Computer Sciences Commons^™

Improving Urban Search And Rescue Team Coordination Through Adaptive Context Awareness, Daniel Reyes Duran

Doctoral Dissertations and Master's Theses

Modern multi-agent Urban Search and Rescue (USAR) operations heavily rely on mobile geospatial Common Operating Pictures (COPs) to maintain team coordination and Situational Awareness (SA). However, the proliferation of high-frequency sensor telemetry at the tactical edge has introduced a data saturation paradox challenge: while information theoretically drives informed decision-making, unmanaged data surges induce increased operator cognitive overload and alert fatigue on mobile End-User Devices (EUDs), while downstream data-broadcasting models inherently strain edge processing and viewport environments.

To resolve these constraints, this dissertation presents a context-aware Value of Information (VoI) data-management framework integrated directly with a custom, event-driven Android Team Awareness …

There Is No Free Benchmark: An Institutional View Of Legal Ai Benchmarking, Neel Guha, Andy K. Zhang, Christine Tsang, Christopher D. Manning, Julian Nyarko, Daniel E. Ho

Faculty Scholarship

Despite substantial excitement around the use of AI in law, little information exists on the performance and associated risks of the domain’s widely marketed tools. Recent work, for instance, has demonstrated the significant potential for “hallucinations” — wherein models make up facts, law, and precedent — leading Chief Justice Roberts to spotlight this risk in his annual report on the judiciary. We argue that there is a need for public AI benchmarking in law. First, relative to other AI application domains, the legal AI ecosystem lacks legibility — there is little information about the design and performance of many commercial …

A Robust Hybrid Security Framework: Integrating Multi-Layered Text Encryption With Barcode-Based Steganography, Mohamed Sayed, Talaat M. Wahbi, Farooq Abdalwahab Haboub

BAU Journal - Science and Technology

The widespread use of the Internet is causing increasing security concerns regarding online communications. One method for achieving secure communication between authorized parties is steganography. We herein employ multilevel technologies, including compression, encryption, barcoding, and steganography to secure a secret text message. Type I multilevel steganography is used with a two-level setup. The first level uses enhanced least significant bit (secure LSB-L1) image steganography; the output is a stego-image file, the cover is an image file, and the secret data in this level is English text. The output from the first level is encrypted using the RSA algorithm, and the …

Unmasking Twitter Bots: An Applied Machine Learning Approach, Rayane El Raba’A, Layal Abu Daher

BAU Journal - Science and Technology

The rapid growth of social networks has led to increased challenges, such as fraud, cyberbullying, and the spread of automated accounts (bots). Detecting anomalies within these networks is essential to maintaining security and trust. This study explored machine learning algorithms: Random Forest, XGBoost, Support Vector Machine (SVM), and Logistic Regression for anomaly detection in social networks, specifically focusing on Twitter bot identification, By applying AI-driven data mining techniques to a dataset of 37,438 Twitter bot accounts dataset, the research evaluates the effectiveness of these models in detecting unusual patterns. XGBoost achieved the highest accuracy (84.9%), with an ROA_AUC of 0.87, …

Enhanced E-Commerce Recommendation Experience With Collaborative Sentiment Analysis And Ranked Content-Based Filtering, Sandi El Zein, Farah Hamze, Amani Merhi, Lynn Noureddine, Lama Affara

BAU Journal - Science and Technology

The rise of online shopping has made informed purchasing decisions increasingly difficult, as consumers face an overwhelming number of product choices and struggle to manually evaluate specifications and user reviews. This paper presents an AI-powered, sentiment-aware product recommendation tool that effectively aligns user preferences with real-world customer feedback from online reviews. The proposed system utilized Instruct ABSA deep learning models for feature extraction and DeBERTa-v3 for sentiment analysis to turn reviews into interpretable scores that would nominate optimal products. An interactive Rasa-based chatbot interface, TopPickAI, was developed to give a seamless user experience, educate users on product features, and conversationally …

