Engineering Commons^™

How Do Social Network Models Compare To All-To-All Models For Forecasting Tuberculosis Epidemics? A Mathematical Modeling Study, Masabho Peter Milali, Hae-Young Kim, George Corliss, Anna Bershteyn

Electrical and Computer Engineering Faculty Research and Publications

Background. Mathematical models guide tuberculosis (TB) target-setting, yet most assume homogeneous “all-to-all” mixing. We compared projected intervention impacts between an all-to-all compartmental model and a Barabási–Albert (BA) scale‑free social network model under otherwise identical disease assumptions.

Methods. We calibrated transmission parameters so both models produced similar baseline trends, then introduced vaccination (coverage 30–70%; efficacy 80–95%) and treatment (20–50% increases in recovery) after a 400‑day burn‑in. Outcomes were assessed 300 days post‑intervention.

Results. Under 60% coverage, increasing vaccine efficacy from 80% to 95% yielded smaller projected reductions in active TB with the network model than with all‑to‑all mixing. Treatment improvements showed …

Efficient Energy Management In Networked Microgrids Using Multi-Agent Deep Reinforcement Learning In The Presence Of Uncertainties, Ayodele Benjamin Chukwuyem

Dissertations

Microgrid technology is essential in facilitating the transition to smart energy grids in developed countries and mitigating energy poverty in developing countries, particularly in areas where grid extensions are not feasible. Recently, the concept of networked microgrids (NMGs) has garnered tremendous attention due to the plausibility of interactions among interconnected microgrids leading to power networks that are more resilient, reliable, and stable. However, because each microgrid has diverse distributed generation resources (renewables and controllable generators) and each microgrid operator (MO) has different objectives, coordinated energy management is required to satisfy local and system-wide goals under conditions with significant uncertainty. Existing …

Condition Monitoring Of Induction Motor Using Motor Current Signature Analysis, Francis Ikechukwu Obianke, Joseph Okhaifoh, Benjamin Akinloye

Al-Bahir

Induction machines serve as the cornerstone and driving force of modern manufacturing and production systems. This research aims to monitor and analyse the operating performance of induction motors using motor current signature analysis (MCSA). MCSA is employed to process the current signal of a motor into a frequency spectrum, known as the current signature by applying the Fast Fourier Transform (FFT) algorithm. The underlying principle is that vibration generated in a motor is closely related to the changes of the magnetic field density, and the induced voltage varies with the stator current.

A simulation model replicating the behaviour of an …

AiXGa1−XN (0.7 < X < 1) Schottky Diodes Using Distributed Polarization Doped Layer With A Current Density Of 14 Ka/Cm2, Tariq Jamil, Abdullah Al Mamun Mazumder, Mafruda Rahman, Muhammad Ali, Grigory Simin, M. Asif Khan

Faculty Publications

High Al-content Al_xGa_1-xN (0.7 < x < 1) quasi-vertical Schottky barrier diodes (SBDs) with distributed polarization doping were grown on the bulk AlN substrate. They exhibit excellent rectification behavior with a large forward current density (~14 kA/cm² ) and a high breakdown field of ~8.3 MV/cm. The SBDs also exhibited low ideality factors of (n~1.2) with a high Schottky barrier height (Φ _b~ 1.7 eV). Thus, this study demonstrates the feasibility of the distributed polarization doping approach for high current–high voltage devices.

An Interpretable Hybrid Deep And Reinforcement Learning Paradigm For Glioma Prognosis, Renuga Devi M Ms

Theses and Dissertations

Brain tumors are highly aggressive and lethal types of cancer, particularly gliomas. These cancerous growths show complicated pathophysiological behaviours and with poor prognosis despite therapeutic advances. Due to their biological differences and infiltrating growth, as well as overlapping radiological characteristics, they pose great difficulty in diagnosis, grading, and survival prediction. Artificial intelligence technology, which includes machine learning , deep learning, and reinforcement learning has developed into a new paradigm for the automation of brain tumor diagnostics and personalized treatment. The main goal of this study is to create an integrated AI-based framework that can perform brain tumor segmentation, grading and …

