International Journal of Computer Theory and Engineering
Enhanced Particle Swarm based Approach for Optimal Firewall Rule Reordering
Firewalls are required to ensure that only trustworthy packets are sent back and forth across the network in order to provide secure network communication. Firewalls employ the rules that network administrators establish to control which packets are allowed access to an organization’s private network in order to enforce security regulations. By classifying packets, network devices can determine how incoming packets behave. A greater communication delay is caused by a higher rule count since it is achieved by a linear search on a list of categorisation rules. The goal of Optimal Rule Ordering (ORO), a generalisation of the issue where the latency is minimised while keeping the classification strategy, is to find the optimal rule sequence. This research suggests a dual approach for reordering the firewall rules using optimization. The first approach uses the probability-based algorithm to order the firewall rules based on the precedence relation. Next, the optimization-based algorithm, Particle swarm optimization (PSO), is used to reorder the firewall rules. Through optimization, firewalls can enhance their performance and filter packets more efficiently by refining their policies.
Copyright (c) 2024 Dhwani Hakani, Palvinder Singh Mann https://creativecommons.org/licenses/by/4.0
Machine Learning-Based Techniques for Assessing Critical Factors for European Tick Abundance
Tick-borne diseases are a significant health risk to humans and animals worldwide. It is important to understand the environmental and climatic factors that contribute to tick occurrence rates in order to reduce the proliferation of tick-borne diseases. Using machine learning and spatial indexing techniques, this study covers tick occurrence rates in Europe over the last 20 years to understand the environmental and climatic factors that contribute to Ixodes ricinus tick abundance. We used biodiversity databases to study land cover categories, climate, vegetation index, and sociological factors. Areas with agriculture and natural vegetation, especially broad-leaved forests, had the strongest tick correlation. Waterways and pastures also showed significant positive correlations, indicating tick habitats. Ticks have moderate associations with urban green spaces, industrial units, and mixed forests suggesting their presence in ecologically disturbed habitats. Geoclimatic factors namely Normalised Difference Vegetation Index and rainfall, showed weak to negative correlations with tick population, indicating that they were less important than previously assumed. Linear Regression, Decision Tree, Random Forest, and Support Vector Machine were compared. We found that feature set and outlier presence significantly affected model performance. After removing outliers, Linear Regression performed best for land use features, with an R² value of 0.81, Normalised Root Mean Square Error (NRMSE) of 1.56, Scatter Index (SI) of 1.56, and Mean Absolute Percentage Error (MAPE) of 1.22. Outlier exclusion improved the model performance results. This research emphasises the importance of specific land uses in predicting the dynamics of tick populations. Our findings lay the groundwork for focused intervention strategies to reduce the spread of tick-borne diseases using an innovative and intelligent approach, while also emphasising the need for further investigation into the complex interactions between environmental factors and tick abundance.
Copyright (c) 2025 Mhd Saeed Sharif, Abin Zorto, Samantha Lansdell, Misaki Seto, Edessa Negera, Sally Cutler https://creativecommons.org/licenses/by/4.0
6th Generation Wireless Spectrum Applications, Opportunities and Challenges
The introduction of the 6th generation spectrum presents opportunities for technology firms, entrepreneurs, and start-ups to develop innovative applications, services, offerings, and business models for experimental purposes, such as regulatory sandboxes, which encourage experimentation and the development of cutting-edge technologies and services. The result promotes the growth of the digital economy and improves economic competitiveness, all of which will contribute to economic expansion. These advancements encourage digital transformation in various industries, increase productivity, and present novel commercial opportunities. This paper thoroughly examines the potential influence of future innovations on the spectrum, like architectural frameworks, network management, new emerging techniques in the 6G spectrum, future spectrum challenges with associated solutions, efficient regulatory frameworks, the impact of Artificial Intelligence, Machine learning, and other emerging technologies. Furthermore, it examines potential challenges for domestic and global networks deployed in future generations. Moreover, it delves into the transformative opportunities and challenges of the 6G wireless spectrum, with a particular emphasis on the enhanced spectrum sharing that is facilitated by cognitive radio technologies, advanced protocols such as IEEE 802.22, IEEE 802.15.4 IEEE 802.1AE, and so on. These advancements enable the optimal utilization of frequency bands by enabling Dynamic Spectrum Access (DSA) and efficient resource management. By effectively balancing these opportunities with security considerations, 6G networks may unlock new applications across diverse sectors, which will pave the way for a more connected and efficient future.
Copyright (c) 2025 FARHAN Ali, Yigang He , Wei Ding Cheng, Atta-ur Rahman, Mohammad Ahmad Saleem Khasawneh https://creativecommons.org/licenses/by/4.0
Deployment of a Deep Learning Model for the Automated Diagnosis of Thai Rubber Leaf Diseases via the LINE Platform
This research presents an approach to real-time detection of rubber leaf diseases using the YOLOv8 deep learning model integrated with the LINE messaging platform. The system allows users to submit rubber leaf images through the LINE platform, where they are processed by an AI-powered backend hosted on Heroku, using Docker and Flask for scalability and efficiency. The YOLOv8 model was trained on a dataset comprising three classes: Healthy, New Disease, and Powdery Mildew. The model achieved an overall mAP50 of 57.9% and an mAP50-95 of 42.8%, with strong performance in detecting healthy leaves (mAP50: 98.3%) but lower accuracy in identifying Powdery Mildew (mAP50: 22.4%) due to significant class imbalance. User testing involved 50 beta testers who submitted 500 images through the chatbot, resulting in a detection accuracy of 72.4%, a misclassification rate of 5.4%, and an average response time of 2.5 seconds. Key performance metrics included a macro-average precision of 78.2%, recall of 73.0%, and an F1 score of 75.5%. User feedback highlighted satisfaction with the system’s ease of use and response speed, though improvements were suggested in handling misclassifications and providing treatment recommendations. These results indicate that the system provides a robust and scalable solution for rubber leaf disease detection, with potential for further optimization.
