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Scheduling Optimization For Cascaded Hybrid Flow Shop Integrating Llm And Rl, Fuqing Zhao, Bin Wu, Tianpeng Xu, Ningning Zhu, Houbin Song, Yukai Yao Oct 2026

Scheduling Optimization For Cascaded Hybrid Flow Shop Integrating Llm And Rl, Fuqing Zhao, Bin Wu, Tianpeng Xu, Ningning Zhu, Houbin Song, Yukai Yao

Journal of System Simulation

Abstract: For the cascaded hybrid flow shop scheduling problem with AGV transportation, a multiobjective optimization model is built to minimize the makespan and total energy consumption. A hybrid algorithm framework integrating the LLM, covariance matrix adaptation evolution strategy, and RL strategy selection mechanism is proposed. LLMs are employed to generate heuristic priority rules and construct high-quality initial solutions. Covariance matrix adaption evolution strategy is adopted to optimize the search in continuous priority space, and an enhanced scalar fitness function integrating hypervolume gain and solution diversity is introduced. An RL strategy selection module based on the upper level information boundary is …

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Q-Learning Enhanced Memetic Algorithm For Robust Scheduling Of Space Launch Missions, Rui Lin, Xiaolu Liu, Yonghao Du, Mingyang Geng, Zhenshun Shi Oct 2026

Q-Learning Enhanced Memetic Algorithm For Robust Scheduling Of Space Launch Missions, Rui Lin, Xiaolu Liu, Yonghao Du, Mingyang Geng, Zhenshun Shi

Journal of System Simulation

Abstract: A robust optimization mathematical model and an enhanced memetic algorithm (MA) for space launch mission scheduling are proposed to address the practical challenges in space launch mission scheduling under the influence of multiple uncertainties such as equipment failure, process rework due to quality issues, and technological changes. A robust optimization scheduling model for space launch missions is built by integrating the makespan under the worst-case scenario, the average maximum makespan, and the average deviation of process start times. A Q-learning enhanced memetic algorithm (QMA) is proposed, which can dynamically select parent solutions based on the population state. Simulation experiments …

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Assessor Experiences In Cmmc Level 2 Certification Assessments: An Interpretative Phenomenological Analysis Of Role Expectations, Samuel Heuchert, John Hastings Oct 2026

Assessor Experiences In Cmmc Level 2 Certification Assessments: An Interpretative Phenomenological Analysis Of Role Expectations, Samuel Heuchert, John Hastings

Research & Publications

The Cybersecurity Maturity Model Certification program requires that third-party assessments be conducted under a non-consultative model. The model is intended to ensure impartiality for organizations seeking certification. While this structure defines expectations for assessor behavior, assessor experiences and interpretations of these constraints remain underexamined. The study examines the lived experiences of CMMC-Certified Assessors and how they navigate role expectations within the non-consultative model. Using Role Conflict Theory as a guiding framework, the study applied Interpretative Phenomenological Analysis (IPA) to semi-structured interviews to explore how assessors make sense of their roles. The analysis identified experiential themes that describe how assessors construct …

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Optimized Fully Sharded Data Parallel For Efficient Distributed Training Of Bilstm With Multi-Head Attention For Network Intrusion Detection Systems, Noor Al-Huda H. Oleiwi, Ameer K. Hadi Oct 2026

Optimized Fully Sharded Data Parallel For Efficient Distributed Training Of Bilstm With Multi-Head Attention For Network Intrusion Detection Systems, Noor Al-Huda H. Oleiwi, Ameer K. Hadi

Journal of Intelligent Informatics, Networking, and Cybersecurity

The expanding volume of network traffic, the growing size of cybersecurity datasets, and the increasing complexity of deep learning models present major computational challenges to training deep learning-based Network Intrusion Detection Systems (NIDSs). Fully Sharded Data Parallel (FSDP) enables distributed training by sharding model parameters across multiple GPUs; however, the synchronization and communication overhead can limit its training efficiency. To overcome these limitations, this paper proposes an Optimized Fully Sharded Data Parallel (Optimized FSDP) framework that improves distributed training efficiency by dynamic gradient accumulation and synchronization scheduling. The proposed framework is integrated with a Bidirectional Long Short-Term Memory (BiLSTM) network …

