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.
A Deep Learning-Based Decision Support System for Early Depression Screening from Multimodal Clinical Interviews
The complexity of the behavioral, emotional, and linguistic features of depressive disorders is still a challenge for depression screening by clinical interviews. Self-report questionnaires, like the PHQ-9, have a number of limitations, including self-report bias and inadequate behavioral representation. In this paper, we thus propose the development of a Deep Learning-Based Decision Support System (DL-DSS) for early depression screening from multimodal clinical interview data. The proposed framework combines the transformer-based text encoding, CNN-based acoustic and visual feature extraction, and LSTM-based temporal modeling approaches to perform multi-modal analyses of transcript, speech, and facial behavior. The system was tested with the DAIC-WOZ benchmark data set. Experimental results show that the proposed multimodal DL-DSS model achieved 87.9% classification accuracy, 89.0% F1-scores, and 92.0% AUC-ROC, outperforming the conventional PHQ-9-based and single-modality deep learning approaches, and also the proposed model has a real-time inference latency of less than 120 ms per interview segment. These results lend support to the effectiveness of AI-based multimodal deep learning for intelligent and scalable depression screening within current clinical decision-support systems.
Copyright (c) 2026 Journal of Soft Computing and Data Mining https://creativecommons.org/licenses/by-nc-sa/4.0
An Explainable Deep Learning Framework for Skin Cancer Diagnosis with Uncertainty Quantification on a Multi-Institutional Dataset
Deep-learning-based diagnosis of skin cancer is now at the dermatologist level, yet further challenges remain in its application in practice: lack of generalization between institutions, lack of confidence in estimates, and lack of transparency in decision-making. This paper presents and tests an explainable deep learning model that utilizes a Bayesian uncertainty quantification to facilitate automatic skin cancer diagnosis from multi-institutional dermoscopic images. The framework combines data harmonization, transfer learning for convolutional features extraction, Monte Carlo Dropout for uncertainty estimation, and Grad-CAM for visual explainability, enhancing the robustness, reliability, and interpretability. A comprehensive experimental evaluation showed that the proposed approach outperforms CNN baselines in terms of accuracy, sensitivity, specificity, and AUC, and that the performance drop with cross-institutional domain shift was less than that of CNN baselines. Uncertainty-sensitive deferral strategies enhanced confidence in the diagnosis of high-confidence cases, and calibration analysis showed good calibration in the probabilistic prediction. Visual attribution analysis confirmed stable patterns of attention to lesions, ensuring an agreement between clinicians' beliefs and transparent human-AI interaction. To conclude, the results indicate the need for a common framework for explainability, uncertainty quantification, calibration, and cross-institutional testing as a crucial step toward clinicians' safety and reliability when using AI for the diagnosis of skin cancers.
Blockchain-Enhanced Edge IIoT Framework with Graph Neural Networks for Real-Time Smart Grid Anomaly Detection
In this paper, the concept of blockchain is combined with the edge IIoT framework and Graph Neural Networks (GNNs) to provide real-time anomaly detection and mitigation in smart grid infrastructures. This proposed architecture is based on three key components: decentralized blockchain security, edge Artificial Intelligence (AI) processing, and spatial-temporal anomaly learning through the Graph Convolutional Network (GCN) and the Temporal Graph Network (TGN). The framework was tested in a simulated smart grid environment with over 10,000 nodes that were interconnected and with various anomaly types such as cyberattacks, line faults, sensor failures, and voltage spikes. The experimental results demonstrate that the TGN model has an anomaly detection accuracy of 94.5% and a recall of 92.75%, which is about 5% higher than the GCN model, and the false positive rate has been reduced by 18%. The framework also reduced computation cost by 22% compared to the traditional centralized approach. The proposed framework required approximately 50 seconds for edge AI processing of all experimental samples, while blockchain validation introduced an additional 25 seconds of overhead, resulting in a total end-to-end processing latency of approximately 75 seconds. In addition, blockchain-based smart contracts facilitated an automated way of anomaly mitigation via node isolation and adaptive load redistribution. The proposed framework shows the potential of how blockchain, edge AI, and GNN models can enhance the smart grid system's resilience, security, and operational intelligence.
Epilepsy; Electroencephalogram (EEG); Chaotic Dynamics; Nonlinear Feature Extraction; Recurrence Quantification Analysis (RQA); Fractal Dimension; Largest Lyapunov Exponent; Hurst Exponent; Support Vector Machine (SVM); Machine Learning; Automatic Seizure Detection; Biomedical Signal Processing.