Copyright (c) 2025 Puriwat Lertkrai, Benjamin Chanakot, Nattapong Kaewboonma https://creativecommons.org/licenses/by/4.0
Deep Learning-Based Estimation of SNS Users’ Place of Residence Using Posts and Following Relationships
In this paper, we propose a new method for estimating the place of residence of Social Networking Service (SNS) users living in Japan. In our previous paper, we proposed a model that estimates the users’ place of residence at the prefecture level based on the content of their posts and the weather and earthquake information described in them. While the weather and earthquake information contributed to an improvement in precision, the information did not enhance recall, particularly in prefectures with a small number of users or those adjacent to large metropolitan prefectures. Therefore, we need to improve the recall for these prefectures in this task. To address this issue, we focus on that the users living in rural areas tend to have closer relationships than those in urban areas. We aim to improve recall, especially in rural prefectures, by utilizing the following relationships between SNS users. In addition, we introduce Bidirectional Encoder Representations from Transformers (BERT) model as feature extraction from tweet content to further improve precision. In our evaluation experiments using the same dataset as in our previous study, we achieved an F1-measure of 0.7747. This is the best result in our study so far. Furthermore, we observed an improvement in the recall of up to 5 points compared to the baseline model using only the tweet content.
Copyright (c) 2025 Ying Chen https://creativecommons.org/licenses/by/4.0
Improvement of the Efficiency of Grape Leaf Disease Classification using VGG12 with Wide Convolution Layer
This article presents a model improvement to increase the efficiency of grape leaf disease classification using the VGG12 (Visual Geometry Group with 12 Layers) with Wide Layer model. It is a new model based on the concept of VGG16 (Visual Geometry Group with 16 Layers) and InceptionV3 Block. It aims to present a small convolutional neural network model. It reduces the number of parameters and computational costs but increases the efficiency of grape leaf disease classification. By reducing the number of layers of the VGG16 model from 16 to 12, horizontal feature maps are forwarded instead of hierarchical feature maps between the second to eighth layers. Feature maps are combined to forward the feature maps hierarchically to the next layer. In addition, the filter size was changed from 3×3 to 1×3 and 3×1 to reduce the number of parameters and help the model process faster. Multiple Dilated Convolution was used to obtain more feature maps and did not increase the parameters. The model’s results were evaluated by experimenting with a four-class grape leaf dataset from the PlantVillage dataset, consisting of three classes of diseased grape leaves and one class of healthy grape leaves. The results showed that the proposed model gave an accuracy of 99.95% in classifying grape leaf disease and comparing the proposed model with the models ResNet50, VGG16, InceptionV3, DenseNet121, and MobileNetV2, whose classification accuracies were 64.14%, 91.16%, 97.92%, 97.71%, and 99.69%, respectively. It was found that the model proposed has the highest accuracy, and it uses 1,571,839 parameters.
Copyright (c) 2025 Jiraporn Thomkaew, Podjana Homhual, Apichai Chanudom https://creativecommons.org/licenses/by/4.0
Multiclass Eye Disease Classification Using Transfer Learning Approach
Ophthalmologists commonly use retinal fundus images for diagnosis. Recently, automation of this process using deep learning has gained significant attention. Multiclass classification, which distinguishes among multiple eye diseases, is more representative of actual clinical settings, however, it presents challenges such as limited availability of annotated datasets, class imbalance, overlapping clinical features across various eye diseases and disease heterogeneity. This study develops deep learning models for multiclass classification of three major eye diseases—cataracts, diabetic retinopathy, and glaucoma—alongside normal cases. A larger and more diverse dataset was obtained by combining multiple publicly available, well-annotated datasets. Four deep learning models: VGG16, Inception-v3, ResNet50 and EfficientNet-B0, were deployed using a transfer learning approach. These models achieved test accuracies ranging from 74.29% to 78.79%, with ResNet50 performing the best, achieving an accuracy of 78.79%, precision of 80.04%, recall of 78.79%, and an F1-score of 78.76%. The results demonstrate the effectiveness of transfer learning for multiclass classification of eye diseases. Notably, the models were trained and evaluated on a heterogeneous dataset that simulates real-world variability in image acquisition, highlighting their generalization capabilities and robustness to inconsistency. The study provides valuable insights about the performance of pre-trained deep learning models under realistic conditions, supporting their potential as assistive diagnostic tools in actual clinical scenarios.