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Calculation Of Alpha-Decay Half-Lives Using A Numerical Approach, Daniel Suescún-Díaz, Rosemberg Gómez-Noguera, Diego Rasero-Causil Oct 2026

Calculation Of Alpha-Decay Half-Lives Using A Numerical Approach, Daniel Suescún-Díaz, Rosemberg Gómez-Noguera, Diego Rasero-Causil

Karbala International Journal of Modern Science

This study introduces a numerical approach for estimating alpha-decay half-lives of alpha-emitting nuclei using a modified five-parameter Woods-Saxon potential. The radial Schrödinger equation is solved with the Numerov method to calculate the wave function and the barrier transmission probability, without relying exclusively on the WKB approximation. The calculations were performed for 398 nuclides taken from the NUBASE2020 database. Of these, 264 nuclides were used for the MWS5 parameter-adjustment stage, whereas the remaining 134 nuclides were reserved for an independent evaluation of the model. The results were compared with those obtained from two reference models: the modified Gamow-Like Model (MGLM) and …

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Antioxidant, Anti-Tyrosinase, Anti-Elastase, And Anti-Collagenase Activities Of Propolis Extracts From The Stingless Bee Tetragonula Laeviceps (Smith, 1857), Tri Hadi Handayani, Dimas Andrianto, Sih Kahono, Pamungkas Rizki Ferdian, Raden Lia Rahadian Amalia, Muhamad Ridwan Oct 2026

Antioxidant, Anti-Tyrosinase, Anti-Elastase, And Anti-Collagenase Activities Of Propolis Extracts From The Stingless Bee Tetragonula Laeviceps (Smith, 1857), Tri Hadi Handayani, Dimas Andrianto, Sih Kahono, Pamungkas Rizki Ferdian, Raden Lia Rahadian Amalia, Muhamad Ridwan

Karbala International Journal of Modern Science

Stingless bees incorporate propolis into their nest as a structural and protective material. Information remains limited on how solvent fractionation affects the antioxidant and enzyme-inhibitory activities of propolis produced by Indo nesian Tetragonula laeviceps. This study examined a crude extract prepared with 70% ethanol together with the n- hexane, ethyl acetate, and water fractions obtained from it. The analysis comprised total phenolic content (TPC), total flavonoid content (TFC), three antioxidant assays (ABTS, CUPRAC, TBARS), and inhibition of tyrosinase, elastase, and collagenase. The largest TPC and TFC values were obtained from the ethyl acetate fraction, at 85.93 ± 1.28 mg GAE/g …

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Constructing The Cyber Workforce Gap: Measurement And Its Consequences, Ryan Straight Oct 2026

Constructing The Cyber Workforce Gap: Measurement And Its Consequences, Ryan Straight

Journal of Cybersecurity Education, Research and Practice

Cyber workforce-gap figures steer federal strategy, industry advocacy, and the growth of educational programs. Rather than estimating the U.S. gap, this review examines how the estimates are made: through job-posting analytics, survey-based estimation, and federal accounting, each of which must assume its way from instrument to labor market. A census of this journal's complete run illustrates what those figures underwrite in one venue: of 176 items published across eleven years, 51 invoke a labor-market workforce claim, 46 treat it as settled fact, and only one, an editorial, interrogates it. Meanwhile, the producers and auditors of those figures have diverged. Between …

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Specification And Internal Assessment Of A Self Reported Protective Application Use Composite Among Computer Science Undergraduates In Enugu State Nigeria, Adesegun Nurudeen Osijirin, Leonard C. Anigbo, Oliver Okechukwu Oct 2026

Specification And Internal Assessment Of A Self Reported Protective Application Use Composite Among Computer Science Undergraduates In Enugu State Nigeria, Adesegun Nurudeen Osijirin, Leonard C. Anigbo, Oliver Okechukwu

