Machine learning is the one of the branch in Artificial Intelligence to work automatically or give the instructions to a particular system to perform a action. The goal of machine Learning is to understand the structure of the data and fit that data into models that can be understood and utilized by the people. The proposed research work is for analysis of various machine algorithms applying on plant disease prediction. A plant shows some visible effects of disease, as a response to the pathogen. The visible features such as shape, size, dryness, wilting, are very helpful to recognize the plant condition. The research paper deals with all such features and apply various machine learning technologies to find out the output. The research work deals with decision tree, Naive Bayes theorem, artificial neural network and k-mean clustering and random forest algorithms. Disease development depends on three conditions-host plants susceptible to disease, favorable environment and viable pathogen. The presence of all three conditions is must for a disease to occur.

The tarai region of Kumaun which represents sub tropical vegetation was explored for the floristic diversity in year 2008-2011. A total of 429 species under 317 genera of vascular plants i.e., Angiosperm belonging to 95 families were recorded. These are listed in present communication.

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Mental disorders have a large impact on individuals, families, and communities, and are one of the main causes worldwide of disability and distress. Correct diagnosis of mental disorders is essential in clinical practice, pharmacological research, and successful treatment. Patients with mental retardation often have multiple and sometimes complicated medical problems. In this paper we have proposed a feed forward back propagation neural network to classify the level of mental retardation by using Matlab software. There are six neurons in the input layer which represent the attribute of a patient. Output layer contains four neurons which represent four different levels of mental retardation in which each patient will be classified

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