Journal of Informatics and Web Engineering
The Journal of Informatics and Web Engineering (JIWE) is a peer-reviewed journal that advances the engineering of user-centric, web-native information systems. We published original research, reviews, and case studies that unite informatics with rigorous web-engineering methods across the full lifecycle, from requirements and design to deployment and evolution. eISSN: 2821-370X | Publisher: MMU Press | Access: Open | Frequency: Triannual (February, June & October) effective from 2024 | Website: https://journals.mmupress.com/jiwe Indexed in:
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Synthetic Data Generation for Healthcare Machine Learning: A Case Study in Vital Signs and Diagnostic Predictions
Healthcare Machine Learning (ML) applications face significant challenges in accessing high-quality training data due to stringent privacy regulations, institutional data silos, and concerns over patient confidentiality. This paper explores synthetic data generation as a viable and privacy-preserving alternative to real patient data for developing ML models in healthcare settings. We present techniques for generating realistic vital signs data including body temperature, blood pressure, heart rate, respiratory rate, and oxygen saturation according to appropriate statistical distributions. In addition, we demonstrate how synthetic datasets generated can be used to train diagnostic prediction models. The generated datasets were applied to multiple diagnostic prediction tasks such as hypertension, fever, Chronic Obstructive Pulmonary Disease, atrial fibrillation, and diabetes mellitus. Experimental results reveal that ML models trained solely on synthetic data achieved comparable predictive performance to those trained on real datasets for conditions with explicit physiological manifestations. In particular, gradient boosting classifiers attained an Area Under the Curve (AUC) of up to 0.89 in predicting hypertension. We also illustrate that augmenting sparse real patient data with artificial samples preserves model accuracy at the expense of decreased reliance on sensitive data. This method has great potential to satisfy healthcare organizations who are interested in creating stable ML applications without compromising on privacy standards like Health Insurance Portability and Accountability Act (HIPAA) and General Data Protection Regulation (GDPR).
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AI-Powered File Security System with Facial Biometrics, QR Code, and OTP Verification
Commonly used file-sharing systems suffer from impermissible security risks such as impersonation, credential theft, and QR code exploitation, which can expose sensitive information. This paper describes and evaluates an AI-driven secure file-sharing system designed to alleviate these risks through a multi-layered authentication strategy. The complete system involves AES-128 encryption, unique QR code referencing, live facial recognition, and One-Time Password (OTP) confirmation. In our proposed workflow, the sender encrypts a reference to the file and links it to facial data of the recipient, if applicable, and embeds this information into a QR code, that is dynamically generated during the send operation. To gain access to the file, the user must scan the QR code, undergo live biometric access verification, and subsequently enter an OTP sent to their email address. Experimental results give confidence in the high level of efficacy and robustness of the system: The facial recognition component achieved 99.0% facial recognition accuracy under optimal conditions and achieved 100% rejection against static image spoofing attacks. The average end-to-end server-side latency was about 3.25 seconds, confirming the system would be viable for file sharing. This method combines strong encryption with two-factor identity verification to guarantee confidentiality, mitigate the use of reusable tokens, and provide a secure way to share sensitive documents across a range of sectors, such as healthcare, education, and legal services. The next iteration will be enhanced with liveness detection, dynamic QR codes, and an app to facilitate the entire process of verification, tokenization, and share sensitive documents.
An Integrated YOLOv8 Based System for Real Time Vehicle Tracking and Identification Using CCTV Footage
Vehicle identification and tracking play a critical role in security and surveillance, supporting the efforts of law enforcing authorities to prevent crime, monitor traffic, and enforce the vehicle rules and regulations. This research presents a proof-of-concept system that employs YOLOv8 for real time vehicle detection and car type classification using live or recorded CCTV footage. YOLOv8 was chosen after rigorous comparison, it was chosen for its ease of use and efficient image training capabilities, making it well suited for rapid prototyping that the concept needed. The system is designed to extract key vehicle features, including colour, model of the vehicle, logo, sticker (if any), its position and license plate information, and supports both real time and recorded video analysis. Although the model has not been fine-tuned, its performance provides a solid foundation for experimental development. The current implementation is at an early experimental stage and has been tested on a personal computer and is not yet ready for commercial deployment in the real world. For training and evaluation, a custom dataset was created using 5 hours of CCTV footage recorded from three different cameras at varied angles and lighting conditions, including both morning and evening scenes to ensure data diversity.
