International Journal of Computer Theory and Engineering
International Journal of Computer Theory and Engineering (IJCTE) is an international academic open access journal which gains a foothold in Singapore, Asia and opens to the world. It aims to promote the integration of computer theory and engineering. The focus is to publish papers on state-of-the-art computer theory and engineering. Submitted papers will be reviewed by technical committees of the journal and association. The audience includes researchers, managers and operators for computer theory and engineering as well as designers and developers. All submitted articles should report original, previously unpublished research results, experimental or theoretical, and will be peer-reviewed. Articles submitted to the journal should meet these criteria and must not be under consideration for publication elsewhere. Manuscripts should follow the style of the journal and are subject to both review and editing. IJCTE will only accept new submissions through online submission system.
A Decision-support Framework for Energy-efficient Batch Optimization in Machine Learning
This paper presents a decision-support framework for selecting energy-efficient batch optimization techniques in machine learning, addressing the absence of reproducible, theory-informed guidance in Green Artificial Intelligence (AI). Eight key methods, including Mixed Precision Training, Dynamic Batching, and Gradient Accumulation are systematically evaluated through a rigorous systematic literature review and assessed across five criteria: energy efficiency, accuracy impact, scalability, technological maturity, and hardware dependency. We operationalize a transparent, weighted Multi-Criteria Decision Analysis (MCDA) protocol, termed the Green AI Score, and a normalized efficiency metric, Energy-Delay Normalized Accuracy (EDNA). These instruments serve as a reproducible analytical scaffold to enable cross-technique comparison and quantification of energy-accuracy trade-offs. Advanced visual analytics, including a heatmap, dual-axis bar chart, bubble plot, and correlation visualization, link sustainability rankings with energy-accuracy trade-offs. Our analysis identifies Mixed Precision Training (Score: 0.84) and Dynamic Batching (Score: 0.77) as top-tier strategies, offering 40–60% and 30–50% energy savings, respectively, with minimal accuracy loss. Furthermore, we theoretically derive a hardware-aware heuristic threshold (memory-bandwidth-to-compute ratio < 0.4) to analytically assess the applicability of Mixed Precision Training under varying system constraints. Finally, a rule-based decision policy is synthesized to support informed technique selection, providing practitioners with a theoretically grounded guide for navigating deployment trade-offs. This work advances beyond descriptive surveying by providing an evidence-based, reproducible analytical synthesis grounded in systems and algorithmic considerations, thereby supporting transparent and sustainable AI deployment.
Copyright (c) 2026 Timothy Adeyi, Adrian D. Cheok, Huihui Song, Steven Z. Zhou, Emma Y. Zhang, Wisura P. Pallewatta, Ishmeal Quist https://creativecommons.org/licenses/by/4.0
Attention-Driven Deep Convolutional Neural Network for Maize Leaf Nutrient Deficiency Recognition
This research focuses on developing an Attention-Driven Deep Convolutional Neural Network (ADCNN) to predict nutrient deficiencies in maize using an image dataset. The main advantage of the ADCNN model is its use of attention mechanisms, which enable it to focus on relevant features at the leaf level. This results in more accurate and transparent classification compared to previous methods that relied solely on Convolutional Neural Networks (CNNs). The ADCNN was trained and tested on a dataset covering six nutrient classes: healthy (ALL Present), general nutrient imbalance (ALLAB), nitrogen (NAB), phosphorus (PAB), potassium (KAB), and zinc (ZNAB). Its performance was compared with three deep learning models: the baseline CNN, Visual Geometry Group 19 (VGG19), and hybrid Convolutional Neural Network-Support Vector Machine (CNN-SVM). Results indicate that the ADCNN performs better than any of the competing models, with accuracy, precision, recall, and F1-scores of nearly 99.48%, which demonstrates its efficacy. The attention mechanism improves the model’s discriminative capability, enabling it to identify signs of deficiencies in appearance. The ADCNN has enhanced generalization, stability, and response time, which are superior to the conventional CNN architecture. This research shows that using a deep attention process enables real-time or automated plant health monitoring based on nutrient status. The ADCNN model is a stable, efficient system that can be used to create intelligent agricultural systems that enhance sustainable agricultural practices by optimizing nutrient use. Future research should focus on collecting real-world data across various environmental conditions and on integrating Internet of Things (IoT)-enabled platforms to advance multimodal data collection for a more comprehensive plant health assessment.
