Digital Commons Network^™

Leveraging Machine Learning For Accurate Prediction Of Nba Player Salaries, Ye Cheng, Yan Song, Mingqi Wang

Iraqi Journal for Computer Science and Mathematics

Basketball players in the NBA are renowned for their talent, athleticism, and commitment to the game. NBA players may make enormous sums of money; however, they vary greatly. Rookie agreements begin at a lower price and go up following performance. NBA players’ pays are influenced by several factors. Because they influence games and the success of the club, exceptional players fetch larger compensation. This study employs Machine Learning (ML) techniques, including Lasso Regression and Random Forest Regression (RFR) models to analyze wage trends, enhanced by the Slime Mould Algorithm (SMA) and Artificial Rabbit Optimization (ARO) for accuracy. The goal is …

Machine Learning Techniques For Optimizing The Efficiency And Costs Of Drill Steel In Sandstone And Granodiorite: A Case Study In Peru, Marco Cotrina, Jairo Marquina, Jose Mamani, Solio Arango, Eusebio Antonio, Eduardo Noriega, Teofilo Donaires, Dominga Cano

Journal of Sustainable Mining

This research aims to optimize the efficiency and costs of drilling steel in sandstone and granodiorite rocks using machine learning techniques in a Peruvian mine. Predictive models, including random forest (RF), XGBoost (XGB), decision trees (DT), and artificial neural networks (ANN), were applied, along with optimization algorithms such as genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), and simulated annealing (SA). A dataset of 705 entries was analyzed, focusing on drill bit wear, percussion and rotation pressures, and cost per meter drilled. Model performance was evaluated using R², RMSE, MAE, and MAPE. The ANN model …

Performance Comparison Of Quantum And Classical Machine Learning Models For Chronic Kidney Disease Prediction, Parama Sridevi, Paramita Basak Upama, Masud Rabbani, Sheikh Iqbal Ahamed

Computer Science Faculty Research and Publications

In this study, we develop and compare quantum and classical machine learning-based chronic kidney disease prediction models. We used the "Chronic_Kidney_Disease Data Set" of the UCI Machine Learning Repository. We performed data preprocessing and applied feature engineering techniques to select the best features. We developed two quantum machine learning-based models and two classical machine learning-based models. We used a hybrid classical-quantum environment for building quantum machine learning models. Finally, we compared the performances of all four models. We found that the Quantum Support Vector Machine performs best among the quantum models. The model’s accuracy was 95% with a k-fold cross-validation …

Data-Driven Prediction Of Binder Rheological Performance In Rap/Ras-Containing Asphalt Mixtures, Eslam Deef-Allah, Magdy Abdelrahman

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Asphalt recycling technologies have advanced considerably over the last few decades with the utilization of reclaimed asphalt pavements (RAP) and recycled asphalt shingles (RAS). Characterizing aged and heterogeneous binders in these mixtures is challenging, particularly with limited extracted binders. This study suggests a data-driven framework that considers the rheological, chemical, and thermal characteristics to predict the binders' performance. Ninety-seven mixtures with 0–35% of the asphalt binder replaced with RAP/RAS binders were included as cores from the field, plant-produced mixtures, and laboratory-fabricated mixtures. The binders were chemically quantified using aging, aromatic, and aliphatic indices. Thermal analyses of the binders involved the …

Process-Grounded Knowledge-Infused Learning And Decision Making, Kaushik Roy

Theses and Dissertations

This dissertation introduces process-grounded knowledge-infused learning and reasoning, a novel framework for integrating domain-expertise-based process knowledge into the learning and reasoning mechanisms of artificial intelligence systems. This approach is designed to produce controlled, transparent, and reliable predictions in critical tasks such as medical diagnosis and recommendation. By focusing on the case study of mental illness diagnosis and recommendation—where decision-making must be grounded in processes such as disorder-specific diagnostic criteria—this work demonstrates methods to embed structured decision-making directly into the system architecture during both training and inference. This integration facilitates end-to-end training and reasoning while ensuring that outputs strictly adhere to …