Enhancing Air Traffic Management Using Spatio-Temporal Deep Learning Predictions
Modern air traffic management faces growing challenges, particularly at busy airports that must handle high flight volumes in limited airspace corridors. The study is especially pertinent for such airports, where daily operations must navigate intricate air traffic networks. To overcome these challenges, innovative approaches are needed to predict and manage congestion effectively which remains a major challenge. The paper presents a Spatio-temporal analytical framework for air traffic flow forecasting, utilizing a dual deep-learning architecture that combines network topology evaluation with time-series analysis. The model contains two major components. The first component employs Graph Neural Networks (GNNs) to model spatial relationships between interconnected flight routes, which are represented as a graph. The second uses Recurrent Neural Networks (RNNs) to analyse temporal patterns in flight delays and traffic density variations. By integrating these approaches, the framework uniquely accounts for both geographical distribution and time-dependent fluctuations in air traffic congestion. Tests with real-world flight data confirm that the model outperforms traditional methods, delivering higher accuracy in identifying high-congestion routes and predicting peak demand periods, thereby enabling more efficient traffic flow management. These improvements enhance operational efficiency at key air traffic hubs, potentially reducing delays and optimizing resource allocation across airspace sectors.
Enhanced Trust-based Security Mechanism to Prevent Temporal DoS Vulnerabilities in IPv6 Link-Local Networks
Many computer networks in operation today currently use both IPv4 and IPv6 stacks. On the other hand, there is a transition towards IPv6-only networks as a result of the limited availability of IPv4 addresses. The primary protocol for link-local IPv6 communication is the Neighbor Discovery Protocol (NDP). Regrettably, its insecure design and basic scope-based security mechanisms make the local network susceptible to insider threats. The Internet Engineering Task Force’s recommended security mechanism for NDP, which is Secure Neighbor Discovery (SEND), is well documented but complex and unsuitable for resource-constrained devices and networks. Trust-ND was positioned as an alternative to SEND as a lightweight trust-based distributed approach using the NDP extension headers. However, its timestamp design and utilization render it susceptible to temporal DoS vulnerabilities. Therefore, this research proposes eTrustND to improve the Trust-ND mechanism for securing IPv6 link-local networks from insider attacks by addressing the existing vulnerabilities by modifying the timestamp reference, format, precision, and validation rules. This paper documents the methodology, the experimentation, and the resulting outcome that show eTrustND eliminates Trust-ND’s temporal DoS vulnerabilities without adding computational and protocol overhead. It also highlights the challenges and best practices of timestamp design and usage in security mechanisms and protocols.
Analysis and Predictive Modelling of EV Charging Patterns and User Behaviour
As more Electric Vehicles (EVs) are released, the ability to predict energy consumption and charging duration becomes crucial in building optimal infrastructure and a proper system of managing energy. This paper proposes a machine learning model that predicts these two important parameters using a real-world dataset. The dataset consists of 1320 EV charging sessions made between January and February 2024 on Kaggle. The data set includes vehicle specifications, time stamps of sessions, environmental conditions, and user behaviour. Feature engineering tasks followed a thorough preprocessing procedure where missing values were imputed, outliers were removed, and the type of data was converted, and included time-based transformations, interaction terms, station popularity measures. Three regression models were developed: Light Gradient Boosting Machine (LGBM), Random Forest (RF), and Support Vector Regression (SVR) to evaluate different modelling approaches and test the predictive efficacy of ensemble against kernel-based methods. These models were trained and tuned using GridSearchCV combined with TimeSeriesSplit cross-validation to maintain temporal integrity. Evaluation metrics included Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R²). Results showed that RF achieved the highest accuracy in predicting energy consumption with an R² of 0.6620, while LGBM performed best in predicting charging duration with an R² of 0.9152. Final testing on unseen data validated the generalization capabilities of these models. The findings support practical infrastructure recommendations and demonstrate the potential of machine learning in enhancing EV charging operations.
