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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Enhancement of IoT Security with Hybrid Cryptosystem of ECC and TinyML Integrated with Blockchain
This study addresses the challenge of securing smart-home Internet-of-Things (IoT) systems under severe resource constraints by proposing and evaluating a lightweight hybrid framework that couples on-device anomaly detection (TinyML) with elliptic-curve cryptography (ECC) and blockchain-based event logging. The approach first classifies incoming sensor readings locally using a TinyML anomaly detector (Isolation Forest); normal data are then encrypted with ECC and transmitted, while all security relevant actions are immutably recorded on a blockchain ledger to provide auditability and device trust. The framework was implemented on a smart home dataset of 49,000 records. The TinyML model achieved strong detection performance (0.98 Precision, 0.97 Recall, 0.975 F1-score, 0.996 Accuracy). Cryptographic and logging overheads were small average ECC key generation in 5.12 ms, encryption 0.85 ms, decryption 0.82 ms and blockchain logging. Overall, the results indicate that combining on device anomaly detection with ECC-secured communication and tamper-evident logging can deliver end-to-end protection, transparency, and scalability for smart-home IoT.
Special Issue: Selected Proceedings of the 1st International Conference on Artificial Intelligence for Sustainability in the Developing World
Copyright (c) 2026 Ibrahim M. Ahmed, Siddeeq Y. Ameen, Yousif Khalid Yousif https://creativecommons.org/licenses/by-nc-sa/4.0
Reinforcement Learning for Join Order Optimization in PostgreSQL: Query Rewriting and Evaluation on JOB and TPC-H Benchmarks
Join order optimization is a critical combinatorial problem in query processing. This paper applies reinforcement learning (RL) techniques to the join order optimization task in PostgreSQL by implementing and evaluating three separate RL-based optimizers: Proximal Policy Optimization (PPO), Deep Q-Network (DQN), and Advantage Actor-Critic (A2C). Each method is trained on the Join Order Benchmark (JOB) and evaluated on both JOB and TPC-H workloads. Performance is compared against PostgreSQL’s default planner without join reordering (PG-Old) and the built-in PostgreSQL optimizer with reordering enabled (PG-Join). Results show that RL-based methods significantly reduce execution times compared to PG-Old and often perform on par with or better than PG-Join, especially for complex multi-join queries. Among the tested methods, PPO achieves the most consistent improvements, with up to 4.47× average speedup over PG-Old on JOB and measurable gains on TPC-H. These findings demonstrate the potential of reinforcement learning as a practical and adaptive approach to join order optimization in relational databases.
Copyright (c) 2026 Mohammed R. Omar, Nawzat S. Ahmed https://creativecommons.org/licenses/by-nc-sa/4.0
Part-of-Speech Tagging-Based Document Clustering for Kurdish Corpora
This study investigates the Natural Language Processing (NLP) challenges of unsupervised clustering for Kurdish documents, focusing on the role of Part-of-Speech (POS) tagging in improving clustering performance. Due to the linguistic complexity of Kurdish and the scarcity of annotated corpora, the proposed method used a TF-IDF (Term Frequency–Inverse Document Frequency) matrix with K-Means clustering, applied POS tagging to the Badini Kurdish corpus. POS tagging is important for capturing the syntactic and grammatical structures of text. The experiments were conducted using the UOZBDN corpus, which includes 231 documents distributed across five categories. To evaluate the impact of POS tag. Additionally, compared the clustering performance using POS tagging and No POS tags of the same corpus. One of the main challenges in this research is the absence of prior studies that apply document clustering techniques to Kurdish corpora. Therefore, there is limited prior work available for direct comparison with the results obtained in this study. The results showed that incorporating POS tags, particularly a carefully selected subset of 22 key of POS categories, significantly improved clustering performance. The proposed approach achieved a Purity score of 0.9714, an NMI score of 0.9477, and a Silhouette score of 0.2403. demonstrated that POS tagging significantly enhanced clustering quality and highlighted the importance of POS represented in Badini Kurdish corpus.
