Journal of Soft Computing and Data Mining
Journal of Soft Computing and Data Mining (JSCDM) is an international journal focusing on the wide scope of soft computing and data mining to solve real-life problems. It invites only original and interesting contributions from worldwide authors throughout the year. The Journal of Soft Computing and Data Mining publishes accepted papers through two regular annual issues. JSCDM is a collection of methodologies that aim to exploit computational research papers in major fields, including Artificial Immune Systems, Bio-Inspired Computing, Computational Statistics, Evolutionary Computing, Distributed Artificial Intelligence, Clustering, Data Mining in Medical Informatics, Data Mining Systems, and Applications.
DLinear Model for Microclimate Prediction of Coffee-Pine Agroforestry
Date fruit is one of the most important economical and cultural agricultural crops in the Middle East that plays a critical role in trade and food sustainability. These merits have attracted increasing interest from researchers and the food industry to improve food sustainability. The advent and integration of computer vision and artificial intelligence (AI) technologies have rapidly accelerated the progress in the development of automated classification, quality assessment, and grading for date fruits. This study offers an inclusive comparative analysis for ten pre-trained convolutional neural network (CNN) models used to classify fifteen different date fruit cultivars. The dataset obtained from two publicly available datasets. They contain images of cultivars from Saudi Arabia and Pakistan. The images are first preprocessed to enhance their quality, segmented, augmented to overcome the imbalance problem, standardized, and normalized to be fed then to the CNNs. Transfer learning was applied to fine-tune the pre-trained models using MATLAB 2023a software package. The performance of models were evaluated based on the overall accuracy, per-class accuracy, training time, execution time, and average inference time per image. Results showed that DarkNet-50 achieved the highest accuracy (99.33%), while MobileNet-V2 and ShuffleNet provided the best balance between accuracy and efficiency, hence they are well-suited for real-time or embedded applications.
Copyright (c) 2025 Journal of Soft Computing and Data Mining https://creativecommons.org/licenses/by-nc-sa/4.0
Dual-Stage Deep Learning Framework for Prostate Cancer Grading Using Swin U-Net and Attention-Based CNNs
Accurate grading of prostatic adenocarcinoma is essential in treatment planning. However, Gleason grading is time-consuming and clinically undependable. We presented a hybrid deep learning framework which comprises Swin U-Net for transformer-based segmentation network and attention-based CNNs for ISUP grade classification task. We incorporated Grad-CAM to aid in model interpretability and to visualize decision crucial areas. Quantitative evaluations on the PANDA, ISUP Grade-wise and transverse datasets achieve 100% accuracy on the smaller balanced Transverse dataset, 90.2 ± 0.7% performance in terms of ISUP with only 3.5M parameters, and a vicious Dice score equal to 0.99 ± 0.005 for segmentation. Notably, this cross-dataset generalization has not deteriorated below 92.3 ± 1.4% in any TIO experiment with no form of retraining applied to the transferred models. Inference time is less than 20 ms, deployment on the edge and mobile. The proposed model has achieved state-of-the-art performance for interpretability, accuracy, and computational complexity. The broadcast-then-categorize platform has been validated in ablation and optimization experiments, which demonstrate the potential for real-time diagnosis of prostate cancer.
EffNetEye: A Multimodal Fusion Model for Multiclass Classification of Retinal Diseases
The Eye is the most sensitive human organ; if affected by any disease, it can hinder the individual’s quality of life. Some Retinal diseases, such as Epiretinal Membrane (ERM), Age-related Macular Degeneration (AMD), glaucoma, and Retinal Vein Occlusion (RVO), are major contributors to eyesight loss. Timely detection of such diseases is essential for successful treatment. A novel model called ‘EffNetEye has been proposed for the classification and diagnosis of retinal diseases by combining two modalities, fundus and OCT. The suggested model provides a simple but effective dual-modality feature-level fusion approach. It uses two EfficientNetB0 backbone networks to extract features from each modality and classifies four retinal diseases: AMD, ERM, RVO, and Normal. None of the studies on multimodal approaches included ERM disease, which distinguishes this model from the existing multimodal approaches. A total of 5,484 image datasets were constructed from three publicly available datasets: OIA-ODIR, RFMid, and OCTDL. Different preprocessing steps are applied to each modality image to address the domain differences among the three datasets. OCT images were preprocessed with Wiener filtering to reduce speckle noise and to improve local contrast in fundus images; the CLAHE technique was applied. Additionally, data augmentation was applied to address class imbalance in the dataset. The model was trained on a combined training and validation dataset and evaluated using 5-fold stratified cross-validation to ensure consistency and eliminate bias. The use of Grad-CAM demonstrated the model’s ability to highlight clinically relevant features in both fundus and OCT scans during prediction. Finally, the model was tested on an independent test set, which showed strong classification performance, achieving an accuracy of 94.2% and a high AUC of 99.99%.
Multimodal imaging, Deep learning, Machine learning, Eye disease, OCT, Fundus.