Machine Learning and Deep Learning Approaches for Multi-Class Land Cover Classification Using High-Resolution Satellite Imagery
Land use and land cover (LULC) classification using Very High Resolution (VHR) satellite data is significant for a wide scope of applications in urban planning, environmental monitoring, engineering, and sustainable development. However, there is a lack of research on the performance comparison of different classifiers in optimal computing environments. In this research, twelve classification algorithms from Pixel-based, object-based image analysis (OBIA), patch-based deep learning, and graph and theory-based approaches are the four methodological paradigms that are looked at. In this study, we make a complete comparison between 12 classification algorithms belonging to four methodological paradigms for LULC classification using SPOT-5 multispectral imagery of the Iraqi city of Mosul. The methods that are being taken into consideration are Support Vector Machine (SVM), Random Forest (RF), Support Vector Machine (SVM) and Random Forest (RF) classifiers in SNIC-based OBIA, Seven models for deep learning (including CNN, ResNet, ViT, DaViT, U-Net with CNN, U-Net with ConvNeXt, and EfficientNet), and a Graph Convolutional Network (GCN). Our results demonstrate that while patch-based deep-learning techniques achieve higher accuracy, there is a significant difference between the accuracy of the various techniques. When using DaViT together with CNN, the best overall accuracy was reached, which is 99.83%, and has a kappa value of 0.9977. EfficientNet was in second place with 99.66%. The best performance was given by U-Net at 99.49%, followed by ViT at 99.41%. The classical pixel-wise RF showed an accuracy of 98.63% and can compete with much lower computational costs. The OBIA method with RF resulted in an accuracy of 98.67%, whereas the accuracy of the GCN-based method was only 78.16%. The McNemar's test result was very significant when comparing the best and worst performing models (p = 0.02). The analysis by class showed that the Road class was the hardest to classify in various approaches, while the Water class had a high level of accuracy. On average, Agriculture yielded the highest percentage?of pixel misclassifications across all paradigms with only 12% pixels being correctly classified. Our findings provide empirical advice for selecting a method for VHR satellite image classification, taking into account the trade-offs among accuracy, computational efficiency, and practical implementation.
Edge-Based Deep Learning for Population-Specific Blood Glucose Level Prediction in Type 1 Diabetes Mellitus
This study evaluates a population-based deep learning (DL) approach to predict blood glucose levels (BGL) in patients with Type 1 Diabetes Mellitus (T1DM) using edge computing. Eight DL architectures, namely Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Neural Hierarchical Interpolation for Time Series Forecasting (NHITS), Kolmogorov-Arnold Network (KAN), Temporal Convolutional Network (TCN), Bidirectional Temporal Convolutional Network (BiTCN), Temporal Fusion Transformer (TFT), and Deep Non-Parametric Time Series Model (DeepNPTS), were optimized using Optuna Auto-Tuning and tested on the ShanghaiT1DM dataset, which consists of 16 subjects with a continuous glucose monitoring (CGM) sampling interval of 15 minutes. Two input configurations (univariate and multivariate) were assessed at prediction horizons (PHs) of 30 and 60 minutes through cloud-based simulations in Google Colab and deployment on an NVIDIA Jetson Orin Nano edge device. Performance is evaluated using MAE, MAPE, and RMSE for predictive performance, along with inference time, latency, throughput, and power consumption for deployment efficiency. In both PHs, KAN and TFT achieve superior predictive performance compared to the LSTM baseline and other models, with minimal differences observed between univariate and multivariate configurations. Crucially, KAN uniquely achieves the highest throughput (4.05 preds./s) at the lowest power consumption (7.64 W), making it the first architecture jointly characterized for population-based BGL prediction and uncompressed edge deployment, with improved predictive performance on edge over cloud at the 60-minute horizon, unseen in other architectures. These characteristics position KAN as a strong candidate for efficient on-device inference. Furthermore, with consistent power consumption of approximately 8 W across all eight models, the edge device implementation yielded predictive performance comparable to cloud-based simulations. These findings confirm that population-specific, edge-based BGL prediction is a feasible and practical solution for contemporary T1DM management.
Biomedical Engineering / Health Informatics
Secure Resource Allocation in IoT Environments Using Deep Reinforcement Learning-Based Adaptive Defenses
The success of Internet of Things (IoT) networks has also created a major problem in resource provisioning, as these networks are becoming increasingly heterogeneous, dynamic, and distributed. Simple and heuristic-based allocation schemes have become obsolete in terms of fulfilling the collective goals of latency reduction, energy efficiency, and secure defense against malicious attacks. To overcome such limitations, this paper suggests a new Deep Reinforcement Learning (DRL) adaptive defense architecture designed to provide resources efficiently and securely in hostile IoT environments. The suggested system combines three major components, viz., Trust Monitoring Module (TMM), Resource Profiling Agent (RPA), and Network Security Orchestrator (NSO), each of which works within the edge and fog layers in cooperation with each other. These modules dynamically evaluate the trustworthiness of devices, keep track of resources, and coordinate real-time policy enforcement in situations of changing threats. Two enhanced DRL models, namely Dueling Deep Q-Network (Dueling DQN) and Asynchronous Advantage Actor-Critic (A3C), are tested in a simulated environment in which adversarial situations involving Distributed Denial of Service (DDoS), flooding, and spoofing attacks are replicated. Quantitative findings: the Dueling DQN comes out with better scores when it comes to important performance parameters: it delivers 89.7 percent packets, has an average latency of 92.3 ms, an energy efficiency of 362 mWh, and a convergence score of 0.88 on the trust score when compared to 83.5 percent packets, 108.2 ms average latency, 295 mWh energy efficiency, and 0.82 convergent score on the trust score of A3C. Dueling DQN further indicates 26.7 percent enhanced policy convergence, 22.7 percent greater energy efficiency, and 18.3 percent superior reward slope stability. These results speculate on the much higher versatility, trust affiliation, and appropriateness of the framework in accommodating the ensuing real-time, mission-centered endeavors of IoT implementation.
