Digital Commons Network^™

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 …

Review Of Energy Storage Systems For Optimal Planning And Operation Of Microgrid Resources, Ibrahim M. Ibrahim Dr., Walid A. Omran Prof., Almoataz Y. Abdelaziz Prof.

Future Engineering Journal

Energy storage systems (ESSs) play a central role in microgrid (MG) research and are widely used in related studies. This is due to the many applications of ESSs that enhance the performance of MGs. These include power quality applications and energy management applications. This paper presents a literature review of various studies that address the use of ESSs in microgrid (MG) systems to perform functions aimed at enhancing overall performance. These studies are classified into two main categories: planning and operational planning, with the MG being either grid-connected or islanded in each case. Finally, the optimization techniques used to obtain …

Centralized, Decentralized And Hybrid Wastewater Management Approaches – Gap Study, Zeyad Kadry, Ahmed M. Ebid Dr., Ibrahim Mahdi

Future Engineering Journal

This literature review highlights advancements in wastewater management, emphasizing hybrid systems that integrate centralized and decentralized approaches. It underscores the cost-effectiveness and scalability of decentralized wastewater management (DWWM) for small urban and rural areas, despite higher land and capital needs. Optimization frameworks utilizing advanced computational tools, such as genetic algorithms and GIS, have proven effective in reducing costs and enhancing resource recovery, with decision-support frameworks tailored for rural planning. The integration of synergistic technologies like vertical farming and photovoltaic energy enhances the sustainability and resilience of decentralized systems in urban contexts. Additionally, innovative treatment methods, including nano-disinfection and advanced coagulation, …

M. Ed. In Teacher Leadership Portfolio, Elizabeth Hemmons

Master of Education in Teacher Leadership Portfolios

No abstract provided.

Statistical Programming For Adaptive Monitoring In Software Defined Networks Using Linear Programming, Fatemeh Amou Aghaei, Ruairí De Fréin

SAML-25 Workshop on Statistical and Machine Learning

Adaptive monitoring in Software Defined Networks (SDNs) is essential to reduce overhead and prioritize critical flows. This paper introduces AdaptMon, a Linear Programming-based model that dynamically allocates monitoring resources based on estimated error rates. By modeling allocation as a probability distribution and enforcing a fairness constraint using an ℓ1-style deviation bound, the approach maximizes expected monitoring utility while preserving balance across the network. Simulations show that AdaptMon reduces monitoring delay by up to 40% without sacrificing anomaly detection accuracy. The model is interpretable, lightweight, and grounded in statistical programming, making it a practical solution for real-time SDN environments.

A Transfer Learning Load Adjusted Approach For Video-On-Demand Systems Given Limited Training Data, Kangogo Kimeli, Ruairí De Fréin

SAML-25 Workshop on Statistical and Machine Learning

Inadequate data complicates planning and allocation of VoD resources, potentially hindering the scalability of VoD services. We propose a Transfer Learning Load Adjusted (TLLA) algorithm for resource management given limited VoD data. TLLA leverages the knowledge gained from pre-trained models by storing features and patterns that can be used to train Machine Learning (ML) related tasks. We model limitations in VoD data by proportionally freezing 50% of the neural layers in models trained from pre-trained and source domains. We evaluate the performance of the frozen neural layers by comparing them to unfrozen data. Freezing 50% of the neural layers in …

Investigation Of Maleic Anhydride In Organic Synthesis, Emma Nissen, Zhijun Wang, Qianli Rick Chu

Arts & Sciences Undergraduate Showcase

This research explores the synthetic utility of maleic anhydride; a versatile and reactive compound widely used in organic and industrial chemistry. The study focuses on the synthesis of N-allyl maleimide and 3-hexyl-1,2-cyclobutanedicarboxylic acid. N-allyl maleimide, confirmed via ^1H NMR spectroscopy, was synthesized in high yield and shows potential as a monomer or intermediate in organic synthesis. Additionally, the photochemical synthesis of a novel compound, 3-hexyl-1,2-cyclobutanedicarboxylic acid, was achieved and characterized. Its long aliphatic side chain contributes to decreased melting point and increased solubility. These findings demonstrate maleic anhydride’s utility in forming intermediates and bicyclic compounds under mild conditions, supporting its …

Real-Time 3d Automated Spotlight Tracking System, Francine Garcia, Mary Hemker

Computer Science and Engineering Senior Theses

Commercially available automated spotlight systems tend to be expensive and unattainable for smaller school or community theaters. This project provides a cheap alternative to automated spotlight systems on the market and is compatible with most stage lighting architecture by using the Digital Multiplex (DMX) Protocol.

