Journal of Applied Science and Technology Trends
type: channel
description: Welcome to the Journal of Applied Science and Technology Trends (JASTT) [ISSN: 2708-0757 | Indexed in: Scopus, CrossRef, Google Scholar] The Journal of Applied Science and Technology Trends (JASTT) is an international, peer-reviewed, and fully open-access journal committed to publishing high-quality research that advances the frontiers of applied science, engineering, and emerging technologies. We provide a dynamic platform for scientists, engineers, technologists, and practitioners to share innovative solutions that address real-world challenges across disciplines such as: Engineering and Technology Environmental and Earth Sciences Computing, AI, and Data Science Applied Life Sciences and Biomedical Engineering Materials and Chemical Engineering Urban Planning, Urban Systems, Transport, and Infrastructure Interdisciplinary and Future-Oriented Technologies All articles published in JASTT are made freely and permanently accessible online under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License (CC BY-NC-SA 4.0). Key Features: Indexed in Scopus and other international databases Rigorous double-blind peer review Fast decision time and transparent publishing process Encouragement of interdisciplinary and collaborative research Special Issues on cutting-edge and cross-disciplinary topics JASTT is published by Interdisciplinary Publishing Academia (IPAcademia) and welcomes submissions from researchers, academics, and professionals globally. We are committed to advancing open science, fostering collaboration, and disseminating knowledge that leads to meaningful technological and societal impact. Submit your manuscript today and join our growing global community of authors and reviewers.
publisher: Interdisciplinary Publishing Academia
language: en
publication name: Journal of Applied Science and Technology Trends
issn: 2708-0757
copyright: Authors who publish with this journal agree to the following terms: Authors retain copyright and grant the journal right of first publication, with the work simultaneously licensed under a Creative Commons Attribution License [CC BY-NC-SA 4.0] that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work, with an acknowledgment of its initial publication in this journal. Authors are permitted and encouraged to post their work online.
items: Intelligent Resource Management and Secure Live Migration in Cloud Environments: A Unified Approach using Particle Swarm Optimization, Machine Learning, and Blockchain on XenServer
items: Real-Time Secure Cloud Transmission Framework for Agricultural Surveillance Using YOLOv5, AES-256, and HMAC-SHA256
items: Physics-Informed Machine Learning Framework for Virtual Screening and Multi-Objective Optimization of Polymer Nanocomposites with Tailored Multifunctional Properties
items: Performance Comparisons Between the Semiconductor Optical Amplifier (SOA) and the Erbium-Doped Fiber Amplifier (EDFA)
items: Adaptive Federated Learning Empowered Wireless Localization Framework Using Vehicle Sensors
items: Designing Remote-Sensed Intelligent Visual Analytics Algorithms for Environmental Monitoring Systems
items: A Proposed Method for Real-Time Automatic Cloud Storage and Analysis of Detected Suspicious Activities to Ensure Data Integrity and Security
items: Window-Based Vision Transformer Network Implementation For Multi-Label Image Classification In Remote Sensing
items: A Hybrid CBIR Framework Using Vision Transformers and Genetic Algorithm for Enhanced Image Retrieval
items: Alzheimer’s Classification with a MaxViT-Based Deep Learning Model Using Magnetic Resonance Imaging
items: Handwritten Recognition of Telugu Characters Using Various Pretrained Convolutional Neural Networks
items: A Hybrid LLM–Knowledge Graph Framework for Accurate Biomedical Question Answering
items: Auto-Tuned Particle Swarm with perturbed velocities - Optimized CLMF Algorithm for DVR based Power Quality Improvement
items: Pancreatic Cancer Detection Using Quaternion Wavelet Transform and Squeeze-and-Excitation Network with SVM Classifier
items: Morphotectonic Controls on the Geomorphic Evolution: A Case Study of Diyala River Basin
items: A Machine Learning Approach to Consumer Behavior Analysis in Social Media-Influenced E-Book Markets
items: An Adaptive State-Augmented Kalman Filter for Robust UAV Altitude Control with Online Sensor Bias Correction and Dynamic Weighting in Degraded Environments
items: SALF: A Blockchain-Based Framework for Scalable Academic Credential Management and Institutional Governance
items: The Impact of Urban Street Characteristics on Walking Attractiveness and Humanization: A Study of The Streets of Kut City Center
items: Feature-Based Child Mortality Prediction Using Ensemble and Traditional Machine Learning Models
items: Software Effort Estimation Using Stacking Ensemble and Bayesian Optimization
items: AppleVit: A Smart Agricultural Software for Apple Leaf Disease Detection Using AI
items: Evaluation of Groundwater Potential Zone in Selected Coastal and Non-Coastal Regions of Nigeria
items: OptiShip: Predictive Freight Cost Modeling Incorporating Regional Fuel Variability
items: Empirical Review of Urban Traffic Challenges and Sustainable Solutions: A Case Study of Muscat, Oman
items: Enhanced algorithm based on Chio-like Method for Non-Square Determinant Calculations for application in CBVR
items: Adaptive Detection and Response System for Post-Installation Cyber Attacks on Smartphones
items: Evaluating Green Space Design Alternatives Using Sustainable Thresholds: A Case Study of Bayti Residential Complex, Najaf, Iraq
items: AQUAPHISH: Leveraging Metaheuristics and Automated Machine Learning for Precision Phishing Detection
items: Text Classification for Clickbait Detection: A Model-Driven Approach Using CountVectorizer and ML Classifiers

Intelligent Resource Management and Secure Live Migration in Cloud Environments: A Unified Approach using Particle Swarm Optimization, Machine Learning, and Blockchain on XenServer
type: item
description: Cloud computing has become the backbone of digital ecosystems, but growing workloads intensify challenges in resource optimization, virtual machine (VM) migration, and security assurance. Existing studies often address these issues in isolation, limiting their practical applicability. This paper presents a unified framework that integrates three complementary components: (i) an Improved Modified Particle Swarm Optimization (IMPSO) algorithm with adaptive inertia scheduling and dynamic mutation control, which outperforms IPSO in convergence speed and load distribution accuracy; (ii) a machine learning–assisted hybrid live VM migration method with dirty-page clustering and workload prediction to minimize downtime; and (iii) a blockchain-enabled secure migration layer to ensure tamper-proof and auditable state transfer. The revised version of this study includes statistical validation (confidence intervals, t-tests) and attack simulation experiments (e.g., man-in-the-middle and replay attacks) to ensure methodological rigor and realistic security assessment. Experimental results on a real XenServer testbed show that the proposed system improves response time by ~30%, reduces migration downtime by ~60%, and ensures 100% migration integrity with ?15% security overhead. Overall, this work represents among the first unified frameworks that jointly optimize resource allocation, downtime reduction, and blockchain-based security in a practically validated, end-to-end cloud migration environment.
subject: Standard Journal Issues
  topic: section
subject: Cloud Computing
  topic: keywords
subject: Load Balancing
  topic: keywords
subject: Virtual Machine Migration
  topic: keywords
subject: Blockchain Security
  topic: keywords
subject: Computer Engineering
  topic: disciplines
creator: Akashbhai Dave
rights: Copyright (c) 2025 Akashbhai Ashokbhai Dave https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-11-17
publication date: 2025-11-17
starting page: 393
ending page: 407
doi: 10.38094/jastt62530

Real-Time Secure Cloud Transmission Framework for Agricultural Surveillance Using YOLOv5, AES-256, and HMAC-SHA256
type: item
description: The agricultural surveillance systems produce unending floods of delicate visual information that needs to be sent safely to cloud-based infrastructures to be subjected to real-time interpretation and decision-making processes. The current paper introduces a new real-time, lossless, and secure edge-to-cloud transmission model which combines YOLOv5-based event detection, AES-256 symmetric encryption, and HMAC-SHA256 integrity checks and verifies into a single system. The system identifies critical events and encrypts locally and sends only the authenticated data to the cloud thus maintaining confidentiality, integrity and availability. It was evaluated on a custom dataset of 8,000 agricultural images with an average encryption time of 0.17 s/image, decryption time of 0.16 s/image and SSIM of 1.00, which validates the lossless image quality. YOLOv5 model attained 98.5 percent mean average precision (mAP @0.5), which guarantees correct detection prior to encryption. Comparison shows that the suggested approach is faster, more scalable, and more robust in comparison with the existing machine learning and standalone encryption systems. The model provides a scalable architecture of safe, smart farming surveillance, which will form the foundation of future updates pertaining to post-quantum encryption and federated edge learning in precision farming.
subject: Special Issue: Remote Sensing based Intelligent Visual Analytics for Real-time Environmental and Earth Monitoring Systems
  topic: section
subject: Smart Agriculture;
  topic: keywords
subject: Cloud Security
  topic: keywords
subject: AES-256
  topic: keywords
subject: HMAC-SHA256;
  topic: keywords
subject: Real-Time Transmission
  topic: keywords
subject: Edge Computing
  topic: keywords
subject: Data Integrity
  topic: keywords
subject: Precision Farming
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: remote sensing
  topic: disciplines
creator: Himanshu
creator: Harwant Singh Arri
creator: Ravi Kumar
creator: Vishal Kumar Singh
creator: Aman Deep
creator: Akash Badhan
rights: Copyright (c) 2025 Himanshu, Harwant Singh Arri, Ravi Kumar, Vishal Kumar Singh, Aman Deep, Akash Badhan https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-11-05
publication date: 2025-11-05
starting page: 15
ending page: 24
doi: 10.38094/jastt605582