Copyright (c) 2026 Toreen Dilshad Masoud, Ismael Ail Ali https://creativecommons.org/licenses/by-nc-sa/4.0
Quantitative Evaluation of Adaptive Threshold-Based Denoising for AWGN-Corrupted Images Using Multiresolution Discrete and Stationary Wavelet Transforms
The integrity of digital images is often degraded by Additive White Gaussian Noise (AWGN), necessitating efficient denoising solutions. This study presents a comprehensive, quantitative benchmark of wavelet-based denoising by systematically evaluating the synergy between transform types and thresholding strategies. The main contributions of this work are threefold. First, it conducts a large-scale comparative analysis of 117 wavelet filters across both the Discrete Wavelet Transform (DWT) and the Stationary Wavelet Transform (SWT). Second, it integrates these transforms with both hard and adaptive SURE Shrink soft thresholding, evaluating their performance under two AWGN levels (sigma=15, sigma=25) on standard test images using Peak Signal-to-Noise Ratio (PSNR), while acknowledging the importance of perceptual metrics such as SSIM for future evaluation. Third, the results establish that SWT consistently yields higher PSNR than DWT, and SURE Shrink outperforms hard thresholding. Their combination achieved the best results, with average PSNR improvements of 15.26% and 13.23% over DWT for low and high noise, respectively. While the study is limited by its use of a single metric and grayscale images, the findings position SWT paired with SURE Shrink soft thresholding as a promising and competitive approach for image restoration in the presence of AWGN. Additionally, the study is primarily experimental, and further theoretical modeling is identified as future work.
Copyright (c) 2026 Firas Mahmood Mustafa, Jalal Sami Issa https://creativecommons.org/licenses/by-nc-sa/4.0
Intelligent Multilingual Academic Journal Search: A Hybrid Semantic and Graph-Based Retrieval Approach
In the rapidly expanding digital landscape of academic research, the sheer volume of scholarly publications presents significant challenges for researchers seeking relevant and high-quality information. Traditional keyword-based search engines often struggle with semantic nuances, interdisciplinary queries, and the growing multilingual nature of global scholarship. This paper introduces a novel, intelligent academic journal search engine designed to overcome these limitations. The system integrates a hybrid search architecture, combining Elasticsearch for lexical retrieval with Sentence-BERT (SBERT) for dense semantic embeddings, alongside Neo4j-powered knowledge graphs for relational exploration. It features robust bilingual support (Arabic-English) with domain-specific query correction and personalized recommendations. Empirical evaluation confirms the system's effectiveness, achieving a mean Precision@10 of 0.808 and an nDCG@10 of 0.889 on a curated bilingual dataset. Furthermore, a user study involving 40 academic participants revealed that 93% found the graph-based visualization essential for exploratory discovery, while load testing demonstrated stable performance under concurrent query loads. These results establish a new benchmark for inclusive, high-performance, and intelligent academic information retrieval.
Copyright (c) 2026 Shireen Fathi Malo, Adel AL-zebari https://creativecommons.org/licenses/by-nc-sa/4.0
A DistilBERT-Based Email Phishing Detection System with SDN-Orchestrated Enforcement
Email phishing is a sophisticated type of cybercrime where perpetrators use credible parties to defraud their targets by using spam emails to spoof their accounts. This paper proposes NetShield-Phish, a novel multi-modal phishing detection system, which combines DistilBERT-based email content analysis with email content text classification and lexical feature engineering in a single framework of late-fusion, in conjunction with Software-Defined Networking (SDN) implementation of threat response. The methodology employs a fine-tuned DistilBERT model for email body classification, a DistilBERT-based email body content text analyzer, and a logistic regression model utilizing engineered lexical features, with probabilistic scores combined through late fusion and calibrated using ROC-based threshold optimization targeting a false positive rate. The system was evaluated using stratified 4-fold cross-validation on a dataset of 28,748 emails (61% legitimate, 39% phishing). The proposed system achieved 0.98 accuracy and 0.98 macro-F1 on held-out data; phishing recall reached 0.99 with a small fraction of legitimate emails routed to the monitor tier. These findings indicate that multi-modal fusion with explicit calibration can deliver high recall while containing user impact, and that coupling detection with programmable SDN actions bridges the gap between lab accuracy and dependable, operations-ready defense.
