http://wiki.math.uwaterloo.ca/statwiki/api.php?action=feedcontributions&user=Eiguray&feedformat=atomstatwiki - User contributions [US]2022-05-29T06:02:48ZUser contributionsMediaWiki 1.28.3http://wiki.math.uwaterloo.ca/statwiki/index.php?title=Traffic_Sign_Recognition_System_(TSRS):_SVM_and_Convolutional_Neural_Network&diff=51235Traffic Sign Recognition System (TSRS): SVM and Convolutional Neural Network2021-12-17T22:44:59Z<p>Eiguray: /* Conclusion */</p>
<hr />
<div>== Presented by == <br />
Egemen Guray<br />
<br />
== Introduction ==<br />
In order to increase the sustainability and decrease incidents of autonomous driving, Hasan Nazmul et.al. presents various techniques and models to build a Traffic Sign Recognition System. In this paper Convolutional Neural Network, and Support Vector Machine models are proposed to address this issue.<br />
<br />
[[Image: TSRS.png|1000px|center]]<br />
<br />
== Previous Work == <br />
Over the years researchers published papers on traffic sign recognition using SVM and CNN. In 2019, Wei-Jong Yang et al proposed a CNN and SVM based TSRS. Using SVM model recognizes the sign and using CNN model predicts which traffic sign it is with a 97% accuracy.<br />
<br />
In 2017, Prashengit Dhar et al. proposed a Traffic Sign Recognition system which achieves 97% accuracy with a HSV color model and a CNN classifier. <br />
<br />
In 2019, Aashrith and et al. used CNN to recognize traffic signs. They achieved 99.18% accuracy on Belgium Data and 99.50% accuracy on German Traffic Sign Benchmark (GTSDB). <br />
<br />
In 2019 Pavly Salah Zaki and et al. published a paper on multiple entity detection system which uses combination of F-RCNN and Single Shot Multi Box Detector along with several feature extractors to detect traffic sign on GTSB dataset.<br />
<br />
== Proposed Methodology ==<br />
<br />
For recognizing traffic signs Hasan Nazmul et al. uses two approaches based on CNN and SVM.<br />
They have considered 12 different classes each with 100 images. Dataset was built from cropping video frame which have been collected random videos.<br />
<br />
[[Image: TrafficSigns.png|1000px|center]]<br />
<br />
== Model Architecture ==<br />
Model is composed of convolutional layers, Relu function, polling layers, and fully connected layer. <br />
<br />
[[Image: CNNFormulaTSRS.png|1000px|center]]<br />
<br />
In the first step, Convolutional layer takes one input and output image in 2D format to extract features from images. After that Relu function replaces all negative values to 0. <br />
<br />
Max polling in the polling layer progressively decrease the size of the input images thus decreases the number of dimensions and the computational power needed while helping model to avoid overfitting as well. <br />
<br />
Fully connected layer combines all features into output stage, calculated error gets to be minimized using backpropagation.<br />
<br />
== Results ==<br />
CNN classification method for Traffic sign prediction scored 99.56% accuracy on training and 96.40% validation accuracy. <br />
Plot of training/validation accuracy and loss of CNN model is visualized in figures below.<br />
<br />
[[Image: TSRSCNNGraphs.png|1000px|center]]<br />
<br />
== Conclusion ==<br />
Hasan Nazmul et.al. represent an traffic sign detection and recognition approach in this paper towards the design of a realtime traffic sing recognition system. Using convolutional neural network classifier this paper suggest that obtained highest training accuracy was 99.56%, while the test accuracy was 96.40%.<br />
<br />
== References ==<br />
<br />
[1] Yang, WJ., Luo, CC., Chung, PC., Yang, JF.: Simplified Neural Networks with Smart Detection for Road Traffic Sign Recognition. Lecture Notes in Networks and Systems, vol 69. Springer, Cham (2020)<br />
<br />
[2] Dhar, M. Z., Abedin, T., Biswas, Datta, A.: Traffic sign detection — A new approach and recognition using convolution neural network. IEEE Region 10 Humanitarian Technology Conference (R10-HTC), Dhaka, pp. 416-419, (2017) <br />
<br />
[3] Aashrith, V., Smriti, S.: Traffic Sign Detection and Recognition using a CNN Ensemble. IEEE International Conference on Consumer Electronics (ICCE). 07 March, (2019)<br />
<br />
[4] Zaki, P. S., et al.: Traffic Signs Detection and Recognition System using Deep Learning. In: Conference on Intelligent Computing and Information Systems (ICICIS) (2019)</div>Eigurayhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Traffic_Sign_Recognition_System_(TSRS):_SVM_and_Convolutional_Neural_Network&diff=51234Traffic Sign Recognition System (TSRS): SVM and Convolutional Neural Network2021-12-17T13:05:57Z<p>Eiguray: /* Conclusion */</p>
<hr />
<div>== Presented by == <br />
Egemen Guray<br />
<br />
== Introduction ==<br />
In order to increase the sustainability and decrease incidents of autonomous driving, Hasan Nazmul et.al. presents various techniques and models to build a Traffic Sign Recognition System. In this paper Convolutional Neural Network, and Support Vector Machine models are proposed to address this issue.<br />
<br />
[[Image: TSRS.png|1000px|center]]<br />
<br />
== Previous Work == <br />
Over the years researchers published papers on traffic sign recognition using SVM and CNN. In 2019, Wei-Jong Yang et al proposed a CNN and SVM based TSRS. Using SVM model recognizes the sign and using CNN model predicts which traffic sign it is with a 97% accuracy.<br />
<br />
In 2017, Prashengit Dhar et al. proposed a Traffic Sign Recognition system which achieves 97% accuracy with a HSV color model and a CNN classifier. <br />
<br />
In 2019, Aashrith and et al. used CNN to recognize traffic signs. They achieved 99.18% accuracy on Belgium Data and 99.50% accuracy on German Traffic Sign Benchmark (GTSDB). <br />
<br />
In 2019 Pavly Salah Zaki and et al. published a paper on multiple entity detection system which uses combination of F-RCNN and Single Shot Multi Box Detector along with several feature extractors to detect traffic sign on GTSB dataset.<br />
<br />
== Proposed Methodology ==<br />
<br />
For recognizing traffic signs Hasan Nazmul et al. uses two approaches based on CNN and SVM.<br />
They have considered 12 different classes each with 100 images. Dataset was built from cropping video frame which have been collected random videos.<br />
<br />
[[Image: TrafficSigns.png|1000px|center]]<br />
<br />
== Model Architecture ==<br />
Model is composed of convolutional layers, Relu function, polling layers, and fully connected layer. <br />
<br />
[[Image: CNNFormulaTSRS.png|1000px|center]]<br />
<br />
In the first step, Convolutional layer takes one input and output image in 2D format to extract features from images. After that Relu function replaces all negative values to 0. <br />
<br />
Max polling in the polling layer progressively decrease the size of the input images thus decreases the number of dimensions and the computational power needed while helping model to avoid overfitting as well. <br />
<br />
Fully connected layer combines all features into output stage, calculated error gets to be minimized using backpropagation.<br />
<br />
== Results ==<br />
CNN classification method for Traffic sign prediction scored 99.56% accuracy on training and 96.40% validation accuracy. <br />
Plot of training/validation accuracy and loss of CNN model is visualized in figures below.<br />
<br />
[[Image: TSRSCNNGraphs.png|1000px|center]]<br />
<br />
== Conclusion ==<br />
<br />
== References ==<br />
<br />
[1] Yang, WJ., Luo, CC., Chung, PC., Yang, JF.: Simplified Neural Networks with Smart Detection for Road Traffic Sign Recognition. Lecture Notes in Networks and Systems, vol 69. Springer, Cham (2020)<br />
<br />
[2] Dhar, M. Z., Abedin, T., Biswas, Datta, A.: Traffic sign detection — A new approach and recognition using convolution neural network. IEEE Region 10 Humanitarian Technology Conference (R10-HTC), Dhaka, pp. 416-419, (2017) <br />
<br />
[3] Aashrith, V., Smriti, S.: Traffic Sign Detection and Recognition using a CNN Ensemble. IEEE International Conference on Consumer Electronics (ICCE). 07 March, (2019)<br />
<br />
[4] Zaki, P. S., et al.: Traffic Signs Detection and Recognition System using Deep Learning. In: Conference on Intelligent Computing and Information Systems (ICICIS) (2019)</div>Eigurayhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Traffic_Sign_Recognition_System_(TSRS):_SVM_and_Convolutional_Neural_Network&diff=51217Traffic Sign Recognition System (TSRS): SVM and Convolutional Neural Network2021-12-02T23:40:45Z<p>Eiguray: /* Results */</p>
<hr />
<div>== Presented by == <br />
Egemen Guray<br />
<br />
== Introduction ==<br />
In order to increase the sustainability and decrease incidents of autonomous driving, Hasan Nazmul et.al. presents various techniques and models to build a Traffic Sign Recognition System. In this paper Convolutional Neural Network, and Support Vector Machine models are proposed to address this issue.<br />
<br />
[[Image: TSRS.png|1000px|center]]<br />
<br />
== Previous Work == <br />
Over the years researchers published papers on traffic sign recognition using SVM and CNN. In 2019, Wei-Jong Yang et al proposed a CNN and SVM based TSRS. Using SVM model recognizes the sign and using CNN model predicts which traffic sign it is with a 97% accuracy.<br />
<br />
In 2017, Prashengit Dhar et al. proposed a Traffic Sign Recognition system which achieves 97% accuracy with a HSV color model and a CNN classifier. <br />
<br />
In 2019, Aashrith and et al. used CNN to recognize traffic signs. They achieved 99.18% accuracy on Belgium Data and 99.50% accuracy on German Traffic Sign Benchmark (GTSDB). <br />
<br />
In 2019 Pavly Salah Zaki and et al. published a paper on multiple entity detection system which uses combination of F-RCNN and Single Shot Multi Box Detector along with several feature extractors to detect traffic sign on GTSB dataset.<br />
<br />
== Proposed Methodology ==<br />
<br />
For recognizing traffic signs Hasan Nazmul et al. uses two approaches based on CNN and SVM.<br />
They have considered 12 different classes each with 100 images. Dataset was built from cropping video frame which have been collected random videos.<br />
<br />
[[Image: TrafficSigns.png|1000px|center]]<br />
<br />
== Model Architecture ==<br />
Model is composed of convolutional layers, Relu function, polling layers, and fully connected layer. <br />
<br />
[[Image: CNNFormulaTSRS.png|1000px|center]]<br />
<br />
In the first step, Convolutional layer takes one input and output image in 2D format to extract features from images. After that Relu function replaces all negative values to 0. <br />
<br />
Max polling in the polling layer progressively decrease the size of the input images thus decreases the number of dimensions and the computational power needed while helping model to avoid overfitting as well. <br />
<br />
Fully connected layer combines all features into output stage, calculated error gets to be minimized using backpropagation.<br />
<br />
== Results ==<br />
CNN classification method for Traffic sign prediction scored 99.56% accuracy on training and 96.40% validation accuracy. <br />
Plot of training/validation accuracy and loss of CNN model is visualized in figures below.<br />
<br />
[[Image: TSRSCNNGraphs.png|1000px|center]]<br />
<br />
== Conclusion ==<br />
<br />
This paper study was to represent an original effective traffic sign detection and recognition approach towards the design of TSRS. As a recent research topic TSRS is getting popular day by day. In this study, it is done using SVM and CNN classification algorithms to decline extensive traffic difficulties. In our ex- periment, we obtained highest training accuracy from CNN 99.56%, while the test accuracy was 96.40%. We showed the real time evaluation results of SVM, where the system performed 98.33% accurately. Many research focused on SVM and CNN to solve this specific problem. <br />
In future, our aim is to increase the number of traffic sign classes with large amount of quality data. As in a machine learning research, to maintain data vol- ume and data quality is most important and time consuming part. To provide a complete system to overcome the traffic issues our ambition is to implement a system with distance calculation form car to traffic sign. <br />
<br />
== References ==<br />
<br />
[1] Yang, WJ., Luo, CC., Chung, PC., Yang, JF.: Simplified Neural Networks with Smart Detection for Road Traffic Sign Recognition. Lecture Notes in Networks and Systems, vol 69. Springer, Cham (2020)<br />
<br />
[2] Dhar, M. Z., Abedin, T., Biswas, Datta, A.: Traffic sign detection — A new approach and recognition using convolution neural network. IEEE Region 10 Humanitarian Technology Conference (R10-HTC), Dhaka, pp. 416-419, (2017) <br />
<br />
[3] Aashrith, V., Smriti, S.: Traffic Sign Detection and Recognition using a CNN Ensemble. IEEE International Conference on Consumer Electronics (ICCE). 07 March, (2019)<br />
<br />
[4] Zaki, P. S., et al.: Traffic Signs Detection and Recognition System using Deep Learning. In: Conference on Intelligent Computing and Information Systems (ICICIS) (2019)</div>Eigurayhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Traffic_Sign_Recognition_System_(TSRS):_SVM_and_Convolutional_Neural_Network&diff=51216Traffic Sign Recognition System (TSRS): SVM and Convolutional Neural Network2021-12-02T23:40:27Z<p>Eiguray: /* Results */</p>
<hr />
<div>== Presented by == <br />
Egemen Guray<br />
<br />
== Introduction ==<br />
In order to increase the sustainability and decrease incidents of autonomous driving, Hasan Nazmul et.al. presents various techniques and models to build a Traffic Sign Recognition System. In this paper Convolutional Neural Network, and Support Vector Machine models are proposed to address this issue.<br />
<br />
[[Image: TSRS.png|1000px|center]]<br />
<br />
== Previous Work == <br />
Over the years researchers published papers on traffic sign recognition using SVM and CNN. In 2019, Wei-Jong Yang et al proposed a CNN and SVM based TSRS. Using SVM model recognizes the sign and using CNN model predicts which traffic sign it is with a 97% accuracy.<br />
<br />
In 2017, Prashengit Dhar et al. proposed a Traffic Sign Recognition system which achieves 97% accuracy with a HSV color model and a CNN classifier. <br />
<br />
In 2019, Aashrith and et al. used CNN to recognize traffic signs. They achieved 99.18% accuracy on Belgium Data and 99.50% accuracy on German Traffic Sign Benchmark (GTSDB). <br />
<br />
In 2019 Pavly Salah Zaki and et al. published a paper on multiple entity detection system which uses combination of F-RCNN and Single Shot Multi Box Detector along with several feature extractors to detect traffic sign on GTSB dataset.<br />
<br />
== Proposed Methodology ==<br />
<br />
For recognizing traffic signs Hasan Nazmul et al. uses two approaches based on CNN and SVM.<br />
They have considered 12 different classes each with 100 images. Dataset was built from cropping video frame which have been collected random videos.<br />
<br />
[[Image: TrafficSigns.png|1000px|center]]<br />
<br />
== Model Architecture ==<br />
Model is composed of convolutional layers, Relu function, polling layers, and fully connected layer. <br />
<br />
[[Image: CNNFormulaTSRS.png|1000px|center]]<br />
<br />
In the first step, Convolutional layer takes one input and output image in 2D format to extract features from images. After that Relu function replaces all negative values to 0. <br />
<br />
Max polling in the polling layer progressively decrease the size of the input images thus decreases the number of dimensions and the computational power needed while helping model to avoid overfitting as well. <br />
<br />
Fully connected layer combines all features into output stage, calculated error gets to be minimized using backpropagation.<br />
<br />
== Results ==<br />
CNN classification method for Traffic sign prediction scored 99.56% accuracy on training and 96.40% validation accuracy. <br />
Plot of training/validation accuracy and loss of CNN model is visualized in figure 5 and 6.<br />
<br />
[[Image: TSRSCNNGraphs.png|1000px|center]]<br />
<br />
== Conclusion ==<br />
<br />
This paper study was to represent an original effective traffic sign detection and recognition approach towards the design of TSRS. As a recent research topic TSRS is getting popular day by day. In this study, it is done using SVM and CNN classification algorithms to decline extensive traffic difficulties. In our ex- periment, we obtained highest training accuracy from CNN 99.56%, while the test accuracy was 96.40%. We showed the real time evaluation results of SVM, where the system performed 98.33% accurately. Many research focused on SVM and CNN to solve this specific problem. <br />
In future, our aim is to increase the number of traffic sign classes with large amount of quality data. As in a machine learning research, to maintain data vol- ume and data quality is most important and time consuming part. To provide a complete system to overcome the traffic issues our ambition is to implement a system with distance calculation form car to traffic sign. <br />
<br />
== References ==<br />
<br />
[1] Yang, WJ., Luo, CC., Chung, PC., Yang, JF.: Simplified Neural Networks with Smart Detection for Road Traffic Sign Recognition. Lecture Notes in Networks and Systems, vol 69. Springer, Cham (2020)<br />
<br />
[2] Dhar, M. Z., Abedin, T., Biswas, Datta, A.: Traffic sign detection — A new approach and recognition using convolution neural network. IEEE Region 10 Humanitarian Technology Conference (R10-HTC), Dhaka, pp. 416-419, (2017) <br />
<br />
[3] Aashrith, V., Smriti, S.: Traffic Sign Detection and Recognition using a CNN Ensemble. IEEE International Conference on Consumer Electronics (ICCE). 07 March, (2019)<br />
<br />
[4] Zaki, P. S., et al.: Traffic Signs Detection and Recognition System using Deep Learning. In: Conference on Intelligent Computing and Information Systems (ICICIS) (2019)</div>Eigurayhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=File:TSRSCNNGraphs.png&diff=51215File:TSRSCNNGraphs.png2021-12-02T23:40:11Z<p>Eiguray: </p>
<hr />
<div></div>Eigurayhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Traffic_Sign_Recognition_System_(TSRS):_SVM_and_Convolutional_Neural_Network&diff=51214Traffic Sign Recognition System (TSRS): SVM and Convolutional Neural Network2021-12-02T23:39:04Z<p>Eiguray: /* Model Architecture */</p>
