Journal of Smart Algorithms and Applications (JSAA)
The Journal of Smart Algorithms and Applications (JSAA) is an international, peer-reviewed, open-access journal dedicated to advancing the theory and practice of intelligent computational methods. Published by the SIRG Publisher, JSAA focuses on the development and application of smart algorithms that solve complex, real-world problems across science, engineering, and technology. The journal publishes original research articles, review papers, and technical reports that present innovative algorithmic strategies, hybrid models, and data-driven solutions. JSAA aims to connect researchers and practitioners working in artificial intelligence, machine learning, optimization, data science, and intelligent systems. By maintaining high editorial standards and a rigorous double-blind peer-review process, JSAA ensures the publication of impactful research that promotes innovation, reproducibility, and global scientific collaboration. ISSN (Online): 3070-4189 Publication Frequency: Bimonthly Publication Editor-in-Chief: Prof. Dr. Diego Alberto Oliva Navarro, University of Guadalajara in Mexico Dr. Diego Oliva received the B.S. degree in Electronics and Computer Engineering from the Industrial Technical Education Center (CETI) of Guadalajara, Mexico in 2007, the M.Sc. degree in Electronic Engineering and Computer Sciences from the University of Guadalajara, Mexico in 2010. He obtained the Ph. D. in Informatics in 2015 from the Universidad Complutense de Madrid. Currently he is an Associate Professor at the University of Guadalajara in Mexico. In 2017 he has been visiting professor at the Tomsk Polytechnic University in Russia. He has the distinction of National Researcher Rank 2 by the Mexican Council of Science and Technology. In 2022 he was recognized as a Highly Cited Researcher by Clarivate WOS. He has been the advisor of 1 Ph. D. Thesis in Spain and 4 master’s degree thesis in Mexico, currently he is the advisor of 3 Ph. D. Thesis in Mexico. Since 2017 he is member of the IEEE and since 2022 he is a Senior Member. Diego Oliva is coauthor of more than 50 papers in international Q1 journals, 5 books in international editorials and he has 2 patents submitted in Mexico. He was the principal investigator of two full funded Mexican projects and participate in two international projects. He is part of the editorial board of IEEE Access, Plos One, Mathematical Problems in Engineering and IEEE Latin America Transactions. His research interest includes evolutionary and swarm algorithms, machine learning, hybrid algorithms, image processing and computational intelligence Scopus: https://www.scopus.com/authid/detail.uri?authorId=55391699600 ============================================================== For more details, please get in touch with the JSAA managing editor or the SIRG editorial office: Journal Editor: Prof. Chin-Shiuh Shieh Ph.D., Professor Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Taiwan Office: I705, Address: No. 415, Jiangong Rd., Kaohsiung City 80778, Taiwan (R.O.C.) ORCID: 0000-0003-3187-458X, Google Scholar, ScholarGPS, Scopus https://bit.nkust.edu.tw/~csshieh/ ================================== Dr. K. Martin Sagayam Associate Professor Department of Electronics and Communication Engineering SRM TRP Engineering college (SRM Group of Institutions) Trichy 621105 ================== Assistant Editor Dr. Arwa Saad Faculty of Computer Science, Nahda University, Beni Suef, City, Egypt. Email: ( editorial.jsaa@scientificirg.com ) =============================
Scientific Innovation Research Group (SIRG)
Journal of Smart Algorithms and Applications (JSAA) content is published under a Creative Commons Attribution 4.0 International (CC BY 4.0) License. This means that content is freely available to all readers upon publication, and content is published as soon as production is complete. Journal of Smart Algorithms and Applications (JSAA) seeks to publish the most influential papers that will significantly advance scientific understanding. Selected articles must present new and widely significant data, syntheses, or concepts. They should merit recognition by the wider scientific community and the general public through publication in a reputable scientific journal.
Beyond Accuracy: Cost-Aware, Explainable Predictive Maintenance for Industrial Machine Health Monitoring Using Sensor RUL Estimation
Section: Original Research Categories: Intelligent Algorithms and Computational Models, Algorithm Engineering and Performance Keywords: Array, Array, Array, Array Subjects: Array, Array, Array Predictive maintenance in safety-critical aerospace systems requires not only accurate remaining useful life (RUL) prediction, but also decision-relevant outputs that are explainable, cost-sensitive, and operationally actionable. However, existing approaches primarily optimize metrics such as RMSE and MAE while overlooking asymmetric error costs, interpretability, and early-warning performance. This paper presents a comprehensive framework evaluated on the NASA C-MAPSS FD001 turbofan dataset, addressing these limitations. Four models, Ridge Regression, Random Forest, XGBoost, and LSTM, are trained on a 112-feature space derived from 14 sensors using rolling statistics, lag features, degradation slopes, and delta transformations. Models are evaluated using RMSE, MAE, R², and the NASA PHM asymmetric score. Results indicate that Random Forest achieves the lowest RMSE (12.13 cycles), while XGBoost attains the best PHM Score (230.02), suggesting improved robustness under asymmetric cost conditions. SHAP-based analysis identifies delta and variability features of key sensors as dominant degradation indicators, offering physically interpretable insights. Additionally, an early-warning system evaluated at a 30-cycle horizon shows that Random Forest achieves an F1-score of 0.737 with minimal missed failures, while LSTM demonstrates higher recall at the cost of increased false alarms. These findings highlight the importance of combining accuracy, cost-awareness, and explainability to support practical predictive maintenance decision-making.