Copyright (c) 2026 Shubhangi Gaikar, Prof. Dr. M. S. Zambare, Prof. A. D. Shaligram, Dr. Kailash Sapnar https://creativecommons.org/licenses/by/4.0
High-Order Cumulants and AI-Based Methods for Blind Identification and Equalization in 5G Networks
This paper explores and evaluates advanced signal processing techniques for blind channel identification and equalization in modern 5G networks. The focus is on traditional statistical methods and emerging artificial intelligence based on the autoencoder method. The study concentrates on two principal frameworks: high-order cumulants, specifically the fourth-order and sixth-order cumulants, which are capable of exploiting non-Gaussian and higher-order statistical dependencies in digitally modulated signals, and Artificial Intelligence (AI)-based models, particularly autoencoders, which use deep learning architectures to reconstruct or denoise received signals without prior knowledge of the transmission channel. To systematically assess the capabilities of each method, three distinct algorithms were developed and implemented: the fourth-order cumulant, the sixth-order cumulant, and the autoencoder algorithm. These algorithms were applied to 5G signals transmitted over Rayleigh multipath fading channels, modeled using standard propagation profiles such as BRAN-A, BRAN-B, and Proakis B. Simulations were conducted over a wide range of Signal-to-Noise Ratios (SNRs) and varying levels of noise and channel dynamics. Key findings from the simulation results are as follows: The sixth-order cumulant demonstrates superior noise resilience compared to the fourth-order cumulant, making it more suitable for environments with stronger interference or non-Gaussian noise. The autoencoder algorithm achieves the highest estimation and identification accuracy after sufficient training, due to its ability to model complex, nonlinear transformations. However, this comes with increased data requirements and computational cost. The fourth-order cumulant algorithm is computationally efficient, theoretically tractable, and performs well under stationary and low-noise conditions. However, it exhibits a notable performance decline in time-varying or high-noise scenarios. In contrast, the autoencoder algorithm shows enhanced robustness and adaptability to nonlinear, time-varying, and imperfectly modeled environments. Its strength lies in learning the underlying channel effects implicitly from raw data, without requiring a structured analytical model. Nevertheless, this benefit is offset by higher computational complexity and a lack of interpretability compared to the cumulant-based methods. In conclusion, while cumulant-based techniques remain valuable in structured or lightly impaired channels, AI based on autoencoder models, especially autoencoders, offers a more powerful and flexible solution for blind equalization under realistic and challenging 5G conditions.
Orthogonal Frequency Division Multiple Access (OFDMA)
Copyright (c) 2026 Said ELKASSIMI, Bouzid Manaut, Said Safi https://creativecommons.org/licenses/by/4.0
Algorithmic Decision-Making Framework for Ship Modernization Strategy Using Nonlinear Multi-Parameter Optimization
The article presents an algorithmic model for decision-making on selecting the optimal strategy for modernizing a seagoing vessel via nonlinear multi-parameter optimization. A set of interrelated technical, economic, and operational factors that influence the feasibility of modernization measures within the vessel’s life cycle is considered. It is shown that traditional approaches to maintenance and periodic assessment of a vessel’s condition are insufficient for strategic planning, as they do not account for the long-term economic consequences, the level of risk, and the investment attractiveness of modernization. A model is proposed that integrates investment cost indicators, modernization parameters, characteristics of the modernization contractor, the projected service life of the vessel after modernization, expected annual profit, risk level, and life cycle indicators (Life Cycle Cost (LCC) and Life Cycle Assessment (LCA)). Net Present Value (NPV) is used as the main optimization criterion, and a system of constraints is formed that takes into account financial, time, and safety factors. The proposed algorithmic system allows for a reasonable choice of modernization type, parameter configuration, and contractor, ensuring maximum economic efficiency under existing conditions. The results indicate the promise of applying optimization algorithms in marine engineering to improve the reliability, profitability, and long-term sustainability of ship operations.