Copyright (c) 2026 Awos Kh. Ali, Ibrahim M. Ahmed, Siddeeq Y. Ameen https://creativecommons.org/licenses/by-nc-sa/4.0
Enabling Privacy-Preserving External Sharing of Electronic Medical Records Through a Tripartite Hybrid Blockchain Architecture
Patient-centric privacy, rapid interoperability, and verifiable auditability remain open problems in smart-healthcare data exchange. We present a tripartite hybrid blockchain framework that splits electronic medical records (EMRs) across three permissioned ledgers-Patient Chain, Provider Chain, and Social Chain-to decouple clinical processing from privacy-preserving external sharing. A crosschain Boneh-Lynn-Shacham aggregate-signature scheme fuses dual-chain approvals into a single 32byte proof, reducing signature storage by <!-- [if gte msEquation 12]><m:oMath><m:r><m:rPr><m:scr m:val="roman"/><m:sty m:val="b"/></m:rPr>99</m:r><m:r><m:rPr><m:scr m:val="roman"/><m:sty m:val="b"/></m:rPr>.</m:r><m:r><m:rPr><m:scr m:val="roman"/><m:sty m:val="b"/></m:rPr>9</m:r><m:r><m:rPr><m:scr m:val="roman"/><m:sty m:val="b"/></m:rPr>%</m:r></m:oMath><![endif]--> <span style="font-size: 11.0pt; line-height: 107%; font-family: 'Calibri',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: 'Times New Roman'; mso-fareast-theme-font: minor-fareast; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: Arial; mso-bidi-theme-font: minor-bidi; position: relative; top: 2.5pt; mso-text-raise: -2.5pt; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;"> and enabling constant-time verification. Zeroknowledge redaction releases only Tier-2 statistical summaries through the Social Chain, embedding GDPR/HIPAA principles of data minimization, purpose limitation, and complete auditability into the protocol rather than post-hoc governance. Security reductions to the Computational Diffie-Hellman problem and gambler's-ruin analysis show that, at <!-- [if gte msEquation 12]><m:oMath><m:r><m:rPr><m:scr m:val="roman"/><m:sty m:val="b"/></m:rPr>45</m:r><m:r><m:rPr><m:scr m:val="roman"/><m:sty m:val="b"/></m:rPr>%</m:r></m:oMath><![endif]--> <span style="font-size: 11.0pt; line-height: 107%; font-family: 'Calibri',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: 'Times New Roman'; mso-fareast-theme-font: minor-fareast; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: Arial; mso-bidi-theme-font: minor-bidi; position: relative; top: 2.5pt; mso-text-raise: -2.5pt; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;"> adversarial hash power, safe external finality requires 118 confirmations, which is <!-- [if gte msEquation 12]><m:oMath><m:r><m:rPr><m:scr m:val="roman"/><m:sty m:val="b"/></m:rPr>61</m:r><m:r><m:rPr><m:scr m:val="roman"/><m:sty m:val="b"/></m:rPr>-</m:r><m:r><m:rPr><m:scr m:val="roman"/><m:sty m:val="b"/></m:rPr>71</m:r><m:r><m:rPr><m:scr m:val="roman"/><m:sty m:val="b"/></m:rPr>%</m:r></m:oMath><![endif]--> <span style="font-size: 11.0pt; line-height: 107%; font-family: 'Calibri',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: 'Times New Roman'; mso-fareast-theme-font: minor-fareast; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: Arial; mso-bidi-theme-font: minor-bidi; position: relative; top: 2.5pt; mso-text-raise: -2.5pt; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;"> fewer than dual- or single-chain baselines. A three-chain prototype on private Ethereum sustains < 6 s mean latency at <!-- [if gte msEquation 12]><m:oMath><m:r><m:rPr><m:scr m:val="roman"/><m:sty m:val="b"/></m:rPr>50tx</m:r><m:r><m:rPr><m:scr m:val="roman"/><m:sty m:val="b"/></m:rPr>/</m:r><m:r><m:rPr><m:scr m:val="roman"/><m:sty m:val="b"/></m:rPr>s</m:r></m:oMath><![endif]--> <span style="font-size: 11.0pt; line-height: 107%; font-family: 'Calibri',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: 'Times New Roman'; mso-fareast-theme-font: minor-fareast; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: Arial; mso-bidi-theme-font: minor-bidi; position: relative; top: 2.5pt; mso-text-raise: -2.5pt; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;"> , while ZK processing averages 288.3 ms per request. The framework therefore delivers stronger security, lower cost, and regulatorready compliance, offering a practical blueprint for privacy-preserving EMR sharing in nextgeneration healthcare networks.
