Journal of Smart Algorithms and Applications (JSAA)
type: channel
description: 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 Frequency: 6 Volumes Annually Editor-in-Chief: Prof. Dr. Diego Alberto Oliva Navarro, University of Guadalajara in Mexico Email: diego.oliva@academicos.udg.mx 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 201…
publisher: Scientific Innovation Research Group (SIRG)
language: en
publication name: Journal of Smart Algorithms and Applications (JSAA)
issn: 3070-4189
copyright: Journal of Smart Algorithms and Applications (JSAA) content is published under a Creative Commons Attribution License (CCBY). 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.
items: Weight-Tied Adaptive Recursive Vision–Language–Action Transformer for Efficient Multimodal Robotic Control
items: Hierarchical Swin Transformer for Multi-Stage Dementia Diagnosis with Clinically-Grounded Visual Explainability
items: Automating COVID-19 Classification in Chest CT Scans Using Advanced CNN Architectures
items: Certainty-Aware Skin Lesion Segmentation with Post-Hoc Reliability Estimation for the Segment Anything Model
items: AI-Guided Grover Search for Simulation-Based Evaluation of Post-Quantum Security in CKKS Homomorphic Encryption
items: A Graph Neural Network Framework for Structural Side-Channel Vulnerability Assessment in Cryptographic Circuits
items: Diagnosing Planning Quality in LLM-Based Penetration Testing Using Execution-Free Evaluation
items: Machine Learning for Chronic Disease Classification and Comorbidity Detection: Methodological Gaps and Future Directions
items: Evidence-Grounded Vision–RAG Framework for Clinically Reliable Visual Reasoning in Chest X-Ray Analysis
items: Reliable Drug–Target Interaction Prediction Using Convolutional Neural Networks with Robust Negative Sample Generation
items: A Deep Learning Framework for Breast Cancer Detection Using Histopathological Imaging
items: Unified Hybrid Self-Supervised Architecture Combining Contrastive and Non-Contrastive Learning for Multi-Level Visual Representation
items: Supervised Machine Learning Approaches for Multimodal Soil and Plant Health Monitoring in Precision Agriculture
items: Adaptive Pod Autoscaling for Enhanced Quality of Service in On-Premise Kubernetes Environments
items: Leveraging Transfer Learning and Fine-Tuning for Improved Skin Cancer Detection in Dermatoscopic Images
items: Smart Irrigation Management System for Precision Agriculture and Climate Resilience
items: 5G OPPORTUNITIES AND IMPACT OF ELECTRIC VEHICLE PERFORMANCE WITH ARTIFICIAL INTELLIGENCE TOWARDS THE SMART CITIES
items: Enhancing Drug-Target Interaction Prediction with CNN-Based Deep Learning and Systematic Encoding Strategies

Weight-Tied Adaptive Recursive Vision–Language–Action Transformer for Efficient Multimodal Robotic Control
type: item
description: Section: Original Research Categories: Artificial Intelligence and Machine Learning, Software Tools and Algorithmic Frameworks Keywords: Vision-Language-Action, Robotic manipulation, Recursive transformers, Multimodal fusion, Embodied AI Subjects: Vision-Language-Action, Robotic manipulation, Embodied AI 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.…
subject: Original Research
  topic: section
subject: Artificial Intelligence and Machine Learning
  topic: category
subject: Software Tools and Algorithmic Frameworks
  topic: category
subject: Vision-Language-Action
  topic: keywords
subject: Robotic manipulation
  topic: keywords
subject: Recursive transformers
  topic: keywords
subject: Multimodal fusion
  topic: keywords
subject: Embodied AI
  topic: keywords
subject: Vision-Language-Action
  topic: subjects
subject: Robotic manipulation
  topic: subjects
subject: Embodied AI
  topic: subjects
creator: Howaida Allam
creator: Inam Ullah Khan
rights: Copyright (c) 2026 Journal of Smart Algorithms and Applications (JSAA) https://creativecommons.org/licenses/by/4.0
date: 2026-04-24
publication date: 2026-04-24
starting page: 36
ending page: 50
doi: 10.66279/pk83n728

