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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HSA-CNN: A Hybrid Spectral-Attention Multi-Agent Framework for Explainable Cloud Detection in Multispectral Remote Sensing Imagery
Cloud detection is an essential preprocessing step in a remote sensing application. However, the presence of clouds and their shadows severely hamper the accuracy of the surface observations and the subsequent analysis. Reliable identification of thin clouds and cloud shadows is still a problem to which even state-of-the-art deep learning-based cloud detection methods have not provided a solution, because of spectral ambiguity, spatial variability, and the lack of awareness of uncertainty of the models. This work presents HSA-CNN, a hybrid spectral, attention, multi-agent deep learning framework that accurately and explainably identifies pixel-wise clouds in multispectral satellite imagery. The proposed architecture is a U-Net-based encoder-decoder architecture that is complemented by SpectralDirectionalKernel (SDK) blocks for multi-scale feature extraction, and integrates a set of specialized agents, including a transformer-based spectral attention agent, a MobileNet-based spatial context agent, a bidirectional LSTM-based temporal sequence agent, and a Bayesian uncertainty agent. This meta-agent orchestration mechanism performs confidence-aware, per-pixel expert selection and ensemble fusion, enabling robust predictions and reliable uncertainty estimation. The experimental results show that HSA and CNN can accurately classify four cloud categories: clear sky, thick cloud, thin cloud, and cloud shadow. Moreover, it significantly improves thin cloud discrimination and prediction stability. Furthermore, the framework can provide interpretable outputs via attention maps, agent-weight visualizations, and pixel-level uncertainty maps, which improve transparency and operational trust. The proposed method is a powerful and interpretable tool in remote sensing that can be employed for atmospheric correction, environmental monitoring, and climate analysis.
Special Issue: Remote Sensing based Intelligent Visual Analytics for Real-time Environmental and Earth Monitoring Systems
Copyright (c) 2025 K.P. Swain, S.K. Mohapatra, Soumya Ranjan Nayak, Ashish Singh https://creativecommons.org/licenses/by-nc-sa/4.0
MRI-Based Brain Tumor Diagnosis Using Preprocessing Pipeline and Transfer Learning Models
Brain tumors represent a significant global health challenge, and their accurate and timely diagnosis is critical for effective treatment planning and improved patient outcomes. While transfer learning has shown promise in this domain, its performance is highly dependent on the quality of input data. This research introduces a novel, comprehensive 8-step preprocessing pipeline designed to significantly enhance the quality and feature visibility of Magnetic Resonance Imaging (MRI) scans for automated brain tumor classification. The pipeline includes resizing, grayscale conversion, Gaussian blurring, Otsu thresholding, contour isolation, Region of Interest (ROI) cropping, normalization, and a Power-law transform. To validate the efficacy of our proposed pipeline, we utilized a merged dataset of 10,287 MRI images from the Masoud and SARTAJ collections, encompassing four classes: glioma, meningioma, pituitary, and normal. This enhanced dataset was used to train and evaluate seven state-of-the-art transfer learning models: Xception, DenseNet-121, GoogLeNet, MobileNet, MobileNet-v2, VGG-19, and ResNet-50. Our rigorous preprocessing resulted in exceptional classification performance, with the Xception model achieving a peak accuracy of 98.68%. This study demonstrates that a meticulous and well-designed preprocessing pipeline is a critical and often overlooked component in developing highly accurate and reliable Computer-Aided Diagnosis (CAD) systems for clinical applications.
Copyright (c) 2026 Mahmood A. Nawar, Ahmed Musa Dinar https://creativecommons.org/licenses/by-nc-sa/4.0
Deep Learning Based Early Detection of Atherosclerosis for Stroke Prevention using Multi-Sensor Data Integration
Atherosclerosis is a progressive cardiovascular condition where arteries narrow due to plaque buildup, significantly increasing the risk of heart attacks and strokes. This study presents a non-invasive, Deep Learning-architecture based system for the timely diagnosis of atherosclerosis by means of real-time physiological and clinical data. This system integrates wearable sensors namely, Electrocardiogram (ECG), Photoplethysmography (PPG), Galvanic Skin Response (GSR), and Blood Pressure (BP) to continuously monitor heart-related parameters. Also, incorporates the clinical indicators namely blood glucose and cholesterol levels to enhance predictive accuracy. Data set comprises 226 records of the subjects having signs of atherosclerosis and 180 records of healthy subjects. The feature extraction involves total 8 original and 5 engineered features. Collected data undergoes preprocessing and is analyzed using various Deep Learning architectures including LSTM, BiLSTM, GRU, CNN-LSTM, and Transformer networks. These models are trained and evaluated using stratified K-fold cross-validation, ensuring consistent and generalized performance. The assessment metrics involves accuracy, precision, recall and F1 score. Among these, CNN-LSTM and Transformer models achieved superior accuracy and robustness in classifying individuals as healthy or at risk of atherosclerosis. The best model is chosen as CNN-LSTM with highest weighted score of 0.98 in comparison with other individual models. The final model is deployed in a user-friendly Streamlit interface, which helps users to input physiological data and receive real-time health predictions. The system provides a diagnostic output, confidence score, and highlights of any abnormal parameters. This solution addresses limitations of traditional diagnostics such as high cost, invasiveness, and lack of real-time feedback by offering a portable, affordable, and continuous monitoring tool. It empowers users, especially in remote or underserved areas, to take proactive measures for stroke prevention and cardiovascular health management.
