Shri Shankaracharya Institute of Professional Management and Technology

A nanocrystalline phosphor, Sr0.97Al2O4: Eu2+x, Dy3+y (with x = 1 mol

In digital world device based payment transfer system makes it faster and ease of use. With this note the vulnerability in the transaction system has major security threat. The threats like phishing, replay attack, SIM clone, and an unauthorized access of mobile device. For such attacks the convention methods for security used is not that much enough. The method used as multi factor authentication, mobile based OTP, this are now more exposed to the attacker, so there must be more secure system required that are reliable for the uses. This paper study targeted to overcome this scenario and proposed the approach TriSecPay for "triple layer secure payment protocol" method used for the security. The approach used to capture the biometric of used closely, identify the device with hardware integration combined with cryptographic keys, and the transaction details. The study finding shows the proposed approach get the 98.6% authentication accuracy and the decrease the chance of replay attacks by 99.2%, and rate of transactions speed increased by 12% compared to traditional OTP-based MFA. The results show that TriSecPay strikes the perfect balance between strong security and a smooth user experience. This makes it a great example for the next wave of secure mobile payment systems.

In today’s times, the increasing problem of brain tumor as Neurological disorder caters to the urgent need for an accurate, efficient, and scalable diagnosis that can be achieved at a high quality. Traditional diagnostics have been successful in the past, but they are too time-consuming to allow early detection and timely treatment. This paper presents NeuroGenius, an intelligent Healthcare system that uses machine learning algorithms onto medical imaging techniques, like MRI and CT scans that help us detect brain tumors. This paper presents NeuroGenius, a smart healthcare platform that has machine learning algorithms for MRI and CT scan technologies quickly detect brain tumors. The framework that we have used relies on preprocessing, feature extraction, and classification techniques by using Convolutional Neural Network (CNNs) based models and Support Vector Machines (SVMs) to achieve reliable results. It does not use any standalone heavy deep learning model but rather focuses on high scalability, and easy integration into real world. Clinical workflows and early detection when combined with easy data handling make NeuroGenius practical and secure. This is a very viable solution for both academic research and clinical use. Also, it supports health professionals through better diagnostic accuracy, reduced workload, and better patient results.

The battery of an electric vehicle (EV) is the spine of the current eco-sustainable transport, requiring proper surveillance and maintenance to ensure safety, durability, and performance. Battery behavior, however, is very temperature-sensitive, with variations of battery temperature and ambient temperature having a large effect on degradation and charging effects. As a solution to this, we offer a very predictive and highly interpretable model that categorizes the charging and performance of the battery under different temperatures. The originality of our method is that it uses a Fine Tree classifier, providing high results in terms of accuracy due to its low computational cost in comparison with deep learning alternatives. In our approach, the three tangible steps of the pipeline are data preprocessing, refined SVM to vet the feature structures, and linear decision trees to train based on parameters including SOC, voltage, current, battery temperature, and charging time. Experimental findings indicate that our model can reach a standard accuracy of 99.99

The prevalence and the long-term nature of chronic diseases such as cardiovascular disorders, diabetes, cancer, and osteoporosis are a major challenge to the health of the world because they are highly prevalent and have long-term effects. This requires careful prediction and early identification in order to minimize morbidity and revise patient outcomes. In this paper, the author discusses machine learning as a predictive model and early chronic disease diagnosis. There were numerous supervised and unsupervised learning algorithms that were utilized to process largescale patient datasets and electronic health records, such as decision trees, random forests, support vector machine, neural networks, and ensemble methods. To improve the performance of the models and their interpretability, a feature selection and data preprocessing were performed. Findings indicated that machine learning models are capable of high predictive accuracy, sensitivity, and specificity in predicting at-risk persons and disease progression. The combination of wearables, deep learning models and predictive analytics enhanced more personal risk assessment and intervention plans. The results demonstrate the possible role of AI-based diagnostic systems to facilitate clinical decision-making, facilitate timely interventions, and streamline the allocation of healthcare resources. Future studies need to increase data size, enhance predictability of modes and incorporate multi-modal health data to achieve greater predictive quality. The paper highlights the disruptive nature of machine learning in the chronic disease management and preventive healthcare.