Digital Commons Network^™

Urban Spatial Development Control In Tanzania: Analysis Of Factors Influencing Gis Application Using Structural Equation Modelling (Sem)., Happiness Protas Mmanda, Nestory Yamungu

Tanzania Journal of Engineering and Technology (TJET)

Rapid urbanization in developing countries has intensified urban expansion, creating challenges for sustainable development. Geographic Information Systems (GIS) enhance spatial planning, but empirical evidence on factors influencing their effectiveness remains limited. This study examines determinants of GIS application in Urban Spatial Development Control (USDC). The objectives are to (1) identify and categorize factors affecting GIS use, (2) assess relative strength, and (3) develop a validated structural model explaining GIS adoption in USDC. Data were collected from 103 LGAs by a mixed sampling method. Exploratory and Confirmatory Factor Analysis classified influencing factors into technology-related (α = 0.869, CR = 0.881), process-related …

Decision Support Tool For The Maintenance Of Meter Gauge Railway Permanent-Way Infrastructures: A Concept Paper, Hamisi J. Maulid, Beatus A.T Kundi, Juma M. Matindana, Ismail W. R. Taifa Dr

Tanzania Journal of Engineering and Technology (TJET)

A Decision Support Tool (DST) represents a transformative way of modernising maintenance practice on the permanent-way infrastructure of Meter Gauge Railway (MGR). Maintenance practice remains largely reactive, as the MGR plays a strategic role in transporting both freight and passengers, resulting in inefficient resource allocation, high operational risk, and growing Lifecycle expenses. This concept paper explores the potential and the design challenges for a DST that combines fuzzy analytic hierarchy process (Fuzzy-AHP) modelling, multi-criteria decision analysis (MCDA), Geographic Information Systems (GIS) and predictive machine learning (ML) analytics for evidence-based, future-oriented maintenance management. The paper draws on peer-reviewed studies from …

Kanmultisign: Multi-Scale Sequence-Based Pose Animation From Sign Language Notation With Kolmogorov-Arnold Networks, Guanyi Du, Lintao Wang, Kun Hu, Ziyang Wang

Research outputs 2022 to 2026

Sign language production from symbolic notation offers a scalable route to accessible sign animation. We present KANMultiSign, a multi-scale sequence generator that translates HamNoSys notation into two-dimensional human pose sequences. Our framework makes two complementary contributions. First, we introduce a coarse-to-fine generation strategy with multi-scale supervision: the model is first guided by an intermediate body–hand–face scaffold to encourage global structural coherence, and then refines fine-grained hand articulation to improve finger-level detail. Second, we investigate integrating Kolmogorov–Arnold Network modules into a Transformer backbone, using learnable univariate function primitives to model the highly non-linear mapping from discrete phonological symbols to continuous body …

When Does Global Search Pay For Itself? A Measured Exploration Cost And Energy Break-Even Analysis Of A Metaheuristic Search Controller In Solar Energy Systems, Mohamed Ali Muammar Ezgour

Communications of the IIMA

Autonomous energy systems increasingly delegate the choice of operating point to embedded search algorithms, trading a fast local optimizer that can settle on a wrong point against a slower global search that guarantees the right one at a measurable cost. This paper reframes maximum power point tracking under partial shading as that decision and measures its economics on a fixed photovoltaic plant in MATLAB/Simulink. A Hippopotamus Optimization global search handed over to incremental conductance is compared with incremental conductance alone across seventeen initial duty cycles and thirty random seeds. The hybrid reached the global peak in all thirty seeds, whereas …

Cyberspace Collaborative Awareness: A Model For Unity Of Effort In Homeland Defense, Mike Knapp, Sean Atkins, Matthew Mclaughlin

Joint Force Quarterly

The increasing frequency and severity of cyberattacks against U.S. critical infrastructure continue to confound homeland defense efforts. Defending against state cyber campaigns that threaten the nation’s most critical systems requires a new awareness model that can enable unity of effort across public and private actors. Examining homeland defense awareness in other domains reveals principles and approaches that can inform the development of a collaborative awareness model in cyberspace. This new framework acknowledges the interconnectedness of government and commercial networks and the independent goals of each player in the domain. Doing so provides a viable path to achieving shared domain awareness …