Intelligent Algorithms and Computational Models
Copyright (c) 2026 Journal of Smart Algorithms and Applications (JSAA) https://creativecommons.org/licenses/by/4.0
Patient-Level Intracranial Aneurysm Detection in Volumetric Neuroimaging Using a 2.5D Deep Learning Framework
Section: Original Research Categories: Intelligent Algorithms and Computational Models Keywords: Array, Array, Array, Array, Array Subjects: Array, Array, Array Intracranial aneurysms (IAs) have a case-fatality rate greater than 50% following rupture, and the majority are clinically silent until hemorrhage occurs. An important research direction in neurovascular imaging informatics is the development of automated screening tools that can identify potentially high-risk cases for expedited expert review. In this work, we propose a binary deep learning framework for patient-level aneurysm detection from volumetric DICOM neuroimaging. The proposed architecture adapts EfficientNetV2-S to a 2.5D input representation of channel-stacked 32 axial slices resized to 384 × 384 pixels. Percentile-based intensity normalization is employed to reduce cross-scanner variability. We measure model generalization using stratified five-fold cross-validation and improve prediction stability using weighted ensemble averaging, test-time augmentation, and temperature-scaled probability calibration. On the RSNA 2025 Intracranial Aneurysm Detection Challenge dataset of 1,147 clinical cases, the framework achieved a weighted AUC-ROC of 0.694 with a cross-validation mean of 0.681±0.005. These results indicate that the proposed method can be considered as a first research baseline for automated aneurysm pre-screening, but the performance obtained is still not enough to be used in the clinical routine without assistance. Before its integration into clinical radiology workflows, it needs external validation on independent multi-site cohorts, threshold-specific sensitivity and specificity analysis, and lesion-level evaluation when annotations are available.
A Multi-Stage AI Framework for Intelligent Incident Detection and Response Automation in Security Operations Centers
Section: Original Research Categories: Intelligent Algorithms and Computational Models Keywords: Array, Array, Array, Array Subjects: Array, Array, Array Security Operations Centers (SOCs) are essential for safeguarding company infrastructure against increasingly sophisticated cyber threats. The expansion of cloud services, Internet of Things (IoT) devices, and extensive dispersed systems has resulted in a significant increase in security event quantities, burdening analysts and revealing the inadequacies of conventional, manual workflows. Rule-based Security Information and Event Management (SIEM) systems, in particular, experience elevated false-positive rates, leading to alert fatigue and protracted incident response times. This paper introduces IRAS (Incident Response Automation and Management System), an AI-driven platform that automates key stages of the incident response lifecycle within enterprise SOC environments. IRAS integrates a two-stage machine learning pipeline combining Isolation Forest for anomaly detection and Random Forest for multi-class attack classification, coupled with Wazuh for centralized log management and deployed within a Dockerized microservice architecture on Ubuntu Linux. Upon detecting malicious activity, IRAS executes predefined mitigation playbooks including IP blocking, endpoint isolation, and account suspension within seconds of detection. IRAS was tested in a controlled experimental setup using an Ubuntu victim machine and a Kali Linux attack machine on the CIC-DDoS2019 and SSH Brute-Force datasets and got a weighted F1-score of 94.5% and, a false-positive rate of 2.8%, and a mean automated response time of 2.46 seconds, representing a substantial reduction compared to manual SOC baselines of 8–15 minutes. The system achieved a peak throughput of 1,200 incidents per hour and reduced analyst workload by 74%, demonstrating significant improvements in SOC efficiency within the evaluated threat scenarios and providing a foundation for semi-autonomous SOC operations. Generalization to additional attack categories such as ransomware, insider threats, and APTs remains a direction for future validation.
Trust-Aware Smart Algorithms for Open Digital Service Ecosystems: A Structured Review
Section: Review Articles Categories: Intelligent Algorithms and Computational Models, Algorithm Engineering and Performance Keywords: Array, Array, Array, Array, Array Subjects: Array, Array Open digital service ecosystems are reshaping how users discover and act on services across commerce, tourism, finance, education, accessibility, and public compliance. However, the literature on underlying algorithms remains fragmented, some prioritize ranking accuracy, others focus on adoption, and a smaller body examines explainability, fairness, privacy, accessibility, and governance. This paper presents a structured review of 74 studies (55 from 2017 to March 2026, plus 19 foundational works) mapping the transition from closed, accuracy-centered recommenders to open, trust-aware, multi-objective smart algorithms. Hybrid and context-aware architectures remain dominant, but recent work increasingly incorporates conversational interfaces, human-in-the-loop control, privacy constraints, and fairness-aware reranking. Five recurring gaps emerge: data fragmentation, weak explanation quality, limited accessibility auditing, poor integration of institutional rules, and weak cross-sector transferability. Trust is best understood as a composite property involving data provenance, procedural transparency, contextual fit, user control, and reliable escalation paths. Based on these findings, the paper proposes a six-layer design model (data interoperability, contextual risk profiling, hybrid inference, governance-aware reranking, explanation interfaces, and human oversight). Contributions: (1) a PRISMA-informed review protocol; (2) a taxonomic classification of five smart algorithm families; (3) a composite trust framework operationalized from Mayer et al. (1995); (4) quantitative comparisons of trust-related metrics; (5) mathematical formulations for trust-aware reranking and explanation generation; (6) a benchmark against NIST AI RMF and EU AI Act; and (7) a six-layer architectural model with implementation examples.
