Open Access. Powered by Scholars. Published by Universities.®

Other Computer Sciences Commons™

Open Access. Powered by Scholars. Published by Universities.®

Machine Learning

Discipline
Institution
Publication Year
Publication
Publication Type

Articles 1 - 30 of 106

Full-Text Articles in Other Computer Sciences

Structured Data Preprocessing Techniques For A Multilingual Healthcare Dataset In Machine Learning Applications, Md. Abu Nishan, Choo-Yee Ting, Chih-Yang Pee, Helmi Zakariah, Thanh-Trung Nguyen Oct 2026

Structured Data Preprocessing Techniques For A Multilingual Healthcare Dataset In Machine Learning Applications, Md. Abu Nishan, Choo-Yee Ting, Chih-Yang Pee, Helmi Zakariah, Thanh-Trung Nguyen

Journal of Informatics and Web Engineering

Data preprocessing is a foundational stage in any machine learning (ML) workflow, especially when dealing with complex, multilingual, and real-world healthcare datasets. The quality, consistency, and structure of input data strongly influence the accuracy, fairness, and interpretability of predictive models. This paper presents a comprehensive and structured preprocessing framework designed for a large multilingual healthcare dataset containing 19,536 patient records with 67 features encompassing demographic, clinical, and socioeconomic information. The framework systematically addresses data heterogeneity, linguistic diversity, and structural inconsistencies through multiple stages of cleaning and transformation. The preprocessing process includes translation of Malay text into English to ensure linguistic …

Go to article

A Comparative Study Of Machine Learning Algorithms For Customer Churn Prediction In Ecommerce, Tan Yi Qi, Lew Sook Ling, Ram Avtar Oct 2026

A Comparative Study Of Machine Learning Algorithms For Customer Churn Prediction In Ecommerce, Tan Yi Qi, Lew Sook Ling, Ram Avtar

Journal of Informatics and Web Engineering

Customer churn rate is one of the main challenges that e-Commerce businesses must address, as it may affect revenue and long-term growth. In this paper, predictive analysis was used to identify potential customers who would be lost. It uses two different types of data to evaluate and compare the performance of five Machine Learning (ML) models, namely logistic regression, decision tree, support vector machine, gradient boost and random forest. Accuracy, precision, recall rate and F1 score are used as indicators of model performance. Among them, random forest achieved the highest performance across both datasets, demonstrating superior predictive power and consistency. …

Go to article

Empirical Evaluation Of Feature Selection Strategies For Interpretable And Robust Healthcare Machine Learning, Md. Abu Nishan, Choo-Yee Ting, Chih-Yang Pee, Helmi Zakariah, Thanh-Trung Nguyen Oct 2026

Empirical Evaluation Of Feature Selection Strategies For Interpretable And Robust Healthcare Machine Learning, Md. Abu Nishan, Choo-Yee Ting, Chih-Yang Pee, Helmi Zakariah, Thanh-Trung Nguyen

Journal of Informatics and Web Engineering

Feature Section (FS) is a fundamental step in developing accurate, robust, and interpretable Machine Learning (ML) models, especially when working with high-dimensional structured datasets commonly found in healthcare, finance, and other dataintensive domains. Selecting the most relevant subset of features not only improves predictive performance but also enhances computational efficiency and model transparency. This paper presents a comprehensive and systematic study of widely adopted feature selection strategies, including filter, wrapper, embedded, model-agnostic methods, and dimensionality reduction techniques. Each category is examined through experimental evaluations across multiple classifiers and benchmark datasets to understand how different feature selection mechanisms affect model performance, …

Go to article

Benchmarking Hybrid Learning Models For Personalized Risk Classification And Prediction In Oncology, Tshikutu Bikengela Anaclet, Nkongolo Kenda Simon, Biduaya Symphorien, Mabela Matendo Rostin, Mbuyi Mukendi Eugène Oct 2026

Benchmarking Hybrid Learning Models For Personalized Risk Classification And Prediction In Oncology, Tshikutu Bikengela Anaclet, Nkongolo Kenda Simon, Biduaya Symphorien, Mabela Matendo Rostin, Mbuyi Mukendi Eugène