Machine Learning-based Prediction of House Sale Prices in Hulu Langat
House price prediction remains a complex task due to the interplay of structural, locational, and economic factors. This study proposes a machine learning-based predictive framework tailored to the residential real estate market in Hulu Langat, Malaysia, a district undergoing rapid urbanization. Using open-source datasets, three structured variations are developed: one containing only housing and locational attributes, another incorporating regional-level macroeconomic data, and a third combining both regional and national macroeconomic indicators. The proposed model (Ensemble Weighted Average) is trained and evaluated using evaluation metrics, then compared with models such as Random Forest, XGBoost, LightGBM, and Ensemble Stacking. The proposed model trained solely on housing and locational data achieved the highest accuracy, outperforming all other models across most evaluation metrics, while XGBoost achieves the fastest computation time. Models trained with the inclusion of macroeconomic indicators consistently underperforms, suggesting that macroeconomic indicators added noise to model prediction, potentially due to spatial resolution mismatches or multicollinearity. The interpretability of the best-performing model was further enhanced with SHapley Additive exPlanations (SHAP), the resulting SHAP analysis reveals that land parcel area, property type, and local housing supply are the top contributing features to model’s performance. These findings validate the effectiveness of ensemble models for localized price prediction and highlight the importance of house attributes over broader economic trends. The proposed framework yields a practical and interpretable approach to house price prediction and may assist policymakers, developers, and planners in making informed decisions.
Machine Learning for Employee Attrition Prediction: A Comparative Study
Employee turnover remains a long-standing issue that can lead to significant economic losses and a decline in workplace productivity. Traditional methods for predicting natural attrition, such as surveys and rumour assessment, usually fail to capture the complexity and multi-factorial nature of natural attrition. To address this limitation, this paper proposes to develop an advanced prediction model using Machine Learning (ML) algorithms to enhance the accuracy and reliability of export prediction. These methods are applicable to a full set of data covering parameters such as demographics, workplace performance and survey scores. The objective of this model is to provide a data-driven model that can identify employees at risk of leaving the organization, thereby enabling intervention at the right time and place. The objectives in the paper are to identify employees who are more likely to leave due to work and social experience, second to understand the main variables that lead to employee turnover, and finally to make the model effective in reducing employee turnover and improving employee satisfaction. In this paper, a ML-based approach is introduced to enhance turnover prediction accuracy by integrating comprehensive features and addressing class imbalance demonstrating improved performance and interpretability as compared to previous studies. Through the application of these ML methods, this paper is beneficial to both academic research and practical practice in human resource management. The results of the paper emphasized the data analysis and ML opportunities, to help solve the problem of employee retention, and create a more close, more effective, more motivated staff. In this paper, the proposed ML model performs well in predicting employee turnover by effectively integrating key features as compared with traditional methods, this method has a higher accuracy rate.
Design of a CNN–NLP Based Visual Chatbot for e-Commerce Systems
E-commerce platforms have limitations to assist interactive and highly personalized experiences using conventional text or voice-based chatbots, particularly in context-aware areas where product discovery and visual identification are necessary. Hence, we propose to integrate an image as a visual-based chatbot. We present a conceptual framework that combines Convolutional Neural Networks (CNN) for image recognition with Natural Language Processing (NLP) for dynamic, context-aware search in a chatbot. The CNN model enables the recognition of products from user-uploaded images, while the NLP component processes and generates appropriate responses to enhance the shopping experience by providing suggestions. This hybrid system will be developed using the Python programming language, which uses libraries such as Keras, OpenCV, and Flask. By allowing users to interact with a chatbot that uses visual inputs, this system aims to create a more intuitive, personalized e-commerce experience that could lead to high engagement and customer satisfaction. The evaluation metrics are measured in terms of the usage of this system by three users in real-world e-commerce applications. This small-scale test may offer insights into how image-based communication can revolutionize the online shopping experience by analysing usability, interaction efficiency, user satisfaction, engagement behaviour, and system responsiveness in practical scenarios during testing.