Internet of Things (IoT), Deep Reinforcement Learning (DRL), Dueling DQN, A3C, Adaptive Defense, Secure Resource Allocation, Edge Computing, Trust Management, Latency Optimization, Energy Efficiency, Cybersecurity, Policy Convergence, Multi-Objective Reward
An Actor-Critic Deep Reinforcement Learning Model with Energy-Awareness and Latency Minimization for Dynamic Spectrum Allocation in 6G-Enabled Aerial Mobile Wireless Networks
Aerial Mobile Wireless Networks (AMWN) play an important role in next-generation communication networks, especially in military operations, disaster recovery, and real-time surveillance. However, the highly dynamic nature of AMWN creates significant challenges for dynamic spectrum allocation (DSA), latency control, energy conservation, and spectrum optimization. These challenges become more critical in 6G-enabled environments that require ultra-low latency, high energy efficiency, and large-scale device connectivity. Deep Reinforcement Learning (DRL) offers a powerful approach by enabling real-time, data-driven decision-making in complex environments. This paper presents an Actor-Critic Deep Reinforcement Learning (AC-DRL) model adapted to a swarm-based behavior model for dynamic spectrum allocation with energy awareness and latency minimization in AMWN. The AC-DRL model is evaluated against a standard Q-learning approach using a custom dataset. The results show improved latency reduction, spectral efficiency, and energy consumption. Simulation results demonstrate up to 27% latency reduction and 22% improvement in energy efficiency compared with traditional models.
Aerial Mobile Wireless Network (AMWN), Machine Learning (ML), Deep Learning (DL), Reinforcement Learning (RL), Deep Reinforcement Learning (DRL), Actor-Critic architecture, 6G Networks, Dynamic Spectrum Allocation, Latency Minimization, Energy Efficiency
Effect of Virtual Reality-Based Instruction on Student Learning Outcomes: An Experimental Study in Higher Education in Oman
Traditional university instruction is predominantly lecture-based, often limiting student interaction and experiential learning opportunities. This study investigates the impact of virtual reality (VR)-based instruction on selected learning outcomes among undergraduate students at Sohar University, Oman. A mixed-methods experimental design was employed, involving 80 students recruited through convenience sampling and randomly assigned to control and experimental groups (n = 40 each). Pre- and post-intervention assessments were conducted to measure academic performance and engagement. Quantitative analysis included paired-samples t-tests to evaluate within-group learning gains, independent-samples t-tests to compare post-test differences, and linear regression to examine the predictive effect of VR exposure while controlling for baseline performance. Assumptions of normality (Shapiro–Wilk W = 0.97, p = 0.18), homogeneity of variance (Levene’s F = 1.26, p = 0.26), and independence were satisfied. Results indicate that the experimental group achieved higher post-test scores (M = 82.4, SD = 6.8) compared to the control group (M = 71.3, SD = 7.5), with a significant mean difference of 11.1 (95% CI [5.2, 17.0]; t(78) = 3.67, p < 0.001; Cohen’s d = 0.82). Qualitative data from purposively sampled focus groups (n = 12) were analyzed using thematic analysis with established reliability (Cohen’s ? = 0.81) and integrated through triangulation to support quantitative findings. The results indicate that VR-based instruction is associated with improved performance and engagement within the studied cohort; however, inference is bounded to the institutional context, warranting further multi-site validation.
Virtual Reality–Based Instruction, Learning Outcomes, Thematic Analysis, Student Engagement, Mixed-Methods Analysis, Higher Education.
A Smart Water Distribution Network Management Framework based on Internet of Things and Reinforcement Learning Technologies
The convergence of the Internet of Things (IoT) and Reinforcement Learning (RL) has transformed the operation of urban water distribution networks (WDNs) from manual, reactive supervision to intelligent, self-optimizing control. This paper presents a hybrid IoT–RL framework that integrates real-time sensing, data synchronization, adaptive control, and predictive decision-making for efficient network management. The proposed architecture employs distributed IoT sensor nodes to continuously acquire pressure, flow, and energy data, which are transmitted via MQTT to a time-series database for feature extraction and anomaly detection. A Proximal Policy Optimization (PPO)–based RL agent learns dynamic control strategies that minimize leakage losses and energy consumption while ensuring hydraulic stability. The framework is evaluated in the EPANET 2.2 environment using the C-Town benchmark under 24-hour diurnal demand conditions. Three control configurations are strictly studied: (i) the SCADA (Model A), which is a static threshold control, (ii) the hybrid PID-Model Predictive Control (PID-MPC) controller (Model C) as a state-of-the-art non-RL control, and finally (iii) the proposed adaptive IoT-RL control (Model B). Quantitative measures show that the performance of Model B is both 93, 22 improved in decision latency and control accuracy compared to Model A and has a 12, 15-percent better latency and accuracy improvement over the sophisticated Model C. Quanti-tative metrics including control accuracy, latency, data throughput, and network efficiency performance index (NEPI) demonstrate that Model B improves decision latency by 93 %, control accuracy by 22 %, and pro-cessing throughput by 95 % relative to Model A, while maintaining superior reliability over Model C. All fig-ures were programmatically generated in MATLAB (vector export) using EPANET-based topological and con-trol visualization scripts. The results confirm that the IoT–RL integration provides a scalable, data-driven pathway for sustainable water infrastructure management and proactive leakage mitigation.