Journal of Informatics and Web Engineering

In the field of oncology, and specifically regarding prostate cancer, recent breakthroughs in artificial intelligence have opened fascinating possibilities for risk classification and prediction. However, moving from theoretical success to actual clinical practice remains a significant challenge. In this study, we evaluated several architectures (generative, discriminant, and hybrid models) to assess their real-world relevance in the context of longitudinal medical data, which is often noisy or incomplete. Our analysis does not merely measure raw performance; it seeks to understand the critical balance between statistical precision and the interpretability that clinicians require for daily decision-making. By examining the inherent strengths and …

Go to article

Designing A Predictive Expense Tracking Mobile Application: A Comparison Of Random Forest, Linear Regression, And Prophet-Based Models, Li Wei Tai, Li Zhe Poh, Md Ismail Hossen Oct 2026

Designing A Predictive Expense Tracking Mobile Application: A Comparison Of Random Forest, Linear Regression, And Prophet-Based Models, Li Wei Tai, Li Zhe Poh, Md Ismail Hossen

Journal of Informatics and Web Engineering

In today’s fast-paced world, people engage in various forms of daily consumption, yet many lack the habit of tracking their expenses due to low financial awareness. Without proper expense recording and budgeting, individuals often struggle to understand their consumption habits and are at a disadvantage when it comes to financial planning, especially during emergencies. To address these issues, this project proposes the development of a user-friendly expense tracking mobile application designed to help adults build healthy financial habits and utilize predictive algorithms to forecast future expenses. The application enables users to record daily transactions, analyse their spending patterns, plan budgets, …

Go to article

Predictive Test Case Prioritization In Agile Workflows: A Machine Learning Approach With Smote-Enn And Pca, Kavinraj Baskaran, Junaidi Abdullah, Sin-Ban Ho, Said Suleiman Naseer Al Harthy, Ian Chai Oct 2026

Predictive Test Case Prioritization In Agile Workflows: A Machine Learning Approach With Smote-Enn And Pca, Kavinraj Baskaran, Junaidi Abdullah, Sin-Ban Ho, Said Suleiman Naseer Al Harthy, Ian Chai

Journal of Informatics and Web Engineering

Test Case Prioritization (TCP) is an essential part of agile software development, allowing for the identification and execution of critical test cases early in the process to maximize fault detection efficiency. Traditional TCP techniques often depend on static metrics, like code complexity and historical pass/fail rates, that do not conform with the dynamic nature of Agile workflows. To overcome this, the proposed research presents a discriminative machine learning-based predictive prioritization model that incorporates historical defect data, execution history, as well as requirement criticality to improve auspiciousness of TCP. The study assesses a range of machine learning models like Logistic Regression, …

Go to article

Intelligent Telemedicine Systems: Machine Learning For Remote Diagnosis And Patient Management, Oras Baker, Kasthuri Subaramaniam, Asiah Lokman Oct 2026

Intelligent Telemedicine Systems: Machine Learning For Remote Diagnosis And Patient Management, Oras Baker, Kasthuri Subaramaniam, Asiah Lokman

Journal of Informatics and Web Engineering

Telemedicine has experienced significant evolution in the past 10 years from a communication-driven health service to an intelligent, data-driven environment driven by AI (artificial intelligence), ML (machine learning), IoMT (Internet of Medical Things) and cloud–edge computing. This development has been further advanced through ongoing global health challenges that have seen an ageing population, rising burden of chronic, multi-morbid conditions, healthcare specialist shortage, soaring cost of healthcare and major disruptions like the COVID-19 pandemic. Modern telehealth services are now integrating more machine learning algorithms for remote diagnosis, real-time monitoring the patient, predictive risk stratification and proactive treatment. This paper offers a …

Go to article

Analysis Of Social Media Trends For Political Election Predictions, Wong Jing Hong, Lew Sook Ling, Tan Li Tao Jun 2026

Analysis Of Social Media Trends For Political Election Predictions, Wong Jing Hong, Lew Sook Ling, Tan Li Tao

Journal of Informatics and Web Engineering

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 …

Go to article

Machine Learning-Based Prediction Of House Sale Prices In Hulu Langat, Kath Moon Yap Choo, Sew Lai Ng, Da An Jun 2026

Machine Learning-Based Prediction Of House Sale Prices In Hulu Langat, Kath Moon Yap Choo, Sew Lai Ng, Da An

Journal of Informatics and Web Engineering

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 …

Go to article

Ai-Powered Threat Hunting For Email Phishing Attack Detection Using Natural Language Processing (Nlp), Mohd Azriy Akmalhazim Mohd Nazariee, S Prabha Kumaresan, Alaa Haddad, Mohamed Uvaze Ahamed Ayoobkhan Jun 2026

