Journal of Applied Science and Technology Trends
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.
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Explainable Multimodal Student Profiling and Personalized Course Recommendation using Attention-Enhanced Heterogeneous Graph Neural Networks
The rapid expansion of digital learning environments has generated rich and diverse student data, yet many existing academic support systems still rely on unimodal predictors that overlook the relational nature of learning. This work introduces an Attention-Enhanced Heterogeneous Graph Neural Network (HGNN) that unifies multimodal student profiling and personalized course recommendation within a single explainable framework. The educational ecosystem is modelled as a heterogeneous graph composed of students, courses, and socio-academic attributes, where distinct relational edges capture course enrolment patterns, peer similarity, and contextual demographic influences. An edge-type-aware attention mechanism enables the model to selectively emphasize the most influential relationships, thereby offering transparent justification for each prediction. Using a real institutional dataset of 400 learners across multiple semesters, the proposed framework achieved 94% classification accuracy surpassing conventional machine learning baselines and homogeneous graph models. The analysis of the attention revealed valuable academic and behavioral variables, which create the learning trajectory of each student. This research takes the field a step closer to trustworthy, context-sensitive, and practical educational analytics by combining classification, recommendation, and interpretability into one pipeline. It prepares the ground for early intervention, improved decision-making, and academic guidance on an individual scale.
Heterogeneous Graph Neural Networks (HGNNs)
Copyright (c) 2026 Aamer Hashmi Syed, Yashpal Singh, Harshit Bhardwaj https://creativecommons.org/licenses/by-nc-sa/4.0
RANSEC: Hybrid Ensemble Learning-based Secure Approach for Ransomware Detection in Cyber-Physical Defence Systems
Sophisticated ransomware attacks increasingly target cyber-physical systems (CPS), therefore seriously compromising security for vital infrastructure. Stronger and more intelligent protection systems are necessary, as conventional detection systems can struggle to adapt to evolving attack patterns. This work proposes a novel hybrid ensemble learning model that is driven by artificial intelligence and makes use of weighted voting, combining Random Forest classifiers with SVM classifiers and another technique, stacking, which utilizes SVM with XGBoost as base classifiers and logistic regression as a meta classifier to improve the accuracy of ransomware detection. Experiments performed on the publicly accessible Kaggle ransomware dataset, containing 62,485 records of process, network activities, validate the superiority of the proposed approach, as the stacking-based hybrid model provides 93.15% accuracy compared to current single and ensemble classifiers. The adaptive resilience of the framework is guaranteed by the dynamic weighting, the meta-learning combination, which reduces the number of false positives and provides low-latency performance that is necessary in the real-world implementation of CPS. This secure model is the first step towards extending the existing literature and provides a scalable means to defend against future ransomware attacks on cyber-physical systems, protecting critical infrastructure in smart manufacturing, healthcare, and energy systems.
Copyright (c) 2026 Om Prakash Suthar, Mohammed Wasim Bhatt, Md. Solaiman Mia, Gaganpreet Kaur, Yogeshwar Prajapati, Rahul Bhandari, Evans Asenso https://creativecommons.org/licenses/by-nc-sa/4.0
Mitigation of NOX Emissions and Enhancement of Combustion Characteristics Using Nano-Emulsified Jatropha B20 Biodiesel in a Diesel Engine
This study experimentally investigates the influence of Al?O? nanoparticle addition on the combustion, performance, and emissions of emulsified Jatropha biodiesel in a compression-ignition engine. An emulsified fuel blend comprising 88% Jatropha methyl ester (JME), 10% water (v/v), and 2% surfactant (B20W10) was prepared using ultrasonication, into which Al?O? nanoparticles were dispersed at concentrations of 25 ppm and 50 ppm. Tests were conducted at varying loads under constant speed to evaluate performance, combustion, and emission characteristics. Among the tested fuels, B20W10Al50 yielded the best outcomes, achieving a 2.03% increase in brake thermal efficiency (BTE) and a 3.84% reduction in brake specific fuel consumption (BSFC) compared to diesel, with statistical analysis confirming the significance of these improvements. Combustion analysis showed a modest increase in peak in-cylinder pressure for B20W10Al50. Emission reductions were substantial relative to diesel: unburned hydrocarbons decreased by 40%, CO by 66.7%, NOx by 22.7%, and smoke opacity by 41.7%. These findings demonstrate that nanoparticle-assisted emulsification can address the common biodiesel trade-offs between efficiency and NOx formation. The study highlights B20W10Al50 as a promising formulation for sustainable transport applications, while also noting the need for further research on long-term nanoparticle stability, injector compatibility, and durability under real-world operating conditions.