Journal of Cybersecurity Education, Research and Practice

This study specifies a Protective Application Use Composite restricted to self-reported protective application use. A cross-sectional survey provided 364 complete records from Computer Science undergraduates at four universities in Enugu State, Nigeria, from 393 distributed questionnaires. The score is the equal-weight average of five questionnaire categories: antivirus, firewall, encryption, multi-factor authentication, and virtual private network use. Reanalysis of the author-supplied item records yielded category means from 2.097 to 2.376 and internal-consistency coefficients from 0.958 to 0.971. The composite mean was 2.170, with a standard deviation of 0.469. The first component of the category-score correlation matrix explained 50.98 percent of variance. …

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Research On Data Risk And Governance Of Generative Artificial Intelligence Empowering Intelligence Work, Yonghui Guo, Xiaobei Guo, Yuan Feng, Yapeng Li Oct 2026

Research On Data Risk And Governance Of Generative Artificial Intelligence Empowering Intelligence Work, Yonghui Guo, Xiaobei Guo, Yuan Feng, Yapeng Li

Journal of Scientific Information Research

[Purpose/significance] Generative Artificial Intelligence（GAI）generates numerous data risks in the process of empowering intelligence work. Fully understanding, identifying, and governing the data risks are the key to the effective empowerment of intelligence work by GAI. [Method/process] Based on the four stages of intelligence work, this paper subdivides the main data activities in intelligence work empowered by generative artificial intelligence from the perspective of the data workflow. Based on risk management theory, the data risks in data input, data processing, data content generation and data content dissemination, are systematically analyzed and identified. From three perspectives of institution, subject, and technology, a data …

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Stewards Of Data: A Practical Handbook For Undergraduate Researchers In Engineering And Applied Sciences, Şenay Purzer, Wei Zakharov, Carla B. Zoltowski Oct 2026

Stewards Of Data: A Practical Handbook For Undergraduate Researchers In Engineering And Applied Sciences, Şenay Purzer, Wei Zakharov, Carla B. Zoltowski

Purdue University Press Books

Filled with actionable guides and real-world examples, Stewards of Data: A Practical Handbook for Undergraduate Researchers in Engineering and Applied Sciences allows readers to utilize the provided templates and guides almost immediately. In today’s world, data is ubiquitous and AI is growing exponentially. Practicing ethical data management is, therefore, crucial. Ensuring that data is findable, accessible, interoperable, and reusable is essential. This book is an interdisciplinary effort aimed at embedding ethical data life-cycle management education into undergraduate research. The authors present a Data Steward Framework, which serves as a guide for undergraduate researchers, the graduate students who mentor them, postdocs, …

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An Intelligent Sdn-Enabled Framework For Real-Time Ipv6 Ip Spoofing Detection And Prevention, Navaneethan C. Arjuman, Ahmad Fikri Sharudin, Zar Chi Hlaing, Supriyanto Praptodiyono Oct 2026

An Intelligent Sdn-Enabled Framework For Real-Time Ipv6 Ip Spoofing Detection And Prevention, Navaneethan C. Arjuman, Ahmad Fikri Sharudin, Zar Chi Hlaing, Supriyanto Praptodiyono

Journal of Informatics and Web Engineering

With the increase in the adoption of Internet Protocol version 6 (IPv6), the issue of Internet Protocol (IP) spoofing has remained a significant risk that damages the integrity of networks. Although some countermeasures, like Secure Neighbor Discovery (SEND) and Router Advertisement (RA) Guard, are available, these solutions are complex to deploy, consume high resources, and can be bypassed by advanced attacks. This paper provides a critical review of the available IPv6 spoofing prevention methods and proposes an enhanced multi-layered framework that uses improved ingress and egress filtering, Artificial Intelligence (AI)-based anomaly detection, and Software-Defined Networking (SDN) to automatically mitigate threats …

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Mask-Resilient Multi-Biometric Authentication For Healthcare, Caleb Yeo, Siew-Chin Chong, Kian-Ming Lim, Lee-Ying Chong Oct 2026

Mask-Resilient Multi-Biometric Authentication For Healthcare, Caleb Yeo, Siew-Chin Chong, Kian-Ming Lim, Lee-Ying Chong