Physics-Informed Machine Learning Framework for Virtual Screening and Multi-Objective Optimization of Polymer Nanocomposites with Tailored Multifunctional Properties
type: item
description: The rational design of polymer nanocomposites with tailored multifunctional properties remains challenging due to complex multi-scale physics and the limitations of traditional empirical approaches, which cannot adequately capture the combinatorial interactions between polymer matrices, nanofillers, and processing conditions. We present a new computational framework for cost-effective virtual screening and optimization of polymer nanocomposites with physically consistent prediction in this series. In a physics-informed neural network, we suggest a combination of the quantum mechanical response, as well as standard molecular dynamics and thermodynamic data. (1) Physics-aware loss functions that incorporate conservation policies and thermodynamic constraints; (2) multiscale descriptor integration of quantum to macroscales; (3) ensemble learning is supplemented by tools to distinguish epistemic and aleatoric uncertainty; and (4) NSGA-III assisted multi-objective optimization coupled with adaptive reference point generation. The neural network architecture consists of multi-branch pathways with 5 hidden layers (256, 512, 512, 256, 128 neurons) using Leaky ReLU activation functions, trained on 23,847 polymer nanocomposite formulations using Adam optimizer (learning rate: 0.001, batch size: 64) with cosine annealing scheduling. The framework achieves prediction accuracies of R² > 0.94 for mechanical properties, R² > 0.91 for thermal characteristics, and R² > 0.88 for electrical conductivity, representing 15-25% improvements over conventional machine learning methods. Virtual screening of 3.2 million candidate formulations identified 1,847 compositions with superior performance. Our NSGA-III optimization identifies Pareto-optimal solutions with 34% higher multifunctional performance than conventional approaches, while reducing experimental validation requirements by 82%. Experimental validation of 127 compositions confirms 89% prediction accuracy within confidence interval…
subject: Standard Journal Issues
  topic: section
subject: polymer nanocomposites
  topic: keywords
subject: physics-informed neural networks
  topic: keywords
subject: multi-objective optimization
  topic: keywords
subject: virtual screening
  topic: keywords
subject: materials informatics
  topic: keywords
subject: computational materials science
  topic: keywords
subject: Computer Science
  topic: disciplines
subject: Artificial Intelligence
  topic: disciplines
creator: Sandeep Gupta
creator: Budesh Kanwer
creator: Udit Mamodiya
creator: Saurabh Shandilya
creator: Deepshikha Bhatia
creator: Nitesh Naik
rights: Copyright (c) 2025 Sandeep Gupta, Budesh Kanwer, Udit Mamodiya, Saurabh Shandilya, Deepshikha Bhatia, Nitesh Naik https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-10-22
publication date: 2025-10-22
starting page: 358
ending page: 371
doi: 10.38094/jastt62459

Performance Comparisons Between the Semiconductor Optical Amplifier (SOA) and the Erbium-Doped Fiber Amplifier (EDFA)
type: item
description: This study provides a comprehensive comparison between erbium-doped fiber amplifiers (EDFAs) and semiconductor optical amplifiers (SOAs), focusing on their potential suitability for long-haul optical transmission. Using identical configurations and data rates in OptiSystem simulations, performance metrics such as gain, Q-factor, and signal-to-noise ratio (SNR) were systematically analyzed over a wide span of input powers. Saturation analysis revealed that SOAs can achieve comparable or even superior gain to EDFAs under certain operating points. The results indicate that SOAs deliver significantly higher Q-factors before deep saturation occurs, with observed improvements of up to 25 dB at an input level of 11.9 dBm. Similarly, the SNR of SOAs exceeded that of EDFAs by nearly 20 dB under identical conditions. These findings suggest that, when biased in the appropriate regime, SOAs hold strong potential for deployment in next-generation long-haul optical systems.
subject: Standard Journal Issues
  topic: section
subject: Semiconductor Optical Amplifier (SOA)
  topic: keywords
subject: Erbium-Doped-Fiber-Amplifier (EDFA)
  topic: keywords
subject: Q-factor
  topic: keywords
subject: Computer Engineering
  topic: disciplines
creator: David Forsyth
creator: Kanar Tariq
creator: Ahmed Al-Gburi
creator: Riyam Johni
rights: Copyright (c) 2025 David I. Forsyth, Kanar R. Tariq, Ahmed Jamal Abdullah Al-Gburi, Riyam Alaa Johni https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-11-03
publication date: 2025-11-03
starting page: 372
ending page: 377
doi: 10.38094/jastt62472

Adaptive Federated Learning Empowered Wireless Localization Framework Using Vehicle Sensors
type: item
description: Currently, wireless localization plays a vital role in supporting a wide range of tasks within smart cities. For example, vehicle tracking services are increasingly used in tunnels and bridges to detect objects and prevent collisions. Both indoor and outdoor localization are equally important for applications such as traffic management and vehicle positioning. However, existing localization techniques still face significant challenges, particularly with respect to accuracy, latency, and resource consumption. Addressing these limitations is therefore essential to ensure reliable and efficient operation in smart city environments. This study proposes an Adaptive Federated Learning–Enabled Wireless Localization Framework (ALFLS) designed specifically for mobility-based vehicle tasks. The novelty of ALFLS lies in its ability to apply pattern learning for both indoor and outdoor localization, leveraging federated learning to execute tasks while maintaining high quality of service. The framework also incorporates an optimized placement strategy for edge and cloud node resources, supported by a training algorithm that enhances real-time localization accuracy. To evaluate performance, experimental localization datasets were tested on a benchmark testbed, highlighting the practical benefits of ALFLS. The simulation results demonstrate that the framework improves localization accuracy by up to 98%, reduces latency by approximately 30%, and achieves significantly higher resource utilization compared to existing methods. These results confirm that ALFLS provides a robust, efficient, and scalable solution for addressing the persistent challenges of wireless localization in smart city environments.
subject: Standard Journal Issues
  topic: section
subject: Federated learning, Wireless Localization Framework, global positioning system, Wearable Sensors, cloud, localization.
  topic: keywords
subject: Computer Science
  topic: disciplines
creator: Mazin Abed Mohammed
creator: Mohd Khanapi Abd Ghani
creator: Sajida Memon
creator: Abdullah Lakhan
creator: Haydar Abdulameer Marhoon
creator: Ahmed Radhi
rights: Copyright (c) 2025 Mazin Abed Mohammed, Mohd Khanapi Abd Ghani, Sajida Memon, Abdullah Lakhan, Haydar Abdulameer Marhoon, Ahmed Dheyaa Radhi https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-10-02
publication date: 2025-10-02
doi: 10.38094/jastt62464