Copyright (c) 2026 Nagesh Babu Vemula, Farooq Shaik https://creativecommons.org/licenses/by-nc-sa/4.0
Integration of IoT and Remote-Sensed Visual Analytics for Smart Environmental Surveillance
This study proposes a unified environmental surveillance system, which fuses IoT sensor networks and the visual analytics of multispectral remote-sensing to address the shortcomings of the conventional surveillance solutions. Edge-based LSTM anomaly detection on distributed nodes of the IoT can offer high-frequency local measurements, whereas a hybrid ResNetVision Transformer (ViT) model can improve the analysis of the satellite image. An adaptive Kalman-based temporal-spatial fusion algorithm incorporates heterogeneous streams of data towards better environmental intelligence. The system was highly performing, indicating the accuracy of the IoT sensors in 91.3-98.1% and a hybrid model at 92.4% and the fused levels at 94% and above respectively. The results were impressive on the system level, since the response time to events was improved significantly, the completeness of data improved, and the accuracy of anomaly detectors increased, as well as the network load decreased. On the whole, the suggested structure has high potential to monitor the environment in real-time, being scalable, in the fields of smart agriculture, air-quality monitoring, water-resource control, climate-risk identification, and smart urban governance.
Special Issue: Remote Sensing based Intelligent Visual Analytics for Real-time Environmental and Earth Monitoring Systems
Deep Learning for Environmental Surveillance
Copyright (c) 2025 A Madhuri, Balamuralikrishna Thati, Suresh Babu Chandolu, Sindhura S, Yalanati Ayyappa, Ruhisulthana Shaik https://creativecommons.org/licenses/by-nc-sa/4.0
Sentiment Analysis Utilizing Artificial Intelligence For Effective Health Crisis Management In Diabetics In Smart Urban Environments
Sentiment analysis utilizing artificial intelligence offers a transformative approach to managing health crises among diabetics in smart urban environments. This research proposes a practical AI-based solution that can be integrated into existing smart urban infrastructure to support real-time health crisis interventions for diabetic patients. Challenges in sentiment analysis for health crisis management in diabetics using AI include the need for high-quality, diverse data to accurately capture sentiment and the potential for privacy issues with sensitive health information in smart urban environments. The objective of this study is to leverage sentiment analysis utilizing artificial intelligence to enhance health crisis management for diabetics within smart urban environments. Adaptive Median Filtering Technique (AMFT) is used in pre-processing to reduce noise in sentiment analysis, as textual data from sources often contains noise such as irrelevant information, spam, and outliers. The combination of AMFT for noise reduction, RNNs for temporal sentiment analysis, and AI-driven optimization introduces a novel, technologically advanced approach to health crisis prediction systems. Recurrent Neural Network (RNN) models are highly effective for sentiment analysis, especially in the health crisis management of diabetics within smart urban environments, due to their ability to process sequential data and capture temporal dependencies. AI-driven optimization (AIDO) can automatically tune hyperparameters of sentiment analysis models in RNNs to improve performance, ensuring the models are both accurate and efficient. The AI-driven sentiment analysis system outperforms traditional monitoring methods, such as rule-based lexicons and keyword frequency-based approaches implemented in Python, achieving an accuracy of 0.92, a precision of 0.90, and a recall of 0.93.The proposed system reflects the focus on applied science and technological innovations by demonstrating a scalable, intelligent health monitoring framework that can be deployed in smart cities and urban health systems. Future advancements in sentiment analysis using artificial intelligence could enhance real-time monitoring and prediction of health crises in diabetics, integrating more diverse data sources and adaptive learning algorithms.