Interval-Valued Neutrosophic Dombi Bonferroni Mean Aggregation Operators In Medical Diagnosis And Sustainable Energy, Maryam Faisal, Muhammad Nadeem, Muhammad Kamran

Neutrosophic Systems with Applications

Medical diagnosis is one of the most difficult fields in which decisions must be made due to the fact that medical information often has characteristics of uncertainty, incompleteness, imprecision and even contradiction. Traditional aggregation and decision-making methods are often not well suited to such complexities, and may result in less reliable diagnostic outcomes. In order to overcome these drawbacks, the authors propose a new approach using a novel representation of Interval-Valued Neutrosophic Sets (IVNSs), the Dombi operational laws, and Bonferroni Mean (BM) aggregation operators. The proposed framework is specifically aimed at coping with uncertainty, indeterminacy and falsity all at once …

An Interactive Multi-Objective Programming Approach For Optimizing Fully Trapezoidal Spherical Fuzzy Linear Programming Problem With Application, Sultan S. Alodhaibi, Hissah Ibrahim Almuzini, Hamiden Abd El-Wahed Khalifa

Neutrosophic Systems with Applications

In this paper, a linear programming framework with completely uncertain parameters is investigated by employing trapezoidal spherical fuzzy numbers (TrSFNs). The proposed formulation incorporates a spherical fuzzy (SF) decision environment in which the optimization process simultaneously maximizes the degree of positive membership while minimizing the corresponding neutral and negative membership degrees. By utilizing the concept of the α -cut associated with TrSFNs, the original fully fuzzy linear programming problem is transformed into an interval-valued linear programming model with confidence levels. To rank and compare the resulting interval objective values, an interval ordering approach based on the decision maker's preferences—considering the …

Exploiting Uncertainty Of Computational Methodology In Optimizing User Interface In Human-Computer Interaction, Nada Mohamed, Alshaimaa A. Tantawy

Neutrosophic Systems with Applications

Human-computer interaction (HCI) evaluation and optimization of user interfaces (UIs) constitute a complex multi-criteria decision-making challenge, marked by conflicting evaluation dimensions, subjective expert judgments, and inherent uncertainty in user experience assessment. Traditional evaluation approaches, such as heuristic expert reviews and user satisfaction surveys, rely on sharp, binary classifications that fail to capture the gradual and overlapping nature of human cognitive and affective states. This limitation necessitates a more robust uncertainty-aware methodology that can model the true complexity of HCI evaluation. This paper proposes a hybrid mathematical model that integrates various Multi-Criteria Decision Making (MCDM) techniques of Entropy, and Simple Additive …

Generative Endurance Logic: An Axiomatic Framework For Reasoning About Outcome-Generating Objects Under Constraints, Hafiz Burhan Ul Haq, Muhammad Nauman Irshad

Neutrosophic Systems with Applications

This paper introduces Generative Endurance Logic (GEL), a formal framework for studying objects through the outcomes they can produce. In many cases, an object cannot be judged only by a fixed truth value, score, or utility value. A rule, model, action, or strategy may behave well in one situation but fail when the context changes or when small perturbations occur. GEL addresses this issue by treating each object as a generator of outcomes. Each object a is linked to a generation map Ga:X×Ω→Y, where X is the context space, Ω is the …

Recursive Neutrosophic Superhypergraphs With Illustrative Applications, Takaaki Fujita, Ajoy Kanti Das, Suman Das, Sankar Prasad Mondal, Volkan Duran

Neutrosophic Systems with Applications

Finite hypergraphs generalize ordinary graphs by permitting each hyperedge to join any nonempty set of vertices, and thus provide a natural model for truly multiway interactions. To represent hierarchical and multi-layer structure, SuperHyperGraphs iterate the powerset operation so that set-valued entities created at one level can be treated as vertices at higher levels. Independently, recursive hypergraphs allow edge recursion: an edge may contain not only vertices but also lower-level edges, yielding nested (and possibly self-referential) incidence controlled by a specified recursion depth. In this work we introduce and axiomatize Recursive Neutrosophic SuperHyperGraphs, a unified framework that combines vertex …