A Survey of Emerging Trends and Algorithmic Solutions inCloud-Native Data Management
Section: Review Articles Categories: Emerging Algorithmic Technologies Keywords: Array, Array, Array, Array Subjects: Array, Array, Array This research paper aims to provide a comprehensive and systematic survey of architectural evolution, implementation frameworks, and empirical evaluation measures for modern cloud database systems. It explores the paradigm shift from traditional on-premise deployments to highly scalable elastic and cloud native data platforms. This work provides a comprehensive architectural analysis of fundamental decoupling mechanisms including storage-compute disaggregation with dynamic partitioning, sharding and geo-replication protocols for Relational, NoSQL and NewSQL models. We build a strong multi-dimensional taxonomy to assess leading enterprise platforms on dimensions of data models, consistency guarantees and operational tradeoffs. We also provide a detailed survey of performance benchmarking techniques (employing standard workloads such as YCSB and TPC) and introduce a new metric, "Throughput-per-Cost," to measure the economic efficiency of cloud. A critical analysis of security infrastructures is provided, covering modern frameworks like Zero-Trust Architectures, homomorphic encryption and post-quantum cryptographic migration pathways. In this paper, we present a forward-looking research roadmap based on real-world case studies on distributed SQL migration and serverless IoT scaling. It points to emerging trends such as AI-driven autonomous database tuning and decentralized Data Mesh architectures. Database practitioners and researchers in the evolving cloud data management need this as a must-have reference. It highlights key emerging trends, including AI-driven autonomous database tuning and decentralized Data Mesh architectures. It offers a definitive guide for both database practitioners and researchers navigating the evolving landscape of cloud data management.
Optimizing YOLOv12-L for Imbalanced Satellite Vehicle Detection via Physics-Informed Data Synthesis and Adaptive Class-Aware Loss Weighting
Section: Original Research Categories: Intelligent Algorithms and Computational Models, Optimization and Metaheuristics Keywords: Array, Array, Array, Array, Array Subjects: Array, Array Vehicle detection in satellite imagery underpins applications in traffic monitoring, urban planning, and disaster response, yet two obstacles routinely degrade detector performance: the small pixel footprint of individual vehicles and severe imbalance between common and rare vehicle categories. This study presents a vehicle detection framework built on the YOLOv12-L architecture and evaluated on the Vehicles in the Middle East (VME) dataset, in which cars outnumber buses and trucks by a wide margin. The framework combines a physics-informed synthetic data generator, which places procedurally rendered bus and truck silhouettes onto realistic backgrounds and enhances them with adaptive contrast and sensor-noise simulation, with a class-aware loss configuration that increases the classification loss weight and applies label smoothing for minority categories. Training on 4,666 images (2,828 original tiles plus 1,838 synthetic tiles contributing 2,476 bus and 2,076 truck instances) for 100 epochs produced a validation mean Average Precision at an IoU threshold of 0.5 (mAP50) of 0.687 and mAP50:95 of 0.391, with per-class mAP50:95 values of 0.476 for cars, 0.374 for buses, and 0.323 for trucks. On a held-out test split, mAP50 reached 0.634 and mAP50:95 reached 0.361. Increasing the inference resolution from 640 to 736 pixels raised validation mAP50:95 to 0.398 without retraining. Test-Time Augmentation at the 1.17× scale further improves overall mAP50 to 0.693. These results confirm that targeted data synthesis combined with class-aware loss weighting yields consistent gains for underrepresented vehicle categories in geospatial imagery.
Weight-Tied Adaptive Recursive Vision–Language–Action Transformer for Efficient Multimodal Robotic Control
Section: Original Research Categories: Intelligent Algorithms and Computational Models Keywords: Array, Array, Array, Array, Array Subjects: Array, Array, Array Vision-Language-Action (VLA) models unify perception, language understanding, and control within a single learning framework, enabling robots to execute manipulation tasks specified through natural language and visual observations. Despite recent progress, many existing VLA systems rely on fixed-depth transformer architectures, resulting in high computational cost and limited adaptability to varying task complexity. We introduce an adaptive recursive VLA architecture that decouples reasoning depth from parameter count through iterative transformer refinement with weight-tied layers. The proposed model processes temporally windowed RGB observations, proprioceptive states, and language instructions using pretrained vision--language encoders and lightweight proprioceptive encoding. Multimodal features are integrated via gated fusion and iteratively refined through recursive transformer iterations, enabling variable-depth reasoning without increasing model size. The refined latent representation conditions structured continuous action prediction, including Cartesian end-effector translation, 6D rotation representation, and gripper actuation. Experimental evaluation on the large-scale DROID robotic manipulation dataset demonstrates substantial improvements over non-recursive baselines. The recursive model achieves a mean squared error (MSE) of 0.020, representing an 82.4\% reduction compared to the baseline (MSE: 0.1137). Prediction accuracy reaches 66.82\% of actions within 0.10 tolerance and 86.15\% within 0.20 tolerance. Position prediction achieves correlations exceeding 0.84-0.96 across all axes, while rotation components show correlations ranging from 0.88 to 0.98. The model maintains computational efficiency with only a 1.5$\times$ inference-time overhead while achieving an 82\% improvement in accuracy. These results validate recursive reasoning as an effective and computationally efficient mechanism for accurate, adaptable multimodal robotic control.