AI-Powered Threat Hunting for Email Phishing Attack Detection Using Natural Language Processing (NLP)
Phishing attacks remain as a significant cybersecurity threat, aiming to steal sensitive information by exploiting human vulnerability. Traditional phishing email detection often struggles to keep up with the latest attack strategies developed by the attackers which results in high false positive rates and the limited contextual understanding on the email contents. Therefore, to address these challenges, this research proposes a solution via an AI-powered threat-hunting model integrating Natural Language Processing (NLP) techniques for phishing email detection in English through the development of PhishGuard AI application. The application is developed as a web-based software solution designed to be accessible to both users with and without technical expertise. The model leverages Word2Vec with TF-IDF weighting for feature extraction and uses an XGBoost classifier. A comprehensive testing process using various metrics will evaluate the computational efficiency and effectiveness of the model. The model's robustness and generalisability were rigorously tested using two distinct datasets which are CEAS_08.csv for in-distribution training and SpamAssasin.csv for out-of-distribution evaluation. The primary value of this model lies in its proactive threat-hunting capability, which distinguishes it from reactive systems that rely on known threat examples. The findings derived from the study aim to enhance to the domain of phishing email detection and contributing to the development of a more robust cybersecurity solution that can help in safeguarding both the individuals and organisations safety in our country.
Analysis of Social Media Trends for Political Election Predictions
The traditional opinion polls are losing the capability to forecast election results because of the biasness in the sampling and the slowness in updating the polls. Although social media has a lot of real-time data, it has a considerable number of drawbacks, such as noise and demographic bias. This paper shows a new end-to-end forecasting pipeline, which is evaluated on a corpus of 1.75 million tweets related to the 2020 election in the U.S. Our approach adopts a dual-path sentiment extracted (VADER & RoBERTa) for a better accuracy and a new state level feature engineering to fix data bias. This plan transforms raw scores into 14 relative indicators, including sentiment differentials and volume ratios of tweets, which adjusts the regional activity imbalances. Average state-level prediction of a tuned Gradient Boosting Tree (GBT) classifier trained using these features was 70.6 per cent (ROC-AUC 0.69). Importantly, the cumulative prognosis was an exact duplicate of the Electoral College 306-232 majority. The feature analysis established that our relative indicators, especially the tweet volume ratio, were the strongest predictors that we engineered. The objectives in this paper are to present an alternative to traditional polling that is both powerful and easy to interpret in terms of bias reduction due to these relative characteristics. This framework has shown that it is a scalable, real-time process of political forecasting that has attained its goals of capturing the dynamics of the electoral process where conventional methods fail to do so.
AI-Powered Dynamic Encryption and Decryption Defense Model
Static cryptographic systems that rely on fixed algorithms and predetermined keys have proven increasingly ineffective in addressing today’s adaptive and AI-powered cyber threats. This paper proposes an Artificial Intelligence (AI)-enabled dynamic encryption and decryption defence model designed to enhance cybersecurity through real-time threat classification and context-aware cryptographic response. The framework combines the Suricata intrusion detection engine with a Random Forest model developed using the CIC-IDS2017 dataset, allowing it to identify and categorize network anomalies into unified groups, including Denial-of-Service (DoS), Distributed Denial of Service (DDoS), Brute Force, and PortScan. Once threats are identified, the system dynamically selects an appropriate encryption scheme, which is AES-128, AES-192, AES-256, or ChaCha20, based on the severity level of the threat. This proportional encryption logic is implemented through a weighted random function, ensuring both computational efficiency and data confidentiality. Logs are periodically encrypted using a scheduled batch system, and any decryption is restricted to time-limited, read-only access, backed by SHA-256 hash verification and secure key storage outside the logging directory. In a simulated environment, the framework demonstrated reliable classification performance with an overall accuracy of 79%, consistent encryption and decryption operations, and high forensic traceability through structured logging. Automation mechanisms, such as Windows Task Scheduler integration and failure recovery logic, ensured robustness against execution overlaps and latency spikes. The proposed architecture is modular, scalable, and designed for potential deployment in enterprise or cloud environments where automated, intelligent cryptographic control is essential. Overall, this work contributes a practical and intelligent solution for real-time, threat-responsive encryption in modern cybersecurity infrastructures.
