Digital Commons Network^™

A Holistic Approach For Workforce Scheduling And Routing, Kerem Can Manalp, Ansel Kaplan Erol, Kutluhan Erol, Cem Evrendi̇lek

Turkish Journal of Electrical Engineering and Computer Sciences

The workforce scheduling and routing problem (WSRP) involves assigning tasks across multiple locations while accounting for varying travel times, service durations, time windows, and skill requirements in a wide range of industries, from healthcare to telecommunications. This paper presents a mixed-integer programming model for the WSRP that balances the trade-off between cost and customer satisfaction using a score-generation function and subsequently evaluates the trade-off between solution quality and computation time for several algorithms on well-known datasets. We demonstrate that our model effectively balances cost, service-level agreement satisfaction, and task priorities while providing high-quality solutions in a timely manner. Observing that …

Cnkg: Harnessing Large Language Models For Cognitive Neuroscience Knowledge Graph Construction, Ali Sarabadani, Kheirollah Rahsepar Fard, Hamid Dalvand

Turkish Journal of Electrical Engineering and Computer Sciences

Textual resources are among the most valuable sources of information in cognitive neuroscience (CN) for understanding and investigating brain activity and cognitive processes. Extracting and constructing knowledge graphs (KGs) from these texts can facilitate medical research by providing deeper insights into neurological diseases and brain function. In recent years, the use of large language models (LLMs) in natural language processing (NLP) has become increasingly widespread, significantly enhancing the extraction of meaningful information from large volumes of text. This study proposes a novel approach for constructing and evaluating a specialized knowledge graph, termed the cognitive neuroscience knowledge graph (CNKG), from scientific …

Dual-Stream Bilstm Framework With Histogram-Based Shape Features For Household Load Forecasting, Chang Xu, Wong Jee Keen Raymond, Hazlee Azil Illias, Hazlie Mokhlis

Turkish Journal of Electrical Engineering and Computer Sciences

This study proposes a dual-stream BiLSTM framework for household load forecasting that integrates time-series dynamics with histogram-based daily shape features. Unlike existing models relying on weather or external data, the proposed method extracts intrinsic load-shape information directly from normalized daily curves. A multihead attention module fuses temporal and shape representations, enabling adaptive weighting of informative dimensions. Experiments on three real-world datasets show consistent improvements over the baseline BiLSTM, with up to 30.12%, 24.27%, and 19.03% reductions in MAE, RMSE, and SMAPE, respectively. The results highlight the framework’s robustness and efficiency for fine-grained load forecasting without external inputs.

Robust Variable-Gain Backstepping Control For Nonlinear Systems With Real-Time Application To Induction Motor, Fadi Alyoussef, İbrahi̇m Kaya, Ahmad Akrad, Rabia Sehab, Cristina Morel

Turkish Journal of Electrical Engineering and Computer Sciences

This study proposes a novel variable-gain mechanism with a minimal number of tuning parameters to enhance the performance of conventional backstepping controllers for nonlinear systems while avoiding singularity and peaking phenomena. The proposed approach is simple, computationally efficient, and well suited for real-time implementation without imposing a significant computational burden. Its effectiveness is validated through real-time experiments conducted using a dSPACE DS1104 controller board and a 7.5-kW induction motor (IM). Simulation results demonstrate that the proposed controller outperforms the conventional backstepping controller. Robustness analyses under variations in stator resistance, load inertia, and viscous friction coefficient reveal substantial reductions in the …

Enhancement Of Nested Hexagonal Fractal Antenna Performance For Multiband Wireless Applications, Abdelbasset Azzouz, Rachid Bouhmidi, Mohammed Chetioui, Redouane Berber, Ahmed Jamal Abdullah Al-Gburi

Turkish Journal of Electrical Engineering and Computer Sciences

This work focuses on developing a compact multiband antenna to meet the growing demand for versatile and efficient radiating structures in modern wireless communication systems. A hexagonal fractal antenna is proposed and analyzed for applications such as mobile communications, WLAN, industrial, scientific and medical (ISM) bands, Wi-Fi, satellite links, radar systems, and military communications. By iteratively modifying the antenna geometry with larger hexagonal elements, the design enhances multiband behavior and improves key performance parameters including gain, S11, voltage standing wave ratio (VSWR), and radiation characteristics. The antenna is modeled using high-frequency structure simulator (HFSS)® and fabricated on a low-cost 0.8 …

