Digital Commons Network^™

Hybrid Sentiment Analysis Model For Customer Feedback Interpretation Using Lexicon, Machine Learning And Deep Learning Techniques, Jou Jia Yi, Lew Sook Ling, Tan Li Tao

Journal of Informatics and Web Engineering

Customer feedback is pivotal in enhancing service quality and user satisfaction across digital platforms. However, traditional sentiment analysis methods often struggle with informal languages, contextual nuances, and aspect-specific opinions. In this paper, a hybrid sentiment analysis framework is proposed, utilizing lexicon-based (VADER), machine learning (Support Vector Machine and Random Forest), and deep learning (BERT) techniques to achieve improved sentiment classification accuracy and interpretability compared to previous studies. The framework incorporates advanced preprocessing techniques, such as emoji normalization, handling of negation, and detection of intensifiers, to better capture emotional information in user-generated content. The objectives of this study are to develop …

Ontology-Based E-Commerce Recommender System: A Hybrid Semantic Filtering Approach, Jocelyn Pua, Su-Cheng Haw, Lucia Dwi Krisnawati, Shaymaa Al-Juboori, Gee-Kok Tong

Journal of Informatics and Web Engineering

The rapid growth of e-commerce has led to product overload, which has resulted in personalized product discovery becoming a crucial problem for consumers. Classic recommender systems (RS), which rely heavily on content-based filtering or collaborative filtering, tend to face well-known problems such as cold start, data sparsity, and ignorance of semantics. Such constraints frequently result in irrelevant or redundant suggestions, thereby lowering the user satisfaction and conversions. This study presents a hybrid ontology-based e-commerce recommender system that combines symbolic reasoning with deep semantic matching. The system is based on a Neo4j graph database to capture structured product relationships and is …

Optimised Data Integration Using Transformer Model And Resource Description Framework, Jerome Aondongu Achir, Muhammad Abdulkarim, Mohammed Abdullahi

Journal of Informatics and Web Engineering

Organizations have become highly reliant on a range of data sources that span structured, semi-structured, and unstructured data types. These repositories allow large-scale storage for faster ingestion and analytics but pose tremendous challenges of integration owing to schema and contextual differences. Traditional data integration methods, such as the ontology-based Resource Description Framework (RDF), are often inadequate when dealing with these challenges. They specifically struggle with the dynamic evolution of the schema of data sources, context-aware interpretation, and achieving interoperability across heterogeneous data sources. This paper presents an integrated system that augments resource description knowledge with token embeddings using the attention …

Xai -Driven Explainability For Cardiovascular Diseases Prediction, Jacqueline Dike, Jarutas Andritsch

Journal of Informatics and Web Engineering

The adoption of artificial intelligence (AI) in cardiovascular disease prediction has significantly improved risk stratification, offering new avenues for early diagnosis and preventive care. With the growing availability of electronic health records and structured clinical datasets, machine learning (ML) and deep learning (DL) models have demonstrated strong predictive capabilities. However, despite their performance, its adoption in healthcare is often constrained by the lack of transparency and interpretability in many ML and DL models. This lack of explainability undermines clinical trust and raises ethical concerns. In high-stakes domains such as CVD prediction, clinicians require not only accurate outputs but also clear …

Sentiment-Based Music Recommendation System Using Natural Language Processing For Emotion-Aware Song Suggestions, Yu Bui Xuan, S. Prabha Kumaresan, Naveen Palanichamy, Mohamed Uvaze Ahamed Ayoobkhan

Journal of Informatics and Web Engineering

Music plays a vital role in influencing emotions, mood, and mental health. However, classical music recommendation systems mostly rely on listening to history, genre preference, or popularity, ignoring the listener’s mood. Motivation to go further in this area gave birth to this study, where a new sentiment-based music recommendation system is being designed by incorporating Natural Language Processing (NLP) and Machine Learning (ML) techniques to provide emotion-aware song recommendations. The system collects various audio features such as valence, energy, tempo, and danceability from music distribution platforms such as Spotify, which are well-known indicators for classifying the emotional tone of a …

A Unified Cloud-Based System For The Tyre Retail Industry: Design And Implementation Of Tracktive, William Theo Wei Loon, Siew-Chin Chong, Kian-Ming Lim, Lee-Ying Chong, Kuok-Kwee Wee