Computer Science, Artificial Intelligence, Medical Imaging, Deep Learning
Artificial Intelligence & Machine Learning
Homomorphic Encryption-Enabled Federated Transfer Learning for Privacy-Preserving of Internet of Vehicles
The swift incorporation of intelligent cars into the Internet of Vehicles (IoV) has contributed to the creation of vast quantities of various and sensitive data, which has caused serious concerns about the privacy and security of data. The conventional centralized learning models tend to face major difficulties associated with loss of privacy, transmission delay, and data handling. In the meantime, the current federated learning (FL) solutions are often unable to be flexible enough when knowledge is transferred into different heterogeneous vehicular settings. In order to overcome these drawbacks, this paper introduces a Privacy-Preserving Federated Transfer Learning (PP-FTL) model that can smoothly combine transfer learning with secure federated model updates based on homomorphic encryption. PP-FTL framework specifically targets data fusion in IoV networks to provide the overall privacy protection and allow the cross-vehicle collaborative learning, even when there is heterogeneous and distributed data. The model uses an aggregation of encrypted weights and a knowledge distillation process that enables successful adaptation between vehicles. Experimental tests based on real-life vehicular network data prove that the suggested PP-FTL framework outperforms the baseline federated learning frameworks in terms of classification, speed of convergence, and preservation of privacy. System attains an average accuracy of 95.7%, a 30% reduction in overheads of communication, and no data leakage, which proves the appropriateness of the system in real-time applications and privacy-conscious IoV applications.
Federated Transfer Learning; Homomorphic Encryption, IoV, Privacy Preservation, Heterogeneous Data Fusion, Domain Adaptation
Computational Modelling and Smart Integration of Bio-Inspired Systems for Enhanced Wireless Localization Using Wearable Sensors
In green buildings, computational intelligence is progressively applied to improve energy efficiency and sustainability, with special attention to passive cooling systems inspired by natural thermoregulatory systems. In this paper, we present a bio-inspired passive cooling framework that is optimized computationally for smart built environments. Inspired by biological methods-such as evaporative cooling in desert beetles, convective ventilation in termite mounds, and radiative dissipation in elephant ears-the system uses physics-based modeling and algorithmic design to reduce energy use. In addition to sustainable architecture, the same computational and sensor-driven paradigm is directly comparable to the wearable-assisted wireless localization systems in WSNs. Accurate localisation consists of distributed sensing, adaptive modeling, and feedback optimisation - all of which structurally resemble thermal regulation in bio-inspired systems. By shifting the approach to wearable sensor fusion, signal propagation modeling, and machine-learning based refinement, the framework can improve localization precision in dynamic IoT environments. Experimental validation combines sensor-enabled prototypes with data-driven surrogate models and machine learning feedback loops for adaptation of parameters in real-time. Experimental testing shows greater thermal stability, increased air flow (~3.2 m/s) and temperature reductions of up to 5degC in the interior. Applied to WSNs, these results point to the possibility of bio-inspired computational optimization to provide scalable, low cost, and adaptive solutions for smart cities, healthcare monitoring, and industrial IoT applications.
AI & Optimization in Business Applications
High-Dimensional Data Stream Classification: Improving Random Patch Online Ensemble Classifier
In recent years, the amount of data produced by human activities has increased massively, giving rise to a constant flow of data generated in real time, known as data streams. Data stream classification requires both incremental and adaptive learning approaches, mainly due to the challenges inherent in the data stream's rapidly changing patterns. The Streaming Random Patches (SRP), investigated in this work, offers a robust online ensemble model for evolving data stream classification. The latter uses incremental decision trees, Hoeffding Trees (HT), as base learners for online forecasts. Each tree is incrementally trained on a unique random patch formed via global feature subspacing and online bagging to ensure ensemble variety. The OB brings bagging to streaming. It suggests instance weights for training frequency instead of sampling with replacement. A drift detection strategy replaces outdated base learners in each tree to keep ensemble relevance to recent data and prevent outdated predictions. The ensemble is incrementally built by testing the HTs before training, updating base learner weights based on testing predictions. Weighted majority voting determines ensemble predictions. Therefore, this study aims to retain good SRP performance when identifying high-dimensional streams. The unpredictable nature of data stream instances and their controllable dimensions can degrade the online classifier's prediction quality, availability, and execution time. To increase SRP classifier performance, we refine the compressed sensing (CS) technique before incremental stream processing to ensure efficient subspace selection. Instead of the HT as the base classifier, we employ the Extremely Fast Decision Tree (EFDT) as a more statistically efficient base learner in the final model. The SRP and other techniques improved high-dimensional data stream prediction performance. Average accuracy gains were +0.15% to +5.43%. The suggested modifications reduced execution time by 95.69%, indicating the method's Green AI alignment.
Artificial Intelligence, Cybersecurity, Machine Learning, Internet of Things (IoT)
Robust OCTA Vessel Segmentation for Early Detection of Neurodegenerative Disorders Using Multi-Scale CNN and Transformer Networks
Early detection of vascular neurodegenerative diseases relies on precise segmentation of retinal vessels in Optical Coherence Tomography Angiography (OCTA) images. However, complex vascular structures, low-contrast micro-vessels, and overlapping vessel types pose significant challenges to conventional segmentation methods. This study proposes a deep learning architecture named CGOctaNet that combines convolutional neural networks (CNNs) with a transformer-based global context modeling framework for robust OCTA vessel segmentation. The model employs a U-Net-like encoder–decoder structure integrated with three modules: (i) a Shallow Feature Extraction Module (SFEM) to preserve vessel boundaries; (ii) a multi-scale convolution encoder for local geometric feature learning; and (iii) a Transformer bottleneck that captures long-range dependencies and inter-vessel relationships for enhanced structural consistency. The Transformer bottleneck enables awareness of global context by learning the spatial relationships between remote areas of the vessels, thereby complementing the small receptive field of CNNs and enhancing segmentation continuity in the presence of noise. This hybrid design achieves better generalization and fine-grained segmentation accuracy than CNN-only models in the past. Experiments on OCTA-500 and ROSE benchmark datasets demonstrate that CGOctaNet outperforms state-of-the-art methods, achieving Dice scores of 93.50%, 90.25%, and 89.50% on OCTA-3M, OCTA-6M, and ROSE datasets, respectively. The improvements are attributed to effective integration of local and global contextual cues, adaptive attention refinement, and balanced optimization through hybrid Dice-Cross Entropy loss.