Copyright (c) 2025 Alvin, Stephanie Chua, Lim Lik Thai, Dayang, Hanafi, Puteri https://creativecommons.org/licenses/by/4.0
An Efficient Convolutional Neural Network Architecture Search Using Multi-Layered Population Structure
The design of optimal Convolutional Neural Network (CNN) architectures has become increasingly complex as networks have grown deeper and more diverse. Neural Architecture Search (NAS) has emerged as a powerful technique to automate the discovery of high-performance architectures, thereby reducing reliance on manual expertise. In this work, we propose a novel approach that integrates NAS with a Multi-Layered Population Structure (MLPS), an effective evolutionary scheme originally developed for Genetic Programming (GP). We term this method Multi-Layered Population Structure Neural Architecture Search (MLPS-NAS). Our approach leverages the hierarchical, pyramid-like population management of MLPS to maintain a diverse set of partial solutions, while systematically exploring the vast search space of CNN architectures. A key feature of MLPS-NAS is its ability to construct complex architectures by repeatedly incorporating effective building blocks. To significantly accelerate the search process, we introduce a weight inheritance mechanism between generations, which drastically reduces the computational cost of training new candidate architectures. Experimental results on a real-world image classification task demonstrate that MLPS-NAS achieves accuracy comparable to an established method, CNN Architecture Design Using Cartesian Genetic Programming (CGP-CNN), while substantially reducing the required search time. This synergy between NAS and MLPS offers a promising direction for the efficient, automated design of high-performance CNNs.
Copyright (c) 2026 Yanni YANG, Kazuki Yabuuchi, Naoki Mori https://creativecommons.org/licenses/by/4.0
Advancing Beyond the Tank: Machine Learning Techniques for Detecting Water Quality and Temperature Anomalies in Intelligent Aquatic Systems
This study aimed to enhance the Beyond the Tank system by integrating machine learning models for real-time anomaly detection in water quality and temperature data. Extensive sensor data was collected and preprocessed, leading to the development and evaluation of various machine-learning algorithms. The most effective model was integrated into the system, enabling continuous monitoring and real-time alerts for anomalies. The mobile app was also upgraded to support immediate notifications, improving user responsiveness to potential issues. The enhancements led to significant improvements in system efficiency, resource utilization, and user satisfaction. The findings demonstrate the potential of machine learning in advancing intelligent aquarium management, providing a solid foundation for future research and practical applications in this field.
Beyond the Tank, Anomaly Detection, IOT, Machine Learning, sensors and actuators
Copyright (c) 2025 William Rey https://creativecommons.org/licenses/by/4.0
SLR-GPoA: A Smart Land Registry System Using Blockchain and Geographic Proof of Authority
The modernization of land registry systems is essential for enhancing transparency, reducing fraud, and driving socio-economic progress. Traditional land administration approaches—whether manual or semi-digital—often suffer from inefficiencies, centralization, and a lack of scalability. This paper introduces a Geographic Proof of Authority (GPoA) based framework for land governance, conceptualized as a Distributed Intelligent System (DIS). Built on a permissioned blockchain architecture, the model integrates geographically aware validator selection, parallel trust-weighted validation, and smart contracts to automate key operations such as ownership transfer and dispute resolution. A prototype implementation using Hyperledger Fabric demonstrated notable improvements in validation accuracy, transaction efficiency, and fault tolerance when compared to conventional approaches. These results affirm the potential of GPoA as a legally-aligned, context-aware, and scalable consensus model for modernizing land registry systems.
Copyright (c) 2025 Aditi Mallya, Ankitha Ananth, Anshul Ramdas Baliga, Anusha M V, Shruti Jadon, Prasad B. Honnavalli https://creativecommons.org/licenses/by/4.0
Innovative Software Development and Project Management Framework for Technology Startups
This paper introduces a new process that integrates inventive problem-solving methods into modern software development. The central research question addresses how tech startups can enhance their software development processes with minimal project management expertise. The Systematic Innovation Mounted Software Development Process, which blends Agile and Systematic Innovation, offers an alternative framework to facilitate idea generation in software products. This intuitive project management framework empowers technology-driven companies to manage their projects more effectively. The study aims to provide guidelines for entrepreneurs to manage projects successfully. By collaborating with the existing Agile model, the Systematic Innovation model fosters creativity and innovative problem-solving. Ultimately, this new software development process and its techniques have the potential to significantly impact the software industry, particularly for startups, as they alleviate managerial burdens and allow companies to focus on their core technologies.
Copyright (c) 2025 SongKyoo Kim https://creativecommons.org/licenses/by/4.0
Beyond OCR: GAN-Driven Restoration of Severely Degrading Document
The recent advancements in deep learning have opened avenues for substantial improvements in text extracting accuracy. These technological innovations have significantly enhanced the capabilities of existing tools, particularly in the realm of Optical Character Recognition (OCR). The OCR plays a pivotal role in digitizing image-based texts. To enhance its efficacy, and advanced preprocessing model aimed at improving text clarity and readability should be implemented prior to the text extraction process. Our works explore the application of Generative Adversarial Networks (GANs) for super resolution tasks, aiming to enhance image quality by increasing resolution. Various GAN architectures and training techniques are being experimented including the usage of state-of-the-art model, Super Resolution Generative Adversarial Network (SRGAN). While the results showed some improvements quantitatively, it also highlighted areas for further optimizations. The findings suggest that GAN-based approach holds promise for super resolution tasks in enhancing text extraction result for document image. Significantly, the experiment showed that OCR successfully improved even when dealing with photos that had experienced 75% damage, which in this stage, an image had experienced substantial information loss. Future work will focus on addressing the identified challenges and enhancing model performance.