Hierarchical Swin Transformer for Multi-Stage Dementia Diagnosis with Clinically-Grounded Visual Explainability
type: item
description: Section: Original Research Categories: Artificial Intelligence and Machine Learning, Nature-Inspired Computing and Optimization, Software Tools and Algorithmic Frameworks Keywords: Dementia Detection, Alzheimer’s Disease, Brain MRI, Swin Transformer, Explainable AI Subjects: Alzheimer’s Disease, Explainable AI 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.
subject: Original Research
  topic: section
subject: Artificial Intelligence and Machine Learning
  topic: category
subject: Nature-Inspired Computing and Optimization
  topic: category
subject: Software Tools and Algorithmic Frameworks
  topic: category
subject: Dementia Detection
  topic: keywords
subject: Alzheimer’s Disease
  topic: keywords
subject: Brain MRI
  topic: keywords
subject: Swin Transformer
  topic: keywords
subject: Explainable AI
  topic: keywords
subject: Alzheimer’s Disease
  topic: subjects
subject: Explainable AI
  topic: subjects
creator: Amr A. Hassanain
creator: Tan Wei Hong
creator: Rajeev Kumar
creator: Mona Ali Abdelrahman
rights: Copyright (c) 2026 Journal of Smart Algorithms and Applications (JSAA) https://creativecommons.org/licenses/by/4.0
date: 2026-04-24
publication date: 2026-04-24
starting page: 105
ending page: 116
doi: 10.66279/j4m1km41

Automating COVID-19 Classification in Chest CT Scans Using Advanced CNN Architectures
type: item
description: Section: Original Research Categories: Artificial Intelligence and Machine Learning, Computational Models and Interdisciplinary Applications, Algorithmic Applications in Emerging Technologies Keywords: Convolutional Networks, COVID-19, CT scan of the lungs, DensNet169, SARS-CoV Subjects: computer vision, Artificial Intelligence, Deep Learning, Healthcare Informatics, Medical Image Analysis 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%.
subject: Original Research
  topic: section
subject: Artificial Intelligence and Machine Learning
  topic: category
subject: Computational Models and Interdisciplinary Applications
  topic: category
subject: Algorithmic Applications in Emerging Technologies
  topic: category
subject: Convolutional Networks
  topic: keywords
subject: COVID-19
  topic: keywords
subject: CT scan of the lungs
  topic: keywords
subject: DensNet169
  topic: keywords
subject: SARS-CoV
  topic: keywords
subject: computer vision
  topic: subjects
subject: Artificial Intelligence
  topic: subjects
subject: Deep Learning
  topic: subjects
subject: Healthcare Informatics
  topic: subjects
subject: Medical Image Analysis
  topic: subjects
creator: Muhammad GamalEldin
creator: Hala Abdel Galil
creator: Hatem M. Noaman
creator: Hamdi A. Mahmoud
rights: Copyright (c) 2026 Journal of Smart Algorithms and Applications (JSAA) https://creativecommons.org/licenses/by/4.0
date: 2026-04-24
publication date: 2026-04-24
starting page: 117
ending page: 131
doi: 10.66279/p8hfyk02