Copyright (c) 2026 Svitlana P. Onyshchenko, Oleksiy M. Melnyk, Oleksandr M. Shumylo, Igor I. Vorokhobin, Igor O. Burmaka, Oleg A. Onishchenko https://creativecommons.org/licenses/by/4.0
Harnessing Explainable AI and Machine Learning for Dual Predictive Modeling of Car Goodwill and Crop MSP in Price and Policy Forecasting
Accurate yet interpretable forecasting remains a major challenge in data-driven price and policy decision-making, as many machine learning models prioritize predictive performance while offering limited transparency. To address this gap, this study proposes an integrated Explainable Artificial Intelligence (XAI)—enabled machine learning framework for dual predictive modelling across two economically significant domains: automotive valuation and agricultural price policy. The framework simultaneously predicts car goodwill values and crop Minimum Support Prices (MSP), enabling cross-domain analysis while maintaining model interpretability. Comparative experiments are conducted using multiple machine learning techniques, including linear and ensemble-based models, applied to automotive and agricultural datasets. Ensemble methods demonstrate superior predictive capability in both domains. To enhance transparency and stakeholder trust, XAI techniques are incorporated to explain model behaviour and identify key influencing factors. The analysis shows that depreciation and brand-related attributes play a dominant role in car goodwill valuation, whereas climatic and cost-related factors significantly influence MSP predictions. The results confirm that integrating XAI with machine learning improves both predictive reliability and interpretability, transforming black-box models into actionable decision-support systems. The proposed dual predictive framework offers a scalable and transparent approach for market optimization and policy evaluation, highlighting the practical value of explainable AI in strategic economic planning and data-driven governance.
Explainable Artificial Intelligence (XAI)
XGBoost and Marketing Mix Modelling (MMM)
Copyright (c) 2026 Meghna Chaudhary, M. Afshar Alam, Dr. Sherin Zafar, Dr. Abubeker K M https://creativecommons.org/licenses/by/4.0
Combining Bioinspired Red Kite Optimization and Deep Learning for Effective COVID-19 Detection in Chest Radiography
Deep Learning (DL)-based systems, employing advanced growths, pave the way for bioinspired methods in almost all domains of life. Healthcare organizations can employ DL approaches due to their precision in recognizing and identifying distinct diseases. The coronavirus disease (COVID-19) epidemic has emerged as the most dangerous disease in recent times, posing a significant burden on health organizations worldwide. Medical imaging and PCR testing have the potential to analyze COVID-19. Given the high spreadability of COVID-19, Chest X-Ray (CXR) analysis is considered safe under various conditions. DL systems are capable of enhancing medical imaging tools and supporting radiologists in making medical decisions for the analysis, diagnosis, and monitoring of distinct diseases. With this motivation, this study presents a novel bioinspired red kite optimizer with a DL-based COVID-19 classification (BRKODL-COVIDC) method on CXR images. The BRKODL-COVIDC method intends to recognize and categorize the presence of COVID-19 using DL models. In the presented BRKODL-COVIDC method, the Bilateral Filtering (BF) model can be used for image pre-processing tasks. In addition, the complex patterns and features in the images can be derived from the DenseNet121 model. For optimal hyperparameter selection of the DL techniques, the Random Key Optimizer (RKO) model can be employed in this research. Last, the BRKODL-COVIDC technique makes use of the Dilated Convolutional Auto-Encoder (DCAE) model and is used for identification purposes. Thus, with this research study, we are contributing to the National Priorities for Research, Development, and Innovation (RDI) in health and wellness to provide and maintain a sustainable environment in the health sector. The simulation outputs of the BRKODL-COVIDC technique can be examined on a standard dataset. The simulated outputs demonstrated the enhanced performance of the BRKODL-COVIDC technique in the COVID-19 detection procedure.
Copyright (c) 2026 Dr. Mohammed Altaf Ahmed, Dr. Suleman Alnatheer, Dr. Sachi Nandan Mohanty, Dr. P. Pavan Kumar, Dr. Bibhuti Bhushan Dash, Qutubuddin Mohammed https://creativecommons.org/licenses/by/4.0
Multi-Task Deep Learning for Automated Tobacco Leaf Grading in a Controlled Environment
Grading tobacco leaves is crucial for ensuring fair pricing and quality control, however, the process is still largely carried out manually, resulting in a slow, subjective, and often inconsistent outcome. In this work, we present a multi-task deep learning approach designed to automate the grading of air-cured Burley tobacco leaves in controlled settings. The model is constructed with shared convolutional layers and separate task-specific branches, allowing it to predict stalk group, quality, and color at the same time, in line with the hierarchical grading system. To improve consistency, images were preprocessed using coin-based size normalization, rotation alignment, and segmentation. In our experiments, the multi-task model with EfficientNetB0 achieved an accuracy of 94.82% and significantly outperformed the multi-class and single-task baselines, while reducing both training time and inference delay. These findings suggest that multi-task learning can be a valuable and robust method for automated tobacco grading, showing gains in accuracy, speed, and scalability compared to other algorithms.
