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
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.
Copyright (c) 2026 International Journal of Innovative Computing https://creativecommons.org/licenses/by/4.0
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.
Copyright (c) 2025 International Journal of Innovative Computing
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.
Tree Inventory using Near-Field Communication (NFC) Tag
Urban green spaces play a vital role in community well-being; however, effectively maintaining and managing these spaces, particularly in terms of individual tree health, can be challenging. Conversely, Geographic Information Systems (GIS) technology offers a promising solution. This study aims to develop a web-based GIS system for tree park maintenance. The research problem stems from risks such as tree death, overgrowth, nutrient deficiencies, and pest infestations, which resulted from inadequate maintenance and monitoring practices, the current manual monitoring methods, and the lack of a dedicated maintenance system. The research objectives encompass reviewing existing approaches, developing a web-based GIS with Near Field Communication (NFC) technology, and conducting testing. The methodology involves the development of a web-based tree inventory system and its integration with the Web NFC API, following the System Development Life Cycle (SDLC). The output is a process flow for updating on-site tree inventory using the NFC tag feature through a web-based GIS application, which enables users to submit real-time maintenance activities and allows administrators to view the data. The benefits include enhanced durability and reliability in the field, as the use of NFC tags, compared to traditional QR codes, offers better resistance to weather conditions. This leads to more consistent data retrieval, improved monitoring and management, and informed decision-making for future maintenance, ultimately contributing to healthier and safer urban green spaces.
A Cost-Benefit Analysis of Zero Trust Architecture (ZTA) Using Hybrid Financial Impact and Threat Mitigation Strategy
Zero Trust Architecture (ZTA) emerges as a pivotal cybersecurity paradigm, fundamentally shifting from traditional perimeter-based security models to a 'Never Trust, Always Verify' ethos, which necessitates continuous authentication and authorization for all network requests, regardless of origin. The increasing adoption of cloud technologies, Internet of Things (IoT) devices, and remote workforces has significantly expanded enterprise network perimeters, rendering conventional security methods such as Virtual Private Networks (VPNs) inadequate against modern attacks like Man-In-The-Middle and Denial of Service (DoS). Despite the growing recognition of ZTA's importance, organizations often exhibit hesitancy in committing resources due to a perceived lack of comprehensive quantitative data on its benefits, available tools, pricing structures, and efficacy rates. This research directly addresses this critical gap by conducting a rigorous cost-benefit analysis, evaluating the financial impact, cost-effectiveness, and threat mitigation outcomes of ZTA implementation. The study's methodology is structured in three phases. In Phase 1, ZTA tools, resources, and components were identified, along with the necessary investments. Vendor pricing data was collected from reputable security providers, including Microsoft, Kaspersky, IBM, CrowdStrike, Google, and BlackBerry. These vendors offer a wide range of security services such as Data Encryption, Identity and Access Management (IAM), Data Protection, Cloud Storage, and Micro-Segmentation. The selected tools and resources function as core threat mitigation strategies designed to reduce data breach risks and associated financial losses. Additionally, data-driven, quantitative methodologies are applied to estimate the total cost of implementing ZTA tools and resources.
Evidence Collection and Handling Process Model in Mobile Forensic for Malaysian Law Enforcement Agencies
The rapid adoption of mobile technology has revolutionized criminal investigations, with mobile devices becoming crucial sources of evidence. The current state of mobile forensics faces challenges from a growing backlog of devices to be analyzed. There have been many initiatives to improve the state of mobile digital forensics. However, most initiatives have focused on relieving the digital forensic analyst on pre-analysis tasks such as triage, extraction workflow and management of cases and evidence. There is still a bottleneck at the actual analysis stage that hampers swift legal resolution. The research objectives include identifying current challenges in mobile forensics, developing an alternative process mobile forensic process model, and evaluating the feasibility of the proposed model. The methodology used in this research is Design Science Research (DSR) as it seeks to enhance knowledge in technology and science through problem-solving and innovative solutions to real-world problems. Through this study, an alternative mobile forensics process model was developed involving a central pseudo air-gapped digital forensics extraction lab with analysis hubs in different locations connected over private networks. The model is validated through surveys with domain experts to offer promising efficiency improvements and feasiblity albeit with potential technical, operational, and security challenges. The significance of this research lies in enriching the knowledge base of digital forensics professionals and legal experts, offering valuable insights for real-world implementation to enhance the efficiency and timelines of mobile forensic analysis, and ultimately contributing to a more just legal system.