Certainty-Aware Skin Lesion Segmentation with Post-Hoc Reliability Estimation for the Segment Anything Model
type: item
description: Section: Original Research Categories: Artificial Intelligence and Machine Learning, Intelligent Decision-Making and Control Systems, Algorithmic Applications in Emerging Technologies Keywords: Image Segmentation, Skin Lesion Segmentation, Pixel-wise Certainty Map, Reliability Estimation Subjects: Image Segmentation, Skin Lesion Segmentation, Reliability Estimation 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 propo…
subject: Original Research
  topic: section
subject: Artificial Intelligence and Machine Learning
  topic: category
subject: Intelligent Decision-Making and Control Systems
  topic: category
subject: Algorithmic Applications in Emerging Technologies
  topic: category
subject: Image Segmentation
  topic: keywords
subject: Skin Lesion Segmentation
  topic: keywords
subject: Pixel-wise Certainty Map
  topic: keywords
subject: Reliability Estimation
  topic: keywords
subject: Image Segmentation
  topic: subjects
subject: Skin Lesion Segmentation
  topic: subjects
subject: Reliability Estimation
  topic: subjects
creator: Hamdi A. Mahmoud
creator: Osamah Ibrahim Khalaf
creator: Ola Farid
rights: Copyright (c) 2026 Journal of Smart Algorithms and Applications (JSAA) https://creativecommons.org/licenses/by/4.0
date: 2026-04-24
publication date: 2026-04-24
starting page: 71
ending page: 86
doi: 10.66279/hzkw5y24

AI-Guided Grover Search for Simulation-Based Evaluation of Post-Quantum Security in CKKS Homomorphic Encryption
type: item
description: Section: Original Research Categories: Artificial Intelligence and Machine Learning, Intelligent Decision-Making and Control Systems, Software Tools and Algorithmic Frameworks Keywords: Fully Homomorphic Encryption, Quantum Cryptanalysis, Grover’s Algorithm, CKKS Scheme, Simulation-Based Security Analysis Subjects: Homomorphic Encryption, Quantum Cryptanalysis, Simulation-Based Security Analysis 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…
subject: Original Research
  topic: section
subject: Artificial Intelligence and Machine Learning
  topic: category
subject: Intelligent Decision-Making and Control Systems
  topic: category
subject: Software Tools and Algorithmic Frameworks
  topic: category
subject: Fully Homomorphic Encryption
  topic: keywords
subject: Quantum Cryptanalysis
  topic: keywords
subject: Grover’s Algorithm
  topic: keywords
subject: CKKS Scheme
  topic: keywords
subject: Simulation-Based Security Analysis
  topic: keywords
subject: Homomorphic Encryption
  topic: subjects
subject: Quantum Cryptanalysis
  topic: subjects
subject: Simulation-Based Security Analysis
  topic: subjects
creator: Howaida Allam
creator: Sonal Trivedi
rights: Copyright (c) 2026 Journal of Smart Algorithms and Applications (JSAA) https://creativecommons.org/licenses/by/4.0
date: 2026-04-24
publication date: 2026-04-24
starting page: 22
ending page: 35
doi: 10.66279/jaksw134

A Graph Neural Network Framework for Structural Side-Channel Vulnerability Assessment in Cryptographic Circuits
type: item
description: Section: Original Research Categories: Computational Models and Interdisciplinary Applications, Software Tools and Algorithmic Frameworks Keywords: Graph neural networks, Circuit topology, Hardware security, Side-channel analysis Subjects: Graph Neural Network, Cryptographic Circuits 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.
subject: Original Research
  topic: section
subject: Computational Models and Interdisciplinary Applications
  topic: category
subject: Software Tools and Algorithmic Frameworks
  topic: category
subject: Graph neural networks
  topic: keywords
subject: Circuit topology
  topic: keywords
subject: Hardware security
  topic: keywords
subject: Side-channel analysis
  topic: keywords
subject: Graph Neural Network
  topic: subjects
subject: Cryptographic Circuits
  topic: subjects
creator: Ola Farid
creator: Azidine Guezzaz
creator: Miguel Sahagun
rights: Copyright (c) 2026 Journal of Smart Algorithms and Applications (JSAA) https://creativecommons.org/licenses/by/4.0
date: 2026-04-24
publication date: 2026-04-24
starting page: 1
ending page: 21
doi: 10.66279/0e5j0983