Intrusion Detection Latency: The Neglected Metric, Sandhyarani Dash, John M. Acken

Electrical and Computer Engineering Faculty Publications and Presentations

Accuracy remains the dominant evaluation metric in Intrusion Detection System (IDS) research, yet an IDS that detects attacks too late is functionally equivalent to one that fails—particularly in Internet of Things (IoT) environments. In operational settings, the timing of detection shapes both the scope of adversarial activity and the feasibility of effective response. To the best of our knowledge, latency (the speed at which intrusions are identified) has received no systematic attention. Our analysis of published IDS papers reveals that latency is defined inconsistently—often referring to inference, communication, computation time, or combinations of these-leading to incomparable performance claims. To close …

Axial Sulfur-Coordination Engineering Boosting Fe–N–C Catalysts For High-Performance Proton Exchange Membrane Fuel Cells, Lin Lin, Xiu-Xuan Hou, Zhe-Chen Fan, Yi-Xuan Yin, Wei-Yi Zhao, Kai Wei, Yu-Die Zhou, Li-Na Hou, Ying Wang, Hao Wan, Jun-Jie Ge

Journal of Electrochemistry

Fe-N-C catalysts have long suffered from kinetically sluggish oxygen reduction reaction (ORR) due to excessive adsorption strength toward oxygen intermediates and low site utilization. Heteroatom doping effectively accelerates ORR reaction kinetics through electronic structure modulation of metal sites for optimal intermediate adsorption, while chemical vapor deposition (CVD) enhances the turnover frequency (TOF) of active sites. Herein, we developed an FeSNC catalyst featuring abundant FeS₁N₄ sites via a dual-precursor CVD strategy. Experimental and theoretical analyses revealed that S incorporation disrupts the symmetric coordination of active sites, which optimizes OH* adsorption energies from 0.212 eV to 1.194 eV. Moreover, …

Deciphering The Role Of Binder Reaction Exothermicity In Thermal Runaway Of Lithium-Ion Cells, Wen Wen, Jing-Hong Zhou, Hao-Tian Lu, Xing-Gui Zhou

Journal of Electrochemistry

Thermal safety associated with lithium-ion cells as power sources remains a critical industry concern. A comprehensive understanding of how internal exothermic side reactions contribute to temperature rise is fundamental for accurately analyzing thermal runaway processes and predicting the thermal safety of lithium-ion cells. While various side-reactions, such as decomposition of solid electrolyte interphase layer, reaction between anode materials and electrolyte, reaction between cathode materials and electrolyte, and electrolyte decomposition, have been identified as heat generation sources in previous studies, the quantification of these reactions remains insufficiently standardized. Particularly, the impact of heat generation from binder decomposition (most commonly polyvinylidene difluoride) …

A Pretraining-Based Framework For On-Device Training Of Imu-Based Locomotion Mode Detection For Wearable Active Exoskeletons, Muhammad Tahir Khan

LSU Master's Theses

Active exoskeletons are being developed to support human movement in physically demanding industries such as construction. For these systems to work effectively, they must be able to correctly identify the user’s current activity. This process is known as locomotion mode detection and plays an important role in selecting the appropriate control parameters for exoskeletons. Many existing approaches use inertial measurement units (IMUs) to recognize these activities and have shown strong performance. However, most of these methods depend on large amounts of labeled data collected under specific conditions. As a result, they often do not perform well when applied to new …

Reconstruction Of Information System Acceptance Model In The Era Of Integrated Artificial Intelligence: A Systematic Literature Review, Ilham, Merlin Apriliyanti

Library Philosophy and Practice (e-journal)

This study aims to explain the rapid development of Artificial Intelligence (AI) which has driven significant transformations in the development and use of information systems. However, most classical information system acceptance models, such as the Technology Acceptance Model (TAM) and  (UTAUT), have not been able to fully explain the unique characteristics of AI-based systems that are autonomous, adaptive, and complex. This study aims to reconstruct the information system acceptance model in the era of integrated AI through a Systematic Literature Review (SLR) approach. This study was conducted using the PRISMA protocol on 130 leading scientific articles indexed by Scopus and …

