Posts categorized Machine Learning Archive

The Pluck & Dispense System (PDSys™) automates the precise transfer of cells from microbubbles to wells, enhancing accuracy and efficiency in laboratory research. This system addresses key challenges such as reducing human error and increasing throughput, making it a valuable tool for small pharmaceutical companies or academic researchers working with microbubbles.

DiscSense is aiming to advance athletes’ throwing skills through the development of a gyroscopic sensor that tracks the end conditions of throws. Throughout our capstone project, we concentrated on building a classification model that will aid athletes in recognizing patterns of successful throws and pinpoint prevalent errors.

Author Sponsor Dr. Zidian Xie Instructor Professor Ajay Anand Professor Cantay Caliskan Abstract In the digital age, Twitter has emerged as ancentral arena for public health issues, particularly those involv-ing…

ML Based classification model to detect triage level for patients arriving at trauma centre, and thus allocate appropriate resources. This was achieved using patients’ data from URMC (Department of Paediatrics).

Team Yihe Chen Harry Huang Junting Chen Kehan Yu Mentor Cantay Caliskan Abstract Predictive Analytics for Demand Responsive Para- transportation Vision & Goal ● Create a productive schedule for Demand…

This project uses the luminescence of the nighttime sky as a predictive features for economic activity.

This project aims to observe, visualize, and model the trends in which COVID-19 patients at the University of Medical Center were allocated ventilators. Descriptive analyses are performed to investigate the relationships between variables such as but not limited to recovery rate and length of ventilator allocation and gender, race, and age.

This project aims to build a model which detects features such as crosswalks and curb ramps at intersections in the city of Rochester.

Team Qianqian Gu (Project Manager) Wei Wu Chen Yao Hanyang Zhang Mentor Ajay Anand Abstract The Goergen Institute for Data Science (GIDS) masters admission office wants to better understand applicants’…

Team Steven Dai Zachary Mustin Uzoma Ohajekwe Duy Pham Sponsor Vnomics Corporation Matt Mayo Mentor Prof. Ajay Anand Abstract Our task is to predict imminent failures in Diesel Particulate Filters…

Team Xiaoen Ding Jiecheng Gu Sung Beom Park Joseph Smith Mentor Ajay Anand Sponsor Lisa Altman Gretchen Briscoe Abstract The Goergen Institute for Data Science wants to understand the types…

Team Yihan Shao Chuqin Wu Melanie Xue Zihe Zheng Mentor Cantay Çalışkan Abstract The goal of this project is to forecast the pest pressure of Grape Powdery Mildew at a…

This project investigates the associations between geriatric assessment based features and relative dose intensity of chemotherapy. It is at the first few phases of Wilmot Cancer Institute’s Ger Oncology Research team at University of Rochester Medical Center. The team refined the data preprocessing pipeline, built predictive models and employed feature selection on the dataset, providing insightful suggestions for future work in cancer studies.

The City of Rochester wants to understand if physical proximity to a convenience store or liquor store affects the likelihood of different types of part 1 crimes.

The Geriatric Oncology Research Team at URMC wants to better understand chemotherapy tolerability in vulnerable older adults.

The goal of the project was to identify upsell opportunities for Paychex’s 401(k) service products to their existing clients.

Rochester Monroe Anti-Poverty Initiative (RMAPI) launched a new survey to better understand the impact of COVID- 19 on community member’s income and basic needs as well as what community members need to be safe and financially secure. The goal of the project was to analyze the survey and responses to inform United Way which kind of assistance needs to be provided, and what features of living necessities are more important for the respondents.

Identify scenarios where DPF (Diesel Particulate Filter) failure is likely to happen so that the trucking customer can be alerted in advance to avoid costly roadside breakdowns.

The main objective is to identify the maximum water flow tolerance of the Moses-Saunders Dam in order not to exceed the permissible limits of Lake St. Louis.

The project supported the goal of UR Utilities and Energy Management deparment to improve the efficiency of chilled water production through predictive modeling

The goal of the project was to explore public perception on COVID-19 vaccine by analyzing social media platform data (Twitter).

The project aim was: 1) Understand how the degree of mental health issues changed over time and space during COVID-19; 2) Find out what topics are people concerned about, and 3) Infer what group of people are more likely to have mental health issues.

To spur museum membership growth, encourage donations from members, and increase overall museum revenue

Development of a telehealth platform that provides digital optical prescriptions, making high quality vision accessible to everyone.

Our senior design project is implementing an AI chess board. It uses Python and the popular chess engine Stockfish on a Raspberry Pi to show the user with LEDs where the computer would like to make its move. Our project was a success and could be used as a beginning step to make a more complex and intuitive chess board that plays chess against a user.

The Caldwell-Fay equation (2002) attempts to model what Lake Ontario’s current water level would be if dam construction had never taken place along the St. Lawrence Seaway (i.e. the natural hydraulic state of the lake).

Newly unearthed Lake Ontario data going back to the 1860s has been discovered, and we had the rare opportunity to be the first to digitize and publicly analyze it.

Since this data set predates any dam construction it actually captures the lake’s natural state. Therefore it can be used to verify Caldwell-Fey’s equation which is being used to govern the lake’s inflow and outflow rate on a daily basis.

MOTLEY (Main Organizing Terminal for Low Energy Usage) focuses on the creation of a Smart Home using a Raspberry Pi as the central device. Similar to the google home, we are able to control our in-house made and thoughtfully designed custom peripherals through the use of this Pi, granting our user the ability to remotely control various devices in their homes from their current location.

We were given a patient reported symptoms dataset PRO-CTCAE and applied a variety of clustering methods. The clusters were then statistically tested for associations with a selection of outcomes such as hospitalization. We found significant associations with clusters and outcomes and compared it to linear regression results.

There is a need to ensure that drivers are alerted of approaching emergency vehicles so that they can remove themselves from the path of the emergency vehicle. It is especially a challenge for deaf, hard of hearing, and distracted drivers to identify emergency signals, which puts them at an increased risk for collision. In this project, we developed a device for use in the car that detects emergency vehicles and notifies the driver of their presence. We used a trained convolutional neural network to detect sirens in noisy environments. On our validation set, we achieved a 95% detection accuracy with a 50% criterion. A demonstration of our real-time detector and design schematic are shown below.

RTS is a regional transportation authority established by New York State and the goal of the project is to find the potential reasons for preventable accidents caused by bus operators. First, descriptive and exploratory analysis is performed on all the data provided and driver-related variables and environmental-related variables. Then, frequent pattern mining is applied and conditional probabilities are calculated for the accident history of operators with high risk of accidents to extract accident patterns.