Hierarchical Swin Transformer for Multi-Stage Dementia Diagnosis with Clinically-Grounded Visual Explainability
Section: Original Research Categories: Intelligent Algorithms and Computational Models, Optimization and Metaheuristics Keywords: Array, Array, Array, Array, Array Subjects: Array, Array This paper presents a novel multi-stage dementia diagnosis framework integrating a Swin Transformer architecture with explainable AI for brain MRI analysis. The proposed approach addresses two critical challenges: capturing both local and global structural features through hierarchical Vision Transformer processing, and providing clinically interpretable decisions via Grad-CAM visualization. Our model was evaluated on a Kaggle dataset comprising 6,400 MRI images across four dementia stages: non-demented (3,200), very mild (2,240), mild (896), and moderate (64). The dataset was split into 70% training, 15% validation, and 15% testing. Experimental results demonstrate superior performance with 97.3% accuracy, precision ranging from 94.8-100%, recall between 91.1-100%, and a macro F1-score of 96.5%. Statistical validation through 5-fold cross-validation (96.8% ± 0.4%) confirms robustness. The SwinGrad-CAM component successfully identifies clinically relevant biomarkers, including hippocampal atrophy and ventricular enlargement, aligning with established neurological indicators. For very mild cases, heatmaps highlight early temporal lobe changes, while moderate cases show intense activation in regions with severe cortical atrophy. This interpretable AI framework offers a robust solution for early intervention, precise staging, and personalized treatment planning in dementia care, enabling clinicians to make informed decisions through visual validation of model reasoning while bridging the gap between deep learning performance and clinical trust.
Automating COVID-19 Classification in Chest CT Scans Using Advanced CNN Architectures
Section: Original Research Categories: Intelligent Algorithms and Computational Models, Emerging Algorithmic Technologies Keywords: Array, Array, Array, Array, Array Subjects: Array, Array, Array, Array, Array This paper proposes an efficient and fully automated method for classifying COVID-19 using CT scan images of a patient's chest. The study utilizes the publicly available SARS-CoV-2 CT scan dataset, which contains 1252 CT scans positive for SARS-CoV-2 (COVID-19) infection, 1230 CT scans from SARS-CoV-2-negative patients, and a total of 2482 cross-sectional scans. This research explores various topologies designed to enhance the classification accuracy of convolutional neural networks, particularly when dealing with images containing small objects of interest. DenseNet169 is particularly effective in handling small-sized infection patterns commonly observed in COVID-19 cases, as it allows the model to analyze images at varying resolutions effectively without compromising small-object data integrity. Several approaches were evaluated, including VGG19, Xception, ResNet101, DenseNet169, and two custom models: a "custom vanilla" model based on the vanilla architecture and a "custom inception" model based on the inception architecture. Among these, DenseNet169 achieved the highest performance, attaining an impressive accuracy rate of 99.731%.
Certainty-Aware Skin Lesion Segmentation with Post-Hoc Reliability Estimation for the Segment Anything Model
Section: Original Research Categories: Intelligent Algorithms and Computational Models, Algorithm Engineering and Performance Keywords: Array, Array, Array, Array Subjects: Array, Array, Array The Segment Anything Model (SAM) represents a major advance in zero-shot visual segmentation, yet it provides purely deterministic outputs without any measure of prediction reliability, a critical limitation for safety-conscious medical imaging applications. This paper introduces a certainty-aware segmentation framework that augments SAM-based zero-shot inference with principled, post-hoc reliability estimation. Three complementary outputs are introduced: a pixel-wise certainty map that identifies spatially localized regions of ambiguity; a global confidence score that provides a scalar measure of overall segmentation trustworthiness; and a quality-flagging mechanism that enables automated screening of unreliable predictions. The framework requires no modification to SAM's architecture and no additional training data, thereby preserving its zero-shot generalization properties. Evaluation on the ISIC 2018 Task 1 skin lesion segmentation benchmark comprising 2,594 dermoscopic images in a fully zero-shot setting yields a mean Dice Similarity Coefficient of 0.820 pm 0.095 and a mean Intersection-over-Union of 0.750 \pm 0.101. A strong positive correlation (Pearson r = 0.84, p < 0.001, n = 2,594) is observed between certainty scores and segmentation quality. High-quality segmentations (DSC> 0.80) are consistently associated with certainty scores above 80%, while low-quality predictions (DSC< 0.70) yield certainty scores below 50%. Stratified analysis confirms a mean DSC difference of over 0.25 between high- and low-certainty tiers (Wilcoxon p < 0.001, Cohen's d = 2.31). These results demonstrate that the proposed certainty metrics reliably track segmentation accuracy and provide a practical mechanism for risk-aware deployment of foundation models in clinical environments.