Deep Learning Based Control System For Multi-Zone Hvac Residential Buildings, Mohammad H. Z. Alghalayeeni

Mechanical Engineering Theses

Heating, ventilation, and air conditioning (HVAC) systems are major contributors to residential energy consumption. However, most homes continue to rely on single zone thermostat control, which regulates temperature based on a single sensor and cannot account for thermal variations across multiple rooms. This often leads to uneven thermal conditions and inefficient energy use in multi-zone residential buildings. This thesis presents a deep reinforcement learning based approach for improving HVAC zoning control through dynamic airflow distribution. A physics based multi zone thermal model of a residential house was developed to simulate heat transfer processes including conduction, convection, solar and internal heat …

Ai-Scm Cmm: A Capability Maturity Model For Artificial Intelligence Integration In Supply Chain Management, Lordt Becklines, Omar F. El-Gayar, Patti Brooks, Insu Park

Annual Research Symposium

Artificial intelligence is increasingly deployed in supply chain management, yet many organizations struggle to align adoption efforts with process readiness, data quality, governance, and workforce capabilities, and they still lack validated supply chain specific roadmap for assessing readiness, sequencing investments, and reducing implementation risk. This study develops and evaluates a Capability Maturity Model for Artificial Intelligence Integration in Supply Chain Management to address that gap. Using a design science research approach, the study synthesizes prior literature and practitioner knowledge to define maturity dimensions, capability indicators, and staged progression levels for AI integration in supply chain contexts. The artifact and assessment …

Multi-Modal Tensor Fusion For Alzheimer’S Disease Recognition, Mason Li, Tiffany Le, Jiajing Huang, Yuxin Wen

Engineering Faculty Articles and Research

Accurate and early diagnosis of Alzheimer’s disease (AD) is critical for effective intervention, disease monitoring, and patient care. Traditional diagnostic approaches rely on a single modality, such as clinical assessments, neuroimaging, or genetic markers, which may fail to capture the complex, multifaceted nature of AD. Multimodal learning has therefore been explored to integrate complementary information across data sources. However, conventional fusion strategies, including early feature concatenation and late decision-level fusion, often model modalities independently and fail to capture high-order cross-modal interactions. To address these limitations, we propose a multimodal tensor fusion network (MTFN) that integrates heterogeneous data sources, including visual …

Arid: Agglomerative Regionalization Via Information Divergence, A Novel Clustering Algorithm For Geo-Spatial Data, Joshua David Sills

Dissertations and Theses

Regionalization is a clustering problem that seeks to partition geospatial data into geographically contiguous regions while remaining internally homogeneous in their attributes. It has been successfully applied towards the development of urban planning, natural resource discovery, and ecological analysis. Existing popular approaches optimize homogeneity using Euclidean or variance-based criteria, which ignore distributional differences such as variance shifts, multimodality, and higher-order dependence. This thesis introduces ARID (Agglomerative Regionalization via Information Divergence), a spatially constrained agglomerative clustering framework that replaces distance-based merging with an information-theoretic, Ward-like criterion that utilizes the Kullback-Leibler (KL) divergence. ARID constructs a neighborhood graph from coordinate space and …

Enhanced Cuckoo Search-Based Optimization For Single Distributed Generation Placement And Sizing In Radial Distribution Systems, Samson Oladayo Ayanlade, Abdulrasaq Jimoh, Richard Oladayo Olarewaju, Ignatius Kema Okakwu, Israel O. Adejumobi, Joseph B. Samson, Oluwadare A. Adebisi, Oluwadare O. Akinrogunde

Al-Bahir

This paper presents an Enhanced Cuckoo Search Algorithm (ECSA) to optimally place and size Distributed Generation (DG) in radial distribution systems to minimize real power loss within operating constraints. The proposed ECSA has exponentially decaying adaptive Lévy flights, constraint-aware solution repair with dynamic penalty coefficients, and diversity-directed stochastic replacement to enhance search robustness and convergence speed. It was tested with 30 independent runs on the IEEE 33-bus, IEEE 69-bus, and a practical Nigerian 32-bus distribution network. The simulations show that the ECSA lowers the active power loss of the IEEE 33-bus system from 201.58 kW to 102.75 kW (49.03%), and …