Copyright (c) 2025 BH Krishna Mohan, Mong-Fong Horng, Siva Shankar S, Chun-Chih Lo https://creativecommons.org/licenses/by-nc-sa/4.0
Leveraging Steel Structures to Measure Real-Time Environmental Hazardous Seismic Activity: A Comprehensive Review
Steel structures are a potential network of distributed sensors to do real-time seismic surveillance and detect environmental hazards. This review discusses the state-of-the-art in existing ways to leverage steel buildings, bridges, and infrastructure as integrated seismic monitoring systems. The present study examines sensor technologies, real-time extensions, communication frameworks, and analytical structures and mechanisms that allow steel structures to serve as both structural resources and environmental apparatus. The combination of accelerometers, strain sensors, fiber-optic systems, and wireless networks turns steel structures into active seismic observatories that will record, characterize, and report real-time dangerous seismic activities. This method has major benefits compared to conventional seismic networks, such as an increased spatial resolution, structure-specific damage, and real-time post-event safety assessment. Some of the major problems are longevity of sensors, bandwidth, variability of the environment, and the necessity to have standardized benchmarking protocols. This review suggests a roadmap through which researchers, engineers, and policymakers can contribute to the development of the field at practical stages of a large-scale implementation. Future research includes multi-sensor fusion, edge computing, the incorporation of digital twins, and field validation research.
Copyright (c) 2025 Husain Rangwala, Dr. Tarak Vora https://creativecommons.org/licenses/by-nc-sa/4.0
Real-Time Environmental Monitoring Systems: Integrating Remote Sensing and Intelligent Visual Analytics for Sustainable Urban Development
The populations in urban centres continue to rise, and cities incur mounting environmental challenges that include worsening air quality, untimely disposal of waste, and uncontrollable changes in the climate. Real-time monitoring of changes in the environment, analysis, and predictions using high-level technologies are necessary to achieve sustainable urban development and resilience. Conventional methods of environmental monitoring do not generally have real-time flexibility, spatial accuracy, and intelligent data analysis, which prevents policymakers from responding in time to impending environmental hazards. A multi-purpose and scalable architecture are needed to combine different sources of data and provide dynamically generated actionable insights. This paper presents the Environmental Cognitive Observation and Intelligent Geospatial Hybrid Tracking (ECO-INSIGHT) model. The approach is a hybrid of unmanned Aerial Vehicles (UAV)-assisted remote sensing, IoT-sensed environmental sensors, and a hybrid deep reinforcement learning model to recognize patterns in real-time and predict events. ECO-INSIGHT deeply integrates a decision fusion layer, which is adaptive, to combine data related to satellites, drones, and ground sensors and provide ongoing environmental intelligence with contextual visualization using AI-powered dashboards. Empirical analysis shows that ECO-INSIGHT increases monitoring accuracy by 94 percent, data redundancy by 38 percent, and predictive response efficiency in a variety of indicators of the ecological condition of cities. ECO-INSIGHT allows proactive environmental management, evidence-based city planning, and sustainable city ecosystems by means of intelligent visual analytics.
Copyright (c) 2025 Kavipriya K, Vilas Namdeo Nitnaware, Balaji V. R, Arul Kumar V P, Krishnaraj Natarajan, Hema Kumar M https://creativecommons.org/licenses/by-nc-sa/4.0
Intelligent Resource Management and Secure Live Migration in Cloud Environments: A Unified Approach using Particle Swarm Optimization, Machine Learning, and Blockchain on XenServer
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.
Copyright (c) 2025 Akashbhai Ashokbhai Dave https://creativecommons.org/licenses/by-nc-sa/4.0
Big Data-Based Remote-Sensed Intelligent Visual Analytics for Environmental and Earth Monitoring Systems
The increasing accessibility of sensor-based earth observations, satellite imaging, and uncrewed aerial vehicle (UAV) data has made environmental monitoring a data-intensive field. However, the size, heterogeneity, and real-time nature of these datasets make it difficult to store, process, and interpret them effectively. This paper proposes a Big Data-driven approach to intelligent visual analytics for earth and environmental monitoring systems. With sophisticated machine learning algorithms, scalable data platforms, and remote sensing technologies, the framework enables automated feature extraction, anomaly detection, and spatiotemporal pattern recognition. Terabytes of satellite and sensor imagery can be analyzed more efficiently through the system's combination of Deep Learning-based Visual Interpretation and Distributed Computing Models (DL-VI-DCM), including Hadoop and Spark. Applications such as land cover classification, deforestation tracking, assessing the effects of climate change, disaster management, and water resource monitoring are all supported by the proposed methodology. To improve decision-making for scientists, environmental agencies, and politicians, a visual analytics dashboard is created to provide interactive maps, time-series trends, and predictive insights. Experimental findings from case studies on urban growth and flood-prone areas show that the framework can process high-resolution remote-sensed data in near real time with high accuracy and interpretability. This paper demonstrates the potential of integrating Big Data technology with intelligent visual analytics to provide flexible, scalable, and decision-focused solutions for earth system monitoring and global environmental sustainability. The proposed DL-VI-DCM achieves superior performance with 40–180?min of processing time, 92–95% classification accuracy, 89–94% anomaly precision, 55–110?GB/min scalability, 80–88% spatiotemporal accuracy, and 1.2–2.5?s response time.