<hr />
<div>== Presented by == <br />
Egemen Guray<br />
<br />
== Introduction ==<br />
In order to increase the sustainability and decrease incidents of autonomous driving, Hasan Nazmul et.al. presents various techniques and models to build a Traffic Sign Recognition System. In this paper Convolutional Neural Network, and Support Vector Machine models are proposed to address this issue.<br />
<br />
[[Image: TSRS.png|1000px|center]]<br />
<br />
== Previous Work == <br />
Over the years researchers published papers on traffic sign recognition using SVM and CNN. In 2019, Wei-Jong Yang et al proposed a CNN and SVM based TSRS. Using SVM model recognizes the sign and using CNN model predicts which traffic sign it is with a 97% accuracy.<br />
<br />
In 2017, Prashengit Dhar et al. proposed a Traffic Sign Recognition system which achieves 97% accuracy with a HSV color model and a CNN classifier. <br />
<br />
In 2019, Aashrith and et al. used CNN to recognize traffic signs. They achieved 99.18% accuracy on Belgium Data and 99.50% accuracy on German Traffic Sign Benchmark (GTSDB). <br />
<br />
In 2019 Pavly Salah Zaki and et al. published a paper on multiple entity detection system which uses combination of F-RCNN and Single Shot Multi Box Detector along with several feature extractors to detect traffic sign on GTSB dataset.<br />
<br />
== Proposed Methodology ==<br />
<br />
For recognizing traffic signs Hasan Nazmul et al. uses two approaches based on CNN and SVM.<br />
They have considered 12 different classes each with 100 images. Dataset was built from cropping video frame which have been collected random videos.<br />
<br />
[[Image: TrafficSigns.png|1000px|center]]<br />
<br />
== Model Architecture ==<br />
Model is composed of convolutional layers, Relu function, polling layers, and fully connected layer. <br />
<br />
[[Image: CNNFormulaTSRS.png|1000px|center]]<br />
<br />
In the first step, Convolutional layer takes one input and output image in 2D format to extract features from images. After that Relu function replaces all negative values to 0. <br />
<br />
Max polling in the polling layer progressively decrease the size of the input images thus decreases the number of dimensions and the computational power needed while helping model to avoid overfitting as well. <br />
<br />
Fully connected layer combines all features into output stage, calculated error gets to be minimized using backpropagation.<br />
<br />
== Results ==<br />
CNN classification method for Traffic sign prediction scored 99.56% accuracy on training and 96.40% validation accuracy. <br />
Plot of training/validation accuracy and loss of CNN model is visualized in figure 5 and 6.<br />
<br />
== Conclusion ==<br />
<br />
This paper study was to represent an original effective traffic sign detection and recognition approach towards the design of TSRS. As a recent research topic TSRS is getting popular day by day. In this study, it is done using SVM and CNN classification algorithms to decline extensive traffic difficulties. In our ex- periment, we obtained highest training accuracy from CNN 99.56%, while the test accuracy was 96.40%. We showed the real time evaluation results of SVM, where the system performed 98.33% accurately. Many research focused on SVM and CNN to solve this specific problem. <br />
In future, our aim is to increase the number of traffic sign classes with large amount of quality data. As in a machine learning research, to maintain data vol- ume and data quality is most important and time consuming part. To provide a complete system to overcome the traffic issues our ambition is to implement a system with distance calculation form car to traffic sign. <br />
<br />
== References ==<br />
<br />
[1] Yang, WJ., Luo, CC., Chung, PC., Yang, JF.: Simplified Neural Networks with Smart Detection for Road Traffic Sign Recognition. Lecture Notes in Networks and Systems, vol 69. Springer, Cham (2020)<br />
<br />
[2] Dhar, M. Z., Abedin, T., Biswas, Datta, A.: Traffic sign detection — A new approach and recognition using convolution neural network. IEEE Region 10 Humanitarian Technology Conference (R10-HTC), Dhaka, pp. 416-419, (2017) <br />
<br />
[3] Aashrith, V., Smriti, S.: Traffic Sign Detection and Recognition using a CNN Ensemble. IEEE International Conference on Consumer Electronics (ICCE). 07 March, (2019)<br />
<br />
[4] Zaki, P. S., et al.: Traffic Signs Detection and Recognition System using Deep Learning. In: Conference on Intelligent Computing and Information Systems (ICICIS) (2019)</div>Eigurayhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Traffic_Sign_Recognition_System_(TSRS):_SVM_and_Convolutional_Neural_Network&diff=51213Traffic Sign Recognition System (TSRS): SVM and Convolutional Neural Network2021-12-02T23:38:53Z<p>Eiguray: /* Model Architecture */</p>
<hr />
<div>== Presented by == <br />
Egemen Guray<br />
<br />
== Introduction ==<br />
In order to increase the sustainability and decrease incidents of autonomous driving, Hasan Nazmul et.al. presents various techniques and models to build a Traffic Sign Recognition System. In this paper Convolutional Neural Network, and Support Vector Machine models are proposed to address this issue.<br />
<br />
[[Image: TSRS.png|1000px|center]]<br />
<br />
== Previous Work == <br />
Over the years researchers published papers on traffic sign recognition using SVM and CNN. In 2019, Wei-Jong Yang et al proposed a CNN and SVM based TSRS. Using SVM model recognizes the sign and using CNN model predicts which traffic sign it is with a 97% accuracy.<br />
<br />
In 2017, Prashengit Dhar et al. proposed a Traffic Sign Recognition system which achieves 97% accuracy with a HSV color model and a CNN classifier. <br />
<br />
In 2019, Aashrith and et al. used CNN to recognize traffic signs. They achieved 99.18% accuracy on Belgium Data and 99.50% accuracy on German Traffic Sign Benchmark (GTSDB). <br />
<br />
In 2019 Pavly Salah Zaki and et al. published a paper on multiple entity detection system which uses combination of F-RCNN and Single Shot Multi Box Detector along with several feature extractors to detect traffic sign on GTSB dataset.<br />
<br />
== Proposed Methodology ==<br />
<br />
For recognizing traffic signs Hasan Nazmul et al. uses two approaches based on CNN and SVM.<br />
They have considered 12 different classes each with 100 images. Dataset was built from cropping video frame which have been collected random videos.<br />
<br />
[[Image: TrafficSigns.png|1000px|center]]<br />
<br />
== Model Architecture ==<br />
Model is composed of convolutional layers, Relu function, polling layers, and fully connected layer. <br />
<br />
[[Image: CNNFormulaTSRS.PNG|1000px|center]]<br />
<br />
In the first step, Convolutional layer takes one input and output image in 2D format to extract features from images. After that Relu function replaces all negative values to 0. <br />
<br />
Max polling in the polling layer progressively decrease the size of the input images thus decreases the number of dimensions and the computational power needed while helping model to avoid overfitting as well. <br />
<br />
Fully connected layer combines all features into output stage, calculated error gets to be minimized using backpropagation.<br />
<br />
== Results ==<br />
CNN classification method for Traffic sign prediction scored 99.56% accuracy on training and 96.40% validation accuracy. <br />
Plot of training/validation accuracy and loss of CNN model is visualized in figure 5 and 6.<br />
<br />
== Conclusion ==<br />
<br />
This paper study was to represent an original effective traffic sign detection and recognition approach towards the design of TSRS. As a recent research topic TSRS is getting popular day by day. In this study, it is done using SVM and CNN classification algorithms to decline extensive traffic difficulties. In our ex- periment, we obtained highest training accuracy from CNN 99.56%, while the test accuracy was 96.40%. We showed the real time evaluation results of SVM, where the system performed 98.33% accurately. Many research focused on SVM and CNN to solve this specific problem. <br />
In future, our aim is to increase the number of traffic sign classes with large amount of quality data. As in a machine learning research, to maintain data vol- ume and data quality is most important and time consuming part. To provide a complete system to overcome the traffic issues our ambition is to implement a system with distance calculation form car to traffic sign. <br />
<br />
== References ==<br />
<br />
[1] Yang, WJ., Luo, CC., Chung, PC., Yang, JF.: Simplified Neural Networks with Smart Detection for Road Traffic Sign Recognition. Lecture Notes in Networks and Systems, vol 69. Springer, Cham (2020)<br />
<br />
[2] Dhar, M. Z., Abedin, T., Biswas, Datta, A.: Traffic sign detection — A new approach and recognition using convolution neural network. IEEE Region 10 Humanitarian Technology Conference (R10-HTC), Dhaka, pp. 416-419, (2017) <br />
<br />
[3] Aashrith, V., Smriti, S.: Traffic Sign Detection and Recognition using a CNN Ensemble. IEEE International Conference on Consumer Electronics (ICCE). 07 March, (2019)<br />
<br />
[4] Zaki, P. S., et al.: Traffic Signs Detection and Recognition System using Deep Learning. In: Conference on Intelligent Computing and Information Systems (ICICIS) (2019)</div>Eigurayhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Traffic_Sign_Recognition_System_(TSRS):_SVM_and_Convolutional_Neural_Network&diff=51212Traffic Sign Recognition System (TSRS): SVM and Convolutional Neural Network2021-12-02T23:38:21Z<p>Eiguray: /* Model Architecture */</p>
<hr />
<div>== Presented by == <br />
Egemen Guray<br />
<br />
== Introduction ==<br />
In order to increase the sustainability and decrease incidents of autonomous driving, Hasan Nazmul et.al. presents various techniques and models to build a Traffic Sign Recognition System. In this paper Convolutional Neural Network, and Support Vector Machine models are proposed to address this issue.<br />
<br />
[[Image: TSRS.png|1000px|center]]<br />
<br />
== Previous Work == <br />
Over the years researchers published papers on traffic sign recognition using SVM and CNN. In 2019, Wei-Jong Yang et al proposed a CNN and SVM based TSRS. Using SVM model recognizes the sign and using CNN model predicts which traffic sign it is with a 97% accuracy.<br />
<br />
In 2017, Prashengit Dhar et al. proposed a Traffic Sign Recognition system which achieves 97% accuracy with a HSV color model and a CNN classifier. <br />
<br />
In 2019, Aashrith and et al. used CNN to recognize traffic signs. They achieved 99.18% accuracy on Belgium Data and 99.50% accuracy on German Traffic Sign Benchmark (GTSDB). <br />
<br />
In 2019 Pavly Salah Zaki and et al. published a paper on multiple entity detection system which uses combination of F-RCNN and Single Shot Multi Box Detector along with several feature extractors to detect traffic sign on GTSB dataset.<br />
<br />
== Proposed Methodology ==<br />
<br />
For recognizing traffic signs Hasan Nazmul et al. uses two approaches based on CNN and SVM.<br />
They have considered 12 different classes each with 100 images. Dataset was built from cropping video frame which have been collected random videos.<br />
<br />
[[Image: TrafficSigns.png|1000px|center]]<br />
<br />
== Model Architecture ==<br />
Model is composed of convolutional layers, Relu function, polling layers, and fully connected layer. <br />
<br />
[[Image: CNNFormulaTSRS.jpg|1000px|center]]<br />
<br />
In the first step, Convolutional layer takes one input and output image in 2D format to extract features from images. After that Relu function replaces all negative values to 0. <br />
<br />
Max polling in the polling layer progressively decrease the size of the input images thus decreases the number of dimensions and the computational power needed while helping model to avoid overfitting as well. <br />
<br />
Fully connected layer combines all features into output stage, calculated error gets to be minimized using backpropagation.<br />
<br />
== Results ==<br />
CNN classification method for Traffic sign prediction scored 99.56% accuracy on training and 96.40% validation accuracy. <br />
Plot of training/validation accuracy and loss of CNN model is visualized in figure 5 and 6.<br />
<br />
== Conclusion ==<br />
<br />
This paper study was to represent an original effective traffic sign detection and recognition approach towards the design of TSRS. As a recent research topic TSRS is getting popular day by day. In this study, it is done using SVM and CNN classification algorithms to decline extensive traffic difficulties. In our ex- periment, we obtained highest training accuracy from CNN 99.56%, while the test accuracy was 96.40%. We showed the real time evaluation results of SVM, where the system performed 98.33% accurately. Many research focused on SVM and CNN to solve this specific problem. <br />
In future, our aim is to increase the number of traffic sign classes with large amount of quality data. As in a machine learning research, to maintain data vol- ume and data quality is most important and time consuming part. To provide a complete system to overcome the traffic issues our ambition is to implement a system with distance calculation form car to traffic sign. <br />
<br />
== References ==<br />
<br />
[1] Yang, WJ., Luo, CC., Chung, PC., Yang, JF.: Simplified Neural Networks with Smart Detection for Road Traffic Sign Recognition. Lecture Notes in Networks and Systems, vol 69. Springer, Cham (2020)<br />
<br />
[2] Dhar, M. Z., Abedin, T., Biswas, Datta, A.: Traffic sign detection — A new approach and recognition using convolution neural network. IEEE Region 10 Humanitarian Technology Conference (R10-HTC), Dhaka, pp. 416-419, (2017) <br />
<br />
[3] Aashrith, V., Smriti, S.: Traffic Sign Detection and Recognition using a CNN Ensemble. IEEE International Conference on Consumer Electronics (ICCE). 07 March, (2019)<br />
<br />
[4] Zaki, P. S., et al.: Traffic Signs Detection and Recognition System using Deep Learning. In: Conference on Intelligent Computing and Information Systems (ICICIS) (2019)</div>Eigurayhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=File:CNNFormulaTSRS.png&diff=51211File:CNNFormulaTSRS.png2021-12-02T23:38:05Z<p>Eiguray: </p>
<hr />
<div></div>Eigurayhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Traffic_Sign_Recognition_System_(TSRS):_SVM_and_Convolutional_Neural_Network&diff=51210Traffic Sign Recognition System (TSRS): SVM and Convolutional Neural Network2021-12-02T23:37:36Z<p>Eiguray: /* Proposed Methodology */</p>
<hr />
<div>== Presented by == <br />
Egemen Guray<br />
<br />
== Introduction ==<br />
In order to increase the sustainability and decrease incidents of autonomous driving, Hasan Nazmul et.al. presents various techniques and models to build a Traffic Sign Recognition System. In this paper Convolutional Neural Network, and Support Vector Machine models are proposed to address this issue.<br />
<br />
[[Image: TSRS.png|1000px|center]]<br />
<br />
== Previous Work == <br />
Over the years researchers published papers on traffic sign recognition using SVM and CNN. In 2019, Wei-Jong Yang et al proposed a CNN and SVM based TSRS. Using SVM model recognizes the sign and using CNN model predicts which traffic sign it is with a 97% accuracy.<br />
<br />
In 2017, Prashengit Dhar et al. proposed a Traffic Sign Recognition system which achieves 97% accuracy with a HSV color model and a CNN classifier. <br />
<br />
In 2019, Aashrith and et al. used CNN to recognize traffic signs. They achieved 99.18% accuracy on Belgium Data and 99.50% accuracy on German Traffic Sign Benchmark (GTSDB). <br />
<br />
In 2019 Pavly Salah Zaki and et al. published a paper on multiple entity detection system which uses combination of F-RCNN and Single Shot Multi Box Detector along with several feature extractors to detect traffic sign on GTSB dataset.<br />
<br />
== Proposed Methodology ==<br />
<br />
For recognizing traffic signs Hasan Nazmul et al. uses two approaches based on CNN and SVM.<br />
They have considered 12 different classes each with 100 images. Dataset was built from cropping video frame which have been collected random videos.<br />
<br />
[[Image: TrafficSigns.png|1000px|center]]<br />
<br />
== Model Architecture ==<br />
Model is composed of convolutional layers, Relu function, polling layers, and fully connected layer. <br />
<br />
[[Image: Example.jpg|1000px|center]]<br />
<br />
In the first step, Convolutional layer takes one input and output image in 2D format to extract features from images. After that Relu function replaces all negative values to 0. <br />
<br />
Max polling in the polling layer progressively decrease the size of the input images thus decreases the number of dimensions and the computational power needed while helping model to avoid overfitting as well. <br />
<br />
Fully connected layer combines all features into output stage, calculated error gets to be minimized using backpropagation.<br />
<br />
== Results ==<br />
CNN classification method for Traffic sign prediction scored 99.56% accuracy on training and 96.40% validation accuracy. <br />
Plot of training/validation accuracy and loss of CNN model is visualized in figure 5 and 6.<br />
<br />
== Conclusion ==<br />
<br />
This paper study was to represent an original effective traffic sign detection and recognition approach towards the design of TSRS. As a recent research topic TSRS is getting popular day by day. In this study, it is done using SVM and CNN classification algorithms to decline extensive traffic difficulties. In our ex- periment, we obtained highest training accuracy from CNN 99.56%, while the test accuracy was 96.40%. We showed the real time evaluation results of SVM, where the system performed 98.33% accurately. Many research focused on SVM and CNN to solve this specific problem. <br />
In future, our aim is to increase the number of traffic sign classes with large amount of quality data. As in a machine learning research, to maintain data vol- ume and data quality is most important and time consuming part. To provide a complete system to overcome the traffic issues our ambition is to implement a system with distance calculation form car to traffic sign. <br />
<br />
== References ==<br />
<br />
