International Journal of Innovative Computing
The International Journal of Innovative Computing is an open-access journal aimed to provide coverage of high-quality, peer-reviewed original research and review articles of interest to academic and industrial researchers in all areas of computing. Submissions must be clearly written and make a significant technical contribution. The International Journal of Innovative Computing publishes online two times a year in June and December. In general, only original papers will be considered. Submission of a manuscript to the journal is a representation by the author that the manuscript has not been published or submitted simultaneously for publication elsewhere. However, papers previously presented in conference proceedings of limited circulation may be submitted in revised form. If a paper has appeared previously, in any form, authors must clearly indicate this in both their cover letter and in a footnote on the first page of the paper. Manuscripts in the following categories will be considered for publication: Research articles, reviews, and new frameworks, models, algorithms, methods, and technologies. Indexed by: MYCITE, MyJurnal
A Dual-Level Rubric Design for Chemical Hazard Evaluation in Research Laboratories
Evaluation of chemical hazards in research laboratories must balance the requirements of regulatory frameworks with the need for tools that are usable by frontline staff. Systems such as the Globally Harmonized System (GHS), the European Union’s CLP Regulation, OSHA’s Hazard Communication Standard, and Malaysia’s CLASS Regulations provide a common language for hazard communication but do not in themselves identify which chemicals in a laboratory inventory should be considered highest priority [3], [24]. This study introduces a dual-level rubric that combines a simplified tool for everyday use with a more detailed scoring framework for deeper analysis. Both rubrics feed into an Environmental Hazard Index (EHI) that integrates hazard categories with exposure-related dimensions such as quantity and frequency of use. The framework was applied to more than 500 records representing nearly 700 unique chemicals across research laboratories at Universiti Teknologi Malaysia (UTM) Kuala Lumpur. Results include a ranked list of Top 20 chemicals, per-laboratory profiles, distribution analyses, and hazard-type composition. The outputs not only enhance chemical safety management but also generate structured, auditable data suitable for AI-driven governance in laboratory safety.
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ScoreSync: Automated Mark Entry App using Image Capture with Computer Vision and Optical Character Recognition
Traditional score recording, whereby educators manually key in scores in their respective education hubs, is time-consuming and susceptible to error when the volume of papers is high. To address this, ScoreSync, an automated mark entry app, was developed to streamline the grading and recording process by leveraging computer vision and machine learning. The primary objective is to develop an application capable of capturing images of exam papers using a lens or phone camera, ensuring a clear and accurate representation of score sheets. This would not only reduce an educator’s administrative burdens but also improve data accuracy and efficiency. Created to be applied by Android devices, ScoreSync captures images of graded papers and extracts scores through Optical Character Recognition (OCR) guided by YOLOv8, a Convolutional Neural Network (CNN) model optimised for bounding box detection. Developed through a Rapid Development Model, the app has been through multiple refines when it comes to the Artificial Intelligence (AI) model, transitioning from TensorFlow to PyTorch for greater flexibility and accuracy. The initial build of the ScoreSync is solely for the purpose of extracting graded papers of any kind, with results ranging from 80% to 90% in terms of detecting the scores in the exam sheets. Future improvements include expanding compatibility across platforms, refining the model adaptability towards different exam paper styles, and a setup that would aid the software when a live-capture solution is implemented, which would minimise more time spent rather than having the educators snap each exam sheet.
Machine Learning–Driven Ontology System for Coronary Heart Disease Diagnosis
Coronary heart disease is a disease that affects the larger coronary arteries on the surface of the heart and has been seen to be one of the major factors contributing to morbidity and death globally. This project presents a machine learning-driven ontology system for diagnosing coronary heart disease (CHD) based on patient data and clinical parameters. The system integrates a trained machine learning model, developed with the Framingham Heart Study dataset on the Weka platform, to predict the risk of CHD risk by extracting classification rules and embedding them within an ontology framework built in Protégé. The ontology models patient demographics, symptoms, and risk factors, providing a structured and semantically rich representation to support accurate diagnosis and clinical decision-making. The decision tree machine learning model achieved an 82.8 percent accuracy, and the ontology model was evaluated to be coherent and consistent. The system was further deployed to the web, enabling user-friendly interaction. By combining machine learning with ontology, this project not only leverages data-driven predictive power but also enhances interpretability, transparency, and adaptability in diagnosis. This synergy bridges the gap between statistical prediction and structured medical knowledge, ultimately contributing to more effective CHD risk assessment and improved patient outcomes in clinical practice.