Copyright (c) 2025 Yosua Kristianto, Benfano Soewito https://creativecommons.org/licenses/by/4.0
A Compact Multi-Step Framework for Packing Identification in Portable Executable Files for Malware Static Analysis
Packing presents a major challenge in cybersecurity, as it complicates malware analysis and extends the operational lifespan of malicious software. This study addresses the issue by developing a robust framework designed to detect packed executable files and identify the specific packers used. The proposed framework leverages 20 optimally selected features extracted from Portable Executable (PE) files to detect packing and recognize packer signatures. A series of extensive experiments was conducted to determine the most effective combination of classification model and feature set. The extreme gradient boost algorithm was selected based on its superior performance. The proposed model achieved a high detection accuracy of 99.27% and an F1-score of 98.84%, outperforming recent methods in the field. In addition, the study introduces a publicly accessible dataset containing 213,784 PE samples and 125 features to facilitate future research. The framework provides a practical tool for security analysts, improving their ability to identify and respond to PE file-based malware in real-world environments. This study focuses exclusively on a static analysis pipeline; no dynamic execution is performed. We also describe how the framework could interface with sandbox-derived dynamic behavioral signals in future work without extending the current study’s scope. Overall, this research contributes a static feature-based approach for packer detection and signature identification, together with a large-scale open dataset that supports ongoing advances in malware classification and analysis.
Malware Detection, Ensemble Learning, Machine Learning, Feature Selection
Copyright (c) 2026 Jong-Wouk Kim, Yang-Sae Moon, Mi-Jung Choi https://creativecommons.org/licenses/by/4.0
Financial Forecasting in Consumer Cyclicals with Economic Indicators and Tokenization
Machine learning in financial Time Series Forecasting (TSF) has a clear application in investment, where predicting stock price movements can inform investment strategies. The Transformer model has emerged as a powerful tool for this purpose, yet significant research gaps remain. Existing studies often focus on a set of stocks without modeling stock behavior within specific sectors. Notably, there is a lack of research on the Consumer Cyclicals (CC) industry, which includes sectors such as automotive, housing, entertainment, and retail. These industries are highly sensitive to economic conditions, making them crucial for understanding broader economic impacts on stock behavior. Furthermore, we observe that many existing works neglect broader economic contexts, which is particularly important for CC analysis due to its sensitivity to economic trends. Additionally, previous studies on financial Transformer models typically use model tokens as feature vectors of multiple variates at a single timestep. This approach may not adequately capture the important relationships between dataset variates for long-term economic trends. To this end, we present the first study on financial TSF for the CC sector along with economic data. To support the research, we propose the first public benchmark dataset for the CC sector, consisting of traditional stock price time series, technical indicators, and temporal data, enriched with economic indicators. Next, we introduce an alternative tokenization approach to enhance the model’s ability to capture long-term trends by preserving information about nonlinear dependencies between dataset variates. We hypothesize that this approach helps capture long-term signals more effectively. Through a comprehensive data ablation study and benchmark testing, we demonstrate that incorporating economic indicators improves the accuracy of longer-term predictions for the CC sector, and the new tokenization method enhances the performance of Transformer models. The dataset and code are made publicly available at: https://github.com/KasperKrawczyk/econtrans_dataset
Copyright (c) 2025 Kasper Krawczyk, Gary Tam, Daniel Archambault https://creativecommons.org/licenses/by/4.0
Logarithmic OWA Operators in Weighted Averages: Theoretical Advances and Decision-Making Applications
Aggregation operators are essential in multi-attribute decision-making, particularly for managing uncertainty and risk. Traditional methods, such as the Ordered Weighted Averaging (OWA) operator, typically address either probability-based weighting or uncertainty-based reordering, but rarely combine both within a unified framework. This paper proposes the Ordered Weighted Logarithmic Averaging Weighted Average (OWLAWA) operator, a novel approach that merges the benefits of weighted averaging and ordered reordering with a logarithmic transformation to better reflect decision-maker preferences under uncertainty. The theoretical properties of this operator including monotonicity, boundedness, and commutativity are formally established. A multi-attribute decision-making framework is then presented, integrating recognized expert weighting methods, including an entropy-based approach, to enhance decision robustness. Through comparative analysis and a sustainability-focused case study involving 20 companies, results demonstrate that the proposed approach yields a controlled sub valuation effect, particularly beneficial in risk-sensitive or compliance-driven environments. These findings indicate a more adaptive and structured decision-making process relative to conventional operators, accommodating both structured probabilities and uncertain preferences. By unifying risk-based and uncertainty-based weighting within a logarithmic formulation, this operator offers a versatile and structured tool for applications in financial risk management, policy evaluation, and supply chain optimization. Future research may explore its integration with fuzzy systems and machine learning methods, further expanding its adaptability in complex decision scenarios.