Designing Remote-Sensed Intelligent Visual Analytics Algorithms for Environmental Monitoring Systems
type: item
description: Increasing climate variability and the rapid degradation of natural ecosystems have necessitated the development of intelligent systems that can track and assess environmental changes in real-time. By combining multi-modal remote sensing data with advanced machine learning and visual analytics techniques, this paper introduces a novel framework for Remote-Sensed Intelligent Visual Analytics (RS-IVA), which aims to improve environmental monitoring systems. To offer a comprehensive, scalable, and adaptable monitoring system, the proposed framework utilizes ground sensor inputs, UAV-based aerial photography, and high-resolution satellite imaging. To identify anomalies such as deforestation, urbanization, water pollution, and changes in air quality, a hybrid deep learning-based algorithm is employed. Explainable AI (XAI) elements make sure that the decision-making process is transparent and accessible. To assist stakeholders, investigate spatiotemporal patterns, forecast environmental hazards, and enhance evidence-based policy decisions, an interactive visual analytics dashboard is being developed. Experiments using benchmark datasets demonstrate that the system is highly accurate in identifying significant environmental changes and exhibits greater adaptability across a wide range of climatic and geographic regions. Intelligent analytics and remote sensing technologies collaborate to improve situational awareness and provide early warnings for sustainable resource planning and disaster management. This research advances the development of next-generation innovative environmental monitoring systems by integrating human-in-the-loop visualization, AI-driven analytics, and remote sensing for informed ecological governance.
subject: Special Issue: Remote Sensing based Intelligent Visual Analytics for Real-time Environmental and Earth Monitoring Systems
  topic: section
subject: Remote Sensing
  topic: keywords
subject: Intelligent Visual Analytics
  topic: keywords
subject: Environmental Monitoring Systems
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: XAI
  topic: keywords
subject: Spatiotemporal Analysis
  topic: keywords
subject: UAV and Satellite Imagery
  topic: keywords
subject: Remote sensing applications
  topic: disciplines
creator: Priya R
creator: Rajiv R Bhandari
creator: Vilas Namdeo Nitnaware
creator: Ramesh P N
creator: Sivakumar R
creator: Dhanapal R
rights: Copyright (c) 2025 Priya R, Rajiv R Bhandari, Vilas Namdeo Nitnaware, Ramesh P N, Sivakumar R, Dhanapal R https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-10-22
publication date: 2025-10-22
starting page: 01
ending page: 14
doi: 10.38094/jastt605480

A Proposed Method for Real-Time Automatic Cloud Storage and Analysis of Detected Suspicious Activities to Ensure Data Integrity and Security
type: item
description: Cloud computing provides flexibility and scalability to enterprises but also poses devastating threats such as data leakage and unauthorized access. This study presents a prototypical security framework of a hybrid cloud system that combines the use of machine learning-based anomaly detection and cryptographic techniques regarding the confidentiality, integrity, and availability (CIA) of dynamic cloud storage to preserve data integrity. The framework uses Random Forest (RF) feature selection and Deep Neural Networks (DNN) to detect anomalies and design a real-time, scalable framework to identify suspicious behavior, supported by a hybrid cryptographic model using Attribute-Based Encryption (ABE) and lightweight algorithms. It is CICIDS2017 and UNSW-NB15 validated and is deployed in AWS and Azure testbeds. Hybrid implemented a high detection rate (98.3 percent) and a low false positive rate (1.2%), and cryptographic operations had an average loading of 4.3 ms/MB. The hybrid model showed better results when compared to standalone RF (95.8%) and DNN (97.6%). Comparisons with other recent articles prove that performance is competitive, although some articles reported accuracy rates above 99.9%. The results establish that the hybrid solution provides high detection, effective cryptography, and great scalability in the context of a multi-cloud environment. Banking, healthcare enterprises and government agencies are examples of enterprises that can implement this framework to limit the risks and realize secure real-time operation. It is possible that future studies can develop quantum-resistant cryptography and federated learning combinations.
subject: Standard Journal Issues
  topic: section
subject: Data Integrity
  topic: keywords
subject: Cryptography
  topic: keywords
subject: Cloud Security
  topic: keywords
subject: Hybrid Anomaly Detection
  topic: keywords
subject: Multi-Cloud Framework
  topic: keywords
subject: Computer Science
  topic: disciplines
subject: Data Science
  topic: disciplines
creator: Himanshu
creator: Harwant Singh Arri
rights: Copyright (c) 2025 Himanshu, Harwant Singh Arri https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-09-17
publication date: 2025-09-17
starting page: 262
ending page: 276
doi: 10.38094/jastt62256

Window-Based Vision Transformer Network Implementation For Multi-Label Image Classification In Remote Sensing
type: item
description: Swift process in technology and widespread availability of low-cost internet have led to a substantial rise in data volume in remote sensing, especially for high-resolution and very-high resolution images. Still, these images contain more complex information, and it is not appropriate to analyze the images using a solitary scene-level label while ignoring the distinct features provided by other labels in the images. In multi-label image classification applications, multiple labels are assigned to an image, reflecting various objects or features present in the scene. The classification of these images is critically important for monitoring environmental changes over large geographical areas, disaster management, urban planning, agriculture and forestry management, natural resource conservation, and military intelligence. Nowadays, many methods are used in such image classification problems, primarily deep learning algorithms. However, current deep learning approaches for multi-label remote sensing images often struggle to capture both local fine-grained details and global contextual relationships simultaneously, leaving a gap for models that can efficiently integrate these complementary representations. In this study, advanced neural networks are explored and evaluated for Multi-label AID dataset which contains 3000 images and 17 different labels; AlexNet, VGG16, DenseNet-201, Inception-v3 and ConvNeXt as the CNN models, ViT, SwinT as transformer models and MaxViT as the hybrid model that initially contains both CNN and transformer network. OneCycleLR as scheduler and AsymmetricLoss (ASL) as loss function are employed for each model to systematically evaluate their impact on model performance. MaxViT was chosen because its multi-scale window-based attention can jointly model local and global dependencies, making it particularly suitable for the complex spatial patterns in remote-sensing imagery compared with other hybrid architectures. The window-based MaxViT algorit…
subject: Standard Journal Issues
  topic: section
subject: Remote Sensing (RS)
  topic: keywords
subject: MaxViT
  topic: keywords
subject: Scene Classification
  topic: keywords
subject: Multi-label AID
  topic: keywords
subject: OneCycleLR
  topic: keywords
subject: Computer Science
  topic: disciplines
subject: Machine Learning
  topic: disciplines
subject: artificial intelligence
  topic: disciplines
subject: Remote Sensing
  topic: disciplines
creator: Emre Akkas
creator: Selda Güney
rights: Copyright (c) 2025 Emre Akkas,, Selda Güney https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-11-07
publication date: 2025-11-07
starting page: 384
ending page: 392
doi: 10.38094/jastt62393

A Hybrid CBIR Framework Using Vision Transformers and Genetic Algorithm for Enhanced Image Retrieval
type: item
description: Content-Based Image Retrieval (CBIR) is an essential tool for arranging and acquiring visual content from large-scale image databases. This research presents a robust hybrid CBIR structure that combines transformer-based deep feature extraction with Genetic Algorithm (GA) optimization to significantly improve retrieval accuracy and efficiency. The proposed system introduces Vision Transformers (ViT) to efficiently capture intricate, global visual figures over the distinctive image categories, supporting both single and multi-object image retrieval scenarios. By influencing the long-range dependency modelling abilities of transformers, the system extracts highly different feature representations. These elements are further optimized with the help of Genetic Algorithm, a powerful adaptive technique that efficiently enhances feature selection and matching through iterative selection, crossover, and mutation processes. Comprehensive experiments were performed on the Corel 1K benchmark dataset illustrates the proposed hybrid model surpasses conventional CBIR model in terms of precision, recall, accuracy, and F1-score. The system achieves a retrieval accuracy of 99.38%, an F1-score of 95.12%, and a reduced error rate of 0.62%, showcasing its superior retrieval performance and computational efficiency. The results highlight the potential of integrating transformer-based deep learning with evolutionary optimization in advancing modern CBIR systems.
subject: Standard Journal Issues
  topic: section
subject: Genetic Algorithm
  topic: keywords
subject: Vision Transformer
  topic: keywords
subject: Feature extraction
  topic: keywords
subject: Corel 1k
  topic: keywords
subject: Optimization
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Computer Science
  topic: disciplines
creator: P Deekshita
creator: Vandana Bonu
creator: Areman Ramyasri
creator: Vaddadi Rani
creator: Keerthana Bodduru
creator: Nagarjuna Karyemsetty
rights: Copyright (c) 2025 P. Deekshita, Vandana Bonu, Areman Ramyasri, Vaddadi Vasudha Rani, Bodduru Keerthana, Nagarjuna Karyemsetty https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-09-30
publication date: 2025-09-30
starting page: 277
ending page: 289
doi: 10.38094/jastt62304