AI-Guided Grover Search for Simulation-Based Evaluation of Post-Quantum Security in CKKS Homomorphic Encryption
Section: Original Research Categories: Intelligent Algorithms and Computational Models, Algorithm Engineering and Performance Keywords: Array, Array, Array, Array, Array Subjects: Array, Array, Array The emergence of quantum computing poses fundamental challenges to the security assumptions underlying modern cryptographic systems, particularly Fully Homomorphic Encryption (FHE) schemes that enable computation on encrypted data. While Grover's algorithm provides a theoretical framework for quantum attacks on symmetric cryptographic primitives, its practical application to complex parameter spaces like those in CKKS FHE has remained limited. This paper presents a simulation-based, exploratory hybrid framework that combines deep neural networks with a simplified quantum search model to evaluate the post-quantum security of CKKS bootstrapping parameters under idealised conditions. The AI-enhanced system learns to identify potentially vulnerable parameter configurations through pattern recognition, then uses this knowledge to optimize the quantum oracle construction in a 4-qubit Grover's algorithm simulator. Experiments conducted on 5,000 synthetically generated parameter sets, with evaluation on 100 boundary-region configurations, demonstrate that this hybrid approach achieves a 73.4\% success rate in identifying insecure parameters under the experimental setup, representing a 30.6\% improvement over standard quantum search in the same simulated environment. Within the experimental model, this analysis indicates that under the experimental model, parameter sets nominally targeting 128-bit quantum security may exhibit effective security levels of only 86–101 bits when subjected to AI-guided search, suggesting that current FHE parameter margins warrant further investigation as quantum capabilities mature. The findings are simulation-based and should not be directly extrapolated to real-world deployments without further validation; however, they indicate that security margins may need to be increased by approximately 2.3 times to maintain true 128-bit quantum resistance against intelligent adversaries. These results have implications for post-quantum cryptographic standards and motivate further study at realistic qubit scales.
A Graph Neural Network Framework for Structural Side-Channel Vulnerability Assessment in Cryptographic Circuits
Section: Original Research Categories: Emerging Algorithmic Technologies Keywords: Array, Array, Array, Array Subjects: Array, Array Traditional side-channel analysis treats power and electromagnetic traces as temporal sequences, applying statistical or sequence-based machine learning methods without regard for the circuit topology responsible for generating observed leakage. This discards structural information intrinsic to digital circuits: gate connectivity, signal propagation topology, and the hierarchical organization of cryptographic modules. A framework is presented that applies Graph Neural Networks (GNNs) to side-channel vulnerability assessment by modeling circuits as attributed graphs in which nodes represent logic gates, edges represent wire connections, and power measurements are encoded as node features. A complete pipeline is developed spanning Verilog netlist parsing, graph construction, and Graph Convolutional Network (GCN) training with multi-head attention for multi-scale circuit analysis. Evaluation on ten AES-128 circuit implementations demonstrates an 86.4% attack success rate, compared with 68.1% for a CNN-LSTM baseline, with required power traces reduced from 988 to 790. Cross-architecture generalization reaches 63.5% accuracy on unseen circuit families, substantially above the 18.7% random baseline. Interpretable vulnerability heatmaps localize leakage sources at the gate level, enabling pre-silicon security assessment before fabrication.
Diagnosing Planning Quality in LLM-Based Penetration Testing Using Execution-Free Evaluation
Section: Original Research Categories: Intelligent Algorithms and Computational Models, Algorithm Engineering and Performance, Mathematical and Computational Intelligence Keywords: Array, Array, Array, Array, Array Subjects: Array, Array, Array Evaluating language model (LLM) penetration testing agents through execution conflates planning quality with environmental factors, including network latency, tool failures, and infrastructure variability. Analysis of AutoPenBench logs reveals that 40\% of agent failures arise from environmental timeouts rather than planning errors, obscuring the true diagnostic signal. We present an \textit{execution-free}, rubric-based evaluation framework that directly assesses the quality of LLM-generated attack plans along five independently scored dimensions: tool relevance, sequence logic, completeness, efficiency, and reasoning quality. Ten penetration testing scenarios spanning reconnaissance to post-exploitation are stratified by difficulty and evaluated across four models: GPT-4o, GPT-4o-mini, Llama-3.3-70B, and Qwen-2.5-14B—using automated scoring via Claude Sonnet 4.5 at temperature=0. Model scores range from 58\% to 74\%, substantially above the random baseline of 18\% but below the 96\% achieved by expert-generated reference plans, confirming a 78-percentage-point discriminative range. Dimension-level analysis reveals balanced performance across rubric criteria (means 7.0–7.3/10) with notable inter-model variation in efficiency (6.0–7.6) and reasoning quality (5.8–8.1). To strengthen validity, we discuss requirements for human expert validation and inter-rater reliability (target Krippendorff's $\alpha \geq 0.67$), multi-sample generation for variance estimation, and the inclusion of evasion and obfuscation scenarios. The framework offers a reproducible, infrastructure-independent diagnostic tool for model development and security education, intended to complement—not replace—execution-based benchmarks.