Copyright (c) 2025 Victor Alfaro, Gerardo G. Alfaro Calderón, José M. Merigó https://creativecommons.org/licenses/by/4.0
Assessing AI-Generated Questions' Alignment with Cognitive Frameworks in Educational Assessment
This study evaluates the integration of Bloom's Taxonomy into OneClickQuiz, an AI-driven plugin for automating multiple-choice question (MCQ) generation in Moodle. Bloom's Taxonomy provides a structured framework for categorizing educational objectives into hierarchical cognitive levels. Our research investigates whether incorporating this taxonomy can improve the alignment of AI-generated questions with specific cognitive objectives. We developed a dataset of 3,691 questions categorized according to Bloom's levels and employed various classification models—Multinomial Logistic Regression, Naive Bayes, Linear Support Vector Classification (SVC), and a Transformer-based model (DistilBERT)—to evaluate their effectiveness in categorizing questions. Our results indicate that higher Bloom's levels generally correlate with increased question length, Flesch-Kincaid Grade Level (FKGL), and lexical density (LD), reflecting the increased complexity of higher cognitive demands. Multinomial Logistic Regression showed varying accuracy across Bloom's levels, performing best for "Knowledge" and less accurately for higher-order levels. Merging higher-level categories improved accuracy for complex cognitive tasks. Naive Bayes and Linear SVC also demonstrated effective classification for lower levels but struggled with higher-order tasks. DistilBERT achieved the highest performance, significantly improving classification of both lower and higher-order cognitive levels, achieving an overall validation accuracy of 91%. This study highlights the potential of integrating Bloom's Taxonomy into AI-driven assessment tools and underscores the advantages of advanced models like DistilBERT for enhancing educational content generation.
Copyright (c) 2025 Antoun Yaacoub, Jérôme Da-Rugna, Zainab Assaghir https://creativecommons.org/licenses/by/4.0
Far Eastern Cultures and the Console User Interface
Even as of 2024, the support of Far Eastern writing systems by computers remains uneven, to say the least. For example, vertical right-to-left user interfaces, as it could naturally be expected in countries like Japan and China, are extremely rare. In fact, it is the opposite that has been happening since the broad adoption of computer systems: people of these cultures have got accustomed to western, left-to-right horizontal interfaces. In previous research, we have started to technically investigate the current situation in the case of Graphical User Interfaces (GUI); the results obtained showed that although noticeable improvements have been made, the support provided by current technologies (e.g. operating system features, browser features) in the realisation of Far Eastern interfaces is still nowhere near that of western ones. In this paper, we propose and evaluate an extension to the Console User Interface (CUI) to improve its support of Far Eastern writing systems. Concretely, the vertical right-to-left application scenario is considered for the CUI. The measured results are positive in that they match those obtained in the case of the conventional, left-to-right horizontal layout. Precisely, our proposal is slightly faster when it comes to the text scrolling speed (between 3% and 9% in our experiments).
Copyright (c) 2025 Antoine Bossard https://creativecommons.org/licenses/by/4.0
An Empirical Investigation of Intelligent Fault-Tolerant Strategy for Cloud Computing System
Cloud Computing has become one of the revolutionary technologies in the distributed computing paradigm. As a result of the increasing demand for accessing data through the Internet by various endpoints, such as laptops, smartphones, and mobile and wearable devices, cloud services continue to grow and thrive. However, the extensive use of cloud-based services increases the risks of failure, compromising the service's reliability and availability. To manage failures, Cloud Computing systems must be fault-tolerant. Although there are many techniques for fault tolerance in cloud computing, they lack a holistic solution for multiple layers of the cloud infrastructure. Additionally, as the complexity of the cloud infrastructure increases, there is an urge to provide an intelligent strategy to deal with failures. In this investigation, we introduce an Intelligent Fault-Tolerant Strategy (IFTS) that will respond to cloud failures efficiently. The proposed IFTS provided a solution for different cloud infrastructure layers. The strategy was simulated using the CloudSim simulator, and the main results of our experiments are as follows; Host selection process in IFTS provided a better makespan by 11.57% than random selection and by 22.05% than the worst-fit process, with a 0% failure rate. IFTS cloudlet to VM mapping process managed to decrease the failure rate by 28.08% in random mapping and 15.46% in available VM mapping to 5.91%, which is a huge difference and improvement. The results for application-level FT show that IFTS makespan is improved by 27.52% when using replication and by 20.83% when using checkpointing.
Fault Tolerance in Cloud Computing; Host Reliability and VM Allocation; Replication and Checkpointing.
Copyright (c) 2025 Kalim Qureshi https://creativecommons.org/licenses/by/4.0
Automated Diagnosis of Mitral Valve Diseases Using Echocardiographic Images: A Comparative Study of GLCM Features and Deep Learning Techniques
Mitral Valve (MV) pathologies such as Mitral Valve Prolapses (MVP), Mitral Stenosis (MS), and type III regurgitation should be diagnosed as early as possible for the better management of the patients. This research work presents a method for the classification of MV diseases using echocardiographic images and texture analysis of the images using Gray Level Co-occurrence Matrix (GLCM) features in conjunction with Machine Learning (ML) classifiers. Initially, a Convolutional Neural Network (CNN) was employed to categorize echocardiographic images into two standard views: Apical Four-Chamber (A4C) and Parasternal Long-Axis (PLA). Next, the energy, contrast, correlation, and the entropy of GLCM-based texture features were obtained. The features were then fed into ML classifiers such as Random Forest (RF), Neural Networks (NN), Ensemble models to classify MV conditions. In the A4C view, the Neural Network Classifier (NNC) obtained an accuracy of 85% while in the Parasternal Long Axis (PLA) view, the accuracy was 84%. Some of the features of GLCM that were deemed important in the performance of the model were revealed. The results show that combining GLCM texture analysis with ML provides a potential way of improving the precision and reliability of MV disease diagnosis. The findings of this study contribute to advancements in cardiovascular disease detection by integrating machine learning techniques with echocardiographic analysis, ultimately supporting efforts to enhance public health and early disease diagnosis, in alignment with global healthcare initiatives.