Alzheimer’s Classification with a MaxViT-Based Deep Learning Model Using Magnetic Resonance Imaging
type: item
description: Alzheimer’s disease (AD), a progressive neurodegenerative disorder, poses significant challenges for early diagnosis due to subtle symptom onset and overlap with normal aging. This study aims to develop an effective deep learning model for classifying four AD stages (Non-Demented, Very Mild Demented, Mild Demented, Moderate Demented) using brain MRI scans. We propose a Multi-Axis Vision Transformer (MaxViT)-based framework, leveraging transfer learning and robust data augmentation on the Kaggle Alzheimer’s MRI Dataset to address class imbalance and enhance generalization. The model employs MaxViT’s multi-axis attention mechanisms to capture both local and global patterns in MRI images. Our approach achieved a classification accuracy of 99.60%, with precision of 99.0%, recall of 98.1%, and F1-score of 98.51%. These results highlight MaxViT’s superior ability to differentiate AD stages, particularly in distinguishing challenging early stages. The proposed model offers a reliable tool for early AD diagnosis, laying a strong foundation for future clinical applications and interdisciplinary research in neurodegenerative disease detection. Future work should explore larger, more diverse datasets and additional biomarkers to further validate and enhance model performance.
subject: Standard Journal Issues
  topic: section
subject: alzheimer
  topic: keywords
subject: classification
  topic: keywords
subject: ddeep learning
  topic: keywords
subject: transfer learning
  topic: keywords
subject: MaxVit
  topic: keywords
subject: MRI
  topic: keywords
subject: Computer Science
  topic: disciplines
creator: Emrah Aslan
creator: Süheyla Demirtas Alpsalaz
creator: Y?ld?r?m Özüpak
creator: Feyyaz Alpsalaz
creator: Hasan Uzel
rights: Copyright (c) 2025 Emrah Aslan, Süheyla Demirtas Alpsalaz, Yildirim Özüpak, Feyyaz Alpsalaz, Hasan Uzel https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-10-03
publication date: 2025-10-03
doi: 10.38094/jastt62453

Handwritten Recognition of Telugu Characters Using Various Pretrained Convolutional Neural Networks
type: item
description: Telugu handwritten character recognition has seen significant advancements in recent years, particularly in the context of deep learning models such as Convolutional Neural Networks (CNNs). However, recognizing handwritten digits and scripts in multilingual settings, especially in a diverse country like India, presents unique challenges due to the variance in handwriting styles and the complexity of regional scripts. The proposed system addresses the challenges of diverse handwriting styles by leveraging the power of CNNs for automated handwriting recognition. Our first contribution is collecting the Telugu characters dataset. A larger dataset of 33,496 handwritten Telugu characters was collected from schoolchildren and adults of varying ages. We collected a larger corpus of 33,496 locally handwritten Telugu characters to study script variability. The experiments in this paper evaluate six vowels (1,200 images) from the public Kaggle set, serving as a focused benchmark; the larger corpus will be used in follow-up work and released separately. This project presents a deep learning-based approach to recognize handwritten Telugu vowels. To ensure optimal recognition performance, five Convolutional Neural Network (CNN) architectures LeNet, VGG16, ResNet50, DenseNet, and AlexNet were selected and evaluated for the task of Telugu character recognition. These models were rigorously trained, and the Adam optimizer is used to optimize the parameters. These models are evaluated, and their performance is compared. From the analysis, it is found that the VGG16 model showed the best results with a high accuracy of 98.14%.
subject: Standard Journal Issues
  topic: section
subject: Handwritten recognition
  topic: keywords
subject: Telugu vowels
  topic: keywords
subject: Convolutional Neural Networks (CNN)
  topic: keywords
subject: Data augmentation
  topic: keywords
subject: Deep learning models
  topic: keywords
subject: Pretrained CNN models
  topic: keywords
subject: Computer Science
  topic: disciplines
subject: Data Science
  topic: disciplines
creator: Vishnu Vardhan Mukkoti
creator: E Jayakiran Reddy
creator: N Srinivas Reddy
creator: Dheeraj Joshi
creator: Shaik Ayyub
creator: Vishal Mahajan
creator: Nishat Ansari
rights: Copyright (c) 2025 Mukkoti Vishnu Vardhan, E Jayakiran Reddy, N Srinivas Reddy, Dheeraj Joshi, Shaik Hujefa Ayyub, Vishal Pratap Mahajan, Nishat Afshan Ansari https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-09-16
publication date: 2025-09-16
starting page: 251
ending page: 261
doi: 10.38094/jastt62267

A Hybrid LLM–Knowledge Graph Framework for Accurate Biomedical Question Answering
type: item
description: Biomedical question answering requires accurate and interpretable systems; however, existing approaches often face challenges such as language model hallucinations and limited reasoning when relying solely on standalone knowledge graphs. To address these limitations, this study proposes a hybrid framework that integrates the LLaMA-3B language model with a Neo4j-based drug–disease–symptom knowledge graph. The system translates natural language questions into executable Cypher queries, operates on an iBKH-derived graph comprising over 65,000 entities and 3 million relationships, and returns answers with supporting evidence through a transparent interface. Experiments conducted on 60 biomedical questions across three levels of difficulty demonstrate the robustness of the approach: 96% exact match for simple queries, 95% for medium queries, and 86.7% for complex queries. Overall, the system achieves Precision@5 of 96.1%, Recall@5 of 89.0%, F1@5 of 91.0%, Hits@k of 96.1%, and an MRR of 94.4%, while maintaining an average response time of only 6.07 seconds. These results indicate that the system retrieves nearly all relevant answers, ranks them correctly, and delivers them with latency low enough for interactive use. Moreover, unlike cloud-based APIs such as ChatGPT, which require internet connectivity and external data transmission, the proposed framework operates fully offline, ensuring privacy, reproducibility, and compliance with biomedical data governance. Overall, this pipeline provides an accurate, efficient, and privacy-preserving solution for biomedical question answering, making it a practical alternative to cloud-dependent approaches in sensitive healthcare contexts.
subject: Standard Journal Issues
  topic: section
subject: Knowledge Graph
  topic: keywords
subject: LLM
  topic: keywords
subject: Question Answering
  topic: keywords
subject: Neo4j
  topic: keywords
subject: Biomedical Informatics
  topic: keywords
subject: Healthcare AI
  topic: keywords
subject: LLaMA 3
  topic: keywords
subject: Computer Science
  topic: disciplines
creator: Havraz Younis Omar Omar
creator: Abdulhakeem Mohammed
rights: Copyright (c) 2025 Havraz Younis Omar, Abdulhakeem O Mohammed Mohammed https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-10-20
publication date: 2025-10-20
starting page: 342
ending page: 357
doi: 10.38094/jastt62404

Auto-Tuned Particle Swarm with perturbed velocities - Optimized CLMF Algorithm for DVR based Power Quality Improvement
type: item
description: Problems pertaining to power quality, like voltage sags and swells as well as harmonics, have the potential to impact the reliability of contemporary power systems. An innovative approach that integrates the Composite Least Mean Fourth (CLMF) algorithm with meta-heuristic algorithms like PSO, DE, PSODE and a hybridization Auto-Tuned PSO and DE (APSODE)—to enhance the performance of a Dynamic Voltage Restorer (DVR) is presented in this study. The CLMF algorithm is used mostly to mitigate voltage distortions, while the meta-heuristic algorithms are employed primarily to optimize the DVR's control parameters for improved operation over different power quality conditions. Extensive simulations, conducted in the MATLAB environment, validate the proposed method, showing a substantial improvement in voltage restoration, reduced Total Harmonic Distortion (THD), and faster dynamic response. The findings of the study highlight the effectiveness of combining CLMF with PSO, DE, PSODE and APSODE in addressing power quality issues, offering improved performance and higher adaptability to changing conditions of power system. The proposed APSODE algorithm introduces auto-tuned acceleration coefficients and hybrid velocity mutation mechanisms, offering real-time adaptability and improved convergence over traditional methods, thereby ensuring strong voltage compensation under dynamic power quality conditions.
subject: Standard Journal Issues
  topic: section
subject: Dynamic Voltage Restorer (DVR)
  topic: keywords
subject: Composite Least Mean Fourth (CLMF) Algorithm
  topic: keywords
subject: Particle Swarm Optimization and Differential Evolution (PSODE),
  topic: keywords
subject: Auto-tuned PSO and Differential Evolution (APSODE)
  topic: keywords
subject: Power Quality Enhancement
  topic: keywords
subject: Total Harmonic Distortion (THD)
  topic: keywords
subject: Voltage Sag/Swell Compensation
  topic: keywords
subject: Computer Science
  topic: disciplines
subject: Computer Engineering
  topic: disciplines
creator: Kranti Kiran
creator: Balakrishnan Rajagopal
creator: Yerramilli Raju
rights: Copyright (c) 2025 Tummala Kranti Kiran, Balakrishnan Rajagopal, Yerramilli Butchi Raju https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-10-03
publication date: 2025-10-03
doi: 10.38094/jastt62300

