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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Noise-Resilient Hybrid EfficientNet–Vision Transformer Framework with Adaptive Symmetric Cross-Entropy Loss for Robust Plant Disease Detection
The errors of human annotation and the noise of the environment such as lighting changes, occlusions and cluttered backdrop limit the correct detection of the plant diseases in the field condition. The research hypothesis is to present a robust deep learning model that can withstand noise and be interpretable in controlled and noisy environments to achieve high plant disease classification. The hybrid EfficientNet-Vision Transformer (ViT) network proposed is based on an EfficientNet-B4 branch of CNN and a branch of Vision Transformer (ViT) network, which focuses on capturing fine-grained lesion features and global contexts information. A data augmentation pipeline based on CycleGAN is used to introduce field-style distortions (e.g., (lighting shifts, shadowing, debris and partial occlusions), to be more robust to environmental noise, and an Adaptive Symmetric Cross-Entropy (ASCE) loss identifies and down-weights uncertain samples with normalized prediction entropy. The training is done in two phases, Stage 1 pretraining with clean images of PlantVillage and Stage 2 with increasingly noisy samples. The framework is tested in two different noise conditions, and these include the controlled synthetic label noise with PlantVillage and the real environmental noise with PlantDoc. The proposed model has an accuracy of 94.5% on the clean PlantVillage test set. It achieves 85.0% accuracy on the PlantVillage dataset under the 20% synthetic label noise protocol, outperforming ResNet-50V2 (76.5%), DenseNet-121 (78.9%), and Co-Teaching (79.5%). Macro-precision, macro-recall and macro-F1 of the model on the external PlantDoc field dataset are 0.718, 0.681, 0.681, respectively with a top-1 accuracy of 72.0, which is a manifestation of cross-domain generalization. The lesion-centric Grad-CAM images indicate that the model places emphasis on symptomatic areas of leaves and represses reactions of background soil, shadows, and clutters. The suggested hybrid EfficientNet-ViT architecture offers, in general, a robust and explainable solution to precision agriculture and intelligent crop tracking systems that are resistant to noise.
Copyright (c) 2026 Pradeep Gupta, Rakesh Singh Jadon https://creativecommons.org/licenses/by-nc-sa/4.0
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
Integrating Vision and Language: An Improved VAD Model
Automatic anomaly detection in video surveillance is crucial for public and private safety. However, it is challenging because of unclear abnormal events, limited labeled data, and mismatches between different types of data. Traditional video anomaly detection methods mainly focus on spatiotemporal visual features. They often ignore semantic information and interactions between different data types. Additionally, many multimodal approaches use basic fusion methods that do not solve the alignment problems between these types of data. To address these issues, we propose a multimodal framework that includes a Hierarchical Multi-scale Temporal Network (H-MSTN). This network models short-, medium-, and long-term dependencies in visual and textual data. A lightweight cross-modal attention module makes sure the semantics align. Meanwhile, a Multimodal Attention-Based Fusion Transformer (MAFT) refines cross-modal representations in real time. We evaluate this framework using the UCF-Crime and XD-Violence benchmarks. The proposed method achieves 92.42% AUC on UCF-Crime and 88.63% AP on XD-Violence with significantly lower computational cost and faster inference than recent multimodal baselines such as ReFLIP-VAD. These results demonstrate a strong efficiency–accuracy trade-off for real-time deployment while maintaining competitive or improved performance over prior methods such as MVAD and TEVAD.
Hierarchical Multi-Scale Temporal Network (H-MSTN)
Multimodal Attention-Based Fusion Transformer (MAFT)
Copyright (c) 2026 Manas Ranjan Biswal, Santos Kumar Baliarsingh 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
Neural Signatures of Alcoholism Revealed by Event-Related Potential Analysis of Open EEG Data
The event-related potential (ERP) analysis allows us to measure brain activity as it reflects sensory processing, attention, and a range of cognitive tasks. In this research article we introduce a comprehensive ERP-based approach for the identification of neurophysiological markers of alcoholism, using the open EEG dataset and the MNE-Python toolkit. All the EEG data recorded during a visual object recognition task were uniformly processed for both the alcoholic and control groups. After filtering and re-referencing, independent component analysis was applied, followed by segmenting the data into epochs and performing baseline correction. We focus on well-known ERP components, notably N2 and P300, occurring roughly 200-300 ms and 300-600 ms after the onset of the stimulus, respectively, which are associated with cognitive evaluation processes. We clearly see two distinct ERP profiles between the two groups. The alcoholic group shows reduced P300 and altered N2 compared with controls. This study presents a transparent and reproducible ERP analysis pipeline, developed solely with open-source tools and data, and highlights the potential of ERP markers as neurophysiological indicators of cognitive changes associated with alcohol use disorder.