Brain Tumor, CNN Segmentation, Deep Learning, Medical Imaging
Copyright (c) 2025 Hani Attar, Jafar Ababneh, Omar A. Sarhan, Ahmed Hamdy, Mohamed Hafez, Mohanad A.Deif https://creativecommons.org/licenses/by/4.0
Bridging Language Barriers in Programming Education: Java Programming Assistance Tool for Sinhala Native Speakers
This study presents an innovative programming assistance tool designed to address language barriers faced by Sinhala-speaking novice Java programmers. The tool provides real-time Java code generation and diagram creation based on Sinhala programming queries, enhancing conceptual understanding. Developed using a Design-Based Research methodology, the tool underwent iterative testing with 122 Sinhala-speaking learners, incorporating user feedback to refine usability and performance. Central to the system is Generative Pre-trained Transformer, version 3.5 Turbo, ensuring accurate translations and programming assistance, alongside a transformer-based model that translates Sinhala queries into English for processing. The translation model achieved 91.37% accuracy, with strong Bilingual Evaluation Understudy scores validating its contextual relevance. The tool’s practical applications extend beyond academia, supporting educational institutions, self-learners, and industry professionals in learning and skill development. Statistical evaluation of user performance demonstrated significant improvements in programming comprehension, reinforcing its effectiveness. By promoting inclusivity and expanding access to programming knowledge, this research contributes to the advancement of Sri Lanka’s technology sector and establishes a scalable framework for broader implementation in multilingual programming education.
Copyright (c) 2025 Kalpani Athukorala, Dilshan De Silva https://creativecommons.org/licenses/by/4.0
Empirical Evaluation of Virtual Machine Migration Policies on Power- and Time-Management in Cloud Computing
Cloud computing has become a norm for enterprises due to its significant advantages in infrastructure management, performance, and expenses. Cloud data centers consume significant power and go against the principles of green computing. It requires efficient management to minimize the environmental impact. Thus, green computing has become an interesting field of research in cloud computing. However, green computing is bundled with a performance-energy trade-off: job completion rate versus power consumption rate. This paper precisely focuses on this topic. A graphical user interface that utilizes the CloudSim simulator to evaluate the performance of various Virtual Machine (VM) power management policies in data centres has been developed. In our study, we test seven VM allocation policies: Dynamic Voltage and Frequency Scaling (DVFS), Interquartile Range (IQR), Local Regression (LR), Local Regression Robust (LRR), Median Absolute Deviation (MAD), Static Threshold (THR), and Single Threshold (STR). We also evaluate the performance of five VM selection policies: None, Maximum Correlation (MC), Minimum Migration Time (MMT), Maximum Utilization (MU), and Random Selection (RS). Utilizing CloudSim’s thirty-six different power management mechanisms across seven designed scenarios, we measure each policy’s power consumption and job completion rates for the analysis. The DVFS mechanism has proven to be the most effective method for conserving power. While preserving the power, its job completion rate is significantly compromised. Our proposed model measures the excessive power consumed by a power management mechanism residing in the system and concludes that there is no clear leader based on the performance-energy trade off.
Copyright (c) 2025 Kalim Qureshi, Abdulatif Albusairi, Paul Manuel https://creativecommons.org/licenses/by/4.0
A Comparison of 2D and 3D CNN For Lung CT Image Tuberculosis Severity Assessment
Tuberculosis is an infectious disease that usually affects the lungs. However, early diagnosis of tuberculosis increases the chance of cure. Practical analysis of lung computed tomography (CT) images from tuberculosis patients is one of the primary methods used to determine the severity of the disease. Handcrafted CT image analysis techniques have been ineffective in medical imaging due to their limitations in extracting discriminating features from the images. The application of deep learning, a branch of machine learning, is gaining increased acceptance in medical image analysis. The challenges such as high cost, human error, and slow speed encountered during manual labelling are gradually eliminated in various scales with deep learning techniques. This study explores two deep-learning approaches to classify TB severity in Lung CT Images. Two-dimensional (2D) and three-dimensional (3D) convolutional neural networks (CNNs) were used separately to classify the ImageCLEF 2021 lung CT dataset into ‘High’ and ‘Low’ severity categories. The proposed 3D-CNN in this study outperformed the 2D CNNs; it produced an overall average accuracy and Area Under the ROC curve (AUC) of 0.9929 and 0.9982 respectively
Copyright (c) 2025 David Alebiosu, Farrukh Hassan Hassan, Adeola Folayan, Mathew Yit Hang Yeow https://creativecommons.org/licenses/by/4.0
Optical Insight: Enhancing Ophthalmic Diagnostics with Automated Detection of Retinal Abnormalities
The human eye is a highly specialized organ responsible for the majority of sensory input that allows individuals to interpret their environment. Understanding its structure is crucial for diagnosing and treating ocular diseases. Traditional diagnostic methods, reliant on human expertise, often suffer from inefficiencies, underscoring the need for advanced imaging technologies to enable early and precise diagnosis. This study introduces deep learning models to automate the detection of four major eye diseases—Glaucoma, Macular Hole, Central Serous Retinopathy, and Drusen—using fundus images. Key innovations include a hybrid ResNet-VGG16 model that improves Drusen detection accuracy and a DenseNet-VGG16 model effective for Central Serous Retinopathy. To address overfitting, a U-Net-ResNet model was developed for Macular Hole detection, while the YOLOv8x object detection model excelled in Glaucoma detection by localizing the optic disc. Integrated into a web application, these models deliver sensitivities and specificities exceeding 95%, setting a new standard in ophthalmic diagnostics. This research contributes to the body of knowledge by demonstrating the efficacy of hybrid model architectures in medical image analysis. The system’s technical soundness is evidenced by robust performance across datasets and clinical scenarios. Its practical applications are substantial, offering healthcare professionals a powerful tool for early disease detection, with the potential to transform clinical workflows and improve patient outcomes.