From Data To Victory: The Race For Analytic Superiority In Warfare, Robert Grossman, Emily Goldman

Joint Force Quarterly

Artificial intelligence technologies have reached a tipping point after decades of development. They are diffusing widely across defense and national security applications. Twenty-first century warfighters rely on analytic models in all systems, at all echelons, and in all domains. As more powerful models built on ever larger data sets become ubiquitous, militaries are in a new competition to deploy artificial intelligence. Operational art must embrace “analytic superiority.” This is the operational advantage from collecting and ingesting data, building robust models and computing infrastructure, deploying the models into operational systems, and denying adversaries' ability to do the same

This article explains …

Bitseat: Reimagining The Financing For Airliners Using Nonfungible Tokens (Blockchain Technology), Edwin S. Ongola

Journal of Aviation Technology and Engineering

This essay describes how blockchain technology, particularly nonfungible tokens, can be used to raise funding for airliners. The essay begins with a brief overview on the costs, categories, and acquisition methods of airliners. After that, the essay introduces concepts on blockchain technology, tokens, and smart contracts. The essay then touches on how nonfungible tokens can be used to facilitate fractional ownership of airliners. From there, the essay discusses Bitseat, a conceptual nonfungible token for fractional ownership of airliners, covering its overall design, appeal, marketplace alternatives, and challenges. Finally, in the discussion, the essay summarizes the overall concept and outlines its …

A Novel Entropy Based Maintainability Measurement Algorithm For Java Source Code., Remi M. Yusuf Mr, Md Shadab Mashuk, Julian Bass

Communications of the IIMA

Software metrics play a central role in assessing and managing the quality of software systems providing quantitative insights into attributes such as complexity, reliability, rigidity, modifiability and maintainability. Among these, maintainability is particularly critical, as it directly influences the ease of system evolution, long-term sustainability, and overall cost effectiveness. Despite the widespread use of metric-based maintainability measurement algorithms, capturing a value that reflects the maintainability situation of software source code remains a challenging task, especially in the presence of design deficiencies such as code smells. To measure changes in maintainability, this study experimentaly characterises the relationship between code smells and …

A Transformer-Based Approach With Data Augmentation For Multilabel Emotional Context Detection, Mohsin Hasan Hussein, Marem H. Abdulabas, Azha Talal Mohammed Ali, Homam Aziz Ghazi

Al-Bahir

Emotion identification in texts is becoming increasingly difficult because of the wide variety of ways emotions are represented. This study uses a fine-tuned Robustly Optimized Bidirectional Encoder Representations from Transformers Approach

(RoBERTa) to offer a Transformer-based model for identifying multilabel emotional context in textual data. To balance emotion categories and enhance the model's capacity for generalization, data augmentation is applied on two different datasets: Semantic Evaluation and Cross-lingual Emotion Dataset (SemEval and XED) English corpus. This stage is considered one of the most important steps in preprocessing as it greatly helps to improve the results. The RoBERTa model was then …

From Dissertation To Deployment: A Unified Software Platform Operationalizing Clinical-Prediction And Sequential-Security Ai For Healthcare, Olsi Shehu, Damiana Teliti, Jasmin Kevrić, Bekir Karlik

Communications of the IIMA

Advances in machine learning for healthcare are abundant, yet most validated models remain confined to research notebooks and never reach secure, usable clinical software. This paper addresses that deployment gap by presenting a unified, security-hardened software platform that operationalizes two complementary streams of doctoral research inside a single, role-based hospital information system. The first stream contributes a clinical-prediction capability: an ultra-hybrid ensemble that couples a quantum-inspired feature transformation, particle-swarm feature selection, and calibrated soft voting for cancer-outcome prediction (96.41% accuracy, AUC-ROC 0.983 on TCGA-BRCA), survival stratification, multi-cancer generalization, and pharmacogenomic drug-response classification (89.31% mean accuracy across 25 compounds). The second …

A Hybrid Rule-Based And Large Language Model Framework For Extracting Acronym–Definition Pairs From Scientific Literature, Petro Skrypnyk