Mathematical and Computational Intelligence
Machine Learning for Chronic Disease Classification and Comorbidity Detection: Methodological Gaps and Future Directions
Section: Review Articles Categories: Intelligent Algorithms and Computational Models Keywords: Array, Array, Array, Array, Array Subjects: Array, Array The rapid proliferation of machine learning (ML) methods across clinical medicine has generated a rich but fragmented body of evidence for chronic disease classification. Despite consistently high reported accuracy, the literature is characterised by five systematic methodological limitations: exclusive reliance on binary single-disease classification tasks, absence of leakage-free preprocessing protocols, lack of non-parametric statistical validation, omission of probability calibration evaluation, and minimal integration of explainability frameworks. This narrative review critically examines 25 representative ML studies spanning musculoskeletal disorders (particularly disc herniation), inflammatory bowel conditions, fibromyalgia, cardiovascular disease, and related chronic comorbidities, published between 2012 and 2025. Studies are analysed thematically across algorithmic approach, task scope, class imbalance strategy, and methodological rigour. Algorithmic families represented include classical support vector machines and tree ensembles, deep learning architectures (CNN, LSTM, U-Net), optimization-enhanced methods (WOA, GGO, PSO, SO), and natural language processing models (RoBERTa). Across all 25 studies, performance metrics range from 82.47% to 99.9% accuracy, yet none simultaneously addresses multiclass comorbidity discrimination, leakage-free preprocessing, and model explainability. The review identifies five critical gaps and maps them to concrete future research directions, with particular emphasis on the unmet need for a unified multiclass framework capable of differential diagnosis among clinically overlapping chronic conditions within fibromyalgia populations. These findings suggest that current ML models are not yet clinically ready for the differential diagnosis of comorbid chronic conditions without methodological reform.
Evidence-Grounded Vision–RAG Framework for Clinically Reliable Visual Reasoning in Chest X-Ray Analysis
Section: Original Research Categories: Intelligent Algorithms and Computational Models Keywords: Array, Array, Array, Array, Array Subjects: Array, Array, Array Vision–language models have shown potential for medical image understanding tasks such as visual question answering (VQA); however, their clinical adoption is limited by diagnostic ambiguity, limited supervision, and the risk of generating hallucinated or clinically unsafe responses. To address these challenges, this paper proposes an evidence-grounded Vision Retrieval-Augmented Generation (Vision–RAG) framework for reliable visual reasoning in chest X-ray analysis. The framework integrates visual retrieval with evidence-aware language generation to support clinically grounded reasoning without task-specific supervised training. A pretrained vision encoder retrieves semantically similar chest X-ray images and corresponding radiology reports from the MIMIC-CXR dataset, providing external clinical evidence to guide the vision–language model. The retrieval index is built from the training split, and evaluation is performed on a held-out validation set for unbiased assessment. The system is evaluated using approximately 2,000 automatically generated clinical questions. Results demonstrate effective evidence retrieval, achieving a Recall@1 of 66.88%, while yes/no question accuracy reaches 56.8%, reflecting the inherent challenge of unsupervised medical reasoning. Concept-level analysis shows clear separation between normal and infectious cases, with most ambiguity occurring between overlapping conditions such as pleural effusion and consolidation. Importantly, the model exhibits conservative prediction behavior with low false-positive tendencies, highlighting clinical safety. These findings indicate that evidence-grounded Vision–RAG provides an interpretable and reliable paradigm for medical visual reasoning in chest X-ray analysis, supporting decision-making in clinical workflows rather than replacing human expertise.
Reliable Drug–Target Interaction Prediction Using Convolutional Neural Networks with Robust Negative Sample Generation
Section: Original Research Categories: Intelligent Algorithms and Computational Models, Algorithm Engineering and Performance, Emerging Algorithmic Technologies Keywords: Array, Array, Array, Array Subjects: Array, Array, Array - Proteins, including receptors, enzymes, and ion channels, represent primary biological targets whose interactions with small-molecule drugs play a critical role in therapeutic discovery and development. Accurate identification of drug–target interactions (DTIs) remains a fundamental challenge in drug discovery due to the high cost, time requirements, and scalability limitations of experimental validation. Consequently, computational approaches have emerged as efficient alternatives for large-scale DTI prediction. This study proposes a convolutional neural network (CNN) based framework for predicting drug–target interactions, with a particular focus on reliable negative sample generation to address the inherent data imbalance and uncertainty present in DTI datasets. The proposed method incorporates feature projection techniques to effectively capture meaningful representations of drug and protein features while reducing noise and redundancy. By constructing more reliable negative instances, the framework improves model robustness and mitigates bias commonly introduced by randomly generated negative samples. The proposed model is evaluated on a benchmark DTI dataset, where it achieves a classification accuracy of 0.9800, demonstrating strong predictive capability. To further assess generalization performance, the model is tested on an independent external dataset derived from DrugBank. On this dataset, the framework attains an accuracy of 0.8814 and an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.9527, indicating effective transferability across datasets. Experimental results confirm that the integration of CNN-based feature learning with reliable negative instance generation significantly enhances DTI prediction performance. The proposed framework offers a robust and generalizable computational tool for drug–target interaction prediction and has the potential to support early-stage drug discovery by reducing experimental search space and accelerating candidate prioritization.