Biomedical Field appplication in Health Care
Copyright (c) 2026 Akshaya D. Shetty, Usha Desai, Akash Saxena https://creativecommons.org/licenses/by-nc-sa/4.0
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
FedGuard-CI: Federated Defense Architecture for Privacy-Preserving Collaborative Learning against Model Inversion Attacks
Federated learning in collaborative intelligence (CI) environments introduces critical privacy risks, including model inversion and gradient leakage, particularly in sensitive domains such as healthcare and finance. This paper presents FedGuard-CI, a novel privacy-preserving framework that integrates dual-stage differential privacy, trust-aware secure aggregation, and a Model Inversion Risk Estimator (MIRE) to mitigate these threats. Experimental evaluation across multiple datasets demonstrates that FedGuard-CI achieves 93.1% accuracy at a privacy budget of ?=3.2\epsilon = 3.2?=3.2, outperforming FLAME and DP-FedAvg in both utility and privacy preservation. The framework reduces inversion success rate by 85% compared to FedAvg, with a 9.6% ISR and a 0.18 SSIM score, while maintaining low communication overhead (585 KB) and efficient runtime (30.2s per round). Ablation studies confirm the importance of MIRE and trust aggregation in enhancing both security and model performance. These results highlight FedGuard-CI’s practicality, scalability, and effectiveness as a foundation for secure and trustworthy federated intelligence.
Copyright (c) 2026 H. K. Al-Mahdawi, Ghada Al-Kateb, Maad M. Mijwil, Hussein Alkattan https://creativecommons.org/licenses/by-nc-sa/4.0
Federated Vision-Language Models for Privacy-Preserving Medical Image Analysis
Deep learning has enhanced the analysis of medical images but privacy issues and institutional variations restrict their large scale application in clinics. FedVLM, a federated vision language model tailored to privacy-preserving multimodal medical image analysis, is one of the solutions to these problems. Contrary to the conventional federated design, which can only process single modal image data, FedVLM takes paired radiological images and clinical reports jointly, which demonstrates high zero-shot and few-shot diagnostic performance. The design consists of secure aggregation, differential privacy and proximal optimization that ensure protection of patient data and minimize variability across sites. Large scale experiments on the NIH ChestX-ray14, MIMIC-CXR, and BraTS datasets indicate that FedVLM is an accurate and interpretable model that achieves near-centralized performance on vision language models without violating privacy. Building on previous works such as FACMIC, BioViL, and FAA-CLIP, FedVLM introduces new methods, including privacy-aware optimization, proximal regularization for varied data, and multimodal contrastive alignment, creating a unified federated framework for clear and secure medical image analysis. Although FedVLM shows promising performance, this work is currently at a research stage and is not yet ready for clinical use. We need validation through future multi-institutional clinical studies.
Copyright (c) 2026 Singamaneni Krishnapriya, Amaravarapu Pramod kumar, Boddupally Janaiah, K S Ranadheer Kumar https://creativecommons.org/licenses/by-nc-sa/4.0
Effective Image and Video Recognition Techniques in Environmental and Earth Monitoring Systems Using Remote-Sensed Intelligent Visual Analytics
Earth and environmental monitoring are very crucial to identify changes in climatic conditions, destruction of an ecosystem and calamities. The increased access to high-resolution satellite, aerial, and UAV imagery requires sophisticated intelligent visual analytics that can be used to derive actionable information on the basis of massive streams of remote-sensed data. The current image and video recognition methods are not always capable of attaining reliable performances in the presence of multimodal data heterogeneity, environmental dynamics, and interference of noise in remote-sensing images. These issues restrict the precision and flexibility of traditional deep learning-based monitoring systems to real-life applications. In this paper, we have suggested the Enhanced Visual Intelligence for Adaptive Recognition Network (EVIAR-Net). This deep learning model is a hybrid one that uses Graph-Convolutional Vision Transformers (GCVT) and Adaptive Multi-Source Fusion (AMSF). EVIAR-Net is able to store spatial correlations along with temporal dependencies using the graph-based spatial reasoning and transformer-based temporal encoding. AMSF actively combines multispectral, hyperspectral and video modalities to provide resistance to illumination, motion, and atmospheric perturbations. Performance assessments of various Earth observation datasets indicate an improvement in recognition accuracy of 21 percent, inference speed of 30 percent, and generalisation to unknown environments are better than CNN, ViT, and LSTM-based models. The suggested EVIAR-Net concept exhibits a smart, adaptable, and energy-saving strategy towards the next-generation environmental monitoring and predictive analytics.