Parameter Optimization Of Dual-Qsg Based Pll For Real-Time Control Of Grid-Connected Ev Chargers, Gaurav Yadav, Sudhanshu Mittal, Vineet Kumar, Sombir Kundu, Praveen Bansal

Turkish Journal of Electrical Engineering and Computer Sciences

Dual-Quadrature Signal Generator (D-QSG) based Phase lock loop (PLL) has been recently proposed to handle the nonideal grid voltage conditions. However, selecting the parameter for D-QSG based controller has been a great challenge, especially for higher-order systems. Inappropriate parameter selection tends to increase settling time both in terms of amplitude as well as harmonics attenuation. Hence, in the proposed work, the main focus is on parameter selection to achieve a faster response. Here, a fourth-order Quasi-Synchronous Generator has been realized by cascading the two nonidentical second order generalized integrators (NISOGIs). Furthermore, the parameters of both the NISOGIs are selected in …

Erratum To “Sgsc-Kko-Lstm: A Deeplearning Classifier Model For Smart Grid” [Turkish Journal Of Electrical Engineering & Computer Sciences 34 (2) 2026 185-213], Samaniba Imchen, Dushmanta Kumar Das

Turkish Journal of Electrical Engineering and Computer Sciences

The first and second authors were incorrectly ordered in the article PDF due to a typesetting error. To rectify this oversight and ensure the accuracy of the published work, the author order have been corrected as follows: 1. Samaniba Imchen – First Author 2. Dushmanta Kumar Das – Second Author

A link to the original article can be found at: https://doi.org/10.55730/1300-0632.4170

A Revenue-Driven Approach For Enhanced Task Utilization In Vehicular Cloud Computing, Ashish Singh Saluja, Satyabrata Das, Sanjib Kumar Nayak, Sohan Kumar Pande

Turkish Journal of Electrical Engineering and Computer Sciences

Vehicular networks support intelligent transportation through vehicle-to-roadside Units (V2R) and vehicle-to-vehicle (V2V) communication but face challenges from dynamic topologies, limited RSU coverage, and bandwidth scarcity, which impact service delivery and revenue. RDA-ITU addresses these challenges by integrating V2R and V2V paradigms to maximize RSU revenue, enhance service availability, and improve system efficiency. It dynamically allocates services based on real-time network conditions and vehicle mobility, leveraging V2V relays to optimize both RSU-direct and cooperative communication. Through extensive simulations, RDA-ITU significantly outperforms four baselines: RBSM, VVMM-U, VVMM-LW, and VVMM-MA. It achieves 81.1% higher total revenue, 154.8% more completed requests, and 103.6% higher …

Class-Aligned Frequency Augmentation Using Variational Mode Decomposition Forfew-Shot Image Classification, Leila Boussaad

Turkish Journal of Electrical Engineering and Computer Sciences

Few-shot image classification benefits from data augmentation, yet most existing methods operate in pixel space with limited control over spectral semantics. We introduce a lightweight, frequency-guided augmentation strategy based on Variational Mode Decomposition (VMD). Our method constructs an offline, per-class ModeBank by decomposing downsampled luminance patches and retaining midband modes that encode class-specific texture patterns. During episodic training, VMD is never executed online: instead, for each support image, a same-class midband mode is selected and blended using PSNR-targeted scaling with a luminance energy cap, ensuring perceptual consistency. The augmentation is fast, reproducible, class-consistent, and integrates seamlessly into standard metric-based pipelines …

Scalefusion: Hierarchical Feature Aggregation For Unified Multiscale Object Detection, Ayşenur Yaylaci, Berru Kaya, Mehmet Kiliçarslan

Turkish Journal of Electrical Engineering and Computer Sciences

Detecting objects across a wide range of scales, particularly small ones, remains a significant challenge in computer vision. Existing methods often improve small object detection at the cost of performance on larger objects or introduce significant computational overhead through external techniques like image slicing. This paper introduces ScaleFusion, a novel, unified, end-to-end object detection architecture designed to provide robust performance across all scales within a single model. The core of our approach is a hierarchical feature aggregation strategy structured like a tree. ScaleFusion processes an image by running a shared backbone network only on fine-grained patches at the lowest level …