[1] Yang, WJ., Luo, CC., Chung, PC., Yang, JF.: Simplified Neural Networks with Smart Detection for Road Traffic Sign Recognition. Lecture Notes in Networks and Systems, vol 69. Springer, Cham (2020)<br />
<br />
[2] Dhar, M. Z., Abedin, T., Biswas, Datta, A.: Traffic sign detection — A new approach and recognition using convolution neural network. IEEE Region 10 Humanitarian Technology Conference (R10-HTC), Dhaka, pp. 416-419, (2017) <br />
<br />
[3] Aashrith, V., Smriti, S.: Traffic Sign Detection and Recognition using a CNN Ensemble. IEEE International Conference on Consumer Electronics (ICCE). 07 March, (2019)<br />
<br />
[4] Zaki, P. S., et al.: Traffic Signs Detection and Recognition System using Deep Learning. In: Conference on Intelligent Computing and Information Systems (ICICIS) (2019)</div>Eigurayhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=File:TrafficSigns.png&diff=51209File:TrafficSigns.png2021-12-02T23:37:16Z<p>Eiguray: </p>
<hr />
<div></div>Eigurayhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Traffic_Sign_Recognition_System_(TSRS):_SVM_and_Convolutional_Neural_Network&diff=51208Traffic Sign Recognition System (TSRS): SVM and Convolutional Neural Network2021-12-02T23:36:47Z<p>Eiguray: /* Introduction */</p>
<hr />
<div>== Presented by == <br />
Egemen Guray<br />
<br />
== Introduction ==<br />
In order to increase the sustainability and decrease incidents of autonomous driving, Hasan Nazmul et.al. presents various techniques and models to build a Traffic Sign Recognition System. In this paper Convolutional Neural Network, and Support Vector Machine models are proposed to address this issue.<br />
<br />
[[Image: TSRS.png|1000px|center]]<br />
<br />
== Previous Work == <br />
Over the years researchers published papers on traffic sign recognition using SVM and CNN. In 2019, Wei-Jong Yang et al proposed a CNN and SVM based TSRS. Using SVM model recognizes the sign and using CNN model predicts which traffic sign it is with a 97% accuracy.<br />
<br />
In 2017, Prashengit Dhar et al. proposed a Traffic Sign Recognition system which achieves 97% accuracy with a HSV color model and a CNN classifier. <br />
<br />
In 2019, Aashrith and et al. used CNN to recognize traffic signs. They achieved 99.18% accuracy on Belgium Data and 99.50% accuracy on German Traffic Sign Benchmark (GTSDB). <br />
<br />
In 2019 Pavly Salah Zaki and et al. published a paper on multiple entity detection system which uses combination of F-RCNN and Single Shot Multi Box Detector along with several feature extractors to detect traffic sign on GTSB dataset.<br />
<br />
== Proposed Methodology ==<br />
<br />
For recognizing traffic signs Hasan Nazmul et al. uses two approaches based on CNN and SVM.<br />
They have considered 12 different classes each with 100 images. Dataset was built from cropping video frame which have been collected random videos. <br />
<br />
== Model Architecture ==<br />
Model is composed of convolutional layers, Relu function, polling layers, and fully connected layer. <br />
<br />
[[Image: Example.jpg|1000px|center]]<br />
<br />
In the first step, Convolutional layer takes one input and output image in 2D format to extract features from images. After that Relu function replaces all negative values to 0. <br />
<br />
Max polling in the polling layer progressively decrease the size of the input images thus decreases the number of dimensions and the computational power needed while helping model to avoid overfitting as well. <br />
<br />
Fully connected layer combines all features into output stage, calculated error gets to be minimized using backpropagation.<br />
<br />
== Results ==<br />
CNN classification method for Traffic sign prediction scored 99.56% accuracy on training and 96.40% validation accuracy. <br />
Plot of training/validation accuracy and loss of CNN model is visualized in figure 5 and 6.<br />
<br />
== Conclusion ==<br />
<br />
This paper study was to represent an original effective traffic sign detection and recognition approach towards the design of TSRS. As a recent research topic TSRS is getting popular day by day. In this study, it is done using SVM and CNN classification algorithms to decline extensive traffic difficulties. In our ex- periment, we obtained highest training accuracy from CNN 99.56%, while the test accuracy was 96.40%. We showed the real time evaluation results of SVM, where the system performed 98.33% accurately. Many research focused on SVM and CNN to solve this specific problem. <br />
In future, our aim is to increase the number of traffic sign classes with large amount of quality data. As in a machine learning research, to maintain data vol- ume and data quality is most important and time consuming part. To provide a complete system to overcome the traffic issues our ambition is to implement a system with distance calculation form car to traffic sign. <br />
<br />
== References ==<br />
<br />
[1] Yang, WJ., Luo, CC., Chung, PC., Yang, JF.: Simplified Neural Networks with Smart Detection for Road Traffic Sign Recognition. Lecture Notes in Networks and Systems, vol 69. Springer, Cham (2020)<br />
<br />
[2] Dhar, M. Z., Abedin, T., Biswas, Datta, A.: Traffic sign detection — A new approach and recognition using convolution neural network. IEEE Region 10 Humanitarian Technology Conference (R10-HTC), Dhaka, pp. 416-419, (2017) <br />
<br />
[3] Aashrith, V., Smriti, S.: Traffic Sign Detection and Recognition using a CNN Ensemble. IEEE International Conference on Consumer Electronics (ICCE). 07 March, (2019)<br />
<br />
[4] Zaki, P. S., et al.: Traffic Signs Detection and Recognition System using Deep Learning. In: Conference on Intelligent Computing and Information Systems (ICICIS) (2019)</div>Eigurayhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=File:TSRS.png&diff=51207File:TSRS.png2021-12-02T23:36:19Z<p>Eiguray: </p>
<hr />
<div></div>Eigurayhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Traffic_Sign_Recognition_System_(TSRS):_SVM_and_Convolutional_Neural_Network&diff=51206Traffic Sign Recognition System (TSRS): SVM and Convolutional Neural Network2021-12-02T23:35:32Z<p>Eiguray: </p>
<hr />
<div>== Presented by == <br />
Egemen Guray<br />
<br />
== Introduction ==<br />
In order to increase the sustainability and decrease incidents of autonomous driving, Hasan Nazmul et.al. presents various techniques and models to build a Traffic Sign Recognition System. In this paper Convolutional Neural Network, and Support Vector Machine models are proposed to address this issue. <br />
<br />
== Previous Work == <br />
Over the years researchers published papers on traffic sign recognition using SVM and CNN. In 2019, Wei-Jong Yang et al proposed a CNN and SVM based TSRS. Using SVM model recognizes the sign and using CNN model predicts which traffic sign it is with a 97% accuracy.<br />
<br />
In 2017, Prashengit Dhar et al. proposed a Traffic Sign Recognition system which achieves 97% accuracy with a HSV color model and a CNN classifier. <br />
<br />
In 2019, Aashrith and et al. used CNN to recognize traffic signs. They achieved 99.18% accuracy on Belgium Data and 99.50% accuracy on German Traffic Sign Benchmark (GTSDB). <br />
<br />
In 2019 Pavly Salah Zaki and et al. published a paper on multiple entity detection system which uses combination of F-RCNN and Single Shot Multi Box Detector along with several feature extractors to detect traffic sign on GTSB dataset.<br />
<br />
== Proposed Methodology ==<br />
<br />
For recognizing traffic signs Hasan Nazmul et al. uses two approaches based on CNN and SVM.<br />
They have considered 12 different classes each with 100 images. Dataset was built from cropping video frame which have been collected random videos. <br />
<br />
== Model Architecture ==<br />
Model is composed of convolutional layers, Relu function, polling layers, and fully connected layer. <br />
<br />
[[Image: Example.jpg|1000px|center]]<br />
<br />
In the first step, Convolutional layer takes one input and output image in 2D format to extract features from images. After that Relu function replaces all negative values to 0. <br />
<br />
Max polling in the polling layer progressively decrease the size of the input images thus decreases the number of dimensions and the computational power needed while helping model to avoid overfitting as well. <br />
<br />
Fully connected layer combines all features into output stage, calculated error gets to be minimized using backpropagation.<br />
<br />
== Results ==<br />
CNN classification method for Traffic sign prediction scored 99.56% accuracy on training and 96.40% validation accuracy. <br />
Plot of training/validation accuracy and loss of CNN model is visualized in figure 5 and 6.<br />
<br />
== Conclusion ==<br />
<br />
This paper study was to represent an original effective traffic sign detection and recognition approach towards the design of TSRS. As a recent research topic TSRS is getting popular day by day. In this study, it is done using SVM and CNN classification algorithms to decline extensive traffic difficulties. In our ex- periment, we obtained highest training accuracy from CNN 99.56%, while the test accuracy was 96.40%. We showed the real time evaluation results of SVM, where the system performed 98.33% accurately. Many research focused on SVM and CNN to solve this specific problem. <br />
In future, our aim is to increase the number of traffic sign classes with large amount of quality data. As in a machine learning research, to maintain data vol- ume and data quality is most important and time consuming part. To provide a complete system to overcome the traffic issues our ambition is to implement a system with distance calculation form car to traffic sign. <br />
<br />
== References ==<br />
<br />
[1] Yang, WJ., Luo, CC., Chung, PC., Yang, JF.: Simplified Neural Networks with Smart Detection for Road Traffic Sign Recognition. Lecture Notes in Networks and Systems, vol 69. Springer, Cham (2020)<br />
<br />
[2] Dhar, M. Z., Abedin, T., Biswas, Datta, A.: Traffic sign detection — A new approach and recognition using convolution neural network. IEEE Region 10 Humanitarian Technology Conference (R10-HTC), Dhaka, pp. 416-419, (2017) <br />
<br />
[3] Aashrith, V., Smriti, S.: Traffic Sign Detection and Recognition using a CNN Ensemble. IEEE International Conference on Consumer Electronics (ICCE). 07 March, (2019)<br />
<br />
[4] Zaki, P. S., et al.: Traffic Signs Detection and Recognition System using Deep Learning. In: Conference on Intelligent Computing and Information Systems (ICICIS) (2019)</div>Eigurayhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Traffic_Sign_Recognition_System_(TSRS):_SVM_and_Convolutional_Neural_Network&diff=51205Traffic Sign Recognition System (TSRS): SVM and Convolutional Neural Network2021-12-02T23:35:02Z<p>Eiguray: </p>
<hr />
<div>== Presented by == <br />
Egemen Guray<br />
<br />
== Introduction ==<br />
In order to increase the sustainability and decrease incidents of autonomous driving, Hasan Nazmul et.al. presents various techniques and models to build a Traffic Sign Recognition System. In this paper Convolutional Neural Network, and Support Vector Machine models are proposed to address this issue. <br />
<br />
== Previous Work == <br />
Over the years researchers published papers on traffic sign recognition using SVM and CNN. In 2019, Wei-Jong Yang et al proposed a CNN and SVM based TSRS. Using SVM model recognizes the sign and using CNN model predicts which traffic sign it is with a 97% accuracy.<br />
<br />
In 2017, Prashengit Dhar et al. proposed a Traffic Sign Recognition system which achieves 97% accuracy with a HSV color model and a CNN classifier. <br />
<br />
In 2019, Aashrith and et al. used CNN to recognize traffic signs. They achieved 99.18% accuracy on Belgium Data and 99.50% accuracy on German Traffic Sign Benchmark (GTSDB). <br />
<br />
In 2019 Pavly Salah Zaki and et al. published a paper on multiple entity detection system which uses combination of F-RCNN and Single Shot Multi Box Detector along with several feature extractors to detect traffic sign on GTSB dataset.<br />
<br />
== Proposed Methodology ==<br />
<br />
For recognizing traffic signs Hasan Nazmul et al. uses two approaches based on CNN and SVM.<br />
They have considered 12 different classes each with 100 images. Dataset was built from cropping video frame which have been collected random videos. <br />
<br />
== Model Architecture ==<br />
Model is composed of convolutional layers, Relu function, polling layers, and fully connected layer. <br />
<br />
[[Image: Hurricane_predictions.png|1000px|center]]<br />
<br />
In the first step, Convolutional layer takes one input and output image in 2D format to extract features from images. After that Relu function replaces all negative values to 0. <br />
<br />
Max polling in the polling layer progressively decrease the size of the input images thus decreases the number of dimensions and the computational power needed while helping model to avoid overfitting as well. <br />
<br />
Fully connected layer combines all features into output stage, calculated error gets to be minimized using backpropagation.<br />
<br />
== Results ==<br />
CNN classification method for Traffic sign prediction scored 99.56% accuracy on training and 96.40% validation accuracy. <br />
Plot of training/validation accuracy and loss of CNN model is visualized in figure 5 and 6.<br />
<br />
== Conclusion ==<br />
<br />
This paper study was to represent an original effective traffic sign detection and recognition approach towards the design of TSRS. As a recent research topic TSRS is getting popular day by day. In this study, it is done using SVM and CNN classification algorithms to decline extensive traffic difficulties. In our ex- periment, we obtained highest training accuracy from CNN 99.56%, while the test accuracy was 96.40%. We showed the real time evaluation results of SVM, where the system performed 98.33% accurately. Many research focused on SVM and CNN to solve this specific problem. <br />
In future, our aim is to increase the number of traffic sign classes with large amount of quality data. As in a machine learning research, to maintain data vol- ume and data quality is most important and time consuming part. To provide a complete system to overcome the traffic issues our ambition is to implement a system with distance calculation form car to traffic sign. <br />
<br />
== References ==<br />
<br />
[1] Yang, WJ., Luo, CC., Chung, PC., Yang, JF.: Simplified Neural Networks with Smart Detection for Road Traffic Sign Recognition. Lecture Notes in Networks and Systems, vol 69. Springer, Cham (2020)<br />
<br />
[2] Dhar, M. Z., Abedin, T., Biswas, Datta, A.: Traffic sign detection — A new approach and recognition using convolution neural network. IEEE Region 10 Humanitarian Technology Conference (R10-HTC), Dhaka, pp. 416-419, (2017) <br />
<br />
[3] Aashrith, V., Smriti, S.: Traffic Sign Detection and Recognition using a CNN Ensemble. IEEE International Conference on Consumer Electronics (ICCE). 07 March, (2019)<br />
<br />
[4] Zaki, P. S., et al.: Traffic Signs Detection and Recognition System using Deep Learning. In: Conference on Intelligent Computing and Information Systems (ICICIS) (2019)</div>Eigurayhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Traffic_Sign_Recognition_System_(TSRS):_SVM_and_Convolutional_Neural_Network&diff=51204Traffic Sign Recognition System (TSRS): SVM and Convolutional Neural Network2021-12-02T23:34:32Z<p>Eiguray: /* Model Architecture */</p>
<hr />
<div>== Presented by == <br />
Egemen Guray<br />
<br />
== Introduction ==<br />
In order to increase the sustainability and decrease incidents of autonomous driving, Hasan Nazmul et.al. presents various techniques and models to build a Traffic Sign Recognition System. In this paper Convolutional Neural Network, and Support Vector Machine models are proposed to address this issue. <br />
<br />
== Previous Work == <br />
Over the years researchers published papers on traffic sign recognition using SVM and CNN. In 2019, Wei-Jong Yang et al proposed a CNN and SVM based TSRS. Using SVM model recognizes the sign and using CNN model predicts which traffic sign it is with a 97% accuracy.<br />
<br />
In 2017, Prashengit Dhar et al. proposed a Traffic Sign Recognition system which achieves 97% accuracy with a HSV color model and a CNN classifier. <br />
<br />
In 2019, Aashrith and et al. used CNN to recognize traffic signs. They achieved 99.18% accuracy on Belgium Data and 99.50% accuracy on German Traffic Sign Benchmark (GTSDB). <br />
<br />
In 2019 Pavly Salah Zaki and et al. published a paper on multiple entity detection system which uses combination of F-RCNN and Single Shot Multi Box Detector along with several feature extractors to detect traffic sign on GTSB dataset.<br />
<br />
== Proposed Methodology ==<br />
<br />
For recognizing traffic signs Hasan Nazmul et al. uses two approaches based on CNN and SVM.<br />
They have considered 12 different classes each with 100 images. Dataset was built from cropping video frame which have been collected random videos. <br />
<br />
== Model Architecture ==<br />
Model is composed of convolutional layers, Relu function, polling layers, and fully connected layer. <br />
[[File:Example.jpg]]<br />
In the first step, Convolutional layer takes one input and output image in 2D format to extract features from images. After that Relu function replaces all negative values to 0. <br />
<br />
Max polling in the polling layer progressively decrease the size of the input images thus decreases the number of dimensions and the computational power needed while helping model to avoid overfitting as well. <br />
<br />
Fully connected layer combines all features into output stage, calculated error gets to be minimized using backpropagation.<br />
<br />
== Results ==<br />
CNN classification method for Traffic sign prediction scored 99.56% accuracy on training and 96.40% validation accuracy. <br />
Plot of training/validation accuracy and loss of CNN model is visualized in figure 5 and 6.<br />
<br />
== Conclusion ==<br />
<br />
This paper study was to represent an original effective traffic sign detection and recognition approach towards the design of TSRS. As a recent research topic TSRS is getting popular day by day. In this study, it is done using SVM and CNN classification algorithms to decline extensive traffic difficulties. In our ex- periment, we obtained highest training accuracy from CNN 99.56%, while the test accuracy was 96.40%. We showed the real time evaluation results of SVM, where the system performed 98.33% accurately. Many research focused on SVM and CNN to solve this specific problem. <br />