Pancreatic Cancer Detection Using Quaternion Wavelet Transform and Squeeze-and-Excitation Network with SVM Classifier
type: item
description: Pancreatic cancer (PC) remains one of the most lethal malignancies worldwide, primarily due to the difficulty of early diagnosis and the subtle radiological signatures it presents. To address this challenge, we propose a hybrid computer-aided diagnostic framework that integrates the Quaternion Wavelet Transform (QWT) for robust multi-scale and phase-preserving feature extraction, a Squeeze-and-Excitation (SE) network for adaptive channel-wise feature recalibration, and a Support Vector Machine (SVM) classifier for reliable categorization of pancreatic lesions. The QWT effectively captures discriminative structural information, while the SE network enhances representational quality by modeling inter-channel dependencies. The fused features are subsequently classified by the SVM to ensure efficient and accurate decision-making. Experiments conducted on the publicly available Kaggle CT dataset demonstrate that the proposed method achieves an accuracy of 96.40%, a precision of 95.50%, a recall of 97.05%, a specificity of 96.72%, and an F1-score of 95.73%, outperforming several state-of-the-art approaches. These results highlight the potential of combining QWT, SE networks, and SVM in advancing computer-aided diagnosis for PC and suggest a promising direction for clinical translation.
subject: Standard Journal Issues
  topic: section
subject: Pancreatic Cancer
  topic: keywords
subject: Quaternion Wavelet Transform
  topic: keywords
subject: Squeeze-and-Excitation Network
  topic: keywords
subject: Support vector machine
  topic: keywords
subject: Computer Science
  topic: disciplines
subject: Machine Learning
  topic: disciplines
creator: Vijaya Shanthi Anagani
creator: Alli Rani
creator: Panuganti Panuganti
creator: Muppalla Tharangini
rights: Copyright (c) 2025 Anagani Vijaya Shanthi, Alli Daisy Rani, Panuganti Ravi, Muppalla Tharangini https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-08-29
publication date: 2025-08-29
starting page: 194
ending page: 202
doi: 10.38094/jastt62269

Morphotectonic Controls on the Geomorphic Evolution: A Case Study of Diyala River Basin
type: item
description: This study investigates the influence of tectonic activity on the geomorphic evolution of the Diyala River Basin, located within variant tectonic zones (Suture, Imbricate, High-folded, and Low-folded zones) of the Arabian and Eurasian plates, and the Mesopotamia Fordeep. Spatial and topographic data, primarily derived from the SRTM v3/NASADEM with a resolution of one arc-second, were used to delineate the basin and extract key morphological parameters. A set of morphotectonic indices was calculated using ArcGIS Pro 3.4.2 to quantify tectonic signatures across the basin. The results reveal a spatial gradient in tectonic activity, with the middle and upstream sections, which are dominated by active ridges and structurally deformed landforms, showing significantly higher tectonic activity than the downstream areas. The methodology demonstrates a practical framework for assessing tectonic impacts on fluvial systems and can support broader applications in natural hazard assessment, watershed management, and regional planning. The study emphasizes the importance of integrated geomorphological analysis in identifying zones of elevated tectonic activity, particularly in structurally complex regions like the Zagros Fold-Thrust Belt.
subject: Standard Journal Issues
  topic: section
subject: Morphotectonic
  topic: keywords
subject: Geomorphometry
  topic: keywords
subject: Tectonic Activity
  topic: keywords
subject: Suture Zone
  topic: keywords
subject: GIS
  topic: keywords
subject: Iraq
  topic: keywords
subject: GeoScience
  topic: disciplines
subject: GIS
  topic: disciplines
subject: Geo-Science
  topic: disciplines
creator: Ayad Ali Faris Beg
creator: Ghadeer F. Al-Kasoob
creator: Ahmed Al-Sulttani
rights: Copyright (c) 2025 Ayad Ali Faris Beg, Ghadeer F. Al-Kasoob, Ahmed H. Al-Sulttani https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-08-25
publication date: 2025-08-25
starting page: 183
ending page: 193
doi: 10.38094/jastt62352

A Machine Learning Approach to Consumer Behavior Analysis in Social Media-Influenced E-Book Markets
type: item
description: Social media has emerged as a dominant marketing channel, significantly influencing consumer purchase decisions. Despite extensive global research, little is known about region-specific dynamics in emerging markets such as India. This study addresses this gap by applying Random Forest and Gradient Boosting models to survey data from 386 respondents in the Delhi-NCR region to analyze e-book purchasing behavior. Data were preprocessed through encoding, normalization, and stratified train–test splitting (80:20), with reproducibility ensured via a fixed random seed. Model evaluation employed R², RMSE, and MAE metrics, alongside a paired-sample t-test. Results showed that Gradient Boosting (R² = 0.82) outperformed Random Forest (R² = 0.78; p = 0.038). Feature importance analysis revealed that behavioral variables—purchase intention, brand awareness, and social media engagement—were the strongest predictors, whereas demographic features contributed minimally. These findings emphasize the primacy of behavioral traits in social media–driven e-book markets and provide evidence for designing region-specific digital marketing strategies in emerging economies.
subject: Standard Journal Issues
  topic: section
subject: Education
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Cognitive learning
  topic: keywords
subject: Non-cognitive learning
  topic: keywords
subject: classification
  topic: keywords
subject: student performance
  topic: keywords
subject: Machine Learning
  topic: subjects
subject: Consumer Behavior Analysis
  topic: subjects
subject: E-Book Markets
  topic: subjects
subject: Computer Science
  topic: disciplines
subject: Management Engineering
  topic: disciplines
creator: Sachin Tripathi
creator: Akhilesh Tiwari
creator: Kamal Upreti
creator: G V Radhakrishnan
rights: Copyright (c) 2025 Sachin Tripathi, Akhilesh Tiwari, Kamal Upreti, G V Radhakrishnan https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-09-10
publication date: 2025-09-10
starting page: 242
ending page: 250
doi: 10.38094/jastt62318

An Adaptive State-Augmented Kalman Filter for Robust UAV Altitude Control with Online Sensor Bias Correction and Dynamic Weighting in Degraded Environments
type: item
description: For unmanned aerial vehicles (UAVs) to operate safely and dependably, accurate state estimation is essential. However, environmental factors that affect measurement quality and sensor biases can impair performance. This paper proposes an Adaptive State-Augmented Kalman Filter (A-SAKF) that integrates two complementary mechanisms: (i) state augmentation for online sensor bias estimation, and (ii) innovation-based adaptive adjustment of measurement covariance. Together, these features enable the filter to maintain robust state estimation performance in the presence of bias errors and uncertain measurement noise conditions. Validation through three simulation scenarios demonstrates the effectiveness of the proposed framework. In Scenario 1, the method correctly estimates and compensates for a 2.0 cm bias in the infrared sensor. In Scenario 2, the velocity estimates eliminate overshoot and reduce settling time by 18% compared to a baseline controller. In Scenario 3, under degraded foggy conditions, the adaptive weighting mechanism recovers LiDAR trust levels within 4.5 s after a 35% drop, thereby preserving altitude tracking accuracy. These results highlight the filter’s capability to address both systematic bias and dynamically varying measurement reliability. By dynamically down-weighting the distorted LiDAR sensor data, the system demonstrates in simulation a steady and precise altitude estimate, showing improved resilience compared to fixed-covariance filters. The proposed filter demonstrates improved state estimation performance for UAVs under uncertain and biased sensor conditions, achieving lower errors than conventional EKF variants in diverse simulation scenarios. The current evidence is limited to simulation-based validation, and future work will extend testing to hardware-in-the-loop and public UAV datasets to further substantiate real-world applicability.
subject: Standard Journal Issues
  topic: section
subject: Unmanned Aerial Vehicle (UAV)
  topic: keywords
subject: Extended Kalman Filter
  topic: keywords
subject: Sensor Fusion
  topic: keywords
subject: Adaptive Control
  topic: keywords
subject: Bias Estimation
  topic: keywords
subject: State Estimation
  topic: keywords
subject: Fault-Tolerant Control
  topic: keywords
subject: Electrical and Electronic Engineering
  topic: disciplines
creator: MOHAMAD HANIFF HARUN
creator: Shahrum Shah Abdullah
creator: Mohd Aras
creator: Mohd Bahar
creator: Md Ashadi Md Johari
creator: Shamsul Fakhar Abd Gani
rights: Copyright (c) 2025 Mohamad Haniff Harun, Shahrum Shah Abdullah, Mohd Shahrieel Mohd Aras, Mohd Bazli Bahar, Md Ashadi Md Johari, Shamsul Fakhar Abd Gani https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-11-06
publication date: 2025-11-06
starting page: 378
ending page: 383
doi: 10.38094/jastt62351