Network And Tree-Based Approaches To Data-Driven Modeling Of Complex Climate Systems, Adam M. Giammarese

Theses

The guiding thread throughout this thesis work is data-driven modeling of Earth's climate. We investigate a variety of settings within the field of complex climate systems, such as gridded, high-resolution reanalysis data, lower-resolution climate indices, and incomplete paleoclimate records with missing values. We begin with reanalysis data of high spatial and temporal resolution, and use climate network analysis to uncover hidden trends in the data in regards to the Amazon rainforest and its changing role in the global climate system. We introduce a novel formulation of k-nearest neighbors climate networks to study changing trends at a local level, and apply …

Causal Neuro-Symbolic Artificial Intelligence: Synergy Between Neuro-Symbolic And Causal Artificial Intelligence, Utkarshani Jaimini

Theses and Dissertations

Understanding and reasoning about cause and effect is innate to human cognition. In everyday life, humans continuously engage in causal reasoning and hypothetical retrospection to make decisions, plan actions, and interpret events. This cognitive ability allows us to ask questions such as: “What caused this situation?”, “What will happen if I take this action?”, or “What would have happened had I chosen differently?” This intuitive capacity to form mental models of the world, infer causal relationships, and reason about alternative scenarios, particularly counterfactuals, is central to our intelligence and adaptability. In contrast, current machine learning (ML) and artificial intelligence (AI) …

Approaches To Enhancing Multiple Hypothesis Testing Methods With Side-Information, Siyu Zheng

Theses and Dissertations

Lesion-symptom mapping (LSM) studies offer insight into the brain areas involved in various aspects of cognition. This is commonly done via behavioral testing in patients with a naturally occurring brain injury or lesions (e.g., strokes or brain tumors). This results in high-dimensional observational data where lesion status (present/absent) is non-uniformly distributed, with some voxels having lesions in very few (or no) subjects. In this situation, mass univariate hypothesis tests have severe power heterogeneity where many tests are known a priori to have little to no power. Additionally, high-dimensional observational data can be grouped according to brain anatomical structure.

In this …

Effect Of Vehicular Electrification On Transportation Emissions In Florida, Shreya Sapkota Dhakal

Doctoral Dissertations and Master's Theses

Vehicular emissions from fuel-based passenger cars emit an array of gases and particles that are detrimental for human health and the environment. In that regard, electric vehicles (EVs) present a viable, sustainable solution. This study investigated the impact of electrification of passenger cars on air quality in Florida, in five major urban counties namely Miami-Dade, Duval, Hillsborough, Orange, and Leon. Between 2018 and 2022, these counties experienced a significant increase of 219.50 ± 52.32% in EV adoption, coupled with a 11.55 ± 6.13% decrease in fuel-based vehicle usage. Herein, we characterize five pollutants primarily generated from fuel-based passenger vehicles- carbon …

Milpbench: A Large-Scale Benchmark Test Suite For Mixed Integer Linear Programming Problems, Huigen Ye, Yaoyang Cheng, Hua Xu, Zhiguang Cao, Hanzhang Qin

Research Collection School Of Computing and Information Systems

Mixed-integer linear programming (MILP) is a cornerstone of optimization with applications across numerous domains. However, the development and evaluation of MILP-solving algorithms are hindered by existing benchmark datasets, which are often limited in scale, lack diversity, and are poorly structured, making them inadequate for systematic testing across different solving approaches, especially for machine learning (ML)-based methods. To address these issues, we introduce MILPBench, a large-scale benchmark suite comprising 100,000 MILP instances organized into 60 well-categorized classes. Using structural properties and embedding similarity metrics, we developed a novel classification framework to ensure both intra-class homogeneity and inter-class diversity. In addition to …

Personal Bankruptcy: A Bibliometric Analysis And Future Research Directions, Aqilah Nadiah Md Sahiq, Saida Farhanah Sarkam, Putri Aliah Mohd Hidzir, Nurwahida Yaakub, Shafinar Ismail, Nurbaity Sabri, Hanifiyah Yuliatul Hijriah, Himmatul Kholidah