In future, our aim is to increase the number of traffic sign classes with large amount of quality data. As in a machine learning research, to maintain data vol- ume and data quality is most important and time consuming part. To provide a complete system to overcome the traffic issues our ambition is to implement a system with distance calculation form car to traffic sign. <br />
<br />
== References ==<br />
<br />
[1] Yang, WJ., Luo, CC., Chung, PC., Yang, JF.: Simplified Neural Networks with Smart Detection for Road Traffic Sign Recognition. Lecture Notes in Networks and Systems, vol 69. Springer, Cham (2020)<br />
<br />
[2] Dhar, M. Z., Abedin, T., Biswas, Datta, A.: Traffic sign detection — A new approach and recognition using convolution neural network. IEEE Region 10 Humanitarian Technology Conference (R10-HTC), Dhaka, pp. 416-419, (2017) <br />
<br />
[3] Aashrith, V., Smriti, S.: Traffic Sign Detection and Recognition using a CNN Ensemble. IEEE International Conference on Consumer Electronics (ICCE). 07 March, (2019)<br />
<br />
[4] Zaki, P. S., et al.: Traffic Signs Detection and Recognition System using Deep Learning. In: Conference on Intelligent Computing and Information Systems (ICICIS) (2019)</div>Eigurayhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Traffic_Sign_Recognition_System_(TSRS):_SVM_and_Convolutional_Neural_Network&diff=51203Traffic Sign Recognition System (TSRS): SVM and Convolutional Neural Network2021-12-02T23:33:26Z<p>Eiguray: /* Model Architecture */</p>
<hr />
<div>== Presented by == <br />
Egemen Guray<br />
<br />
== Introduction ==<br />
In order to increase the sustainability and decrease incidents of autonomous driving, Hasan Nazmul et.al. presents various techniques and models to build a Traffic Sign Recognition System. In this paper Convolutional Neural Network, and Support Vector Machine models are proposed to address this issue. <br />
<br />
== Previous Work == <br />
Over the years researchers published papers on traffic sign recognition using SVM and CNN. In 2019, Wei-Jong Yang et al proposed a CNN and SVM based TSRS. Using SVM model recognizes the sign and using CNN model predicts which traffic sign it is with a 97% accuracy.<br />
<br />
In 2017, Prashengit Dhar et al. proposed a Traffic Sign Recognition system which achieves 97% accuracy with a HSV color model and a CNN classifier. <br />
<br />
In 2019, Aashrith and et al. used CNN to recognize traffic signs. They achieved 99.18% accuracy on Belgium Data and 99.50% accuracy on German Traffic Sign Benchmark (GTSDB). <br />
<br />
In 2019 Pavly Salah Zaki and et al. published a paper on multiple entity detection system which uses combination of F-RCNN and Single Shot Multi Box Detector along with several feature extractors to detect traffic sign on GTSB dataset.<br />
<br />
== Proposed Methodology ==<br />
<br />
For recognizing traffic signs Hasan Nazmul et al. uses two approaches based on CNN and SVM.<br />
They have considered 12 different classes each with 100 images. Dataset was built from cropping video frame which have been collected random videos. <br />
<br />
== Model Architecture ==<br />
Model is composed of convolutional layers, Relu function, polling layers, and fully connected layer. In the first step, Convolutional layer takes one input and output image in 2D format to extract features from images. After that Relu function replaces all negative values to 0. <br />
<br />
Max polling in the polling layer progressively decrease the size of the input images thus decreases the number of dimensions and the computational power needed while helping model to avoid overfitting as well. <br />
<br />
Fully connected layer combines all features into output stage, calculated error gets to be minimized using backpropagation.<br />
<br />
== Results ==<br />
CNN classification method for Traffic sign prediction scored 99.56% accuracy on training and 96.40% validation accuracy. <br />
Plot of training/validation accuracy and loss of CNN model is visualized in figure 5 and 6.<br />
<br />
== Conclusion ==<br />
<br />
This paper study was to represent an original effective traffic sign detection and recognition approach towards the design of TSRS. As a recent research topic TSRS is getting popular day by day. In this study, it is done using SVM and CNN classification algorithms to decline extensive traffic difficulties. In our ex- periment, we obtained highest training accuracy from CNN 99.56%, while the test accuracy was 96.40%. We showed the real time evaluation results of SVM, where the system performed 98.33% accurately. Many research focused on SVM and CNN to solve this specific problem. <br />
In future, our aim is to increase the number of traffic sign classes with large amount of quality data. As in a machine learning research, to maintain data vol- ume and data quality is most important and time consuming part. To provide a complete system to overcome the traffic issues our ambition is to implement a system with distance calculation form car to traffic sign. <br />
<br />
== References ==<br />
<br />
[1] Yang, WJ., Luo, CC., Chung, PC., Yang, JF.: Simplified Neural Networks with Smart Detection for Road Traffic Sign Recognition. Lecture Notes in Networks and Systems, vol 69. Springer, Cham (2020)<br />
<br />
[2] Dhar, M. Z., Abedin, T., Biswas, Datta, A.: Traffic sign detection — A new approach and recognition using convolution neural network. IEEE Region 10 Humanitarian Technology Conference (R10-HTC), Dhaka, pp. 416-419, (2017) <br />
<br />
[3] Aashrith, V., Smriti, S.: Traffic Sign Detection and Recognition using a CNN Ensemble. IEEE International Conference on Consumer Electronics (ICCE). 07 March, (2019)<br />
<br />
[4] Zaki, P. S., et al.: Traffic Signs Detection and Recognition System using Deep Learning. In: Conference on Intelligent Computing and Information Systems (ICICIS) (2019)</div>Eigurayhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Traffic_Sign_Recognition_System_(TSRS):_SVM_and_Convolutional_Neural_Network&diff=51202Traffic Sign Recognition System (TSRS): SVM and Convolutional Neural Network2021-12-02T23:33:08Z<p>Eiguray: </p>
<hr />
<div>== Presented by == <br />
Egemen Guray<br />
<br />
== Introduction ==<br />
In order to increase the sustainability and decrease incidents of autonomous driving, Hasan Nazmul et.al. presents various techniques and models to build a Traffic Sign Recognition System. In this paper Convolutional Neural Network, and Support Vector Machine models are proposed to address this issue. <br />
<br />
== Previous Work == <br />
Over the years researchers published papers on traffic sign recognition using SVM and CNN. In 2019, Wei-Jong Yang et al proposed a CNN and SVM based TSRS. Using SVM model recognizes the sign and using CNN model predicts which traffic sign it is with a 97% accuracy.<br />
<br />
In 2017, Prashengit Dhar et al. proposed a Traffic Sign Recognition system which achieves 97% accuracy with a HSV color model and a CNN classifier. <br />
<br />
In 2019, Aashrith and et al. used CNN to recognize traffic signs. They achieved 99.18% accuracy on Belgium Data and 99.50% accuracy on German Traffic Sign Benchmark (GTSDB). <br />
<br />
In 2019 Pavly Salah Zaki and et al. published a paper on multiple entity detection system which uses combination of F-RCNN and Single Shot Multi Box Detector along with several feature extractors to detect traffic sign on GTSB dataset.<br />
<br />
== Proposed Methodology ==<br />
<br />
For recognizing traffic signs Hasan Nazmul et al. uses two approaches based on CNN and SVM.<br />
They have considered 12 different classes each with 100 images. Dataset was built from cropping video frame which have been collected random videos. <br />
<br />
== Model Architecture ==<br />
Model is composed of convolutional layers, Relu function, polling layers, and fully connected layer. <br />
<br />
In the first step, Convolutional layer takes one input and output image in 2D format to extract features from images. After that Relu function replaces all negative values to 0. <br />
<br />
Max polling in the polling layer progressively decrease the size of the input images thus decreases the number of dimensions and the computational power needed while helping model to avoid overfitting as well. <br />
<br />
Fully connected layer combines all features into output stage, calculated error gets to be minimized using backpropagation.<br />
<br />
== Results ==<br />
CNN classification method for Traffic sign prediction scored 99.56% accuracy on training and 96.40% validation accuracy. <br />
Plot of training/validation accuracy and loss of CNN model is visualized in figure 5 and 6.<br />
<br />
== Conclusion ==<br />
<br />
This paper study was to represent an original effective traffic sign detection and recognition approach towards the design of TSRS. As a recent research topic TSRS is getting popular day by day. In this study, it is done using SVM and CNN classification algorithms to decline extensive traffic difficulties. In our ex- periment, we obtained highest training accuracy from CNN 99.56%, while the test accuracy was 96.40%. We showed the real time evaluation results of SVM, where the system performed 98.33% accurately. Many research focused on SVM and CNN to solve this specific problem. <br />
In future, our aim is to increase the number of traffic sign classes with large amount of quality data. As in a machine learning research, to maintain data vol- ume and data quality is most important and time consuming part. To provide a complete system to overcome the traffic issues our ambition is to implement a system with distance calculation form car to traffic sign. <br />
<br />
== References ==<br />
<br />
[1] Yang, WJ., Luo, CC., Chung, PC., Yang, JF.: Simplified Neural Networks with Smart Detection for Road Traffic Sign Recognition. Lecture Notes in Networks and Systems, vol 69. Springer, Cham (2020)<br />
<br />
[2] Dhar, M. Z., Abedin, T., Biswas, Datta, A.: Traffic sign detection — A new approach and recognition using convolution neural network. IEEE Region 10 Humanitarian Technology Conference (R10-HTC), Dhaka, pp. 416-419, (2017) <br />
<br />
[3] Aashrith, V., Smriti, S.: Traffic Sign Detection and Recognition using a CNN Ensemble. IEEE International Conference on Consumer Electronics (ICCE). 07 March, (2019)<br />
<br />
[4] Zaki, P. S., et al.: Traffic Signs Detection and Recognition System using Deep Learning. In: Conference on Intelligent Computing and Information Systems (ICICIS) (2019)</div>Eigurayhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Traffic_Sign_Recognition_System_(TSRS):_SVM_and_Convolutional_Neural_Network&diff=51201Traffic Sign Recognition System (TSRS): SVM and Convolutional Neural Network2021-12-02T23:32:21Z<p>Eiguray: </p>
<hr />
<div>== Presented by == <br />
Egemen Guray<br />
<br />
== Introduction ==<br />
In order to increase the sustainability and decrease incidents of autonomous driving, Hasan Nazmul et.al. presents various techniques and models to build a Traffic Sign Recognition System. In this paper Convolutional Neural Network, and Support Vector Machine models are proposed to address this issue. <br />
<br />
== Previous Work == <br />
Over the years researchers published papers on traffic sign recognition using SVM and CNN. In 2019, Wei-Jong Yang et al proposed a CNN and SVM based TSRS. Using SVM model recognizes the sign and using CNN model predicts which traffic sign it is with a 97% accuracy.<br />
<br />
In 2017, Prashengit Dhar et al. proposed a Traffic Sign Recognition system which achieves 97% accuracy with a HSV color model and a CNN classifier. <br />
<br />
In 2019, Aashrith and et al. used CNN to recognize traffic signs. They achieved 99.18% accuracy on Belgium Data and 99.50% accuracy on German Traffic Sign Benchmark (GTSDB). <br />
<br />
In 2019 Pavly Salah Zaki and et al. published a paper on multiple entity detection system which uses combination of F-RCNN and Single Shot Multi Box Detector along with several feature extractors to detect traffic sign on GTSB dataset.<br />
<br />
== Proposed Methodology ==<br />
<br />
For recognizing traffic signs Hasan Nazmul et al. uses two approaches based on CNN and SVM.<br />
They have considered 12 different classes each with 100 images. Dataset was built from cropping video frame which have been collected random videos. <br />
<br />
<br />
== Model Architecture ==<br />
Model is composed of convolutional layers, Relu function, polling layers, and fully connected layer. <br />
<br />
In the first step, Convolutional layer takes one input and output image in 2D format to extract features from images. After that Relu function replaces all negative values to 0. <br />
<br />
Max polling in the polling layer progressively decrease the size of the input images thus decreases the number of dimensions and the computational power needed while helping model to avoid overfitting as well. <br />
<br />
Fully connected layer combines all features into output stage, calculated error gets to be minimized using backpropagation.<br />
<br />
== Results ==<br />
CNN classification method for Traffic sign prediction scored 99.56% accuracy on training and 96.40% validation accuracy. <br />
<br />
Plot of training/validation accuracy and loss of CNN model is visualized in figure 5 and 6.<br />
<br />
<br />
== Conclusion ==<br />
<br />
This paper study was to represent an original effective traffic sign detection and recognition approach towards the design of TSRS. As a recent research topic TSRS is getting popular day by day. In this study, it is done using SVM and CNN classification algorithms to decline extensive traffic difficulties. In our ex- periment, we obtained highest training accuracy from CNN 99.56%, while the test accuracy was 96.40%. We showed the real time evaluation results of SVM, where the system performed 98.33% accurately. Many research focused on SVM and CNN to solve this specific problem. <br />
In future, our aim is to increase the number of traffic sign classes with large amount of quality data. As in a machine learning research, to maintain data vol- ume and data quality is most important and time consuming part. To provide a complete system to overcome the traffic issues our ambition is to implement a system with distance calculation form car to traffic sign. <br />
<br />
== References ==<br />
<br />
[1] Yang, WJ., Luo, CC., Chung, PC., Yang, JF.: Simplified Neural Networks with Smart Detection for Road Traffic Sign Recognition. Lecture Notes in Networks and Systems, vol 69. Springer, Cham (2020) <br />
[2] Dhar, M. Z., Abedin, T., Biswas, Datta, A.: Traffic sign detection — A new <br />
approach and recognition using convolution neural network. IEEE Region 10 <br />
Humanitarian Technology Conference (R10-HTC), Dhaka, pp. 416-419, (2017) <br />
[3] Aashrith, V., Smriti, S.: Traffic Sign Detection and Recognition using a CNN Ensemble. IEEE International Conference on Consumer Electronics (ICCE). 07 March, (2019)<br />
<br />
[4] Zaki, P. S., et al.: Traffic Signs Detection and Recognition System using Deep Learning. In: Conference on Intelligent Computing and Information Systems (ICICIS) (2019)</div>Eigurayhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Traffic_Sign_Recognition_System_(TSRS):_SVM_and_Convolutional_Neural_Network&diff=51200Traffic Sign Recognition System (TSRS): SVM and Convolutional Neural Network2021-12-02T23:30:56Z<p>Eiguray: </p>
<hr />
<div>== Presented by == <br />
Egemen Guray<br />
<br />
== Introduction ==<br />
In order to increase the sustainability and decrease incidents of autonomous driving, Hasan Nazmul et.al. presents various techniques and models to build a Traffic Sign Recognition System. In this paper Convolutional Neural Network, and Support Vector Machine models are proposed to address this issue. <br />
<br />
== Previous Work == <br />
Over the years researchers published papers on traffic sign recognition using SVM and CNN. In 2019, Wei-Jong Yang et al proposed a CNN and SVM based TSRS. Using SVM model recognizes the sign and using CNN model predicts which traffic sign it is with a 97% accuracy.<br />
<br />
In 2017, Prashengit Dhar et al. proposed a Traffic Sign Recognition system which achieves 97% accuracy with a HSV color model and a CNN classifier. <br />
<br />
In 2019, Aashrith and et al. used CNN to recognize traffic signs. They achieved 99.18% accuracy on Belgium Data and 99.50% accuracy on German Traffic Sign Benchmark (GTSDB). <br />
<br />
In 2019 Pavly Salah Zaki and et al. published a paper on multiple entity detection system which uses combination of F-RCNN and Single Shot Multi Box Detector along with several feature extractors to detect traffic sign on GTSB dataset.<br />
<br />
== Proposed Methodology ==<br />
<br />
For recognizing traffic signs Hasan Nazmul et al. uses two approaches based on CNN and SVM.<br />
They have considered 12 different classes each with 100 images. Dataset was built from cropping video frame which have been collected random videos. <br />
<br />
<br />
== Model Architecture ==<br />
<br />
== Results ==<br />
<br />
== Conclusion ==<br />
<br />
== References ==</div>Eigurayhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat441F21&diff=50826stat441F212021-11-25T04:14:27Z<p>Eiguray: /* Paper presentation */</p>
<hr />
<div><br />
<br />
== [[F20-STAT 441/841 CM 763-Proposal| Project Proposal ]] ==<br />
<br />
<!--[https://goo.gl/forms/apurag4dr9kSR76X2 Your feedback on presentations]--><br />
<br />
=Paper presentation=<br />
{| class="wikitable"<br />
<br />
{| border="1" cellpadding="3"<br />
|-<br />
|width="60pt"|Date<br />
|width="250pt"|Name <br />
|width="15pt"|Paper number <br />
|width="700pt"|Title<br />
|width="15pt"|Link to the paper<br />
|width="30pt"|Link to the summary<br />
|width="30pt"|Link to the video<br />
|-<br />
|Sep 15 (example)||Ri Wang || ||Sequence to sequence learning with neural networks.||[http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Going_Deeper_with_Convolutions Summary] || [https://youtu.be/JWozRg_X-Vg?list=PLehuLRPyt1HzXDemu7K4ETcF0Ld_B5adG&t=539]<br />
|-<br />
|Week of Nov 16 || Ali Ghodsi || || || || ||<br />
|-<br />