Diagnosing Planning Quality in LLM-Based Penetration Testing Using Execution-Free Evaluation
type: item
description: Section: Original Research Categories: Artificial Intelligence and Machine Learning, Intelligent Decision-Making and Control Systems, Robotics, Automation, and Industrial Applications Keywords: Cybersecurity, Penetration Testing, Tool-Use Planning, Security Evaluation, LLM Agents Subjects: Cybersecurity, Penetration Testing, LLM Agents 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 educa…
subject: Original Research
  topic: section
subject: Artificial Intelligence and Machine Learning
  topic: category
subject: Intelligent Decision-Making and Control Systems
  topic: category
subject: Robotics, Automation, and Industrial Applications
  topic: category
subject: Cybersecurity
  topic: keywords
subject: Penetration Testing
  topic: keywords
subject: Tool-Use Planning
  topic: keywords
subject: Security Evaluation
  topic: keywords
subject: LLM Agents
  topic: keywords
subject: Cybersecurity
  topic: subjects
subject: Penetration Testing
  topic: subjects
subject: LLM Agents
  topic: subjects
creator: Howaida Allam
creator: Azidine Guezzaz
rights: Copyright (c) 2026 Journal of Smart Algorithms and Applications (JSAA) https://creativecommons.org/licenses/by/4.0
date: 2026-04-24
publication date: 2026-04-24
starting page: 51
ending page: 70
doi: 10.66279/enzxq198

Machine Learning for Chronic Disease Classification and Comorbidity Detection: Methodological Gaps and Future Directions
type: item
description: Section: Review Articles Categories: Artificial Intelligence and Machine Learning Keywords: Machine Learning;, Chronic Disease Classification, Comorbidity Detection, Fibromyalgia, Explainable AI Subjects: Chronic Disease Classification, Comorbidity Detection 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 m…
subject: Review Articles
  topic: section
subject: Artificial Intelligence and Machine Learning
  topic: category
subject: Machine Learning;
  topic: keywords
subject: Chronic Disease Classification
  topic: keywords
subject: Comorbidity Detection
  topic: keywords
subject: Fibromyalgia
  topic: keywords
subject: Explainable AI
  topic: keywords
subject: Chronic Disease Classification
  topic: subjects
subject: Comorbidity Detection
  topic: subjects
creator: Asmaa Atwa
creator: Ahmed Yehia Ismaeel
creator: Ahmed A. Elngar
rights: Copyright (c) 2026 Journal of Smart Algorithms and Applications (JSAA) https://creativecommons.org/licenses/by/4.0
date: 2026-04-24
publication date: 2026-04-24
starting page: 87
ending page: 104
doi: 10.66279/5a6sr902

Evidence-Grounded Vision–RAG Framework for Clinically Reliable Visual Reasoning in Chest X-Ray Analysis
type: item
description: Section: Original Research Categories: Artificial Intelligence and Machine Learning, Algorithmic Applications in Emerging Technologies, Software Tools and Algorithmic Frameworks Keywords: Medical Vision-Language Models, Retrieval-Augmented Reasoning, Visual Evidence Retrieval, Chest X-ray Interpretation, Clinical Decision Support Subjects: Grounded Vision, Visual Reasoning, Chest X-Ray 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 finding…
subject: Original Research
  topic: section
subject: Artificial Intelligence and Machine Learning
  topic: category
subject: Algorithmic Applications in Emerging Technologies
  topic: category
subject: Software Tools and Algorithmic Frameworks
  topic: category
subject: Medical Vision-Language Models
  topic: keywords
subject: Retrieval-Augmented Reasoning
  topic: keywords
subject: Visual Evidence Retrieval
  topic: keywords
subject: Chest X-ray Interpretation
  topic: keywords
subject: Clinical Decision Support
  topic: keywords
subject: Grounded Vision
  topic: subjects
subject: Visual Reasoning
  topic: subjects
subject: Chest X-Ray
  topic: subjects
creator: Shahd Ahmed
creator: Norhan Alaa
creator: Alaa Khaled
creator: Sama Ali
creator: Lara Zein
creator: Neelakrishnan Subramanian
rights: Copyright (c) 2026 Journal of Smart Algorithms and Applications (JSAA) https://creativecommons.org/licenses/by/4.0
date: 2026-02-08
publication date: 2026-02-08
starting page: 49
ending page: 60