Circuit boards with Ultra-Wideband (UWB) radio frequency track an actor’s coordinates in a 3D space defined by four UWB anchors. A Raspberry Pi single-board computer acts as a controller and converts the coordinates into pan and tilt angles and maps these calculations to values that the spotlight can read in order to move. These values …

Shape-Based Nanoparticle Classification Using Machine Learning, Caitlin Caitlin Robertson, Hender Lopez

SAML-25 Workshop on Statistical and Machine Learning

The accurate classification of nanoparticles (NPs) based on their shapes is crucial for understanding their physical-chemical properties and predict their bioactivity. Nowadays, synthesis method are able to produce a broad range of shapes, such as spheres, cubes and branched NPs and commonly these NP shapes are only described qualitative. This study presents NP descriptors obtained from NPs contours extracted from electron microscopy images. Descriptors such as Fourier descriptors, aspect ratio, and compactness are then used as input for machine learning classifiers. In particular, XGBoost, Random Forest, and neural networks are explored and the their performances are compared and discussed.

Enhancing Dermatological Skin Lesion Classification With Multi-Modal Attention-Based Models And Explainability, Conan Oreilly

SAML-25 Workshop on Statistical and Machine Learning

Accurate classification of skin lesions is critical for early detection of melanoma and other malignancies, particularly in resource-limited settings. This study presents a novel multi-modal machine learning framework that integrates dermoscopic images and structured clinical metadata to improve diagnostic performance. Leveraging the PAD-UFES-20 dataset, which includes over 2,000 smartphonecaptured lesion images and associated patient metadata, we benchmark a series of unimodal and multimodal models. Our results demonstrate that modality attention fusion (MAF) applied to a frozen SwinV2-Tiny vision transformer and metadata multi-layer perceptron (MLP), augmented with focal loss, yields a state-ofthe- art weighted F1-score of 0.84 and balanced accuracy of …

Analyzing Option Chain Bid–Ask Spreads With Machine Learning, Brian Byrne, Qianru Shang

SAML-25 Workshop on Statistical and Machine Learning

This paper investigates the determinants of option bid–ask spreads using machine learning techniques. We analyze a cross-sectional dataset of Apple Inc. (AAPL) call options, focusing on the relative bid–ask spread as the target variable. By comparing linear models with ensemble methods such as Random Forests and XGBoost, we find that nonlinear machine learning methods significantly outperform traditional OLS regression. The most influential factors are moneyness, implied volatility, and time to expiration, while volume and open interest have limited predictive power. Results suggest that spreads are driven by a mix of market microstructure dynamics, capital constraints, and regulatory requirements such as …

Intention To Commute By Public Transportation And/Or By Foot: Findings From A Pls Structural Equation Model, Simona Balzano, Houyem Demni,, Edoardo Pascucci,, Luisa Natale, Giuseppe Cappelli, Sofia Nardoianni, Giovanni C. Porzio

SAML-25 Workshop on Statistical and Machine Learning

Sustainable mobility stands at the forefront of contemporary discussions, driven by the clear imperative to transition towards more environmentally friendly transportation and patterns. This shift is widely recognized as a crucial opportunity to address the challenges and inherent dangers posed by climate change. It is then crucial to introduce attitudes to encourage voluntary behavioral changes toward different sustainable solutions. In this perspective, to foster a future where sustainable personal mobility options are widely embraced and integrated, it is crucial to comprehend the inclination of younger generations to use them. For this reason, a survey on the use of sustainable mobility …

Shedding Light On Cellular Glycolysis Pathway Kinetics Using A Spectralomics Approach, Integrating Multivariate Statistical And Machine Learning Analytical Approaches, Nitin Patil, Zohreh Mirveis, Hugh Byrne

SAML-25 Workshop on Statistical and Machine Learning

The potential of time resolved label-free Raman microspectroscopy to elucidate the kinetics of cellular and subcellular glycolysis pathway was explored in this study. A549, human lung cells were cultured in an unbuffered minimal medium with glucose as a sole carbon source under three different modulated conditions. Modulator drugs oligomycin and 2-deoxyglucose were used to stimulate and inhibit the glycolysis pathway. Initially the kinetic glycolysis assay was used to monitor the glycolysis end-point kinetics followed by development of a numerical model capable of simulating the end-point kinetics. For Raman spectroscopy, samples at different timepoints from the experiments with similar conditions as …