Voltage-Mode Driver With Sar-Based Termination Calibration, Moustafa M. Elsayed, Abeer T. Khalil, Sameh A. Ibrahim, Mohy Eldin A. Abo-Elsoud

Mansoura Engineering Journal

This paper introduces a DAC-based four-level pulse-amplitude modulation (PAM-4) driver capable of operating at data rates up to 80 Gb/s in 65-nm CMOS technology. A replica-based termination calibration loop is proposed to preserve driver linearity across process, voltage, and temperature (PVT) variations. The proposed driver achieves a relative level mismatch (RLM) of 99.3%. In addition, the transmitter demonstrates a vertical eye opening of 134.8 mV and a horizontal eye opening of 0.44 UI under worst-case conditions.

Identification Of Thruster Faults In Underwater Vehicles By Using Custom Encodings In Spiking Neural Networks, Donovan Gegg

LSU Master's Theses

Autonomous Underwater Vehicles (AUVs) are untethered robotic platforms used for tasks such as seafloor mapping, infrastructure inspection, and environmental monitoring. Recent technological advances have produced smaller, more affordable platforms, broadening access to research teams and small companies alike. This miniaturization comes at the cost of them handling drawbacks associated with a more compact machine such as reduced battery capacity as well as limited processing and sensing capabilities. These constraints make small-sized marine vehicle’s reliability critical as they can cause malfunctions, making the loss of a vehicle more likely. Actuator faults are particularly consequential as unintended and unstable control in an …

Polymer Microstructures For Advanced Biomanufacturing, Tongyao Wu

LSU Doctoral Dissertations

With the continued growth of the biopharmaceutical industry, the demand for scalable, robust, and resource-efficient platforms for large-scale mammalian cell culture is amplified. Recent developments in microfluidic technology, such as precise control of the microenvironment, showed the potential to improve the performance of cell culture systems. However, constrained by scalability and operational efficiency, applying such approaches to large-scale cell culture and biopharmaceutical production presents challenges. This dissertation addresses these challenges through three independent but conceptually related technological developments. First, a roll-to-roll (R2R) fabrication process was developed for the scalable production of hollow microcarriers (HMCs). HMCs provide three-dimensional microenvironments suitable for …

Graph-Based And Uncertainty-Aware Machine Learning For Academic Performance Prediction, Anca Olivia Muresan

Electronic Theses and Dissertations

Early identification of students at risk of academic failure is essential for timely pedagogical interventions and reducing dropout rates. While Artificial Intelligence (AI) has significantly advanced predictive modeling in education, two primary challenges persist: effectively modeling the complex, evolving relationships within heterogeneous educational data, and ensuring the reliability of model outputs for high-stakes decision-making. This dissertation addresses these challenges by proposing a comprehensive framework for early and continuous student performance prediction applied to the Open University Learning Analytics (OULA) dataset. First, we introduce a Heterogeneous Graph Neural Network (HGNN) approach that utilizes metapath structures to capture latent interactions between diverse …

Sonochemically Synthesized Al-Doped Zno Nanorods-Based Flexible Piezoelectric Nanogenerators For Durable Energy Harvesting, Tiham Fayaz, Ahmed Hasnain Jalal, Fahmida Alam

Electrical and Computer Engineering Faculty Publications

This study presents a high-performance piezoelectric nanogenerator (PENG) based on aluminum-doped zinc oxide (Al:ZnO) nanorods synthesized via a novel sonochemical method. This rapid, cost-effective, and reproducible approach enables the synthesis of ZnO nanorods (ZnO NRs) under ambient conditions. The PENG, fabricated on a 177 µm flexible Indium Tin Oxide (ITO) coated polyethylene terephthalate (PET) substrate, benefits from optimized aluminum doping, enhancing the output voltage. Structural analyses conducted using atomic force microscopy (AFM), scanning electron microscopy (SEM), and X-ray diffraction (XRD) demonstrated that the synthesized ZnO nanorods possessed a high degree of crystallinity. The synthesis process was carefully fine-tuned to avoid …