Educational AR Mystery Puzzle Game for Children
AR (AR) has been on the rise in which it has been implemented in many fields. Children these days have low attention span when they have no electronic devices on them. To deal with this issue, the aim of this research is to design an educational AR mystery puzzle for children aged 4 to 6. Therefore, the objective of this research is to analyse the user engagement, problem solving skills and the learning outcome with the AR mystery puzzle with an interesting storyline and lastly, to evaluate the AR mystery puzzle design where it focuses on the usability. There are 2 levels provided in the prototype, for level 1 the users were required to choose one marker out of the two markers provided, and for level 2 the users were required to choose two markers out of the three markers provided to go through with the story. Moreover, players can inspect the items before making the final decision and the leader board score is based on the time taken to finish the game. Waterfall methodology is used for this research since the workflow of the proposed game is an initial prototype to better understand its potential based on the objectives and scopes covered with a clear requirement. For the evaluation, usability tests are done as well as the functionality testing. The results showed that this proposed game design can inspire children for them to be exposed to an AR mystery puzzle game and be educated with essential life skills.
The Halting Problem in Complexity Theory: A Review
The Halting Problem is a pivotal concept in computational theory, questioning whether a general algorithm can determine if a program halts or runs indefinitely on a given input. While foundational works have established its undecidability, a critical comparative evaluation of how the problem has evolved across theoretical, educational, quantum, and dynamical domains remains lacking in the literature. This gap limits comprehensive understanding and cross-domain applicability for researchers. Therefore, this paper aims to address that void by analyzing and synthesizing five influential contributions: Turing’s 1937 foundational proof, Pavlotskaya’s 2002 mathematical formalization, Sipser’s 2013 pedagogical perspective, Aaronson’s 2016 exploration into quantum undecidability, and Cotler and Rezchikov’s 2024 application in computational dynamical systems. Through structured analysis of their methodologies, assumptions, and implications, this paper highlights the strengths, limitations, and evolving interpretations of the Halting Problem. Results confirm that its undecidability has far-reaching effects on algorithm design, software verification, and emerging areas like quantum computing and chaotic systems. By identifying key gaps such as lack of empirical validation and practical tools, this paper proposes future directions including approximation algorithms, interdisciplinary modeling, and AI safety applications. This study thus offers a consolidated foundation for researchers seeking to explore the Halting Problem’s role across both classical theory and modern computational frontiers.
Theoretical Foundations and Applications of Space Complexity in Distributed and Geometric Computation
Space Complexity. This paper presents a formal examination of space complexity across distributed consensus, geometric clustering, decision-tree inference, and functional convolution. Drawing on five recent research contributions, the study unifies theoretical models and structural bounds to characterize minimal spatial requirements under diverse computational frameworks. The analysis employs indistin-guishability and valency arguments, coreset-based compression theory, functional-space metrics, and convolutional operations. The results establish several tight lower bounds and structural equivalences, providing methodological insights for the design of algorithms operating under stringent memory constraints.
A Systematic Mapping Study of Code-mixed Embedding Technique
The mix of languages with other languages in a conversation or text is a phenomenon known as code-mixing. This paper presents the existing research on code-mixed embedding techniques, with a focus on identifying research gaps, trends, performance, and methodology approaches. A comprehensive review of 44 peer-reviewed publications from 2016 to 2024 was conducted using online digital libraries. The selected studies were analyzed based on publication trends, language pairs, strengths, and limitations. The result shows a growing interest in the publication trend for papers involving code-mixed embedding techniques, with coverage of several common techniques such as character embedding and word embedding, including Hindi-English language pairs being the most studied. However, several limitations remain in unoptimized models and low-resource language coverage. This mapping provides a structured overview of the field and offers direction for future research in developing more robust and inclusive code-mixed embedding techniques.