Shallow Feature Extraction Module (SFEM)
Convolution Block Attention Modules (CBAM)
Optical Coherence Tomography Angiography (OCTA)
AI-Driven Secure Emergency Message Dissemination in 5G-Enabled VANETs Using LSTM-Based Intrusion Detection and CP-ABE Encryption
The number of vehicles has expanded rapidly due to advances in automobile technology and global population growth, resulting in an increase in the frequency of traffic accidents. Wireless Vehicle-to-Vehicle (V2V) connections are used by event-driven safety applications to alert drivers to potentially dangerous situations. For emergency vehicles to respond to urgent emergency services, there must be uninterrupted traffic on the roads. Even a small delay in an emergency journey time can be costly and potentially result in lost lives. To overcome this limitation in this research, we designed an AI-driven emergency message dissemination framework for “Vehicular Ad hoc Networks (VANETs)” with 5G, for the efficient, secure, and privacy-preserving communication of messages at critical times. Using time-series data analysis, an AI-based Intrusion Detection System (IDS) that uses Long Short-Term Memory (LSTM) networks classifies emergency messages through recognizing abnormalities and false warnings. To ensure secure emergency message dissemination in the VANET environment, this research proposes a Multi-Authority Ciphertext-Policy Attribute-Based Encryption (CP-ABE). Priority-based scheduling leverages are the Edge-DENM Prioritization Algorithm, which categorizes emergency messages based on urgency levels to prevent delays in high-risk scenarios. The proposed approach ensures secure, scalable, and privacy-preserving emergency communication, improving overall VANET performance by dynamically adapting to traffic conditions and vehicle mobility.
Ciphertext-Policy Attribute-Based Encryption
Burst-Aware Weighted Fair Queueing for Serverless Inference: Mitigating Noisy Neighbor Effects in Multi-Tenant Systems
Multi-tenant serverless inference often devolves into noisy-neighbor scenarios where a single tenant’s bursty LLM batch floods the fleet, pushing interactive calls beyond their latency budgets. We are proposing a Burst-Aware Weighted Fair Queueing (BWFQ) - a scheduler that requires only two counters per tenant (tokens earned, tokens spent) and a constant-time heap pop to pick the next invocation. In BWFQ, we use the classic token-bucket shaper where tokens accumulate at a tenant-specific base rate and are reduced on each dispatch. When a tenant exhausts all its tokens, its requests are queued, giving chances to other quieter tenant s to run. Techniques described in other papers like Dominant-Resource Fairness, BWFQ requires neither per-invocation resource profiling nor multi-dimensional share accounting, making it easy to integrate onto existing Lambda-style dispatchers. To evaluate our algorithm, we built a prototype using AWS Lambda and observed that BWFQ reduces the P99 latency gap between interactive and batch tenants from 8.5s to 2.1s; a 4.0X improvement, while preserving 94% of the throughput achieved by First-Come-First-Serve. The algorithm adds only 35 µs of scheduling overhead per decision and fits in approximately in 150 lines of Go code. These results demonstrate that a simple token-bucket fair queueing is a practical, immediately usable step towards building fairness in production serverless inference.
The Use of Transformer-Based Models for Automatic Short-answer Scoring in Education: A Systematic Literature Review
This review focuses on recent advancements in the Automatic Short-Answer Scoring (ASAS) system in education. The primary objective of this review is to identify current trends in utilizing transformer-based models for the ASAS system. This review also aims to discuss future directions for ASAS technology. ASAS’s conventional machine learning methods were inconsistent because they rely on statistical similarity and are prone to bias. Meanwhile, transformer-based models were typically used for feature extraction, embedding, and score calculation via classification or regression. They generally served as a similarity calculator comparing students’ answers to the reference answer. We applied the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to explore ASAS’s state of the art and uncover its future trends. Our findings reveal that transformer-based models significantly outperform traditional machine learning approaches by capturing complex context. On the other hand, LLMs excel at providing feedback and score justification. Recent studies have shown a shift toward using transformer-based models for ASAS’s complex tasks, including data augmentation and feedback generation. However, further research is needed to use LLMs and GPTs to generate explainable, fairer scores and to address data scarcity through reasoning and augmentation.