The Indonesian Capital Market Review

Personal bankruptcy is a pertinent topic to discuss due to the surge in personal bankruptcy cases around the world and it is an important indicator of household financial problems nationally. However, only few comprehensive reviews have been conducted to date. Hence, this study provides a bibliometric review of 210 studies on personal bankruptcy authored by 496 scholars. Bibliographical data were extracted from the Scopus database and analyzed it using the Bibliometrix-R software. Based on the citation analysis metrics, we revealed the most influential articles, journals, authors, and institutions. Using the network and conceptual structure analysis, we identified three underlying research …

A Visual-Omics Foundation Model To Bridge Histopathology With Spatial Transcriptomics, Weiqing Chen, Pengzhi Zhang, Tu N Tran, Yiwei Xiao, Shengyu Li, Vrutant V Shah, Hao Cheng, Kristopher W Brannan, Keith Youker, Li Lai, Longhou Fang, Yu Yang, Nhat-Tu Le, Jun-Ichi Abe, Shu-Hsia Chen, Qin Ma, Ken Chen, Qianqian Song, John P Cooke, Guangyu Wang

Faculty, Staff and Student Publications

Artificial intelligence has revolutionized computational biology. Recent developments in omics technologies, including single-cell RNA sequencing and spatial transcriptomics, provide detailed genomic data alongside tissue histology. However, current computational models focus on either omics or image analysis, lacking their integration. To address this, we developed OmiCLIP, a visual-omics foundation model linking hematoxylin and eosin images and transcriptomics using tissue patches from Visium data. We transformed transcriptomic data into 'sentences' by concatenating top-expressed gene symbols from each patch. We curated a dataset of 2.2 million paired tissue images and transcriptomic data across 32 organs to train OmiCLIP integrating histology and transcriptomics. Building …

Leveraging Lime For Trustworthy Apple Quality Assessment, İsmail Kırbaş, Ahmet Çifci

Platform: A Journal of Engineering

This study explores the integration of machine learning (ML) models and explainable artificial intelligence (XAI) techniques to enhance the accuracy and transparency of apple quality assessment. Leveraging the Apple Quality dataset from Kaggle, we evaluated the performance of random forest (RF), logistic regression (LR), support vector machine (SVM), and eXtreme gradient boosting (XGBoost) across accuracy, precision, recall, and F1-score metrics. Among these models, RF demonstrated superior performance, achieving an accuracy of 87.6%, underscoring its robustness in managing complex feature interactions. A novel contribution of this study lies in employing the local interpretable model-agnostic explanations (LIME) method to elucidate model predictions, …

Adaptive Control Of Air Purification Processes In Industrial Painting Workshops: A Review Of Scientific Literature, N.A Kabulov, Sh. Mamatbekov

Chemical Technology, Control and Management

This article examines modern approaches to adaptive control of air purification processes in industrial facilities, particularly in paint shops for parts coating. The relevance of the study is driven by the need to enhance air purification efficiency, ensure worker safety, and comply with environmental regulations. The paper analyzes adaptive control methods, including the use of neural networks, machine learning, and adaptive control algorithms, which enable real-time optimization of purification systems. Special attention is given to the utilization of sensor data for predicting pollution levels and automatically adjusting purification parameters. The article also discusses the advantages and limitations of implementing adaptive …

Estimating Order Picking Distances Using Machine Learning, Hector J. Carlo, Carlos A. Morel-Figueroa, Victoria Méndez-González, Yamilette Boscio-Sánchez, Alex Demel-Pacheco

International Material Handling Research Colloquium

No abstract provided.