|Week of Nov 22 || Jared Feng, Xipeng Huang, Mingwei Xu, Tingzhou Yu|| || Don't Just Blame Over-parametrization for Over-confidence: Theoretical Analysis of Calibration in Binary Classification || [http://proceedings.mlr.press/v139/bai21c/bai21c.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Don%27t_Just_Blame_Over-parametrization Summary] ||<br />
|-<br />
|Week of Nov 29 || Kanika Chopra, Yush Rajcoomar || || Automatic Bank Fraud Detection Using Support Vector Machines || [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.863.5804&rep=rep1&type=pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Automatic_Bank_Fraud_Detection_Using_Support_Vector_Machines Summary] ||<br />
|-<br />
|Week of Nov 22 || Zeng Mingde, Lin Xiaoyu, Fan Joshua, Rao Chen Min || || Do Vision Transformers See Like Convolutional Neural Networks? || [https://proceedings.neurips.cc/paper/2021/file/652cf38361a209088302ba2b8b7f51e0-Paper.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Do_Vision_Transformers_See_Like_CNN Summary] ||<br />
|-<br />
|Week of Nov 22 || Justin D'Astous, Waqas Hamed, Stefan Vladusic, Ethan O'Farrell || || A Probabilistic Approach to Neural Network Pruning || [http://proceedings.mlr.press/v139/qian21a/qian21a.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Summary_of_A_Probabilistic_Approach_to_Neural_Network_Pruning Summary] ||<br />
|-<br />
|Week of Nov 22 || Cassandra Wong, Anastasiia Livochka, Maryam Yalsavar, David Evans || || Patch-Based Convolutional Neural Network for Whole Slide Tissue Image Classification || [https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Hou_Patch-Based_Convolutional_Neural_CVPR_2016_paper.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Patch_Based_Convolutional_Neural_Network_for_Whole_Slide_Tissue_Image_Classification Summary] ||<br />
|-<br />
|Week of Nov 29 || Jessie Man Wai Chin, Yi Lin Ooi, Yaqi Shi, Shwen Lyng Ngew || || CatBoost: unbiased boosting with categorical features || [https://proceedings.neurips.cc/paper/2018/file/14491b756b3a51daac41c24863285549-Paper.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=CatBoost:_unbiased_boosting_with_categorical_features Summary] ||<br />
|-<br />
|Week of Nov 29 || Eric Anderson, Chengzhi Wang, Kai Zhong, YiJing Zhou || || || || ||<br />
|-<br />
|Week of Nov 29 || Ethan Cyrenne, Dieu Hoa Nguyen, Mary Jane Sin, Carolyn Wang || || || || ||<br />
|-<br />
|Week of Nov 29 || Bowen Zhang, Tyler Magnus Verhaar, Sam Senko || || Deep Double Descent: Where Bigger Models and More Data Hurt || [https://arxiv.org/pdf/1912.02292.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Deep_Double_Descent_Where_Bigger_Models_and_More_Data_Hurt Summary] ||<br />
|-<br />
|Week of Nov 29 || Chun Waan Loke, Peter Chong, Clarice Osmond, Zhilong Li|| || XGBoost: A Scalable Tree Boosting System || [https://www.kdd.org/kdd2016/papers/files/rfp0697-chenAemb.pdf Paper] || ||<br />
|-<br />
|Week of Nov 22 || Ann Gie Wong, Curtis Li, Hannah Kerr || || The Detection of Black Ice Accidents for Preventative Automated Vehicles Using Convolutional Neural Networks || [https://www.mdpi.com/2079-9292/9/12/2178/htm Paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=The_Detection_of_Black_Ice_Accidents_Using_CNNs&fbclid=IwAR0K4YdnL_hdRnOktmJn8BI6-Ra3oitjJof0YwluZgUP1LVFHK5jyiBZkvQ Summary] ||<br />
|-<br />
|Week of Nov 22 || Yuwei Liu, Daniel Mao|| || Depthwise Convolution Is All You Need for Learning Multiple Visual Domains || [https://arxiv.org/abs/1902.00927 Paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Depthwise_Convolution_Is_All_You_Need_for_Learning_Multiple_Visual_Domains Summary] ||<br />
|-<br />
|Week of Nov 29 || Lingshan Wang, Yifan Li, Ziyi Liu || || Deep Learning for Extreme Multi-label Text Classification || [https://dl.acm.org/doi/pdf/10.1145/3077136.3080834 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Deep_Learning_for_Extreme_Multi-label_Text_Classification Summary]||<br />
|-<br />
|-<br />
|Week of Nov 29 || Kar Lok Ng, Muhan (Iris) Li || || || || ||<br />
|-<br />
|Week of Nov 29 ||Kun Wang || || Convolutional neural network for diagnosis of viral pneumonia and COVID-19 alike diseases|| [https://doi-org.proxy.lib.uwaterloo.ca/10.1111/exsy.12705 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Convolutional_neural_network_for_diagnosis_of_viral_pneumonia_and_COVID-19_alike_diseases Summary] ||<br />
|-<br />
|Week of Nov 29 ||Egemen Guray || || Traffic Sign Recognition System (TSRS): SVM and Convolutional Neural Network || [https://www.researchgate.net/publication/344399165_Traffic_Sign_Recognition_System_TSRS_SVM_and_Convolutional_Neural_Network Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Traffic_Sign_Recognition_System_(TSRS):_SVM_and_Convolutional_Neural_Network Summary] ||<br />
|-<br />
|Week of Nov 29 ||Bsodjahi || || Bayesian Network as a Decision Tool for Predicting ALS Disease || https://www.mdpi.com/2076-3425/11/2/150/pdf || ||<br />
|-<br />
|Week of Nov 29 ||Xin Yan, Yishu Duan, Xibei Di || || Predicting Hurricane Trajectories Using a Recurrent Neural Network || [https://arxiv.org/pdf/1802.02548.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Predicting_Hurricane_Trajectories_Using_a_Recurrent_Neural_Network Summary]||<br />
|-<br />
|Week of Nov 29 ||Ankitha Anugu, Yushan Chen, Yuying Huang || || A Game Theoretic Approach to Class-wise Selective Rationalization || [https://arxiv.org/pdf/1910.12853.pdf Paper] || ||<br />
|-<br />
|Week of Nov 29 ||Aavinash Syamala, Dilmeet Malhi, Sohan Islam, Vansh Joshi || || Research on Multiple Classification Based on Improved SVM Algorithm for Balanced Binary Decision Tree || [https://www.hindawi.com/journals/sp/2021/5560465/ Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Research_on_Multiple_Classification_Based_on_Improved_SVM_Algorithm_for_Balanced_Binary_Decision_Tree Summary]||<br />
|-<br />
|Week of Nov 29 ||Christian Mitrache, Alexandra Mossman, Jessica Saini, Aaron Renggli|| || U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging|| [https://proceedings.neurips.cc/paper/2019/file/57bafb2c2dfeefba931bb03a835b1fa9-Paper.pdf?fbclid=IwAR1dZpx9vU1pSPTSm_nwk6uBU7TYJ2HNTrsqjaH-9ZycE_PFpFjJoHg1zhQ]||</div>Eigurayhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Traffic_Sign_Recognition_System_(TSRS):_SVM_and_Convolutional_Neural_Network&diff=50825Traffic Sign Recognition System (TSRS): SVM and Convolutional Neural Network2021-11-25T04:13:53Z<p>Eiguray: Created page with "== Presented by == Egemen Guray == Introduction == == Previous Work == == Motivation == == Model Architecture == == Results == == Conclusion == == References =="</p>
<hr />
<div>== Presented by == <br />
Egemen Guray<br />
<br />
== Introduction ==<br />
<br />
<br />
== Previous Work == <br />
<br />
== Motivation ==<br />
<br />
== Model Architecture ==<br />
<br />
== Results ==<br />
<br />
== Conclusion ==<br />
<br />
== References ==</div>Eigurayhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat441F21&diff=50824stat441F212021-11-25T04:11:38Z<p>Eiguray: </p>
<hr />
<div><br />
<br />
== [[F20-STAT 441/841 CM 763-Proposal| Project Proposal ]] ==<br />
<br />
<!--[https://goo.gl/forms/apurag4dr9kSR76X2 Your feedback on presentations]--><br />
<br />
=Paper presentation=<br />
{| class="wikitable"<br />
<br />
{| border="1" cellpadding="3"<br />
|-<br />
|width="60pt"|Date<br />
|width="250pt"|Name <br />
|width="15pt"|Paper number <br />
|width="700pt"|Title<br />
|width="15pt"|Link to the paper<br />
|width="30pt"|Link to the summary<br />
|width="30pt"|Link to the video<br />
|-<br />
|Sep 15 (example)||Ri Wang || ||Sequence to sequence learning with neural networks.||[http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Going_Deeper_with_Convolutions Summary] || [https://youtu.be/JWozRg_X-Vg?list=PLehuLRPyt1HzXDemu7K4ETcF0Ld_B5adG&t=539]<br />
|-<br />
|Week of Nov 16 || Ali Ghodsi || || || || ||<br />
|-<br />
|Week of Nov 22 || Jared Feng, Xipeng Huang, Mingwei Xu, Tingzhou Yu|| || Don't Just Blame Over-parametrization for Over-confidence: Theoretical Analysis of Calibration in Binary Classification || [http://proceedings.mlr.press/v139/bai21c/bai21c.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Don%27t_Just_Blame_Over-parametrization Summary] ||<br />
|-<br />
|Week of Nov 29 || Kanika Chopra, Yush Rajcoomar || || Automatic Bank Fraud Detection Using Support Vector Machines || [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.863.5804&rep=rep1&type=pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Automatic_Bank_Fraud_Detection_Using_Support_Vector_Machines Summary] ||<br />
|-<br />
|Week of Nov 22 || Zeng Mingde, Lin Xiaoyu, Fan Joshua, Rao Chen Min || || Do Vision Transformers See Like Convolutional Neural Networks? || [https://proceedings.neurips.cc/paper/2021/file/652cf38361a209088302ba2b8b7f51e0-Paper.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Do_Vision_Transformers_See_Like_CNN Summary] ||<br />
|-<br />
|Week of Nov 22 || Justin D'Astous, Waqas Hamed, Stefan Vladusic, Ethan O'Farrell || || A Probabilistic Approach to Neural Network Pruning || [http://proceedings.mlr.press/v139/qian21a/qian21a.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Summary_of_A_Probabilistic_Approach_to_Neural_Network_Pruning Summary] ||<br />
|-<br />
|Week of Nov 22 || Cassandra Wong, Anastasiia Livochka, Maryam Yalsavar, David Evans || || Patch-Based Convolutional Neural Network for Whole Slide Tissue Image Classification || [https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Hou_Patch-Based_Convolutional_Neural_CVPR_2016_paper.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Patch_Based_Convolutional_Neural_Network_for_Whole_Slide_Tissue_Image_Classification Summary] ||<br />
|-<br />
|Week of Nov 29 || Jessie Man Wai Chin, Yi Lin Ooi, Yaqi Shi, Shwen Lyng Ngew || || CatBoost: unbiased boosting with categorical features || [https://proceedings.neurips.cc/paper/2018/file/14491b756b3a51daac41c24863285549-Paper.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=CatBoost:_unbiased_boosting_with_categorical_features Summary] ||<br />
|-<br />
|Week of Nov 29 || Eric Anderson, Chengzhi Wang, Kai Zhong, YiJing Zhou || || || || ||<br />
|-<br />
|Week of Nov 29 || Ethan Cyrenne, Dieu Hoa Nguyen, Mary Jane Sin, Carolyn Wang || || || || ||<br />
|-<br />
|Week of Nov 29 || Bowen Zhang, Tyler Magnus Verhaar, Sam Senko || || Deep Double Descent: Where Bigger Models and More Data Hurt || [https://arxiv.org/pdf/1912.02292.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Deep_Double_Descent_Where_Bigger_Models_and_More_Data_Hurt Summary] ||<br />
|-<br />
|Week of Nov 29 || Chun Waan Loke, Peter Chong, Clarice Osmond, Zhilong Li|| || XGBoost: A Scalable Tree Boosting System || [https://www.kdd.org/kdd2016/papers/files/rfp0697-chenAemb.pdf Paper] || ||<br />
|-<br />
|Week of Nov 22 || Ann Gie Wong, Curtis Li, Hannah Kerr || || The Detection of Black Ice Accidents for Preventative Automated Vehicles Using Convolutional Neural Networks || [https://www.mdpi.com/2079-9292/9/12/2178/htm Paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=The_Detection_of_Black_Ice_Accidents_Using_CNNs&fbclid=IwAR0K4YdnL_hdRnOktmJn8BI6-Ra3oitjJof0YwluZgUP1LVFHK5jyiBZkvQ Summary] ||<br />
|-<br />
|Week of Nov 22 || Yuwei Liu, Daniel Mao|| || Depthwise Convolution Is All You Need for Learning Multiple Visual Domains || [https://arxiv.org/abs/1902.00927 Paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Depthwise_Convolution_Is_All_You_Need_for_Learning_Multiple_Visual_Domains Summary] ||<br />
|-<br />
|Week of Nov 29 || Lingshan Wang, Yifan Li, Ziyi Liu || || Deep Learning for Extreme Multi-label Text Classification || [https://dl.acm.org/doi/pdf/10.1145/3077136.3080834 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Deep_Learning_for_Extreme_Multi-label_Text_Classification Summary]||<br />
|-<br />
|-<br />
|Week of Nov 29 || Kar Lok Ng, Muhan (Iris) Li || || || || ||<br />
|-<br />
|Week of Nov 29 ||Kun Wang || || Convolutional neural network for diagnosis of viral pneumonia and COVID-19 alike diseases|| [https://doi-org.proxy.lib.uwaterloo.ca/10.1111/exsy.12705 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Convolutional_neural_network_for_diagnosis_of_viral_pneumonia_and_COVID-19_alike_diseases Summary] ||<br />
|-<br />
|Week of Nov 29 ||Egemen Guray || || Traffic Sign Recognition System (TSRS): SVM and Convolutional Neural Network || [https://www.researchgate.net/publication/344399165_Traffic_Sign_Recognition_System_TSRS_SVM_and_Convolutional_Neural_Network Paper] || ||<br />
|-<br />
|Week of Nov 29 ||Bsodjahi || || Bayesian Network as a Decision Tool for Predicting ALS Disease || https://www.mdpi.com/2076-3425/11/2/150/pdf || ||<br />
|-<br />
|Week of Nov 29 ||Xin Yan, Yishu Duan, Xibei Di || || Predicting Hurricane Trajectories Using a Recurrent Neural Network || [https://arxiv.org/pdf/1802.02548.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Predicting_Hurricane_Trajectories_Using_a_Recurrent_Neural_Network Summary]||<br />
|-<br />
|Week of Nov 29 ||Ankitha Anugu, Yushan Chen, Yuying Huang || || A Game Theoretic Approach to Class-wise Selective Rationalization || [https://arxiv.org/pdf/1910.12853.pdf Paper] || ||<br />
|-<br />
|Week of Nov 29 ||Aavinash Syamala, Dilmeet Malhi, Sohan Islam, Vansh Joshi || || Research on Multiple Classification Based on Improved SVM Algorithm for Balanced Binary Decision Tree || [https://www.hindawi.com/journals/sp/2021/5560465/ Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Research_on_Multiple_Classification_Based_on_Improved_SVM_Algorithm_for_Balanced_Binary_Decision_Tree Summary]||<br />
|-<br />
|Week of Nov 29 ||Christian Mitrache, Alexandra Mossman, Jessica Saini, Aaron Renggli|| || U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging|| [https://proceedings.neurips.cc/paper/2019/file/57bafb2c2dfeefba931bb03a835b1fa9-Paper.pdf?fbclid=IwAR1dZpx9vU1pSPTSm_nwk6uBU7TYJ2HNTrsqjaH-9ZycE_PFpFjJoHg1zhQ]||</div>Eigurayhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat441F21&diff=50823stat441F212021-11-25T04:11:16Z<p>Eiguray: /* Paper presentation */</p>
<hr />
<div><br />
<br />
== [[F20-STAT 441/841 CM 763-Proposal| Project Proposal ]] ==<br />
<br />
<!--[https://goo.gl/forms/apurag4dr9kSR76X2 Your feedback on presentations]--><br />
<br />
=Paper presentation=<br />
{| class="wikitable"<br />
<br />
{| border="1" cellpadding="3"<br />
|-<br />
|width="60pt"|Date<br />
|width="250pt"|Name <br />
|width="15pt"|Paper number <br />
|width="700pt"|Title<br />
|width="15pt"|Link to the paper<br />
|width="30pt"|Link to the summary<br />
|width="30pt"|Link to the video<br />
|-<br />
|Sep 15 (example)||Ri Wang || ||Sequence to sequence learning with neural networks.||[http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Going_Deeper_with_Convolutions Summary] || [https://youtu.be/JWozRg_X-Vg?list=PLehuLRPyt1HzXDemu7K4ETcF0Ld_B5adG&t=539]<br />
|-<br />
|Week of Nov 16 || Ali Ghodsi || || || || ||<br />
|-<br />
|Week of Nov 22 || Jared Feng, Xipeng Huang, Mingwei Xu, Tingzhou Yu|| || Don't Just Blame Over-parametrization for Over-confidence: Theoretical Analysis of Calibration in Binary Classification || [http://proceedings.mlr.press/v139/bai21c/bai21c.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Don%27t_Just_Blame_Over-parametrization Summary] ||<br />
|-<br />
|Week of Nov 29 || Kanika Chopra, Yush Rajcoomar || || Automatic Bank Fraud Detection Using Support Vector Machines || [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.863.5804&rep=rep1&type=pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Automatic_Bank_Fraud_Detection_Using_Support_Vector_Machines Summary] ||<br />
|-<br />
|Week of Nov 22 || Zeng Mingde, Lin Xiaoyu, Fan Joshua, Rao Chen Min || || Do Vision Transformers See Like Convolutional Neural Networks? || [https://proceedings.neurips.cc/paper/2021/file/652cf38361a209088302ba2b8b7f51e0-Paper.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Do_Vision_Transformers_See_Like_CNN Summary] ||<br />
|-<br />
|Week of Nov 22 || Justin D'Astous, Waqas Hamed, Stefan Vladusic, Ethan O'Farrell || || A Probabilistic Approach to Neural Network Pruning || [http://proceedings.mlr.press/v139/qian21a/qian21a.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Summary_of_A_Probabilistic_Approach_to_Neural_Network_Pruning Summary] ||<br />
|-<br />
|Week of Nov 22 || Cassandra Wong, Anastasiia Livochka, Maryam Yalsavar, David Evans || || Patch-Based Convolutional Neural Network for Whole Slide Tissue Image Classification || [https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Hou_Patch-Based_Convolutional_Neural_CVPR_2016_paper.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Patch_Based_Convolutional_Neural_Network_for_Whole_Slide_Tissue_Image_Classification Summary] ||<br />
|-<br />
|Week of Nov 29 || Jessie Man Wai Chin, Yi Lin Ooi, Yaqi Shi, Shwen Lyng Ngew || || CatBoost: unbiased boosting with categorical features || [https://proceedings.neurips.cc/paper/2018/file/14491b756b3a51daac41c24863285549-Paper.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=CatBoost:_unbiased_boosting_with_categorical_features Summary] ||<br />
|-<br />
|Week of Nov 29 || Eric Anderson, Chengzhi Wang, Kai Zhong, YiJing Zhou || || || || ||<br />
|-<br />
|Week of Nov 29 || Ethan Cyrenne, Dieu Hoa Nguyen, Mary Jane Sin, Carolyn Wang || || || || ||<br />
|-<br />
|Week of Nov 29 || Bowen Zhang, Tyler Magnus Verhaar, Sam Senko || || Deep Double Descent: Where Bigger Models and More Data Hurt || [https://arxiv.org/pdf/1912.02292.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Deep_Double_Descent_Where_Bigger_Models_and_More_Data_Hurt Summary] ||<br />
|-<br />
|Week of Nov 29 || Chun Waan Loke, Peter Chong, Clarice Osmond, Zhilong Li|| || XGBoost: A Scalable Tree Boosting System || [https://www.kdd.org/kdd2016/papers/files/rfp0697-chenAemb.pdf Paper] || ||<br />
|-<br />
|Week of Nov 22 || Ann Gie Wong, Curtis Li, Hannah Kerr || || The Detection of Black Ice Accidents for Preventative Automated Vehicles Using Convolutional Neural Networks || [https://www.mdpi.com/2079-9292/9/12/2178/htm Paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=The_Detection_of_Black_Ice_Accidents_Using_CNNs&fbclid=IwAR0K4YdnL_hdRnOktmJn8BI6-Ra3oitjJof0YwluZgUP1LVFHK5jyiBZkvQ Summary] ||<br />
|-<br />
|Week of Nov 22 || Yuwei Liu, Daniel Mao|| || Depthwise Convolution Is All You Need for Learning Multiple Visual Domains || [https://arxiv.org/abs/1902.00927 Paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Depthwise_Convolution_Is_All_You_Need_for_Learning_Multiple_Visual_Domains Summary] ||<br />