An Enhanced Hybrid Binary Grey Wolf and Harris Hawk Optimization Algorithm Based on Cumulative Binomial Probability for Feature Selection in Classification
Feature selection is a widely used approach for reducing dimensionality in datasets by eliminating irrelevant and redundant features. It significantly enhances the accuracy and efficacy of classification models. Hybrid binary grey wolf with Harris hawk optimization (HBGWOHHO) is a metaheuristic algorithm that has been effectively employed for feature selection in classification. However, the HBGWOHHO algorithm has a limitation in unbalanced exploration and exploitation in achieving the sub-optimal solution. This limitation refers to the linearly declining value of a balancing parameter, which lacks regulation between the exploration and exploitation phases. This paper presents an enhanced HBGWOHHO that employs an adaptive technique based on cumulative binomial probability (CBP) called hybrid grey wolf Harris hawk optimization-CBP (HBGWHHO_CBP) to fine-tune the balancing parameter. This adaptive adjustment technique ensures a more effective trade-off between exploration and exploitation, thus improving the algorithm's search efficiency and solution quality. Dimension-wise diversity metric is used to quantitatively assess this balance during the optimization process. Eleven UCI benchmark datasets were utilized to assess the efficacy of the proposed HBGWHHO_CBP. The proposed algorithm demonstrated superior performance across the evaluated datasets, yielding an average accuracy of 0.94, a mean of 8.51 selected features, and a mean fitness value of 0.06, while requiring less computational time. The Wilcoxon signed-rank test results indicate that the proposed algorithm significantly outperforms the native HBGWOHHO and three other metaheuristic-based feature selection algorithms. The proposed metaheuristic can be applied for addressing the feature selection in classification.
Classification, Feature selection, Grey wolf optimization, Harris hawk optimization, Hybrid algorithm
Computer Science, Artificial Intelligence, Optimization.
An Approach of Hopfield Network-Based Optimization for Task Offloading and Resource Allocation in Mobile Edge Computing
Mobile edge computing (MEC) has become an effective solution to alleviate the delay and energy burdens of cloud computing infrastructures. By enabling task execution at edge servers located near user devices, MEC reduces transmission delay and improves responsiveness. Nevertheless, uncontrolled task offloading may degrade overall system performance, and optimizing task offloading and resource allocation (TORA) can ensure efficient workload distribution. Prior studies have primarily applied metaheuristic techniques such as genetic algorithms (GA) and particle swarm optimization (PSO), while machine learning-based solutions remain relatively limited. This work introduces a Hopfield network-based optimization model, where the TORA target is reformulated as an energy function, and a Hopfield network performs the minimization process. Simulation experiments confirm that the Hopfield approach reduces both delay and energy consumption more effectively than GA and PSO, with its longer runtime. However, this limitation can be mitigated by advances in modern processing power.
Enhancing Pressure Vessel Design Optimization with a Hybrid CSA-PSO Algorithm
The optimization of the Pressure Vessel Design Problem (PVDP) plays a crucial role in various engineering applications where structures with the most efficient material utilization are required. However, the intricate interplay between design parameters and strict constraints presents a significant barrier for classical optimization algorithms to efficiently traverse the convoluted landscape of PVDP. Applying metaheuristic algorithms has been introduced as a solution to tackle these problems. The techniques introduced have the potential to confront the difficulties associated with nonlinearity, multimodality, and complex constraints that are frequently encountered in complex engineering optimization problems. This work carries out a comprehensive and comparative analysis of the performance of well-established metaheuristic algorithms in solving the PVDP. The methods under consideration at present are Big Bang –Big Crunch (BB-BC), Cuckoo Search Algorithm (CSA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Vibrating Particles System (VPS), and Water Evaporation Optimization (WEO). Third, we introduced the CSA-PSO algorithm. Through extensive numerical experiments, their rates of convergence, the quality of the solutions, and the amount of computation required are compared and analyzed, revealing their relative pros and cons in terms of the PVDP. As a result, the knowledge of how metaheuristic algorithms perform on the PVDP can be exploited when selecting optimization methods.
Metaheuristics Algorithms, Constrained Problem, Welded Beam Design problem, Big Bang–Big Crunch, Cuckoo Search Algorithm, Artificial Bee Colony, Vibrating Particles System, Water Evaporation Optimization.
Interpreting the Smart Healthcare Model Using Shapley Values
Data analysis and treatment specifications are important in healthcare, but there are many challenges, such as specifying metrics to measure and monitor patient health. Machine learning appears to improve the prediction of patients’ outcomes at the lowest cost in healthcare systems. The machine learning used real data collected by Internet of Things (IoT) devices and analyzed them to detect potential future risks. There are many techniques for detecting potential future risks, but the random forest model was the strongest. Unfortunately, its results were complex and difficult to interpret. Therefore, this study used Shapley values, a powerful analysis method that helps interpret the ambiguity in ML results, often referred to as a black box. This led to the development of the random forest model, which interprets its outcomes by assigning equal importance to features based on their contribution to the results. This study is divided into two steps. Firstly, the random forest model classifies the data based on relationships among feature attributes to predict the outcome. Secondly, interpreting the prediction results from the previous step using Shapley Tree Interpreter (SHAP) values to distribute importance across attributes based on their contributions to the prediction. The proposed method showed that the age, ID, and AP-LO are the most important attributes in predicting diastolic blood pressure. Where age had (-157), id (+17.53), and ap-lo (-213.03) effects. Gender, height, and glu were the most important predictors of systolic blood pressure. Where gender had (-4.41), height (+2.78), and glu (+2.48) effects.