Swindeitvit: A Soft Voting Vision Transformer Ensemble For Accurate And Explainable Solar Panel Fault Detection, Mahe Zabin

Turkish Journal of Electrical Engineering and Computer Sciences

Solar panels are becoming very essential in providing sustainable energy but they are usually affected by defects on the surface like dust, snow, bird droppings, physical damages and electrical faults which interfere with their performance. These faults must be identified accurately and in a timely manner to enhance energy efficiency, lower the maintenance cost, and supplement the traditional manual methods of inspection which are labor-intensive, time-consuming and subject to human errors in judgment. The most common methods, such as traditional CNNs and hybrid architectures tend to be less accurate, less explainable and cannot be properly evaluated to be deployed in …

Llm-As-A-Judge For Infection Prevention And Control And Antimicrobial Resistance Impact: Comparing Three Main Llms Vs. Human Experts' Assessment, Marcello Di Pumpo, Leonardo Villani, Maria Rosaria Gualano, Danilo Buonsenso, Francesca Raffaelli, Daniele Donà, Patrizia Laurenti, Vittorio Maio, Stefania Boccia, Walter Ricciardi

College of Population Health Faculty Papers

BACKGROUND: Large language models (LLMs) are increasingly used to generate health information, yet their reliability as evaluators remains unclear. This study investigated the feasibility of an LLM-as-a-judge methodology in the context of infection prevention and antimicrobial resistance (AMR), comparing automated ratings with human expert benchmarks.

METHODS: We performed a secondary analysis of an expert-annotated dataset of health messages. Three leading LLMs (ChatGPT, Claude, Gemini) independently evaluated the same messages using an adapted DISCERN tool across five domains: information reliability, quality, AMR impact, persuasiveness, and overall score. We utilized descriptive statistics, intra-rater reliability tests, and mixed-effects ordinal regression to analyze divergence …

Building The Next Cybersecurity Workforce: A Grades 7–12 Curriculum To Close The Cyber Talent Gap, Mohammed A. Salam, Iqbal Shareef, Rich P. Manprisio

Journal of Cybersecurity Education, Research and Practice

In today’s rapidly evolving technological landscape, cyberattacks pose increasing threats, yet a global shortage of cybersecurity and digital forensics professionals leaves industries vulnerable, similar to having too few law enforcement officers in a densely populated city. The judicial system faces rising digital crimes and fraud cases, further strained by the lack of experts to analyze and extract digital evidence. Despite high demand, millions of positions remain unfilled. This paper identifies the root causes of the cybersecurity workforce shortage and proposes a targeted solution: a curriculum for Grades 7–12 designed to foster cybersecurity awareness and interest. The methodology included a comprehensive …

Contemporary Cybersecurity Challenges In Emerging Technologies: A Systematic Literature Analysis, Faztudo Languisse Prof

Journal of Cybersecurity Education, Research and Practice

The accelerating convergence of artificial intelligence (AI), the Internet of Things (IoT), cloud computing, blockchain, and quantum computing has fundamentally transformed the global threat landscape, introducing cybersecurity challenges of unprecedented complexity and scale. This systematic literature review synthesizes findings from peer-reviewed publications, institutional reports, and regulatory documents published primarily between 2020 and 2025 to provide an integrated analysis of contemporary cybersecurity challenges across five key emerging technology domains. The review identifies critical vulnerabilities inherent to each domain, documents the evolution of threat actors and attack methodologies — including AI-powered ransomware, adversarial machine learning, and harvest-now-decrypt-later quantum attacks — and evaluates …

Cybersecurity Governance Of Industrial Iot In Sub-Saharan Africa: Policy Gaps, Threat Landscape, And Lessons From Comparative African Contexts, Faztudo Languisse Eng.