Copyright (c) 2025 Pramoda Patro, Sowmya M, Srinivas N, Subhranginee Das, Arul Kumar V P, Dhanapal R https://creativecommons.org/licenses/by-nc-sa/4.0
Real-Time Secure Cloud Transmission Framework for Agricultural Surveillance Using YOLOv5, AES-256, and HMAC-SHA256
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.
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
An Explainable Multi-Agent Framework for Real-Time Tree Detection and Canopy Segmentation in Remote-Sensed Imagery
Monitoring tree cover and canopy architecture is important for sustainable forest management, biodiversity assessments, and climate adaptation planning. However, most current methods rely on large labeled datasets or specific sensors, which limit scalability and adaptability. This study hypothesizes that a zero-shot explainable multi-agent system can successfully detect and segment trees from standard RGB satellite imagery without having to retrain on task-specific data. A new framework is proposed that combines YOLO11m for tree detection and SAM2 for crown segmentation. The system utilizes a combination of vegetation, edge, and color-based agents that work in concert under an IoU based fusion strategy to increase robustness under varying brightness, shadows, and canopy overlap. Explainability includes Grad-CAM, SHAP, and LIME-based agents to visualize model attention to establish user trust. Experiments were conducted on a dataset of 2400 high-resolution satellite imagery (0.5–1.5 m). Moreover, the framework produced a 97.3% overall accuracy score, 97.6% precision score, 97.0% recall score, and 0.92 IoU, processing each image in under 10 seconds. The results of this study demonstrate that the `multi-agent zero-shot' method achieves high accuracy, fast inference, and transparent predictions for real-time vegetation monitoring, deforestation evaluations, and the urban canopy.
Copyright (c) 2025 K.P. Swain, Soumya Ranjan Nayak, Tarini Prasad Pattanaik, Ashish Singh https://creativecommons.org/licenses/by-nc-sa/4.0
Deep Learning-Driven Visual Analytics Framework for Next-Generation Environmental Monitoring
In this paper, we propose a deep learning-based visual analytics pipeline for next-generation environmental monitoring with multispectral and temporal remote sensing data. We used large-scale benchmark images (MODIS, Landsat-8, Sentinel-2 to record a wide range of the land-use/land-cover, vegetation cover, atmospheric and water-body features. The pre-processing pipeline consisted of noise filtering, normalization and dimensionality reduction through PCA to improve the quality of data and model parsimony. The key hyperparameters like learning rate, batch size and layers depth- of system were optimized with hybrid PSO optimization technique which enhanced the convergence behaviour and classification ability of model.Deep learning models, such as convolutional neural networks (CNNs) like VGG16, GoogleNet, and ResNet50, and transformer-based ones, have been used to extract spatial-temporal information out of the satellite images. The three different types of networks provided more generalization based on transfer learning to utilize the already trained ImageNet weights and then fine-tune them in the domain. The models proposed were tested in various environmental surveillance problems such as land-cover classification, vegetation health monitoring and detection of water-quality anomalies, which proved to be robust and adjustable to a variety of remote sensing problems.Experiments illustrate that ResNet50 can outperform other architectures in all datasets, i.e., it attains highest accuracy 95.2%, 94.6% and 90.8% for Sentinel-2, Landsat-8 and MODIS data sources, respectively with corresponding F1-score greater than 94% and AUC >0:96. These results demonstrate the successful application of optimized deep-learning models, which can ensure real-time and scalable deployed monitoring with high precision for remote-sensing images.