Journal of Informatics and Web Engineering

In the Malaysian concurrent tyre retailing industry, most of the business activities are carried out in the traditional labour-intensive ways. This manual process is prone to inefficiencies, increases operating costs and increases the likelihood of error. The current paper outlines Tracktive, a unified and cloud-based solution that was conceptualized and built to automate the industry by centralizing the ordering of tyres, inventory and order management to a single cloud-based platform. Tracktive is built on microservice architecture and event-driven architecture and deployed with the help of a simulated cloud environment. All the core functional modules are independent, scalable modules, each having …

An Empirical Evaluation Of Machine Learning Methods And Text Classifiers For Sentiment Analysis Of Online Consumer Reviews, Pei Qin Lo, Sew Lai Ng, Li-Xian Jiao

Journal of Informatics and Web Engineering

This study aims to identify the best predictive model for analysing online product reviews (OPRs) in the electronics industry, with a secondary focus on leveraging unstructured customer feedback to support product improvement. Using a dataset of 9,675 Oppo mobile phone reviews, this study employs three classification models—Random Forest, Support Vector Machine (SVM) and Logistic Regression–paired with Term Frequency-Inverse Document Frequency (TF-IDF) or bidirectional encoder representation transformer (BERT) as the embedding models to analyse customer sentiment and derive actionable insights. The methodology features a comprehensive analysis pipeline that includes text preprocessing with the Natural Language Toolkit (NLTK), feature extraction using) vectorization …

Real-Time Posture Monitoring For Effective Exercise Using Mediapipe Python, Wan Izzul Wafiq Wan Noor Asmawi, S. Prabha Kumaresan

Journal of Informatics and Web Engineering

Maintaining proper posture during exercise is crucial for preventing injuries and maximizing workout efficiency. This project aims to develop a real-time posture monitoring system using MediaPipe and OpenCV to provide instant feedback on the exercise form. The system captures video input through a webcam, processes it using OpenCV, and utilizes MediaPipe’s pose-estimation model to detect key body landmarks. By analysing the joint angles and comparing them to predefined optimal postures, the system evaluates the user’s form and provides corrective feedback in real time. This approach eliminates the need for expensive wearable sensors, making posture monitoring more accessible and user friendly. …

Design And Implementation Of A Web-Based Lecture Timetable Scheduling System, Suraj Abubakar, Mohammed Kabir Dauda, Maryam Abdullahi Musa, Bala Muhammad Muhammad

Journal of Informatics and Web Engineering

Timetable management is a controversial but core administrative activity at tertiary learning institutions, normally saturated with timetabling clashes, resource wastage, and communication breakdowns. This study describes the development and validation of a Web-Based Lecture Timetable Scheduling System (WLTSS) for Abubakar Tafawa Balewa University (ATBU) Department of Computer Science. The system was implemented to automatically and effectively build, modify, and publicize lecture timetables. On Hypertext Markup Language (HTML), Cascading Style Sheets (CSS), JavaScript, and React as frontend technologies, the system provides an adaptable interface with real-time data updates through the Reacts component model foundation. MySQL and Node.js are used to run …

Flood Disaster Preparedness And Response Using A Web-Based Integrated Flood Management System (Ifms), Mohamed Abdiraman Abdirahim, Abd Samad Bin Hasan Basari, Malik Bader Alazzam, Muhammad Shukri Bin Che Lah, Rabiah Binti Ahmad, Aida Binti Mustapha

Journal of Informatics and Web Engineering

Floods are one of the most frequent and damaging natural hazards in Malaysia, especially in low-lying places, such as Batu Pahat and Johor. National flood monitoring systems, such as InfoBanjir and Malaysian Meteorological Department (MET) Malaysia, have disjointed data pools, late updates, and inadequate public access. In this paper, we present an IFMS, which is a contemporary web platform developed to integrate national flood management systems through data collection, automatic processing, dynamic visualization tools, and others. The system architecture consists of three main layers: IoT-enabled flood sensors, centralized web server, and responsive user interfaces. Backend processing is performed using Laravel, …

Evaluating Accuracy Latency And Robustness Of Face Recognition Models For Real-Time Web Applications, Alani Fatini Sharzizi, Nur Afiqah Sahadun, Abdulkadir Hassan Disina, Harinda Fernando

Journal of Informatics and Web Engineering

Face-recognition technology is one of the most important advancements in the field of computer vision. They play a crucial role in many applications, including biometric authentication, surveillance, online security, and interactive web systems. The use of web-based solutions is increasing continuously. Therefore, accurate and fast recognition models employing few resources are required in real-world applications. However, because of the challenges related to such environments, including the lighting, occlusion, pose, and computing power of client devices, it is difficult to ascertain which model will be most successful in a real-life scenario. The purpose of this research is to compare four deep …