EduSign: A Multi-Modal Deep Learning Model for Educational Sign Language Recognition
Sign language recognition (SLR) systems aim to improve accessibility to deaf and hard-of-hearing people. The existing SLR systems fail to meet educational needs because they only support common vocabulary. The paper proposes EduSign as a solution to existing problems through its multi-modal deep learning system which uses spatial landmark data and temporal motion data to recognize educational sign language. The system uses MediaPipe to extract hand and upper body and facial landmarks which combine with dense optical flow to create a complete educational sign language system that shows both structural and dynamic features. The system processes fused features through a lightweight hybrid architecture which includes multi-scale temporal convolutions and squeeze-and-excitation attention and hierarchical residual learning and multi-head temporal attention. The assessment of the framework proceeds through testing on a new educational Kannada Sign Language (KSL) dataset which contains ten academic signs used in classroom settings. The experimental results show that the training achieved an accuracy of 96.04% while the validation accuracy reached 94.57% with only a 1.47% generalization gap. Additional comparative and ablation studies illustrate the efficiency of multimodal fusion and attention mechanism. The results suggest that joint use of spatial representation based on landmark and temporal modeling based on motion presents a good framework for educational SLR within the intelligent learning system.
Hardware-Optimized Explainable Machine Learning for Fair Predictive Decision Making in Business Applications
To enhance the objectivity of decision-making on predicting business applications, the paper presents a hardware-optimized explainable machine learning framework. The model unites explainability and fairness policies and hardware-conscious optimization to mitigate the shortcomings of black-box and hardware-agnostic models. Examination of representative business data shows prediction accuracy of up to 87.9%, which translates to an increase of 14.8%- 17.3% compared to the baseline methods. Parity inequality falls below 21.6% because it guarantees that outcomes of program decisions are fairer across sensitive attributes. Moreover, the proposed framework obtains a reduction in inference latency of 18.9% and a reduction in energy consumption per decision cycle of 13.7%. The consistency of explainability is increased by 19.4% and offers consistent and trustworthy feature-level factualization. In general, the findings substantiate the idea that explainability, fairness, and hardware efficiency optimization, when combined, result in scalable, transparent, and high-performance predictive decision-making systems in practice in businesses.
Cyberthreat Forensics Using AI-Neutrosophic Reasoning for SQL Injection Detection in Web Applications
SQL Injection (SQLi) is a type of threat that is among the most resilient to web applications, as it allows an antagonist to alter the backend database and steal valuable information. The simplest detectors, effective in conforming patterns, fail to function with an obfuscation scheme and are characterised by a high false alarm rate in general. The article prescribes an AI-Neutrosophic Forensic Framework, a machine-learning classifier powered by neutrosophic rationale needed to create a more practical and intuitively understandable forensic specification. The framework ranks open source SQLi datasets and web application honeypot logs and identifies structural query features and semantic query features. A hybrid encoder of both Decision Tree and Support Vector Machine produces probability estimates, which are, in turn, converted to a neutrosophic stimulus of truth, falsity, and indeterminacy. This indeterminacy is alleviated in marginal cases. The results presented in the 10000 SQL query simulation indicate that the framework has an accuracy and precision of 97.86 and 96.45, respectively, a recall of 98.21 with a F1-score of 97.32, a false positive value of 2.1 and a false negative value of 1.9, and an average latency to detect parallels of 12.6 ms. The proposed framework has demonstrated that the error levels have substantially reduced and that is able to provide forensics data that is meaningful when analysing web applications in comparison to the original AI-only practices.
Analysis of Normalization, Resampling, and Weighted Voting to Improve k-NN Performance in Diabetes Disease Detection
Diabetes is a global health issue, and machine learning has become crucial for early detection. The k-Nearest Neighbors (k-NN) machine learning algorithm is widely used because it is simple and easy to implement; however, its performance is sensitive to feature scale differences, class imbalance, and the assumption of uniform decision-making, as commonly found in clinical datasets such as the Pima Indians Diabetes (PID) dataset. Many studies have been conducted to address these issues; however, very few have systematically evaluated the combined effects of preprocessing and distance-based decision-making methods on k-NN performance. This study evaluates the combined effects of preprocessing strategies and distance-based decision-making methods on k-NN performance. Two preprocessing methods, namely normalization and resampling, were evaluated. The normalization methods evaluated are MinMax and Z-Score, while the resampling methods evaluated are SMOTE, ADASYN, and Random Undersampling. Two weighted decision-making schemes, namely Dual Weighted and Gaussian Weighted Voting, were also investigated. Performance was evaluated using Accuracy, Precision, Recall, Specificity, F1-score, AUC, and execution time analysis. The results show that Z-Score normalization combined with SMOTE improves the accuracy of the baseline k-NN from 74.48% to 84.10% and increases recall from 54.90% to 90.80%. Incorporating Dual Weighted Voting achieves the highest overall performance, with 84.20% accuracy, 91.80% recall, 85.32% F1-score, and an AUC of 0.85, while maintaining low computational cost. However, the additional improvement over the best preprocessing configuration is not statistically significant. These findings indicate that performance gains are primarily driven by normalization and resampling strategies, while distance-sensitive voting provides marginal additional refinement for classical k-NN in imbalanced medical classification tasks.