Tumor-Immune Dynamics With Memory And Time Delay: A Fractional-Order Model With Ctla-4 Regulation, Mutaz Mohammad, Mohyeedden Sweidan, Alexander Trounev, Fathalla Rihan

All Works

This study develops a fractional-order tumor-immune interaction model incorporating Caputo memory effects, delayed immune activation, and CTLA-4 checkpoint regulation. The model describes the coupled dynamics of tumor cells, CD4^+ T cells, IFN-γ, and CTLA-4, and extends classical integer-order tumor-immune models by accounting for hereditary immune responses and biologically motivated latency effects. Theoretical properties, including positivity, boundedness, equilibrium structure, and fractional-order stability, are examined to establish the biological and mathematical consistency of the model. The delayed fractional system is then investigated computationally by comparing several numerical methods, including finite difference discretization, Daubechies wavelet collocation, Euler wavelet collocation, and a predictor-corrector scheme. …

Analysis Of Normalization, Resampling, And Weighted Voting To Improve K-Nn Performance In Diabetes Disease Detection, Agustiyar, R. Rizal Isnanto, Budi Warsito, Adi Wibowo, Ferry Jie

Research outputs 2022 to 2026

Diabetes is a global health issue, and machine learning has become crucial for early detection. The k-Nearest Neighbors (k-NN) machine learning algorithm is widely used because it is simple and easy to implement; however, its performance is sensitive to feature scale differences, class imbalance, and the assumption of uniform decision-making, as commonly found in clinical datasets such as the Pima Indians Diabetes (PID) dataset. Many studies have been conducted to address these issues; however, very few have systematically evaluated the combined effects of preprocessing and distance-based decision-making methods on k-NN performance. This study evaluates the combined effects of preprocessing strategies …

A Physically Unclonable Function Authentication Protocol To Secure Ieee C37.118 Communications, Taylor Griffiths, Mohiuddin Ahmed, Chadni Islam, Paul Haskell-Dowland

Research outputs 2022 to 2026

This paper outlines the need for security on the smart grid, specifically for Phasor Measurement Units (PMUs) and Phasor Data Concentrators (PDCs). These devices are targeted due to their criticality to the smart grid and the insecure communication protocols used. Common attacks on these devices target the data sent between them. As such, integrity-based attacks are the focus of this paper, more specifically on False Data Injection attacks (FDIAs) and Time Synchronisation Attacks (TSAs). Security solutions are proposed throughout literature from mitigation and protection techniques to detection and resolution techniques. To secure the PMU and PDC, this paper proposes an …

Operationalizing Supply-Chain Hygiene In Graduate Is Education: A Hands-On Module For Secure Software And Ai/Ml Pipelines, Dominic A. Wilson

Journal of Cybersecurity Education, Research and Practice

Supply-chain attacks (including typosquatting, dependency confusion, compromised builds, dataset poisoning, and backdoored models) pose growing threats to analytics platforms central to Information Systems (IS). While frameworks like the Secure Software Development Framework (SSDF) and Supply-chain Levels for Software Artifacts (SLSA) offer guidance, IS curricula often lack accessible, infrastructure-light modules that build practical skills for mitigating these risks. This experience report presents a two-week module embedded in a graduate Secure Coding course required for a Master’s in Applied Security and Analytics degree. The module operationalizes secure development habits across both traditional software and machine learning (ML) pipelines. The module addresses a …

The Nist Artificial Intelligence Risk Management Framework: Adoption Challenges And Opportunities, Gillian Kennedy, Devin Patel, Humza Sheikh, Paul Wagner, Robert J. Honomichl