Ai-Driven Personalized Radiotherapy Planning, Nithin Venkatesh, Marco Pota, Maged Shaban

SAML-25 Workshop on Statistical and Machine Learning

The planning of radiation oncology treatment is made more dynamic and individualized by Artificial Intelligence (AI). Routine radiotherapy practice applies normative procedures indifferent to patient-specific parameters such as tumor volume, patient anatomy, and heterogeneity in the delineation of treatment response. Inadequate and over-radiation treatment is the most prevalent outcome. Further, with the inclusion of AI, it can facilitate enhancing the healthcare industry through optimizing radiotherapy using an array of patient information such as molecular profiles and imaging data. The product offers an end-to-end AI-driven solution to all aspects of radiotherapy, from initial consultation (diagnosis) to adaptive treatment planning. All the …

Early Lane Change Prediction For Mixed Traffic With V2x Communication, Muhammed Fatih Koc, Nouman Ashraf, Pramod Pathak, Sachin Sharma

SAML-25 Workshop on Statistical and Machine Learning

Lane change prediction is essential for ensuring road safety and effective decision-making in autonomous vehicles (AVs). AVs will probably take several decades to penetrate new vehicle sales. As AVs and human-driven vehicles (HDVs) will coexist in traffic for the long term, AVs must understand the lane change intentions of surrounding HDVs. Lane changing is a critical manoeuvre that can cause a crash if it is performed late or if incorrect lane adjustments are made. Therefore, forecasting surrounding vehicles’ lane change intentions in advance is essential to ensure safe driving in mixed traffic environments having both AVs and HDVs. The unpredictability …

A Machine Learning Approach To Improve Prediction In Chemical Exposure Risk Assessment, Michele Marro, Cédric Koller, Hasnaa Chettou, David Vernez

SAML-25 Workshop on Statistical and Machine Learning

Exposure models play a crucial role in predicting chemical exposure in workplaces, offering an essential alternative to measurements, which are resource-intensive and time-consuming and sometimes not possible. Despite their widespread use and continuous development, significant challenges persist, including variability in predictions, limited model updates, and difficulties in accessing the required input data. In this study, we investigate how modern machine learning techniques can contribute to the improvement of exposure models by addressing these limitations. To overcome the frequent lack of data, we explore the use of synthetic datasets generated through existing exposure models. This approach allows for the study of …

Interpretable Ai In Education: A Comparison Of Glass-Box Models For Predicting Student Success, Jan Glazenborg

SAML-25 Workshop on Statistical and Machine Learning

This Master’s thesis addresses early identification of first-year Computer Science students at risk of underperformance by comparing inherently interpretable (“glass-box”) predictive models with the existing Naïve Bayes–based PreSS tool. The PreSS dataset was originally compiled by Quille & Bergin from 692 first-year CS1 students across eleven institutions in Ireland and Denmark, who completed surveys on programming and mathematics backgrounds, gaming habits and a short programming test four to six hours into the course. Seventeen normalized features capturing demographic, academic and behavioural factors were extracted. In this thesis, four machine learning models are evaluated: Naïve Bayes, explainable boosting machines, automatic piecewise …

Pros & Cons Of Reinforcement Learning - Illustrated By The Problem Of Controlling Gantry Robots, Horst Zisgen

SAML-25 Workshop on Statistical and Machine Learning

In this talk a solution for the dynamic scheduling of flexible flow shop systems using gantry robots for material handling by means of simulation and Reinforcement Learning (RL) is presented. Subsequently the pros and cons of a RL approach are briefly discussed and illustrated at the robot control problem.