Integrated GenAI Data Observatory Framework for Evidence-based Analytics of Malaria Burden
Minimal size of data can degrade the performance of machine learning models, leading to overfitting. This limitation hinders the transferability and generalizability of the models to different contexts or datasets. In resource-constrained settings, the lack of adequate data inhibits the development of robust, data-driven systems capable of delivering accurate insights. However, Generative Artificial Intelligence (GenAI) leverage Large Language Models (LLMs) to create and synthesize a wide array of meaningful content, including text, images, audio, video, and multimedia data. These models generate synthetic data that replicate the statistical properties of real datasets, thereby augmenting existing data and improving the overall quality of inputs available for machine learning tasks. In this paper, an integrated GenAI-enabled data observatory framework designed to enhance the storage and analytical processing is proposed to improve the quality of evidence of predictive analytics tasks. This framework employs advanced data warehouse technology combined with the capabilities of LLMs to facilitate a more effective data management system. To validate the machine analytics layer, data is sourced from the repository of Malaria Indicator Survey (MIS), which provide in-depth insights into the malaria burden faced by rural communities across Nigeria. The machine analytical layer of the proposed framework demonstrates percentage increases in overall accuracies with Decision Tree (DT) and Extreme Gradient Boosting (XGBoost) algorithms, achieving improvements of 23.81% and 15.38%, respectively, when compared to using the MIS data alone.
Design and Implementation of an AI-Integrated Power BI Dashboard for Data-Driven Logistics Management: A Case Study of Ninja Van Pengkalan Chepa
The exponential growth of e-commerce has intensified operational demands on courier and logistics services, making real-time performance monitoring and customer insight generation vital for competitiveness. In Malaysia, regional hubs such as Ninja Van Pengkalan Chepa (TrustCo) face recurring challenges, including parcel overload during peak seasons, manual reporting processes, and limited visibility into customer behavior. This study addresses these issues through the novel development of an integrated Power BI dashboard enhanced with AI chatbot capabilities, specifically tailored for regional courier operations, a combination not previously implemented in Malaysia’s logistics context. Employing a design and development research approach based on the Software Development Life Cycle (SDLC) framework with Agile principles, operational data from 2022–2025 including parcel volumes, delivery metrics, and customer feedback were analyzed and visualized. The proposed system integrates three innovative modules: (1) Operational Monitoring, for courier performance tracking; (2) Customer Analytics, enabling demographic segmentation and sentiment analysis; and (3) AI Interaction, introducing natural language query processing through a chatbot interface. User Acceptance Testing (UAT) involving logistics professionals validated the system’s usability, with 86.7% reporting intuitive navigation, 80% confirming data accessibility, and 93.3% verifying filter accuracy.
A Review on Responsible AI in Smart City
As smart cities become a global agenda among governments and municipalities, the integration of Artificial Intelligence (AI) has accelerated due to its potential to enhance urban efficiency, sustainability, and governance. However, this rapid adoption raises ethical and societal challenges that demand the implementation of Responsible AI principles. This paper adopts surveyed literature approach, surveying academic works, industry frameworks, and government policies related to Responsible AI in the smart city context. The review synthesizes key dimensions of Responsible AI including ethics and governance, bias and fairness, privacy and security, sustainability, and human AI collaboration while comparing strategic approaches adopted by major technology companies and national AI policies. Findings indicate that most current efforts remain conceptual, focusing on principles and governance rather than technical deployment. The study highlights the need for practical implementations such as algorithmic transparency, bias mitigation mechanisms, and clear policy technology alignment. Recommendations are proposed to guide future integration of Responsible AI in smart city initiatives to ensure ethical, transparent, and human-cantered outcomes.
Career-Driven Study Pathways Prediction for Secondary School Students Using Hybridized Machine Learning
Future study pathway determination is paramount for the secondary students’ further academic development especially in preparing themselves for their careers. Without sufficient information and guidance, students might be exposed to information-limited advice and choices which tend to risk their future undertaking. Addressing this crucial decision-making challenge in choosing the study pathway while considering the students’ career dreams, this paper proposes hybridized machine learning models which are career-driven in guiding the students to make the right study pathway decision. Upon pre-processing the dataset containing essential students’ academic performance-related data as well as their career aspirations, four different hybridized machine learning models are developed to be trained and tested with the data for predicting the students’ study pathways. The machine learning models studied include random forests, decision trees as well as neural networks which are hybridized between each other to benefit the diversifying strengths of each model. The performance metrics such as accuracy and precision have been measured cross-validated to verify the model’s dependability. The obtained results demonstrated that the proposed career-driven hybridized models perform better than the typical existing machine learning models, which echo into better decisions offered by the proposed hybridized models in advising students’ future study pathway more effectively.