Copyright (c) 2025 Dilshan De Silva, Dinuka R. Wijendra, Kithmina S. Siriwardana, Shehan N. W. Gunasekara, Udesh Piyumantha, Sahan P. Thilakaratne https://creativecommons.org/licenses/by/4.0
Enhancing Tag Recommendation Precision on Stack Overflow Data Warehouse: An Integrated Approach Combining Numeric Attributes, Feature Extraction Techniques, and Multiple Machine Learning Algorithms
Building upon our previous work on extracting and analyzing Stack Overflow data to uncover trends in programming languages, community contributions, and talent availability, this research investigates the impact of numeric attributes on tag recommendation. Utilizing the Stack Overflow Data Warehouse System developed in our prior study, we conduct a comprehensive analysis of multiple machine learning (ML) algorithms to evaluate their effectiveness in recommending tags based on an integration of specific numeric attributes with feature extraction techniques. The methodology involves extracting relevant data, preprocessing it, and applying Term Frequency-Inverse Document Frequency (TF-IDF) as a feature extraction technique alongside diverse ML algorithms, including Support Vector Machines (SVM), Gradient Boosting, Random Forest, and Decision Tree, to assess their performance. Our results indicate that this combination improves evaluation metrics, including F1 Score, Recall, and Precision, with a particularly significant influence on the Precision of tag recommendations, providing insights into the optimization of tagging systems on Q&A platforms. Future research will focus on integrating advanced models and refining data preprocessing techniques to further enhance tag prediction accuracy. This study extends the application of the Stack Overflow Data Warehouse System and contributes to the improvement of tag recommendation mechanisms in online technical communities.
Copyright (c) 2025 loeyl Loey https://creativecommons.org/licenses/by/4.0
A Digital Signature on Cubic Pell Cryptosystem CP256-1299
Elliptic curves have proven to be a suitable foundation for cryptosystems, with Elliptic Curve Cryptosystems (ECC) offering strong security with smaller key sizes. Recent advancements in ECC design aim to create more efficient and secure curves. In this paper, we introduce a new digital signature scheme, named Cp256-1299. It is a 256-bit scheme based on a cubic Pell curve where the arithmetic operations are efficient and straightforward. In previous works, cubic Pell curves have been used to design public key cryptosystems. Our main motivation in proposing the new digital signature algorithm is to exploit the effectiveness of the arithmetic of cubic Pell curves, while maintaining reasonable keys and high security. We compare our new scheme to three widely-used digital signature algorithms based on ECC, namely Ed25519, Secp256k1 and Secp256r1. It turns out that our cubic Pell curve based digital signature algorithm is designed to operate with a larger periodic order while maintaining at least similar computational requirements to most popular elliptic curve cryptosystems. Our new scheme is also suitable to support a central bank digital currency.
Special Issue: Network and Information Security in Cyber Systems (Cybersecurity)
Copyright (c) 2025 Nur Azman Abu, Abderrahmane Nitaj, Muhammad R. Kamel Ariffin https://creativecommons.org/licenses/by/4.0
Adding Common Sense To Robots Using Ontology
This work investigates how ontological frameworks might improve robots’ ability to reason using common sense. The goal of the project was to enhance robot decision-making in dynamic real-world situations by developing an ontology-based model retraining technique. The researchers wanted to incorporate organized commonsense knowledge into robotic systems, so they built extensive ontologies that captured knowledge about the physical world and human interactions. The research compared the performance of robots with conventional models (control group) to those with ontology-enhanced models (experimental group) across various measures. The results indicate that this strategy may be used to develop more competent and user-friendly robotic helpers for a variety of sectors, including industry, healthcare, and education. Although the study has limitations related to data quality and experimental design, it does demonstrate the promise of ontology-based techniques to advance autonomous systems and human-robot interactions. Extending ontology databases, multidisciplinary cooperation, and investigating applications in other sectors are some of the future research goals.