A Multi-Model Ensemble Approach Using Deep and Traditional Learning for Autism Spectrum Disorder Classification
Conventional diagnostic tests for Autism Spectrum Disorder (ASD) involve the use of subjective behavioral observations and questionnaires completed by the clinician, which can be time-consuming and subject to human bias. The challenge encourages the development of innovative, data-driven methods to facilitate early and accurate identification of ASD. The research proposes a Multi-Model Ensemble Approach Using Deep and traditional learning for ASD classification (MME-ASD) model. The MME-ASD model encompasses three traditional machine learning (ML) and two deep learning (DL) algorithms that perform according to a weighted majority voting strategy. The five learning paradigms are Random Forest (RF), Decision Tree (DT), Neural Network (NN), Convolutional Neural Network (CNN), and Deep Recurrent Neural Network (DRNN), which are utilized to enhance classification accuracy and generalization. An ensemble evaluation method is proposed to complete this study and assess the efficiency of the proposed MME-ASD model. The MME-ASD model acquires complementary properties by using numeric and textual data from a publicly available dataset of ASD, which includes information on 704 adults, both with and without a diagnosis. Initially, during the evaluation phase, the performance of the standalone traditional ML and DL algorithms was assessed across several train-test ratios. Subsequently, the proposed MME-ASD ensemble was evaluated with a 60-40 split to ensure compatibility with the baseline models. Finally, a 3-fold cross-validation experiment was conducted to assess the robustness and generalization of the proposed MME-ASD model. The experimental outcomes reveal that the MME-ASD model outperforms individual learners for both cross-validation and train-test assessments. It records evaluation metrics of accuracy 99.57%, precision 99.48%, and recall 98.94% across the 3-fold cross-validation experiments. The findings verify that incorporating deep and traditional learning models in an ensemble framework can significantly enhance the classification of ASD, offering a dependable and scalable computational model to aid clinical specialists in the initial diagnosis of ASD.
Closed-Source vs. Open-Weights Language Models: Accuracy, Stability, and Deployment Trade-Offs in Educational Assessment (GPT-4o vs. LLaMA 3.1)
Large language models (LLMs) are increasingly used to support educational assessment, yet institutional adoption requires balancing performance with governance constraints related to auditability, data control, and deployability. We compare a closed-source model (GPT-4o) and an open-weights model (LLaMA 3.1) on two expert validated assessment formats across five academic domains (Science, History, Literature, Technology, and Social Studies). Experiment 1 evaluates multiple choice question (MCQ) option selection using 148 items, five prompting strategies (Raw, Brief Instruction, Long Instruction, Chain-of-Thought, and Question-Answer Prompt Generation), and five independent repetitions per condition (7,400 total model calls) to estimate both accuracy and run-to-run stability under a strict A to E output constraint. Experiment 2 evaluates 147 one sentence short answer items under a fixed instruction prompt. In the multiple choice questions (MCQs), the accuracy and stability of the results for GPT-4o were higher compared to those of LLaMA 3.1, and the incorporation of the additional prompt structure was associated with higher stability compared to the average improvement in correctness. In the short answer questions, the performance of GPT-4o was slightly higher compared to that of LLaMA 3.1 for ROUGE-L and METEOR, and these results mostly corresponded to the results obtained for the semantic similarity questions and the evaluation rubric. The results of the study are important for understanding the accuracy-stability trade-off for the two language generators and for supporting the evaluation approach that considers the correctness of the results for MCQs, their stability for multiple runs, and the validation of the results for short answer questions using the evaluation rubric and the results for the semantic similarity questions.
Artificial Intelligence & Machine Learning
Advancing Flood Prediction and Classification: A Systematic Literature Review
Floods are the most frequent and destructive natural hazards, intensified by climate change and rapid urbanization, which create an urgent need for accurate prediction and classification techniques to mitigate the challenges. While machine learning (ML) and deep learning (DL) techniques have shown great promise, Unlike previous reviews that remain fragmented by examining only forecasting, prediction or susceptibility mapping, this study integrates prediction and classification tasks, systematically analyzes datasets and evaluation metrics, and develops a taxonomy linking data, methods, and performance by conducting a systematic literature review of 30 peer-reviewed articles published between the span of 2020 and 2024, retrieved from major academic databases, using explicit search strings and inclusion criteria. The synthesis reveals that traditional ML models such as Random Forests (RF), Support Vector Machines (SVM), and Artificial Neural Networks (ANN) remain effective for structured hydrological datasets, whereas DL models, particularly Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN), excel in capturing spatiotemporal flood dynamics. Beyond model performance, the review highlights critical backwards for research in Africa and the South American continent and methodological gaps, including data scarcity in underrepresented regions, inconsistent evaluation standards, limited adoption of explainable AI, and weak integration of socio-environmental variables. By providing a structured taxonomy that links datasets, methods, and performance metrics, this review advances beyond prior work and offers actionable insights for researchers and policymakers to strengthen flood risk modeling, disaster preparedness, and climate resilience.