Journal of Cybersecurity Education, Research and Practice

Artificial intelligence (AI) is being adopted at an exponential rate to improve efficiency, decision-making, and cybersecurity, but its rapid integration introduces new and often poorly understood risks, including system errors, algorithmic bias, data privacy concerns, security vulnerabilities, and ethical dilemmas. This paper examines how organizations are implementing AI and evaluates the National Institute of Standards and Technology's AI Risk Management Framework (NIST AI RMF) as a tool for managing these risks. It reviews the benefits of AI adoption alongside the risks emerging from its use in business and broader society and examines the legal and ethical challenges organizations face when …

Parametric Modular Answer Set Programs Made Declarative, Jorge Fandinno, Yuliya Lierler, Torsten Schaub

Computer Science Faculty Publications

In this paper, we explore the concept of modularity in first-order answer set programming (ASP). We introduce a new formalism called parametric modular logic programs, which allows defining subprograms with parameters and intensionality statements. We demonstrate how this formalism can capture the semantics of clingo-programs with collective control , a feature that enables structuring and instantiating subprograms. We provide theoretical foundations for modular ASP, illustrate its usefulness, and connect to traditional non-modular ASP.

Forecasting Covid-19 New Cases Using Nbeats Deep Learning And Mobility Data, Amril Nazir, Mohammad Shorfuzzaman, Muhammad Lujaini Lotfi, Firuz Kamalov, Sufian Badawi, Maen Takruri, Abdul Halim Jallad

All Works

COVID-19 is a highly contagious disease transmitted primarily through human contact. Therefore, understanding population mobility is essential for predicting COVID-19 case trends. In this paper, we propose a novel deep learning approach for forecasting new COVID-19 cases using a neural architecture called Neural Basis Expansion Analysis for Interpretable Time Series (N-BEATS). The N-BEATS model effectively handles long input sequences and large output horizons without information loss or increased computational complexity. We compare the performance of N-BEATS with a state-of-the-art benchmark model, LSTM-Markov, across four major countries: the United States, the United Kingdom, Russia, and Brazil. Three distinct COVID-19 datasets from …

Autoencoders As Classifiers Trained On Single Sources Of Radiographic Images For Generalizability Across Unseen Sources, Ryan M. Putney

USF Tampa Graduate Theses and Dissertations

Artificial neural networks trained to classify X-ray images according to disease state will learn to distinguish between technical or procedural variations in the images rather than features relevant to the disease. For example, the model might learn to recognize the machine that captured the image or to distinguish an image taken while the patient is lying supine or standing upright. The present work is aimed at using autoencoders as a generalizable classifier to detect COVID vs pneumonia (PNA) X-ray images. The first experiment was to find an architecture for a fully convolutional autoencoder (CAE) and for a convolutional autoencoder with …

Modern Architecture Of Muqarnas In Damascus: An Original Computational Methodology, Nahed Jawad Chakouf

All Works

The research aimed to revive muqarnas using digital computational tools, drawing on the techniques of Damascene craftsmen and ancient manuscripts. It involved designing contemporary muqarnas with double-curvature vaults and large spans, incorporating new unit designs and compositional techniques. In Section I, the researcher conducted pre-design studies on four muqarnas types, each associated with one of the four Damascene architectural styles. These studies examined the geometry and behavior of muqarnas types when used on traditional domed surfaces. Section II included a design study of the Dome of the Eagle of the Umayyad Mosque in Damascus, using modern software tools such as …

On The Use Of Lorawan For Smart Fishing Applications In The Blue Economy Sector, Eva Shayo

Tanzania Journal of Engineering and Technology (TJET)

The growth of digital technology is expected to transform small-scale fishery sectors, where a need for robust, low-cost, long-range communication networks becomes critical. There exist several technologies that are used in the fishery sector but they are never affordable to small scale fisheries. This study evaluates the feasibility of using low cost Long Range Wide Area Network (LoRaWAN) technology specifically tailored for smart fishing environments to small scale fishery sector. Using simulation, we assess the performance of the key performance metrics including probability of success and energy efficiency under varying device densities and time. During evaluation, we considered end devices …