Gamification by Design: Methodological Foundations of the GAMESPro Speaking Model for Primary Education
This study presents the development and validation of the GAMESPro model, a structured gamification model designed to enhance English-speaking proficiency among Malaysian primary school students. Despite national reforms and policy initiatives aimed at improving communicative competence, oral English skills remain underdeveloped, particularly in rural and resource-constrained schools. Existing gamification practices in Malaysian classrooms often rely on isolated digital tools and lack pedagogical coherence. Addressing this gap, the study employed the Design and Development Research (DDR) methodology, encompassing three phases: Needs Analysis, Design and Development, and Evaluation. A Systematic Literature Review identified key gamification principles, while validation of ten experts confirmed the clarity and relevance of six core components: Education, Challenges, Engagement, Collaboration, Feedback, and Rewards. Content Validity Index (CVI) analysis yielded strong expert agreement (S-CVI = 0.95), validating the model’s theoretical and contextual integrity. GAMESPro is designed for both digital and non-digital classroom contexts and aligns with the CEFR-aligned KSSR English curriculum. The model offers a comprehensive structure that integrates motivational design with pedagogical goals, aiming to foster student confidence, reduce speaking anxiety, and encourage active verbal participation. The next phase involves classroom implementation to assess the model’s practical impact on student engagement, teacher usability, and speaking outcomes. The GAMESPro model contributes a validated instructional model that bridges theory and practice, offering educators a systematic approach to integrating gamification in ESL instruction.
The Development of Decentralized Ticketing System Using Web3
Traditional ticketing systems often suffer from major drawbacks such as ticket fraud, duplication, inflated resale prices, lack of transparency, and centralized control over transactions. These issues result in reduced trust and limited flexibility for both event organizers and ticket buyers, especially in unregulated secondary markets. To address these gaps, this paper presents the design and development of a Decentralized Ticketing System (DTS) using Web3 technologies. The system leverages Ethereum blockchain, smart contracts written in Solidity, and NFT-based ticket issuance to ensure security, transparency, and verifiable ownership. Features include wallet-based login via MetaMask, multi-ticket purchasing, QR-based validation, controlled resale pricing, and seller revenue withdrawal. Smart contract reliability is enhanced using OpenZeppelin libraries and tested with Mocha and Chai. By decentralizing control and automating ticket processes, the proposed DTS enhances current practices by offering a more secure, tamper-proof, and user-centric ticketing alternative that mitigates fraud and enables transparent peer-to-peer interactions. The architecture of this system integrates a decentralized storage and interaction layer that connects the blockchain smart contracts with a web-based user interface which allow organizers to create events and sell tickets while buyers can securely browse, purchase, and manage their digital assets. The system also demonstrates how blockchain-based ticketing can improve traceability, reduce intermediaries, and support fairer event ecosystems for stakeholders across industry.
AI-Driven Intrusion Detection System for Network Security
In the evolving landscape of cybersecurity, Intrusion Detection Systems (IDS) play a vital role in safeguarding computer networks against malicious activity. Traditional signature-based IDS approaches are increasingly ineffective in detecting novel, complex, or zero-day attacks due to their reliance on predefined rules. To overcome these limitations, this study proposes an AI-driven IDS that integrates both classical machine learning and modern deep learning techniques. The framework introduces and compares three models such as Support Vector Machine (SVM), Convolutional Neural Network (CNN), and a hybrid CNN-SVM model. The system is designed to analyze network traffic patterns and classify them as either benign or malicious, enhancing detection capabilities through intelligent feature learning and classification. Two widely recognized benchmark datasets are used to train and validate the models, ensuring the system's applicability to a variety of network environments. Furthermore, the research includes the development of a real-time detection component that incorporates live packet capture, feature extraction, and dynamic visualization via a dashboard interface. This paper contributes to the field by demonstrating how hybrid AI models can effectively address the challenges of network intrusion detection. The study emphasizes the importance of combining traditional and deep learning approaches to build scalable, adaptive, and accurate intrusion detection systems for modern network infrastructures.