Enhancing Smart Home Security Using Adaptive Access Control with Blockchain and Machine Learning
Smart homes, equipped with interconnected IoT devices such as locks, cameras, and sensors, face critical security challenges due to the limitations of static access control mechanisms like Role-Based Access Control (RBAC) and Attribute-Based Access Control (ABAC), which lack adaptability to dynamic, multi-user environments and evolving threats. To address this problem, this research introduces a hybrid Blockchain–Machine Learning (ML) framework that ensures secure, adaptive, and context-aware access control for smart home ecosystems. The proposed system integrates IoT devices with ML algorithms, including Support Vector Machines (SVM) and Neural Networks, to predict user behaviours and dynamically adjust access permissions in real time, while Blockchain ensures immutable, decentralized, and tamper-proof logging of access events. The methodology employed a mixed approach, beginning with an extensive literature review to identify shortcomings in existing static models, followed by system design using smart contracts, caching strategies to reduce latency, and a user perception survey involving 25 participants to validate acceptance and usability. Results demonstrated high user trust and readiness to adopt the proposed system, with 96% of respondents favouring Blockchain-ML-enabled dynamic access control over conventional methods despite concerns about privacy risks, costs, and implementation complexity. This work contributes to society by offering a scalable and intelligent smart home security solution that enhances trust, improves user experience, and strengthens resilience against cyber threats, ultimately supporting safer and smarter living environments.
AI-Powered Collaborative Filtering for Hyper-Personalized: Leveraging Machine Learning and Business Intelligence
This study offers a novel method for attaining hyper-personalization in e-commerce by combining business intelligence (BI) and machine learning technologies with collaborative filtering driven by artificial intelligence (AI). The main goal is to improve recommendation systems so that they can provide highly relevant and personalized product recommendations that suit the tastes and habits of specific users. The suggested method enhances recommendation accuracy and diversity by utilizing sophisticated deep learning architectures like convolutional and recurrent neural networks to record intricate user-item interaction patterns. Hybrid models are used to overcome issues like data sparsity and cold-start issues. These models combine content-based and collaborative filtering, and they make use of auxiliary data sources like social networks. Real-time data analysis, user segmentation, and performance monitoring are further made possible by the integration of BI tools, which promotes operational enhancements and strategic decision-making. According to experimental data, this all inclusive methodology performs noticeably better than conventional techniques, attaining greater precision, recall, and user engagement metrics. In the end, a strong, scalable solution that can offer a smooth, customized shopping experience, boost customer satisfaction, cultivate loyalty, and propel revenue growth in cutthroat e-commerce environments is produced by the synergistic use of AI, machine learning, and BI.
Modelling Temperature-Induced Performance Losses in Rooftop Solar Panels Using Machine Learning
This study investigates the critical impact of temperature in the performance of rooftop solar photovoltaic (PV) systems. Particularly focusing on energy generation efficiency losses when temperatures exceed 28°C. High temperatures increase the internal resistance of PV cells, reducing their ability to convert sunlight into electricity, the research identifies an optimal operating temperature range for solar panels between 24°C and 26°C, where efficiency is maximized. To predict these temperature-dependent performance losses, machine learning models, are employed. A comparation between Linear regression in capturing the complex, non-linear relationships between temperature, irradiance, and energy output. While Linear Regression showed a perfect R2 score of 1.0 indicative of overfitting. The Random Forest model achieved a robust R2 of 0.8987, demonstrating superior generalizability and accuracy of real- world applications. This research validates the effectiveness of machine learning techniques for more reliable solar energy forecasting and optimization and highlights the necessity of considering thermal management in solar system design, especially in hot climates.
Performance Analysis of the Sam Poo Kong Signalized Intersection for the Implementation of Priority Bus Signal Technology in Semarang City
Currently, public transportation in Semarang City is still not optimal. Efforts are needed to shorten public transport travel times to make public bus services more attractive to passengers. This research aims to analyze the Sam Poo Kong signalized intersection, as a preliminary study before implementing priority bus signal technologyThis study analyzes the Sam Poo Kong signalized intersection to evaluate and implement Priority Bus Signal (PBS) technology using Verkehr In Städten – SIMulationsmodell (VISSIM) simulation software — a microscopic traffic simulation tool for evaluating vehicle movement dynamics. Results show that implementing the proposed PBS system reduces average bus waiting times by 27.5% and queue lengths by 18% during peak hours. These findings demonstrate that integrating smart signal control can improve urban mobility efficiency and support Semarang’s Smart City initiative. The data obtained included the highest speed recorded during the afternoon on the road section towards Kaligarang reaching 44.9 km/h. Apart from that, the longest duration of the red light is at Traffic Light B, while the green light is at Traffic Light A. For the shortest duration, the red light is at Traffic Light A, while the green light is at Traffic Light B. Results of the study will be used to implement priority bus signal technology.