Journal of Informatics and Web Engineering

The fast integration of digital healthcare platforms, including telemedicine and electronic health records, has created a significant need for secure, contactless authentication methods. Traditional login methods such as passwords and PINs are increasingly insufficient for ensuring the security of sensitive patient data. This paper presents a robust, lightweight Healthcare Authentication System that five biometric modules to address major challenges in facial recognition: spoofing attacks, liveness detection, and facial occlusion due to mask-wearing. The system leverages RetinaFace for face detection, a MobileNetV2 model for mask classification, a lightweight CNN for blink-based liveness verification, and MiniFASNet for spoof detection. For identity matching, …

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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 …

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Comparative Analysis Of Time Series Forecasting Models For Stock Price Prediction: Arima, Lstm, And Prophet, Muhammad Syahid Pebriadi, Tino Kemal Fattah, Putriana Salman, Rizky Amelia Oct 2026

Comparative Analysis Of Time Series Forecasting Models For Stock Price Prediction: Arima, Lstm, And Prophet, Muhammad Syahid Pebriadi, Tino Kemal Fattah, Putriana Salman, Rizky Amelia

Journal of Informatics and Web Engineering

Stock price forecasting with a uniform approach is a challenging process due to nonlinearity, regime changes, and seasonality. The following paper would like to address the practical problem of selecting an appropriate model through comparing three widely used models such as Auto Regressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and Prophet based on how they are applied to different attributes of data and forecast-related tasks. The structured direct comparison will be our proposal, as to be able to identify the strengths and shortcomings in abiding by each model. Each of the model is methodologically trained and tested on …

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Haptic Technology In Networked Digital Applications: A Literature Review, Tharanitharan Chandran, Ngu War Hlaing, Athirah Mohd Ramly Oct 2026

Haptic Technology In Networked Digital Applications: A Literature Review, Tharanitharan Chandran, Ngu War Hlaing, Athirah Mohd Ramly

Journal of Informatics and Web Engineering

Haptic-enhanced network applications provide a breakthrough merger of haptic technology with digital environments, allowing for immersive and responsive interactions. In contrast to traditional interfaces that rely primarily on visual and auditory feedback, these applications add tactile sensations, boosting user engagement in domains such as telemedicine, Virtual Reality (VR), Augmented Reality (AR), gaming, and remote collaboration. This study adopts a systematic literature review methodology to identify, filter, and analyse relevant academic sources published between 1998 to 2024, to examine accomplishments, challenges, and future directions of haptic technology in network applications. The study investigates actuator technology, tiny haptic devices, and adaptive algorithms …

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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. …

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Recycle Tracker Application: A Gamified Mobile Platform To Enhance Recycling Behaviour, Yi Han Ng, Pey Yun Goh, Lee Yeng Ong, Kian Ming Lim Oct 2026

Recycle Tracker Application: A Gamified Mobile Platform To Enhance Recycling Behaviour, Yi Han Ng, Pey Yun Goh, Lee Yeng Ong, Kian Ming Lim

Journal of Informatics and Web Engineering

Malaysia's recycling efforts have long been challenged by limited accessibility, low motivation, and insufficient environmental awareness among the public. Despite various national campaigns, recycling participation rates remain inconsistent, highlighting the need for more engaging and user-centred approaches. To address these challenges, this paper presents the Recycle Tracker Application, a gamified mobile solution designed to encourage consistent recycling behaviour and strengthen environmental responsibility among Malaysians. The application integrates multiple core features, including a real-time interactive map of recycling centres, an educational waste-sorting and DIY recycling guide, personalized eco-goal setting, and an innovative Dino Evolution gamification system that rewards users as they …

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Sql Optimization In E-Commerce Platforms Using Large Language Models: A Multi-Pattern Query Empirical Study, Min Ming Lee, Nabhan Salih Oct 2026

Sql Optimization In E-Commerce Platforms Using Large Language Models: A Multi-Pattern Query Empirical Study, Min Ming Lee, Nabhan Salih

Journal of Informatics and Web Engineering

The rapid expansion in e-Commerce domains has presented an unprecedented challenge to database systems that support the execution of complex analytic queries on multi-billion transaction data stream while keeping queries’ response latency under one second. It is commonly observed that Large Language Models (LLMs) are very good at processing natural language queries, rather than struggling with syntax or semantics. This feature is instrumental to various tasks like analytics acceleration, transaction speed-up and handling large workloads across different domains. In this paper, a detailed examination of how LLMs can be employed as intelligent assistants for Structure Query Language (SQL) query optimization …