Theses and Dissertations

Authors of scientific papers rely heavily on acronyms and often use them without defining them, making the literature harder to read and index. This thesis develops and evaluates a hybrid rule-based and large language model (LLM) framework that extracts acronym–definition pairs from scientific PDF documents. It extends an earlier Rowan University system that combined a regular-expression parser with a single LLM on 200 papers. That system showed that neither the parser nor the LLM alone is sufficient for accurate extraction of the pairs. The framework is a fully automated pipeline from PDF input to scored results. It compares four LLM …

Gc-Ms Profiling And Antibacterial Activity Of Cold-Macerated Garlic Extracts Against Multidrug-Resistant Uropathogens, Fatima A. Khalaf, Saeed A. Fayadh

Karbala International Journal of Modern Science

The growing number of cases of multidrug-resistant (MDR) urinary tract infections has made the clinical management of urinary tract diseases a significant challenge; therefore, the need for effective adjunctive therapies cannot be overemphasized. This study evaluated the bioactive components and antibacterial efficacy of cold-macerated garlic extracts obtained using distilled water, 70% ethanol and hexane solvents against antimicrobial-resistant urinary isolates. Out of 250 urine samples that were analyzed, 124 (49.6%) showed significant microbial growth, with Escherichia coli being the most frequently isolated organism. The results of the phytochemical screening and Gas Chromatography-Mass Spectrometry (GC-MS) analysis showed that the ethanolic extract had …

Hyperlattice-Valued And Superhyperlattice-Valued Uncertain Sets With Decision Applications, Takaaki Fujita, Ajoy Kanti Das, Sankar Prasad Mondal, Arif Mehmood, Arkan Ghaib

Neutrosophic Systems with Applications

Fuzzy set theory enriches classical sets by assigning to each element a graded membership in [0,1], thereby capturing partial inclusion and uncertainty. The notion of an Uncertain Set further abstracts this idea by allowing membership to take values in a general degree-domain, providing a unified language that subsumes fuzzy, intuitionistic fuzzy, neutrosophic, plithogenic, and related models. On the algebraic side, a hyperlattice replaces one lattice operation by a multivalued hyperoperation, enabling the representation of ambiguous or non-deterministic combinations, while a superhyperlattice iterates this structure through powerset lifting to obtain higher-order layers of interaction. Motivated by these developments, we introduce HyperLattice-valued …

A Neutrosophic Event-Graph Legal Ai System For Detecting Contradictions In Witness Testimonies Under Egyptian Law, Shimaa Abdelghany Attalla, Alaa Elmor, Nada Hesham, Abduallah Gamal

Neutrosophic Systems with Applications

Witness testimony is an important source of evidence in criminal proceedings, but it may contain contradictions, incomplete details, or conflicts with other case-file materials. This paper proposes a neutrosophic event-graph legal AI framework for detecting materially contested claims in witness testimonies under the Egyptian criminal-procedure context. The framework converts testimony and related records into structured claims containing actor, action, object, time, location, source, and modality. These claims are then connected through an event graph and evaluated using neutrosophic components of support, indeterminacy, and opposition. The system produces source-grounded legal-review alerts when a claim has sufficient opposition from other claims or …

An Interval-Valued Spherical Fuzzy Critic–Waspas Framework For Prioritizing Healthcare Delivery Models To Enhance Patient Satisfaction Under Uncertainty, Mariam Hamada, Ahmed Samy, Mohamed M. Abdelhafeez, Shrouk El-Amir

Neutrosophic Systems with Applications

Selecting an appropriate healthcare delivery model is important for improving the quality of healthcare services and enhancing patient satisfaction. However, this decision is complex because it involves several criteria, uncertainty, and different expert opinions. To handle this uncertainty, this paper uses Interval-Valued Spherical Fuzzy Sets (IVSFSs), which allow experts to express their evaluations more flexibly. This paper proposes an integrated interval-valued spherical fuzzy CRITIC-WASPAS approach to prioritize healthcare delivery models. The CRITIC method is used to determine the objective weights of the evaluation criteria, while the WASPAS method is used to rank the healthcare delivery models. Expert evaluations are expressed …