Copyright (c) 2026 Mohammed Baker Yousif, Omar Akram Khaleel Alsaffar, Siddeeq Y. Ameen https://creativecommons.org/licenses/by-nc-sa/4.0
Security Checkpoints at University Campus Entrances and Their Impact on Social Integration: A Case Study of the University of Wasit
Historically, universities are more of an open social and cultural system where interaction, accessibility, and engagement among neighbors are favored. Rise in pressure on security has seen most institutions adopt policies on physical control like security checkpoints at college gates. These interventions are supposed to improve the safety but might create some physical and psychological distance between the universities and the city. In this paper, the researcher will assess how security checkpoints influence the perceived Spatial and Social Impact of Security Checkpoints at University Campus Entrances: A Case Study of the University of Wasit. A combined mixed-method design was employed, creating a spatial analysis of four campus entry points and a structured survey of a questionnaire consisting of more than 40 questions, which was implemented among the students, employees, visitors, and residents of the location. The results show that security checkpoints boost perceived safety in internal users to a great extent. Nevertheless, they diminish the spatial receptivity as well as limit free motion and inhibit impromptu social manifestation. The indicators of social integration, especially the sense of belonging and interaction, have always registered low scores. The non-student users of the campus find it to be hard to enter and very monitored, which further assists in the perception of social and spatial isolation. The research outcomes are based on the findings suggests that although security checkpoints address protective goals, their design and functioning have the significant impact on the campus experience and campus-community relationships. It claims transparency in the checkpoint design; dispersed entrances and well balanced security measures will enhance the social connectivity without jeopardizing security.
Copyright (c) 2026 Hussain A. Saifan, Haider Majid Hasan https://creativecommons.org/licenses/by-nc-sa/4.0
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
Road Network Index: A Novel Spectral Index for Urban Road Network Extraction from Landsat Imagery
Urban road information extraction from low- to medium-resolution remote sensing imagery remains challenging due to the limited spectral separability between road surfaces and surrounding urban features. This work proposes a road network index (RNI), a novel spectral index for urban road network extraction, to delineate the urban road network from Landsat satellite imagery. In the first step, the Landsat data is pre-processed to handle the atmospheric correction followed by the selection of the spectral bands used to develop RNI by performing spectral analysis. The proposed RNI is used for the identification of road information from Landsat imagery, and the road and non-road information is separated using thresholding. Furthermore, the non-road components are eliminated by using geometric feature analysis. By applying RNI to Landsat multispectral imagery, we can extract urban road networks accurately and efficiently. Results reveal that RNI is automated, convenient in asphalt road extraction from medium-resolution satellite imagery. Experimental results demonstrate that the proposed approach achieves an overall accuracy of 91.67%, a Kappa coefficient of 0.8334, and an F1-score of 91.64%, indicating strong agreement and balance between precision and recall. These findings confirm that RNI provides an automated and reliable solution for asphalt road extraction from medium-resolution satellite imagery, particularly in complex urban environments.
Copyright (c) 2026 Yashwant Soni, Uma meena, Vikash Kumar Mishra 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
AI-Driven Intelligent Visual Analytics of Remote-Sensed Data for Sustainable Earth and Environmental Monitoring
The ecosystem degradation and climate change are increasing at a very high rate, and this phenomenon must be monitored with the aid of the most advanced technologies to organize the management of resources in the most efficient manner. In the paper, a smart visual analytics system is proposed that may be applied to utilize the remote-sensed data on the holistic monitoring of the environment. The model applies multispectral and hyperspectral satellite images and machine learning and deep learning models to determine crucial indicators of the environment (such as vegetation cover, water quality, and urban growth patterns). A graph-based clustering approach with Convolutional Neural Networks (CNNs) can be used to extract features and cluster data to identify anomalies in real time to interpret geospatial data on a big scale. The results show that the specified system would be effective in the land cover classification with an accuracy of 93%, in contrast to the accuracy of the conventional remote sensing analytics tools, which is 15 percent higher. In addition, the use of normalized different indexes estimation of water quality led to the R² of 0.87 that indicated the high predictive reliability. The visual analytics dashboard can dynamically interact with spatial-temporal patterns in such a way that the sustainability of the available resources can be managed by the decision-making process.
Artificial Intelligence and Machine Learning
Copyright (c) 2025 R.Vignesh, Vivek Parashar, Gayatri Hegde, Ravi Kishore Veluri, Nusrat Parveen, Mutkule Prasad Raghunath, Sandipan Biswas, Kantilal Pitambar Rane, Abhishek Raghuvanshi 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