|-<br />
|Week of Nov 29 || Lingshan Wang, Yifan Li, Ziyi Liu || || Deep Learning for Extreme Multi-label Text Classification || [https://dl.acm.org/doi/pdf/10.1145/3077136.3080834 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Deep_Learning_for_Extreme_Multi-label_Text_Classification Summary]||<br />
|-<br />
|-<br />
|Week of Nov 29 || Kar Lok Ng, Muhan (Iris) Li || || || || ||<br />
|-<br />
|Week of Nov 29 ||Kun Wang || || Convolutional neural network for diagnosis of viral pneumonia and COVID-19 alike diseases|| [https://doi-org.proxy.lib.uwaterloo.ca/10.1111/exsy.12705 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Convolutional_neural_network_for_diagnosis_of_viral_pneumonia_and_COVID-19_alike_diseases Summary] ||<br />
|-<br />
|Week of Nov 29 ||Egemen Guray || || Traffic Sign Recognition System (TSRS): SVM and Convolutional Neural Network || [https://www.researchgate.net/publication/344399165_Traffic_Sign_Recognition_System_TSRS_SVM_and_Convolutional_Neural_Network Paper] || [Paper] ||<br />
|-<br />
|Week of Nov 29 ||Bsodjahi || || Bayesian Network as a Decision Tool for Predicting ALS Disease || https://www.mdpi.com/2076-3425/11/2/150/pdf || ||<br />
|-<br />
|Week of Nov 29 ||Xin Yan, Yishu Duan, Xibei Di || || Predicting Hurricane Trajectories Using a Recurrent Neural Network || [https://arxiv.org/pdf/1802.02548.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Predicting_Hurricane_Trajectories_Using_a_Recurrent_Neural_Network Summary]||<br />
|-<br />
|Week of Nov 29 ||Ankitha Anugu, Yushan Chen, Yuying Huang || || A Game Theoretic Approach to Class-wise Selective Rationalization || [https://arxiv.org/pdf/1910.12853.pdf Paper] || ||<br />
|-<br />
|Week of Nov 29 ||Aavinash Syamala, Dilmeet Malhi, Sohan Islam, Vansh Joshi || || Research on Multiple Classification Based on Improved SVM Algorithm for Balanced Binary Decision Tree || [https://www.hindawi.com/journals/sp/2021/5560465/ Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Research_on_Multiple_Classification_Based_on_Improved_SVM_Algorithm_for_Balanced_Binary_Decision_Tree Summary]||<br />
|-<br />
|Week of Nov 29 ||Christian Mitrache, Alexandra Mossman, Jessica Saini, Aaron Renggli|| || U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging|| [https://proceedings.neurips.cc/paper/2019/file/57bafb2c2dfeefba931bb03a835b1fa9-Paper.pdf?fbclid=IwAR1dZpx9vU1pSPTSm_nwk6uBU7TYJ2HNTrsqjaH-9ZycE_PFpFjJoHg1zhQ]||</div>Eigurayhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat441F21&diff=50822stat441F212021-11-25T04:08:13Z<p>Eiguray: /* Paper presentation */</p>
<hr />
<div><br />
<br />
== [[F20-STAT 441/841 CM 763-Proposal| Project Proposal ]] ==<br />
<br />
<!--[https://goo.gl/forms/apurag4dr9kSR76X2 Your feedback on presentations]--><br />
<br />
=Paper presentation=<br />
{| class="wikitable"<br />
<br />
{| border="1" cellpadding="3"<br />
|-<br />
|width="60pt"|Date<br />
|width="250pt"|Name <br />
|width="15pt"|Paper number <br />
|width="700pt"|Title<br />
|width="15pt"|Link to the paper<br />
|width="30pt"|Link to the summary<br />
|width="30pt"|Link to the video<br />
|-<br />
|Sep 15 (example)||Ri Wang || ||Sequence to sequence learning with neural networks.||[http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Going_Deeper_with_Convolutions Summary] || [https://youtu.be/JWozRg_X-Vg?list=PLehuLRPyt1HzXDemu7K4ETcF0Ld_B5adG&t=539]<br />
|-<br />
|Week of Nov 16 || Ali Ghodsi || || || || ||<br />
|-<br />
|Week of Nov 22 || Jared Feng, Xipeng Huang, Mingwei Xu, Tingzhou Yu|| || Don't Just Blame Over-parametrization for Over-confidence: Theoretical Analysis of Calibration in Binary Classification || [http://proceedings.mlr.press/v139/bai21c/bai21c.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Don%27t_Just_Blame_Over-parametrization Summary] ||<br />
|-<br />
|Week of Nov 29 || Kanika Chopra, Yush Rajcoomar || || Automatic Bank Fraud Detection Using Support Vector Machines || [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.863.5804&rep=rep1&type=pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Automatic_Bank_Fraud_Detection_Using_Support_Vector_Machines Summary] ||<br />
|-<br />
|Week of Nov 22 || Zeng Mingde, Lin Xiaoyu, Fan Joshua, Rao Chen Min || || Do Vision Transformers See Like Convolutional Neural Networks? || [https://proceedings.neurips.cc/paper/2021/file/652cf38361a209088302ba2b8b7f51e0-Paper.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Do_Vision_Transformers_See_Like_CNN Summary] ||<br />
|-<br />
|Week of Nov 22 || Justin D'Astous, Waqas Hamed, Stefan Vladusic, Ethan O'Farrell || || A Probabilistic Approach to Neural Network Pruning || [http://proceedings.mlr.press/v139/qian21a/qian21a.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Summary_of_A_Probabilistic_Approach_to_Neural_Network_Pruning Summary] ||<br />
|-<br />
|Week of Nov 22 || Cassandra Wong, Anastasiia Livochka, Maryam Yalsavar, David Evans || || Patch-Based Convolutional Neural Network for Whole Slide Tissue Image Classification || [https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Hou_Patch-Based_Convolutional_Neural_CVPR_2016_paper.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Patch_Based_Convolutional_Neural_Network_for_Whole_Slide_Tissue_Image_Classification Summary] ||<br />
|-<br />
|Week of Nov 29 || Jessie Man Wai Chin, Yi Lin Ooi, Yaqi Shi, Shwen Lyng Ngew || || CatBoost: unbiased boosting with categorical features || [https://proceedings.neurips.cc/paper/2018/file/14491b756b3a51daac41c24863285549-Paper.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=CatBoost:_unbiased_boosting_with_categorical_features Summary] ||<br />
|-<br />
|Week of Nov 29 || Eric Anderson, Chengzhi Wang, Kai Zhong, YiJing Zhou || || || || ||<br />
|-<br />
|Week of Nov 29 || Ethan Cyrenne, Dieu Hoa Nguyen, Mary Jane Sin, Carolyn Wang || || || || ||<br />
|-<br />
|Week of Nov 29 || Bowen Zhang, Tyler Magnus Verhaar, Sam Senko || || Deep Double Descent: Where Bigger Models and More Data Hurt || [https://arxiv.org/pdf/1912.02292.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Deep_Double_Descent_Where_Bigger_Models_and_More_Data_Hurt Summary] ||<br />
|-<br />
|Week of Nov 29 || Chun Waan Loke, Peter Chong, Clarice Osmond, Zhilong Li|| || XGBoost: A Scalable Tree Boosting System || [https://www.kdd.org/kdd2016/papers/files/rfp0697-chenAemb.pdf Paper] || ||<br />
|-<br />
|Week of Nov 22 || Ann Gie Wong, Curtis Li, Hannah Kerr || || The Detection of Black Ice Accidents for Preventative Automated Vehicles Using Convolutional Neural Networks || [https://www.mdpi.com/2079-9292/9/12/2178/htm Paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=The_Detection_of_Black_Ice_Accidents_Using_CNNs&fbclid=IwAR0K4YdnL_hdRnOktmJn8BI6-Ra3oitjJof0YwluZgUP1LVFHK5jyiBZkvQ Summary] ||<br />
|-<br />
|Week of Nov 22 || Yuwei Liu, Daniel Mao|| || Depthwise Convolution Is All You Need for Learning Multiple Visual Domains || [https://arxiv.org/abs/1902.00927 Paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Depthwise_Convolution_Is_All_You_Need_for_Learning_Multiple_Visual_Domains Summary] ||<br />
|-<br />
|Week of Nov 29 || Lingshan Wang, Yifan Li, Ziyi Liu || || Deep Learning for Extreme Multi-label Text Classification || [https://dl.acm.org/doi/pdf/10.1145/3077136.3080834 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Deep_Learning_for_Extreme_Multi-label_Text_Classification Summary]||<br />
|-<br />
|-<br />
|Week of Nov 29 || Kar Lok Ng, Muhan (Iris) Li || || || || ||<br />
|-<br />
|Week of Nov 29 ||Kun Wang || || Convolutional neural network for diagnosis of viral pneumonia and COVID-19 alike diseases|| [https://doi-org.proxy.lib.uwaterloo.ca/10.1111/exsy.12705 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Convolutional_neural_network_for_diagnosis_of_viral_pneumonia_and_COVID-19_alike_diseases Summary] ||<br />
|-<br />
|Week of Nov 29 ||Egemen Guray || || Traffic Sign Recognition System (TSRS): SVM and Convolutional Neural Network || [https://www.researchgate.net/publication/344399165_Traffic_Sign_Recognition_System_TSRS_SVM_and_Convolutional_Neural_Network Paper] || ||<br />
|-<br />
|Week of Nov 29 ||Bsodjahi || || Bayesian Network as a Decision Tool for Predicting ALS Disease || https://www.mdpi.com/2076-3425/11/2/150/pdf || ||<br />
|-<br />
|Week of Nov 29 ||Xin Yan, Yishu Duan, Xibei Di || || Predicting Hurricane Trajectories Using a Recurrent Neural Network || [https://arxiv.org/pdf/1802.02548.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Predicting_Hurricane_Trajectories_Using_a_Recurrent_Neural_Network Summary]||<br />
|-<br />
|Week of Nov 29 ||Ankitha Anugu, Yushan Chen, Yuying Huang || || A Game Theoretic Approach to Class-wise Selective Rationalization || [https://arxiv.org/pdf/1910.12853.pdf Paper] || ||<br />
|-<br />
|Week of Nov 29 ||Aavinash Syamala, Dilmeet Malhi, Sohan Islam, Vansh Joshi || || Research on Multiple Classification Based on Improved SVM Algorithm for Balanced Binary Decision Tree || [https://www.hindawi.com/journals/sp/2021/5560465/ Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Research_on_Multiple_Classification_Based_on_Improved_SVM_Algorithm_for_Balanced_Binary_Decision_Tree Summary]||<br />
|-<br />
|Week of Nov 29 ||Christian Mitrache, Alexandra Mossman, Jessica Saini, Aaron Renggli|| || U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging|| [https://proceedings.neurips.cc/paper/2019/file/57bafb2c2dfeefba931bb03a835b1fa9-Paper.pdf?fbclid=IwAR1dZpx9vU1pSPTSm_nwk6uBU7TYJ2HNTrsqjaH-9ZycE_PFpFjJoHg1zhQ]||</div>Eigurayhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat441F21&diff=50821stat441F212021-11-25T04:07:24Z<p>Eiguray: </p>
<hr />
<div><br />
<br />
== [[F20-STAT 441/841 CM 763-Proposal| Project Proposal ]] ==<br />
<br />
<!--[https://goo.gl/forms/apurag4dr9kSR76X2 Your feedback on presentations]--><br />
<br />
=Paper presentation=<br />
{| class="wikitable"<br />
<br />
{| border="1" cellpadding="3"<br />
|-<br />
|width="60pt"|Date<br />
|width="250pt"|Name <br />
|width="15pt"|Paper number <br />
|width="700pt"|Title<br />
|width="15pt"|Link to the paper<br />
|width="30pt"|Link to the summary<br />
|width="30pt"|Link to the video<br />
|-<br />
|Sep 15 (example)||Ri Wang || ||Sequence to sequence learning with neural networks.||[http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Going_Deeper_with_Convolutions Summary] || [https://youtu.be/JWozRg_X-Vg?list=PLehuLRPyt1HzXDemu7K4ETcF0Ld_B5adG&t=539]<br />
|-<br />
|Week of Nov 16 || Ali Ghodsi || || || || ||<br />
|-<br />
|Week of Nov 22 || Jared Feng, Xipeng Huang, Mingwei Xu, Tingzhou Yu|| || Don't Just Blame Over-parametrization for Over-confidence: Theoretical Analysis of Calibration in Binary Classification || [http://proceedings.mlr.press/v139/bai21c/bai21c.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Don%27t_Just_Blame_Over-parametrization Summary] ||<br />
|-<br />
|Week of Nov 29 || Kanika Chopra, Yush Rajcoomar || || Automatic Bank Fraud Detection Using Support Vector Machines || [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.863.5804&rep=rep1&type=pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Automatic_Bank_Fraud_Detection_Using_Support_Vector_Machines Summary] ||<br />
|-<br />
|Week of Nov 22 || Zeng Mingde, Lin Xiaoyu, Fan Joshua, Rao Chen Min || || Do Vision Transformers See Like Convolutional Neural Networks? || [https://proceedings.neurips.cc/paper/2021/file/652cf38361a209088302ba2b8b7f51e0-Paper.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Do_Vision_Transformers_See_Like_CNN Summary] ||<br />
|-<br />
|Week of Nov 22 || Justin D'Astous, Waqas Hamed, Stefan Vladusic, Ethan O'Farrell || || A Probabilistic Approach to Neural Network Pruning || [http://proceedings.mlr.press/v139/qian21a/qian21a.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Summary_of_A_Probabilistic_Approach_to_Neural_Network_Pruning Summary] ||<br />
|-<br />
|Week of Nov 22 || Cassandra Wong, Anastasiia Livochka, Maryam Yalsavar, David Evans || || Patch-Based Convolutional Neural Network for Whole Slide Tissue Image Classification || [https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Hou_Patch-Based_Convolutional_Neural_CVPR_2016_paper.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Patch_Based_Convolutional_Neural_Network_for_Whole_Slide_Tissue_Image_Classification Summary] ||<br />
|-<br />
|Week of Nov 29 || Jessie Man Wai Chin, Yi Lin Ooi, Yaqi Shi, Shwen Lyng Ngew || || CatBoost: unbiased boosting with categorical features || [https://proceedings.neurips.cc/paper/2018/file/14491b756b3a51daac41c24863285549-Paper.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=CatBoost:_unbiased_boosting_with_categorical_features Summary] ||<br />
|-<br />
|Week of Nov 29 || Eric Anderson, Chengzhi Wang, Kai Zhong, YiJing Zhou || || || || ||<br />
|-<br />
|Week of Nov 29 || Ethan Cyrenne, Dieu Hoa Nguyen, Mary Jane Sin, Carolyn Wang || || || || ||<br />
|-<br />
|Week of Nov 29 || Bowen Zhang, Tyler Magnus Verhaar, Sam Senko || || Deep Double Descent: Where Bigger Models and More Data Hurt || [https://arxiv.org/pdf/1912.02292.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Deep_Double_Descent_Where_Bigger_Models_and_More_Data_Hurt Summary] ||<br />
|-<br />
|Week of Nov 29 || Chun Waan Loke, Peter Chong, Clarice Osmond, Zhilong Li|| || XGBoost: A Scalable Tree Boosting System || [https://www.kdd.org/kdd2016/papers/files/rfp0697-chenAemb.pdf Paper] || ||<br />
|-<br />
|Week of Nov 22 || Ann Gie Wong, Curtis Li, Hannah Kerr || || The Detection of Black Ice Accidents for Preventative Automated Vehicles Using Convolutional Neural Networks || [https://www.mdpi.com/2079-9292/9/12/2178/htm Paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=The_Detection_of_Black_Ice_Accidents_Using_CNNs&fbclid=IwAR0K4YdnL_hdRnOktmJn8BI6-Ra3oitjJof0YwluZgUP1LVFHK5jyiBZkvQ Summary] ||<br />
|-<br />
|Week of Nov 22 || Yuwei Liu, Daniel Mao|| || Depthwise Convolution Is All You Need for Learning Multiple Visual Domains || [https://arxiv.org/abs/1902.00927 Paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Depthwise_Convolution_Is_All_You_Need_for_Learning_Multiple_Visual_Domains Summary] ||<br />
|-<br />
|Week of Nov 29 || Lingshan Wang, Yifan Li, Ziyi Liu || || Deep Learning for Extreme Multi-label Text Classification || [https://dl.acm.org/doi/pdf/10.1145/3077136.3080834 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Deep_Learning_for_Extreme_Multi-label_Text_Classification Summary]||<br />
|-<br />
|-<br />
|Week of Nov 29 || Kar Lok Ng, Muhan (Iris) Li || || || || ||<br />
|-<br />
|Week of Nov 29 ||Kun Wang || || Convolutional neural network for diagnosis of viral pneumonia and COVID-19 alike diseases|| [https://doi-org.proxy.lib.uwaterloo.ca/10.1111/exsy.12705 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Convolutional_neural_network_for_diagnosis_of_viral_pneumonia_and_COVID-19_alike_diseases Summary] ||<br />
|-<br />
|Week of Nov 29 ||Egemen Guray || || Traffic Sign Recognition System (TSRS): SVM and Convolutional Neural Network || https://www.researchgate.net/publication/344399165_Traffic_Sign_Recognition_System_TSRS_SVM_and_Convolutional_Neural_Network || ||<br />
|-<br />
|Week of Nov 29 ||Bsodjahi || || Bayesian Network as a Decision Tool for Predicting ALS Disease || https://www.mdpi.com/2076-3425/11/2/150/pdf || ||<br />
|-<br />
|Week of Nov 29 ||Xin Yan, Yishu Duan, Xibei Di || || Predicting Hurricane Trajectories Using a Recurrent Neural Network || [https://arxiv.org/pdf/1802.02548.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Predicting_Hurricane_Trajectories_Using_a_Recurrent_Neural_Network Summary]||<br />
|-<br />
|Week of Nov 29 ||Ankitha Anugu, Yushan Chen, Yuying Huang || || A Game Theoretic Approach to Class-wise Selective Rationalization || [https://arxiv.org/pdf/1910.12853.pdf Paper] || ||<br />
|-<br />
|Week of Nov 29 ||Aavinash Syamala, Dilmeet Malhi, Sohan Islam, Vansh Joshi || || Research on Multiple Classification Based on Improved SVM Algorithm for Balanced Binary Decision Tree || [https://www.hindawi.com/journals/sp/2021/5560465/ Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Research_on_Multiple_Classification_Based_on_Improved_SVM_Algorithm_for_Balanced_Binary_Decision_Tree Summary]||<br />
|-<br />
|Week of Nov 29 ||Christian Mitrache, Alexandra Mossman, Jessica Saini, Aaron Renggli|| || U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging|| [https://proceedings.neurips.cc/paper/2019/file/57bafb2c2dfeefba931bb03a835b1fa9-Paper.pdf?fbclid=IwAR1dZpx9vU1pSPTSm_nwk6uBU7TYJ2HNTrsqjaH-9ZycE_PFpFjJoHg1zhQ]||</div>Eigurayhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=F21-STAT_441/841_CM_763-Proposal&diff=50820F21-STAT 441/841 CM 763-Proposal2021-11-25T03:32:00Z<p>Eiguray: </p>
<hr />
<div>Use this format (Don’t remove Project 0)<br />
<br />
Project # 0 Group members:<br />
<br />
Last name, First name<br />
<br />
Last name, First name<br />
<br />
Last name, First name<br />
<br />
Last name, First name<br />
<br />
Title: Making a String Telephone<br />
<br />
Description: We use paper cups to make a string phone and talk with friends while learning about sound waves with this science project. (Explain your project in one or two paragraphs).<br />
<br />
--------------------------------------------------------------------<br />
Project # 1 Group members:<br />
<br />
Feng, Jared<br />
<br />
Huang, Xipeng<br />
<br />
Xu, Mingwei<br />
<br />
Yu, Tingzhou<br />
<br />
Title: Patch-Based Convolutional Neural Network for Cancers Classification<br />
<br />
Description: In this project, we consider classifying three classes (tumor types) of cancers based on pathological data. We will follow the paper ''Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification''.<br />
--------------------------------------------------------------------<br />
Project # 2 Group members:<br />
<br />
Anderson, Eric<br />
<br />
Wang, Chengzhi<br />
<br />
Zhong, Kai<br />
<br />
Zhou, Yi Jing<br />
<br />
Title: Application of Neural Networks<br />
<br />
Description: Using neural networks to determine content/intent of emails.<br />
<br />
--------------------------------------------------------------------<br />
Project # 3 Group members:<br />
<br />
Chopra, Kanika<br />
<br />
Rajcoomar, Yush<br />
<br />
Bhattacharya, Vaibhav<br />
<br />
Title: Cancer Classification<br />
<br />
Description: We will be classifying three tumour types based on pathological data. <br />