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Leveraging Meaningful Intrusion Detection Mechanism For Voice Traffic Patterns, Aizam Nazmien Alinoordin, Liana Ameerah Rosli, Sin-Ban Ho, Said Suleiman Naseer Al Harthy, Ian Chai Oct 2026

Leveraging Meaningful Intrusion Detection Mechanism For Voice Traffic Patterns, Aizam Nazmien Alinoordin, Liana Ameerah Rosli, Sin-Ban Ho, Said Suleiman Naseer Al Harthy, Ian Chai

Journal of Informatics and Web Engineering

Voice over IP (VoIP) communication has emerged as an important way of communicating as a modern digital medium but it has been facing a lot of cyber threats because of the protocols involved and the real-time aspect associated with it. conventional Intrusion Detection Systems (IDS) do not cope well with VoIP traffic, as signature-based solutions fail to handle various types of intrusions, while anomaly-based approaches tend to produce a high false positive rate. In this research, a hybrid intrusion detection mechanism is integrated, a lightweight and application-based system built around signature matching and anomaly detection to offer advanced protection to …

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A Web-Based Multi-Modal Ai Framework For Intelligent Content Moderation With Constructive Feedback, Marwah Zaid Mohammed Al-Helali, Naveen Palanichamy, Su-Cheng Haw, Subhashini Gopal Oct 2026

A Web-Based Multi-Modal Ai Framework For Intelligent Content Moderation With Constructive Feedback, Marwah Zaid Mohammed Al-Helali, Naveen Palanichamy, Su-Cheng Haw, Subhashini Gopal

Journal of Informatics and Web Engineering

Traditional content moderation systems primarily rely on strict measures that focus on removing offending or inappropriate content without offering users any form of educational feedback or guidance. This lack of constructive interaction often leads to a poor user experience, leaving users frustrated and uninformed regarding the reasons behind content removal. Moreover, such a system fails to encourage long-term positive behavioural changes within online communities, as users do not gain insights on how to improve their conduct. This paper proposes a new constructive Artificial Intelligence (AI) intervention system that integrates a number of AI models in order to conceptualize content moderation …

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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, …

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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 …

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Next-Gen Fitness: Ai-Based Diet Plans And Real-Time Exercise Monitoring With Gradient Boosting, Gaussian Mixture Models And Mediapipe, I. Kavitha, B. Vanathi, K. Revathi, K. Sanjaikumar Oct 2026

Next-Gen Fitness: Ai-Based Diet Plans And Real-Time Exercise Monitoring With Gradient Boosting, Gaussian Mixture Models And Mediapipe, I. Kavitha, B. Vanathi, K. Revathi, K. Sanjaikumar

Journal of Informatics and Web Engineering

Global shifts toward sedentary habits and refined dietary choices are magnifying public-health pressures from obesity, metabolic dysregulation, and cardiovascular disease, positioning targeted, personalized fitness coaching tools at the forefront of preventive-care strategy. By embedding tailored nutritional and physical-activity plans into daily life, these tools reinforce adherence, enhance intrinsic motivation, and catalyze sustained wellness achievements—each moderated by real-time analytics, feedback loops, and progressive adaptation to evolving behavior and physiology. Their leading benefits include prolonged engagement, diminished risk of relapse, and deep integration of healthful routines, culminating in lowered lifetime healthcare costs and improved individual life satisfaction. Current machine-learning approaches deliver commendable …

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Calibrated Probabilistic Predictions For Phishing Email Detection Using Sentence-Bert With Machine Learning Models, Oluwasegun Abiodun Abioye, Abraham Evwiekpaefe, Philip Odion Oct 2026

Calibrated Probabilistic Predictions For Phishing Email Detection Using Sentence-Bert With Machine Learning Models, Oluwasegun Abiodun Abioye, Abraham Evwiekpaefe, Philip Odion

