Sanjivani College of Engineering

This research aims to develop a comprehensive mental health prediction system based on Internet of Things (IoT) and deep learning methods. Specifically, this prediction system is especially sensitive to changing stress levels, to allow prompt intervention and personalized support, in response to the critical demand related to precise, convenient, and stigma-free mental health observation. The proposed system utilizes Interval type-2 Fuzzy Gradient recurrent mixed multimodal Convolutional Neural Network (IFG-CNN) that has three major modules. (i) Stress detector component performs a question–answer based evaluation with the help of Robustly Optimized BERT Approach (RoBERTa). (ii) and A facial emotion recognition module takes visual data and uses Scale-Invariant Feature Transform (SIFT) to extract features and then uses emotion classification using a Rotation-Invariant Surface Attention Radial basis function neural Network (RISAR-Net) trained by the white-faced capuchin optimizer. (iii) The classification module is a mental health status module that classifies them into highly stressed, moderately stressed and normal. The proposed model has a better prediction accuracy with accuracy of 97.85

Skin cancer has recently become the fifth most common cancer worldwide, burdening both the economy and global health. Industrialization, genetic modification, and the rapidly changing environment have all contributed to an increase in the incidence of skin cancer. To overcome this challenge, this research suggests employing Fuzzy Self-Guided Structure Convolution Retention Generative Adversarial Network with Greater Cane Rat Algorithm (FSGSCR-GAN-GCRA) approaches to improve the identification accuracy of skin cancer. Initially, dermoscopic images are collected from the ISIC 2017 and ISIC 2018 datasets for analysis. Before this, preprocessing is performed using the Dual Bilateral Least Squares Hybrid Filter (DBLSF), which helps protect edges and remove outliers effectively. This is followed by the Feedback DenseNet201 Network (FDNet201), which enhances feature extraction and accentuates clinically significant regions by jointly using feedback attention strategies with the dense connections of DenseNet201. The Fuzzy Self-Guided Structure Convolution Retention GAN (FSGSCR-GAN) then applies Fuzzy logic and convolutional transformers to the extracted features to retain the structural integrity of lesions, local textures, and global patterns simultaneously. To overcome structural information loss, feature under-representation, and training instability, the GAN enables structure-preserving adversarial learning; the Feedback attention-enhanced DenseNet201 is more effective at lesion-specific feature discrimination; and optimization with the GCRA ensures efficient convergence of hyperparameters. The FSGSCR-GAN-GCRA shows better skin cancer identification than the current methodology. The proposed model has excellent Accuracy (99.71

A decision-centric workflow was developed to link a QbD-defined design space with interpretable surrogate modeling for rapid selection of ultrasound-triggered doxorubicin-loaded chitosan microbubbles. A 15-run Box–Behnken design spanning chitosan, palmitic acid, and Pluronic F68 was used to quantify three critical quality attributes (CQAs)—mean size, encapsulation efficiency (EE), and burst release at 40 s (Burst40)—and to train response-specific surrogates evaluated by leave-one-out cross-validation (LOOCV). The DoE produced a broad performance envelope (size 2.35–4.85 µm; EE 60–82%; Burst40 55.6–95%) with clear composition-driven trade-offs, notably between EE and acoustic responsiveness. A regularized polynomial surrogate (PolyRidge) provided strong LOOCV performance for size (R²=0.885; RMSE = 0.264 µm) and EE (R²=0.871; RMSE = 2.18%), whereas Burst40 was only moderately predictable (R²=0.501; RMSE = 8.61%), consistent with threshold-like cavitation and microstructure sensitivity. Digital screening with multi-objective desirability and explainability (SHAP, permutation importance, partial dependence, LIME) shortlisted manufacturable candidates; experimental validation showed QbD/RSM was better calibrated for absolute size and EE, while both modeling approaches remained similarly limited for Burst40. Importantly, shortlisted formulations preserved suppressed baseline release and pronounced ultrasound-enhanced release, yielding trigger-dependent cytotoxicity and increased apoptotic commitment in MCF-7 cells.

Global agriculture is seriously threatened by plant diseases, which drastically reduce crop yields, economic productivity, and food security. Conventional methods of disease detection rely on manual inspections, which are time-consuming, error-prone, and often inconsistent under various field conditions. To overcome these limitations, the current study proposes an explainable hybrid CNN (Convolutional neural networks) method for precise plant disease identification and yield enhancement. The proposed architecture combines CNNs for dependable feature extraction with classifiers that make complex decisions using machine learning. A comprehensive preprocessing pipeline that incorporates noise reduction, augmentation, and normalization improves the model's predictive power in a variety of lighting scenarios, crop types, and occlusions. Explainable AI (XAI) methods, like Grad-CAM, demonstrate the important elements affecting classification results, improving interpretability and boosting user confidence. Even in the face of challenging lighting and background changes, experimental evaluations show superior efficacy over traditional models, achieving high F1-scores, recall, accuracy, and precision. Furthermore, by including yield-related insights that support data-driven agricultural decision-making, the methodology extends beyond simply identifying illnesses. A comprehensive strategy reduces crop losses, promotes sustainable farming methods, and allows for proactive disease management. For modern precision agriculture, the suggested system provides an open, scalable, and useful solution.

Artificial Intelligence has emerged as a pivotal advancement in textile systems, integrating intelligent sensing, wearable computing, IoT, and machine learning applications on smart fabrics for real-time monitoring of object behavior through predictive analytics. The primary areas of application identified in the investigation include healthcare, industrial safety, sports and fitness. Emerging domains represent; AI-enhanced wearable fabrics are perpetually evolving for ongoing patient surveillance and preventive health measures. The most significant security risk identified during this analysis is data breaches, closely followed by limited system access, adversarial AI assaults and denial of service attacks. Privacy-preserving technologies have emerged as a significant domain, with encryption along with blockchain, edge computing, and federated learning. This meta-analysis offers insights regarding contemporary research trends and obstacles in cybersecurity which will assist researchers in creating secure and sustainable AI-integrated wearable textile systems.