Advancing Smart Industries with Internet of Things and Artificial Intelligence: A Digital Ecosystem Perspective
The digital transformation of industrial systems under the Industry 4.0 paradigm has introduced cyber-physical systems (CPS) as a core enabler of vertical integration and data-driven production environments. The convergence of Internet of Things (IoT) and Artificial Intelligence (AI) technologies has accelerated this transformation, fostering the development of the Industrial Internet of Things (IIoT) and creating smart industries characterized by real-time monitoring, automation, and human–robot collaboration (HRC). While these advancements establish a digital ecosystem capable of optimizing production through intelligent data analysis and decision-making, their practical implementation remains constrained by unresolved challenges and gaps in validation. With the emergence of Industry 5.0, the focus shifts toward human-centric, sustainable, and resilient industrial ecosystems, where AI-driven cognitive computing further enhances interaction between humans and machines. This review examines the application of AI–IoT integrated technologies across multiple industrial domains to identify their strengths, limitations, and recurring challenges. By categorizing existing literature into key application areas, the study highlights both the opportunities and risks inherent in current approaches, bridging the conceptual design of smart industries with their real-world realizations. The findings underscore the importance of scalable, secure, and efficient frameworks to ensure the safe and reliable adoption of AI–IoT in the industrial ecosystem.
An AI-Based Smart Study Planner for University Class Scheduling Optimization
Efficient course scheduling remains a major challenge in higher education, where manual registration and limited predictive tools often delay student progression and strain institutional resources. This study presents an AI-based Smart Study Planner that integrates predictive analytics, real-time student input, and an AI-driven chatbot advisory system to optimize academic scheduling. The planner forecasts cumulative grade point average (CGPA), automates course recommendations, and provides administrators with real-time demand analytics for resource allocation. Developed using the Laravel framework with MySQL and ApexCharts, the system was validated through User Acceptance Testing (UAT) involving 10 undergraduate students and 3 academic administrators. Quantitative evaluation indicated a 29% improvement in planning efficiency, 25% reduction in registration conflicts, and overall user satisfaction of 4.6/5, while benchmarking against Coursicle, CGPA Forecaster, and Ellucian Degree Works confirmed higher predictive accuracy (91%) and usability. These findings demonstrate that the proposed planner is a cost-effective, scalable, and empirically validated solution that enhances student decision-making and administrative planning. The research contributes both a practical implementation framework and empirical evidence supporting AI-driven optimization as a viable approach to addressing persistent inefficiencies in academic scheduling. Overall, the findings demonstrate that the AI-based smart study planner is cost-effective, scalable, and user-friendly, addressing critical shortcomings in existing solutions.
Improving Latent Space Diversity in SMILES Generation Using Transformer VAE
This work presents TransMolVAE, a Transformer-based variational autoencoder for molecular generation using SMILES. RNN-based models such as SmilesVAE often face posterior collapse. This happens when the latent space is not well used. To solve this, TransMolVAE applies self-attention. This allows the model to capture long-range patterns in SMILES strings. It improves reconstruction accuracy, keeps KL divergence stable, and makes better use of the latent space. The experiments showed strong results. Token accuracy increased steadily, reaching 0.72 by the fifth epoch. In contrast, SmilesVAE stayed low at 0.25. Validity, uniqueness, and novelty of molecules generated by TransMolVAE also reached above 70%. For SmilesVAE, these values were only between 50% and 55%. These findings show that TransMolVAE can generate more valid, novel, and diverse molecules compared to the baseline. The number of active units also stayed high, proving that the latent space was fully used. There was a trade-off in computation. TransMolVAE needed longer CPU time per epoch compared to SmilesVAE. This is because Transformer models are more complex and require higher computation. Even so, the improved quality of molecule generation makes TransMolVAE more suitable for drug discovery, where novelty and diversity are more important than speed.
Exploring Explainable AI and Reinforcement Learning in Educational Conversational Agents with Metacognitive Self-Regulated Learning: A Systematic Literature Review
This systematic literature review explores integrating conversational AI, reinforcement learning (RL), and interpretable AI (XAI) in programming education to enhance metacognitive self-regulated learning (MSRL). Conversational AI offers programming support and feedback, and combining it with RL optimizes interaction strategies, yet existing studies suffer from overreliance on generative AI, insufficient long-term empirical evidence, and AI "black box" issues. XAI, while addressing the black box problem, struggles to balance performance and interpretability. Core research questions include effective RL integration for MSRL, XRL ’ s role in building trust, conversational AI’s support for code generation and metacognitive training, application challenges, and long-term experiment design. A "conversational AI+RL+XAI+affective computing" framework was established via database searches, with mixed methods analyzing quantitative and qualitative data. Findings reveal a focus on paired technology combinations rather than overall synergy. Key challenges are mismatched XAI interpretation strategies, difficulty quantifying MSRL as RL rewards, and lack of standardized evaluation. The study fills critical research gaps, providing a foundation for transparent adaptive educational AI. Future research should prioritize process interpretability, large-scale long-term teaching experiments, and optimized multi-objective reward functions to advance programming education tutoring systems.