Identification Accuracy Of Cost-Effective Visible (Vis) To Near-Infrared (Nir) Sensor As7265x On Plastic Waste In The Philippines, Ma. Tricia Jasmin M. Ocho, Chloe Lizanne R. Bravo, Jennika Love R. Loyola, Kierra Jamielle V. Taroma, Leianne Nina Arabelle E. Lampayan

DLSU Senior High School Research Congress

In the Philippines, plastic waste persists as a significant issue caused by the widespread use of single-use plastics and the lack of recycling systems. The lack of proper systems made waste segregation and identification inefficient and disorganized. Past studies explain the use of VIS/NIR in plastic segregation and potential of spectral frequencies. However, there is limited investigation of the AS7265x sensor’s consistency when integrated with machine learning to further improve detection reliability; thus, the study evaluates the effectiveness of the AS7265x triad spectroscopy sensor, incorporated with machine learning algorithms. An experimental setup was created using the AS7265x sensor and Arduino …

Classification Of Human Trust In Ai Using Brain Activity Data, Danushka Bandara, Ruhuan Liao, Fatima Chowdhury, Leslie Abbott

Northeast Journal of Complex Systems (NEJCS)

Trust plays a crucial role in human-computer interaction, particularly in scenarios involving artificial intelligence (AI) systems. This study explores the feasibility of using functional near-infrared spectroscopy (fNIRS) data to classify trust levels in human-AI interaction scenarios. A total of 18 participants completed an image classification task with an AI team member while their hemodynamic responses were recorded using fNIRS. Preprocessing of fNIRS data involved motion artifact removal, filtering, and normalization. Exploratory analysis identified significant associations between hemodynamic responses in the prefrontal cortex and trust levels. An across-subject binary trust classification model was developed using machine learning techniques, achieving an F1 …

Predicting Energy Consumption Trends In The Philippines (2025-2030) Using Machine Learning Models, Christine Angeline F. Vermudo, Hannah Ashley A. Mendoza, Angelica Nicole B. Constantino, Rhian Consuelo C. Ledesma

DLSU Senior High School Research Congress

Energy security remains a significant concern for the Philippines amid rising demand, growing population, and continuous modernization. With the history of the Philippines’ problems in energy, attention to energy is necessary. With this, the study aims to forecast the Philippines' possible annual energy consumption from 2025 to 2030, considering population growth as a potential variable influencing the Philippines’ projected energy consumption trends. The study employs a machine learning approach using the linear regression model, which was the model with the best performance in forecasting future energy consumption based on MATLAB’s Linear Regression Learner app. Prior to forecasting, historical data from …

Enhancing Software Effort Estimation: A Comparative Analysis Of Machine Learning Models With Correlation-Based Feature Selection, Muhammad Abid, Momina Liaqat Ali

Sustainable Machine Intelligence Journal

Software effort estimation is a critical process in the management of software projects, essential for predicting the resources and time required for application development. Accurate estimation is paramount for delivering projects on time and computational resources. This paper investigates techniques that utilize machine learning models to ameliorate software effort estimation. We employ four popular machine learning models including the Linear Regression, Gradient Boosting, Random Forest, and Decision Tree and evaluate their performance across five diverse climate-related datasets: Antarctica Hotpoints/Climate Change NASA, Crop Production and Climate Change, Global Warming on Earth (Global Temp), Climate Change, and Climate Treasure (Marine). A key …

Modeling And Estimation Of Co2 Capture By Porous Liquids Through Machine Learning, Farid Amirkhani, Amir Dashti, Hossein Abedsoltan, Amir H. Mohammadi, John L. Zhou, Ali Altaee

Chemical and Biochemical Engineering Faculty Research & Creative Works

Porous liquids (PLs) are newly developed porous materials that combine unique fluidity with permanent porosity, which exhibit promising functionalities. They have shown ability to efficiently absorb greenhouse gases such as carbon dioxide (CO₂). Experimental measurement is one approach to determining the solubility of various greenhouse gases in PLs, which has drawbacks such as being expensive and time-consuming. Hence, simulation models are valuable to predict the solubility of CO₂ in various PLs. This work aims to develop machine learning (ML) modeling methods for accurately estimating CO₂ solubility under varying conditions (e.g. PLs, temperature, pressure). Adaptive Neuro-Fuzzy Inference …