Journal of Cybersecurity Education, Research and Practice

The rapid deployment of Industrial Internet of Things (IIoT) systems across Sub-Saharan Africa's extractive, energy, logistics, and agro-industrial sectors has introduced a cybersecurity challenge of growing urgency: industrial networks that were designed for operational efficiency are increasingly exposed to cyber threats for which neither the organizations nor the regulatory frameworks are adequately prepared. This article examines the cybersecurity governance of IIoT systems in a developing African economy, using Mozambique as a primary case study and drawing comparative lessons from South Africa, Rwanda, and Kenya. Through an integrative literature review and documentary analysis of national digital, cybersecurity, and industrial policies, the …

Performance Evaluation Of Approximate Nearest Neighbor Search On Nvidia Bluefield-3 Dpu, Sophia Bhoria, Nathan Tibbetts, Arjun Kirubakaran, Alima Subedi, Satish Puri

Computer Science Faculty Research & Creative Works

Advanced SmartNICs known as Data Processing Units (DPU) enable in-network data analytics, being equipped with standard processors and accelerators capable of doing custom computation on-NIC. These SmartNICs are advantageous because the host CPU can delegate simpler data analytics tasks, like filtering, to the NIC where the data first arrives. Only data needing further refinement must be passed on to the host CPU. Our benchmarks focus on NVIDIA's commercially available Bluefield-3 DPU. Similarity search, particularly Approximate Nearest Neighbor (ANN) search, is an important domain with wide usage across numerous applications. We explore ANN search on SmartNICs, providing insight into the performance …

Genedit: A Context-Aware Equity, Diversity And Inclusion Principles Integration Tool For Software Engineering Education, Chetan Arora, Ajanie Kodagoda Bammanna Arachchige, Jinchun Du, Muhammad Aamir Cheema, Aster Cosmos, Antonette Shibani, Vasudha Malhotra, Naeem Janjua, Afaq Shah

Research outputs 2022 to 2026

Equity, diversity and inclusion (EDI) is widely acknowledged as essential in software engineering (SE), yet day-to-day integration into teaching remains uncommon due to time pressures, low instructor confidence, and fragmented resources. We present GenEDIt, a purpose-built, LLM-backed chatbot that helps educators weave EDI into SE education artefacts without altering intended learning outcomes. Unlike generic chat interfaces, GenEDIt provides a tailored UI and workflow, and embeds retrieval over a vetted EDI knowledge base. The tool supports five educator-centred modes - (1) methods for integrating EDI into current activities; (2) examples/datasets; (3) EDI-integrated assessments and rubrics; (4) reflective prompts; and (5) rapid …

Challenges In Scaling R-Tree Spatial Search On Processing-In-Memory, Tasmia Jannat, Michael Gowanlock, Satish Puri

Computer Science Faculty Research & Creative Works

Spatial query processing is important in scientific, geospatial, and data-intensive applications. R-trees are widely used to index spatial objects, but their query-dependent traversal creates irregular work across different regions. This poster studies the challenges of scaling R-tree spatial search on a commercial Processing-in-Memory (PIM) system. Although PIM reduces CPU to memory data movement by executing search near memory, it does not remove full-pipeline overheads: the host still manages data placement, query batching, kernel launches, result retrieval, and aggregation. Our results show strong DPU-side search acceleration, with PIM kernel speedup ranging from about 20 x to 73 x, but end-to-end speedup …

Learning Programming In Informal Spaces: Using Emotion As A Lens To Understand Novice Struggles On R/Learnprogramming, Alif Al Hasan, Subarna Saha, Mia Mohammad Imran

Computer Science Faculty Research & Creative Works

Novice programmers experience emotional difficulties in informal online learning environments, where Confusion and Frustration can hinder motivation and learning outcomes. This study investigates novice programmers' emotional experiences in informal settings, identifies causes of emotional struggle, and explores design opportunities for affect-aware support systems. We manually annotated 1,500 posts from r/learnprogramming using the Learning-Centered Emotions framework, applying clustering, and axial coding. Confusion, Curiosity, and Frustration dominated emotional experiences, sometimes co-occurring and linked to early learning stages. Positive emotions were infrequent. The primary emotional triggers included ambiguous errors, unclear learning pathways, and misaligned resources. We identify five key areas where novice programmers …

Decoding The Allosteric Grammar Of Protein Kinases: A Dual-Stream Framework Integrating Protein Language Models And Energy Landscape Frustration Analysis, Will Gatlin, Max Ludwick, Lucas Turano, Brandon Foley, Kamila Riedlova, Vít Škrhák, Marian Novotný, David Hoksza, Gennady M. Verkhivker