Solar GenAI: An Offline LLM-Based Generative AI Agent for Solar Photovoltaics in Malaysia
The adoption of solar photovoltaic (PV) systems in Malaysia is often limited by fragmented information and restricted access to reliable, localized knowledge. To address this challenge, Solar GenAI is introduced as an offline-capable, generative AI agent built on the Mistral-7B-Instruct model and deployed entirely on local hardware using the Ollama platform. Designed to function without internet connectivity, Solar GenAI ensures data privacy, institutional autonomy, and broad accessibility, particularly in bandwidth-limited or secure environments. The system integrates a Retrieval-Augmented Generation (RAG) pipeline grounded in a curated corpus of Malaysia-specific solar policies and technical documents. This architecture eliminates hallucinations and achieves 100% factual accuracy across representative queries, compared to a 60% hallucination rate in an LLM-only baseline. A caching mechanism further enhances responsiveness, reducing average query latency by 35%. The user interface was developed based on Nielsen’s usability principles to support intuitive interaction across diverse user groups. By combining fully offline LLM deployment, grounded retrieval, and performance optimization, Solar GenAI provides a secure, accessible, and scalable platform to support solar PV awareness and education, institutional applications, and policy implementation in Malaysia.
Hybrid Wavelet-PCA and DNN-LSTM-Kalman Framework for Robust State-of-Charge Estimation Using Minimal Sensors
An accurate prediction of state of charge (SOC) is essential for the operational performance and durability of lithium (Li)-ion battery systems in electric vehicles. Its accuracy in dynamic operation, however, is often limited by noise sensitivity, drift buildup and rest-conditioning dependence of conventional procedures such as Coulomb counting and open-circuit voltage. This work presents a two-stage hybrid framework for SOC estimation to overcome these limitations. The first stage includes wavelet transform denoising and principal component analysis (PCA) to enhance the quality of signal, eliminate redundant information, and achieve a signal-to-noise ratio (SNR) improvement up to 77.9 dB with 95% variance retention. The second stage combines a deep neural network (DNN)–long short-term memory (LSTM) model with Kalman filtering to capture nonlinear temporal dynamics and generate smoothed SOC predictions. Experimental results show that the root mean squared error (RMSE) of baseline SOC estimation methods was 21.8%, while that of the proposed method was only 0.52%, achieving over forty-fold accuracy improvement. The proposed framework demonstrates strong robustness under noisy and dynamic operating conditions and can be further extended to state of health (SOH) prediction by incorporating battery degradation features, enabling predictive maintenance and enhancing long-term reliability in battery management systems.
Cyberbullying Detection on Social Media: A Review of Features and Machine Learning Approaches
The rapid growth of digital communication platforms has facilitated global connectivity while simultaneously intensifying the prevalence of cyberbullying, a form of online aggression with severe psychological consequences for victims. The research problem addressed in this paper concerns the limitations of manual moderation methods in detecting harmful online behavior at scale. Consequently, this review paper provides a comprehensive synthesis of recent research on cyberbullying detection, with an emphasis on feature engineering and supervised learning algorithms, including Support Vector Machine (SVM) and Random Forest. Feature engineering strategies that include lexical, sentiment, user, and network-based features are examined for their role in enhancing detection accuracy. Analysis of 13 key studies reveals that SVM and Random Forest consistently outperform other classifiers, especially when combined with robust optimization techniques. The review identifies that content-based features, particularly TF-IDF, combined with hyperparameter tuning, achieve accuracies exceeding 94% in optimal configurations. The study concludes that while current models demonstrate substantial progress, challenges related to linguistic ambiguity, data imbalance, cross-platform generalizability, and lack of multilingual support remain critical directions for future research.