Survival Predictions From Classification Algorithms – Concepts And Application To Graft And Patient Survival After Kidney Transplantation, Antje Jahn

SAML-25 Workshop on Statistical and Machine Learning

Clinical prediction models are developed to predict long-term patient outcomes following medical interventions. One example motivating this research is the prediction of graft and patient survival after kidney transplantation, using data from the German organ transplantation registry. A practical issue in this context is to deal with incomplete information due to right-censoring, which arises when patients are lost to follow-up or enter the study at different times, resulting in varying durations of observation. This is particularly relevant in the registry data, where follow-up is frequently incomplete or irregular. While traditional survival analysis methods handle censoring by modeling the hazard function, …

Pathology’S Place In Understanding The Bias And Inequalities In Women’S Healthcare, Andrea Heaney, Eugene Hickey, Emma Murphy

SAML-25 Workshop on Statistical and Machine Learning

Women’s healthcare is a complex, multifaceted issue with both historic and implicit biases, along with biological differences between men and women. With the advancement of AI tools in healthcare and the potential for biased data to create biased models, it is vital to consider how women are represented in data. Previously conducted semi-structured semantic interviews with clinicians were analysed via Braun and Clark’s method of thematic analysis. The analysis of these interviews yielded the following themes: Gender Influencing Health, Pregnancy, Social Factors, General Health, Treatment, Training, and Research. These themes highlight that context is key to understanding the biases in …

Impact Of Spatial Diversity And Subject Variability On Wifi-Based Human Activity Recognition, Amany Elkelany, Robert J. Ross, Susan Mckeever

SAML-25 Workshop on Statistical and Machine Learning

In recent years, WiFi-based Human Activity Recognition (HAR) has gained substantial attention due to the ubiquity of WiFi infrastructure and advancements in wireless communication. Unlike camera-based systems that raise privacy concerns or wearable sensors that require user compliance, WiFi-based HAR provides a noninvasive and practical alternative that operates seamlessly with existing infrastructure. WiFi-based HAR leverages fluctuations in wireless signals, particularly Channel State Information (CSI), to passively detect and classify human activities. WiFi-based HAR models often achieve high accuracy in a single environment but suffer significant performance drops when applied to new environments due to variations in spatial settings, human movement, …

Optimising Ai For Chemical Imaging: Benchmarking Performance Against Foundation Models For Task-Specific Applications In Histopathology, Rahul Suresh, Mohd Rifqi Rafsanjani, Karin Jirstrom, Arman Rahman, William M. Gallagher, Aidan Meade

SAML-25 Workshop on Statistical and Machine Learning

The integration of chemical imaging with artificial intelligence presents a compelling route toward fully digital, label-free histopathology, yet it also introduces notable challenges. While deep learning models from domains like machine vision, digital pathology, and remote sensing are readily accessible, they frequently struggle to generalize effectively to chemical imaging data, as highlighted in recent research [1]. Additionally, although foundational pathological models show potential for advancing AI-based histopathological diagnostics and prognostics, our preliminary assessments suggest they may fall short in addressing the broad spectrum of classification tasks encountered in clinical settings. In this presentation, we highlight some recent published work from …

2025-06-05 Minutes & Appendices, Longwood University

Board of Visitors Meeting Minutes 1964-

No abstract provided.

Exploitation Of Plasma Treatment-Assisted Monocarboxylic Cobalt Phthalocyanine Nanorod Growth For High-Efficiency Photodetection Applications, Ahmed Ramzy, Ahmed M. El-Mahalawy, M A. Abd El Ghaffar, Wael A. Abbas

Nanotechnology Research Centre

Here, the effect of cobalt phthalocyanine (CoPC) functionalization by a monocarboxylic group in the form of thermally evaporated thin films followed by plasma treatment for variable time intervals is introduced. The effect of plasma treatment on crystalline structure, molecular structure, and morphology was examined using the XRD, FT-IR, and FE-EEM techniques. Significant modifications were observed in the crystal structure and morphology with increasing plasma treatment time intervals. The plasma treatment for 5 min enhanced the formation of the nanorods along the deposited surface with improved crystallinity. Tracking the optical properties variation of the deposited MCCoPc thin films under plasma treatment …

Automated Approaches To Interpreting And Explaining Machine Learning Models, Tilak Chandrashekar, Tarry Singh, Maged Shaban

SAML-25 Workshop on Statistical and Machine Learning

This paper proposes a novel framework for automating ML model interpretation and explainability across different applications with an emphasis on transparency, trust, and human-centric decisionmaking assistance. Although ML models, particularly advanced structures such as deep neural networks, possess superior predictive powers, their interpretability tends to be obscure, and thus their application in sensitive or regulated domains is impeded. Current XAI techniques, though promising, tend to be post-hoc and are not scalable for real-time or large-scale deployments. This study addresses such concerns by presenting an automated, modular pipeline where interpretation techniques are embedded in the process of developing the ML model. …