SALF: A Blockchain-Based Framework for Scalable Academic Credential Management and Institutional Governance
type: item
description: This study introduces SALF (Secure Academic Ledger Framework), a technically innovative blockchain-based system engineered to overcome persistent challenges in academic credential management, including latency bottlenecks, governance opacity, and integration inflexibility. SALF pioneers a hybrid on-chain/off-chain architecture optimized for low-latency operations while preserving blockchain immutability, and it employs a role-based smart contract suite tailored to institutional hierarchies. Unlike prior frameworks, SALF integrates a degree-based incentive mechanism that quantifies data quality metrics—legibility, correctness, and non-redundancy—to ensure equitable institutional participation and discourage centralization. Built upon a Proof of Authority (PoA) consensus model, SALF achieves high performance under load, maintaining a throughput of over 30 transactions per second (TPS) and P95 latency below 300 milliseconds. RESTful APIs ensure real-time interoperability with existing systems such as ERPs and academic dashboards. Compared to benchmark systems like EduCert-Chain and EduCopyRight-Chain, the proposed framework achieves a 41.3% reduction in latency and maintains stable throughput under high-load conditions, even as other systems exhibit significant degradation or integration constraints. These distinctive technical contributions position SALF as a scalable, governance-aware, and future-ready infrastructure for decentralized academic credentialing across heterogeneous institutions.
subject: Standard Journal Issues
  topic: section
subject: academic records
  topic: keywords
subject: blockchain
  topic: keywords
subject: secure academic ledger framework (salf)
  topic: keywords
subject: proof of authority (poa)
  topic: keywords
subject: encryption
  topic: keywords
subject: Computer Science
  topic: disciplines
subject: AI
  topic: disciplines
creator: Mustafizul Haque
creator: Bui Thanh Hung
creator: Kamal Upreti
creator: Ritu Sapra
creator: Rituraj Jain
creator: G V Radhakrishnan
creator: Pravin R. Kshirsagar
rights: Copyright (c) 2025 Mustafizul Haque, Bui Thanh Hung, Kamal Upreti, Ritu Sapra, Rituraj Jain, G V Radhakrishnan, Pravin R. Kshirsagar https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-09-04
publication date: 2025-09-04
starting page: 203
ending page: 218
doi: 10.38094/jastt62295

The Impact of Urban Street Characteristics on Walking Attractiveness and Humanization: A Study of The Streets of Kut City Center
type: item
description: Recently, many cities have faced significant challenges that directly impact the quality of life of their residents due to rapid urban growth. One of the most pressing issues is the encroachment on public spaces due to vehicle traffic, resulting in a decline in pedestrian-friendly environments. The concept of "humanizing cities" is central to contemporary urban design, focusing on improving the quality of urban life by redesigning public spaces to meet the needs of individuals and enhance comfort, safety, and social interaction. The importance of this study lies in addressing the challenges Iraqi cities face in creating pedestrian-friendly urban environments, particularly in medium-sized cities like Kut, which have witnessed rapid urban development in recent decades. The study adopted a descriptive-analytical methodology, combining field observations and user surveys to assess street characteristics, including sidewalk width, availability of green spaces, and safety levels. A statistical analysis using a five-point Likert scale was conducted to demonstrate the relationship between street design and pedestrian satisfaction. This study, therefore, identifies the most significant street features that enhance humanization and evaluates how each street in the Kut city center applies these principles. It also compared city center streets based on these attributes. The results indicated that pedestrian satisfaction is closely related to key physical features, such as sidewalk width, green spaces, and safety measures. Streets with these attributes scored higher in comfort and walkability, while those lacking them experienced lower pedestrian participation. That highlights the importance of prioritizing human-centered design to create inclusive, safe, and vibrant urban spaces.
subject: Standard Journal Issues
  topic: section
subject: Humanize City
  topic: keywords
subject: City Center Street
  topic: keywords
subject: Humanization Streets
  topic: keywords
subject: Physical Street Features
  topic: keywords
subject: Urban Planning
  topic: disciplines
creator: yaqeen Hadi
creator: Ihsan Jassim
rights: Copyright (c) 2025 Yaqeen Haider Hadi, Ihsan Abbas Jassim https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-09-08
publication date: 2025-09-08
starting page: 231
ending page: 241
doi: 10.38094/jastt62344

Feature-Based Child Mortality Prediction Using Ensemble and Traditional Machine Learning Models
type: item
description: Child mortality is a big problem around the world, especially in low- and middle-income nations where there are big differences in health care and social conditions. This investigation seeks to create a predictive model for child mortality and pinpoint the key factors that significantly contribute to it, employing machine learning (ML) methodologies. The dataset includes various features such as parental age, maternal education, birth weight, wealth index, and access to healthcare services. Thirteen machine learning classifiers were used, categorized into four model groups: Traditional Models (Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Naive Bayes), Tree-Based Models (Decision Tree, Random Forest, Extra Trees), Boosting Models (AdaBoost, Gradient Boosting, XGBoost), and Ensemble Learning Models (Soft Voting, Hard Voting, Stacking). The efficacy of each model was assessed using classification metrics, including Accuracy, Precision, Recall, and F1-Score within a 10-fold cross-validation framework to guarantee robustness. Results indicate that ensemble models, particularly AdaBoost, achieved the highest predictive accuracy, with perfect scores across all metrics (1.00). XGBoost and Stacking also demonstrated strong and consistent performance. The findings indicate that ensemble learning methods are effective in predicting child mortality and can assist policymakers and healthcare planners in identifying high-risk populations and implementing targeted interventions to reduce child mortality.
subject: Standard Journal Issues
  topic: section
subject: Child Mortality Prediction
  topic: keywords
subject: Machine Learning Classifiers
  topic: keywords
subject: Ensemble Learning Models
  topic: keywords
subject: Explainable AI (XAI)
  topic: keywords
subject: Healthcare Data Analytics
  topic: keywords
subject: Computer Science
  topic: disciplines
creator: Aruna Sampathirao
creator: Modalavalasa Divya
creator: Promod Sahu
rights: Copyright (c) 2025 Sampathirao Aruna, Modalavalasa Divya, Promod Kumar Sahu https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-08-08
publication date: 2025-08-08
starting page: 169
ending page: 182
doi: 10.38094/jastt62264

Software Effort Estimation Using Stacking Ensemble and Bayesian Optimization
type: item
description: Accurately estimating software costs is a vital step in ensuring the successful completion of a software project. There is a need for estimation techniques that ensure projects are completed on time, within budget, and with the desired quality. Accurate estimation plays a crucial role in crafting realistic budget plans and ensuring that projects are completed on time with sufficient resources. When estimations are precise, teams can spot potential issues early, distribute resources more effectively, and handle risks with greater confidence. This research focuses on boosting the reliability of software effort estimation by applying a stacking method enhanced with Bayesian hyperparameter optimization. It leverages three core algorithms: SVM, Random Forest, and Decision Tree, each fine-tuned using the proposed approach. Evaluations across 11 public datasets reveal noteworthy improvements, ranging from 0.2 to 0.5. A significance test confirms the model’s strong performance, showing a p-value greater than 0.5, which indicates that the results are statistically meaningful. These findings suggest that combining stacking with Bayesian tuning holds promise for refining software effort predictions. It can serve as a valuable reference for future project planning across diverse modeling approaches.
subject: Standard Journal Issues
  topic: section
subject: Software Estimation, Ensemble Learning, Stacking, Hyperparameter Tuning, Bayesian Optimization, Machine Learning, Model Performance.
  topic: keywords
subject: Software effort estimation
  topic: subjects
subject: Computer Science
  topic: disciplines
subject: software engginering
  topic: disciplines
creator: Muhammad Yusuf
creator: Daniel Siahaan
rights: Copyright (c) 2025 Muhammad Yusuf, Daniel Siahaan https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-08-07
publication date: 2025-08-07
starting page: 161
ending page: 168
doi: 10.38094/jastt62310