Reliable Drug–Target Interaction Prediction Using Convolutional Neural Networks with Robust Negative Sample Generation
type: item
description: Section: Original Research Categories: Artificial Intelligence and Machine Learning, Intelligent Decision-Making and Control Systems, Computational Models and Interdisciplinary Applications Keywords: Drug–target interaction (DTI) prediction, Convolutional neural networks (CNNs), Area under the ROC curve (AUC), Negative instances Subjects: Drug–Target, Convolutional Neural Networks, Robust Negative Sample Generation - 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 transferabi…
subject: Original Research
  topic: section
subject: Artificial Intelligence and Machine Learning
  topic: category
subject: Intelligent Decision-Making and Control Systems
  topic: category
subject: Computational Models and Interdisciplinary Applications
  topic: category
subject: Drug–target interaction (DTI) prediction
  topic: keywords
subject: Convolutional neural networks (CNNs)
  topic: keywords
subject: Area under the ROC curve (AUC)
  topic: keywords
subject: Negative instances
  topic: keywords
subject: Drug–Target
  topic: subjects
subject: Convolutional Neural Networks
  topic: subjects
subject: Robust Negative Sample Generation
  topic: subjects
creator: Heba Saeed
creator: Mayar Adel
creator: Yasmeen Ataa
creator: Mayar Mohamed
creator: Hasnaa Ahmed
creator: Tan Wei Hong
creator: Niresh Jayarajan
rights: Copyright (c) 2026 Journal of Smart Algorithms and Applications (JSAA) https://creativecommons.org/licenses/by/4.0
date: 2026-02-08
publication date: 2026-02-08
starting page: 34
ending page: 48

A Deep Learning Framework for Breast Cancer Detection Using Histopathological Imaging
type: item
description: Section: Original Research Categories: Artificial Intelligence and Machine Learning, Intelligent Decision-Making and Control Systems Keywords: Breast cancer detection, Histopathology images, BreakHis dataset, Early tumor detection, Computer-aided diagnosis. Subjects: Breast cancer detection, Computer-aided diagnosis 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 t…
subject: Original Research
  topic: section
subject: Artificial Intelligence and Machine Learning
  topic: category
subject: Intelligent Decision-Making and Control Systems
  topic: category
subject: Breast cancer detection
  topic: keywords
subject: Histopathology images
  topic: keywords
subject: BreakHis dataset
  topic: keywords
subject: Early tumor detection
  topic: keywords
subject: Computer-aided diagnosis.
  topic: keywords
subject: Breast cancer detection
  topic: subjects
subject: Computer-aided diagnosis
  topic: subjects
creator: Shahd A. AbdElraouf
creator: Esraa Emad Ahmed
creator: Amer Ibrahim
creator: Eman Ebrahim Salah
creator: M. A. El-Mowafy
rights: Copyright (c) 2026 Journal of Smart Algorithms and Applications (JSAA) https://creativecommons.org/licenses/by/4.0
date: 2026-02-08
publication date: 2026-02-08
starting page: 26
ending page: 33

Unified Hybrid Self-Supervised Architecture Combining Contrastive and Non-Contrastive Learning for Multi-Level Visual Representation
type: item
description: Section: Original Research Categories: Artificial Intelligence and Machine Learning, Intelligent Decision-Making and Control Systems Keywords: Contrastive learning, Representation learning, Non-contrastive learning, Unlabeled image datasets Subjects: Self-Supervised, Contrastive learning, Non-contrastive learning, Visual Representations 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.
subject: Original Research
  topic: section
subject: Artificial Intelligence and Machine Learning
  topic: category
subject: Intelligent Decision-Making and Control Systems
  topic: category
subject: Contrastive learning
  topic: keywords
subject: Representation learning
  topic: keywords
subject: Non-contrastive learning
  topic: keywords
subject: Unlabeled image datasets
  topic: keywords
subject: Self-Supervised
  topic: subjects
subject: Contrastive learning
  topic: subjects
subject: Non-contrastive learning
  topic: subjects
subject: Visual Representations
  topic: subjects
creator: Ali N. K Alhejoh
creator: Ibrahim AbuNahleh
creator: Mohammed Hashem Almourish
rights: Copyright (c) 2026 Journal of Smart Algorithms and Applications (JSAA) https://creativecommons.org/licenses/by/4.0
date: 2026-02-08
publication date: 2026-02-08
starting page: 12
ending page: 25