The Impact Of Gender Sensitization On Requirements Elicitation: A Controlled Experiment, Ruthbertha Kateule, Salome Maro, Leonard Peter Binamungu

Tanzania Journal of Engineering and Technology (TJET)

Previous studies in software engineering have reported the importance of considering gender aspects in various software engineering activities, including requirements engineering, however, to the best of our knowledge, no work has investigated the impact of gender sensitisation on eliciting gender inclusive software requirements. The objective of this study was to understand the impact of gender sensitisation on software requirements elicitation. We conducted a controlled experiment using 40 undergraduate students from three different computing programs at the University of Dar es Salaam. The 40 participants were divided into 9 groups with both males and females. The participants were asked to elicit …

Enhancing Community Engagement Through ‘Nitunze Kilombero’ Mobile App: Case Of Climate Land Use And Cover Management For Kilombero Basin, Ghanima Chanzi, Subira Munishi

Tanzania Journal of Engineering and Technology (TJET)

The Kilombero Basin in southeastern Tanzania faces significant challenges due to rapid land use and land cover (LULC) changes driven by climate change, population growth, and unsustainable farming practices. This study assessed the role of community engagement and digital reporting through the 'NITUNZE KILOMBERO', mobile application in supporting water resources management in the basin. The App facilitates real-time reporting of environmental issues, provides educational resources, and enables collaboration between local communities and authorities. A mixed-methods approach, including household surveys and key informant interviews, was employed to assess the app's effectiveness. Results indicate high community awareness of LULC impacts, with 85% …

(R2077) Enhancing Queue Management: Dynamic Server Allocation And Optional Services In Stochastic Modeling, G. Ayyappan, S. Sankeetha

Applications and Applied Mathematics: An International Journal (AAM)

Consider a queueing system with a single server, where customer arrivals follow a Markovian arrival process and service times follow a phase-type distribution. The main server has the capability to recruit an additional server when the number of customers in the system exceeds a certain threshold, denoted as L. Both servers provide normal service to customers, and optional service is provided upon request. The main server takes multiple vacations, with the durations following an exponential distribution with rate parameter η, until there is at least one customer in the system. This system can be represented as a Markov chain process, …

(R2142) Queueing System With Batched Services, Server-Controlled Re-Service, Set-Up Time And Multiple Vacation, S. Karpagam, N. Aarthy, B. Somasundaram

Applications and Applied Mathematics: An International Journal (AAM)

To analyze a bulk queueing system with server-controlled admission for reservice, low-batch service, multiple vacation and set-up time. The study explores how the server dynamically adjusts between low-batch and bulk services based on the queue size. After completing a bulk service, the departing batch may request reservice with a certain probability. However, accepting the request is not mandatory, and the server admits it with some probability. Whenever the queue becomes empty, whether after reservicing, completing service with no reservice request, or ending the low-batch service, the server enters a random-length vacation. If the queue still has less than ‘a’ customers …

Trustworthy Reinforcement Learning And Communication-Efficient Multi-Agent Systems, Rui Zuo

Dissertations - ALL

The rapid proliferation of autonomous systems, such as Unmanned Aircraft Systems (UAS) transitioning to large-scale Beyond Visual Line of Sight (BVLOS) operations, demands a paradigm shift toward reliable, transparent, and resilient autonomy. While Deep Reinforcement Learning (DRL) and Multi-Agent Reinforcement Learning (MARL) have demonstrated exceptional capabilities in complex decision-making and coordination, their real-world deployment in safety-critical domains is hindered by two fundamental challenges. First, the opaque, "black-box" nature of DRL models prevents human operators from understanding and trusting the agents' underlying logic. Second, as multi-agent operations scale, they become severely constrained by the stochastic link qualities and strict bandwidth limitations …