<br />
--------------------------------------------------------------------<br />
Project # 4 Group members:<br />
<br />
Li, Shao Zhong<br />
<br />
Kerr, Hannah <br />
<br />
Wong, Ann Gie<br />
<br />
Title: Classification of text<br />
<br />
Description: Being to automatically grade answers on tests can save a lot of time and teaching resources. But unlike a multiple-choice format where grading can be automated, the other formats involving text answers is more through in testing knowledge but still requires human evaluation and marking which is a bottleneck of teaching resources and personnel on a large scale with thousands of students. We will use classification techniques and machine learning to develop an automated way to predict the rightness of text answers with good accuracy that can be used by and suppport graders to reduce the time and manual effort needed in the grading process.<br />
<br />
--------------------------------------------------------------------<br />
Project # 5 Group members:<br />
<br />
Chin, Jessie Man Wai<br />
<br />
Ooi, Yi Lin<br />
<br />
Shi, Yaqi<br />
<br />
Ngew, Shwen Lyng<br />
<br />
Title: The Application of Classification in Accelerated Underwriting (Insurance)<br />
<br />
Description: Accelerated Underwriting (AUW), also called “express underwriting,” is a faster and easier process for people with good health condition to obtain life insurance. The traditional underwriting process is often painful for both customers and insurers. From the customer's perspective, they have to complete different types of questionnaires and provide different medical tests involving blood, urine, saliva and other medical results. Underwriters on the other hand have to manually go through every single policy to access the risk of each applicant. AUW allows people, who are deemed “healthy” to forgo medical exams. Since COVID-19, it has become a more concerning topic as traditional underwriting cannot be performed due to the stay-at-home order. However, this imposes a burden on the insurance company to better estimate the risk associated with less testing results. <br />
<br />
This is where data science kicks in. With different classification methods, we can address the underwriting process’ five pain points: labor, speed, efficiency, pricing and mortality. This allows us to better estimate the risk and classify the clients for whether they are eligible for accelerated underwriting. For the final project, we use the data from one of the leading US insurers to analyze how we can classify our clients for AUW using the method of classification. We will be using factors such as health data, medical history, family history as well as insurance history to determine the eligibility.<br />
<br />
--------------------------------------------------------------------<br />
Project # 6 Group members:<br />
<br />
Wang, Carolyn<br />
<br />
Cyrenne, Ethan<br />
<br />
Nguyen, Dieu Hoa<br />
<br />
Sin, Mary Jane<br />
<br />
Title: TBD<br />
<br />
Description: TBD<br />
<br />
--------------------------------------------------------------------<br />
Project # 7 Group members:<br />
<br />
Bhattacharya, Vaibhav<br />
<br />
Chatoor, Amanda<br />
<br />
Prathap Das, Sutej<br />
<br />
Title: PetFinder.my - Pawpularity Contest [https://www.kaggle.com/c/petfinder-pawpularity-score/overview]<br />
<br />
Description: In this competition, we will analyze raw images and metadata to predict the “Pawpularity” of pet photos. We'll train and test our model on PetFinder.my's thousands of pet profiles.<br />
<br />
--------------------------------------------------------------------<br />
Project # 8 Group members:<br />
<br />
Xu, Siming<br />
<br />
Yan, Xin<br />
<br />
Duan, Yishu<br />
<br />
Di, Xibei<br />
<br />
Title: TBD<br />
<br />
Description: TBD<br />
--------------------------------------------------------------------<br />
Project # 9 Group members:<br />
<br />
Loke, Chun Waan<br />
<br />
Chong, Peter<br />
<br />
Osmond, Clarice<br />
<br />
Li, Zhilong<br />
<br />
Title: TBD<br />
<br />
Description: TBD<br />
<br />
--------------------------------------------------------------------<br />
<br />
Project # 10 Group members:<br />
<br />
O'Farrell, Ethan<br />
<br />
D'Astous, Justin<br />
<br />
Hamed, Waqas<br />
<br />
Vladusic, Stefan<br />
<br />
Title: Pawpularity (Kaggle)<br />
<br />
Description: Predicting the popularity of animal photos based on photo metadata<br />
--------------------------------------------------------------------<br />
Project # 11 Group members:<br />
<br />
JunBin, Pan<br />
<br />
Title: TBD<br />
<br />
Description: TBD<br />
--------------------------------------------------------------------<br />
Project # 12 Group members:<br />
<br />
Kar Lok, Ng<br />
<br />
Muhan (Iris), Li<br />
<br />
Wu, Mingze<br />
<br />
Title: NFL Health & Safety - Helmet Assignment competition (Kaggle Competition)<br />
<br />
Description: Assigning players to the helmet in a given footage of head collision in football play.<br />
--------------------------------------------------------------------<br />
Project # 13 Group members:<br />
<br />
Livochka, Anastasiia<br />
<br />
Wong, Cassandra<br />
<br />
Evans, David<br />
<br />
Yalsavar, Maryam<br />
<br />
Title: TBD<br />
<br />
Description: TBD<br />
--------------------------------------------------------------------<br />
Project # 14 Group Members:<br />
<br />
Zeng, Mingde<br />
<br />
Lin, Xiaoyu<br />
<br />
Fan, Joshua<br />
<br />
Rao, Chen Min<br />
<br />
Title: TBD<br />
<br />
Description: TBD<br />
--------------------------------------------------------------------<br />
Project # 15 Group Members:<br />
<br />
Huang, Yuying<br />
<br />
Anugu, Ankitha<br />
<br />
Dave, Meet Hemang<br />
<br />
Chen, Yushan<br />
<br />
Title: TBD<br />
<br />
Description: TBD<br />
--------------------------------------------------------------------<br />
Project # 16 Group Members:<br />
<br />
Wang, Lingshan<br />
<br />
Liu, Ziyi<br />
<br />
Zheng, Hanxi<br />
<br />
Li, Yifan<br />
<br />
Title: Implement and Improve CNN in Multi-Class Text Classification<br />
<br />
Description: We are going to apply Convolutional Neural Network (CNN) to classify real-world data (application to build an efficient insurance contract classifier) and improve CNN algorithm-wise in the context of text classification, being supported with real-world data set. With the implementation of CNN, it allows us to further analyze the efficiency and practicality of the algorithm.<br />
The dataset is composed of insurance contracts containing client and policy information. We will implement a multi-class classification to break down the information contained in each insurance contract into some pre-determined subcategories (eg, short-term renewable/long-term non-renewable). We will attempt to process the complicated data into several data types(e.g. JSON, pandas data frames, etc.) and choose the most efficient raw data processing logic based on runtime and algorithm optimization.<br />
--------------------------------------------------------------------<br />
Project # 17 Group members:<br />
<br />
Malhi, Dilmeet<br />
<br />
Joshi, Vansh<br />
<br />
Syamala, Aavinash <br />
<br />
Islam, Sohan<br />
<br />
Title: Kaggle project: Brain Tumor Radiogenomic Classification<br />
<br />
Description: In this project, we will predict the genetic subtype of glioblastoma using MRI (magnetic resonance imaging) scans to train and test your model to detect the presence of MGMT promoter methylation.<br />
--------------------------------------------------------------------<br />
<br />
Project # 18 Group members:<br />
<br />
Yuwei, Liu<br />
<br />
Daniel, Mao<br />
<br />
Title: Sartorius - Cell Instance Segmentation (Kaggle) [https://www.kaggle.com/c/sartorius-cell-instance-segmentation]<br />
<br />
Description: Detect single neuronal cells in microscopy images<br />
<br />
--------------------------------------------------------------------<br />
<br />
Project #19 Group members:<br />
<br />
Samuel, Senko<br />
<br />
Tyler, Verhaar<br />
<br />
Zhang, Bowen<br />
<br />
Title: NBA Game Prediction<br />
<br />
Description: We will build a win/loss classifier for NBA games using player and game data and also incorporating alternative data (ex. sports betting data).<br />
<br />
-------------------------------------------------------------------<br />
<br />
Project #20 Group members:<br />
<br />
Mitrache, Christian<br />
<br />
Renggli, Aaron<br />
<br />
Saini, Jessica<br />
<br />
Mossman, Alexandra<br />
<br />
Title: Classification and Deep Learning for Healthcare Provider Fraud Detection Analysis<br />
<br />
Description: TBD<br />
<br />
--------------------------------------------------------------------<br />
<br />
Project # 21 Group members:<br />
<br />
Wang, Kun<br />
<br />
Title: TBD<br />
<br />
Description : TBD<br />
<br />
--------------------------------------------------------------------<br />
<br />
Project # 22 Group members:<br />
<br />
Guray, Egemen<br />
<br />
Title: Traffic Sign Recognition System (TSRS): SVM and Convolutional Neural Network<br />
<br />
Description : I will build a prediction system to predict road signs in the German Traffic Sign Dataset using CNN.<br />
--------------------------------------------------------------------<br />
<br />
Project # 23 Group members:<br />
<br />
Bsodjahi<br />
<br />
Title: Modeling Pseudomonas aeruginosa bacteria state through its genes expression activity<br />
<br />
Description : Label Pseudomonas aeruginosa gene expression data through unsupervised learning (eg., EM algorithm) and then model the bacterial state as function of its genes expression</div>Eigurayhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=F21-STAT_441/841_CM_763-Proposal&diff=50819F21-STAT 441/841 CM 763-Proposal2021-11-25T03:30:59Z<p>Eiguray: </p>
<hr />
<div>Use this format (Don’t remove Project 0)<br />
<br />
Project # 0 Group members:<br />
<br />
Last name, First name<br />
<br />
Last name, First name<br />
<br />
Last name, First name<br />
<br />
Last name, First name<br />
<br />
Title: Making a String Telephone<br />
<br />
Description: We use paper cups to make a string phone and talk with friends while learning about sound waves with this science project. (Explain your project in one or two paragraphs).<br />
<br />
--------------------------------------------------------------------<br />
Project # 1 Group members:<br />
<br />
Feng, Jared<br />
<br />
Huang, Xipeng<br />
<br />
Xu, Mingwei<br />
<br />
Yu, Tingzhou<br />
<br />
Title: Patch-Based Convolutional Neural Network for Cancers Classification<br />
<br />
Description: In this project, we consider classifying three classes (tumor types) of cancers based on pathological data. We will follow the paper ''Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification''.<br />
--------------------------------------------------------------------<br />
Project # 2 Group members:<br />
<br />
Anderson, Eric<br />
<br />
Wang, Chengzhi<br />
<br />
Zhong, Kai<br />
<br />
Zhou, Yi Jing<br />
<br />
Title: Application of Neural Networks<br />
<br />
Description: Using neural networks to determine content/intent of emails.<br />
<br />
--------------------------------------------------------------------<br />
Project # 3 Group members:<br />
<br />
Chopra, Kanika<br />
<br />
Rajcoomar, Yush<br />
<br />
Bhattacharya, Vaibhav<br />
<br />
Title: Cancer Classification<br />
<br />
Description: We will be classifying three tumour types based on pathological data. <br />
<br />
--------------------------------------------------------------------<br />
Project # 4 Group members:<br />
<br />
Li, Shao Zhong<br />
<br />
Kerr, Hannah <br />
<br />
Wong, Ann Gie<br />
<br />
Title: Classification of text<br />
<br />
Description: Being to automatically grade answers on tests can save a lot of time and teaching resources. But unlike a multiple-choice format where grading can be automated, the other formats involving text answers is more through in testing knowledge but still requires human evaluation and marking which is a bottleneck of teaching resources and personnel on a large scale with thousands of students. We will use classification techniques and machine learning to develop an automated way to predict the rightness of text answers with good accuracy that can be used by and suppport graders to reduce the time and manual effort needed in the grading process.<br />
<br />
--------------------------------------------------------------------<br />
Project # 5 Group members:<br />
<br />
Chin, Jessie Man Wai<br />
<br />
Ooi, Yi Lin<br />
<br />
Shi, Yaqi<br />
<br />
Ngew, Shwen Lyng<br />
<br />
Title: The Application of Classification in Accelerated Underwriting (Insurance)<br />
<br />
Description: Accelerated Underwriting (AUW), also called “express underwriting,” is a faster and easier process for people with good health condition to obtain life insurance. The traditional underwriting process is often painful for both customers and insurers. From the customer's perspective, they have to complete different types of questionnaires and provide different medical tests involving blood, urine, saliva and other medical results. Underwriters on the other hand have to manually go through every single policy to access the risk of each applicant. AUW allows people, who are deemed “healthy” to forgo medical exams. Since COVID-19, it has become a more concerning topic as traditional underwriting cannot be performed due to the stay-at-home order. However, this imposes a burden on the insurance company to better estimate the risk associated with less testing results. <br />
<br />
This is where data science kicks in. With different classification methods, we can address the underwriting process’ five pain points: labor, speed, efficiency, pricing and mortality. This allows us to better estimate the risk and classify the clients for whether they are eligible for accelerated underwriting. For the final project, we use the data from one of the leading US insurers to analyze how we can classify our clients for AUW using the method of classification. We will be using factors such as health data, medical history, family history as well as insurance history to determine the eligibility.<br />
<br />
--------------------------------------------------------------------<br />
Project # 6 Group members:<br />
<br />
Wang, Carolyn<br />
<br />
Cyrenne, Ethan<br />
<br />
Nguyen, Dieu Hoa<br />
<br />
Sin, Mary Jane<br />
<br />
Title: TBD<br />
<br />
Description: TBD<br />
<br />
--------------------------------------------------------------------<br />
Project # 7 Group members:<br />
<br />
Bhattacharya, Vaibhav<br />
<br />
Chatoor, Amanda<br />
<br />
Prathap Das, Sutej<br />
<br />
Title: PetFinder.my - Pawpularity Contest [https://www.kaggle.com/c/petfinder-pawpularity-score/overview]<br />
<br />
Description: In this competition, we will analyze raw images and metadata to predict the “Pawpularity” of pet photos. We'll train and test our model on PetFinder.my's thousands of pet profiles.<br />
<br />
--------------------------------------------------------------------<br />
Project # 8 Group members:<br />
<br />
Xu, Siming<br />
<br />
Yan, Xin<br />
<br />
Duan, Yishu<br />
<br />
Di, Xibei<br />
<br />
Title: TBD<br />
<br />
Description: TBD<br />
--------------------------------------------------------------------<br />
Project # 9 Group members:<br />
<br />
Loke, Chun Waan<br />
<br />
Chong, Peter<br />
<br />
Osmond, Clarice<br />
<br />
Li, Zhilong<br />
<br />
Title: TBD<br />
<br />
Description: TBD<br />
<br />
--------------------------------------------------------------------<br />
<br />
Project # 10 Group members:<br />
<br />
O'Farrell, Ethan<br />
<br />
D'Astous, Justin<br />
<br />
Hamed, Waqas<br />
<br />
Vladusic, Stefan<br />
<br />
Title: Pawpularity (Kaggle)<br />
<br />
Description: Predicting the popularity of animal photos based on photo metadata<br />
--------------------------------------------------------------------<br />
Project # 11 Group members:<br />
<br />
JunBin, Pan<br />
<br />
Title: TBD<br />
<br />
Description: TBD<br />
--------------------------------------------------------------------<br />
Project # 12 Group members:<br />
<br />
Kar Lok, Ng<br />
<br />
Muhan (Iris), Li<br />
<br />
Wu, Mingze<br />
<br />
Title: NFL Health & Safety - Helmet Assignment competition (Kaggle Competition)<br />
<br />
Description: Assigning players to the helmet in a given footage of head collision in football play.<br />
--------------------------------------------------------------------<br />
Project # 13 Group members:<br />
<br />
Livochka, Anastasiia<br />
<br />
Wong, Cassandra<br />
<br />
Evans, David<br />
<br />
Yalsavar, Maryam<br />
<br />
Title: TBD<br />
<br />
Description: TBD<br />
--------------------------------------------------------------------<br />
Project # 14 Group Members:<br />
<br />
Zeng, Mingde<br />
<br />
Lin, Xiaoyu<br />
<br />
Fan, Joshua<br />
<br />
Rao, Chen Min<br />
<br />
Title: TBD<br />
<br />
Description: TBD<br />
--------------------------------------------------------------------<br />
Project # 15 Group Members:<br />
<br />
Huang, Yuying<br />
<br />
Anugu, Ankitha<br />
<br />
Dave, Meet Hemang<br />
<br />
Chen, Yushan<br />
<br />
Title: TBD<br />
<br />
Description: TBD<br />
--------------------------------------------------------------------<br />
Project # 16 Group Members:<br />
<br />
Wang, Lingshan<br />
<br />
Liu, Ziyi<br />
<br />
Zheng, Hanxi<br />
<br />
Li, Yifan<br />
<br />
Title: Implement and Improve CNN in Multi-Class Text Classification<br />
<br />
Description: We are going to apply Convolutional Neural Network (CNN) to classify real-world data (application to build an efficient insurance contract classifier) and improve CNN algorithm-wise in the context of text classification, being supported with real-world data set. With the implementation of CNN, it allows us to further analyze the efficiency and practicality of the algorithm.<br />
The dataset is composed of insurance contracts containing client and policy information. We will implement a multi-class classification to break down the information contained in each insurance contract into some pre-determined subcategories (eg, short-term renewable/long-term non-renewable). We will attempt to process the complicated data into several data types(e.g. JSON, pandas data frames, etc.) and choose the most efficient raw data processing logic based on runtime and algorithm optimization.<br />