Fatigue Driving Recognition Using Improved YOLOv8
Fatigue driving contributes to 21% of fatal accidents, leading to slower driver responses and increases crash risks. This study presents research to detect driver fatigue. Fatigue driving systems face challenges in real-time detection due to limited computing resources and poor accuracy detection. To address this, we introduce the YOLOv8-FCA model, enhancing the YOLOv8 algorithm to boost detection accuracy and efficiency in controlled environments. YOLOv8 was selected for its proven performance in object detection tasks. Our model integrates a feature pyramid network (RepFPN) to improve detection of subtle fatigue indicators and details, and utilizes the convolutional block attention module (CBAM) to focus attention on critical regions. Experimental results demonstrate a 1.3% increase in detection accuracy (mAP50), achieving 99.1%, while reducing computational costs by 40.73%, thus significantly enhancing detection and response capabilities.
Development of Threats and Vulnerability Assessment Model for Electric Vehicles (EVs) Transportation Internet of Things (T-IoT)
The paper describes the proposal of a model for threats and vulnerability assessment for Electric Vehicles (EVs) in the Transportation Internet of Things (T-IoT) model. EV systems are becoming more and more reliant on IoT devices and systems that encounter many security challenges. This reliance on many interconnected devices, sensors, and communication networks creates vulnerabilities where cyberattacks, such as hacking and data breaches, or manipulations can stem as entry points to EVs. In this study, the researcher addresses the problems outlined above by discovering orders of critical threats, evaluating the remaining approaches working to produce threat modeling and vulnerability assessments, and adapting those approaches to the requirements of EV systems. Ultimately the study details a model to provide systematic attack scenario analysis and models for vulnerability assessment to provide a solid framework of identification and risk reduction to EV security and reliability in the future of transportation networks. The model developed in this study demonstrates improved capability in predicting, identifying, and mitigating cyber threats targeting electric vehicle (EV) systems. The results provide empirical evidence of the model's capacity to strengthen protection mechanisms against evolving cyber risks, offering a tangible solution to current security challenges in EV infrastructure. This research contributes to ongoing efforts to improve cybersecurity in transportation technologies, presenting a functional approach that can be implemented in real-world scenarios. Finally, this will improve the measures of the future of security and safety for the integration of EVs into the T-IoT.
An Intelligent Business-to-Consumer Ecommerce Framework for Direct Agricultural Product Distribution
The rising demand for fresh, locally sourced agricultural products, along with the need for efficient distribution approaches, has prompted interest in direct-to-consumer ecommerce models within the agriculture industry. Existing agricultural distribution systems frequently incorporate intermediaries, resulting in inefficiencies, inflated costs and delays in the delivery of perishable goods. The development of advanced technologies, including artificial intelligence (AI), has facilitated the development of more efficient, transparent, and consumer-oriented agricultural distribution systems. This paper outlines the framework of an intelligent business-to-consumer ecommerce platform for the direct distribution of agricultural products using machine learning to improve shipments, transparency and user experience. The proposed framework analyzes key factors such as price, freshness, and shipping conditions to deliver customized product recommendations and improve shipment assignment. The intelligent product recommendation and shipment assignment ensures user preference as well as the freshness of perishable goods while reducing delays and transportation expenses. Also, the proposed framework comprises of overall conceptual framework including, data collection and preprocessing steps, feature extraction, the use of recurrent neural network and singular value decomposition to train data points and evaluation metrics such as RMSA, MAE, ranking quality and cold start testing to validate intelligent model efficiency. Also, it facilitates interaction between consumers and producers, promoting a transparent, efficient, and economical distribution process. The intelligent model continuously adjusts to customer preferences and market dynamics, improving efficiency in operation and user satisfaction.