Scaling Logical Reasoning On Modern Hpc Hardware, Yihao Sun

Dissertations - ALL

Deductive logic reasoning has evolved from a theoretical symbolic artificial intelligence tool into a massive computational workload. Modern domains, ranging from static program analysis and binary reverse engineering to knowledge-graph reasoning, rely on Datalog, a logical query language, to express deeply recursive, declarative specifications. However, scaling logic reasoning to these industrial workload exposes two interlocking ceilings in traditional engines. Architecturally, the memory bandwidth and parallel throughput of single-node CPUs fall drastically short of the read- and write-heavy demands of semi-naive evaluation. Asymptotically, traditional query processing algorithms, such as binary join algorithms generate massive intermediate relations that exhaust device memory on …

From Detection To Segmentation: A Foundation Model Approach To Organoid Brightfield Image Analysis Using Sam 3, Hiren Manani

Theses - ALL

This thesis presents the first systematic application of SAM 3, a unified foundation model for promptable segmentation, to mouse small intestinal organoid brightfield microscopy image analysis. The work spans the complete pipeline from zero-shot baseline evaluation through domain-specific fine-tuning on a GPU cluster, and documents the full engineering process required to adapt a state-of-the-art foundation model to a novel biomedical imaging domain. A comprehensive literature review of over 20 papers spanning detection-based, classical segmentation, foundation model, and morphological analysis approaches identified a clear research gap that this thesis addresses. Five critical compatibility patches were developed to deploy SAM 3 on …

Constrained Neural Intelligence: Investigations Into Deep Multi-Objective Optimization, Naveed Tahir

Dissertations - ALL

Neural intelligence is identified with complex learning problems optimized over the high-dimensional, non-convex parameter spaces of deep neural networks. Solving such problems generally requires handling competing objectives and conflicting constraints. This is traditionally dealt with using heuristic methods that collapse such complexity into unconstrained, singular objectives. While computationally convenient, this invites a tradeoff against the precision and stability afforded by non-heuristic, geometry-aware approaches. This dissertation explores constrained multi-objective learning in various applied and theoretical contexts and highlights the feasibility of approximate methods as well as the necessity of exact methods. We propose a framework for equality-constrained deep learning via approximate …

Multi-Modal Survival Prediction On Breast Cancer Mammograms And Enabling Multi-Institutional Collaborations With Federated Learning, Nikolas Koutsoubis

USF Tampa Graduate Theses and Dissertations

Cancer remains one of the leading causes of mortality worldwide, and machine learning is well positioned to leverage the large volumes of routinely collected clinical and imaging data to improve patient outcomes. Realizing this potential at scale requires three capabilities that current practice does not yet deliver in combination: models that integrate the multiple data modalities clinicians use, training procedures that respect institutional data-sharing constraints, and tooling that enables sharing of de-identified data when federated approaches are not sufficient. This dissertation develops methods and infrastructure addressing each of these capabilities in the context of breast cancer and oncology more broadly. …

Molecular Identification Of Cryptococcus Albidus Isolated From A Gingivitis Infection In A Male From Karbala City, Iraq, Maysaa Taqi Al-Khazali, Zahraa Raheem Murshidy, Sarah Kadhim Al-Rahimy, Sura Abd Ali Kadhim Mohammed, Dhuha Ali Hussein

Karbala International Journal of Modern Science

The current study presents a case of gingivitis caused by Cryptococcus albidus in a 35-year-old male from the city of Karbala in Iraq. This fungal species was identified via morphology, microscopy, additionally to the VITEK system. C. albidus was further confirmed using the polymerase chain reaction (PCR) technique by amplifying the 18S rRNA gene as a universal primer and sequencing. Sequences analysis revealed that this strain of C. albidus had not been previously documented in the NCBI database. Hence, the newly obtained sequences were submitted to the GenBank repository and assigned the accession number OQ975882.1, marking the first report of …