Project G.L.E.N. (Enhancing Tornado Research: A Foundation For Transistioning The Enhanced Fujita Scale), Alexis Peshehonoff, Makaylei Huggins, Bernard Muich, Nicholas Fazzolari, Liam John, Harris Meyer, Jakob Graudons, Nicholas Krasznavolgyi, Stephen Syvertson, Aditya Bhattacharya, Kai Hong, Augustine Balish, Zachary Ort, Erin Rhoden

Student Research Symposium (SRS)

"Project G.L.E.N., formally known as “Enhancing Tornado Research: A Case for Transitioning the Enhanced Fujita Scale,” is an ongoing initiative aimed at advancing tornado research through the development and deployment of an unmanned tornado probe. Through the Gale Level Environmental Navigator (G.L.E.N.) probe, a remote-controlled unit equipped with specialized meteorological sensors and innovative design features, we aim to collect real-time, onsite data from within tornadoes. This approach allows for direct measurement of internal atmospheric conditions such as wind speeds, pressure fluctuations, temperature, humidity, and other storm dynamics while removing human risk from tornado interception, significantly enhancing the safety of storm …

Investigation Of Biofilm Formation And Osmotic Stress Tolerance Of Nonmotile E. Coli Mg1655 In Simulated Microgravity, Tyler Jenkins

Student Research Symposium (SRS)

Simulated microgravity presents multiple physiological stressors for astronauts, including bone demineralization, muscle loss, and weakening of the immune system (Chavez et al., 2024). Bacteria serve as viable model organisms to study physiological changes because of their abundance in the human digestive system and skin. Motility in bacteria plays a significant role in cell survival, but few studies have been conducted on microgravity’s effect on motility phenotypes. While motile strains like K12 have been studied extensively under microgravity conditions (Topolski et al., 2022; Chavez et al., 2024), the effects of simulated microgravity on non-motile strains are largely unknown. To better understand …

An Ai Approach To Lunar Phase Detection: Enhancing The Identification Of The New Crescent With Astronomical Data Integration, Murad Al-Rajab, Samia Loucif, Raed Abu Zitar, Mubarak Gwaza Abdu-Aguye

All Works

Introduction: The observation of the lunar crescent is significant in astronomy, cultural traditions, and religious lunar calendar determinations. However, earth-based imaging that captures all lunar phases, particularly the new crescent across multiple months, remains limited. This study explores the feasibility of using artificial intelligence (AI) techniques to detect and analyze the birth of the new lunar crescent using space-borne imagery from NASA’s Lunar Reconnaissance Orbiter (LRO), spanning over 13 years. Methods: This study evaluates both deep learning and traditional machine learning approaches for new crescent detection. Convolutional Neural Networks (CNN), Random Forests (RF), and Support Vector Machines (SVM) were applied …

Re: Comment Letter For The Butte Priority Soils Operable Unit (Bpsou) 2026 Revised Draft Final Backfill And Cover Soil Quality Assurance Project Plan (Qapp) (Dated January 5, 2026), Molly Roby

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Who Lets Ai Take Over? Cross-National Variation In Willingness To Delegate Socially Important Roles To Artificial Intelligence, Ala Yankouskaya, Mohamed Basel Almourad, Magnus Liebherr, Fahad Beyahi, Guandong Xu, Raian Ali

All Works

Delegating socially significant roles to artificial intelligence (AI) is an emerging reality, yet little is known about how publics evaluate this transfer of responsibility across contexts and countries. This study applied a structural model to a large cross-national dataset (30,994 individuals in 35 countries) to test how cognitive appraisals, affective dispositions, and contextual factors jointly shape willingness to delegate socially important roles of companionship, mental health advisor, doctor and teacher to children to AI. The results revealed a robust hierarchy of delegation preferences, with companionship most frequently entrusted to AI, followed by mental-health advisor, teacher, and doctor. Cognitive appraisals emerged …

Emerging Threats In Ai: A Detailed Review Of Misuses And Risks Across Modern Ai Technologies, Niyat Seghid, Farkhund Iqbal, Khalifa Al-Room, Áine Macdermott