Deep Learning Model for Burn Injury Assessment with Enhanced Diagnosis Rate
The skin is considered as the largest organ of our body specifically satisfies the environment. As a result, the skin is vulnerable to various injuries, especially burns. Burns are linked to high rates of morbidity and mortality and can be fatal. Effective diagnosis supported by precise evaluation of the burn area and the depth of wound is critical for enabling efficacy in clinical settings. The characteristics of a burn wound on the skin include: the wound is abnormal, the skin is infected, there is discomfort, the skin is tight, and there are raised areas of skin. To properly diagnose burn injuries a genomic analysis for skin tissue is necessary. In existing studies, various machine learning algorithms exercised over vast datasets to identify the wound patterns and classify the same accordingly. The multilayered neural network used in the deep learning is renowned as a subset of machine learning and resembles the architecture of human neural circuits. Automatically extracting features from a burn image and classifying the wound based on severity is possible with deep neural networks. Our method involves using deep learning techniques to analyse the genome in skin burn wounds according to the extent of damage. Our goal is to examine various deep learning approaches that can support skin genomic analysis and to improve the diagnostic rate of burn injury with the help of tissue segmentation. Further the scope for repairing the tissue which enables quick advancements in skin care is discussed. The development of deep learning techniques has opened a significant avenue for medical image processing and burn trauma cases.
Performance Analysis of Faster R-CNN and YOLOv8 Model for Mango Fruits Detection
Mango cultivation is a vital agricultural sector in many regions, providing significant economic benefits and contributing to environmental sustainability. Traditional mango detection and harvesting methods are prone to error and are labour-intensive. Recent advances in aerial imagery and Artificial Intelligence (AI) offer innovative solutions to these challenges. An automated yield estimation and fruit harvesting system will require an accurate fruit object detection model. This study explores the application of deep learning models for mango detection on two diverse datasets. This research focuses on evaluating two primary deep learning models: Faster Region-based Convolutional Neural Network (Faster R-CNN) and You Only Look Once (YOLO) variants. Experiments were conducted using datasets from the ACFR mango dataset and a locally collected dataset. The ACFR dataset is acquired from a moving ground vehicle while the local custom dataset is taken from a low flying drone. The Faster R-CNN model was tested with ResNet-50 backbones. YOLOv8 with simple training image augmentation demonstrated superior performance on both datasets, achieving a mean Average Precision with 0.5 Intersection over Union (mAP@0.5) of 0.959 on the ACFR dataset and 0.756 on the custom local mango image dataset. The YOLOv8 model outperforms Faster R-CNN by a large margin on both datasets. The advantage of simple image augmentation for improving mango detection has also been demonstrated. The YOLOv8 model is found to be able to detect mango fruits effectively in both dark and bright lighting conditions.
A Review of Cyber-Attacks on Web Applications and Their Countermeasures
Leaders have always wondered where and how precisely Information Technology (IT) can be of value in their organizations. However, the pacing rate of technology development and cutting-edge features that technologies can provide can cause the decision-maker of an organization to rush to invest in technology upgrades without taking risks into account. The risks that may cost the existence of organizations can be caused by an adversary who commits a cyberattack against the IT assets of an organization, seizing leaders' underestimations of the risks of IT upgrades. Inevitably, there will always be new vulnerabilities in IT assets, especially in information systems. This study aims to analyse the most recent cyberattacks and their criticalities. In addition, a review of countermeasures was conducted to withstand the attacks. Furthermore, this study addresses the key factors that contribute to the neglect of vulnerabilities in web applications. This study provides researchers and organizations with a review of recent real-world cyber-attack incidents using industrial reports and case studies as sources. The results of this study will provide practitioners and researchers with blueprint knowledge about cybercrimes and how critical cybersecurity is, especially in the field of software engineering, regardless of the size of the development team.
Editorial: Artificial Intelligence in Healthcare and Wellness — Emerging Frameworks, Intelligent Monitoring, and Precision Medicine
AI is changing our understanding of health and health-care systems in ways that go beyond the traditional one-click-fits-all approach to care. It is also making care more personalised than ever before, with delivery occurring on an ongoing basis and at all times. The six papers in this special issue of the Journal of Informatics and Web Engineering will give you a clear view of where we are headed. They examine several different areas, including: monitoring chronic illness through wearable technology; using deep learning techniques for contactless heart monitoring via rPPG; developing new cancer risk models; identifying neuroplasticity therapeutic pathways using multi-agent systems; generating synthetic health data; and developing new tri-domain frameworks to examine the interrelationships among the heart, brain, and body. Each of these papers addresses some aspect of a true issue, such as lack of data, limited access, and clinical challenges that traditional methods cannot solve. Collectively, the studies presented in this issue do not just point toward new revolutionary computing; they also provide insight into the future of the health care system. A more intelligent health care system that can adapt to its environment; respond to its client, and ground itself in both ethical and clinical realities. This editorial will outline the common themes that are found throughout the contributions in this special issue. The editorial is an integrative perspective across multiple fields including real-time physiological sensing, explainability and trust, data scarcity solutions, and holistic AI wellness model development.