20 Years Of Neutrosophic Statistics: A Bibliometric Analysis, Hammad Khalid, Arshad Hameed

Neutrosophic Systems with Applications

Florentin Smarandache introduced neutrosophic statistics (NS), a formalism for representing uncertainty and indeterminacy in observations, parameters and statistics in an extension to classical and interval statistics. Although widespread in various contexts, such as statistical quality control, hypothesis testing, medical diagnosis, and decision science, and increasingly used and developed, the scientific evolution and conceptual framework of the science of decision making remain largely unexamined. To fill this gap, the present study has been conducted with a comprehensive bibliometric analysis of NS research articles published between 2003 to 2025 from Scopus which has been conducted following the SPAR-4-SLR protocol. In all, 501 …

A Unified Framework For Neutrosophic Estimation Using Fractional Power, Exponential, And Logarithmic Functions With Bivariate Auxiliary Information, Anchal Yadav, Anuj Yadav

Neutrosophic Systems with Applications

This study develops a generalized neutrosophic ratio-type estimator for estimating the population mean by incorporating information from two auxiliary variables under Simple Random Sampling Without Replacement (SRSWOR). The proposed methodology extends the conventional single-auxiliary-variable approach by jointly incorporating bivariate auxiliary information within the neutrosophic framework, thereby accounting for uncertainty, indeterminacy, and inconsistency in the available information. The bias and mean squared error of the proposed estimator are derived using first-order approximations, and the corresponding efficiency conditions are established through theoretical comparisons with existing neutrosophic estimators. The performance of the proposed estimator is further examined using a real medical dataset represented …

Learning-Based Entanglement Generation For Quantum Routing, Tasdiqul Islam, Rasman Mubtasim Swargo, Md Arifuzzaman

Computer Science Faculty Research & Creative Works

Entanglement generation in long-distance quantum networks is challenging because resources are limited and entanglement swapping is probabilistic. To maximize the rate of successful requests, existing quantum routing algorithms often rely on computationally expensive methods such as Integer Linear Programming (ILP) to determine which links to entangle and use for end-To-end entanglement generation. However, these approaches fail to meet the latency requirements of real-world quantum networks. In this study, we propose a Reinforcement Learning (RL)-based model that determines which links to entangle in each time slot, replacing the slow ILP-based link-selection phase used in prior algorithms. The proposed Deep Q-learning model …

Fast Discovery Of Motivic Patterns In Symbolic Music Via Lossy Compression, Adam James Wilson

Publications and Research

Generative systems that react to live musicians require rapid analysis of musical data, which rules out deep learning models: they cannot be trained within the time constraints of live performance. But because analysis results are often transformed before use, we are free to reduce the parameters that undergo transformation to a small set of primitive states. We address this coincidence of constraint and opportunity with an algorithm for online discovery of maximal musical motives that achieves speed through lossy compression: the pitch and inter-onset-interval deltas for all pairs of events in a potential motive are reduced to two-bit values, conceptualized …

Geographical Pattern Analysis Of Gis Images With Deep Learning And Voronoi Network, Nidaa Kareem, Tawfiq A. Al-Assadi

Journal of Intelligent Informatics, Networking, and Cybersecurity

Localization is not enough for the analysis of spatial patterns; a principled geometric and statistical framework is required. This paper proposes an integrated spatial intelligence system combining deep learning, computational geometry, and spatial statistics, which is a unified and interpretable system. It is based on segmentation localization that accurately localizes the centroid of each object without the disadvantages of the bounding box. These centroids form a natural Voronoi tessellation of regions of spatial influence intrinsic to the data instead of imposing any artificial restrictions. A geometry-based density formulation is used to improve representation, which includes Voronoi cell areas and neighborhood …

Epistemic Resilience In Ai-Driven Innovation, James C. Ryan, Massimiliano Cappuccio, Effrosyni Georgiadou, Alessandro Lanteri