Journal of Informatics and Web Engineering

The growing sophistication of phishing attacks requires better detection than classic conventional filters. Previous work has focused on the accuracy, precision and recall of classifiers but there is a lack in testing well-calibrated probability predictions, which are crucial for reliable decision-making using cybersecurity systems. To fill this gap, we present in this work a phishing email detection framework that combines semantically rich Sentence-BERT all-mpnet-base-v2 model embeddings with a wide variety of machine learning classifiers. A total of nine model configurations were tested, including Sentence-BERT combined with GRU, SVM, Random Forest, Logistic Regression, k-Nearest Neighbours, Gaussian Naïve Bayes, Multi-Layer Perceptron, LightGBM …

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Iot Applications In Fire Safety Management: Bibliometric Study On Resilience And Emergency Response, Nur ‘Amirah Mhd Noh, Nor Rima Muhamad Ariff, Ahmad Amru Mohamad Zaid, Rabeah Md Zin Oct 2026

Iot Applications In Fire Safety Management: Bibliometric Study On Resilience And Emergency Response, Nur ‘Amirah Mhd Noh, Nor Rima Muhamad Ariff, Ahmad Amru Mohamad Zaid, Rabeah Md Zin

Journal of Informatics and Web Engineering

In the past few years, the Internet of Things (IoT) has been used more often in fire safety. Simple devices like smoke sensors, alarms, and connected networks are now able to detect signs of fire earlier and send alerts faster. These systems are not perfect, but they do make it easier to react before a small incident becomes serious. This paper reviews how research in this area has developed. The study looked at three main databases—Scopus, Web of Science (WoS), and IEEE Xplore. At first, the searches gave thousands of results, most of which were not directly related to fire …

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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, …

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Web Accessibility And Search Engine Optimization: Best Approach For Building Corporate Websites, Jeremiah Charles, Khaled Mahbub Oct 2026

Web Accessibility And Search Engine Optimization: Best Approach For Building Corporate Websites, Jeremiah Charles, Khaled Mahbub

Journal of Informatics and Web Engineering

The rapid digitization of products and services necessitates the development of safeguards to enforce equitable access to internet resources. Although legislative measures have been implemented to compel public-sector organizations to provide accessible web and mobile applications, most websites are still not fully accessible. There is an urgent need for organizations to integrate accessibility into current and future developments. The integration of solutions derived from decades of accessibility research into organizational processes and initiatives is beneficial to all users. This study provides a guide for businesses and organizations to improve their web accessibility and Search Engine Optimization (SEO). This research covers …

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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, …

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Deep Learning For Skin Cancer Classification Using Data Augmentation And Pre-Trained Cnn Models, Pa Pa Min, Fang Yong Lim, Jashila Nair Mogan, Astie Darmayantie Oct 2026

Deep Learning For Skin Cancer Classification Using Data Augmentation And Pre-Trained Cnn Models, Pa Pa Min, Fang Yong Lim, Jashila Nair Mogan, Astie Darmayantie

Journal of Informatics and Web Engineering

Skin cancer is one of the most common forms of cancer worldwide, and its early detection is critical for effective treatment and improved survival rates. In recent years, advancements in Artificial Intelligence (AI), particularly deep learning, have shown great promise in enhancing the accuracy and efficiency of medical image analysis. This study aims to develop a non-invasive, AI-powered diagnostic system for the early detection and classification of skin cancer using dermoscopic images. The proposed approach leverages deep learning techniques to automatically recognize and differentiate between various types of skin lesions, including both malignant and benign cases. The project focuses on …

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Adaptive And Learned Query Optimization: Techniques, Architectures, And Emerging Trends, Rehab Mohammed Abdelhalim Mokhtar, Palanichamy Naveen Oct 2026

Adaptive And Learned Query Optimization: Techniques, Architectures, And Emerging Trends, Rehab Mohammed Abdelhalim Mokhtar, Palanichamy Naveen

Journal of Informatics and Web Engineering

As databases continue to grow and complexity there is an urgent need to achieve more efficient and faster query processing. This paper presents a technical review of methods that work towards optimization of various types of queries. It progresses by focusing on basic approaches like rules-based optimization and cost-based optimization. Based on this basis, the paper presents a synthesized reference architecture for adaptive and learned query optimizers as a generic framework derived from prior system. It explains on how the classical optimizer components can be enhanced through runtime monitoring integration and feedback mechanisms. From this perspective, it becomes possible to …

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