Flood Prediction, Forecasting, Classification, Machine Learning, Deep Learning, Reinforcement Learning
Explainable Artificial Intelligence (XAI)
Leveraging Cellular Automata for Strip-Cut Document Reconstruction: A Path Travelling Salesman Problem Formulation
Reconstructing shredded documents is a long-standing problem in information security, forensics, and archival recovery. This work tackles the Reconstruction of Shredded Documents Problem (RoSDP) by formulating it as a path Travelling Salesman Problem (TSP) and solving it using Cellular Automata (CA). The proposed method is framed as an intelligent control system, where the CA operates as a decision-making engine that determines optimal sequential actions for strip arrangement—similar to automated control strategies employed in robotics. The pipeline includes dataset construction, edge-based dissimilarity computation, an order-respecting 2D coordinate mapping that overcomes non-metric multidimensional scaling (NMDS) failures, CA-driven search to recover the left-to-right shred order, and deterministic page reassembly. Evaluated on 120 pages stratified by content density and shredded into 20, 25, and 30 strips, the method delivers a mean page-level reconstruction accuracy of 70.74%, achieves perfect 100% recovery on many pages, and runs in approximately 30–55 seconds per page on a single CPU. Analysis shows errors stem primarily from the boundary dissimilarity cost rather than the CA mechanism itself. Compared with a closely related optimization-based baseline, the proposed approach yields higher overall accuracy. These results position CA as a practical, reproducible, and competitive intelligent-control framework for strip-cut reconstruction and motivate future improvements to the cost function and extensions to cross-cut and hand-torn documents.
Computer Science, Artificial Intelligence, Optimization.
Ensemble CNN Model for Detecting Rice Leaf Diseases by Integrating MobileNetV2 and EfficientNet-B1 Architectures
Rice diseases significantly impact agricultural productivity, requiring effective methods for early detection to prevent substantial damage. This paper introduces an ensemble Convolutional Neural Network (CNN) model that integrates MobileNetV2 and EfficientNet-B1 architectures for the identification of rice leaf diseases. The algorithm employs a dataset comprising 5,936 rice leaf photos from Mendeley Data to classify five disease categories: Bacterial blight, Blast, Brownspot, Tungro, and Healthy. The ensemble method employs hyperparameter optimization by Bayesian and Random techniques, integrating dropout, learning rate adjustment, and kernel regularization to achieve a compromise between model accuracy and generalization. The model attained a training accuracy of 99.99% and a validation accuracy of 99.90%, showcasing robustness and accuracy. The TensorFlow Lite format facilitates integration into mobile applications, providing farmers with a portable and effective solution. This system enhances agriculture by equipping stakeholders with advanced disease management tools, increasing yields, and promoting sustainable practices.
Agentic AI for Knowledge Management in the Agriculture Community
The integration of Artificial Intelligence (AI) into Knowledge Management Systems (KMS) offers new opportunities to address persistent challenges in agriculture, particularly in crop disease identification and treatment. A novel conceptual model of an Agentic AI-empowered KMS tailored for the agricultural community of farmers and farming experts is proposed. Unlike conventional decision-support systems, the model introduces agency through AI components capable of autonomously acquiring, retrieving, storing, and applying knowledge while engaging in continuous feedback with human experts. Multimodal interfaces, i.e., chatbots for natural language queries and image recognition for both text and visual diagnoses, enable farmers to contribute real-time field data. Knowledge graphs and Large Language Models (LLMs) mediate the transformation of inputs into validated, context-aware treatment and pesticide recommendations. The model is grounded in Activity Theory, providing a socio-technical lens to align user requirements, community participation, and role distribution with KM processes. The originality of this approach lies in combining agentic AI capabilities with collaborative knowledge exchange, creating a self-improving agricultural knowledge ecosystem. Anticipated contributions include enhanced decision-making, improved crop yields, and sustainable farming practices, establishing a foundation for future empirical validation and system implementation.
Agentic AI, artificial intelligence, knowledge management system, crop disease, pesticide management, Activity Theory
A Variable Strength Interaction Test Suites Generation Strategy using Sewing Training Optimization
This paper reports on a study in which a new model for the development of Variable Strength (VS) interaction test suits, identified as Sewing Training Based Optimization (STBO). This approach is based on the principle of how tailor apprentices learn through a stage of imitation, a stage of practice, and a stage of evaluation, which together bring about a balance between wide exploration and focused search. To evaluate its performance in terms of test suite size reduction and scalability, STBO was applied to commonly used base models. The results indicate that STBO produced 26 optimal solutions, outperforming nearly 90% of existing methods. Consistent superiority over traditional algorithms was observed, while performance remained comparable to that of more complex metaheuristic approaches. In addition, a real-time software system case study was conducted, in which STBO demonstrated effective fault detection capability, thereby indicating that it is a reliable and adaptive strategy for software testing.
