Digital Commons Network^™

Simulation Method For Multi-Crew Construction Processes Based On Large Language Model-Powered Agent, Yifan Wang, Bin Yang, Congjun Wang

Journal of System Simulation

Abstract: Traditional construction simulation methods typically rely on predefined rules or static scheduling mechanisms, making it difficult to simulate interactive decision-making under complex constraints by dynamically adapting multi-crew construction scenarios. As a result, workforce idleness caused by process dependencies and spatial occupation fails to be solved. A simulation method for multicrew construction processes based on a LLM-powered agent was proposed. The distributed crew agents endowed with construction scenario understanding and reasoning capabilities were constructed, as well as a centralized project manager agent. A “single-manager and multiple-crew” decision-making mechanism for multi-agent interaction in construction was designed. Autonomous decision-making and coordination mechanism …

Resource-Efficient Continuous Learning Framework For Edge Real-Time Video Analytics, Shuxia Wu, Junjie Zhang, Delong Chen, Zheyi Chen

Journal of System Simulation

Abstract: By deploying lightweight models at the network edge, edge systems can provide services of real-time video analytics. However, due to the data drift caused by the discrepancy between model training and actual deployment, it is challenging to construct lightweight models that match real-world environments. To address this challenge, a resource-efficient continuous learning framework for edge real-time video analytics (CL4VA) was proposed. A region of interest-granularity predictor for accuracy degradation was introduced to efficiently select key samples from real-time video streams. A two-layer mixed sample pool was constructed to adaptively trigger the model's continuous learning and avoid the issue of …

Reinforcement Learning Based Method For Uav Team Orienteering Optimization Under Multi-Constraint Condition, Can Yang, Kai Chen, Feng Zhu

Journal of System Simulation

Abstract: Traditional optimization methods struggle with efficiency, while reinforcement learning approaches often yield low solution quality and high training costs. In response, this paper proposes an attention mechanism-based reinforcement learning method. A dynamic attention strategy network with multi-information fusion is designed to improve solution quality. A visibility-graph approach is employed to simplify threat zone constraints and speed up convergence, and a decoding sequence reordering mechanism is introduced for further performance optimization of the solution. The simulation results show that the method generates high-quality solutions within milliseconds, achieving total rewards that approach or even surpass those obtained by traditional solvers …

Strike Strategy Planning Method Of Unmanned Ground Vehicles Based On Improved Ppo Algorithm, Bingkun Wang, Yue Wang, Mei Yang, Pengnian Zhang, Bohao Fan, Jie Tang

Journal of System Simulation

Abstract: An improved PPO algorithm based on the hybrid action space and gated recurrent unit (GRU) is proposed to address the limitations of predefined strike rules in maximizing the hitting accuracy of unmanned ground vehicles and the difficult coupling and optimization of continuous motion planning and discrete strike decision-making. The environmental model and target model are built for the process of unmanned ground vehicles' strike missions, coupled with a three-layer model for unmanned ground vehicles that fuses kinematic constraints, situational awareness, and dynamic decision-making. Two distinct policy networks are employed, including the continuous motion planning network for path planning, and …

Knowledge Closed-Loop Driving-Based Intelligent Game Confrontation Simulation, Quan Liu, Yu Wang, Linyue Liu, Hao Chen, Jian Huang

Journal of System Simulation

Abstract: For human-machine intelligence integration and collaborative intelligence enhancement, a “knowledge-model-data-knowledge” closed-loop paradigm for combat simulation is proposed to guide the design of a DRL-based game confrontation simulation architecture. By building a combat priori knowledge-guided DRL agent model, mining and analyzing the time series data of agent interactions generated during simulations, and extracting combat posterior knowledge that expands the cognition boundaries of commanders, the knowledge closed-loop driving mechanism for intelligent combat simulations is achieved. The experimental results indicate that the proposed mechanism can effectively endow the combat simulation system with intelligence growth capabilities, providing valuable reference for the deepening …

Intelligent Decision-Making Method In Imbalanced Air Combat Based On Asymmetric Self-Play, Wei Zheng, Jiahao Tang, Xiaoping Xiong, Xin Fan

Journal of System Simulation

Abstract: To solve the problem of strategy convergence caused by role homogenization in traditional self-play for imbalanced air combat, an intelligent decision-making method based on asymmetric selfplay was proposed. This method decoupled tactics from control by employing a hierarchical reinforcement learning framework and designed differentiated reward functions for advantaged and disadvantaged sides. Bidirectional independent policy pools were constructed to promote the co-evolution of strategies. The proximal policy optimization algorithm was utilized to train the model. Experiments in 1v1 weapon-imbalanced and 2v1 numerically-imbalanced scenarios demonstrate that compared to symmetric self-play, the proposed method increases the kill rate of the advantaged …

Agent-Based Pathfinding Method For Indoor Fire Emergency Evacuation, Ao Tian, Jianqin Zhang, Zheng Wen, Chaonan Hu, Hong Zhao, Bo Shen