Best Practices For Electrical Safety At Home, Joshua Henderson, Derek Centrone

Student Research Symposium (SRS)

The household, although it’s a comfortable and relaxing environment, can in fact be riddled with everyday hazards that homeowners and residents overlook or are too complacent in acknowledging or even recognizing their presence. One such common oversight is electrical hazards. This may be due to the high usage of electricity in people’s day-to-day lives, especially since the perceived risk of electrical hazards by the public can be very low. In a household, there are many hazards present each and every day. One such hidden deadly hazard is electricity. While on the surface it doesn’t seem that dangerous, in reality, it …

Enhanced Antenna Selection Techniques For Energy-Efficient Code Index Modulation Aided Spatial Modulated Wireless Communication Systems, Fati̇h Çögen, Burak Ahmet Özden, Erdoğan Aydin

Turkish Journal of Electrical Engineering and Computer Sciences

This study proposes an integrated multiple-input multiple-output (MIMO) transceiver framework, termed CIM-HQAM-SM, which combines code index modulation (CIM) and spatial modulation (SM) with energy-efficient hexagonal quadrature amplitude modulation (HQAM). In the proposed bit mapping, the information bits jointly select (i) the active transmit-antenna index, (ii) the Walsh–Hadamard spreading-code indices for the in-phase and quadrature branches, and (iii) an HQAM symbol. Hence, the payload is conveyed through the constellation symbol as well as through antenna and code indices. For the considered Rayleigh-fading scenarios and matched spectral-efficiency settings, the proposed framework offers BER improvements over conventional SM and quadrature SM (QSM), while …

An Ensembled Two-Phase Deep Learning Approach For A Psychiatric Disorder Detection, Prajna Paramita Debata, Midhun Chakkaravarthy, Brojo Kishore Mishra

Turkish Journal of Electrical Engineering and Computer Sciences

Computational Psychiatry represents a burgeoning realm within scientific inquiry, delving into the intricate interplay of neurobiology within the brain. The escalating prevalence of mental illness underscores the urgency to confront this challenge. Among the prevalent disorders, Schizophrenia and Bipolar Disorder loom large, affecting a significant portion of the population at some point in their lives. However, pinpointing psychiatric disorders poses a formidable challenge. Genetic predispositions significantly influence the development of mental illnesses, with intriguing overlaps observed among certain disorders. This convergence complicates accurate diagnosis. Here, a deep learning approach is considered for significant gene biomarker identification and classification of Schizophrenia …

Cover And Contents

Turkish Journal of Electrical Engineering and Computer Sciences

No abstract provided.

Sentisec: Combining Keyword Heuristics And Sentiment Modeling For Ai-Powered Threat Detection, Ridho Surya Kusuma, Erum Ashraf, Selvakumar Manickam, Shankar Karuppayah

Turkish Journal of Electrical Engineering and Computer Sciences

This work presents SENTISEC, a hybrid LLM-based threat detection framework designed to classify security logs by integrating keyword heuristics, domain-adapted sentiment scoring, and Retrieval-Augmented Generation (RAG). The system achieves an overall accuracy of 93.67%, with 91.46% macro recall, 89.07% macro F1, and 95.15% threat recall, while maintaining a low false-positive rate of 1.68%. Its methodology incorporates strict keyword and IOC matching, a domain-tuned DistilBERT sentiment module, hybrid BM25–MiniLM retrieval enhanced with BGE reranking, adaptive quantile-based threshold calibration, and SHAP-based explainability. Comparative evaluations against keyword-only, sentiment-only, classical machine-learning models, and DistilBERT-only baselines show that SENTISEC consistently improves both true-positive and true-negative …

Sgsc-Kko-Lstm: A Deeplearning Classifier Model For Smart Grid, Dushmanta Kumar Das, Samaniba Imchen