Copyright (c) 2025 Sammy F., Thiyagarajan Mani Chettier, Venkata Ashok Kumar Boyina, Harshala Shingne, Kamal Saluja, Manisha Mali, R.Sugumar, Shobana A https://creativecommons.org/licenses/by-nc-sa/4.0
Physics-Informed Machine Learning Framework for Virtual Screening and Multi-Objective Optimization of Polymer Nanocomposites with Tailored Multifunctional Properties
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 intervals (95% confidence intervals: ±8.3% for mechanical, ±9.1% for thermal, ±11.2% for electrical properties). The present physics-informed machine learning approach enables computational materials design with accounting for the most relevant physical laws and data-driven techniques to discover optimal high-performance polymer nanocomposites yet offers a robust uncertainty quantification to inform risk-conscious design decisions.
Copyright (c) 2025 Sandeep Gupta, Budesh Kanwer, Udit Mamodiya, Saurabh Shandilya, Deepshikha Bhatia, Nitesh Naik https://creativecommons.org/licenses/by-nc-sa/4.0
A Computational Framework and Pipeline Architecture for Sustainable Online Consumer Behavior
This study proposes a design-oriented computational framework and pipeline architecture for e-commerce systems to address the gap between pro-environmental attitudes and sustainable purchase behaviors. Despite growing availability of green personal care products (GPCP) on e-commerce platforms, consumer engagement remains limited. Prior studies often overlook this niche, focusing instead on broader green product categories or offline retail contexts. This study also addresses the gap by examining how pro-environmental and consumption values shape consumer behavior for GPCPs in digital commerce, and then translates the validated determinants into a computational design. A quantitative design guided by the Value–Attitude–Behavior framework was employed. Data were collected from 302 consumers who purchased GPCPs online in the past six months. Constructs were measured using validated scales and analyzed through Partial Least Squares Structural Equation Modelling (PLS-SEM). The empirically significant determinants were then mapped into a multi-stage pipeline architecture to demonstrate how behavioral factors can be operationalized within e-commerce systems. The findings indicate that emotional value, functional quality value, and perceived environmental knowledge significantly shape positive consumer attitudes toward GPCPs in e-commerce settings. Green purchase attitudes were strongly predictive of purchase intentions (? = 0.781, p < 0.000), which subsequently influenced actual purchase behavior (? = 0.726, p < 0.000). These empirically validated determinants inform the computational pipeline, where values act as inputs, attitudes and intentions as intermediate system states, and behaviors as measurable outcomes. Theoretically, this study advances the Value–Attitude–Behavior framework by confirming the distinct and context-dependent roles of emotional and functional quality values, as well as environmental knowledge, in shaping sustainable consumer attitudes in digital contexts. Practically, by integrating these behavioral insights into a pipeline design, the study contributes a computational framework that links consumer psychology with system architecture.
Copyright (c) 2025 Nikita Panwar, Dasharathraj k shetty, Sandeep S Shenoy, Anusha Pai https://creativecommons.org/licenses/by-nc-sa/4.0
Performance Comparisons Between the Semiconductor Optical Amplifier (SOA) and the Erbium-Doped Fiber Amplifier (EDFA)
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.
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
Adaptive Statistical Quality Assessment and Enhancement of Infrared and Optical Remote Sensing Imagery for Intelligent Environmental Monitoring in Real?Time
Infrared and optical remote sensing imagery plays a critical role in environmental applications such as wildfire detection, urban thermal profiling, climate monitoring, and sustainable resource management. Nevertheless, sensor noise, low contrast, motion blur, and atmospheric clutter usually affect the utility of such data, reducing its usefulness in real-time environmental monitoring and intelligent visual analytics. To address these challenges, the paper suggests a new adaptive statistical quality profiling and refinement architecture with future functionality in real time work of infrared and optical satellite images. The proposed framework incorporates the modeling of the temporal-spatial consistency with adaptive histogram-based statistical assessments and edge-preserving denoising techniques to reconstruct degraded scenes and deliver high-fidelity outcomes. The system can be used as a diagnostic layer in intelligent visual analytics pipelines as well as an enhancement module by incorporating both perceptual quality measures and statistical measures. Experiments on Sentinel and Landsat images indicate that the quantitative metrics are significantly enhanced: PSNR was improved to 29.8 dB and 32.3 dB when using an IR data and 24.1 dB and 26.5 dB when using an RGB optical data respectively. Similarly, SSIM, BRISque and NIQE increased by over 30 meaning that there was an improvement in perceptions. The framework not only is rapid in image fidelity, but also satisfies near real time performance, and can sustain throughput of up to 25 fps with GPU-accelerated inference and maintains the mean latency low, at less than 50 ms. These findings support the scalability of the given approach to the edge deployment within the process of satellite and ground-station. In addition to technical efficiency, the enhancements have a direct positive impact on environmental monitoring, making it possible to track the location of the hotspots of wildfires much better, haze less effectively to analyze air quality, and profile urban heat islands. In general, the research paper reflects a viable and scalable quality diagnostics and improvement system that facilitates sustainable environmental monitoring, smart visual analytics, and real-time Earth observation decision-making.