Recent Trends on Multi-omics Studies in Cancer Research: A Bibliometric Study
The integration of multi-omics approaches has revolutionized cancer research by providing a comprehensive understanding of cancer pathogenesis beyond single-omics methods. By combining diverse omics data types, multi-omics analyses improve precision in identifying intricate disease-related mechanisms. Despite increasing interest, bibliometric analyses on multi-omics research in oncology remain limited. This study addresses this gap by conducting a bibliometric analysis of multi-omics cancer research trends over the past six years (2019 to February 2025), utilizing data from the Web of Science Core Collection (WoSCC) accessed on 28 February 2025, and analysing it with VOSviewer. The analysis of 3386 publications indexed in WoSCC reveals a significant surge in multi-omics research. China leads with 2055 publications, while the University of Toronto in Canada and the Institut National de la Santé et de la Recherche Médicale (Inserm) in France emerge as major contributors, each accounting for more than 50% of their country's total publications in this domain. Dominant keywords such as multi-omics, prognosis, immunotherapy, machine learning and tumor microenvironment highlight current research priorities. This study provides a comprehensive overview of publication trends, offering valuable insights to guide future research in multi-omics cancer studies. By highlighting major contributors and emerging focal points, this study aspires to foster advancements and inspire future exploration in this pivotal domain.
An Enhancement of Coverage-Based Test Case Prioritization Technique Using Hybrid Genetic Algorithm
Test Case Prioritization plays a defining role in regression testing by optimizing the ordering of test case execution for early fault detection. This ensures that modifications to the code do not adversely affect existing functionalities. TCP is considered an effective approach in regression testing that optimizes test execution by ordering test cases according to a criterion. While previous research works are based on a single criterion using Genetic or Cuckoo search to solve optimization problems in TCP, these single metrics limit their effectiveness. This study proposes a novel hybrid evolutionary algorithm approach that addresses this limitation by significantly improving the Average Percentage Fault Detection (APFD) rates in regression testing. Our adopted approach enhances the strengths of Cuckoo Search Optimization (CSO) to overcome the limitations of premature convergence in GA by parameter tuning helps to overcome the limitations of single algorithm solutions. We evaluate the performance by incorporating APFD and CE for improvement in the rate of fault detection and an increase in coverage effectiveness for a more comprehensive test suite application. Statistical evaluation using ANOVA strengthens our resolve for the adopted approach, with significant results tabulated. Experimental evaluations on Siemens Test Suite datasets demonstrate how our hybrid approach achieves an improvement in APFD of 0.271% over both GA and CSO and an improvement in CE of 6.35% over GA and 6.62%. over CSO across all test suite datasets. These findings highlight the applicability of a well-thought-out hybridized evolutionary algorithm that can be used in TCP to advance software testing practices.
Intrusion Detection System using Convolutional Neural Network for Industrial Internet of Things Security
The rise of Industry 4.0 has led to the widespread adoption of Industrial Internet of Things (IIoT) devices, enhancing manufacturing efficiency while introducing significant cybersecurity risks. IIoT environments are highly susceptible to cyber threats such as Denial-of-Service (DoS), SQL injection, and ransomware, which can lead to production downtime and data breaches. Traditional intrusion detection systems (IDS) often fail to detect evolving threats, resulting in high false negative rates. This research proposes an advanced IDS integrating Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) to enhance IIoT security. By leveraging both spatial and temporal feature extraction, the proposed model effectively identifies network anomalies in real-time industrial environments. This study contributes to IIoT cybersecurity by developing an IDS capable of improving threat detection through the integration of CNN and LSTM architectures. The approach enhances pattern recognition and sequential dependency modeling, making it more adaptive to dynamic cyber threats. The model is trained and evaluated on a large-scale IIoT dataset, achieving a binary classification accuracy of 71%, outperforming several state-of-the-art models. The CNN-LSTM IDS demonstrates a strong ability to recognize normal traffic, with a recall of 99%, significantly reducing false alarms. In multi-class classification, the model successfully identifies certain high-volume attack types, such as DDoS. These findings underscore both the strengths and limitations of deep learning-based intrusion detection in IIoT environments. While the proposed model offers significant improvements, further research is needed to address the detection of low-frequency attacks and optimize classification performance.