Tracking The Kinetics Cellular Glycolysis And Glutaminolysis Pathways Using Vibrational Spectroscopy, Combined With Multivariate Statistical And Machine Learning Approaches For Data Mining, Zohreh Mirveis, Nithin Patil, Hugh Byrne

SAML-25 Workshop on Statistical and Machine Learning

Understanding dynamic metabolic processes within living cells is crucial for gaining insights into cellular function and disease mechanisms. The kinetics of glycolysis and glutaminolysis pathways play significant roles, as alterations in their activity have been linked to various disorders, including cancer and mental health conditions such as bipolar disorder. These pathways therefore hold potential as biomarkers for disease diagnosis and therapy. However, real-time monitoring of their kinetics remains challenging due to the lack of suitable non-invasive techniques. Current gold-standard fluxomics approaches, such as mass spectrometry, are destructive to cells and thus unsuitable for time-resolved studies. In this study, we evaluate …

A Framework For Scalable And Controlled Hallucination Data Collection, Lin Ting Liang

Computer Science Senior Theses

This thesis addresses a key bottleneck in hallucination research: the scarcity and limitations of hallucination benchmark datasets. Existing datasets typically focus on a single type of hallucination and are expensive to produce due to the need for manual prompt creation and annotation. To overcome these challenges, we propose a novel mixture-of-experts (MoE) adversarial framework that actively induces hallucinations. Our framework employs three large language model (LLM) agents that iteratively and adversarially revise prompts to provoke hallucinated responses from a target question-answering model. It automates the generation of both intrinsic hallucinations (logical inconsistencies) and extrinsic hallucinations (inclusion of unverifiable external information). …

Ensemble-Based Binding Free Energy Profiling And Network Analysis Of The Kras Interactions With Darpin Proteins Targeting Distinct Binding Sites: Revealing Molecular Determinants And Universal Architecture Of Regulatory Hotspots And Allosteric Binding, Mohammed Alshahrani, Vedant Parikh, Brandon Foley, Gennady M. Verkhivker

Mathematics, Physics, and Computer Science Faculty Articles and Research

KRAS is a pivotal oncoprotein that regulates cell proliferation and survival through interactions with downstream effectors such as RAF1. Despite significant advances in understanding KRAS biology, the structural and dynamic mechanisms of KRAS allostery remain poorly understood. In this study, we employ microsecond molecular dynamics simulations, mutational scanning, and binding free energy calculations together with dynamic network modeling to dissect how engineered DARPin proteins K27, K55, K13, and K19 engage KRAS through diverse molecular mechanisms ranging from effector mimicry to conformational restriction and allosteric modulation. Mutational scanning across all four DARPin systems identifies a core set of evolutionarily constrained residues …

Benchmarking Energy And Performance Of Parallel Machine Learning Models Using Hardware And Software Power Meters, Urooj Asgher, Tania Malik

SAML-25 Workshop on Statistical and Machine Learning

The growing reliance on machine learning algorithms across domains such as healthcare, transportation, and finance has led to their increased deployment on high-performance computing platforms. While performance optimization remains a central concern, energy efficiency is emerging as a critical design consideration, particularly in light of global sustainability goals. This study presents a comparative analysis of the energy consumption and performance of serial and parallel implementations of four machine learning algorithms, K-means clustering, Ant Colony Optimization, Logistic Regression, and Random Search. Experiments were conducted on an HPC testbed using both hardware-based and software-based power meters to measure energy consumption. The results …

Improving Node Classification For Graphs Withweak Feature Signals: A Similarity-Entropy Aggregation Approach, Brian Daniel Bernhardt, Chiara Marciano, Mario Rosario Guarracino

SAML-25 Workshop on Statistical and Machine Learning

Graph Neural Networks (GNNs) have established themselves as powerful tools for graph-structured data. However, when feature separability among nodes is low, conventional neighborhood aggregation strategies often result in performance degradation due to over-smoothing and noisy information propagation. In this work, we introduce a novel GNN framework that refines the aggregation process by integrating feature similarity and neighborhood entropy into node message passing. Unlike standard models that uniformly aggregate neighbor information, this new model dynamically adjusts neighbor influence, prioritizing nodes with high similarity and low entropy. We evaluate the model on synthetic graphs generated using the Stochastic Block Model (SBM), varying …