Proactive Risk Identification In New Zealand Infrastructure Projects: A Machine Learning Approach To Classifying Project Risk, Dr Tirth Patel, Assoc. Prof. Eric Scheepbouwer, Dr. Jacobus Daniel Van Der Walt

CIB Conferences

In the construction industry, proactive identification and management of challenges such as delays, budget constraints, and regulatory hurdles are essential for effective risk management and successful project delivery. Traditional methods, relying on manual analysis and expert judgment, are often time-consuming, inconsistent, and subjective. This study proposes a data-driven framework to automate the identification and classification of challenges within project descriptions using Natural Language Processing (NLP) and machine learning (ML) techniques. Utilizing the Pacificon dataset, comprising 254,923 infrastructure projects across New Zealand, the study defines challenge categories through regular expressions and implements a machine learning pipeline with TfidfVectorizer and Logistic Regression. …

Anomaly Detection In Electrical Systems Using Machine Learning And Statistical Analysis, Mustafa I. Al-Karkhi, Grzegorz Rzadkowski, Latif Ibraheem, Muhammad Aqib

Terra Joule Journal

The reliability of electrical systems is essential for uninterrupted power supply across residential, industrial, and commercial sectors. This research utilizes the various techniques of ML in identifying all sorts of abnormal electrical power variables, including voltage fluctuations, line faults, and frequency variations that emanate from several causes like equipment failures and environmental ones. Voltage and current measurements were pre-processed along with other additional parameters such as frequency and THD, then visualized to assure its quality and relevance for training the ML models. The work implemented the algorithms of Random Forest, Decision Tree, XGBoost, SVM, and Logistic Regression-the Random Forest Classifier …

Machine Learning: Neural Networking With Relu And Optimization, Aidan Redmond Brownell

Undergraduate Theses, Capstones, and Recitals

At its core, learning is an algorithmic process: it begins with input data, undergoes a series of transformations or computations, and yields an output intended to solve a specific task. This output is then compared against a target or desired result, and the internal mechanisms are updated based on how well the output aligns with expectations. While this feedback-driven process occurs almost effortlessly in humans, it is a far more structured, deliberate, and computationally intensive undertaking for machines.

Enhancing Water Scarcity Resilience In Egypt Through Machine Learning-Driven Phenological Crop Mapping And Water Use Efficiency Analysis, Surendra Maharjan, Wenzhao Li, Shahryar Fazli, Aqil Tariq, Rejoice Thomas, Cyril Rakovski, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Agriculture forms the backbone of Egypt’s economy, with the Nile Valley and Delta serving as key production zones for crops like wheat, rice, and clover. However, the sector faces mounting pressure from water scarcity, as it depends almost entirely on the Nile for irrigation, making it necessary to map major crops for assessing Water Use Efficiency (WUE) and informing agricultural planning. In this study, we used machine learning (ML) techniques—specifically Support Vector Machine (SVM) to time-series phenological data and optical indices (Enhanced Vegetation Index (EVI), Bare Soil Index (BSI), Land Surface Water Index (LSWI), Normalized Difference Vegetation Index (NDVI), and …

Multi-Label Classification Of Acoustic And Electronic Drum Sounds Using Machine Learning, Sean Perman

Electronic Theses and Dissertations

This paper presents a system for multi-class classification of drum sounds using audio signal processing and machine learning techniques. The project utilizes a diverse dataset of both acoustic and electronic drum samples and extracts ten distinct audio features to capture the timbral and temporal characteristics of each sound. The methodology includes signal preprocessing, feature extraction, and the application of supervised classification algorithms to distinguish between multiple drum classes. Experimental evaluations demonstrate that the selected features significantly enhance classification accuracy across a varied dataset. These findings underscore the effectiveness of combining traditional audio processing with modern machine learning, offering promising applications …

Enhancing Multi-Step Stock Price Forecasting With Social Media Sentiment And Engagement Metrics, Damilare Olaniyan