Software testing, Variable-strength interaction, T-way testing, metaheuristic optimization, Combinatorial Testing, Sewing Training Based Optimization (STBO)
Multimodal Alignment and Fusion for Diabetic Retinopathy Detection using Deep Learning Approach
This study proposes a label-driven multimodal alignment with late fusion framework for diabetic retinopathy (DR) detection, integrating fundus images, OCT scans, and structured electronic health records (EHR). Unlike patient-wise paired datasets, which are often unavailable, the proposed alignment strategy groups and matches modalities based on diagnostic labels (DR/No_DR), ensuring semantic consistency across heterogeneous sources. Each modality is modeled using an architecture tailored to its data type—CNNs for fundus and OCT images, and an ANN for EHR—before predictions are combined via four late fusion strategies: Simple Average, Weighted Average, Majority Voting, and Stacked Ensemble. By enforcing label-driven alignment, the framework ensures that multimodal integration leverages coherent diagnostic cues from aligned class distributions, even without patient- level pairing. Experimental results, evaluated on accuracy, specificity, sensitivity, and F1-Score, show that while unimodal CNN-OCT and ANN-EHR models achieved strong accuracy, the Simple Average and Weighted Average fusion methods attained the highest F1-Scores (0.999), demonstrating an optimal precision–recall balance. Confusion matrix analysis further confirms high specificity and sensitivity, underscoring the ability of label-aligned multimodal fusion to exploit complementary diagnostic strengths. These findings highlight that label-driven alignment, coupled with averaging-based late fusion, not only improves predictive performance but also enhances robustness and clinical applicability, offering a scalable and interpretable AI- assisted DR screening solution for real-world ophthalmology practice.
Multimodal alignment, late fusion, diabetic retinopathy, Fundus, OCT Scan, EHR
Optimizing Nurse Knowledge Management in Healthcare Services using the Jaro-Winkler Algorithm
Effective nurse knowledge management is essential for supporting clinical decision-making and ensuring high-quality patient care. However, hospitals often face challenges such as data duplication, information inconsistency, and inefficient data retrieval. This study explores the integration of the Jaro-Winkler algorithm into a nurse knowledge management model to enhance health services at Lamongan District Hospital. The algorithm was selected for its precision in measuring string similarity, which facilitates accurate data identification and the elimination of redundant entries. The methodology included data collection, system requirements analysis, algorithm implementation, and performance evaluation based on accuracy and retrieval speed. The findings revealed a 30% reduction in data duplication and a 40% increase in data retrieval speed. These improvements underscore the algorithm's effectiveness in optimizing knowledge management processes. The study contributes to the development of intelligent, technology-based systems that can be adopted by other healthcare institutions to improve service quality and operational efficiency.
Jaro-Winkler Algorithm, Knowledge Management, Nurses, Health Services, Lamongan Regional Hospital
Enhancing Nonlinear Feature Reduction through Logistic Regression-Guided Hybrid PCA
The usage of Principal Component Analysis (PCA) to reduce the number of dimensions is commonly because it is easy to use and works well. But in the real world, a lot of datasets have parts with high variance that don't always give useful information for classification. This reserach solves this problem by suggesting a Hybrid PCA Guided by Logistic Regression (PCA–LR), which chooses components based on how well they work for supervised classification instead of just variance. The proposed method tested on four datasets with different structures: Mental Health, Breast Cancer, Fetal Health, and Pistachio. The result shown PCA–LR achieved better accuracy performance than basic PCA in these datasets. Proposed method also works better than Kernel PCA (KPCA) and Sparse PCA (SPCA). The Mental Health dataset shows the biggest improvement, with PCA–LR getting an accuracy of 98.52%, which is 25.71% better than basic PCA. The method gets 97.37% on Breast Cancer and the same best performance as KPCA on Fetal Health and Pistachio. These results suggest that adding lightweight supervised guidance to PCA can make it much more useful on datasets that aren't strictly linear.
Dimensionality reduction, Hybrid-PCA, Logistic regression, Nonlinear feature reduction
Smart In-Car Speed Monitoring System, The Effect of Grace Period on Driving Behavior
High and excessive speed is one of the leading causes of traffic accidents, leading to fatalities and serious injuries worldwide. In Iraq, there was a 21% increase in casualties by 2017. This study presents a speed monitoring system in which the driver gets alerted by an audible alarm in case of over-speeding. A grace period of 10 seconds is allowed so that the driver tries to reduce the car's speed. A ticket is issued via SMS message if the driver fails to decelerate within that period. Thus, prioritizing safety awareness over immediate punitive measures. The system was tested through 40 car trips, carried out by four drivers, 20 trips without an alarm and 20 with an alarm. Multiple statistical tests were used to evaluate the system. Results revealed that incorporating the alarm achieved a reduction of 21%, with *p*=.039 in speed mean, and 14% with *p*=.002 in speed variability. Additionally, tickets were decreased by 89% with high statistical significance (?p? < .001). Furthermore, statistically, trips with an alarm revealed 84% lower odds of receiving a ticket in comparison with the trips without an alarm. On the other hand, results showed that there is no correlation between temperature and speeding (p > .05, indicating that there is no statistical significance).