Copyright (c) 2026 Charlie Marzan, Conrado Ruiz Jr., Oya Aran https://creativecommons.org/licenses/by/4.0
Interpretable Machine Learning for Ambient Temperature Prediction: Insights from SHAP, ICE, PDP, and ALE
Accurate ambient temperature prediction is essential for climate monitoring, urban planning, and environmental management, particularly in regions experiencing rapid climatic variability such as Sri Lanka. This study investigates the application of explainable machine learning models for short-term ambient temperature prediction in Battaramulla, Sri Lanka. Five regression algorithms-K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Support Vector Regression (SVR), and Histogram-based Gradient Boosting Regressor (HGBR) were evaluated using 14 meteorological and environmental predictors, including temporal variables, relative humidity, solar radiation, rainfall, wind speed, and air pollutant concentrations (CO2, NOₓ, CH4, O3, CO, PM2.5, and PM10). Among the models tested, HGBR demonstrated superior predictive performance, achieving R² values of 1.00 (training) and 0.96 (testing), with corresponding mean squared error values of 0.01 and 0.11. Model interpretability was examined using SHapley Additive exPlanations (SHAP), Partial Dependence Plots (PDP), Individual Conditional Expectation (ICE) analyses, and Accumulated Local Effects (ALE), which identified several features as the most influential predictors. Model validation using 192 real-time observations showed close agreement between predicted and measured temperatures, although the evaluation was limited to a single location and time period. A web-based application, ‘Therma’, was developed to facilitate practical deployment of the model for localized temperature estimation. Overall, this study demonstrates the utility of explainable machine learning for localized climate prediction while highlighting the need for broader spatiotemporal validation in future work.
Histogram-Based Gradient Boosting Regression (HGBR)
Copyright (c) 2026 Hirushan Sajindra, Thilina Abekoon, Salani Buthpitiya, Yasith Alahakoon, Namal Rathnayake, Komali Kantamaneni, Upaka Rathnayake https://creativecommons.org/licenses/by/4.0
Hybrid 3D Object Detection in Logistic Environments Using a Histogram Depth Filter
In intralogistics, accurate 3D object detection is essential for enhancing robotic perception and enabling autonomous navigation. This paper presents a novel hybrid method that combines 2D object detection with depth-based point cloud segmentation for efficient and real-time 3D object pose estimation. The key contribution of this paper is the Cumulative Histogram Depth Filter (HDF), a lightweight algorithm that segments dominant depth regions corresponding to detected objects. This approach uniquely enables the reuse of existing 2D-labeled datasets, eliminating the need for extensive 3D annotations. The proposed method was evaluated using both simulated and real-world data, obtaining high detection accuracy. In the simulated environment, it achieved a mean distance error of 0.14 m, an Intersection over Union (IoU) of 0.90, and a mean Average Precision at 50 (mAP@50) of 0.95. Real-world experiments using Azure Kinect and ZED2 cameras yielded an average distance error of 0.13 m, IoU of 0.78, and mAP@50 of 0.65. Additionally, the system runs at 105 FPS, significantly outperforming more complex hybrid architectures in terms of computational efficiency, making it particularly suitable for real-time robotic applications.
Copyright (c) 2026 Daniel Vidal, Prof. Dr. Ing. Johannes Fottner, Boyan Liu https://creativecommons.org/licenses/by/4.0
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
Edge Federated Learning for Privacy Aware IoT Sensor Networks
Privacy issues and communication overhead are bottlenecks to the machine learning implementation in IoT sensor network. In this paper, we introduce a lightweight Edge-enabled Federated Learning (EFL) framework utilizing Personalized Federated Learning (FedPer) over edge computing for privacy-preserving collaborative training. Our solution mitigates the challenges of non-independent and identically distributed (non-IID) data, using client-specific personalization and secure aggregation without raw data exchange. Extensive experiments over five real-world IoT datasets (UCI HAR, Ambient, DOO-RE, SHL and WISDM) show that FedPer can achieve up to 96% accuracy—surpassing baseline methods (FedAvg, FedProx) by 2–12% within non-IID scenarios—meanwhile decreasing communication overhead by up to 20%. The hardware evaluation on Raspberry Pi 4 and Jetson Nano validates the realization of real-time inference (<30 ms/sample) for Decision Tree (DT) with compact model sizes (<500 KB). Our system offers a large-scale, privacy-assured way of intelligent sensing in smart home, wearables and industrial IoT.
