The heart is the most important part of the human body. Any abnormality in heart results heart related illness in which it obstructs blood vessels which causes heart attack, chest pain or stroke. Care and improvement of the health by the help of identification, prevention, and care of any kind of diseases is the main goal. So for this various prediction analysis methods are used which job is to identify the illness at prelim phase so that prevention and care of heart disease is done. This paper emphasizes on the care of heart diseases at a primitive phase so that it will lead to a successful cure. In this paper, diverse data mining classification method like Decision tree classification, Naive Bayes classification, Support Vector Machine classification, and k-NN classification are used for determination and safeguard of the diseases.

The numbers of educational institutions are growing at par with the lost student rate in a country like India. When a missing student is found we need to identify the student on the strength of some common parameter like student name, his/her institution name, branch or class etc. But we never get accurate and complete information in most of the cases to identify or recognize a lost student. In such a situation, a soft computing model can be a striking choice to track a lost student on the basis of partial information. In the past we propose soft computing model for the same. This paper proposes a more optimized parallel soft computing model which takes half of the time taken by the earlier single thread model for identifying a lost student on the basis of imprecise and partial information. The system is tested meticulously on a database of 50000 records and an efficiency of 94% is obtained.

Data is one of the most important and vital aspect of different activities in today's world. Therefore vast amount of data is generated in each and every second. A rapid growth of data in recent time in different domains required an intelligent data analysis tool that would be helpful to satisfy the need to analysis a huge amount of data. Map Reduce framework is basically designed to process large amount of data and to support effective decision making. It consists of two important tasks named as map and reduce. Optimization is the act of achieving the best possible result under given circumstances. The goal of the map reduce optimization is to minimize the execution time and to maximize the performance of the system. This survey paper discusses a comparison between different optimization techniques used in Map Reduce framework and in big data analytics. Various sources of big data generation have been summarized based on various applications of big data.The wide range of application domains for big data analytics is because of its adaptable characteristics like volume, velocity, variety, veracity and value .The mentioned characteristics of big data are because of inclusion of structured, semi structured, unstructured data for which new set of tools like NOSQL, MAPREDUCE, HADOOP etc are required. The presented survey though provides an insight towards the fundamentals of big data analytics but aims towards an analysis of various optimization techniques used in map reduce framework and big data analytics.