Annasaheb Dange College of Engineering & Technology (ADCET) is an engineering education institute in the city of Ashta in the Indian state of Maharashtra. It is located about 20 km from Sangli. The ADCET campus is situated on nearly 32 acres of land. The college was established in 1999 by Annasaheb Dange. The institute is NAAC-accredited and NBA-accredited. The institute was once affiliated with Shivaji University, Kolhapur, but as of academic year 2017–2018, it gained autonomous status.
The Web of Clinical Things (WCT) coordinates the medical care industry with the Web of Things (WoT) environment. It enables the creation, collection, transmission, and analysis of clinical data through IoT integration. These IoT networks comprise various medical services, IT frameworks, clinical sensors, and management software. The IoMT enhances the medical care framework by creating a seamless, open, integrated, and reliable system. Nonetheless, ensuring secure verification in IoMT is challenging due to the diverse communication environment of various IoMT devices. Although various examinations have investigated potential IoMT device authentication techniques, more work is required, especially in client validation, to develop long-term IoMT solutions. Secret key administration remains one of the significant challenges in this specific circumstance. This paper presents an IoMTrelated electronic passwordless verification strategy that is quick, powerful, and secure. To achieve cross-stage similarity, it integrates a version of FIDO2/WebAuthn, one of the latest standards for passwordless authentication. This streamlines the process of obtaining and further developing client accreditations while maintaining compliance with regulations. The paper also assesses the delays in IoMT gadget verification and enrollment processes. Results and reproductions demonstrate how the proposed framework can achieve rapid validation on cloud servers with minimal enrollment and approval processes, regardless of device types.
Early and accurate detection of medical conditions is vital for improving patient outcomes, yet traditional diagnostic methods often face challenges such as time intensity, reliance on expert analysis, and susceptibility to errors. This project presents a Tumor Detection System that utilizes machine learning, particularly Convolutional Neural Networks (CNNs), to automate the detection and classification of proposed work from Imaging MRI scans. The system integrates advanced image processing techniques and leverages a comprehensive action dataset to train and optimize the model for high diagnostic accuracy. Beyond tumor detection, it features real-time MRI image analysis, a repository of tumor-specific symptoms and treatment recommendations, and an AI-powered chatbot to assist users. Designed with a userfriendly interface, the system ensures accessibility for healthcare professionals, enabling efficient decision-making and enhanced patient care. Scalable and adaptable, this solution holds the potential for broader applications in medical diagnostics, extending beyond proposed work to other imaging-based use cases. By bridging gaps in healthcare services, particularly in underserved areas, this project aspires to provide a reliable tool for early diagnosis and intervention, contributing to better patient outcomes and advancing medical research.
This article reports the structural characterization and luminescence study of Ca2La3(SiO4)3F:Dy3+ synthesized by solid state reaction (SSR) route. It belongs to hexagonal system with space group P6_3/m . SEM and EDAX study confirm formation of micro-crystalline powder and pure phase formation. The phosphor exhibits well intense peak around 573 nm attributed to electronic transition 4F9/2 → 6H13/2 along with other weak emission peaks when excited at 386 nm. Yellow-green emission is confirmed by the CIE chromatic coordinate diagram. This phosphor thus finds applications in solid state lighting, display devices and other optical applications.
The strategy of IoT security is based on a cybersecurity strategy in protecting IoT devices and the vulnerable networks they connect to from cyber-attacks. Traditional intrusion detection systems (IDS) cannot understand the complexity and volume of IoT network traffic and need advanced solutions for such a system. The proposed paper enforces a multi-phase model for IDS in IoT systems with considerations of the collection of datasets, preprocessing of data, feature extraction, hybrid optimization, ensemble Deep Learning (DL) models, and evaluation. The preprocessing phase involves all those essential data-cleaning techniques to handle missing values, duplicate records, encoding categorical features, and standardizing the numerical data. Feature extraction extracts the statistical and frequency-based features, such as flow-based metrics and N-gram analysis, that can highlight patterns in abnormal traffic. Correlation analysis, removing redundancy and improving informative power, while DL methods such as CNN are used for spatial feature extraction. A new hybrid approach is proposed in this paper, Hybrid Waterwheel Plant Algorithm and a Mother Optimization Algorithm (HWPAMO), for selecting the best features and improving performance. Finally, design an ensemble model involving various DL architectures: InceptionV3, VGG16, and Long Short-Term Memory (LSTM) networks with autoencoders with attention mechanisms and residual blocks. The developed technique is validated with other prevailing techniques in terms of kappa score, accuracy, MCC, recall, and precision. The proposed model demonstrates superior performance across all evaluated metrics for both 70
Electric vehicles (EVs) are increasingly recognized as a solution to transportation-related air pollution, yet their widespread adoption is limited by restricted driving range per charge. This manuscript proposes a novel hybrid technique to optimize energy use and extend EV range by integrating Sea-Horse Optimization (SHO) with Hamiltonian Deep Neural Networks (HDNNs), termed the SHO-HDNN technique. The proposed method enhances EV range and motor drive efficiency by using SHO to optimize energy consumption and HDNNs to predict EV range based on current driving conditions. The system's performance is evaluated through control error and speed analysis. The SHO-HDNN model is implemented in MATLAB and contrasted with other methods already in use, including Multi-Island Genetic Algorithm, Particle Swarm Optimization and Non dominated Sorting Genetic Algorithm (NSGA-II).Results demonstrate that the SHO-HDNN method improves prediction accuracy to 99