All Works

Artificial intelligence (AI) has become an integral component of innovation infrastructure, facilitating information retrieval, knowledge recombination, and decision support. While these applications accelerate innovation cycles, they simultaneously obscure evidential provenance, compromise output reliability, and diffuse accountability when algorithmic insights fail. To address these vulnerabilities, this paper introduces the concept of epistemic resilience, defined as an organizational and ecosystem capability to preserve, adapt, and restore trustworthy knowledge processes amid technological disruption, uncertainty, and epistemic risk. Synthesizing literature across innovation management, organizational learning, knowledge management, social epistemology, research integrity, and human–AI collaboration, this study conceptualizes AI-driven innovation as a sociotechnical knowledge process …

Agentic Ai For Autonomous Marketing: Taxonomy, Benchmark, And An Open-Source Agent, Prashidda Thapa, Shraddha Khadka, Pragyan Sharma, Ana Rai

Student Publications

Marketing automation is moving beyond rule-based systems and prompt-driven generative tools toward agentic AI: large language model (LLM) based agents that plan, act, and adapt under high-level objectives. Existing analyses are largely conceptual and commercial platforms are proprietary, leaving the community without reproducible baselines. We contribute three artifacts. First, a compact taxonomy organized around an augmentation–automation-autonomization continuum and a Perceive–Reason–Act(PRA)cycle that distinguishes autonomous agents from rule-based automation. Second, MarketingAgent, an open-source ReAct agent with six tools spanning SQL access, RFM segmentation, XGBoost churn prediction, campaign and email generation, and budget allocation. Third, the Marketing Agent Benchmark (MAB), a 15-task suite …

Do Ensemble Models Always Win? A Comparative Machine Learning Evaluation For Financial Time-Series Prediction, Shraddha Khadka, Prashidda Thapa, Pragyan Sharma, Shreya Silwal

Student Publications

Equal-weighted ensembling is often treated as a default upgrade over individual machine learning models. We test this assumption on one-day-ahead stock-price prediction across five sector diversified U.S. assets (2015–2024) by comparing Random Forest (RF), Support Vector Regression (SVR), Long Short-Term Memory (LSTM), and their equal-weighted average under identical features, preprocessing, and chronological splits. Pooled across assets, LSTM attains the lowest test-set RMSE (17.70), SVR is statistically tied with LSTM under the Diebold and Mariano test (𝑝 = 0.494), the equal-weighted ensemble is third (18.13), and RF is last (20.33); SVR beats the ensemble at 𝑝 = 0.001 (Bonferroni-significant) while LSTMbeats …

Enhancing Programming Productivity For Individuals With Adhd Through Generative Artificial Intelligence: An Inductive Analysis, Lionel Mew

School of Professional and Continuing Studies Faculty Publications

Attention-deficit/hyperactivity disorder (ADHD) significantly impacts computer programmers through challenges in sustained attention, executive functioning, and organizational skills. While traditional intervention strategies have shown varying degrees of success, the emergence of generative artificial intelligence (AI) presents novel opportunities to address ADHD-related programming challenges. This paper presents an inductive analysis synthesizing current research on ADHD's effects on programming, traditional productivity enhancement techniques, and the potential of generative AI tools. Through examination of recent literature and field studies, we propose that generative AI can serve as a transformative intervention by providing personalized cognitive support, reducing executive function demands, and enhancing code generation efficiency. …

Facevalue: Exploring Real-Time Self-View Overlays To Prompt Meaning-Oriented Self-Awareness In Remote Meetings, Gun Woo (Warren) Park, Anthony Tang, Fanny Chevalier

Research Collection School Of Computing and Information Systems

In remote video meetings, visual non-verbal cues, such as facial expressions or head movements, are seen continuously but often only partially. This increases ambiguity compared to in-person settings and can cause misinterpretation or misalignment between intended and perceived meaning. Motivated by communication theories, we designed FaceValue, a technology probe that augments the self-view with private, real-time overlays. These overlays are subtle, suggestive prompts intended to help attendees reflect on how their cues might be interpreted by others. To invite personal interpretation, FaceValue avoids behavioral labeling and instead aims to support meaning-oriented self-awareness: recognizing when visible cues may unintentionally (mis)communicate intent. …