Multi-Class Flood Classification Model Using Spatial Topo-Hydrological Features and Interpretable Machine Learning
Rapid urban expansion of cities in many developing countries, such as Nigeria, is aggravating occurrences of devastating urban floods because of sudden changes in climate and uncontrolled land use. Methods based on traditional flood prediction are expensive, primarily binary-classification-based, and lacking generality, which hampers their capability for disaster preparedness purposes. In this paper, we propose a standardized multiclass flood classification framework with spatial, topographic, hydrological and meteorological covariates based on three ML classifiers, including Random Forest, Support Vector Machine and Logistic Regression. The model was evaluated strictly using stratified 5-fold cross-validation and a 20% held-out test set. From the right, the RF model recorded the highest performance accuracy, at 92%, indicating desirable generalisation and resistance to overfitting. SVM was successful with 87% and LR achieved 83%, both being relatively unstable at minor flood classification. The experimentation-based feature importance analysis indicated that the environmental data index is the most important predictor, increasing interpretation and transparency in modelling. The results introduce RF as a trustworthy multi-class urban flood classification tool in data-poor contexts, with potential applications for early warning systems, city management and climate-resilient policies in the Global South.
Beef Freshness Classification Using CNN with DCT and GLCM Feature Extraction
The increasing global demand for beef, which has risen by 13.9% over the past decade, underscores the growing importance of ensuring meat quality and freshness in the food industry. Conventional methods for assessing beef freshness rely on manual visual inspection, which is time-consuming, subjective, and often inaccurate. To address these limitations, this study proposes a hybrid approach that integrates the Discrete Cosine Transform (DCT), Gray Level Co-occurrence Matrix (GLCM), and Convolutional Neural Network (CNN) techniques for automated beef freshness classification. A dataset of fresh and spoiled beef images was used, followed by a series of preprocessing steps, feature extraction using DCT and GLCM, and classification through a CNN-based model. The integration of frequency-domain and texture-based features enhances the model’s ability to capture discriminative visual patterns associated with meat freshness. Experimental results demonstrate that the proposed model achieves an overall classification accuracy of 93%, with F1-scores of 0.94 for fresh meat and 0.93 for spoiled meat. These findings indicate that the DCT, GLCM, and CNN framework provides an efficient and reliable alternative to traditional inspection methods. The proposed approach contributes to the advancement of computer vision applications in food quality assessment, promoting improved automation, objectivity, and quality control across the meat supply chain.
Copyright (c) 2026 Journal of Soft Computing and Data Mining https://creativecommons.org/licenses/by-nc-sa/4.0
Diabetes Prediction Using The Smote-Cart Framework Model for Imbalanced Data Case
Diabetes mellitus (DM) is described by chronic high blood glucose levels, which can result in long-term damage, dysfunction, and organ failure. As a result of technological advancements, many researchers are employing machine learning to predict diabetes. They collect patients’ demographics and health information, organizing them into a dataset. However, in most real-world data, the non-diabetic cases exceed the diabetic cases, contributing to bias in the majority class and resulting in low predictive diabetic cases. Therefore, a Synthetic Minority Oversampling Technique (SMOTE) has been proposed to improve diabetic prediction on the dataset samples before training the Classification and Regression Tree (CART) model. The proposed framework involved the preprocessing step (SMOTE and categorical conversion), CART training, hyperparameter tuning, and evaluation metrics. With a combination of 8 leaf numbers per node, a maximum of 10 splits, and deviance as the split criterion, the model achieves an overall accuracy of 98.72%, a precision of 98.94%, a sensitivity of 98.44%, and an F1-score of 98.67%. In conclusion, the proposed SMOTE-CART framework can effectively address the imbalanced data in a diabetes dataset and improve the accuracy of diabetes prediction.
Synthetic Minority Oversampling Technique
A Comparative Study of Filter-Based Feature Selection in High-Dimensional Medical Datasets Using Whale Optimization Variants
High-dimensional medical datasets, such as microarray gene expression profiles, pose significant challenges for feature selection (FS) due to their large feature space and limited sample size, often resulting in unstable and inconsistent outcomes. Metaheuristic algorithms like the Whale Optimization Algorithm (WOA) have shown promise in FS; however, their reliance on a single leader and linear control parameter typically leads to poor exploration-exploitation balance and reduced stability. This study proposes a Hierarchical Whale Optimization Algorithm (HiWOA) for filter-based FS, incorporating two key enhancements: a hierarchical leadership strategy, where three leaders guide the search, and an arcsine-based control parameter that enables a smoother transition between exploration and exploitation. Unlike earlier HiWOA applications in optimization or wrapper-based selection, this is the first to adapt HiWOA to filter-based FS. Experiments were conducted on five benchmark medical datasets using four filter methods (ANOVA, Chi-square, Mutual Information, and Pearson Correlation), with performance evaluated in terms of algorithmic stability, FS stability, and classification accuracy using kNN. Results demonstrate that HiWOA consistently achieves well-balanced exploration-exploitation ratio with higher exploration rates (+25.66%), improved FS stability (+0.00261 in MKCI), and superior classification accuracy (+11.28%) compared to WOA and its variants. Among the filter methods, ANOVA combined with HiWOA delivered the most discriminative feature subsets, establishing a robust framework for high-dimensional medical data analysis.
