Raisoni Group of Institutions

Plant diseases threaten food security, especially in pulse crops essential for sustainable agriculture. This study explores Convolutional Neural Networks (CNNs) for rapid detection of major diseases such as Ascochyta blight, Fusarium wilt, and phytoplasma infections, which cause severe losses in countries like Ethiopia, India, and Taiwan. Using advanced imaging, we propose a web-based diagnostic tool to empower farmers with timely, accurate disease identification, enabling effective management decisions. This approach offers not just technological advancement but also economic sustainability for smallholder farmers by improving crop health and yields through early intervention and precision agriculture practices.

<title>Abstract</title> Cloud computing has become an influential paradigm for facilitating effective storage, access, and sharing of medical information. In spite of its merits, issues related to data security and privacy have remained central limitations to its large-scale adaptation in the healthcare industry. In a bid to overcome such challenges, this article suggests a privacy-preserving framework to improve the privacy of healthcare data as well as for securing data sanitization and restoration based on a new hybrid algorithm called Memetic-Enhanced Glowworm Swarm Optimization with Adaptive Particle Refinement (ME-GSO-APR). The algorithm proposed combines Glowworm Swarm Optimization (GSO) for global exploration, Dynamic Particle Swarm Optimization (DPSO) for faster convergence, Memetic Algorithms (MA) for optimized local utilization, and Simulated Annealing (SA) to break free from optima. Altogether the algorithms are used to secure peak performance in data hiding as well as recovery operations. ME-GSO-APR has adaptive neighborhood scaling and improved key extraction methods that maintain data integrity without losing too much information. The performance of the proposed model is compared with benchmark optimization techniques like Artificial Bee Colony (ABC), Firefly Algorithm (FF), Glowworm Swarm Optimization (GSO), and Genetically Modified Glowworm Swarm Optimization (GMGW). Comparative study validates the superior efficiency of ME-GSO-APR in terms of statistical robustness, sanitization accuracy, restoration fidelity, convergence rate, and robustness to significant sensitivity variations between 10% to 70% as well as in the radar chart showing multi-metric performance. Made to address the rigorous demands of privacy maintenance in cloud environments of healthcare, the superiority of the developed model is established.

Wildfire forecasting is a critical challenge in environmental signal processing and disaster response planning. The ability to interpret multimodal spatiotemporal signals is essential for early warning systems and resource deployment. This study addresses these limitations by proposing a unified prediction-to-action framework. We utilized four open-access datasets—wildland fire emissions database (WFED), fire information for resource management system (FIRMS), Sentinel Hub, and a custom moderate resolution imaging spectroradiometer+shuttle radar topography mission (ERA5+MODIS+SRTM) fusion—covering fire occurrences, vegetation indices, meteorological parameters, and topographic features. These heterogeneous signals were preprocessed, aligned, and transformed into structured tensors for model training and evaluation. We use a transformer-based system to understand long-term patterns in space and time, enhanced by a belief–desire–intention (BDI) reasoning module that connects our predictions to flexible wildfire response plans. The novelty lies in the integration of signal-aware attention mechanisms with symbolic decision modeling. Model performance was evaluated using F1-score, intersection over union (IoU), mean absolute error (MAE), and directional accuracy. The suggested framework did better than the basic convolutional neural network (CNN) models, reaching an F1-score of 0.74, a directional accuracy of 84.3%, and lowering the MAE to 7.6 km², while also providing clear and relevant action suggestions.

Abstract Piles react nonlinearly and time-dependently to fast groundwater level fluctuations due to pore pressure and effective stress changes. Traditional design models use static stress assumptions and cannot update soil–structure interactions in real time, limiting their hydrologically active prediction power. This research presents an integrated multi-model framework with sensor networks, adaptive soil–mechanics formulations, machine-learning-based modulus development, probabilistic failure evaluation, and actuator-driven stiffness control. The framework includes a real-time groundwater–Terzaghi interaction model for dynamic effective stress evaluation, a transformer-network architecture for evolving soil modulus prediction, a Bayesian load-redistribution model for failure probability estimation, and a self-actuated response optimizer for localized stiffness corrections. Hydro-mechanical conditions are updated continuously via closed-loop elements. The integrated system outperforms earlier methods in settlement prediction, directional stress estimation, modulus evolution tracking, and risk-aware load control using full-scale pile testing datasets &amp; samples. Pile foundations subjected to different groundwater regimes improved in accuracy and reliability, enabling intelligent, self-regulating geotechnical infrastructure sets.

Abstract Histamine receptors (HRH1–HRH4) are G-protein coupled receptors (GPCRs) that mediate essential physiological functions, including neurotransmission, gastric acid secretion, immune regulation, and presynaptic autoregulation. Despite their importance, systematic comparative analyses across mammalian histamine receptor sequences remain scarce. In this study, we performed a comprehensive evaluation of HRH1–HRH4 across multiple mammalian species, integrating sequence homology, invariant residue mapping, amino acid substitutions, compositional frequencies, Shannon entropy, polystring distributions, intrinsic disorder profiling, and phylogenetic clustering. HRH2 and HRH1 were highly conserved among primates, HRH3 showed strong cohesion within rodents, while HRH4 exhibited pronounced divergence consistent with immune-related specialization. Invariant residues localized to transmembrane helices and activation motifs (D107, W428/Y431, NPxxY), underscoring strict evolutionary constraints on ligand binding, receptor stability, and G-protein coupling. Substitutions were confined to non-essential lipid-facing and loop regions, predominantly conservative in nature, enabling diversification without disrupting the GPCR fold. Amino acid frequency and entropy analyses revealed hydrophobic dominance with subtype-specific enrichment of polar residues, while disorder profiling identified HRH1 as the most dynamic and HRH2 as the most structurally constrained. Polystring analysis highlighted conserved motifs (WWW, PP) alongside subtype- and species-specific repeats, reflecting evolutionary strategies balancing receptor stability with adaptive flexibility. Phylogenetic clustering confirmed subtype-specific cohesion, with HRH3 and HRH4 forming compact clades, HRH1 showing moderate dispersion, and HRH2 forming the most isolated cluster. Collectively, these findings demonstrate that mammalian histamine receptor evolution is governed by conserved biophysical cores and selective variability, offering insights into structural conservation, functional diversification, and translational relevance for drug design and model selection.