Foreground segmentation in dynamic videos is a challenging task for many researchers. Many researchers worked on various methods that were traditionally developed; however, the performance of those state-of-art procedures has not yielded encouraging results. Hence, to obtain efficient results, a deep learning-based neural network model is proposed in this paper. The proposed methodology is based on Convolutional Neural Network (CNN) model incorporated with Visual Geometry Group (VGG) 16 architecture, which is further divided into two sections, namely, Convolutional Neural Network section for feature extraction and Transposed Convolutional Neural Network (TCNN) section for un-sampling feature maps. Then the thresholding technique is employed for effective segmentation of foreground from background in images. The Change Detection (CDNET) 2014 benchmark dataset is used for the experimentation. It consists of 11 categories, and each category contains four to six videos. The baseline, camera jitter, dynamic background, and bad weather are the categories considered for the experimentation. The performance of the proposed model is compared with the state-of-the-art techniques, such as Gaussian Mixture Model (GMM) and Visual Background Extractor (VIBE) for its efficiency in segmenting foreground images.

Identification of the foreground objects in dynamic scenario video images is an exigent task, when compared to static scenes. In contrast to motionless images, video sequences offer more information concerning how items and circumstances change over time. Pixel based comparisons are carried out to categorize the foreground and the background based on frame difference methodology. In order to have more precise object identification, the threshold value is made static during both the cases, to improve the recognition accuracy, adaptive threshold values are estimated for both the methods. The current article also highlights a methodology using Generalized Rayleigh Distribution (GRD). Experimentation is conducted using benchmark video images and the derived outputs are evaluated using a quantitate approach.

The heart is the one of the most typical and important organ in our human body. Over few decades Cardiovascular Diseases became one of the most frequent reasons of deaths. This threatening not only in India but also the whole world. The heart was attacked by so many factors like age, sex, diet, stress, smoking etc. So there is a need to early diagnosing the disease accurately so that immediate treatment can be provided and saves millions of lives .The incorrect prediction may also cause side effects or loss of life. In the last few decades eminent researchers are proposed many approaches to predict the heart diseases. In this article, we are reviewed different types of efficient machine learning algorithms for heart disease prediction with correlation matrices; visualize the features and performance metrics like precision, recall, accuracy. In our survey the logistic regression approach gives the best accuracy result which is 81.9%.