Copyright (c) 2025 Rami Baazeem https://creativecommons.org/licenses/by-nc-sa/4.0
Adaptive Federated Learning Empowered Wireless Localization Framework Using Vehicle Sensors
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.
Federated learning, Wireless Localization Framework, global positioning system, Wearable Sensors, cloud, localization.
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
Designing Remote-Sensed Intelligent Visual Analytics Algorithms for Environmental Monitoring Systems
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.
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
A Proposed Method for Real-Time Automatic Cloud Storage and Analysis of Detected Suspicious Activities to Ensure Data Integrity and Security
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.
Copyright (c) 2025 Himanshu, Harwant Singh Arri https://creativecommons.org/licenses/by-nc-sa/4.0
Window-Based Vision Transformer Network Implementation For Multi-Label Image Classification In Remote Sensing
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 algorithm, which has not been previously applied to the Multi-label AID dataset in the current literature, has been evaluated. This constitutes the first application of MaxViT to this dataset and provides a novel benchmark for multi-label remote-sensing classification. This algorithm has demonstrated superior performance on this dataset, significantly outperforming existing models and setting a new benchmark with an mAP of 84.98%.
Copyright (c) 2025 Emre Akkas,, Selda Güney https://creativecommons.org/licenses/by-nc-sa/4.0
A Hybrid CBIR Framework Using Vision Transformers and Genetic Algorithm for Enhanced Image Retrieval
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.
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
Alzheimer’s Classification with a MaxViT-Based Deep Learning Model Using Magnetic Resonance Imaging
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.
Copyright (c) 2025 Emrah Aslan, Süheyla Demirtas Alpsalaz, Yildirim Özüpak, Feyyaz Alpsalaz, Hasan Uzel https://creativecommons.org/licenses/by-nc-sa/4.0
Handwritten Recognition of Telugu Characters Using Various Pretrained Convolutional Neural Networks
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%.
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
A Hybrid LLM–Knowledge Graph Framework for Accurate Biomedical Question Answering
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.
Copyright (c) 2025 Havraz Younis Omar, Abdulhakeem O Mohammed Mohammed https://creativecommons.org/licenses/by-nc-sa/4.0
Auto-Tuned Particle Swarm with perturbed velocities - Optimized CLMF Algorithm for DVR based Power Quality Improvement
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.
Composite Least Mean Fourth (CLMF) Algorithm
Particle Swarm Optimization and Differential Evolution (PSODE),
Auto-tuned PSO and Differential Evolution (APSODE)
Copyright (c) 2025 Tummala Kranti Kiran, Balakrishnan Rajagopal, Yerramilli Butchi Raju https://creativecommons.org/licenses/by-nc-sa/4.0
Pancreatic Cancer Detection Using Quaternion Wavelet Transform and Squeeze-and-Excitation Network with SVM Classifier
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.
Copyright (c) 2025 Anagani Vijaya Shanthi, Alli Daisy Rani, Panuganti Ravi, Muppalla Tharangini https://creativecommons.org/licenses/by-nc-sa/4.0
Morphotectonic Controls on the Geomorphic Evolution: A Case Study of Diyala River Basin
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.
Copyright (c) 2025 Ayad Ali Faris Beg, Ghadeer F. Al-Kasoob, Ahmed H. Al-Sulttani https://creativecommons.org/licenses/by-nc-sa/4.0
A Machine Learning Approach to Consumer Behavior Analysis in Social Media-Influenced E-Book Markets
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.
Copyright (c) 2025 Sachin Tripathi, Akhilesh Tiwari, Kamal Upreti, G V Radhakrishnan https://creativecommons.org/licenses/by-nc-sa/4.0
An Adaptive State-Augmented Kalman Filter for Robust UAV Altitude Control with Online Sensor Bias Correction and Dynamic Weighting in Degraded Environments
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.
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
SALF: A Blockchain-Based Framework for Scalable Academic Credential Management and Institutional Governance
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.
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