Governing Agentic Ai: The Human Values Alignment Framework (Hvaf-A) As A Policy Tool, Mousa Al-Kfairy

All Works

Agentic artificial intelligence (AI) systems—software that plans, acts, and decides without step-by-step human approval—are already deployed in finance, healthcare, recruitment, and public services. They differ from earlier AI in one critical way. They do not produce outputs for a human to accept or reject. They take actions. Those actions may be irreversible. They may affect people who never used the system. Existing governance frameworks were not designed for this. Current alignment approaches—including reinforcement learning from human feedback, constitutional AI, and preference aggregation—assume that a well-aligned system satisfies what users ask for. This paper argues that the assumption fails in agentic …

Individualized Bayesian Inference Identifies Novel Genetic Variants For Parkinson's Disease, Jin Ren, Yasaman J. Soofi, Md Asad Rahman, Qing Lu, Jinling Liu

Engineering Management and Systems Engineering Faculty Research & Creative Works

Parkinson's disease (PD) is a complex neurodegenerative disorder with a significant genetic component. While genome-wide association studies (GWAS) have been instrumental in identifying genetic variants associated with PD, the reliance on large sample sizes and population-level analyses may overlook variants with lower minor allele frequencies or individual-specific relevance. Individualized Bayesian Inference (IBI) offers a promising method to complement GWAS by identifying and prioritizing candidate genetic markers at both the individual and patients-like-me subgroup levels. This study evaluates the application of IBI to PD genetics, using GWAS as a baseline for comparison. We analyzed genetic data from the Fox Insight online …

Trustworthy And Explainable Malware Threat Intelligence Through Social Media Analytics And Nature-Inspired Optimization, Feras Al-Obeidat, Muhammad Saad Rashad, Muhammad Amin, Waqas Ali, Bilal Khan, Sajid Anwar

All Works

The convergence of media analytics, Cyber threat Intelligence (CTI) and trustworthy artificial intelligence has become essential for modern cybersecurity systems operating over large-scale, heterogenous data sources. In particular, Social Media Intelligence (SOCMINT) and Open Source Intelligence (OSINT) provide high-volume, real-time signals that complement structured CTI frameworks for early-stage malware and adversarial threat detection. However, integrating these unstructured and dynamic sources with Structured Threat Information Expression (STIX) remains challenging due to its hierarchical complexity, semantic redundancy, and computational overhead in resource-constrained environments. This paper proposes an explainable and optimized intelligence pipeline (BERT-STIX) that unifies SOCMINT, OSINT, and STIX-based CTI using deep …

Bayesian And Multi-Objective Decision Support For Incident Mitigation In Cyber-Physical Systems, Shaofei Huang, Christopher M. Poskitt, Lwin Khin Shar

Research Collection School of Computing and Information Systems

Cyber-physical systems increasingly rely on interconnected physical and digital systems whose security incidents can escalate rapidly into safety and operational failures. Existing decision-support approaches struggle to support incident response because they rely on static assumptions, incomplete vulnerability data, and single-objective risk models that do not adequately capture trade-offs between attack success likelihood, impact severity, and system availability. This paper proposes an adaptive decision-support framework for incident mitigation in cyber-physical systems that integrates hierarchical Bayesian Network modelling, confidence-calibrated exposure estimation, and multi-objective optimisation into a unified, adaptive pipeline. The framework constructs probabilistic models from system architecture and vulnerability data, incorporating complementary …

Towards More Inclusive Ai Systems In Cities, Siew Ying Shee, Orlando Woods

Research Collection School of Social Sciences

Artificial Intelligence (AI) is increasingly embedded in urban infrastructures and governance, shaping how people, spaces, and futures are classified, prioritised, and managed. Yet, most AI systems are developed within a narrow set of linguistic and geopolitical contexts and exported globally, embedding particular epistemic assumptions into diverse urban environments. Even where formal inclusion metrics are met, such asymmetries can render certain populations and realities less legible within algorithmic systems. Prevailing approaches in digital inclusion—centred on fairness metrics, representation, or access—presume technologies as politically inert and bounded. Yet, the adaptive and probabilistic behaviour of contemporary AI disrupts this premise, challenging the idea …