Doctor of Philosophy (Ph.D.)

Computer Science
Western Michigan University
2018

Master of Science (M.S.)

Computer Science
Western Michigan University
2018

Master of Science (M.S.)

Computer Science
University of Mosul
2011

Bachelor of Science (B.S.)

Computer Science
University of Mosul
2008

Artificial Intelligence

Speciality Areas: Artificial Intelligence in Healthcare, Natural Language Processing (NLP), Explainable AI, Human-Centered AI, Health Equity and AI, Health Equity and AI, Translational Artificial Intelligence, Large Language Models (LLMs), Federated Learning

Data Science

Specialty Areas: Big Data Analytics, Biomedical Data Science

Machine Learning

Specialty Area: Deep Learning

Medicine

Speciality Areas: Population Health Analytics, Clinical Decision Support Systems, Smart Health Systems, Alzheimer's Disease Research, HIV Outcomes Research, Sleep Medicine Analytics, Digital Health and Wearable Technologies, Computational Medicine, Precision Medicine, Medical Imaging Analytics

Advanced Python for AI and Data Science

Biostatistics & Bioinformatics

Graduate Capstone

Practicum/Internship

Department-Level Graduate Research and Creative Scholars Award. Computer Science Department. Western Michigan University; March 2018

College of Engineering and Applied Science, Western Michigan University. Dean office travel grants for graduate students; Nov 2017

Patents

F. Alsaleem, M. A. Takallou, S. Myers, I. Pipinos, and B. Qolomany, “Systems and Methods for Diagnosing Peripheral Arterial Disease (PAD) Using Gait Acceleration Characteristics,” US Patent 20240237902A1, Jul. 18, 2024.

Predicting High-Risk Colorectal Polyps in African Americans Using Pre-Colonoscopy Clinical Features

Predicting High-Risk Colorectal Polyps in African Americans Using Pre-Colonoscopy Clinical Features: Machine Learning Model Development and Temporal Validation

Risk stratification for advanced colorectal polyps typically relies on colonoscopy and/or pathology findings. However, there is growing interest in whether non-invasive features available prior to colonoscopy can help identify patients at higher risk. Such approaches may enhance clinical decision-making by prioritizing surveillance for individuals most likely to harbor high-risk polyps, when colonoscopy resources are limited while potentially reducing unnecessary procedures in lower-risk patients. Importantly, the use of non-invasive, pre-procedural information may also help promote more equitable access to risk stratification, particularly in settings where colonoscopy resources are limited or unevenly distributed. We aimed to develop and externally validate machine learning models to predict high-risk colorectal polyps using only non-invasive, pre-colonoscopy demographic, clinical, and behavioral features in a diverse, predominantly African American, urban cohort. 

We conducted a retrospective cohort study using demographic, lifestyle, and comorbidity data from patients who underwent colonoscopy at Howard University Hospital to develop and validate several machine learning models, including neural networks, random forest, support vector machines (SVM), Naive Bayes, logistic regression, decision trees, k-nearest neighbors (KNN), and XGBoost, for predicting high-risk colorectal polyps. High-risk polyps (HRP) were defined as villous or tubullovillous adenomas, high-grade dysplasia, polyps >= 10 mm in size, and/or the presence of >= 3 polyps per procedure; all other cases were classified as low-risk polyps (LRP). The dataset included 4,681 patients from 2015-2022 used for internal validation and 1,562 patients from 2023-2024 used for external validation.

Diagnosis of disease affecting gait

Diagnosis of disease affecting gait with a body acceleration-based model using reflected marker data for training and a wearable accelerometer for implementation

Large Language Model Enhanced Particle Swarm Optimization for Hyperparameter Tuning for Deep Learning Models

Large Language Model Enhanced Particle Swarm Optimization for Hyperparameter Tuning for Deep Learning Models 

Determining the ideal architecture for deep learning models, such as the number of layers and neurons, is a difficult and resource-intensive process that frequently relies on human tuning or computationally costly optimization approaches. While Particle Swarm Optimization (PSO) and Large Language Models (LLMs) have been individually applied in optimization and deep learning, their combined use for enhancing convergence in numerical optimization tasks remains underexplored. Our work addresses this gap by integrating LLMs into PSO to reduce model evaluations and improve convergence for deep learning hyperparameter tuning. The proposed LLM-enhanced PSO method addresses the difficulties of efficiency and convergence by using LLMs (particularly ChatGPT-3.5 and Llama3) to improve PSO performance, allowing for faster achievement of target objectives. Our method speeds up search space exploration by substituting underperforming particle placements with best suggestions offered by LLMs. Comprehensive experiments across three scenarios—(1) optimizing the Rastrigin function, (2) using Long Short-Term Memory (LSTM) networks for time series regression, and (3) using Convolutional Neural Networks (CNNs) for material classification—show that the method significantly improves convergence rates and lowers computational costs. Depending on the application, computational complexity is lowered by 20% to 60% compared to traditional PSO methods. Llama3 achieved a 20% to 40% reduction in model calls for regression tasks, whereas ChatGPT-3.5 reduced model calls by 60% for both regression and classification tasks, all while preserving accuracy and error rates. This groundbreaking methodology offers a very efficient and effective solution for optimizing deep learning models, leading to substantial computational performance improvements across a wide range of applications.

A survey of social cybersecurity

A survey of social cybersecurity: Techniques for attack detection, evaluations, challenges, and future prospects

In this comprehensive review, we delve into various techniques, attacks, challenges, potential solutions, and trends within the realm of detecting social cybersecurity attacks. Additionally, we explore the potential of readily available public datasets and tools that could expedite research in this vital domain. Our objective is not only to tackle the existing challenges but also to illuminate potential pathways for future exploration. Through this survey, our primary focus is to provide valuable insights into the rapidly evolving landscape of social cybersecurity. By doing so, we aim to assist researchers and practitioners in developing effective prediction models, enhancing defense strategies, and ultimately fostering a safer digital environment.

Safeguarding connected autonomous vehicle communication

Safeguarding connected autonomous vehicle communication: Protocols, intra- and inter-vehicular attacks and defenses

The advancements in autonomous driving technology, coupled with the growing interest from automotive manufacturers and tech companies, suggest a rising adoption of Connected Autonomous Vehicles (CAVs) in the near future. Despite some evidence of higher accident rates in AVs, these incidents tend to result in less severe injuries compared to traditional vehicles due to cooperative safety measures. However, the increased complexity of CAV systems exposes them to significant security vulnerabilities, potentially compromising their performance and communication integrity. This paper contributes by presenting a detailed analysis of existing security frameworks and protocols, focusing on intra- and inter-vehicle communications. We systematically evaluate the effectiveness of these frameworks in addressing known vulnerabilities and propose a set of best practices for enhancing CAV communication security. The paper also provides a comprehensive taxonomy of attack vectors in CAV ecosystems and suggests future research directions for designing more robust security mechanisms. Our key contributions include the development of a new classification system for CAV security threats, the proposal of practical security protocols, and the introduction of use cases that demonstrate how these protocols can be integrated into real-world CAV applications. These insights are crucial for advancing secure CAV adoption and ensuring the safe integration of autonomous vehicles into intelligent transportation systems.