Copyright (c) 2025 Ranathunga Arachchige Ranathunga, Samantha Rajapaksha https://creativecommons.org/licenses/by/4.0
AI-Powered Histopathological Analysis for Intelligent and Early Detection of Colon Cancer: A Synergistic Approach with Inception v3 and Machine Learning
Colon cancer (CC) is one of the major global concerns, as it is the third most common cancer worldwide and nearly 10% of all the cancer cases. The mortality rate of this cancer is also high. Age and sedentary lifestyle are the two major causes of this cancer. Colon cancer does not show any significant signs for detection at the early stage. Advanced stages of diagnosis leaves with very less treatment options. It is for this reason that Artificial Intelligence (AI) can step-in to identify the disease at an early stage. Integrating AI with screening of Histopathological Images will aid in intelligent colon classification. This technique will streamline the process, saving time and maximizing the expertise of medical professionals. In the proposed approach, Deep Learning (DL) based Inception v3 model (IV3M) is integrated with three Machine Learning (ML) models like, Neural Network (NN), Gradient Boosting (GB) and Decision Tree (DT) for an automated colon classification. Integrating DL and ML can solve the complexing in understanding of histopathological images and further aid in classification process. Results, so obtained reveals a very good classification accuracy of 98.8%. Results are further validated by Confusion Matrix (CM), Calibration Plot (CP) and Receiver Operating Characteristic (ROC) Analysis. Integrating DL and ML for CC classification has the potential to reform clinical practice by saving lives with more accurate prediction and optimizing healthcare resources. For clinical purpose, experts like Gastroenterologist and Colon Surgeon must be consulted for necessary diagnosis. The paper intents to provide a dedicated technique for classification of colon cancers at an initial stage, so as to offer patients with early possible treatment options.
Copyright (c) 2024 Vineet Mehan https://creativecommons.org/licenses/by/4.0
An Energy-Efficient VM Selection Using Updated Dragonfly Algorithm in Cloud Computing
Cloud computing is popular among industries, academia, and government to supply reliable and scalable computational power. High speed networks in cloud data centers connect Virtual machines with Physical Machines. Virtualization assists the cloud service providers to manage resources effectively but unoptimized and inefficient services degrade the performance of the system. The scheduling architecture of cloud computing includes Physical Machines (PMs), Virtual Machines (VMs) and the allocation and migration policy of the VMs over the PMs. The overutilized PMs get a few migrations and this paper introduces a novel behaviour of VM selection from overutilized PM using Swarm intelligence. The evaluation of the proposed algorithm architecture is compared with other state of art optimization algorithm from the same series. The evaluation has been done on the base of Quality of Service (QoS) parameters such as SLA-Violation, energy consumption against various load variation scenario to support elasticity. The proposed work has outcasted other techniques with significant margin in terms of QoS and the illustrations are discussed in the result.
Copyright (c) 2024 Ajay Prashar, Jawahar Thakur https://creativecommons.org/licenses/by/4.0
Feature Selection by ModifiedBoostARoota and Classification by CatBoost model on High Dimensional Heart Disease Datasets
As many human beings are losing lives due to heart disease, early detection and prevention of the same would help save lives. Various Machine Learning Algorithms are applied in classifying patients with/ without heart disease. To efficiently predict the outcome, it is desirable to select the features which highly contribute to the prediction. Feature Selection algorithms would save time and improve the prediction. In this paper, ModifiedBoostARoota (MBAR) algorithm is used for Feature Selection and classifiers CatBoost, XGBoost, Decision Tree, Extra Trees Classifier, Support Vector Classifier, Logistic Regression, K Nearest Neighbors, Naive Bayes and Random Forest were applied on UCI Arrhythmia dataset and UCI Z-Alizadeh Sani dataset. Synthetic Minority Over-sampling Technique (SMOTE) was applied to balance the dataset. A comparison on the accuracy achieved with and without SMOTE shows that after applying SMOTE, MBAR with CatBoost classifier gives better accuracy of 92.76% on Z-Alizadeh Sani dataset and 86.33% on Arrhythmia dataset.
Heart disease, Feature Selection, CatBoost, Classification
Copyright (c) 2022 International Journal of Computer Theory and Engineering
Business Intelligence Technology as Support for Web-Based PAUD Reports at HIMPAUDI Bekasi Regency
Himpunan Pendidik dan Tenaga Kependidikan Anak Usia Dini (Association of Early Childhood Educators and Personnel of Education or HIMPAUDI) of Bekasi Regency is a group of 1680 entities of Pendidikan Anak Usia Dini (Early Childhood Education Programs or PAUD) spread across 23 sub-districts, 187 villages and 176 villages. HIMPAUDI Bekasi Regency strives to realize the application of computer technology to evaluate routine reports from PAUD educational institutions every month. At this time there are still many obstacles in processing reports into a form that is easy to understand. This study aims to build a computer-based system in reporting activities at HIMPAUDI Bekasi Regency which is useful for making it easier for each PAUD to send reports to the Regency HIMPAUDI, making it easier for District HIMPAUDI to monitor and recap all reports, and facilitate the analysis of HIMPAUDI reports for Bekasi Regency. The report system built is a web-based system that uses Business Intelligence technology to analyze reports so that reports uploaded in the form of excel files can be automatically recapitulated by the system into graphs that can be viewed based on parameters such as year, age, study group, and so on. The website development method uses the System Development Life Cycle (SDLC) which starts with data collection, system analysis and design, implementation, testing and system maintenance. The result of the research is a web-based Busisness Intelligence application to support PAUD reports in Himpaudi Bekasi Regency which is submitted and then managed by Himpaudi of Bekasi Regency.
Business Intelligence, reports, SDLC, web