Journal of System Simulation

Abstract: To improve emergency evacuation efficiency and reduce casualties in dynamic fire scenarios, a real-time path re-planning method based on agents and dynamic A* algorithm framework is proposed. The behavior and actions of the agent is modeled. A reward function is designed, and an agent-based evacuation framework is constructed. Based on fire simulation data, a dynamic cost network involving parameters such as thermal radiation, smoke visibility, and CO concentration is constructed to achieve spatiotemporally continuous modeling of fire environments. By optimizing the composite cost function through dynamic weight allocation, combined with an improved heuristic function and dynamic search mechanism, local …

Pediatric Inflammatory Bowel Disease Tissue Classification From Pathology Slide Images: Detecting Phenotypes Using Computer Vision, Chloe Martin-King, Ali Nael, Louis Ehwerhemuepha, Blake Calvo, Quinn Gates, Jamie Janchoi, Elisa Ornelas, Melissa Perez, Andrea Venderby, John Miklavcic, Peter Chang, Aaron Sassoon, Brian Rubio, Ghislaine Barrigan, Kenneth Grant

Food Science Faculty Articles and Research

Background and Aims

Methods

Results …

Robust Medical Image Prediction Via Adaptive Reconstruction: Bridging The Gap In Low-Quality Data, Prateek Singhal, Madan Singh

Journal of Informatics and Web Engineering

Medical image prediction plays a very significant role in clinical decision-making and early detection and diagnosis of different diseases. However, the quality of medical images has a huge impact on the predictive models' accuracy. Poor-quality data usually occurs due to problems like noise, artifacts, and low resolution and poses a major challenge for reliable medical image prediction. Framework advances medical image analysis through three novel contributions firstly, A hybrid architecture combining wavelet-based denoising with deep learning (DL) enhancement (unlike existing single-approach methods). Secondly, Cross-modality robustness validated on low-quality CT/MRI/X-rays from real clinics (versus modality-specific solutions), and lastly, A closed-loop system …

Radiology Report Generation Using Deep Learning And Web-Based Deployment For Chest X-Ray Analysis, David Agbolade, Peyman Heydarian, Shakeel Ahmad

Journal of Informatics and Web Engineering

The huge rise in the number of medical images has caused a major problem in radiology departments. Radiologists are now working harder than ever, which affects the quality of their diagnoses and patient care. It takes 15 to 30 minutes to write a manual radiological report for each case, and different people may see things differently. Modern departments process over 230 cases a week, which causes long delays in diagnosis. Automated report generation systems that are already in use have a lot of problems, such as not being able to be interpreted clinically, not having enough Digital Imaging and Communications …

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 …

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 …

Enhancing Fraud Detection In Financial Transactions Using Lightgbm And Random Forest, Wan-Ping Khor, Kah-Ong Michael Goh, Check-Yee Law, Connie Tee, Yong-Wee Sek, Riasat Khan

Journal of Informatics and Web Engineering

In recent years, the frequency and complexity of financial fraud have been rising and have become an urgent challenge for the global financial system. Traditional rule-based detection methods struggle to cope with new types of fraud, especially in terms of real-time detection, generalization ability, and accuracy. To overcome these limitations, machine learning techniques have gradually emerged as a promising solution for identifying fraudulent transactions with better flexibility and scalability. Based on the publicly available European credit card fraud transaction dataset, this study proposes a hybrid model that combines the advantages of LightGBM and Random Forest, aiming to improve the accuracy, …

Vehicle Re-Identification System Using Residual Network With Instance-Batch Normalization, Wei Jie Low, Kah-Ong Michael Goh, Check-Yee Law, Connie Tee, Yong-Wee Sek, Md Ismail Hossen

Journal of Informatics and Web Engineering

Vehicle Re-identification (Re-ID) has become extremely important due to the increasing number of vehicles on the road and its potential to address traffic-related challenges. As a result, there is also a growing need for efficient methods to track and identify vehicles across multiple traffic cameras. One of the biggest challenges of this task is the variations in vehicle appearances across different camera angles. This is because vehicles can appear significantly different when captured from various camera angles and viewpoints. Furthermore, the current vehicle Re-ID solutions typically require extensive coding knowledge, making it inaccessible to many potential users. Therefore, we focus …

Editorial: Intelligent Systems And The Next Wave Of Digital Innovation, Hairulnizam Mahdin

Journal of Informatics and Web Engineering

The field of artificial intelligence (AI), machine learning and intelligent automation has become pervasive in our modern digital world. It extends from business and public services to environmental management and even into people's daily experiences with technology. In this special issue "Intelligent Systems and the Next Wave of Digital Innovation," published in the Journal of Informatics and Web Engineering, reviews several studies to explore the increasing role of intelligent systems in current society and their importance. Some of the more significant areas of discussion are how to formalize the expectations for explainable AI, evaluating face recognition models in the real …