Multi-Agent AI Systems for Detecting Emerging Therapeutic Targets and Intervention Patterns in Neuroplasticity Research
The exponential growth of neuroplasticity research presents profound challenges to the manual synthesis of literature, hindering the identification of emerging therapeutic targets and intervention patterns. While multi-agent Artificial Intelligence (AI) systems have been proposed conceptually to address this, concrete implementations demonstrating end-to-end utility are scarce. This paper details the successful implementation and application of a novel four-agent AI system designed to automate the discovery, extraction, analysis, and validation of patterns in neuroplasticity literature. Our platform consists of four parts: a Literature Discovery Agent (LDA) for corpus gathering, a Concept Extraction Agent (CEA) with a multi-level NLP strategy fallback mechanism for improving resilience, a Pattern Analysis Agent (PAA) utilizing machine learning for thematic grouping and trend analysis, and a Validation Agent (VA) purely for the validation phase. The authors have a specific case study on neuroplasticity with respect to stroke rehabilitation research, where the platform automatically processed 533 scientific papers, extracted 4,393 biomedical entities, and isolated four research sub-fields that are not only statistically significant but also relevant to the topic: (1) Vagus Nerve Stimulation, (2) Molecular and Synaptic Processes involving Brain-Derived Neurotrophic Factor (BDNF), Clinical and Music Therapies, and finally (3) Brain-Computer Interface and Motor Training. This research project illustrates the efficacy and utilization capacity of collective AI validation beyond any purely conceptual framework.
Synthetic Data Generation for Healthcare and Wellness: Methods, Applications, and Future Directions
Artificial intelligence in healthcare relies heavily upon the availability of high-quality datasets, but very rigid privacy regimes, institutional silos, ethical issues, and heterogeneous data still serve to limit the availability of real-world clinical data. To combat these limitations, you focus on synthetic data generation as a privacy-preserving and scalable alternative to traditional data generation for healthcare and wellness studies. More relevant is a work on a broad and clear framework of synthesis methods based on statistical modelling, rule-based generation and domain-specific clinical logic, in contrast to recent studies that mainly target sophisticated methods for the deep learning architectures. The paper reviews the major synthetic data generation approaches like statistical distributions, machine learning techniques, Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), diffusion models, and hybrid methods. Furthermore, the paper demonstrates how physiological constraints, time structures, prevalence modelling, and clinically meaningful associations could be incorporated into synthetic datasets in a range of healthcare fields including clinical risk prediction, wearable analytics, mental health text generation, genomics, epidemiological modeling and medical imaging. To ensure reproducibility and accessibility for both research and practice, practical Python-based examples along with domain-aware probabilistic models are provided. Further, it discusses evaluation approaches for evaluating statistical accuracy, downstream utility, and privacy in the process, and stresses their trade-offs on realism, technical efficiency, and disclosure potential. Future avenues for research are also presented, (e.g., digital twins, multimodal patient simulation, long-term disease progression modelling, differentially private generative systems). This work would be a theoretical basis as well as a practical guide for researchers, clinicians, and educators striving to create safe, transparent, and trustworthy synthetic healthcare data for advance of healthcare artificial intelligence.
