In Computer-Aided Detection (CAD) brain disease classification is a vital issue. Alzheimer’s Disease (AD) and brain tumors are the primary reasons of death. The studies of these diseases are carried out by Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), and Computed Tomography (CT) scans which require expertise to understand the modality. The disease is the most prevalent in the elderly and can be fatal in its later stages. The result can be determined by calculating the mini-mental state exam score, following which the MRI scan of the brain is successful. Apart from that, various classification algorithms, such as machine learning and deep learning, are useful for diagnosing MRI scans. However, they do have some limitations in terms of accuracy. This paper proposes some insightful pre-processing methods that significantly improve the classification performance of these MRI images. Additionally, it reduced the time it took to train the model of various pre-existing learning algorithms. A dataset was obtained from Alzheimer’s Disease Neurological Initiative (ADNI) and converted from a 4D format to a 2D format. Selective clipping, grayscale image conversion, and histogram equalization techniques were used to pre-process the images. After pre-processing, we proposed three learning algorithms for AD classification, that is random forest, XGBoost, and Convolution Neural Networks (CNN). Results are computed on dataset and show that it outperformed with exiting work in terms of accuracy is 97.57% and sensitivity is 97.60%.

Every real-world scenario is now digitally replicated in order to reduce paperwork and human labor costs. Machine Learning (ML) models are also being used to make predictions in these applications. Accurate forecasting requires knowledge of these machine learning models and their distinguishing features. The datasets we use as input for each of these different types of ML models, yielding different results. The choice of an ML model for a dataset is critical. A loan risk model is used to show how ML models for a dataset can be linked together. The purpose of this study is to look into how we could use machine learning to quantify or forecast mortgage credit risk. This phrase refers to the process of evaluating massive amounts of data in order to derive useful information for making decisions in a variety of fields. If credit risk is considered, a method based on an examination of what caused and how mortgage credit risk affected credit defaults during the still-current economic crisis of 2021 will be tried. Various approaches to credit risk calculation will be examined, ranging from the most basic to the most complex. In addition, we will conduct a case study on a sample of mortgage loans and compare the results of three different analytical approaches, logistic regression, decision tree, and gradient boost to see which one produced the most commercially useful insights.