Physical Sciences and Mathematics Commons^™

Temporal Neural Team Formation With Negative Sampling, Seyed Sobhan Dashti

Electronic Theses and Dissertations

Predicting future successful teams of experts who can synergistically work in concert with each other and en masse cover a set of required skills of a degree necessary for the achievement of the desired outcome is challenging due to several reasons, including 1) the magnitude of the pool of plausible expert candidates with diverse backgrounds and skills, and 2) the drift and variability of collaborative ties of experts and their level of expertise in each area in time. Prior works in team formation have neglected the fact that experts’ skill, interests, and collaborative ties change over time. We can categorize …

Comparative Analysis Of Membership Inference Attacks In Federated Learning, Saroj Dayal

Electronic Theses and Dissertations

Given a federated learning model and a record, a membership inference attack can determine whether this record is part of the model’s training dataset. Federated learning is a machine learning technique that enables different parties to train a model without the need to centralize or share their local data. Membership inference attack risks the private datasets if those datasets are used to train the federated learning model and access to the generated model is available. There is a need to study the membership inference attack in the federated learning setting. In this thesis, we empirically investigated and compared various membership …

Online Sexual Predator Detection, Muhammad Khalid

Electronic Theses and Dissertations

Online sexual abuse is a concerning yet severely overlooked vice of modern society. With more children being on the Internet and with the ever-increasing advent of web-applications such as online chatrooms and multiplayer games, preying on vulnerable users has become more accessible for predators. In recent years, there has been work on detecting online sexual predators using Machine Learning and deep learning techniques. Such work has trained on severely imbalanced datasets, and imbalance is handled via manual trimming of over-represented labels. In this work, we propose an approach that first tackles the problem of imbalance and then improves the effectiveness …

Tree-Based Approaches For Predicting Financial Performance, Ahmed Shafeek Abouhassan

Electronic Theses and Dissertations

The lending industry commonly relied on assessing borrowers’ repayment performance to make lending decisions. This is to safeguard their assets and maintain their profitability. With the rise of Artificial Intelligence, lenders resorted to Machine Learning (ML) algorithms to solve this problem.

In this study, the novelty introduced is applying ML’s Tree-based methods to a large dataset and accurately predicting financial repayment performance without using any repayment history, which was utilized in all literature reviewed. Instead, the attributes used were demographics and psychographics of applicants, only. The study’s proprietary US-based dataset comprises an anonymous population whose owner does not wish to …

Identifying Network Biomarkers For Each Breast Cancer Subtypes Along With Their Effective Single And Paired Repurposed Drugs Using Network-Based Machine Learning Techniques, Forough Firoozbakht

Electronic Theses and Dissertations

Breast cancer is a complex disease that can be classified into at least 10 different molecular subtypes. Appropriate diagnosis of specific subtypes is critical for ensuring the best possible patient treatment and response to therapy. Current computational methods for determining the subtypes are based on identifying differentially expressed genes (i.e., biomarkers) that can best discriminate the subtypes. Such approaches, however, are known to be unreliable since they yield different biomarker sets when applied to data sets from different studies. Gathering knowledge about the functional relationship among genes will identify “network biomarkers” that will enrich the criteria for biomarker selection. Cancer …

Deep Learning Applications In Medical Bioinformatics, Ziad Omar

Electronic Theses and Dissertations

After a patient’s breast cancer diagnosis, identifying breast cancer lymph node metastases is one of the most important and critical factor that is directly related to the patient’s survival. The traditional way to examine the existence of cancer cells in the breast lymph nodes is through a lymph node procedure, biopsy. The procedure process is time-consuming for the patient and the provider, costly, and lacks accuracy as not every lymph node is examined. The intent of this study is to develop an artificial neural network (ANNs) that would map genetic biomarkers to breast lymph node classes using ANNs. The neural …

Comparative Study Of Reinforcement Learning Methods In Path Planning, Daniel Obawole

Electronic Theses and Dissertations

In order to perform a large variety of tasks and achieve human-level performance in complex real-world environments, an intelligent agent must be able to learn from its dynamically changing environment. Generally speaking, agents have limitations in obtaining an accurate description of the environment from what they perceive because they may not have all the information about the environment. The present research is focused on reinforcement learning algorithms that represent a defined category in the field of machine learning because of their unique approach based on a trial-error basis. Reinforcement learning is used to solve control problems based on received rewards. …