Ai-Powered Threat Hunting For Email Phishing Attack Detection Using Natural Language Processing (Nlp), Mohd Azriy Akmalhazim Mohd Nazariee, S Prabha Kumaresan, Alaa Haddad, Mohamed Uvaze Ahamed Ayoobkhan

Journal of Informatics and Web Engineering

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 …

Go to article

Ai-Driven Intrusion Detection System For Network Security, Zhi Lin Sarah Teoh, Wee How Khoh, Hui Yen Yap, Pin Shen Teh Jun 2026

Ai-Driven Intrusion Detection System For Network Security, Zhi Lin Sarah Teoh, Wee How Khoh, Hui Yen Yap, Pin Shen Teh

Journal of Informatics and Web Engineering

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 …

Go to article

Synthetic Data Generation For Healthcare Machine Learning: A Case Study In Vital Signs And Diagnostic Predictions, Sellappan Palaniappan, Rajasvaran Logeswaran, Kasthuri Subaramaniam, Oras Baker, Bui Ngoc Dung Jun 2026

Synthetic Data Generation For Healthcare Machine Learning: A Case Study In Vital Signs And Diagnostic Predictions, Sellappan Palaniappan, Rajasvaran Logeswaran, Kasthuri Subaramaniam, Oras Baker, Bui Ngoc Dung

Journal of Informatics and Web Engineering

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 …

Go to article

Analysis And Predictive Modelling Of Ev Charging Patterns And User Behaviour, Yi Xuan Law, Kah Ong Michael Goh, Check Yee Law, Connie Tee, Yong Wee Sek, S M Hasan Mahmud Jun 2026

Analysis And Predictive Modelling Of Ev Charging Patterns And User Behaviour, Yi Xuan Law, Kah Ong Michael Goh, Check Yee Law, Connie Tee, Yong Wee Sek, S M Hasan Mahmud

Journal of Informatics and Web Engineering

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, …

Go to article

Machine Learning For Employee Attrition Prediction: A Comparative Study, Kai Jia Low, Sook Ling Lew, Sri Winarno Jun 2026

Machine Learning For Employee Attrition Prediction: A Comparative Study, Kai Jia Low, Sook Ling Lew, Sri Winarno

Journal of Informatics and Web Engineering

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 …

Go to article

An Ai-Based Framework For Heart–Brain–Body Coherence In Wellness Monitoring: A Longitudinal Simulation Study, Asiah Lokman, Akalpita Tendulkar, Yujiao Zhang, Mantena Sireesha Jun 2026

An Ai-Based Framework For Heart–Brain–Body Coherence In Wellness Monitoring: A Longitudinal Simulation Study, Asiah Lokman, Akalpita Tendulkar, Yujiao Zhang, Mantena Sireesha

Journal of Informatics and Web Engineering

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 …

Go to article

Scenarioxp: A Complete Scenario-Based Testing Framework For The Exploration And Exploitation Of Autonomous Vehicle Validation Scenarios, Quentin Goss Apr 2026

Scenarioxp: A Complete Scenario-Based Testing Framework For The Exploration And Exploitation Of Autonomous Vehicle Validation Scenarios, Quentin Goss

Doctoral Dissertations and Master's Theses

Today is an age of exciting emerging technology where cutting-edge research in autonomous vehicles (AVs) reduces the active human participation in driving and extends awareness beyond human limitations of perception and reaction, improving driving safety and quality of the user experience as a result. The ever-increasing complexity of these autonomous systems poses many challenges towards the validation and verification (V\&V) of these complex systems under time and resource constraints, as the use of artificial intelligence and also the intricacy of the operating environment means that these systems are also black-box and non-deterministic. Scenario-based V\&V testing of such systems, which involves …

Go to article

Anfis And Rbfnn Efficacy And Timescale Dependence In Spei-Based Drought Prediction Using Meteorological Inputs, Alisa Afendi, Muhamad Usman Tariq, Shuhaida Ismail, Azizul Azhar Ramli Feb 2026

Anfis And Rbfnn Efficacy And Timescale Dependence In Spei-Based Drought Prediction Using Meteorological Inputs, Alisa Afendi, Muhamad Usman Tariq, Shuhaida Ismail, Azizul Azhar Ramli