Ai-Driven Malware Analysis And Detection: A Comprehensive Survey Of Techniques, Trends And Challenges, Salman Khan, Hasnat Raza, Mansoor Alam

Journal of Informatics and Web Engineering

Malware represents the most critical threat in cybersecurity, meant to compromise the security for any individual or any organization. These are covert software, designed to perform malicious act like data theft, data alteration, and to interrupt a normal operation of the services. The persistent evolution of malware has called for more sophisticated techniques in its detection and prevention, resulted into direct need of Artificial Intelligence in cybersecurity. Artificial intelligence, using machine learning techniques and rising concepts like neural networks has greatly improved the traditional static and dynamic ways of detecting malware. Advances in AI-driven solutions have made them much more …

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 …

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 …

Editorial Preview For February 2026 Issue, Fong-Yee Lee, Su-Cheng Haw

Journal of Informatics and Web Engineering

The February 2026 issue presents 17 research papers within its regular section, covering a wide range of areas including Artificial Intelligence (AI), Machine Learning (ML), Web and Cloud Technologies, Recommender Systems and Software Engineering. The collection reflects JIWE’s ongoing effort to advance research in the informatics and web engineering domain. A special thematic section, guest-edited by Prof. Ts. Dr. Hairulnizam Bin Mahdin, centers on “Intelligent Systems and the Next Wave of Digital Innovation”. This section studies the pervasive role of AI and machine learning in multiple sectors of industrial automation and disaster preparedness. These studies also emphasize on trust, transparency, …

Efficiency Of Neo4j In Designing And Analysing Graph Database For Air Quality Analysis Of Indian Metro Cities, Jiten Chavda, Kishan Bharvad, Rishap Parmar, Nidhi Arora

Journal of Informatics and Web Engineering

The analysis of the Air Quality Index (AQI) is currently a popular subject in the area of sustainable research, as it is crucial for investigating and analysing the effects of air pollutants on human health in urban environments. It has been identified over the last decade that airborne pollution has become a critical issue and will remain an important concern in India in the coming years. In recent years, a variety of models and algorithms utilizing big data techniques have been developed for the analysis of air quality data. In this paper, we suggest monitoring and feature analysis of air …

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 …

Deep Learning-Based Automatic Detection And Diagnosis Of Tuberculosis From Chest X-Ray Images: A Comprehensive Analysis, Paschal C. Ahanotu, Deborah A. Adedigba, Raza Hasan, Sellappan Palaniappan

Journal of Informatics and Web Engineering

Tuberculosis (TB) continues to be one of the foremost public health issues in the world, and remains the second most salient communicable cause of death after COVID-19. In 2022, TB accounted for 10.6 million new infections and 1.3 million deaths globally. Conventional diagnostic approaches involving sputum smear microscopy, culture assays, and GeneXpert MTB/RIF are characterized by excessive turnaround times, elevated costs, and dependency on specialised infrastructure and trained personnel. These constraints are exacerbated in resource-poor countries, resulting in delayed diagnosis, delayed therapy initiation, and enhanced disease transmission. This work investigates the application of deep learning algorithms to automatically diagnose TB …

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 …

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 …

Test Case Prioritization Using Ant Colony Optimization To Improve Fault Detection And Time, Nurezayana Zainal, Muhammad Sh Salleh, Nur Atiqah Wahidah Sulaiman, Ammar Alazab, Nur Liyana Sulaiman

Journal of Informatics and Web Engineering

Regression testing plays a critical role in ensuring the reliability and quality of software following continuous integration and development. However, executing all test cases during regression testing can be time-consuming and resource-intensive. Test Case Prioritization (TCP) addresses this challenge by determining an optimal execution order of test cases that maximizes early fault detection while minimizing execution time. Optimization algorithms contribute significantly to enhancing the effectiveness of TCP while utilizing limited resources. This study proposes an Ant Colony Optimization (ACO) algorithm to address the TCP problem, leveraging its strength in navigating complex search spaces inspired by the foraging behavior of real …

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 …

An Enhanced Glowworm Swarm Optimization For Minimizing Surface Roughness In Die Sinking Electrical Discharge Machining, Nurezayana Zainal, Muhammad Ammar S.M. Shahrom, Mohamad Firdaus Ab. Aziz, Salama A. Mostafa, Anis Farhan Kamaruzzaman, Nor Bakiah Abd. Warif

Journal of Informatics and Web Engineering

Electric Discharge Machining (EDM) is a non-traditional machining process that utilizes electric sparks between an electrode and a workpiece submerged in a dielectric fluid to ablate a material. It is commonly used in die-making, aerospace, automotive manufacturing, and medical manufacturing, because it can machine hard and complex materials with high precision. In this work, a Surface Roughness Optimization for EDM (SRO-EDM) model is proposed to investigate the machining performance of the die-sinking EDM process of titanium alloys. A regression-based combined approach of Glowworm Swarm Optimization (GSO) and a Two-Factor Interaction (2FI) model has been proposed to investigate the impact of …