Data Mining, Distributed Computing, Inventory, Federated Learning, Machine learning
Copyright (c) 2026 MOHAMMAD ABU KAUSAR, MOHAMMAD NASAR 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
ZK-FLGuard: Verifiable Privacy via Zero-Knowledge Proofs in Federated Anomaly Detection for 5G Edge-IoT Systems
This paper presents Zero-Knowledge Federated Learning Guard (ZK-FLGuard), a privacy-preserving and verifiable federated learning framework for real-time anomaly detection in Fifth-Generation Mobile Network (5G)-enabled Internet of Things (IoT) environments. Building on the integration of zero-knowledge proofs (zk-SNARK—Zero-Knowledge Succinct Non-interactive Argument of Knowledge) and blockchain-based access control, ZK-FLGuard ensures the integrity of model updates without exposing private data. Using real-world intrusion detection datasets (CICIDS2017—Canadian Institute for Cybersecurity Intrusion Detection System 2017, TON_IoT—Telecommunications Organisation of the National Security—IoT) and a synthetic adversarial dataset, our evaluation shows that ZK-FLGuard achieves up to 0.96 F1-score (harmonic mean of precision and recall), improves recall in low-frequency attack detection, and introduces less than 10% additional latency overhead compared to standard Federated Learning (FL). Compared with centralized Long Short-Term Memory (LSTM) and FL without Zero-Knowledge Proof (ZKP), ZK-FLGuard provides competitive accuracy while ensuring verifiable computation and strong privacy guarantees. We address the critical challenge of securing federated anomaly detection in 5G-enabled IoT systems against data leakage, model poisoning, and unauthorized access. While FL preserves privacy by keeping raw data local, it remains vulnerable to gradient leakage and adversarial manipulation. Our hypothesis is that combining zero-knowledge proofs and blockchain with FL can deliver a scalable, tamper-resistant, and privacy-preserving detection pipeline suitable for resource-constrained edge environments.
Copyright (c) 2026 Manuel José Cabral dos Santos Reis 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
Optimizing Medical MRI Brain Image Classification through Compression Analysis on Deep learning Models with Light Weight Implementation
Medical imaging is essential for diagnosing neurological diseases. Advances in Deep Learning (DL) have greatly improved brain Magnetic Resonance Imaging (MRI) classification, enabling more accurate anomaly detection. However, the high computational and memory demands of DL models pose challenges for deployment on resource-constrained platforms such as portable medical devices and edge computing systems. This study aims to address these limitations by reducing storage and transmission demands through deep learning-based tumour classification on compressed MRI data. JPEG2000 lossless compression was applied at varying ratios to examine its effect on classification performance. The analysis focuses on understanding how different compression levels impact the accuracy and reliability of DL models. Three deep learning architectures—Convolutional Neural Network (CNN), ResNet50, and MobileNetV2—were selected to represent baseline, high-capacity, and lightweight models, respectively, and were trained on compressed MRI datasets to classify brain images into glioma, meningioma, pituitary tumor, and no tumor. Evaluation metrics included Peak Signal-to-Noise Ratio (PSNR), entropy, and Structural Similarity Index (SSIM) for image quality, and precision, recall, accuracy, F1 score, and Area Under the Receiver Operating Characteristic Curve (AUROC) for classification performance. The experimental results indicate that CNN and ResNet50 exhibit higher classification metrics at specific compression levels. However, MobileNetV2 consistently maintains acceptable performance across all tested compression ratios. MobileNetV2 features a lightweight architecture with minimal memory requirements, making it well-suited for deployment in point-of-care devices. The model was optimized and deployed on the NVIDIA Jetson TX2 Developer Board, enabling the development of a portable, lightweight diagnostic tool for brain tumor detection.
edge Artificial Intelligence (AI) deployment
Copyright (c) 2026 Neena K A, Anil Kumar M N 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
Self Determining Structure Model for Depth Map Completion
In this paper, we present a comprehensive approach for depth map completion using a variable pipeline model. The pipeline is designed to be flexible, and its structure is dynamically determined during the training phase by minimizing a fitness function. To achieve this objective, we used the Particle Swarm Optimization (PSO) technique, which efficiently ex-plores the solution space to optimize key parameters. The pipeline itself consists of three main stages: a convolutional stage (Lab Convolution), an Interpolation Model (IM), and a post-Convolutional Stage (SC2). During training, our model automatically adjusts several aspects, including the selection of filter parameters, interpolator settings, and the number of filters used at each stage. Moreover, the pipeline is capable of interchanging the execution order between the stages, either processing in the sequence of Lab Convolution-IM-SC2 or SC2-IM-Lab Convolution. This flexible structure allows for the discovery of better configurations that significantly im-prove the performance of depth map completion tasks. Our approach demonstrates superior results by adapting to the specific characteristics of the input data.
Copyright (c) 2026 Vanel Lazcano, Dr. Anthony D. Cho, Dr. Felipe Calderero 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