Recent Studies on Motion Detection for Education Environment using Computer Vision with Deep Architecture
The integration of computer vision and deep learning within educational environments offers innovative methods for enhancing student engagement and focus assessment. Traditional classroom observation is often limited by time constraints and subjective biases, creating a need for objective and automated monitoring solutions. Recent advancements in AI-based motion detection, particularly using deep learning architectures such as CNNs, YOLO variants, R-FCN, and LSTMs, show promising results in tracking and analyzing student behaviors and engagement through movement and positioning. This review explores state-of-the-art models used in educational settings to classify and detect behaviors, emotional states, and engagement levels based on observable cues, including facial expressions, gestures, and posture. Furthermore, optimizing camera placement and using multi-angle configurations are identified as crucial factors for improving detection accuracy in complex classroom setups. Experimental findings on model performance, including precision, box loss, and classification accuracy, reveal these systems’ efficacy and limitations, with some challenges persisting in precise localization. Despite these, the practical implications of real-time motion detection systems are significant, enabling educators to create responsive and interactive learning environments. This study underscores the potential of AI-enhanced monitoring in transforming classroom dynamics and promoting an engaging educational experience.
Exploring Agent Behavior and Performance in Shortened Simulations via Multiagent Reinforcement Learning
This study explores the potential of Multiagent Reinforcement Learning (MARL) for autonomous navigation in a discrete two-dimensional environment. We design and implement an agent that learns an optimal path through a 5×5 grid via repeated interactions and a reward-based mechanism. Over 500 training episodes, we examine the convergence speed of the learned policy, the stability of agent behavior, and the success rate in reaching the goal. Our approach combines artificial neural networks with a multiagent framework, enabling decentralized decision making and scalable adaptation. We discuss critical factors affecting learning stability, including reward function design and network architecture, and outline avenues for extending the methodology to more complex, real-time tasks. The findings demonstrate MARL’s promise in solving navigation problems efficiently and provide concrete recommendations for tuning training parameters and network structures to enhance performance and robustness.
Applying Multimodal Large Language Models for Visual Question Answering: Toward Vietnamese Educational Reasoning Systems
Visual Question Answering (VQA) is rapidly advancing due to Multimodal Large Language Models (MLLMs), which demonstrate powerful complex reasoning capabilities. However, this progress is predominantly centered on English and general-domain contexts. Domain-specific fields, such as STEM education, and low-resource languages, such as Vietnamese, remain significantly underserved, lacking both standardized datasets and specialized reasoning models. This research addresses this gap by investigating how MLLMs can be adapted for Vietnamese educational settings. As a foundational step, the ViPPS dataset (Vietnamese Physics Problem Solving) has been constructed and publicly released, the first multimodal dataset for physics problem solving in Vietnamese. Initial experiments on ViPPS show that current MLLMs achieve promising results, but still struggle with domain-specific reasoning and numerical accuracy. Based on these observations, the next stage of this research will focus on expanding the ViPPS dataset in both scale and scope, developing and evaluating advanced text-image reasoning and calculation mechanisms and extending these capabilities to VideoQA and model explainability. This research will contribute critical resources and methods, advancing the field of educational VQA for low-resource languages.
Enhancing Vision Transformer with Vision language Model Text Embeddings for Robust Air Pollution Classification
Far from arguing the importance of ecological awareness in our current era it is however necessary to highlight the pressing global environmental challenges that are faced by our society. While air pollution is only a sector of concern it is essentially one of the most critical factors influencing human health and environmental sustainability. Under this premise, monitoring air quality is necessary. While the Air quality index has mostly been measured using Internet of Things (IoT) sensors, detecting visible air pollution has garnered interest due to its accessibility. However, the existing works based on vision only methods (CNNs, Vits) have shown limitations in capturing generalized correlations that are essential for a robust air pollution detection system. The proposed solution investigates the capability of semantic information to broaden the scope of features learned by the model. A Vision language Model (VLM) based text encoder with the objective of introducing knowledge anchors across any datasets. The model generates language tokens to guide a Vision transformer. The proposal also investigates tuning mechanisms for the VLM and image filtering for the input data. The key innovation targets a cross modal integration of vision transformers with a vision language model to create a few shots classification model for air pollution classification. The aim is to produce a model with flexible data integration and capable of leveraging visual and semantic correlations. The research demonstrated an improved generalization across a broad dataset variance. The model outperforms baseline CNN in accuracy when it comes to cross data implementation.
