Agriculture plays a vital role in the food production process that occupies nearly one-third of the total surface of the earth. Rice is propagated from the seeds of paddy and it is a stable food almost used by fifty percent of the total world population. The extensive growth of the human population alarms us to ensure food security and the country should take proper food steps to improve the yield of food grains. This paper concentrates on improving the yield of paddy by predicting the factors that influence the growth of paddy with the help of Evolutionary Computation Techniques. Most of the researchers used to relay on historical records of meteorological parameters to predict the yield of paddy. There is a lack in analyzing the day to day impact of meteorological parameters such as direction of wind, relative humidity, Instant Wind Speed in paddy cultivation. The real time meteorological data collected and analysis the impact of weather parameters from the day of paddy sowing to till the last day of paddy harvesting with regular time series. A Robust Optimized Artificial Neural Network (ROANN) Algorithm with Genetic Algorithm (GA) and Multi Objective Particle Swarm Optimization Algorithm (MOPSO) proposed to predict the factors that to be concentrated by farmers to improve the paddy yield in cultivation. A real time paddy data collected from farmers of Tamilnadu and the meteorological parameters were matched with the cropping pattern of the farmers to construct the database. The input parameters were optimized either by using GA or MOPSO optimization algorithms to reconstruct the database. Reconstructed database optimized by using Artificial Neural Network Back Propagation Algorithm. The reason for improving the growth of paddy was identified using the output of the Neural Network. Performance metrics such as Accuracy, Error Rate etc were used to measure the performance of the proposed algorithm. Comparative analysis made between ANN with GA and ANN with MOPSO to identify the recommendations for improving the paddy yield.

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Big Data is a noteworthy environment to maintain the diversity of the huge amount of data. The big data utilizes machine learning algorithms to process large datasets which comes from various places such as histories, weblogs, and data repositories, large datasets and data warehousing, etc. In an existing method, most of the data mining approaches might not be able to maintain the large dataset. Using datamining, the big data are having lack of compatibility with database systems and analysis tools; large dataset clustering and analyzing is a big issue in big data. For this reason, the research work uses machine learning algorithms which are implemented in the Hadoop tool to collect and process the large amount of data which is structured, semi-structured or unstructured in a reasonable amount of time. Also, it gives more accurate prediction system and accurate information. Using Machine Learning Algorithm computational cost and complexities is minimized. The overall research work is implemented in the Hadoop tool with the help of the python programming language and it is compared with some existing algorithms. The proposed work tested with suitable parameters such as accuracy, Kappa T and Kappa M.

Educational Data Mining is an Emerging research domain which focus on extracting knowledge from educational databases to promote the learning environment. Educational psychology helps to understand the differences in learning process of a person from cognitive and behavioral perspective. This research aims to predict the personal intelligence of adolescence and early adulthood who are studying under graduate and post graduate courses in educational institution. Adolescence plays a very import role in the development of personality to new dimensions in human life. The Adolescence physical, mental, social, moral and spiritual outlooks undergo revolutionary changes. Many teachers and parents fail to asses these changes and they do not like to slacken their control over them. Psychologist has stressed to properly channelize the behavior of adolescence and give them adequate education. In this paper machine learning technique with decision tree induction algorithm was used to analyze the personal intelligence of college students. To construct the decision tree, Entropy and Information Gain are used as attribute selection measures in ID3 algorithm. Applying data mining techniques in the field of Educational Psychology is a new method and the rules generated from the decision tree helps to identify the personal intelligence of the students. This result helps the Educators to improve the learning environment better for Adolescence.