Difference between revisions of "F21-STAT 441/841 CM 763-Proposal"
|Line 308:||Line 308:|
Revision as of 23:30, 24 November 2021
Use this format (Don’t remove Project 0)
Project # 0 Group members:
Last name, First name
Last name, First name
Last name, First name
Last name, First name
Title: Making a String Telephone
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).
Project # 1 Group members:
Title: Patch-Based Convolutional Neural Network for Cancers Classification
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.
Project # 2 Group members:
Zhou, Yi Jing
Title: Application of Neural Networks
Description: Using neural networks to determine content/intent of emails.
Project # 3 Group members:
Title: Cancer Classification
Description: We will be classifying three tumour types based on pathological data.
Project # 4 Group members:
Li, Shao Zhong
Wong, Ann Gie
Title: Classification of text
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.
Project # 5 Group members:
Chin, Jessie Man Wai
Ooi, Yi Lin
Ngew, Shwen Lyng
Title: The Application of Classification in Accelerated Underwriting (Insurance)
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.
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.
Project # 6 Group members:
Nguyen, Dieu Hoa
Sin, Mary Jane
Project # 7 Group members:
Prathap Das, Sutej
Title: PetFinder.my - Pawpularity Contest 
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.
Project # 8 Group members:
Project # 9 Group members:
Loke, Chun Waan
Project # 10 Group members:
Title: Pawpularity (Kaggle)
Description: Predicting the popularity of animal photos based on photo metadata
Project # 11 Group members:
Project # 12 Group members:
Kar Lok, Ng
Muhan (Iris), Li
Title: NFL Health & Safety - Helmet Assignment competition (Kaggle Competition)
Description: Assigning players to the helmet in a given footage of head collision in football play.
Project # 13 Group members:
Project # 14 Group Members:
Rao, Chen Min
Project # 15 Group Members:
Dave, Meet Hemang
Project # 16 Group Members:
Title: Implement and Improve CNN in Multi-Class Text Classification
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. 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.
Project # 17 Group members:
Title: Kaggle project: Brain Tumor Radiogenomic Classification
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.
Project # 18 Group members:
Title: Sartorius - Cell Instance Segmentation (Kaggle) 
Description: Detect single neuronal cells in microscopy images
Project #19 Group members:
Title: NBA Game Prediction
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).
Project #20 Group members:
Title: Classification and Deep Learning for Healthcare Provider Fraud Detection Analysis
Project # 21 Group members:
Description : TBD
Project # 22 Group members:
Title: Traffic Sign Recognition System (TSRS): SVM and Convolutional Neural Network
Description : I will build a prediction system to predict road signs using CNN and German Traffic Sign Dataset
Project # 23 Group members:
Title: Modeling Pseudomonas aeruginosa bacteria state through its genes expression activity
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