Journal of Informatics and Web Engineering

Drought is a slow-onset natural disaster that has far-reaching effects on agriculture, water security, and socio-economic systems, especially in climate-vulnerable countries such as Malaysia. It is imperative to predict droughts for prompt mitigation efforts. In this paper, the influence of temporal scale on drought modelling has been put into discussion by analyzing a comparison between two machine learning (ML) models: Adaptive Neuro-Fuzzy Inference System (ANFIS); Radial Basis Function Neural Network (RBFNN) based on Standardized Precipitation Evapotranspiration Index (SPEI) as depicting drought. The SPEI of four timescale categories (SPEI-3, SPEI-6, SPEI-9, and SPEI-12) were calculated weekly and monthly (two different temporal …

Go to article

Editorial: Intelligent Systems And The Next Wave Of Digital Innovation, Hairulnizam Mahdin Feb 2026

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 …

Go to article

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

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, …

Go to article

Ai-Driven Malware Analysis And Detection: A Comprehensive Survey Of Techniques, Trends And Challenges, Salman Khan, Hasnat Raza, Mansoor Alam Feb 2026

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 …

Go to article

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 Feb 2026

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 …

Go to article

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 Feb 2026

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, …

Go to article

Editorial Preview For October 2025 Issue, Su-Cheng Haw, Hui-Ngo Goh Oct 2025

Editorial Preview For October 2025 Issue, Su-Cheng Haw, Hui-Ngo Goh

Journal of Informatics and Web Engineering

The October 2025 issue of the Journal of Informatics and Web Engineering (JIWE) concludes the journal’s triannual publication cycle and precedes its transition to a quarterly schedule commencing in 2026. This issue presents 21 research papers within its regular section, covering a wide range of areas including Artificial Intelligence (AI), Machine Learning (ML), Software Engineering, Recommender Systems, Cybersecurity, and Web Technologies. 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 “AI-Enhanced Computing and Digital Transformation”. The featured studies examine …

Go to article

Zero Trust - The Future Of Cybersecurity, Danial Haider, Shougfta Mushtaq, Hasnat Ali, Mazliham Mohd Su’Ud Oct 2025

Zero Trust - The Future Of Cybersecurity, Danial Haider, Shougfta Mushtaq, Hasnat Ali, Mazliham Mohd Su’Ud

Journal of Informatics and Web Engineering

The recent Zero-Trust Architecture (ZTA) is progressively adopted to the develop network security by assuming no implicit trust within or outside an organization’s boundary. Though, ZTA faces substantial challenges in detecting sophisticated and developing cyber threats, particularly due to its trust on traditional security mechanisms that struggle to manage internal threats and sophisticated attack techniques. To report these shortcomings, the proposed study discovers the combination of advanced machine learning (ML) and deep learning (DL) performances to improve the anomaly detection proficiencies within ZTA environments. The study develops the CICIDS2017 dataset, which contains diverse and realistic network traffic patterns, to assess …

Go to article

Hybrid-Based Movie Recommender System: Techniques, Case Studies, Evaluation Metrics, And Future Trends, Cheng-Yung Lai, Noramiza Hashim, Lucia Dwi Krisnawati Oct 2025

Hybrid-Based Movie Recommender System: Techniques, Case Studies, Evaluation Metrics, And Future Trends, Cheng-Yung Lai, Noramiza Hashim, Lucia Dwi Krisnawati

Journal of Informatics and Web Engineering

The necessity for sophisticated recommender systems in the movie recommendation sphere has become particularly pronounced, generating a more personalized movie recommendation due to people nowadays who like to watch movies online. Efficient recommender systems make use of advanced machine learning (ML) techniques in the pursuit of accurate and meaningful recommendations. This paper endeavours to give a comprehensive overview of technologies known as recommender systems, concentrating on ML methods found at the base. Different strategies have been applied in this work, which include collaborative filtering (CF), content-based filtering (CB), hybrid approaches, Generative AI and so on. The merits and demerits of …

Go to article

Hybrid Filtering For Personalized And Health-Conscious Recipe Recommendations In Unieats, Hui Lek Liew, Xinying Chew, Khai Wah Khaw, Shiuh Tong Lim Oct 2025

Hybrid Filtering For Personalized And Health-Conscious Recipe Recommendations In Unieats, Hui Lek Liew, Xinying Chew, Khai Wah Khaw, Shiuh Tong Lim