Enhancing Fake News Analysis in Bangla: A Hybrid Model with LDA, and LIME for Interpretable Classification
The proliferation of fake news presents a critical challenge in ensuring information authenticity, especially in the context of Bangla text. This study addresses the issue by developing a robust pipeline for detecting and categorizing fake news using advanced machine learning (ML) and natural language processing (NLP) techniques. A dataset comprising 5,000 fake news articles was prepared, collected from 20 different newspapers in Bangladesh between 2020 and 2024. Topic modeling was performed using Latent Dirichlet Allocation (LDA), whose results in topic categorization (World, Business, Entertainment, Bangladesh, and Sports) were significantly better compared to other models. The proposed model, consisting of Bidirectional Encoder Representations from Transformers (BERT), Support Vector Machine (SVM), and Bidirectional Long Short-Term Memory (BiLSTM), outperforms the traditional models in all evaluation measures, yielding a mean accuracy of 97%. Moreover, the integration of Local Interpretable Model-agnostic Explanations (LIME) adds interpretability to the model by explaining individual predictions, thereby enhancing transparency. This holistic approach sets a new benchmark for Bangla fake news detection with high accuracy, interpretability, and reliability, opening avenues for further research in combating misinformation in Bangla.
Design and Scalability Performance Evaluation of Permissioned Blockchain Architecture for Online Voting System
This research addresses critical challenges in online voting systems by integrating blockchain technology, specifically leveraging Hyperledger Fabric. The objectives include designing a permissioned blockchain architecture tailored for online voting systems, developing and implementing smart contracts to manage the entire voting lifecycle, including voter registration, vote casting, and result viewing within the permissioned blockchain environment and assessing the scalability and performance of the proposed voting system architecture using Hyperledger Caliper. The study begins with a comprehensive review of literature and case studies to identify security gaps within existing online voting systems, focusing on both on-chain and off-chain aspects. Subsequently, an online voting system architecture with a single channel blockchain network is designed, using Hyperledger Fabric to enhance the scalability of online voting processes. Then, smart contracts are developed to implement the logic of the voting application. To assess the effectiveness of the designed architecture, extensive testing and evaluation are conducted. Key scalability performance metrics are measured using Hyperledger Caliper on critical operational functions. The Hyperledger Fabric Network was incrementally scaled from a single-peer network to a five-peer network configuration to assess scalability. The performance assessment suggests that a five-peer network is optimal for the proposed online voting system. The findings pave the way for future advancements in blockchain-based solutions, offering a secure, scalable and transparent framework for online voting systems.
Group Project Monitoring System (TeamWatch
Group projects in courses offer a valuable method of teaching students how to work in a collaborative setting, mirroring real-world scenarios. However, several challenges decrease the effectiveness of these projects, including the presence of free riders, the absence of a robust grading method, a lack of validation to ensure active participation, and the prevalence of lone wolves. These issues, if left unaddressed, degrade the educational benefits of group projects for students. Manual intervention by lecturers to tackle these challenges isn’t practical as lecturers have many courses to follow and tracking each group manually will be difficult to do. Therefore, TeamWatch proposed web-based system with a desktop agent, designed to assist students and lecturers in the process of programming group projects. The development of TeamWatch employed the waterfall model, providing a structured approach to ensure efficient and systematic progress. The front-end is built using Django Templates, while Django served as the back-end framework. Postgres was employed as the database system, and Python was utilized for the development of the desktop agent. TeamWatch offers a range of features aimed at enhancing the group working experience. Students will be able to track their working hours in real-time, access the working hours of other group members, and even log offline working hours. Lecturers will have comprehensive visibility into each student's working hours and can generate reports providing valuable insights into individual performance and participation.
Anomaly Network-based Intrusion Detection Model Based on Hybrid CNN-LSTM and Attention Mechanism
With the growing frequency of network attacks, traditional anomaly-based intrusion detection models often fail to identify advanced attack patterns and suffer from high false positive rates. This paper proposes a hybrid deep learning model integrating Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and an Attention Mechanism to enhance detection accuracy and robustness. Leveraging CNNs for spatial feature extraction, LSTMs for temporal pattern recognition, and Attention Mechanisms for prioritizing critical data, the model effectively identifies diverse intrusion types. Using the NF-UNSW-NB15-v2 dataset, this research incorporates advanced preprocessing techniques such as Recursive Feature Elimination with Cross-Validation (RFECV) and Synthetic Minority Oversampling Technique (SMOTE). Experimental results demonstrate improved performance across key metrics, offering a robust framework for real-time intrusion detection in complex network environments.