Copyright (c) 2025 Gnana Rubini R, Raja J, Kamaraja pandian P, Vilas Namdeo Nitnaware, Sheemona Joseph, Ponni Alias Sathya S https://creativecommons.org/licenses/by-nc-sa/4.0
Life Cycle Assessment of Conventional and Self Compacting Concrete- A Cradle to Grave Approach
Concrete is widely used construction material all over the world. The environmental issues related to concrete production have been a concern for long time. Concrete production is known to be energy intensive and heavily dependent on natural resources like limestone and natural aggregates. Additionally, the production of cement, which is a crucial raw material for concrete, generates a significant amount of CO2. To cut down these impacts it is important to understand the whole life cycle of concrete. The present research performs a cradle-to-grave Life Cycle Assessment (LCA) of concrete to provide insight into each and every stage associated with it. For analysis study develops an Excel-based tool using data from Indian regions. The evaluation compares conventional concrete with Self Compacting Concrete (SCC). The evaluation examines the impact of cement type, mix design proportions, and End-of-Life (EOL) scenarios on overall outcomes. The findings demonstrate that replacing Ordinary Portland Cement (OPC) with blended cements such as Portland Pozzolana Cement (PPC) and Portland Slag Cement (PSC) can significantly diminish the environmental impact of conventional concrete. It cuts the amount of clinkers by 58% and Global Warming Potential (GWP) by 40%, However the results are not similar for SCC. The PPC based SCC required almost double the binder content to achieve necessary strength and workability. This mix has 27% higher GWP and 23% higher production cost than the OPC + fly ash based mix. This outcome shows that choosing an alternative cement type and replacing OPC alone does not ensure better performance unless the quantities are carefully optomized.
Supplementary Cementitious materials (SCMs)
Copyright (c) 2025 Trupti Parmar, Dr. Tarak Voraa 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.
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
Hybrid Enhanced Underwater Dark Channel Prior Framework with Vision Transformer Refinement, Mamba-Based Fusion, and Diffusion-Driven Dehazing
Underwater imaging can often exhibit colour casts, reduced contrast, and scattering effects due to wavelength-dependent absorption and light turbidity. This paper presents an enhanced underwater image restoration technique called Enhanced Underwater Dark Channel Prior with Advanced Refinement (EUDCP-AR), which combines a physics-based dehazing approach with modern refinement mechanisms for effective visibility recovery. The proposed framework incorporates a variation of Underwater Dark Channel Prior (UDCP) to determine the intensity of initial haze, a Vision Refinement with global attention based on transformer (ViT) to adjust local differences in the dark channel. A Mamba-fusion approach which is inspired by state-space refinements helps to improve the atmospheric light estimation through bidirectional brightness propagation, which results in better color balance and more uniform lighting.Subsequently, Diffusion helps in dehazing the transmission by way of a diffusion process, mapping and maintain edge information. Quantitative and qualitative experiments were done on various underwater datasets, measures of PSNR, SSIM, MSE and entropy as measures to evaluate the performance. The experimental findings prove that EUDCP-AR has better contrast enhancement, color fidelity and structural clarity with conventional and deep learning-based efficient methods of underground improvement. The reconstructed images stabilise natural tone, better contour, and definition of edges and are natural, noise artefacts were reduced, which proved the soundness of the hybrid physical-learning model. The proposed EUDCP-AR framework showcases a more robust, perceptually consistent and computationally efficient solution which helps in enhancing the underwater images. Its ability to preserve the fine details, balancing color attenuation and restoring the structural integrity, makes it good for applications in marine research, autonomous underwater vehicles (AUVs), submersible robotics, and scientific imaging.