Supervised Machine Learning Approaches for Multimodal Soil and Plant Health Monitoring in Precision Agriculture
type: item
description: Section: Original Research Categories: Artificial Intelligence and Machine Learning, Intelligent Decision-Making and Control Systems Keywords: Machine Learning, Precision Agriculture, Soil Health Monitoring, Plant Stress Detection, Ensemble Learning Subjects: Machine Learning, Agriculture 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 …
subject: Original Research
  topic: section
subject: Artificial Intelligence and Machine Learning
  topic: category
subject: Intelligent Decision-Making and Control Systems
  topic: category
subject: Machine Learning
  topic: keywords
subject: Precision Agriculture
  topic: keywords
subject: Soil Health Monitoring
  topic: keywords
subject: Plant Stress Detection
  topic: keywords
subject: Ensemble Learning
  topic: keywords
subject: Machine Learning
  topic: subjects
subject: Agriculture
  topic: subjects
creator: Alaa Khaled
creator: Fatma Saad
creator: K. Martin Sayagam
creator: Azidine Guezzaz
rights: Copyright (c) 2026 Journal of Smart Algorithms and Applications (JSAA) https://creativecommons.org/licenses/by/4.0
date: 2026-02-08
publication date: 2026-02-08
starting page: 1
ending page: 11

Adaptive Pod Autoscaling for Enhanced Quality of Service in On-Premise Kubernetes Environments
type: item
description: Section: Original Research Categories: Software Tools and Algorithmic Frameworks Keywords: -Kubernetes, -On-premises, -Quality of Service, -Docker, -Container Disciplines: -Kubernetes, Quality of Service 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.
subject: Original Research
  topic: section
subject: Software Tools and Algorithmic Frameworks
  topic: category
subject: -Kubernetes
  topic: keywords
subject: -On-premises
  topic: keywords
subject: -Quality of Service
  topic: keywords
subject: -Docker
  topic: keywords
subject: -Container
  topic: keywords
subject: -Kubernetes
  topic: disciplines
subject: Quality of Service
  topic: disciplines
creator: Vani Rajasekar 1
creator: Malliga S 2
creator: Gokula Krishnan K 3
creator: Sathya K 4
creator: Vandana Sharma *5
rights: Copyright (c) 2025 Journal of Smart Algorithms and Applications https://creativecommons.org/licenses/by/4.0
date: 2025-12-11
publication date: 2025-12-11
starting page: 43
ending page: 49

Leveraging Transfer Learning and Fine-Tuning for Improved Skin Cancer Detection in Dermatoscopic Images
type: item
description: Section: Original Research Categories: Computational Models and Interdisciplinary Applications Keywords: -Machine learning, -Skin cancer, -Diseases, -Patterns, -Skin detection, -Artificial intelligence Disciplines: Artificial intelligence, Skin cancer 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
subject: Original Research
  topic: section
subject: Computational Models and Interdisciplinary Applications
  topic: category
subject: -Machine learning
  topic: keywords
subject: -Skin cancer
  topic: keywords
subject: -Diseases
  topic: keywords
subject: -Patterns
  topic: keywords
subject: -Skin detection
  topic: keywords
subject: -Artificial intelligence
  topic: keywords
subject: Artificial intelligence
  topic: disciplines
subject: Skin cancer
  topic: disciplines
creator: Prashant Johri *1
creator: Ahmed Hammad 2
creator: Hegazi Ibrahim 3
rights: Copyright (c) 2025 Journal of Smart Algorithms and Applications https://creativecommons.org/licenses/by/4.0
date: 2025-12-11
publication date: 2025-12-11
starting page: 37
ending page: 42

