The purpose and value of higher education is transforming. Technology is strengthening the capabilities of institutions to face the new challenges. Higher education has access to realms of data which can be used to improve decision making. The use of Big Data and analytics in higher education is relatively new area. The relevance of analytics is profoundly seen in many areas and higher education is no different. This paper examines the role of Big Data and analytics in higher education.

Kidney disease is a major public health concern that has only recently emerged. Toxins are removed from the body by the kidneys through urine. In the early stages of the condition, the patient has no problems, but recovery is difficult in the later stages. Doctors must be able to recognize this condition early in order to save the lives of their patients. To detect this illness early on, researchers have used a variety of methods. Prediction analysis based on machine learning has been shown to be more accurate than other methodologies. This research can help us to better understand global disparities in kidney disease, as well as what we can do to address them and coordinate our efforts to achieve global kidney health equity. This study provides an excellent feature-based prediction model for detecting kidney disease. Various machine learning algorithms, including k-nearest neighbors algorithm (KNN), artificial neural networks (ANN), support vector machines (SVM), naive bayes (NB), and others, as well as Re-cursive Feature Elimination (RFE) and Chi-Square test feature-selection techniques, were used to build and analyze various prediction models on a publicly available dataset of healthy and kidney disease patients. The studies found that a logistic regression-based prediction model with optimal features chosen using the Chi-Square technique had the highest accuracy of 98.75 percent. White Blood Cell Count (Wbcc), Blood Glucose Random (bgr), Blood Urea (Bu), Serum Creatinine (Sc), Packed Cell Volume (Pcv), Albumin (Al), Hemoglobin (Hemo), Age, Sugar (Su), Hypertension (Htn), Diabetes Mellitus (Dm), and Blood Pressure (Bp) are examples of these traits.

The advent of technology has led to rise in data being captured, stored and analyzed. The requirement of improving the computational models along with managing the voluminous data is a primary concern. The transition of the High Performance Computing from catering to traditional problems to the newer domains like finance, healthcare etc. necessitates the joint analytical model to include Big Data. The rise of Big Data and subsequently Big Data analytics has changed the entire perspective of data and data handling. Ever growing analytical needs for Big Data can be satisfied with extremely high performance computing models. As a result of enormous research in this field, recent years have seen the emergence diverse paradigms for Big Data analytics. With the spread of Big Data analytics in varied domains, newer concerns regarding the effectiveness of analytical paradigms are also observed. This paper highlights the major analytical models and concerns and challenges in High Performance Data Analytics.

Sports play a very significant role in the development of the human persona. Getting involved in games like Cricket and other various sports help us to build character, discipline, confidence and physical fitness. Indian Premier League, IPL provides the most successful form of cricket as it gives opportunities to young and talented players to show case their talents on various pitch. Decision-makers are the utmost customers for all fundamentals in the sports analytics framework. Sports analytics has been a smash hit in shaping success for many players and teams in various sports. Sports analytics and data visualization can play a crucial role in selecting the best players for a team. This paper is about the Toss Related analysis and the breadth of data visualization in supporting the decision makers for identifying inherent players for their teams.

<p class="Body">A recommender system is an approach to give an appropriate solu-tion to a particular problem. This helps in recognising the pattern or behaviour of a user to suggest future possible likes of the user. Nowa-days people like to travel during their spare time, it has become a rigid task to decide where to go. This paper represents a customised recommender system to help users in destining their itinerary. A model is designed to suggest the best places to visit in Rome. A questionnaire was prepared to get information about users interest during their travel. The model generates the best five places to visit with respect to the choice picked by the user. The top five places for each category will be displayed to the user and the user was asked to pick a starting point for the itinerary. Then the model generates another set off a filtered list of places to enhance their travel experi-ence. It includes displaying the top 5 restaurants to visit during their travel.</p>