Mathematics, Physics, and Computer Science Faculty Articles and Research

The spatial and energetic encoding of allosteric regulatory sites remains a major challenge in structural biology, frequently representing a “blind spot” for sequence-based artificial intelligence (AI) models. We present a protein language model (PLM)-guided approach complemented by the energy landscape frustration analysis as a dual-stream framework to investigate the relationship between AI prediction of binding sites and biophysical organization of regulatory pockets across the human kinome. By probing a fine-tuned residue-level PLM classifier across 453 kinase structures, a clear performance gap is discovered between highly predictable orthosteric pockets (Types I, I.5, and II) and poorly resolved distal allosteric sites (Type …

How Can Accessibility In Computing Education Be Improved Through Hci Research?, Dr David Santandreu Calonge, Linda Smail, Firuz Kamalov, Dima Yousef, Melody Sylvain

All Works

Accessibility - accommodation of diverse sensory, motor, cognitive, and linguistic needs - remains critically underrepresented in computing education despite broad societal acknowledgment of its importance. This position paper argues that systemic inequities and persistent barriers to integrating accessibility and inclusion - ranging from curricular inertia to limited faculty capacity - require new methodological approaches drawn from Human-Computer Interaction (HCI) research. HCI provides tested frameworks that pair participatory design with inclusive pedagogy, guided by empirical evaluation to drive systemic change. We identify three key areas where HCI can advance accessibility education: (1) embedding accessibility within core curricula through design-based pedagogies, (2) …

Research On Image Feature Analysis Of Intangible Cultural Heritage Brocade Integrating Multi-Scale Visual Perception, Ruiyang Yuan, Hao Wang, Shu Zhou, Hui Zhu, Jingwen Qiu

Journal of Scientific Information Research

[Purpose/significance] Addressing the challenges posed by the complex semantic characteristics of intangible cultural heritage brocade imagery, the difficulty in extracting their profound connotations, and the inadequate utilisation of multi-scale features by traditional deep learning models, this paper aims to explore a method for analysing the characteristics of intangible cultural heritage brocade images that integrates multi-scale visual features. [Method/process] This paper constructs a multi-scale feature analysis framework for intangible cultural heritage brocade images (ICH_BC), integrating convolutional neural networks with Transformer architectures. The framework employs ResNet to extract local texture and detail features from brocade images, utilises VIT to capture global structural …

Expert Interview: "The Mirror Of Ai" In The Domain Of Scientifc Information Research, Taitian Mao, Yulai Bao, Jianxiang Wei, Peng Wu, Chuanming Yu, Gan Tang, Dongyan Wei, Yifei Ma, Wei Wang, Yu Ma

Journal of Scientific Information Research

Professor Mao Taitian and colleagues argues elucidates the adaptation logic, practical pathways, and prerequisites of intelligent agents to empower the high-quality development of scientific information. Professor Bao Yulai and colleagues advocate that integrating the perceptual elasticity of domain-specific large models with the cognitive rigidity of ontology can establish a new paradigm for intelligence services in complex scenarios. The synergy between the two can not only expand the theoretical boundaries of information science and serve national strategies, but also advance intelligence services from assisted analysis to intelligent decision-making. Professor Wei Jianxiang and colleagues point out that generative artificial intelligence has triggered …

Religious Bias In Llms Is Significantly Understudied, Sheryl Carty, Nancy Fulda, Walter Reade

Faculty Publications

In the earlier years of development of LLMs, it was relatively easy to prompt an LLM to respond with toxic or biased statements about religion. Subsequent improvements in frontier models addressed many of the issues of bias and toxicity in general, including against religion. At the same time, the adoption and usage of these models has grown exponentially. Small and implicit biases, therefore, have a magnified overall impact. In this paper, we (1) briefly review previous efforts to measure religious bias in LLMs, (2) show, by reviewing over 12,000 papers dealing with bias in LLMs, that religious bias has been …

Security Architecture Decision Framework For Endpoint Protection In Resource-Constrained K-12 Environments, Jason Folker