Journal of Informatics and Web Engineering

This research paper introduces UniEats, a recipe recommendation website designed to help university students better organize their meals and adopt healthier eating habits. The system aims to address common challenges students face, including time constraints, limited cooking skills, and insufficient nutritional awareness, which often lead to unhealthy food choices. At the core of UniEats is its recommendation engine, which employs a hybrid filtering approach, combining content-based and collaborative filtering techniques to provide personalized recipe suggestions based on users' dietary preferences, rating history, and recipe attributes. UniEats offers a range of features, including recipe search, weekly meal planning, nutritional analysis through …

Go to article

Implementation Of Lightweight Machine Learning Models For Real-Time Text Classification On Resource-Constrained Devices, Marwah Zaid Mohammed Al-Helali, Naveen Palanichamy, K. Revathi Oct 2025

Implementation Of Lightweight Machine Learning Models For Real-Time Text Classification On Resource-Constrained Devices, Marwah Zaid Mohammed Al-Helali, Naveen Palanichamy, K. Revathi

Journal of Informatics and Web Engineering

This paper addresses the growing need for implementing intelligent Natural Language Processing (NLP) systems on low-power, memory-limited devices such as Raspberry Pi, mobile phones, and IoT edge hardware. As edge computing and smart devices proliferate, there is an urgent need for more advanced NLP technology that does not require constant cloud access and is efficient in computing and provides results in real time. While deep learning and cloud-based models typically offer high text-classification accuracy and have demonstrated exceptional performance across a range of NLP tasks, they are often too resource-intensive for real-time deployment in constrained environments. To overcome these limitations, …

Go to article

Review On Secure And Efficient Iot-Based Healthcare System With The Integration Of Machine Learning And Firewalls, Muhammad Awais, Syeda Samar Fatima, Jawaid Iqbal Oct 2025

Review On Secure And Efficient Iot-Based Healthcare System With The Integration Of Machine Learning And Firewalls, Muhammad Awais, Syeda Samar Fatima, Jawaid Iqbal

Journal of Informatics and Web Engineering

The integration of the Internet of Things (IoT) into healthcare would mean a revolutionized approach in patient monitoring, diagnosis, and treatment, making this quite some development in healthcare delivery. This review has focused on how the integration of IoT with Machine Learning (ML) and stringent security measures tackle the challenging situation of data privacy and cyber threats in healthcare. Current methodologies point toward how essential advanced sensors, cloud computing, and wireless technologies for IoT-based healthcare systems necessary to secure patient data. patient record kept in files and now forward to the cloud database system so that in any case of …

Go to article

Addressing Iot Security Challenges Through Advanced Machine Learning And Encryption, Ahmad Aziem Khushairi Anuar, Ahmad Anwar Zainuddin, Ahmad Adlan Abdul Halim, Dek Rina Oct 2025

Addressing Iot Security Challenges Through Advanced Machine Learning And Encryption, Ahmad Aziem Khushairi Anuar, Ahmad Anwar Zainuddin, Ahmad Adlan Abdul Halim, Dek Rina

Journal of Informatics and Web Engineering

The rapid growth of Internet of Things (IoT) devices, including smartwatches, home assistants, and connected appliances, has brought significant convenience to daily life, but it has also introduced serious security challenges. These devices often transmit sensitive data, making them vulnerable to theft, misuse, and unauthorized access. Current security measures are insufficient to address the complex and evolving nature of IoT systems, leaving many of them exposed to potential breaches and cyberattacks. This review explores recent developments in IoT security, focusing on how advanced technologies, such as machine learning, can be utilized to enhance the protection of IoT systems. The main …

Go to article

Developing A Predictive Model For Football Players’ Market Value Using Machine Learning, Muhammad Afif Jazimin Idris, Sew Lai Ng Oct 2025

Developing A Predictive Model For Football Players’ Market Value Using Machine Learning, Muhammad Afif Jazimin Idris, Sew Lai Ng

Journal of Informatics and Web Engineering

Football is the world’s most popular sport, and evaluating the market value of players is crucial for clubs and managers in making informed decisions regarding transfers, contracts, and financial planning. This study aims to develop a predictive model to estimate the market value of football players using machine learning (ML) algorithms and real-life statistics performance data from the top five European leagues such as English Premier League, Italian Serie A, Spanish La Liga, German Bundesliga, and French Ligue 1 between the 2017/18 and 2019/20 seasons. By reviewing past research, various ML methods such as Random Forest, LightGBM, XGBoost, and Gradient …

Go to article

Next Last