--------------------------------------------------------------------<br />
Project # 17 Group members:<br />
<br />
Malhi, Dilmeet<br />
<br />
Joshi, Vansh<br />
<br />
Syamala, Aavinash <br />
<br />
Islam, Sohan<br />
<br />
Title: Kaggle project: Brain Tumor Radiogenomic Classification<br />
<br />
Description: In this project, we will predict the genetic subtype of glioblastoma using MRI (magnetic resonance imaging) scans to train and test your model to detect the presence of MGMT promoter methylation.<br />
--------------------------------------------------------------------<br />
<br />
Project # 18 Group members:<br />
<br />
Yuwei, Liu<br />
<br />
Daniel, Mao<br />
<br />
Title: Sartorius - Cell Instance Segmentation (Kaggle) [https://www.kaggle.com/c/sartorius-cell-instance-segmentation]<br />
<br />
Description: Detect single neuronal cells in microscopy images<br />
<br />
--------------------------------------------------------------------<br />
<br />
Project #19 Group members:<br />
<br />
Samuel, Senko<br />
<br />
Tyler, Verhaar<br />
<br />
Zhang, Bowen<br />
<br />
Title: NBA Game Prediction<br />
<br />
Description: We will build a win/loss classifier for NBA games using player and game data and also incorporating alternative data (ex. sports betting data).<br />
<br />
-------------------------------------------------------------------<br />
<br />
Project #20 Group members:<br />
<br />
Mitrache, Christian<br />
<br />
Renggli, Aaron<br />
<br />
Saini, Jessica<br />
<br />
Mossman, Alexandra<br />
<br />
Title: Classification and Deep Learning for Healthcare Provider Fraud Detection Analysis<br />
<br />
Description: TBD<br />
<br />
--------------------------------------------------------------------<br />
<br />
Project # 21 Group members:<br />
<br />
Wang, Kun<br />
<br />
Title: TBD<br />
<br />
Description : TBD<br />
<br />
--------------------------------------------------------------------<br />
<br />
Project # 22 Group members:<br />
<br />
Guray, Egemen<br />
<br />
Title: Traffic Sign Recognition System (TSRS): SVM and Convolutional Neural Network<br />
<br />
Description : I will build a prediction system to predict road signs using CNN and German Traffic Sign Dataset<br />
--------------------------------------------------------------------<br />
<br />
Project # 23 Group members:<br />
<br />
Bsodjahi<br />
<br />
Title: Modeling Pseudomonas aeruginosa bacteria state through its genes expression activity<br />
<br />
Description : Label Pseudomonas aeruginosa gene expression data through unsupervised learning (eg., EM algorithm) and then model the bacterial state as function of its genes expression</div>Eigurayhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat441F21&diff=50309stat441F212021-11-15T16:10:14Z<p>Eiguray: /* Paper presentation */</p>
<hr />
<div><br />
<br />
== [[F20-STAT 441/841 CM 763-Proposal| Project Proposal ]] ==<br />
<br />
<!--[https://goo.gl/forms/apurag4dr9kSR76X2 Your feedback on presentations]--><br />
<br />
=Paper presentation=<br />
{| class="wikitable"<br />
<br />
{| border="1" cellpadding="3"<br />
|-<br />
|width="60pt"|Date<br />
|width="250pt"|Name <br />
|width="15pt"|Paper number <br />
|width="700pt"|Title<br />
|width="15pt"|Link to the paper<br />
|width="30pt"|Link to the summary<br />
|width="30pt"|Link to the video<br />
|-<br />
|Sep 15 (example)||Ri Wang || ||Sequence to sequence learning with neural networks.||[http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Going_Deeper_with_Convolutions Summary] || [https://youtu.be/JWozRg_X-Vg?list=PLehuLRPyt1HzXDemu7K4ETcF0Ld_B5adG&t=539]<br />
|-<br />
|Week of Nov 16 || Ali Ghodsi || || || || ||<br />
|-<br />
|Week of Nov 22 || Jared Feng, Xipeng Huang, Mingwei Xu, Tingzhou Yu|| || Don't Just Blame Over-parametrization for Over-confidence: Theoretical Analysis of Calibration in Binary Classification || [http://proceedings.mlr.press/v139/bai21c/bai21c.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Don%27t_Just_Blame_Over-parametrization Summary] ||<br />
|-<br />
|Week of Nov 22 || Kanika Chopra, Yush Rajcoomar || || Automatic Bank Fraud Detection Using Support Vector Machines || [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.863.5804&rep=rep1&type=pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Automatic_Bank_Fraud_Detection_Using_Support_Vector_Machines Summary] ||<br />
|-<br />
|Week of Nov 22 || Zeng Mingde, Lin Xiaoyu, Fan Joshua, Rao Chen Min || || || || ||<br />
|-<br />
|Week of Nov 22 || Justin D'Astous, Waqas Hamed, Stefan Vladusic, Ethan O'Farrell || || A Probabilistic Approach to Neural Network Pruning || [http://proceedings.mlr.press/v139/qian21a/qian21a.pdf] || ||<br />
|-<br />
|Week of Nov 22 || Cassandra Wong, Anastasiia Livochka, Maryam Yalsavar, David Evans || || Patch-Based Convolutional Neural Network for Whole Slide Tissue Image Classification || [https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Hou_Patch-Based_Convolutional_Neural_CVPR_2016_paper.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Patch_Based_Convolutional_Neural_Network_for_Whole_Slide_Tissue_Image_Classification Summary] ||<br />
|-<br />
|Week of Nov 29 || Jessie Man Wai Chin, Yi Lin Ooi, Yaqi Shi, Shwen Lyng Ngew || || || || ||<br />
|-<br />
|Week of Nov 29 || Eric Anderson, Chengzhi Wang, Kai Zhong, YiJing Zhou || || || || ||<br />
|-<br />
|Week of Nov 29 || Ethan Cyrenne, Dieu Hoa Nguyen, Mary Jane Sin, Carolyn Wang || || || || ||<br />
|-<br />
|Week of Nov 29 || Chun Waan Loke, Peter Chong, Clarice Osmond, Zhilong Li|| || || || ||<br />
|-<br />
|Week of Nov 22 || Ann Gie Wong, Curtis Li, Hannah Kerr || || The Detection of Black Ice Accidents for Preventative Automated Vehicles Using Convolutional Neural Networks || [https://www.mdpi.com/2079-9292/9/12/2178/htm Paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=The_Detection_of_Black_Ice_Accidents_Using_CNNs&fbclid=IwAR0K4YdnL_hdRnOktmJn8BI6-Ra3oitjJof0YwluZgUP1LVFHK5jyiBZkvQ Summary] ||<br />
|-<br />
|Week of Nov 22 || Yuwei Liu, Daniel Mao|| || Depthwise Convolution Is All You Need for Learning Multiple Visual Domains || [https://arxiv.org/abs/1902.00927 Paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Depthwise_Convolution_Is_All_You_Need_for_Learning_Multiple_Visual_Domains Summary] ||<br />
|-<br />
|Week of Nov 29 || Lingshan Wang, Yifan Li, Ziyi Liu || || Deep Learning for Extreme Multi-label Text Classification || [https://dl.acm.org/doi/pdf/10.1145/3077136.3080834 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Deep_Learning_for_Extreme_Multi-label_Text_Classification Summary]||<br />
|-<br />
|-<br />
|Week of Nov 29 || Kar Lok Ng, Muhan (Iris) Li || || || || ||<br />
|-<br />
|Week of Nov 29 ||Kun Wang || || || || ||<br />
|-<br />
|Week of Nov 29 ||Egemen Guray || || || || ||<br />
|-<br />
|Week of Nov 22 ||Bsodjahi || || Bayesian Network as a Decision Tool for Predicting ALS Disease || https://www.mdpi.com/2076-3425/11/2/150/pdf || ||<br />
|-<br />
|Week of Nov 22 ||Xin Yan, Yishu Duan, Xibei Di || || Predicting Hurricane Trajectories Using a Recurrent Neural Network || [https://arxiv.org/pdf/1802.02548.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Predicting_Hurricane_Trajectories_Using_a_Recurrent_Neural_Network Summary]||</div>Eigurayhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=F21-STAT_441/841_CM_763-Proposal&diff=50170F21-STAT 441/841 CM 763-Proposal2021-11-12T18:19:38Z<p>Eiguray: </p>
<hr />
<div>Use this format (Don’t remove Project 0)<br />
<br />
Project # 0 Group members:<br />
<br />
Last name, First name<br />
<br />
Last name, First name<br />
<br />
Last name, First name<br />
<br />
Last name, First name<br />
<br />
Title: Making a String Telephone<br />
<br />
Description: We use paper cups to make a string phone and talk with friends while learning about sound waves with this science project. (Explain your project in one or two paragraphs).<br />
<br />
--------------------------------------------------------------------<br />
Project # 1 Group members:<br />
<br />
Feng, Jared<br />
<br />
Huang, Xipeng<br />
<br />
Xu, Mingwei<br />
<br />
Yu, Tingzhou<br />
<br />
Title: Patch-Based Convolutional Neural Network for Cancers Classification<br />
<br />
Description: In this project, we consider classifying three classes (tumor types) of cancers based on pathological data. We will follow the paper ''Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification''.<br />
--------------------------------------------------------------------<br />
Project # 2 Group members:<br />
<br />
Anderson, Eric<br />
<br />
Wang, Chengzhi<br />
<br />
Zhong, Kai<br />
<br />
Zhou, Yi Jing<br />
<br />
Title: Application of Neural Networks<br />
<br />
Description: Using neural networks to determine content/intent of emails.<br />
<br />
--------------------------------------------------------------------<br />
Project # 3 Group members:<br />
<br />
Chopra, Kanika<br />
<br />
Rajcoomar, Yush<br />
<br />
Bhattacharya, Vaibhav<br />
<br />
Title: Cancer Classification<br />
<br />
Description: We will be classifying three tumour types based on pathological data. <br />
<br />
--------------------------------------------------------------------<br />
Project # 4 Group members:<br />
<br />
Li, Shao Zhong<br />
<br />
Kerr, Hannah <br />
<br />
Wong, Ann Gie<br />
<br />
Title: Classification of text<br />
<br />
Description: Being to automatically grade answers on tests can save a lot of time and teaching resources. But unlike a multiple-choice format where grading can be automated, the other formats involving text answers is more through in testing knowledge but still requires human evaluation and marking which is a bottleneck of teaching resources and personnel on a large scale with thousands of students. We will use classification techniques and machine learning to develop an automated way to predict the rightness of text answers with good accuracy that can be used by and suppport graders to reduce the time and manual effort needed in the grading process.<br />
<br />
--------------------------------------------------------------------<br />
Project # 5 Group members:<br />
<br />
Chin, Jessie Man Wai<br />
<br />
Ooi, Yi Lin<br />
<br />
Shi, Yaqi<br />
<br />
Ngew, Shwen Lyng<br />
<br />
Title: The Application of Classification in Accelerated Underwriting (Insurance)<br />
<br />
Description: Accelerated Underwriting (AUW), also called “express underwriting,” is a faster and easier process for people with good health condition to obtain life insurance. The traditional underwriting process is often painful for both customers and insurers. From the customer's perspective, they have to complete different types of questionnaires and provide different medical tests involving blood, urine, saliva and other medical results. Underwriters on the other hand have to manually go through every single policy to access the risk of each applicant. AUW allows people, who are deemed “healthy” to forgo medical exams. Since COVID-19, it has become a more concerning topic as traditional underwriting cannot be performed due to the stay-at-home order. However, this imposes a burden on the insurance company to better estimate the risk associated with less testing results. <br />
<br />
This is where data science kicks in. With different classification methods, we can address the underwriting process’ five pain points: labor, speed, efficiency, pricing and mortality. This allows us to better estimate the risk and classify the clients for whether they are eligible for accelerated underwriting. For the final project, we use the data from one of the leading US insurers to analyze how we can classify our clients for AUW using the method of classification. We will be using factors such as health data, medical history, family history as well as insurance history to determine the eligibility.<br />
<br />
--------------------------------------------------------------------<br />
Project # 6 Group members:<br />
<br />
Wang, Carolyn<br />
<br />
Cyrenne, Ethan<br />
<br />
Nguyen, Dieu Hoa<br />
<br />
Sin, Mary Jane<br />
<br />
Title: TBD<br />
<br />
Description: TBD<br />
<br />
--------------------------------------------------------------------<br />
Project # 7 Group members:<br />
<br />
Bhattacharya, Vaibhav<br />
<br />
Chatoor, Amanda<br />
<br />
Prathap Das, Sutej<br />
<br />
Title: PetFinder.my - Pawpularity Contest [https://www.kaggle.com/c/petfinder-pawpularity-score/overview]<br />
<br />
Description: In this competition, we will analyze raw images and metadata to predict the “Pawpularity” of pet photos. We'll train and test our model on PetFinder.my's thousands of pet profiles.<br />
<br />
--------------------------------------------------------------------<br />
Project # 8 Group members:<br />
<br />
Xu, Siming<br />
<br />
Yan, Xin<br />
<br />
Duan, Yishu<br />
<br />
Di, Xibei<br />
<br />
Title: TBD<br />
<br />
Description: TBD<br />
--------------------------------------------------------------------<br />
Project # 9 Group members:<br />
<br />
Loke, Chun Waan<br />
<br />
Chong, Peter<br />
<br />
Osmond, Clarice<br />
<br />
Li, Zhilong<br />
<br />
Title: TBD<br />
<br />
Description: TBD<br />
<br />
--------------------------------------------------------------------<br />
<br />
Project # 10 Group members:<br />
<br />
O'Farrell, Ethan<br />
<br />
D'Astous, Justin<br />
<br />
Hamed, Waqas<br />
<br />
Vladusic, Stefan<br />
<br />
Title: Pawpularity (Kaggle)<br />
<br />
Description: Predicting the popularity of animal photos based on photo metadata<br />
--------------------------------------------------------------------<br />
Project # 11 Group members:<br />
<br />
JunBin, Pan<br />
<br />
Title: TBD<br />
<br />
Description: TBD<br />
--------------------------------------------------------------------<br />
Project # 12 Group members:<br />
<br />
Kar Lok, Ng<br />
<br />
Muhan (Iris), Li<br />
<br />
Wu, Mingze<br />
<br />
Title: NFL Health & Safety - Helmet Assignment competition (Kaggle Competition)<br />
<br />
Description: Assigning players to the helmet in a given footage of head collision in football play.<br />
--------------------------------------------------------------------<br />
Project # 13 Group members:<br />
<br />
Livochka, Anastasiia<br />
<br />
Wong, Cassandra<br />
<br />
Evans, David<br />
<br />
Yalsavar, Maryam<br />
<br />
Title: TBD<br />
<br />
Description: TBD<br />
--------------------------------------------------------------------<br />
Project # 14 Group Members:<br />
<br />
Zeng, Mingde<br />
<br />
Lin, Xiaoyu<br />
<br />
Fan, Joshua<br />
<br />
Rao, Chen Min<br />
<br />
Title: TBD<br />
<br />
Description: TBD<br />
--------------------------------------------------------------------<br />
Project # 15 Group Members:<br />
<br />
Huang, Yuying<br />
<br />
Anugu, Ankitha<br />
<br />
Dave, Meet Hemang<br />
<br />
Chen, Yushan<br />
<br />
Title: TBD<br />
<br />
Description: TBD<br />
--------------------------------------------------------------------<br />
Project # 16 Group Members:<br />
<br />
Wang, Lingshan<br />
<br />
Liu, Ziyi<br />
<br />
Zheng, Hanxi<br />
<br />
Li, Yifan<br />
<br />
Title: Implement and Improve CNN in Multi-Class Text Classification<br />
<br />
Description: We are going to apply Convolutional Neural Network (CNN) to classify real-world data (application to build an efficient insurance contract classifier) and improve CNN algorithm-wise in the context of text classification, being supported with real-world data set. With the implementation of CNN, it allows us to further analyze the efficiency and practicality of the algorithm.<br />
The dataset is composed of insurance contracts containing client and policy information. We will implement a multi-class classification to break down the information contained in each insurance contract into some pre-determined subcategories (eg, short-term renewable/long-term non-renewable). We will attempt to process the complicated data into several data types(e.g. JSON, pandas data frames, etc.) and choose the most efficient raw data processing logic based on runtime and algorithm optimization.<br />
--------------------------------------------------------------------<br />
Project # 17 Group members:<br />
<br />
Malhi, Dilmeet<br />
<br />
Joshi, Vansh<br />
<br />
Syamala, Aavinash <br />
<br />
Islam, Sohan<br />
<br />
Title: Kaggle project: Brain Tumor Radiogenomic Classification<br />
<br />
Description: In this project, we will predict the genetic subtype of glioblastoma using MRI (magnetic resonance imaging) scans to train and test your model to detect the presence of MGMT promoter methylation.<br />
--------------------------------------------------------------------<br />
<br />
Project # 18 Group members:<br />
<br />
Yuwei, Liu<br />
<br />
Daniel, Mao<br />
<br />
Title: Sartorius - Cell Instance Segmentation (Kaggle) [https://www.kaggle.com/c/sartorius-cell-instance-segmentation]<br />
<br />
Description: Detect single neuronal cells in microscopy images<br />
<br />
--------------------------------------------------------------------<br />
<br />
Project #19 Group members:<br />
<br />
Samuel, Senko<br />
<br />
Tyler, Verhaar<br />
<br />
Zhang, Bowen<br />
<br />
Title: NBA Game Prediction<br />
<br />
Description: We will build a win/loss classifier for NBA games using player and game data and also incorporating alternative data (ex. sports betting data).<br />
<br />
-------------------------------------------------------------------<br />
<br />
Project #20 Group members:<br />
<br />
Mitrache, Christian<br />
<br />
Renggli, Aaron<br />
<br />
Saini, Jessica<br />
<br />
Mossman, Alexandra<br />
<br />
Title: Classification and Deep Learning for Healthcare Provider Fraud Detection Analysis<br />
<br />
Description: TBD<br />
<br />
--------------------------------------------------------------------<br />
<br />
Project # 21 Group members:<br />
<br />
Wang, Kun<br />
<br />
Title: TBD<br />
<br />
Description : TBD<br />
<br />
--------------------------------------------------------------------<br />
<br />
Project # 22 Group members:<br />
<br />
Guray, Egemen<br />
<br />
Title: TBD<br />
<br />
Description : TBD</div>Eiguray