Turkish Journal of Electrical Engineering and Computer Sciences

Maintaining smart grid stability is crucial for the reliable operation of decentralized electricity networks, especially as the energy sector becomes more complex. The process of ensuring grid stability begins with collecting consumer data and comparing it to power supply requirements. Ultimately, consumers receive a report showing their energy use and pricing details. However, this process is time-consuming and can be improved by leveraging artificial intelligence to predict smart grid stability more efficiently. Specifically, an optimized Long Short-Term Memory (LSTM) network is proposed to predict smart grid stability, addressing the challenges associated with traditional data collection and evaluation methods. Simulations from …

A Joint Optimization-Based Novel Attack For Genomic Beacon Reconstruction, Kousar Saleem, Si̇nem Sav

Turkish Journal of Electrical Engineering and Computer Sciences

Genomic data sharing has become an essential component of biomedical research, enabling large-scale collaborations and accelerating discoveries in human genetics. To balance the need for accessibility with privacy concerns, several controlled-access mechanisms have been proposed, including genomic beacons. Genomic beacons answer simple presence/absence queries about specific genetic variants. However, prior work has demonstrated that beacons remain vulnerable to genome reconstruction attacks, where an adversary can recover large portions of participants’ genomes using summary statistics. Building on insights from prior reconstruction attacks, we introduce an approach that unifies SNP correlation and allele frequency alignment objectives within a single-stage joint optimization framework. …

Automated Software Size Measurement Using Multilingual Domain-Adapted Language Models, Samet Tenekeci̇, Hüseyi̇n Ünlü, Burak Keçeci̇, Muhammed Efe İnci̇r, Onur Demi̇rörs

Turkish Journal of Electrical Engineering and Computer Sciences

Software Size Measurement (SSM) is crucial for estimating required project effort as well as budget and schedule. However, many small and medium-sized companies struggle to apply objective SSM due to limited resources and lack of expertise. This often leads to inaccurate estimates and project overruns. There is a need for practical, low-resource solutions that support these tasks without requiring expert involvement. Motivated by this challenge, this study proposes an automated software size measurement approach that formulates the measurement task as supervised regression over natural language requirements, using domain-adapted transformer models. We construct large-scale Turkish and English software engineering corpora to …

Reducing Complexity In Versatile Video Coding Intra-Coding Through Machine Learning-Based Optimization Of Partitioning And Prediction, Amina Kessentini, Amna Maraoui, Imen Werda, Fatma Ezahra Sayadi

Turkish Journal of Electrical Engineering and Computer Sciences

The escalating demand for high-resolution multimedia content has necessitated more efficient video compression solutions. The Versatile Video Coding (VVC) standard, despite achieving remarkable compression gains, introduces significant computational complexity, primarily due to its exhaustive Rate-Distortion Optimization (RDO) process. To address this, we propose an intelligent approach leveraging supervised machine learning techniques to streamline the VVC encoding process. Specifically, we introduce a Lightweight Neural Network (LNN) for efficient coding unit partitioning decisions and a Decision Tree (DT) classifier for optimizing the intra prediction process. This dual-method framework, tailored for All Intra coding configuration, significantly reduces encoder complexity while maintaining compression performance …

Designing Risk-Aware Mixed-Mode Evacuation Strategies For Tsunamis: Insights From İstanbul, Vedat Bayram, Doruk Ergez, Ada Arikanoğlu

Turkish Journal of Electrical Engineering and Computer Sciences

​Tsunamis pose severe and time-critical risks to densely populated coastal cities, where limited warning times and infrastructure constraints demand carefully coordinated evacuation strategies. This study develops an integrated, risk-aware optimization framework that jointly considers vertical and horizontal sheltering options together with mixed pedestrian-vehicular evacuation dynamics. The proposed mixed-integer second-order cone programming (MISOCP) model simultaneously determines vertical shelter location, evacuee assignment, road-use designation for pedestrians and vehicles, and route selection under congestion, capacity, and budget constraints. Vehicle travel times incorporate congestion effects through a convex flow-dependent function, while pedestrian routing ensures convergent and conflict free evacuation paths. A risk-minimization objective accounts …