Hierarchical Whale Optimization Algorithm
Multi-Classes of Microorganisms Classification Using Hybrid Deep Stacked Autoencoder
Classification of microorganisms is critical for clinical and medical research; however, it remains challenging due to two key limitations: the limited availability of datasets of microscope images of microorganisms and the high morphological similarity between different classes. These limitations often result in misclassification and poor accuracy. To address these issues, the study proposes a hybrid model that combines a convolutional network and recurrent neural networks to extract more efficient features. This study evaluated the model on benchmark datasets. The model achieved 86% accuracy on the large-scale dataset and 97% accuracy on the small-scale dataset. This raises the probability of overfitting. For this, we utilized regularization and data augmentation techniques to reduce this risk. The main contribution is the development of a hybrid architecture that combines CNN, Bi-LSTM, and LSTM to capture bidirectional temporal dependencies and spatial sequential patterns, and compares the results between the proposed model and baseline model, which used CNN, BILSTM, LSTM, CNN-BILSTM, and CNN-LSTM. This integration of the proposed model, which enhances feature extraction and provides a more robust learning compared to conventional CNN–LSTM or CNN–BiLSTM approaches. These results emphasize the model's potential to enhance diagnostic processes, reduce time and labor, and support global health efforts by alleviating the workload on biologists and specialists.
Date Fruit Varieties Identification Using Convolutional Neural Networks: A Comparative Study
A Cognitive-Driven Stacking Ensemble Approach for Dyslexia Handwriting Classification using LeNet-Based Deep Learning Models
Dyslexia, a specific learning disability, affects cognitive processing in approximately 5-20% of children worldwide. In India, the prevalence of dyslexia is alarmingly high, affecting about 10% of the population. Despite advancements in technology, there is a concerning lack of attention towards screening children with Dyslexia. People with dyslexia often struggle with interpreting words and visual stimuli, yet with timely intervention through right education and training, can effectively improve their learning outcomes. Handwriting analysis has emerged as a promising approach for dyslexia detection by using the cognitive motor patterns linked with dyslexia. Although researchers have developed different ways to analyse handwriting patterns in dyslexia, there is still need for developing more accurate and efficient methods for classifying dyslexic handwriting using advanced machine learning techniques. This research introduces a novel approach utilizing a Stacking ensemble deep neural network for classifying dyslexic handwriting. Our method employs four variants of the LeeNet-5-based Convolutional Neural Network (CNN), distinguished by different numbers of triple convolution layers used to extract cognitive-motor features from handwriting patterns. These models were trained and validated on a dataset using standard performance metrics. Experimental results revealed that increasing the number of feature extraction layers enhances model performance. Ensembling has been performed to combine the strengths of individual models and achieve better accuracy. Significantly, by stacking the four variants of the models, our Stacking ensemble approach attained an impressive accuracy of 96.86% in classifying the three classes of dyslexic handwriting. Notably, the Receiver Operating Characteristic (ROC) curves demonstrate perfect classification for the Corrected and Reversed classes with an Area Under the Curve (AUC) of 1.00, and an AUC of 0.96 for the Normal class, indicating the robustness and reliability of our proposed model. The promising results underscore the potential of deep learning and cognitive-driven handwriting analysis in advancing dyslexia screening, emphasizing the effectiveness of the Stacking ensemble approach in addressing this critical issue.
Dyslexia, Stacking, Ensemble, LeNet-5, CNN, Classification
Intelligent Trajectory Planning Algorithms for Autonomous Mobile Robots
Given the widespread utilization of autonomous mobile robots in the past two decades, the path planning problem has received considerable attention from researchers. The goal is to find a collision-free path from a start point to a target point in an environment full of obstacles whilst satisfying some criteria, such as time, distance, and safety. This study proposes an intelligent hybrid optimisation method for autonomous mobile robots (AMRs) to reach a target or multiple targets in environments filled with obstacles. The proposed method combines two algorithms. The first algorithm is the probabilistic roadmap algorithm, which tries to explore the search space and generate several free collision paths to be utilised as a population for the second algorithm. The second algorithm is the enhanced genetic algorithm (EGA), which is utilised to plan the shortest and smoothest path depending on the population generated by PRM. The EGA presents an effective and accurate fitness function, improves the genetic operators of a conventional genetic algorithm (GA), and proposes a new genetic modification operator.The efficiency of the proposed method is verified by several simulations in various environments and real-time experiments on a nonholonomic wheeled mobile robot. Results show that the proposed hybrid approach outperforms the conventional GA, probabilistic roadmap, ant colony optimisation, the Bezier smoothing algorithm, and other algorithms in planning a smooth, near-optimal, collision-free path in competitive time.
An Agent-Based Decision Fusion Regression Model for Analyzing Cyberspace Users' Engagement in Informatics Blogs
Direct interactive communication technology, called a blog, enables users to post comments and share links to maintain interaction with fellow users. In modern information systems, a blog platform allows individuals or groups to write their opinions on a specific topic. Broad informatics blogs provide spaces for people to share ideas an exchange opinions on various issues. Driven informatics-based learning methods are used to detect and analyze users tendencies in these blog posts. Blog features exhibit a direct correlation with the extend to which bloggers adhere to both national cultural patterns and social and political developments in their respective countries. This paper proposes an agent-based decision fusion regression (ADFR) model for analyzing cyberspace users engagement in blog posts. The ADFR model is tested using the BlogFeedback dataset, which has been used in several previous research studies. The model results are compared with three machine learning algorithms: Linear Regression (LR), Decision Tree (c4.5), and Decision Forest (DF) for performing blog post analysis tasks. The R2 score reaches 0.0695 with ADFR, while DT shows only 0.0002, DF reveals 0.0104, and LR demonstrates 0.0544 in blog comment the highest level of predictive power and outstanding generalization potential.