AppleVit: A Smart Agricultural Software for Apple Leaf Disease Detection Using AI
type: item
description: Apple leaf diseases endanger global apple production at such an intensity that it demands precise detection systems to control disease spread effectively. Traditional inspection methods and Convolutional Neural Network (CNN)-based models face challenges when processing extended image dependencies in leaf images, which subsequently affects their ability to identify diseases accurately. This research develops AppleViT, a lightweight Vision Transformer (ViT)-based model that applies Vision Transformer technology with self-attention approaches to enhance leaf disease classification accuracy and feature extraction within apple leaf detection systems. AppleViT was trained using a public dataset comprising 9,714 apple leaf images, categorized into four classes: Apple Scab, Black Rot, Cedar Apple Rust, and Healthy. The accuracy rate of AppleViT reached 97.8%, which exceeded the ResNet-50 and EfficientNet-B3 and MobileNetV3 models while operating with 1.3 million parameters suitable for precision agriculture real-time usage. The proposed approach demonstrates both high generalization skills alongside precise precision and recall value measurements for disease categories. Future research will create attention visualization features and mobile application compatibility before expanding the architecture to identify multiple diseases across different plant types. AppleViT highlights the potential of Vision Transformer (ViT) technology as a powerful tool to revolutionize plant disease detection for improving crop yield and disease management worldwide.
subject: Standard Journal Issues
  topic: section
subject: Apple Leaf Disease Classification
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Vision Transformer
  topic: keywords
subject: Vision Transformer
  topic: subjects
subject: Deep Learning
  topic: subjects
subject: Self-Attention Mechanism
  topic: subjects
subject: Computer Science
  topic: disciplines
creator: Pradeep Gupta
creator: Rakesh Singh Jadon
rights: Copyright (c) 2025 Pradeep Gupta, Rakesh Singh Jadon https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-09-06
publication date: 2025-09-06
starting page: 219
ending page: 230
doi: 10.38094/jastt62315

Evaluation of Groundwater Potential Zone in Selected Coastal and Non-Coastal Regions of Nigeria
type: item
description: For socioeconomic development, groundwater is a vital resource, especially in areas with limited water supplies. This study assesses groundwater potential zones (GWPZs) in two distinct Nigerian regions, the inland crystalline basement complex of Ile-Ife and the coastal sedimentary basin of Ilaje using integrated geospatial techniques. The novelty of this research lies in its direct comparative analysis of these two disparate hydrogeological and anthropogenic contexts, which fills a critical gap in the existing literature. The study utilized Remote Sensing, GIS, and the Analytical Hierarchy Process (AHP), including a sensitivity analysis to improve methodological robustness. For GWPZ delineation, nine key thematic layers, including geology, land use/land cover, NDWI, NDVI, drainage density, lineament density, rainfall, DEM, and slope, were processed and weighted using AHP. Significant differences were found in the results. Ilaje had a higher percentage of highly available GWPZs (6.15%) than Ile-Ife (4.00%), which was indicative of fundamental variations in hydrogeological, geomorphological, and hydrological controls. Importantly, the results highlight how these differences call for tailored management approaches; Ile-Ife's resources are being depleted by increasing urbanization, while Ilaje's potential is accompanied by serious risks of pollution and saltwater intrusion. This research demonstrates that a “one-size-fits-all’’ approach to groundwater management is untenable in diverse environments and offers fresh empirical insights for both hydrogeological theory and practical policy formulation. It is important to note that the resulting GWPZ maps, based on secondary data, should be interpreted as preliminary indicators requiring future validation through borehole logs and pump test data.
subject: Standard Journal Issues
  topic: section
subject: Groundwater Potential Zone
  topic: keywords
subject: Analytical Hierarchy Process
  topic: keywords
subject: Remote Sensing (RS)
  topic: keywords
subject: Sensitivity Analysis
  topic: keywords
subject: Ilaje
  topic: keywords
subject: Ile-Ife
  topic: keywords
subject: Hydrology
  topic: disciplines
subject: Remote Sensing
  topic: disciplines
subject: GIS
  topic: disciplines
creator: Caleb Olutayo Oluwadare
creator: John Eyinade
rights: Copyright (c) 2025 Caleb Olutayo Oluwadare, John Adeyemi Eyinade https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-10-02
publication date: 2025-10-02
starting page: 301
ending page: 315
doi: 10.38094/jastt62287

OptiShip: Predictive Freight Cost Modeling Incorporating Regional Fuel Variability
type: item
description: Properly estimating the cost of freight transportation has always been a challenge to the logistics sector, especially when considered in a region like India, where some factors are dynamic, such as the price of diesel, which keeps on fluctuating and the variable nature of delivery costs to be accounted for. The traditional estimation methods tend to be based on either contractual or stationary models, which do not capture spatial and time eras of fuel prices, and as such, they cannot cope with reality in the real world. The proposed study presents OptiShip, machine learning-based infrastructure that could provide realistic and situationally aware freight cost forecasts. The major goal is to improve the accuracy of cost estimation, which involves incorporating regional avenues of fuel price variability alongside the fundamental logistical components, namely distance and delivery time. The framework is based on a five-phase approach, which includes the data gathering, preprocessing, and pre-alignment of the diesel prices based on the spatial locations in terms of a KDTree model, training and analyzing of the models. To model the non-linear, multidimensional components of the relationship between the input features, three algorithms of ensemble learning, Random Forest, Gradient Boosting, and XGBoost are used. The hyperparameter tuning with the help of GridSearchCV and the evaluation of the performance of the models are performed through R2, Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) scores. The Random Forest Regressor is found to be the best according to experimental results, having an R2 value of 0.97, an RMSE of 12.69 and an MAE of 4.94 which proves that it can model the actual representations of the highly accurate real-world logistic situation. The results indicate that regional economic indicators can be very useful to integrate into cost forecasting models and indicate that OptiShip could also be employed in real-time logistics platforms.
subject: Standard Journal Issues
  topic: section
subject: ensemble models
  topic: keywords
subject: freight cost
  topic: keywords
subject: fuel pricing
  topic: keywords
subject: logistics
  topic: keywords
subject: machine learning
  topic: keywords
subject: Computer Science
  topic: disciplines
creator: Aye Thiri Nyunt
creator: Brij Kotak
creator: Ravi Chauhan
creator: Rituraj Jain
creator: Vedant Keshariya
rights: Copyright (c) 2025 Aye Thiri Nyunt, Brij Kotak, Ravi Chauhan, Rituraj Jain, Vedant Keshariya https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-07-22
publication date: 2025-07-22
starting page: 116
ending page: 123
doi: 10.38094/jastt62244

Empirical Review of Urban Traffic Challenges and Sustainable Solutions: A Case Study of Muscat, Oman
type: item
description: This paper presents an empirical and bibliometric analysis of the issue of traffic in Muscat, Oman, combining statistics and world research trends. According to the empirical results, 15,195 traffic accidents and 1,086 deaths were recorded in Muscat between 2012 and 2022, with the root cause of road fatalities being overspeeding (54%). This has an impact of over 38 hours of congestion delay per driver annually, and its air pollution level of PM 2.5 is 48 µg/m³, far exceeds the WHO levels, which highlights severe environmental and health aspects. The study is methodologically descriptive and a trend analysis using secondary data from government and international sources. An analogous bibliometric was based on 3,799 articles indexed in Scopus and mapped via co-occurring keywords, thematic grouping, and author collaborations in VOSviewer. The factors used as inclusion criteria were peer-reviewed journal articles in the field of engineering that were written in English. The analysis compounds one of the gaps in the research spheres of the Gulf region that supports the novice nature of this muscat-focused paper. The conclusions also underline the necessity of infrastructure improvement, intelligent transport systems (ITS), transportation improvements, and behavioral changes. This review provides actionable policy recommendations and other research directions that should be undertaken, such as stakeholder views and long-term intervention evaluations. In the current study, I will lead researchers and policymakers to the specifics of data-driven, long-term urban mobility policies in Muscat.
subject: Standard Journal Issues
  topic: section
subject: traffic congestion
  topic: keywords
subject: urbanization
  topic: keywords
subject: public transportation
  topic: keywords
subject: intelligent transportation systems
  topic: keywords
subject: road infrastructure
  topic: keywords
subject: Urban Planning
  topic: disciplines
creator: Khuloud Balushi
creator: Sitesh Singh
creator: Rituraj Jain
rights: Copyright (c) 2025 Khuloud Nasser Al Balushi, Sitesh Kumar Singh, Rituraj Jain https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-07-24
publication date: 2025-07-24
starting page: 124
ending page: 139
doi: 10.38094/jastt62261

Enhanced algorithm based on Chio-like Method for Non-Square Determinant Calculations for application in CBVR
type: item
description: In this paper, we propose an enhanced algorithm based on the Chio-like method for calculating non-square determinants, optimized for content-based video retrieval (CBVR) systems. The algorithm accelerates the computation of determinant kernels used for similarity score generation, which is critical for efficient video indexing and retrieval. While the classical Chio-like method reduces the determinant order by one at each step, our improved approach reduces the order by four, providing notable computational benefits. Although the asymptotic time complexity remains the same, considering the fact that the resulting determinant is decreased by four orders compared to one and two orders, respectively, from existing Chio-like methods, the proposed method demonstrates clear practical performance improvements. The computer implementation of the proposed algorithm in MATLAB shows an average execution time reduction of approximately 24.5% compared to the standard Chio-like method and 3.2% compared to its modified version. These enhancements make the method well-suited for large-scale or real-time CBVR applications, where fast and accurate similarity evaluation is essential.
subject: Standard Journal Issues
  topic: section
subject: Non-square determinants
  topic: keywords
subject: Chio’s-like
  topic: keywords
subject: Algorithm optimization
  topic: keywords
subject: Execution time
  topic: keywords
subject: Kernels
  topic: keywords
subject: Similarity score
  topic: keywords
subject: CBVR
  topic: keywords
subject: Applied Mathematics
  topic: disciplines
creator: Besnik Duriqi
creator: Halil Snopçe
creator: Armend Salihu
creator: Artan Luma
creator: Majlinda Fetaji
rights: Copyright (c) 2025 Besnik Duriqi, Halil Snopçe, Armend Salihu, Artan Luma, Majlinda Fetaji https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-08-01
publication date: 2025-08-01
starting page: 149
ending page: 160
doi: 10.38094/jastt62253