Smart Irrigation Management System for Precision Agriculture and Climate Resilience
type: item
description: Section: Original Research Categories: Algorithmic Applications in Emerging Technologies Keywords: -Green technology, -smart irrigation, -precision agriculture, -Internet of Things Disciplines: Green technology, smart irrigation, Internet of Things 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.
subject: Original Research
  topic: section
subject: Algorithmic Applications in Emerging Technologies
  topic: category
subject: -Green technology
  topic: keywords
subject: -smart irrigation
  topic: keywords
subject: -precision agriculture
  topic: keywords
subject: -Internet of Things
  topic: keywords
subject: Green technology
  topic: disciplines
subject: smart irrigation
  topic: disciplines
subject: Internet of Things
  topic: disciplines
creator: Abdelmoty M. Ahmed 1
creator: Nesma Abd El-Mawla * 2
creator: Hegazi Ibrahim 3
rights: Copyright (c) 2025 Journal of Smart Algorithms and Applications https://creativecommons.org/licenses/by/4.0
date: 2025-12-11
publication date: 2025-12-11
starting page: 28
ending page: 36

5G OPPORTUNITIES AND IMPACT OF ELECTRIC VEHICLE PERFORMANCE WITH ARTIFICIAL INTELLIGENCE TOWARDS THE SMART CITIES
type: item
description: Section: Original Research Categories: Computational Models and Interdisciplinary Applications Keywords: Future road-map, Electric Vehicle, Artificial Intelligence, IoT Disciplines: Electric Vehicle, Artificial Intelligence, IoT 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.
subject: Original Research
  topic: section
subject: Computational Models and Interdisciplinary Applications
  topic: category
subject: Future road-map
  topic: keywords
subject: Electric Vehicle
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: IoT
  topic: keywords
subject: Electric Vehicle
  topic: disciplines
subject: Artificial Intelligence
  topic: disciplines
subject: IoT
  topic: disciplines
creator: E.B. Priyanka 1*
creator: Thangavel 2
creator: K. Martin Sagayam 3
rights: Copyright (c) 2025 Journal of Smart Algorithms and Applications https://creativecommons.org/licenses/by/4.0
date: 2025-12-11
publication date: 2025-12-11
starting page: 17
ending page: 27

Enhancing Drug-Target Interaction Prediction with CNN-Based Deep Learning and Systematic Encoding Strategies
type: item
description: Section: Original Research Categories: Artificial Intelligence and Machine Learning Keywords: Drug-Target Interaction Prediction, - Convolutional Neural Networks (CNNs), - Feature Representation Techniques, - Deep Learning Disciplines: Drug-Target, Deep Learning, Protein Families, Feature Representation Techniques - 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 representati…
subject: Original Research
  topic: section
subject: Artificial Intelligence and Machine Learning
  topic: category
subject: Drug-Target Interaction Prediction
  topic: keywords
subject: - Convolutional Neural Networks (CNNs)
  topic: keywords
subject: - Feature Representation Techniques
  topic: keywords
subject: - Deep Learning
  topic: keywords
subject: Drug-Target
  topic: disciplines
subject: Deep Learning
  topic: disciplines
subject: Protein Families
  topic: disciplines
subject: Feature Representation Techniques
  topic: disciplines
creator: Abdelmoty M. Ahmed1
creator: Heba Saeed 2
creator: Mayar Adel 3
creator: Yasmeen Ataa 4
creator: Mayar Mohamed 5
creator: Hasnaa Ahmed *6
rights: Copyright (c) 2025 Journal of Smart Algorithms and Applications https://creativecommons.org/licenses/by/4.0
date: 2025-12-11
publication date: 2025-12-11
starting page: 5
ending page: 16