Combining the Group Method of Data Handling with Firefly Algorithm for Rainfall Forecasting
Rainfall forecasting continues with the evolution of mixed and new research, where a variety of the same models have been suggested to make some improvements in prediction validity. However, at the same time there is a lack of models that seems to be single capable of handling all features of the data to the best of its ability, with multi-method selection difficult. One way to overcome this constraint is that model combination methods have rapidly emerged in time series forecasting, especially precipitation forecasting. These can be an array of simple but complex model pairs. This study proposes a rainfall forecasting framework for the work based on Group Method of Data Handling (GMDH) and Firefly Algorithm (FA). Forecasting is first of all done based on the GMDH (with polynomial transfer function, GMDH-Poly). Then the model is further enlarged with transfer functions (Sigmoid function, Tangent function, and Radial Basis Function). After that, FA is used to heuristically update the weights of each GMDH variant, taking into account the output from this optimisation for a better prediction. This proposed approach is applied in the case of rainfall data in Kuching, Sarawak for the years 2010–2019. The experimental results clearly show the hybrid GMDH-FA model significantly outperforms the standard GMDH model. In particular, GMDH-FA scoring RMSE, MAE and MAPE is 0.0550, 0.0455 and 24.0733 respectively, which is significantly lower than that of, GMDH-Poly (0.0981, 0.0761 and 47.3864) in testing data set.
Towards Resource-Constrained Event Extraction: A Knowledge-Augmented Framework for Overcoming Challenges in Vietnamese NLP
Event Extraction (EE) is a crucial task in Natural Language Processing (NLP), instrumental in capturing meaningful activities and contributing to the tracking of narratives and developments within textual documents. Extensive research has been dedicated to improving the accuracy of event trigger identification and argument role classification, spanning from traditional machine learning to modern deep learning architectures. Recently, driven by the rapid advancements of Large Language Models (LLMs), these models have been applied to EE, primarily through data augmentation or fine-tuning approaches. However, the computational and resource overhead associated with LLMs remains a significant challenge. Furthermore, existing state-of-the-art methods predominantly focus on high-resource languages such as English and Chinese, leaving low-resource languages, like Vietnamese, largely under-explored due to their unique linguistic ambiguities. Consequently, our research direction focuses on leveraging a Small Language Model-based (SLM-based) approach, augmented with external knowledge, to address the EE task in Vietnamese. The ultimate objective is to develop a compact model capable of effectively addressing core EE challenges, such as rare events, semantic ambiguity, and long-range dependencies—thereby establishing an efficient and robust framework specifically tailored for the Vietnamese low-resource language domain.
A Sigmoid-Based Multi-Label U-Net with Weighted Tversky Loss for Cardiac Fat Segmentation in CT Imaging
Cardiovascular diseases remain among the leading causes of mortality worldwide. Cardiac fat, particularly epicardial adipose tissue and mediastinal adipose tissue, has increasingly been recognized as a key indicator in the early detection of cardiovascular diseases. However, this detection remains challenging due to the thin and often indistinct pericardial membrane that separates epicardial and mediastinal in non-contrast computed tomography (CT) images. Accurate segmentation of these fat regions and the pericardial membrane in CT imaging is essential to support clinical risk assessment and timely medical interventions. Traditional manual segmentation techniques have been employed in the past, but they are time-consuming and prone to inter-observer variability. Meanwhile, conventional machine learning algorithms often struggle to capture the complex spatial information inherent in tomographic CT images. To address these challenges, this study proposes an improved U-Net model designed to enhance the accuracy and adaptability of coronary fat segmentation in non-contrast CT scans. The model specifically addresses issues such as class imbalance, ambiguous labeling, and limited generalizability by incorporating a sigmoid activation function, which enables binary predictions for each anatomical structure and allows the model to effectively handle overlapping regions in multi-class segmentation tasks. The proposed model is evaluated on an annotated CT dataset using standard segmentation performance metrics. The results demonstrate that the advanced U-Net architecture developed in this study achieves improved accuracy in cardiac fat segmentation, thereby offering potential to enhance clinical diagnosis and support better patient treatment in medical imaging.
Modelling and Improving Efficiency of Emergency Department’s Green Zone Using Discrete Event Simulation
This study aims to optimize patient flow within the Green Zone of a Hospital Emergency Department in Kuala Lumpur by focusing on reducing patient waiting times and improving staff workload caused by overcrowding and operational inefficiencies. A model was developed to replicate current Green Zone operations and evaluate scenarios to optimize staff allocation and identify the bottlenecks by using Discrete Event Simulation (DES) in ARENA software. The model has been verified by medical professionals and validated against real-world data with an acceptable deviation of less than 10%. Implementation of the optimized model significantly reduced average patient waiting times from 174.50 minutes to 60.65 minutes. Doctor utilization rates also decreased from 97.00% to 76.66%, indicating improved resource efficiency and workload distribution. The application of DES in optimizing patient flow within the Green Zone demonstrated significant improvements in both patient experience and operational efficiency. The approach holds potential for broader implementation across other departments and healthcare facilities in Malaysia.