The Hybrid Local Maximum Distance Algorithm with Dissimilarity-Based Test Case Prioritization for Software Product Line Testing
Software Product Line (SPL) testing presents significant challenges due to configuration variability. The software tester needs to ensure a good quality of common and variant-specific behaviours across product variants. Test Case Prioritization (TCP) is a well-established strategy for improving regression testing efficiency by enabling early fault detection. This paper proposes the hybrid prioritization approach that integrates a dissimilarity-based with the Enhanced Local Maximum Distance Algorithm (ELMDA). The proposed approach introduces three hybrid string distance techniques, NEHT1, NEHT2, and NEHT3 combining Jaro-Winkler and Manhattan distance to quantify test case diversity more effectively. These dissimilarity scores guide the selection of structurally and behaviourally distinct test cases, enhancing both fault detection and execution efficiency. Empirical evaluations using two SPL case studies which are GPS and i-Robot Roomba. Each case studies are tested against five mutant versions. Results shown that the proposed approach significantly outperforms existing techniques in terms of Average Percentage of Faults Detected (APFD) and execution time. Notably, NEHT1 achieves the highest APFD and lowest execution time across both experiments. Statistical analysis confirms the significance of these improvements. The results establish ELMDA with NEHT1 as a promising solution for effective and scalable SPL regression testing.
Information Technology-Software Testing and Quality Assurance
Data Science-Algorithm Design and Optimization
Detection and Classification of Emergency Vehicles from Audio and Video Inputs using Deep Learning Techniques
With the rapid advancement of autonomous vehicles, ensuring road safety is one of the biggest concerns of the automotive industry. One critical aspect of safety is the accurate and timely detection of emergency vehicles such as ambulances, and fire trucks and promptly switching lanes to ensure smooth passage. This paper proposed an efficient and straightforward method to locate and label emergency vehicles with the help of the most updated deep learning algorithms known as YOLOv8 and long short-term memory (LSTM). The accuracy and efficiency of emergency vehicle detection in terms of perfecting the models is the focus. Data augmentation methods are carried out to enhance day-night and low visibility performance of the model on the dataset. The system is capable of identifying and classifying emergency vehicles on the basis of audio and video data using several signal/image processing methods and accomplished with the means of explainable artificial intelligence (XAI) mechanisms providing detailed information. The given system can be used with self-driving and human-driven vehicles which can be fitted to advanced driver assistance systems (ADAS). It is found that the accuracy and performance of emergency vehicle detection have improved significantly with the 96.6% accuracy rate that will ameliorate the interaction safety of autonomous vehicles and emergency vehicles.
Emergency Vehicle Detection, Image Processing, Audio Classification, YOLO, Object Detection, LSTM, Explainable AI
On-Device Training Based Anomaly Detection Platform
Anomaly Detection (AD) identifies issues in monitored systems, allowing timely responses. It's used in areas like industrial systems, healthcare, and networks. Among various AD methods, Autoencoder (AE)-based models are popular for their unsupervised training, which doesn’t require labeled data. This study proposes a full platform that implements AE-based AD on tiny devices and tests it on motor operating noise. The hardware includes two microcontroller units (MCUs) responsible for (1) real-time data acquisition and processing, (2) AE training and threshold setting, and (3) running inference for anomaly alerts. To boost training speed, the platform supports both Stochastic Gradient Descent (SGD) and Adaptive Moment Estimation (Adam). The algorithm uses real-time data instead of pre-collected data to improve practical performance. Tests show the platform effectively trains on normal motor noise and accurately sets the anomaly threshold. It achieves a real-time anomaly detection accuracy of 100%. The system runs fully automatically, making it suitable for integration into edge AIoT systems.
An Ensemble Learning Model for Multi-Type Cancer Prediction in Clinical Diagnostic Decision Support Systems
Cancer attacks various parts of the human body, causing different effects that result in numerous health conditions. Diagnosing the type of cancer at an early stage is critically important, as it ensures timely clinical management of the patient, which has been a necessity in relevant research. Machine learning (ML) and Deep Learning (DL) algorithms are widely used for identifying cancer cases by collecting data from various fields, including medical, biomedical, and bioinformatics. These algorithms can reveal important features and detect cancer cases within complex cancer datasets. In this paper, an ensemble learning method comprising five widely used ML and DL algorithms has been proposed for constructing an ensemble-based Multi-Type Cancer Prediction (eMTCP) model. The algorithms employed in building the eMTCP are Naive Bayes (NB), Random Forest (RF), Support Vector Machines (SVM), Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM). The eMTCP is deployed for developing a Cancer Diagnostic Clinical Decision Support System (CDCDSS). Subsequently, four cancer diagnostic datasets: liver cancer, breast cancer, brain cancer, and cervical cancer, are utilized to test the performance of the eMTCP model. The most promising algorithm is eMTCP (stacked ensemble), which yields the highest F1-scores on all datasets: breast (0.979), liver (0.765), brain (0.898), and cervical (0.482), illustrating better performance in multi-type cancer prediction. CNN and LSTM achieved high stability with superior F1-scores of 0.957 (breast), 0.669 (liver), and 0.853 (brain). CNN outperformed in the case of liver cancer and showed similar performance in other cancer types. The SVM ML model has the lowest scores of all: 0.969 (breast), 0.716 (liver), 0.844 (brain), and only 0.153 (cervical), indicating that its representation in the clinical-only dataset is not very reliable.