All Works

The swift evolution of artificial intelligence (AI) has enabled unprecedented capabilities across domains, while simultaneously introducing critical vulnerabilities that can be maliciously exploited or cause unintended harm. Although multiple initiatives aim to govern AI-related risks, a comprehensive and systematic understanding of how AI systems are actively misused in practice remains limited. This paper presents a systematic review of AI misuse across modern AI technologies. We analyze documented incidents, attack mechanisms, and emerging threat vectors, drawing from existing AI risk repositories, prior taxonomies, and empirical case reports. These sources are synthesized into a unified analytical framework that categorizes AI misuse across …

Nonenzymatic Glucose Biosensor Based On Selective Reduction Of Silver Ions, Maryam Sarlak, Shankar B. Rananavare

Chemistry Faculty Publications and Presentations

A nonenzymatic conductivity-based glucose biosensor, reported here, designed for potential noninvasive measurement in accessible biofluids such as saliva, sweat, and tears. The sensor exploits a classic Tollens’ silvering process that deposits metallic silver on a sensitized surface via a quantitative, selective reduction of silver ions by glucose. Of the two surface sensitization methods used, the self-assembled bifunctional thiol/silane surfactant yields a more stable and sensitive sensor than tin(II) chloride. The conductometric sensor consists of two spiral interdigitated silver electrodes fabricated by lithography and wet etching. The sensor impedance changes after selective metallic silver deposition between the electrodes. The sensor exhibits …

An Autonomous Robotic System For Object Retrieval And Delivery: Enhancing Independence For Users Living With Disability And Older Adults, Jincheng Li, Chenghao Lin, Amna Mazen, Youssef A. Bazzi

Michigan Tech Publications

As the global population ages, there is a growing need for assistive technologies to help older adults maintain their independence. This work presents a cost-effective autonomous socially assistive robot designed for object retrieval and delivery, enhancing accessibility in home environments. The system is built on the Robot Operating System (ROS) framework and integrates three key components: the Pioneer P3-DX mobile robot for autonomous navigation, the ReactorX-200 robotic arm for pick-and-place operations, and the Kinect v2 RGB-D camera for object detection and localization. Users interact with the robot through natural language processing by issuing voice commands to retrieve various objects. Microsoft …

Exploring The Efficacy Of Storytelling In Education About Climate Change, Nina Adjanin, Srijana Katuwal, Heidi Sevestre, Gordon Brooks

Mid-Western Educational Researcher

From bedtime tales to digital narratives, stories wield unparalleled influence, shaping perceptions, and fostering empathy. Through mediums like social media and digital platforms, storytelling becomes a tool for advocacy, amplifying voices, and catalyzing social change. In this qualitative study, 107 higher education students from diverse backgrounds participated. The purpose of this study was to uncover salient beliefs about climate change stories on popular social media platforms among full-time college students. The results are based on three key components of the theory of planned behavior (TPB): affective beliefs, normative beliefs, and control beliefs. The respondents expressed both positive and negative attitudes …

News Briefs, Marvin H. Jeter Iii, Joyce Shaw

Mississippi Libraries

News updates from Gunter Library at the Gulf Coast Research Laboratory and from the Mississippi Library Commission.

Long-Range Fit: A Software Package For The Representation And Study Of Long-Range Molecular Interactions, Adrian L. Batista-Planas, Ernesto Quintas-Sánchez, Richard Dawes

Chemistry Faculty Research & Creative Works

Describing intermolecular forces is fundamental to modeling and predicting the behavior of molecular systems. In particular, long-range molecular interactions with electrostatic, induction, and dispersion as the main components play a critical role, especially for low-temperature and low-density regimes. Long-range interactions are often described through perturbation theory, representing the electronic charge distribution via a multipolar series of the moments and polarizability tensors corresponding to each molecule. However, while the theory is well established, obtaining the resulting analytical expressions (and their practical implementation) constitutes a highly complex and system-dependent task. To address this challenge, we developed long-range-fit (LRF), an interactive and user-friendly …

Cybersecurity In Higher Education Institutions: Awareness, Policy, And Experience On Employee Behaviour, Abdullahi Abiodun Yusuf, Adriana A. Steyn

