Every data and kind of data need a physical drive to store it. There has been an explosion in the volume of images, video, and other similar data types circulated over the internet. Users using the internet expect intelligible data, even under the pressure of multiple resource constraints such as bandwidth bottleneck and noisy channels. Therefore, data compression is becoming a fundamental problem in wider engineering communities. There has been some related work on data compression using neural networks. Various machine learning approaches are currently applied in data compression techniques and tested to obtain better lossy and lossless compression results. A very efficient and variety of research is already available for image compression. However, this is not the case for video compression. Because of the explosion of big data and the excess use of cameras in various places globally, around 82% of the data generated involve videos. Proposed approaches have used Deep Neural Networks (DNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs), and various variants of Autoencoders (AEs) are used in their approaches. All newly proposed methods aim to increase performance (reducing bitrate up to 50% at the same data quality and complexity). This paper presents a bibliometric analysis and literature survey of all Deep Learning (DL) methods used in video compression in recent years. Scopus and Web of Science are well-known research databases. The results retrieved from them are used for this analytical study. Two types of analysis are performed on the extracted documents. They include quantitative and qualitative results. In quantitative analysis, records are analyzed based on their citations, keywords, source of publication, and country of publication. The qualitative analysis provides information on DL-based approaches for video compression, as well as the advantages, disadvantages, and challenges of using them.

Question Answer System (QAS) automatically answers the question asked in natural language. Due to the varying dimensions and approaches that are available, QAS has a very diverse solution space, and a proper bibliometric study is required to paint the entire domain space. This work presents a bibliometric and literature analysis of QAS. Scopus and Web of Science are two well-known research databases used for the study. A systematic analytical study comprising performance analysis and science mapping is performed. Recent research trends, seminal work, and influential authors are identified in performance analysis using statistical tools on research constituents. On the other hand, science mapping is performed using network analysis on a citation and co-citation network graph. Through this analysis, the domain’s conceptual evolution and intellectual structure are shown. We have divided the literature into four important architecture types and have provided the literature analysis of Knowledge Base (KB)-based and GNN-based approaches for QAS.

Clouds play a vital role in Earth’s water cycle and the energy balance of the climate system; understanding them and their composition is crucial in comprehending the Earth–atmosphere system. The dataset “Understanding Clouds from Satellite Images” contains cloud pattern images downloaded from NASA Worldview, captured by the satellites divided into four classes, labeled Fish, Flower, Gravel, and Sugar. Semantic segmentation, also known as semantic labeling, is a fundamental yet complex problem in remote sensing image interpretation of assigning pixel-by-pixel semantic class labels to a given picture. In this study, we propose a novel approach for the semantic segmentation of cloud patterns. We began our study with a simple convolutional neural network-based model. We worked our way up to a complex model consisting of a U-shaped encoder-decoder network, residual blocks, and an attention mechanism for efficient and accurate semantic segmentation. Being an architecture of the first of its kind, the model achieved an IoU score of 0.4239 and a Dice coefficient of 0.5557, both of which are improvements over the previous research conducted in this field.

In this fast-growing world, there is huge competition in the market for employees. It becomes a tough task from an HR perspective to keep the most talented resources in the company to benefit the productivity of the company because every employee is a valuable asset. Employees tend to shift their current jobs numerous times due to various reasons and therefore employee turnover becomes a serious issue in this challenging world. This paper focuses on resolving the problem of employee attrition using classification algorithms like random forest, logistic regression and SVM on the IBM attrition dataset. If a valuable employee leaves an organization or gets promoted it becomes a difficult and tedious task to replace the employee. Architecture has been proposed in this paper which uses Random forest, SVM, Decision tree classifiers and similarity techniques to find the closest employees suitable for the vacancy. This includes finding similarities in the skills, qualifications and experience. Pre-trained word vectors are used to generate GloVe embeddings for finding document similarity. A personality match between two employees is calculated by taking a big five personality test, followed by clustering and finding the euclidean distance between two answer vectors in the same cluster. Best-Fit will finally recommend best-fit employees on the basis of resume match, personality match and retention probability.

Every element of life is being transformed by machine learning, and this has had a big influence on actual events. One of the many industries that has profited from machine learning is education. Other industries that have benefited include healthcare, engineering, business, entertainment, and transportation. Due to its disregard for how customers really make purchases, the conventional strategy for attaining sales and marketing objectives is no longer advantageous to businesses. The sales and marketing sectors have benefited greatly from advances in machine learning. These advances have made it much simpler for the sales team to create plans for expanding their business by looking at crucial factors like target market demographics and sales estimates for the coming years. This study's goal is to propose a factor for estimating Big Mart Companies' future sales while accounting for their past real sales. extended analysis of machine learning techniques for predicting sales, such as linear regression, random forest and K-Nearest Regression. The forecast takes into account information about the outlet's founding year, size, kind of outlet location, item weight, fat content, visibility, and MRP.