A Deep Learning Framework for Breast Cancer Detection Using Histopathological Imaging
Section: Original Research Categories: Intelligent Algorithms and Computational Models, Algorithm Engineering and Performance Keywords: Array, Array, Array, Array, Array Subjects: Array, Array Breast cancer remains a significant global public health challenge, representing the most frequently diagnosed cancer among women and one of the leading causes of cancer-related mortality. Early detection and accurate diagnosis are critical for improving survival rates and expanding treatment options. Conventional diagnostic modalities such as mammography, ultrasound imaging, and biopsy are widely used; however, these approaches are often limited by inter-observer variability, image quality issues, and time-intensive clinical workflows. Recent advances in artificial intelligence, particularly deep learning, have demonstrated strong potential to support and enhance breast cancer detection through automated and consistent image analysis. This paper presents a comprehensive review of state-of-the-art deep learning techniques applied to breast cancer detection, with a focus on convolutional neural network–based approaches. In addition to the literature review, a practical implementation is presented to evaluate the effectiveness of deep learning in histopathological image classification. The proposed implementation employs the EfficientNetB3 architecture as a feature extraction backbone and is evaluated using the BreakHis dataset, which contains labeled breast histopathology images. The methodology includes image preprocessing, data augmentation, model training, and performance evaluation using standard validation metrics. Experimental results demonstrate that the proposed model achieves a validation accuracy of 97.4%, indicating strong discriminative capability and competitive performance relative to existing approaches reported in the literature. Despite these promising results, several challenges remain before widespread clinical adoption can be realized. These include ensuring model generalizability across diverse clinical settings, addressing data scarcity and class imbalance, improving interpretability to gain clinician trust, and navigating regulatory and ethical considerations. Overall, this study highlights both the progress and limitations of deep learning–based breast cancer detection systems and underscores their potential role as decision-support tools in future clinical workflows.
Unified Hybrid Self-Supervised Architecture Combining Contrastive and Non-Contrastive Learning for Multi-Level Visual Representation
Section: Original Research Categories: Intelligent Algorithms and Computational Models, Algorithm Engineering and Performance Keywords: Array, Array, Array, Array Subjects: Array, Array, Array, Array This paper presents a hybrid self-supervised learning framework for fine-grained visual representation using the STL-10 dataset. Addressing the challenges of limited labeled data and complex image variations, the proposed approach integrates four self-supervised paradigms, BYOL, SimCLR, MoCo v3, and DINO into a unified architecture. The model uses a shared backbone with projection and predictor heads, along with a target network updated via an exponential moving average (EMA), to extract robust representations from unlabeled images. Data augmentations, including random resized cropping (RRC), horizontal flipping, color jittering, and grayscale conversion, are employed to generate multiple correlated views for combined contrastive and non-contrastive learning. Experimental results demonstrate stable convergence with an average BYOL loss of –0.88. Downstream evaluation confirms the high quality of the learned embeddings, achieving a test accuracy of (87.20%), a recall of (87.20%), and an F1 score of (87.19%). Furthermore, the framework attained a mean Average Precision (mAP) of (93.70%), indicating highly discriminative and transferable feature representations. We demonstrate that hybrid self-supervision effectively leverages mutually complementary learning paradigms, yielding superior representation quality and faster convergence compared to single-method baselines.
Supervised Machine Learning Approaches for Multimodal Soil and Plant Health Monitoring in Precision Agriculture
Section: Original Research Categories: Intelligent Algorithms and Computational Models, Algorithm Engineering and Performance Keywords: Array, Array, Array, Array, Array Subjects: Array, Array Precision agriculture increasingly depends on intelligent data-driven systems to enhance crop productivity while supporting sustainable resource management. Early and accurate identification of plant stress is essential for timely intervention and improved yield. This study presents a supervised machine learning framework for real-time soil and plant health monitoring using multimodal sensor data. The proposed approach integrates environmental factors, soil parameters, and plant physiological measurements to provide a comprehensive assessment of crop health conditions. A heterogeneous dataset of sensor readings is utilized to classify plant health into three categories: Healthy, Moderate Stress, and High Stress. Four supervised machine learning algorithms, K-Nearest Neighbors (KNN), Decision Tree, Random Forest, and Gradient Boosting, are implemented, optimized, and evaluated using standard performance metrics, including accuracy and the Area Under the Receiver Operating Characteristic Curve (AUC). The evaluation strategy ensures reliable comparison across models. Experimental results demonstrate that ensemble-based models significantly outperform non-ensemble approaches in capturing the complex and nonlinear relationships present in agricultural sensor data. Random Forest and Gradient Boosting achieve near-perfect classification performance, with the Random Forest model attaining an AUC score of 1.00, indicating excellent class separability. The Decision Tree model also shows strong predictive capability, while the KNN model records comparatively lower accuracy of approximately 64%, reflecting its limited suitability for this application. The findings confirm the effectiveness of ensemble machine learning techniques as robust decision-support tools for precision agriculture and establish a practical baseline for model selection in real-time soil and plant health monitoring systems.
Adaptive Pod Autoscaling for Enhanced Quality of Service in On-Premise Kubernetes Environments
Section: Original Research Categories: Intelligent Algorithms and Computational Models Keywords: Array, Array, Array, Array, Array Disciplines: Array, Array Micro-services and cloud systems are developing into effective business tools nowadays. These container-based designs have made it possible to construct sophisticated SaaS applications efficiently. Managing and creating microservices with a wide range of different capabilities is a difficult task, from data processing and data warehousing to computing, prescriptive, and predictive analytics. Data centers made up of enormous, heterogeneous virtualized systems that are constantly expanding and diversifying over time are the foundation for computing service providers. Additionally, these technologies must be integrated with existing designs while adhering to Quality of Service (QoS) requirements. The primary objective of the proposed work is to provide an on-premises architecture based on Kubernetes and Docker.