Copyright (c) 2026 Akash Rajak 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
Urban Injection in Historic Centers: Space Syntax Approach
This study presents a comprehensive analytical-applied framework for the revitalization of historic urban centers through the application of an urban injection strategy guided by the results of spatial structure analysis. The historic Rusafa district of Baghdad serves as a case study. Field surveys, digital maps, and Depth map X analysis were employed to measure indices of integration, clarity, intensity, and selection. This allowed for the identification of areas with high structural coherence, visually prominent axes, and centers of urban activity. The results reveal a dual urban structure within the historic core. This structure is characterized by highly integrated and spatially dominant main axes, such as Al Khulafa Street and Al-Khilani Square, contrasted with secondary streets and pathways suffering from weak coherence and functional performance, particularly sections of Al Rashid Street. Activity is clearly concentrated along the Al-Rashid-Al-Khulafa axis. Based on this diagnosis, the study proposes the Urban Injection strategy as a precise intervention approach. It draws inspiration from therapeutic and aesthetic intervention patterns in the medical field (intravenous, intramuscular, subcutaneous, intraosseous, cosmetic, preventative, and spiritual injections) and repurposes them within the field of architecture and urban planning, adapting them to the spatial functioning of the historic fabric. This approach allows for targeted interventions that revitalize urban areas while preserving heritage values. It provides planners and decision-makers with an evidence-based and replicable tool that supports the achievement of the Sustainable Development Goals, particularly Goal 11 (Sustainable Cities and Communities) and Goal 13 (Climate Action). The study is constrained by limited socio-economic data and restricted access to parts of the study area. Moreover, the analysis focuses mainly on spatial structure, with limited consideration of social and environmental variables. Future research should incorporate behavioral and environmental indicators to broaden the framework’s analytical scope.
Copyright (c) 2026 Hawraa Oleiwi Shakeer, Sabeeh Lafta Farhan 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
Artificial Intelligence and Knowledge-Based Engineering for the Design of Environmental Nanomaterials: A Review
AI and KBE have been commonly applied into designing environmental nanomaterials to enhance performance, sustainability, and innovation. This review focused on how applying AI’s tools such as machine learning, expert systems, predictive modeling techniques and KBE frameworks accelerates material discovery time in addition to enhancing structural design optimization and decision making for environmental applications also. Case studies illustrate applications of the computing tools to integrate experimental work in water purification, pollution control, and renewable energy. The paper makes recommendations for the revision of existing methods and new areas of investigation for intelligent adaptive and sustainable frameworks for nanomaterial design.
Copyright (c) 2026 Konda Reddy Gondi, Sreenivasulu Surisetty, Damodhar Reddy, S. Bhaskar, P. Sneha 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
Interpretable XAI-Driven Crop Price Prediction System Integrating Climatic and Market Dynamics for Trustworthy and Informed Agricultural Decision-Making
Stability of agriculture, food security and economic planning depend on precise price prediction of crops. By incorporating both climatic and market dynamics and explainable artificial intelligence (XAI), one can make agricultural systems more transparent and build trust in their predictions. Nevertheless, most current models of crop price forecasting are plagued by low interpretability, weak nonlinear dependence treatment, and they cannot provide information on how climatic and economic variables affect the results of the predictions. To overcome these shortcomings, the proposed Explainable Hybrid Deep Learning using SHAP-based Interpretability (EHDL-SHAP) framework uses Recurrent Neural Networks (RNNs) to find temporal climatic effects, and Gradient Boosted Regression Trees (GBRT) to identify non-temporal market dynamics. SHAP (SHapley Additive exPlanations) is also used to give explainable information by measuring the impact that each feature makes to the eventual prediction. The suggested system allows making agricultural choices based on the data and being transparent at the same time, correlating the predictions of the models with the reality of climatic and economic conditions. The experimental analysis of multi-regional crop data data shows that E HDL-SHAP has a stronger prediction accuracy, interpretability, and stakeholder confidence than traditional black-box models do. The framework is a smart, articulable and dependable instrument that farmers, policymakers, and agribusiness can use as a reference to make a substantial decision on crop management and pricing. The suggested approach significantly enhances the prediction performance of 94.8 percent, regional generalization performance by 0.9 percent or so, and the prediction performance of the temporal dependency captures the highest correlation.
Copyright (c) 2025 R. Mahesh Kumar, Sukanya N S, L. Meenachi, Vilas Namdeo Nitnaware, Rohini V, V. B. kirubanand 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
Hydrological Analysis of Al-Rafee Watershed for Sustainable Rainwater Harvesting
This study aimed to conduct a hydrological analysis and assess the potential for rainwater harvesting using GIS-AL technologies for the Al-Rafee’ watershed, located in the eastern part of Iraq within the governorates of Salah Al-Din and Diyala Provinces. The total area of the basin is 212.19 km², lying between latitudes (34°33’18"–34°33,49"N) and longitudes (44°35’22"–44°46’17"E). The study relied on the SCS-CV model to estimate surface water runoff volume. The findings indicated the presence of three hydrological soil groups: B, C, and D. The land cover was found to be diverse, with agricultural lands occupying the largest area (91.12 km²), while barren lands had the smallest coverage (0.22 km²). The study also showed high Curve Number (CN) values and low retention (S) values. Furthermore, it concluded that it is possible to establish reservoirs to collect rainwater at the identified locations, with the aim of developing the studied region for future investment opportunities.
Copyright (c) 2026 Ali Kahdim Tilab, Mohammed Abdullah Mohammed, Mohammed Bahjat Thamer 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