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 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 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 Editors: Dr. K. Martin Sagayam Associate Professor Department of Electronics and Communication Engineering SRM TRP Engineering college (SRM Group of Institutions) Trichy 621105 ========================= Dr. ِ Ahmed Hammad Faculty of Information Technology Department of Computer Science, Al-Isra University, Amman, Jordan. Email: ( editorialoffice@pub.scientificirg.com )
Scientific Innovation Research Group (SIRG)
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.
Evidence-Grounded Vision–RAG Framework for Clinically Reliable Visual Reasoning in Chest X-Ray Analysis
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 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.
Artificial Intelligence and Machine Learning
Algorithmic Applications in Emerging Technologies
Software Tools and Algorithmic Frameworks
Copyright (c) 2026 Journal of Smart Algorithms and Applications (JSAA) https://creativecommons.org/licenses/by/4.0
Reliable Drug–Target Interaction Prediction Using Convolutional Neural Networks with Robust Negative Sample Generation
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 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.
Intelligent Decision-Making and Control Systems
Computational Models and Interdisciplinary Applications
Drug–target interaction (DTI) prediction
A Deep Learning Framework for Breast Cancer Detection Using Histopathological Imaging
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 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: 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.
Supervised Machine Learning Approaches for Multimodal Soil and Plant Health Monitoring in Precision Agriculture
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 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: 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.
Copyright (c) 2025 Journal of Smart Algorithms and Applications https://creativecommons.org/licenses/by/4.0
Leveraging Transfer Learning and Fine-Tuning for Improved Skin Cancer Detection in Dermatoscopic Images
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
Smart Irrigation Management System for Precision Agriculture and Climate Resilience
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.
5G OPPORTUNITIES AND IMPACT OF ELECTRIC VEHICLE PERFORMANCE WITH ARTIFICIAL INTELLIGENCE TOWARDS THE SMART CITIES
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.
Enhancing Drug-Target Interaction Prediction with CNN-Based Deep Learning and Systematic Encoding Strategies
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 the robustness and accuracy of the predictions. 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 the integration of deep learning with advanced feature representation techniques offers a promising tool for accelerating drug discovery and understanding drug mechanisms.