An Effective Cyberbullying Detection Model for the Malay Language Using Transformer Model in Social Media Platform X
In the digital era, social media has transformed communication but has also facilitated cyberbullying, significantly affecting individuals' mental health, particularly within the Malay-speaking community. Despite the growing concerns, developing effective cyberbullying detection systems for low-resource languages like Malay has been limited. This research addresses this gap by introducing transformer learning model specifically designed for detecting cyberbullying in Malay language tweets. The work begins with an extensive literature review to consolidate the current understanding of cyberbullying detection techniques. A substantial dataset will be curated from X and manually annotated, forming the basis for model training and evaluation. The research employs machine learning model and transformer models to improve overall detection accuracy and robustness. Advanced NLP techniques, including transformer models and transfer learning, will be utilized to navigate the complexities of the Malay language and ensure accurate detection. The proposed model’s performance will be rigorously evaluated using metrics such as accuracy, precision, recall, and F1-score with tests to ensure its robustness against different cyberbullying tactics. This research has produced a high-performing detection model that enhances the safety of Malay-speaking internet users and provides insights into cyberbullying dynamics within the community with advanced NLP for low-resource languages. The results demonstrate that deep learning Transformer models, particularly DeBERTa, which has outperformed traditional machine learning models in accuracy for Malay language cyberbullying detection.
MultiPhishNet: A Multimodal Approach of QR Code Phishing Detection using Multi-Head Attention and Multilingual Embeddings
Phishing attacks leveraging QR codes have become a significant threat due to their increasing use in contactless services. These attacks are challenging to detect since QR codes typically encode URLs leading to phishing websites designed to steal sensitive information. Existing detection methods often rely on blacklists or handcrafted features, which are inadequate for handling obfuscated URLs and multilingual content. This paper proposes MultiPhishNet, a multimodal phishing detection model that integrates advanced embedding techniques, Convolutional Neural Networks (CNNs), and multi-head attention mechanisms to automatically extract and learn key features from URLs and HTML content. The model leverages FastText embeddings for word-level representation, custom character embeddings for obfuscated URLs, and SBERT (Sentence-Bidirectional Encoder Representations from Transformers) embeddings for HTML content. To address class imbalance, ADASYN (Adaptive Synthetic Sampling) oversampling was applied, ensuring balanced training. The proposed method was evaluated on a moderately multilingual dataset, achieving an accuracy of 97.76% and an AUC of 0.9946. These results demonstrate that MultiPhishNet outperforms the baseline HTMLPhish model in phishing detection. Future research will focus on expanding the dataset to cover a broader range of languages and regional phishing tactics.
Transforming Workplace Efficiency: Using Agile Development Methodologies for Developing Meeting Management System
This In today's digital era, individuals are seamlessly connected across various digital platforms, from social media giants like Facebook, Twitter, and Instagram to knowledge-sharing hubs like StackOverflow and Quora. The relentless advancement of digital technologies has ushered in a wave of unprecedented accessibility and connectivity. While this transformation has brought tremendous benefits to society, organizations face new and intricate challenges in adapting to evolving task preferences, performance expectations, and operational requirements. In response to this dynamic landscape, this paper proposes an innovative paradigm shift aimed at enhancing quality, efficiency, and time-to-completion for critical tasks. Central to this transformation is the concept of digitizing manual processes, which offers a transformative approach capable of significantly reducing task durations while ensuring heightened efficiency and minimizing associated risks. One illustrative example involves the automation of employee attendance tracking within a company, seamlessly integrating data into a centralized database. This digitized process not only expedites task completion but also facilitates the generation of detailed reports presented graphically, allowing human resource managers to make informed decisions swiftly. This is just one instance where digitalization outperforms traditional manual methods, leading us to conclude that digitalization is a pivotal strategy for boosting organizational productivity. Moreover, traditional practices in managing meetings, characterized by manual participant notifications, have presented substantial inefficiencies. Secretaries often bear the brunt of coordinating meeting schedules, making phone calls, and sending emails to ensure all involved parties possess accurate information. This paper embarks on a comprehensive investigation to ameliorate this situation by injecting a digitalization approach into the existing meeting management system.
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