Adaptive Detection and Response System for Post-Installation Cyber Attacks on Smartphones
type: item
description: Smartphone cyber-attacks are one of the major security threats of the present-day mobile computing era. Such attacks are used on the weaknesses that arise upon initial device configuration and they tend to extract sensitive data of the users, the system integrity and the functionality of a given device. This paper presents the post-installation attack vectors and detection together with a new Dynamic Security Assessment Framework (DSAF) that can be used to detect and mitigate the attacks in real time. The proposed method integrated behavior analysis, machine learning, and anomaly detection methods to detect post-installed suspicious activity. The proposed method uses two prominent algorithms, i.e., the Adaptive Threat Detection Algorithm (ATDA) and the Risk-Based Response Algorithm (RBRA). We have carried out an experimental study that shows that our strategy reaches a 94.7 percent accuracy in detection with a false positive of 2.3 percent that is much higher than what the current security solutions are capable of. The effectiveness of the framework is proved through thorough testing on 1,500 smartphones of various platforms whereby a 78 percent post-installation invasion is curbed using the framework as compared to conventional security systems.
subject: Standard Journal Issues
  topic: section
subject: Cyber attacks
  topic: keywords
subject: Dynamic Secuirty
  topic: keywords
subject: Threat Detection
  topic: keywords
subject: Smartphones
  topic: keywords
subject: post installation attacks
  topic: keywords
subject: Computer Science
  topic: disciplines
creator: Kireet Muppavaram
creator: Vijay Koka
rights: Copyright (c) 2025 Kireet Muppavaram, Vijay Koka https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-07-28
publication date: 2025-07-28
starting page: 140
ending page: 148
doi: 10.38094/jastt62293

Evaluating Green Space Design Alternatives Using Sustainable Thresholds: A Case Study of Bayti Residential Complex, Najaf, Iraq
type: item
description: This study evaluates green space design alternatives in the Bayti residential complex, Najaf, Iraq, using the concept of sustainable thresholds as a comprehensive planning and assessment tool. The research addresses the lack of integrated sustainability criteria in green space design within emerging urban environments, where climate adaptation, social inclusion, and economic resilience are often neglected. A descriptive-analytical methodology was adopted, incorporating ENVI-MET climate simulation and the LEED-ND v4.1 evaluation system. Three design alternatives were proposed to enhance thermal comfort, spatial equity, and local economic activation. The results revealed that the first alternative achieved the highest performance, significantly improving surface temperature distribution, social interaction, and access to services, while integrating economic functions through flexible green spaces. The study confirms that applying sustainable threshold indicators from the early stages of planning enhances decision-making and ensures better environmental, social, and economic outcomes. It recommends adopting threshold-based frameworks in residential green sector design and integrating simulation tools to align with climatic and contextual needs.
subject: Standard Journal Issues
  topic: section
subject: Green Sectors
  topic: keywords
subject: Sustainable Thresholds
  topic: keywords
subject: Urban Planning
  topic: keywords
subject: Urban Design
  topic: keywords
subject: Residential Complexes
  topic: keywords
subject: Urban Planning
  topic: disciplines
creator: Ahmed Alkhafaji
creator: Zuhair A. Nasar
creator: Ahmed F Almudhafar
creator: Zaid J alyasiri
creator: Ghaith J. Althabhawi
rights: Copyright (c) 2025 Ahmed S. Al-Khafaji, Zuhair A. Nasar, Ahmed F. Almudhafar, Zaid J. Alyasiri, Ghaith J. Althabhawi https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-07-20
publication date: 2025-07-20
starting page: 103
ending page: 115
doi: 10.38094/jastt62241

AQUAPHISH: Leveraging Metaheuristics and Automated Machine Learning for Precision Phishing Detection
type: item
description: Phishing is an ongoing and dynamic threat in the field of cybersecurity, targeting user trust to capture sensitive data through fraudulent websites. Conventional detection systems tend to use binary classification and static features, which make them less flexible to new attack paradigms. This paper seeks to design a solid and comprehensible phishing detection system that alleviates the drawbacks of binary labeling by proposing a regression-based risk scoring model. The aim is to improve accuracy, feature interpretability, and deployment in real-time settings. The new method combines Whale Optimization Algorithm (WOA) for feature selection and H2O AutoML for model creation and assessment. A filtered dataset of 10,000 phishing and normal websites is operated upon using 48 features, which are then reduced to 36 using WOA. The last models are optimized with H2O AutoML, encompassing ensemble learners, and tested on various regression metrics. Interpretability is achieved with SHAP analysis. The best model had an R² of 0.9534, RMSE of 0.1079, and MSE of 0.0116, better than traditional classification-based phishing detectors. The system, with only 36 features, had training time decreased by 23.6% and inference latency reduced by ~18%, without any sacrifice in detection accuracy (98.3%). Regression-based scoring also supported adaptive threat ranking in real time. By posing phishing detection as a regression problem and integrating metaheuristic feature selection with AutoML, this work introduces a scalable and explainable framework ready for real-world deployment. The low-latency yet high-accuracy model is best suited for integration into browser-level phishing filters and cloud-based threat intelligence platforms.
subject: Standard Journal Issues
  topic: section
subject: phishing attack
  topic: keywords
subject: optimization algorithm
  topic: keywords
subject: whale optimization algorithm
  topic: keywords
subject: automl framework
  topic: keywords
subject: automl h2o
  topic: keywords
subject: regression analysis
  topic: keywords
subject: random forest algorithm
  topic: keywords
subject: Computer Science
  topic: subjects
subject: Computer Science
  topic: disciplines
creator: Kamal Upreti
creator: Uma Shankar
creator: Shitiz Upreti
creator: G V Radhakrishnan
creator: Sheela Hundekari
creator: Saroj Date
rights: Copyright (c) 2025 Kamal Upreti, Uma Shankar, Shitiz Upreti, G V Radhakrishnan, Sheela Hundekari, Saroj Date https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 87
ending page: 102
doi: 10.38094/jastt61238

Text Classification for Clickbait Detection: A Model-Driven Approach Using CountVectorizer and ML Classifiers
type: item
description: In both digital and mainstream media, clickbait headlines are rampant forms of receiving and distributing attention, designed to maximize click through rates which in turn delegitimize content and help spread misinformation. In this paper we propose an efficient and interpretable machine learning framework for the binary classification of clickbait vs non-clickbait headlines using traditional models. Our pipeline involves the preprocessing step, followed by n-gram feature extraction via CountVectorizer and the classification done using Multinomial Naive Bayes, Logistic Regression and XGBoost. The models were trained and evaluated on accuracy, precision, recall, F1 score and ROC AUC, using a publicly available dataset. Our results indicate that Naive Bayes and Logistic Regression models enjoyed better performance with an accuracy of 95.88% and an F1-score of 95.88% and an AUC of 0.99, performing better than the more complex XGBoost classifier. Confirming the ability of lightweight models for real time clickbait detection, we further show that traditional machine learning is also interpretable and scalable.
subject: Standard Journal Issues
  topic: section
subject: Clickbait Detection
  topic: keywords
subject: Text Classification
  topic: keywords
subject: CountVectorizer
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Naive Bayes Classifier
  topic: keywords
subject: Computer Science
  topic: disciplines
creator: Nay Oo Lwin
creator: Rituraj Jain
creator: Rabnaz Dal
creator: Htet Oo Yan
creator: Kaung Khant Thaw
creator: Saw Yan Naung
rights: Copyright (c) 2025 Nay Oo Lwin, Rituraj Jain, Rabnaz Dal, Htet Oo Yan, Kaung Khant Thaw, Saw Yan Naung https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-06-24
publication date: 2025-06-24
starting page: 43
ending page: 49
doi: 10.38094/jastt61237