Machine learning (ML), ensemble learning (EL), ensemble learning, Decision Support System (DSS), multi-cancer diagnosis, breast cancer, liver cancer, cervical cancer, brain cancer
A Multi-Agent-Based Deep Learning Model for Protecting Cloud Computing Environment Against Distributed Denial of Service Flooding Attacks
Distributed Denial of Service (DDoS) flooding attacks pose a significant threat to the resilience of modern cloud computing infrastructures and their ability to sustain operational stability. Unlike traditional DoS and DDoS attacks, it is the legitimate user who inadvertently causes the damage; the applications exploit the architecture, latency limits, and resource bottlenecks, rendering the service unavailable to genuine users. The current mitigation strategies struggle to keep pace with the diverse attack vectors and fluctuating traffic patterns, particularly in a cloud-native environment where a more intelligent and distributed approach is necessary. A high-fidelity detection system that employs deep learning and self-driving agents offers an effective defensive mechanism. This paper proposes a deep learning model based on a multi-agent and Dynamic Ensemble Selection (DES) strategy, combined with five separately trained Long Short-Term Memory (LSTM) models, to form a DES-LSTM model for identifying and mitigating DDoS flooding attacks in real-time. TS allocates intelligent agents across multiple nodes in the cloud infrastructure, allowing each node to conduct local traffic analysis and contribute to collective threat detection. The system employs DES to facilitate context-based model selection through dynamically evaluated accuracy values, enabling adaptive decision-making. The CIC-DDoS2019 dataset, which encompasses the full spectrum of DDoS attack types, is utilized to train, validate, and evaluate the model's performance. This paper provides a detailed description of the architecture, integration methodology, and simulation, as well as model training, traffic modeling equations, and visualizations. Evaluations against a baseline LSTM model demonstrate that the proposed ensemble achieves superior detection accuracy, reduced false-positive rates, and enhanced robustness in varying attack conditions. The DES-LSTM architecture effectively works based on the experimental outcomes. It possesses real time feasibility, as the classification accuracies (97.8%), precision (96.6%) and recall (97.2%) were in all likelihood enhanced, meanwhile the false alarm rate (2.1%) and the detection latency (19 ms) were tremendously diminished. The agent-based, decentralized structure is both scalable and delivers low latency, making it suitable for deployment in existing cloud security systems.
Adaptive Intrusion Response via Federated Meta-Learning for IIoT Zero-Day Mitigation
The industrial evolution has meant that Industrial Internet of Things (IIoT) devices have exponentially increased, and as such, industrial automation has now become a reality since they are capable of monitoring in real-time, making predictions, and improving efficiency. These environments are fluid and privacy-sensitive environments that need collaborative and privacy-preserving learning models that are able to rapidly adapt to the emerging threats. Federated meta-learning has become a potentially promising method that unites the flexibility of meta-learning and the distributed and privacy-concerned design of federated learning. This document suggests an adaptive security solution to employ Model-Agnostic Meta-Learning (MAML) and Reptile-based federated approaches to intrusion response and zero-day attacks prevention of IIoT systems. The experimental data needed to train and test involves synthetic traffic of IIoT networks that is simulated using stochastic attack generator modules. Indicators of performance, namely detection performance, false positive rate, latency, and convergence efficiency, were provided by means of classification tools in the form of confusion matrix visualization, ROC curves, and loss progression graphs. The framework has been tested and run in a simulated environment of controlled tests in which MATLAB code driven by a matrix has internal data and inbuilt comparative agents. The experiments reveal that MAML-FL has a better result when it comes to generalization and zero-day threats mitigation, whereas Reptile-FL is more efficient in terms of seeking communication and faster convergence rates. In this paper, the authors present a scalable and robust architecture capable of providing a trade-off between real-time learning, adversarial robustness, and communication efficiency, thereby making the IIoT ecosystems intelligent and secure in their automation.
Federated Learning, Meta-Learning, IIoT Security, Zero-Day Attacks, Intrusion Response, Adaptive Intelligence, Distributed Systems.
CAFED-Net: Cross-Adaptive Federated Learning with Dy-namic Adversarial Defense for Real-Time Privacy-Preserving and Threat Detection in Distributed IoT Ecosystems
The industrial, urban, and healthcare sectors now require immediate cybersecurity measures due to the rapid expansion of distributed Internet of Things (IoT) networks across these domains. Centralized threat detection systems struggle to adapt to diverse IoT environments that cover large-scale systems with highly sensitive response times. New threats rapidly develop against defense systems that maintain fixed positions, creating both mobile threats and expanding across data domains. Federated Artificial Intelligence (Federated AI) introduces a training methodology that addresses data privacy concerns while safeguarding against severe data breaches that can result from centralization. A new Federated AI system architecture has been created to manage cross-domain threat detection activities in distributed IoT environments through the implementation of dynamic adverse adaptation strategies. The system performs local training operations at distributed nodes and combines them with federated aggregation while implementing an adaptive intelligence-sharing framework. This method establishes resilient mod-els along with domain-independent capabilities that protect data independence. The proposed system utilizes adversarial training as it dynamically adjusts to emerging attack vectors during operation. The detection capabilities, coupled with the adaptability of simulation-based evaluation, demonstrate supe-riority over baseline models during adversarial drift conditions. This research presents an approach that enables real-time privacy-safe IoT threat detection at a scalable level to address evolving cybersecurity threats.
Federated Learning, IoT Security, Adversarial Adaptation, Cross-Domain Detection, Distributed Net-works, Privacy-Preserving AI