Speed monitoring, With alarm, Without alarm, Grace period, Driving behavior
ARIMA-GARCH Based Time Series Analysis of Cryptocurrency Volatility
Cryptocurrencies are a new type of asset that is changing the game. They are decentralized, very volatile, and their prices change quickly. Investors, policymakers, and researchers all need to know how bitcoin markets work. This article examines the volatility patterns of significant cryptocurrencies by employing the Autoregressive Integrated Moving Average (ARIMA) in conjunction with the GARCH model to assess historical price data and predict future trends. We also do a full volatility analysis to show how unpredictable cryptocurrency markets are. We also do a seasonality analysis and calculate the Relative Strength Index (RSI) for the technical analysis indicators. The study uses the R programming environment to clean up data, model time series, and check performance indicators. Our results show that ARIMA models accurately capture the time-based relationships in bitcoin time series, which makes them good for long-term predictions.
Cryptocurrency, time series, volatility analysis, ARIMA, GARCH, seasonality analysis
Enhancing Digital Evidence Analysis: Fakeseeker for Real-Time Deepfake Detector using EfficientNet
Deepfake technology poses significant challenges to the authenticity of digital evidence. This creates an urgent need for reliable and effective verification methods. This work introduces Fakeseeker, a lightweight, real-time detection framework designed to assist digital forensic investigations. The system uses a fine-tuned EfficientNet-B0 architecture to analyze both uploaded and live-streamed media. The approach involves preparing datasets from publicly available sources, including Celeb-DF, FaceForensics++, and DFDC. Subsequently, MTCNN-based face extraction, data augmentation, and targeted optimization of EfficientNet-B0 were undertaken. Comparative evaluations with EfficientNet variants B0, B1, and B2 show that EfficientNet-B0 offers the best balance between accuracy and efficiency. It takes about nine minutes per training epoch, achieves a validation LogLoss of 0.0021, and has an AUC score of 0.9996. Based on these results, Fakeseeker was built using EfficientNet-B0 as its backbone. It includes separate components for media processing, classification, and reporting. Evaluation results show a detection accuracy of 99.2% with minimal resource usage. This makes the system suitable for real-time forensic workflows and field applications. Overall, this study provides a practical and scalable solution for improving the verification and analysis of digital evidence.
Artificial Intelligence, Deep Learning, Forensic Analysis, Media Analysis
Evaluating Rainfall Predictions using Statistical Decomposition and Neural Network–Based Approaches
The ability to accurately predict impacts of rainfall is essential for managing our water supply, developing agricultural plans and protecting ourselves from natural disasters. For the purpose of this study, this research compares statistical and neural network-based methods used in the prediction of rainfall, particularly Singular Spectrum Analysis (SSA), Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM). According to those models, time series can be decomposed into trend, seasonal and noise components during SSA approaches. Meanwhile, other deep learning models offer solutions that utilize historical data to model nonlinear rainfall behavior and account for random events. Data collected from Mersing on daily rainfall are analysed for statistical properties throughout this study and used to develop and test forecasting models. In this regard, metrics can be analysed to determine the performance of models such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Coefficient of correlation (R) and Nash-Sutcliffe Efficiency (NSE). The results indicate that SSA performed the best reconstruction with MAE=4.1931, RMSE=6.8078, R=0.9362, and NSE=0.8727, however both recurrent and vector SSA had poor forecasting scenarios with negative NSE values. Based on training results, LSTM model showed MAE=4.0391, RMSE=10.5818, R=0.7495 and NSE=0.5307 which indicates that the model has a strong capacity to learn. Although the ANN model outperformed the LSTM model marginally in the test, it achieved a MAE of 9.9117 and NSE of -0.2332 which gives it a marginal edge over the LSTM model. It is suggested that rather than concentrating solely on statistical or neural models, both structured and chaotic rainfall behaviours in Malaysian coastal areas can be better understood by integrating SSA with deep learning techniques for a more effective understanding of the phenomenon. A combined approach can provide valuable insights into the implementation of decomposition-based and data-driven forecasting methods specifically for moist climates. Thus, the findings established the foundation for further studies on developing improved and enhance hybrid rainfall forecasting systems.
Singular Spectrum Analysis, Artificial Neural Network, Long-Short Term Memory, forecasting
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.
Optimized Tsunami Vulnerability Area Classification using Hybrid Fuzzy-SVM Model Through Spatial Data Processing
Tsunami disasters pose serious threats to human life and coastal infrastructure and require accurate mapping of tsunami-prone areas for effective disaster mitigation and coastal planning. Machine learning methods, including weighted overlay and Support Vector Machine (SVM), are widely applied but often face limitations in identifying transitional vulnerability zones with gradual class boundaries. This study proposes a hybrid fuzzy-SVM approach to improve the accuracy of tsunami vulnerability classification, which is an important parameter in classifying tsunami vulnerability. Three geospatial parameters elevation, land cover, and inundation extent, were used as primary inputs, each transformed through fuzzy membership functions to handle uncertainty and spatial ambiguity. The Fuzzy Vulnerability Index (FVI) smooths the resulting values ??and then classifies using SVM with linear and RBF kernels under a one-vs-rest scheme to generate multi-class vulnerability maps across the southern coast of East Java. Experimental results demonstrated that the proposed hybrid fuzzy–SVM outperformed both conventional SVM and weighted overlay methods. The model achieved an overall accuracy of 91.3%, precision of 0.911, recall of 0.910, and F1-score of 0.910, indicating strong agreement between predicted and reference vulnerability maps. Overall, the hybrid fuzzy–SVM framework provides a more flexible and data-driven approach to tsunami vulnerability assessment.