Journal of Cybersecurity Education, Research and Practice

K-12 educational institutions face an ongoing challenge in protecting endpoints when budgets and staffing prevent the implementation of standard security best practices. Technology directors routinely make difficult decisions about administrative rights, software controls, and security tooling, but they lack frameworks designed for the constraints and priorities specific to educational environments. This paper develops a security architecture decision framework tailored for K-12 endpoint protection. The framework integrates five weighting dimensions to help technology directors evaluate competing architectural choices. These dimensions include educational impact, security risk reduction, resource requirements, compliance obligations, and operational feasibility. The framework creates structured documentation that helps decision-makers …

Cooperative Evolution For Discovering Scalable Spiking Neural Network Architectures, Catherine C. Rodriquez

LSU Master's Theses

SNNs are a foundational model in neuromorphic computing, where efficient architectures must be discovered for a wide range of applications. Evolutionary methods, such as EONS, offer a flexible approach to this search but become increasingly difficult to scale as task complexity grows due to the rapidly expanding combined topology–parameter search space and associated memory demands. To address this challenge, we propose a co-evolutionary ensemble framework in which a population of candidate SNNs is evolved with fitness defined by each network’s marginal contribution to group performance. Grounded in cooperative game theory and difference evaluation functions from multiagent systems, this formulation provides …

Dynlp: Parallel Dynamic Batch Update For Label Propagation In Graph-Based Semi-Supervised Learning, S. M. Shovan, Arindam Khanda, S. M. Ferdous, Sajal K. Das, Mahantesh Halappanavar

Computer Science Faculty Research & Creative Works

Semi-supervised learning aims to infer class labels using only a small fraction of labeled data. In graph-based semi-supervised learning, this is typically achieved through label propagation to predict labels of unlabeled nodes. However, in real-world applications, new data often arrives in batches, and stale data often becomes irrelevant. Each time a new batch appears, reapplying the traditional label propagation algorithm to recompute all labels is redundant, computationally intensive, and inefficient. To address the absence of an efficient label propagation update method, we propose DynLP, a novel GPU-centric Dynamic Batched Parallel Label Propagation algorithm that performs only the necessary updates, propagating …

Real-Time Image Denoising And Reconstruction Using Generative Adversarial Networks (Gans), Roa'a M. Al_Airaji, Haider Th. Salim Alrikabi, Rula Kamil, Svyd Iryna

Karbala International Journal of Modern Science

This paper presents a real-time image denoising and reconstruction system based on a compact Generative Adversarial Network (GAN) designed for embedded systems and edge computing. The proposed model employs a [ generator and a PatchGAN discriminator, trained using hybrid loss function that combines L1, perceptual, and adversarial terms to balance pixel and perceptual realism. Evaluations were conducted on multiple datasets, including DIV2K, BSD68, SIDD, DND, RENOIR, and PolyU at noise levels (σ = 15, 25, 50). Quantitatively, the proposed model achieves an average PSNR of 32.8 dB and an SSIM of 0.88, which is higher by more than 3 dB …

Energy Efficiency Limits And Future Electricity Demand Of Computing Devices, Ricardo Pinto, Tiago Domingos, Paul E. Brockway, Matthew Kuperus Heun, Tânia Sousa

University Faculty Publications and Creative Works

• ICT (information and communication technologies) represented 4% of the world electricity consumption in 2020; 
• Computing devices represented 2% of the world electricity consumption in 2020; 
• In recent scenarios datacentre electricity demand reaches 3% of world electricity in 2030, and more than 4% in 2035

Toward Intelligent Machines: Conscious Learning And Hardware-Accelerated Ai, Pavia Bera

USF Tampa Graduate Theses and Dissertations

Artificial intelligence systems excel at narrow, well-defined tasks but remain brittle at theboundaries of their training distributions: they cannot quantify uncertainty, adapt continuously to non-stationary data, or operate efficiently on energy-constrained hardware. This dissertation addresses these limitations through four coordinated contributions spanning probabilistic learning algorithms, biologically inspired temporal memory, cross-entity warning propagation via distributed associative memory, and spintronic processing-in-memory.

The first contribution introduces Boosted Bayesian Neural Networks (BBNNs), which extend standard mean-field variational inference by iteratively constructing a mixture posterior through Boosting Variational Inference. On five clinical medical classification datasets, BBNNs achieve superior uncertainty calibration—lower Negative Log-Likelihood and Expected Calibration …