A Review of Virtual Conferencing, Interaction and Collaboration in Virtual Reality
With the rapid advancement of technology, virtual conferencing, as a new communication medium, are changing people's traditional communication methods and work models. Through the Internet and advanced communication technology, virtual conferencing eliminates geographical distance and enable people to conduct various communication methods such as video conferencing, audio calls, and real-time chat, making communication easy regardless of time and space. Since COVID-19, there has been a wealth of research on virtual conferencing, especially augmented reality (AR) and virtual reality (VR) conferencing. They are either based on discussions of virtual conferencing technology, reflections on completed virtual conferencing events, or surveys on the user experience of virtual conferencing, but a comprehensive summary of the shared features of virtual conferencing is currently lacking. In this study, we will summarize the key features of virtual conferencing and propose existing issues and challenges in virtual conferencing based on these features, in order to provide directions for improving the quality of virtual conferencing.
Design and Implementation of a Transparent and Modular Animal Welfare System Using React Native and Firebase
This study presents Pethero KPH UUM, a centralized mobile application designed to streamline stray animal welfare management for Kelab Pencinta Haiwan Universiti Utara Malaysia (KPH UUM). The application addresses key organizational challenges, including inefficient communication, opaque donation processes, and disorganized volunteer coordination. Through interviews, surveys, and a comparative analysis of existing systems, critical limitations in current practices were identified. Developed using React Native, Firebase, and other modern tools, the application integrates key features such as donation tracking, real-time animal updates, volunteer management, and adoption services to enhance transparency and engagement. The Rapid Solo Software Development (RSSD) methodology was employed to ensure a structured, modular, and efficient development process. System testing and user evaluations confirm the platform's usability, effectiveness, and positive impact on operational transparency and community engagement. By digitizing KPH UUM’s workflows, Pethero not only optimizes resource management but also cultivates a more proactive and compassionate approach to animal welfare within academic communities. The findings highlight the potential of tailored digital solutions in strengthening non-profit initiatives.
Deep Learning-based Ransomware Detection Model with Hybrid Analysis
Ransomware continues to advance as a major cybersecurity threat integrating obfuscation techniques to evade detection systems. Existing machine learning approaches often struggle to identify novel ransomware variants due to their limited ability to capture temporal and behavioral patterns. To address this gap, this study proposes a hybrid ransomware detection framework that integrates both static and behavioral analysis using Long Short-Term Memory (LSTM) network architectures. The models investigated include Vanilla LSTM, Bidirectional LSTM, Stacked LSTM, and Convolutional LSTM (ConvLSTM). Datasets containing labeled Windows-based ransomware and benign samples were collected from open-source repositories and pre-processed into structured feature vectors suitable for time-series modeling. The proposed hybrid framework was evaluated using accuracy, precision, recall, and F1-score metrics to determine which LSTM performed the best. Among the tested models, ConvLSTM achieved the highest accuracy of 97.36%, with a precision of 97.2%, recall of 97.39%, and F1-score of 97.3%, outperforming other LSTM architectures. These results demonstrate that combining static and behavioral features with deep learning significantly improves ransomware detection performance, suggesting the approach’s strong potential for real-world cybersecurity applications.
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Comparative Study of Consensus Mechanisms for Digital Image Evidence Validation using Smart Contracts on Layer 2 Polygon Blockchain
Ensuring the authenticity, integrity, and reliability of digital image evidence is a persistent challenge in forensic and legal domains due to the vulnerabilities of centralized evidence management systems. This study compares three blockchain consensus mechanisms—Proof of Existence (PoE), Proof of Ownership (PoOW), and Zero-Knowledge Ethereum Virtual Machine (zkEVM)—to assess their effectiveness in securing and validating digital image evidence on the Layer 2 Polygon network. Forensic images were stored on the InterPlanetary File System (IPFS), with each consensus model registering tamper-evident Content Identifiers (CIDs) on-chain via dedicated smart contracts. The evaluation considered performance metrics including latency, gas usage, transaction fees, throughput, scalability, and privacy protection. The findings revealed that PoE demonstrated the best overall efficiency, achieving a latency of 3730ms, a transaction fee of 0.001321 ETH, and a throughput of 0.176 TPS, making it well-suited for real-time applications such as timestamping and immediate evidence submission. PoOW, although more computationally demanding, achieved the highest gas-refund rate at 89%, making it ideal for ownership verification and traceability, such as copyright and asset provenance. Meanwhile, zkEVM provided a well-rounded performance profile with moderate transaction costs and latency. It is powerful for privacy-preserving applications that require cryptographic guarantees, especially in enterprise and regulatory settings. This comparative evaluation highlights the unique advantages and limitations of each approach, providing critical insights into selecting the most suitable blockchain-based consensus mechanism for the transparent and tamper-resistant validation of digital forensic evidence.