Journal of Cybersecurity Education, Research and Practice

The digital transformation of higher education institutions (HEIs) has introduced unprecedented connectivity and operational efficiency, but it has also heightened their exposure to cyber threats. South African HEIs, in particular, face increasing vulnerability due to their reliance on technology, openness, and diverse user communities. This study examines the influence of the institutional cybersecurity environment on employee cybersecurity-compliant behaviour (CCB), emphasising the critical roles of awareness, policy engagement, and experience. Drawing on Protection Motivation Theory and the Theory of Planned Behaviour, a conceptual model was developed to explore how cybersecurity awareness, policy familiarity, and prior experience shape employees’ attitudes, subjective norms, …

Early Detection Of Infectious Diseases: A Review Of Recent Advances In Pathogen Identification, Molecular Tools, And Metabolomics-Driven Biomarker Discovery, Patrica Ivy Agorsor, Michael O. Eze

Chemistry Faculty Research & Creative Works

The recent COVID-19 pandemic has heightened public interest in noninvasive methods for early diagnosis of infectious diseases. In addition, various government agencies have implemented "infectious disease preparedness" to mitigate future outbreaks. This review highlights conventional and advanced methods for infectious disease diagnosis with an emphasis on emerging mass spectrometry methods. Conventional methods for pathogen identification, such as culture-based techniques and molecular methods, have limitations with respect to sensitivity, specificity, and turnaround time. Recent advances in high-resolution mass spectrometry have revolutionized the field of infectious disease biomarker discovery. These techniques enable the comprehensive profiling of metabolites in various biological samples, identification …

Silver Bow Creek Conservation Area Final Principles For A Greener Cleanup Report, Woodard & Curran

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Time And Water, Sheila Nayar

Journal of Religion & Film

This is a film review of Time and Water (2026), directed by Sara Dosa.

On The Misattribution Of Reassurance: A Structural Account, Griselda Poe

Publications and Research

This paper challenges the conventional assumption that empathy generates reassurance in interpersonal services. Reassurance is treated not as an emotion transmitted from the outside, but as an internal state transition that arises when a fixed and erroneous world model—a False Fantasy—undergoes collapse and the world becomes computable again. The study identifies a systematic misattribution pattern where providers and receivers treat empathy as a causal mechanism rather than a post hoc explanatory label. By introducing Base AI as an external reference—a system capable of providing structural information without emotional modulation—this paper demonstrates that reassurance is generated through operations such as distraction …

Responsible Ai In Teaching And Learning, Asa B. Stone, Mark C. Stone, Derek M. Heeren, Santosh Pitla

PRAIRIE: Pioneering Responsible AI for Research, Innovation, and Education

The rapid adoption of generative artificial intelligence (AI) in higher education presents both transformative opportunities and significant pedagogical risks. While AI tools are becoming embedded in academic and professional environments, their integration into teaching and learning raises critical questions about cognitive engagement, academic integrity, equity, and skill development. This white paper proposes a principled framework for the responsible integration of AI in higher education, grounded in the dual commitment to AI literacy and the cultivation of durable skills.

The framework articulates six core principles: purposefulness; transparency; integrity and attribution; critical AI literacy; equity and access; and privacy and data protection. …

Future Trends In Cybersecurity: A Meta-Review, Yara Mohammed, Manar Alsaid, Gahangir Hossain

Faculty Publications

The increasing importance of cybersecurity in protecting digital assets, data and infrastructures necessitates a reevaluation of research priorities within the discipline. As of today, numerous emerging cybersecurity topics are gaining significant importance in both academic research and industry applications. To identify recent trends in cybersecurity topics, this study extracts scholarly articles from two prestigious academic databases, the ACM Digital Library, and Google Scholar, covering the period from early 2015 to late 2024.Through a systematic identification of trends and focal points in cybersecurity research, a comprehensive analysis is facilitated, including Latent Dirichlet Allocation (LDA), Biterm Topic Modeling (BTM), keyword frequencies, and …

The Behavior And Complexation Of Technetium Under Conditions Relevant For Nuclear Fuel Reprocessing, Rachel E. Greenberg

Dissertations, Theses, and Capstone Projects

Spent nuclear fuel is reprocessed by utilizing several large-scale solvent extraction processes. One of the most prevalent of these processes is the plutonium uranium reduction extraction (PUREX) process. This process involves an organic phase composed of tributyl phosphate (TBP) in kerosene and an aqueous phase comprised of concentrated nitric acid. Both uranium (U) and plutonium (Pu) are extracted into the organic phase by nitrate and TBP complexation while the various fission products remain in the initial aqueous phase. Pu is then chemically reduced to its trivalent state and back-extracted into a second aqueous phase, thereby separating the two fissile materials. …