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Leveraging Transfer Learning and Fine-Tuning for Improved Skin Cancer Detection in Dermatoscopic Images
Section: Original Research Categories: Emerging Algorithmic Technologies Keywords: Array, Array, Array, Array, Array, Array Disciplines: Array, Array This study presents a deep learning-based approach to improve skin cancer detection using dermoscopic images. The proposed system employs the ResNet50 architecture with transfer learning to enhance the classification accuracy of skin lesions. Training and evaluation were conducted on the HAM10000 dataset, with specialized techniques applied to mitigate class imbalance and improve model generalization. To ensure interpretability, the study integrates SHAP (Shapley Additive explanation), which identifies key image regions influencing classification decisions. The model leverages texture and morphological features extracted by the convolutional neural network (CNN) to distinguish between benign and malignant lesions effectively. A comprehensive evaluation framework was developed, incorporating random sampling and visualization methods to rigorously assess performance. The results demonstrate consistent accuracy across diverse lesion types, highlighting the model’s clinical applicability
Smart Irrigation Management System for Precision Agriculture and Climate Resilience
Section: Original Research Keywords: Array, Array, Array, Array Disciplines: Array, Array, Array In recent years, there have been significant climate alterations and substantial increases in pollution levels. The globe must prioritize the consideration of temperature fluctuations and increases in extreme weather phenomena. Unanticipated precipitation or it lack can significantly impact water levels, soil quality, crops, and food security. Conversely, adaptation may be as simple as enhancing efficiency through plug-and-play systems and implementing minimal modifications to irrigation management. This study aims to deliver a thorough examination of the global difficulties and potential impact of smart irrigation management systems on environmental preservation and sustainability attainment. These solutions are designed to build complementary frameworks that support modern administrations in boosting their adaptability, productivity, and overall efficiency. The document outlines the key environmental challenges, organized by likelihood and potential impact, and proposes an intelligent, user-friendly irrigation system for precision agriculture. This system is designed to maximize water efficiency in farming with minimal human involvement while incorporating soil characteristics and surrounding environmental factors.
5G OPPORTUNITIES AND IMPACT OF ELECTRIC VEHICLE PERFORMANCE WITH ARTIFICIAL INTELLIGENCE TOWARDS THE SMART CITIES
Section: Original Research Categories: Emerging Algorithmic Technologies Keywords: Array, Array, Array, Array Disciplines: Array, Array, Array This research aims to evolve the National Clean Air Program (NCAP) Operational Strategy, as announced by the Government of India in 2017; the wide-ranging epidemiological synecdoche is to resolve the air contamination concern at a 20% to 30% target of reduction in 2024. According to public authority guidelines, it is compulsory to convert approximately 25% of existing vehicles into electric vehicles by 2024. In the car area, vehicle planning and maintainability are focused on using Electric Vehicle innovation with zero discharge and the minimization of carbon. Electric Vehicles are more eco-accommodating along their lifecycle than traditional petroleum derivative vehicles, particularly on the off chance that they are powered with clean power. Further, current advancements like AI (Artificial Intelligence) and IoT have likewise improved the plan and development of Electric Vehicle manufacturing and battery charging innovation. The fundamental goal of computer-based intelligence is to provide closed-loop enhancement of electric vehicles in the areas of quick charging conventions for batteries, output control, monitoring, and preservation of force with an inspired way to a superior sustainable environment. The proposed work enumerates in detail the features of electric vehicles with the influence on 5G-IoT (Internet of Things) and comprehends advancement strategies of AI and ML to improve the highlights of EV in frequency band analysis with SNR and downlink power utilization.
Enhancing Drug-Target Interaction Prediction with CNN-Based Deep Learning and Systematic Encoding Strategies
Section: Original Research Categories: Intelligent Algorithms and Computational Models Keywords: Array, Array, Array, Array Disciplines: Array, Array, Array, Array - Drug-target interaction (DTI) prediction is a crucial task in drug discovery, aiming to identify novel therapeutic applications for existing drugs and reduce the time and cost associated with drug development. This study proposes a deep learning-based approach for DTI prediction, leveraging convolutional neural networks (CNNs) and advanced feature representation techniques. The methodology involves encoding drug-target pairs using a combination of drug molecular descriptors generated by PaDEL-Descriptor and protein sequence properties derived from the AAindex1 database. Moran autocorrelation is employed to capture the structural and functional characteristics of the proteins. The concatenated feature vectors are then projected onto a lower-dimensional space using random projection and reshaped into matrices to serve as inputs for the CNN model. The CNN architecture, based on LeNet-5, learns hierarchical feature representations from the input matrices. An ensemble of multiple CNN predictors is used to enhance prediction robustness and accuracy. The model is evaluated on benchmark datasets, demonstrating superior performance compared to traditional machine learning algorithms and existing deep learning-based methods. The proposed framework exhibits high accuracy, sensitivity, precision, and area under the curve (AUC) values, highlighting its effectiveness in capturing complex drug-target relationships. Furthermore, the model shows strong generalization ability on external validation datasets and benefits from a systematic approach for generating reliable negative samples. The results suggest that integrating deep learning with advanced feature representation techniques offers a promising approach to accelerating drug discovery and understanding drug mechanisms.