Enhanced SOC estimation of lithium ion batteries with RealTime data using machine learning algorithms.

Accurately estimating Battery State of Charge (SOC) is essential for safe and optimal electric vehicle operation. This paper presents a comparative assessment of multiple machine learning regression algorithms including Support Vector Machine, Neural Network, Ensemble Method, and Gaussian Process Regression for modelling the complex relationship between real-time driving data and battery SOC. The models are trained and tested on extensive field data collected from diverse drivers across varying conditions.

Lung Diseases Detection Using Various Deep Learning Algorithms.

The primary objective of this proposed framework work is to detect and classify various lung diseases such as pneumonia, tuberculosis, and lung cancer from standard X-ray images and Computerized Tomography (CT) scan images with the help of volume datasets. We implemented three deep learning models namely Sequential, Functional & Transfer models and trained them on open-source training datasets. To augment the patient's treatment, deep learning techniques are promising and successful domains that extend the machine learning domain where CNNs are trained to extract features and offers great potential from datasets of images in biomedical application.

Analysis of Electric Motor Magnetic Core Loss under Axial Mechanical Stress.

The electrical machine core is subjected to mechanical stresses during manufacturing processes. These stresses include radial, circumferential and axial components that may have significant influence on the magnetic properties and it further leads to increase in iron loss and permeability in the stator core. In this research work, analysis of magnetic core iron loss under axial mechanical stress is investigated.