Copyright (c) 2025 Lakshay Rana, Ayush Dogra, Shalli Rani, M Sravan Kumar Reddy https://creativecommons.org/licenses/by-nc-sa/4.0
Adaptive Pan-sharpening via Contourlet Decomposition and Anisotropic Diffusion for Multispectral-Panchromatic 2D signals
Pan-sharpening is now indispensable in remote-sensing computing by means of its ability to render not only high-spatial-resolution images but also precise spectral fidelity to the images. These features are essential for competent environmental measurement and investigation. The paper presents a contourlet-based pan-sharpening algorithm, which aims to combine the high spatial resolution of the panchromatic (PAN) images with the multispectral (MS) ones to extract high spectral resolution of the fused image. The algorithm tends to enhance the details of the space and reduce the spectral distortion of a multi-stage fusion procedure to some extent. The structure initiates with contourlet decomposition images captured by both PAN and MS, thus resembling directional and multi-scale structure. At the high-frequency sub-bands, an anisotropic filter is used to reduce noise, leaving salient edges intact, and a maximum-absolute selection rule is applied iteratively to inject spatial details effectively. In order to have spectral fidelity and to maintain global contrast on low-frequency components, saliency maps are used in conjunction with adaptive weight maps. The sub-bands reconstructed progressively give a high-resolution, artifact-minimal fused image. The proposed framework is validated through benchmark remote sensing datasets. The proposed methodology is based on objective metrics. Performance evaluation based on a benchmark remote sensing dataset demonstrates that the proposed methodology outperforms existing approaches in terms of sharper edges, contrast, and spectral features and offers an efficient solution for environmental applications.
Copyright (c) 2025 Suraj Thakur, Ayush Dogra, Shalli Rani, Bhawna Goyal, M Sravan Kumar Reddy https://creativecommons.org/licenses/by-nc-sa/4.0
Transparent Crop Price Forecasting Framework Using Explainable AI for Multi-Factor Driver Analysis and Stakeholder Decision Support
Reliable open crop price forecasts are needed for the future of agriculture, food availability, and making informed decisions by the stakeholders. The uncertain nature of the markets of farmers and traders, the fluctuation of their incomes, are typical results of the difficult work of prediction, which is caused by the interacting effect of meteorological, economic, and policy factors. Problem Statement: The conventional approaches to market movement forecasting are not always effective due to their incoherent nature and lack of transparency to the stakeholders. This renders it hard to ascertain the nature of the variables that are driving price changes. A predictability and responsiveness system that is explainable and data-driven is becoming more and more important, and this paper introduces the Explainable AI-based Crop Price Transparency Framework to Factor Analysis and Resilient Management (EXACT-FARM), one of the Crop Price Transparency Frameworks that is driven by Explainable AI. EXACT-FARM is a hybrid modelling (SARIMAX-XGBoost-LSTM) strategy coupled with feature-level explanations based on SHAP values and counterfactual reasoning. The model takes into account a number of factors in order to come up with forecasts and driver attributions that are easy to comprehend. These are the production trends, events in the policy, weather indices, and trade dynamics. The use of multiple-year datasets in experiments indicates that EXACT-FARM increases predictive accuracy by 1520 percent, and visually explains the influence of drivers. EXACT-FARM is an open and trusted decision-support system that gives stakeholders actionable pricing, planning, and sustainable agriculture management information.
Copyright (c) 2026 Pramoda Patro, Vilas Namdeo Nitnaware, Subhranginee Das, Harikishore R, Srinivas G, Akshaya Kumar Dash 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
Trust-Aware and Adaptive Malicious Node Detection in Fog Network using Independent DQN with Centralized Training
The proliferation of decentralized and dynamic networks, such as fog computing and the Internet of Things (IoT), has significantly raised the demand for network security and resilience solutions. This study presents a decentralized trust management framework for detecting malicious nodes in fog-to-fog networks using Multi-Agent Reinforcement Learning (MARL). Our approach utilizes Independent Deep Q-Networks (I-DQN) for decentralized decision making at each fog node with dynamic trust evaluation, allowing them to learn an optimal detection policy based on local observations. Importantly, we enhance this by centralized training controlled by a central orchestrator, which uses a shared global critic and parameter sharing. The proposed system was evaluated on a 10-15 nodes fog network under three distinct attack scenarios: aggressive, stealth, and gradual. Experimental results demonstrate superior performance with detection rates of 92.0% for aggressive attacks, 78.0% for stealth attacks, and 67.9% for gradual attacks. Security focused results demonstrate exceptional false negative performance with FNR values of 8.3% ± 2.0% for aggressive attacks (excellent performance), 22.0% ± 2.0% for stealth attacks (good performance), and 31.9% ± 1.9% for gradual attacks (acceptable performance), ensuring minimal malicious nodes remain undetected across all attack types. The proposed approach provides a highly secure and scalable solution for detecting malicious nodes in fog networks, offering superior threat detection through intelligent trust based decision making and coordinated multi-agent learning.
Copyright (c) 2026 Sheenu Singh, Meetu Kandpal, Harshad B Bhadka 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