Electronic Theses and Dissertations

This thesis investigates whether social media sentiment can improve the accuracy of stock price prediction beyond traditional historical data. While financial markets have long relied on structured numerical indicators, the growing influence of public discourse on platforms like Twitter has introduced new opportunities for extracting market-relevant signals from unstructured text. The study focuses on four major technology firms and combines sentiment features derived from Twitter with historical stock prices in a hybrid machine learning framework. Engagement-weighted sentiment, linguistic complexity, and polarity intensity were extracted using natural language processing techniques and incorporated into classification and regression models. Results show that including …

Intelligent Intrusion Detection In Clustered Wireless Sensor Networks: A Dynamic Clustering And Machine Learning-Based Approach, Abdullah R. Abdulwahhab, Mohd Fadzli Mohd Salleh, Muhammad Firdaus Akb, Mohammed Najm Abdullah

Iraqi Journal for Computer Science and Mathematics

Traditional Intrusion Detection Systems (IDS) designed for more conventional network infrastructures are often ill-equipped to handle the unique challenges WSNs pose, leading to significant gaps in security and resilience. This paper introduces an Intelligent Intrusion Detection System (IIDS) explicitly tailored for clustered WSNs to address these critical challenges. The proposed IIDS integrates dynamic clustering with advanced machine learning algorithms to create a robust and adaptive security solution capable of real-time threat detection and mitigation. The dynamic clustering mechanism is designed to continuously monitor and respond to changes in sensor node network topology and energy levels, ensuring that energy consumption is …

Enhancing Dementia Classification For Diverse Demographic Groups: Using Vision Transformer-Based Continuous Scoring Of Clock Drawing Tests., Mengyao Hu, Yi Lu Murphey, Tian Qin, Edmundo R Melipillán, Laura B Zahodne, Richard Gonzalez, Vicki A Freedman

Faculty, Staff and Student Publications

Objectives: Alzheimer's disease and related dementias significantly affect older adults' quality of life. The clock-drawing test (CDT) is a widely used dementia screening tool due to its ease of administration and effectiveness. However, manual CDT-coding in large-scale studies can be time-intensive and prone to coding errors and is typically limited to ordinal responses. In this study, we developed a continuous CDT score using a deep learning neural network (DLNN) and evaluated its ability to classify participants as having dementia or not.

Methods: Using a nationally representative sample of older adults from the National Health and Aging Trends Study (NHATS), we …

Revised Unsteady Drag Force And Its Impact On Particle Dynamics At High Reynolds Numbers, Ahmad Talaei, Timothy J. Garrett

Physics Student Research

The accurate modeling of particle motion in viscous fluids at high Reynolds numbers remains a fundamental challenge, particularly under unsteady flow conditions where nonlinear effects dominate. This study introduces a revised unsteady drag formulation, extending its applicability beyond previous studies. By integrating classical solutions of the Navier–Stokes equations, the new formulation overcomes the limitations of the Maxey–Riley–Gatignol (MRG) equation, addressing non-physical memory effects associated with Basset drag and extending its applicability to higher Reynolds numbers.

Through experimental comparisons and direct numerical simulations (DNS), the revised equation of motion is compared to the MRG equation, both during steady-state conditions and non-equilibrium …

A Hybrid Ai-Based Framework For Real-Time Epileptic Seizure Detection In Intracranial Eeg Signals Using Brain-Computer Interfaces, Ahmed, M. Salaheldin Dr., Manal Abdel Wahed Prof., Neven Saleh

Future Engineering Journal

Epilepsy is a serious neurological disorder that can significantly impact an individual's quality of life. This study proposes a novel method for the detection and mitigation of epileptic seizures through the integration of artificial intelligence (AI) and a brain-computer interface (BCI) system. Statistical features were extracted from intracranial electroencephalography (IEEG) signals using a multi-resolution decomposition technique and used to train five classification algorithms: support vector machines (SVM), k-nearest neighbors (KNN), Naïve Bayes (NB), artificial neural networks (ANN), and long short-term memory (LSTM). The model demonstrated strong performance, achieving classification accuracies of 98.28% (SVM), 93.88% (KNN), 95.73% (NB), 95.73% (ANN), and …