AI-Powered Continuous Health Monitoring for Chronic Disease Management in Malaysia: Real-Time Risk Prediction Using Wearable Sensors and Digital Biomarkers
Chronic Non-Communicable Diseases (NCDs), such as diabetes mellitus, hypertension, and cardiovascular disease, make up a significant portion of the mortality cases in Malaysia, as well as impose consistent demands for costs related to health care expenditure. According to the cost reports at the national level, major NCDs (cardiovascular disease, diabetes, and cancer) are responsible for RM 9.65 billion annually as direct healthcare cost [3]. However, the current approach to managing chronic diseases focuses heavily on periodic assessments in clinic setting at weekly or monthly intervals. Here, a predictive analysis framework based on AI technology, leveraging continuous monitoring with patient data collected from consumer wearables to predict potential health events up to five days ahead is proposed. A synthetic dataset was generated to simulate the behaviour of 500 patients affected by diabetes and/or hypertension, who were continuously monitored throughout a year for heart rate, heart rate variability, physical activity, sleep indices, and blood pressure features. The models are compared to determine the optimal algorithm in the case study. The LSTM model showed 93.7% accuracy in predicting five days in advance, outperforming the random forest model (91.5%), XGBoost model (90.6%), and the simple threshold rule-based approach (78.6%). The sensitivity and specificity scores of the LSTM model were 87.6% and 95.9%, respectively, while the ROC-AUC score was 0.983. According to the SHAP analysis, previous heart rate values (history for three days and seven days) have the largest contributions in predicting patient outcomes, followed by changes in heart rate variability and low activity levels. Although evaluation is based on synthetic data for methodological validation, the framework is intended for extension to real-world wearable streams to support earlier risk detection and more proactive chronic disease management.
An AI-Based Framework for Heart–Brain–Body Coherence in Wellness Monitoring: A Longitudinal Simulation Study
Present wellness monitoring systems primarily emphasize single-modality metrics, neglecting the intricate, interdependent interactions among cardiac, neural, and behavioral regulatory systems. This paper introduces a machine learning framework that implements heart–brain–body coherence as a dynamic, longitudinally evaluated wellness metric. In contrast to previous simulation-based studies that utilized independently generated, cross-sectional data categorized by deterministic IF-THEN rules, the current study adopts a longitudinal simulation design featuring 80 virtual subjects monitored across 30 consecutive daily profiles (N=2,400 records). This design incorporates physiologically realistic inter-variable correlations and probabilistic coherence labelling derived from a continuous risk-scoring function. The subject-level train/test split (56 training subjects, 1,680 records; 24 test subjects, 720 records) stops data from leaking over time. The precision for logistic regression was 79.2% (ROC-AUC = 0.887; five-fold CV AUC=0.904±0.010), while random forest's precision was 78.1% (ROC-AUC = 0.871; five-fold CV AUC=0.894±0.010). The values were significantly varied across all inputs, with an HRV RMSSD of 22.6%, self-reported fatigue with 21.2%, hours of sleep with 19.9%, and stress score at 18.7%. This shows real multi-signal coherence instead of artefacts that are caused by variables that create labels. These results establish a reliable simulation benchmark for subsequent validation with authentic wearable datasets, including MIMIC-IV and PhysioNet.
Intelligent Telemedicine Systems for Contactless Heart Rate Estimation using Deep Learning-based rPPG
Over the last ten years, telemedicine has undergone significant developments, from simple communication-based healthcare to smart and data-driven solutions. The solutions are now powered by Artificial Intelligence (AI), Machine Learning (ML), and Internet of Medical Things (IoMT) technologies. Physiological monitoring is traditionally done with contact-based sensors. The sensors include Electrocardiogram (ECG) and pulse oximeters. The sensors have various disadvantages, including hardware costs, difficulty in continuous usage, and discomfort for patients. Photoplethysmography (rPPG), a remote method of physiological monitoring, is a breakthrough technology. The method is used to estimate physiological signals, including Heart Rate (HR), from video streams captured by standard RGB cameras. The paper aims to explore spatial-temporal deep learning frameworks for remote rPPG as part of a standard five-layer telemedicine architecture. We explain a general ML pipeline, along with the benefits of decomposing the spatial and temporal features of images and motion to improve signal extraction against environmental noise. Besides, the paper presents some of the deployment issues, which include motion effects, lighting, as well as bias in the algorithms, specifically with respect to melanin absorption in human skin. The research also presents some of the avenues of future research, specifically with respect to model compression, which will help move from a cloud to an edge device, thus helping to improve the privacy of users.