Now days from health care industries large volume of data is generating. It is necessary to collect, store and process this data to discover knowledge from it and utilize it to take significant decisions. Diabetic Mellitus (DM) is from the Non Communicable Diseases (NCD), and lots of people are suffering from it. Now days, for developing countries such as India, DM has become a big health issue. The DM is one of the critical diseases which has long term complications associated with it and also follows with various health problems. With the help of technology, it is necessary to build a system that store and analyze the diabetic data and predict possible risks accordingly. Predictive analysis is a method that integrates various data mining techniques, machine learning algorithms and statistics that use current and past data sets to gain insight and predict future risks. In this work machine learning algorithm in Hadoop MapReduce environment are implemented for Pima Indian diabetes data set to find out missing values in it and to discover patterns from it. This work will be able to predict types of diabetes are widespread, related future risks and according to the risk level of patient the type of treatment can be provided.

In recent times it is seen that the climatic and weather conditions not only in India but also in other countries have become uncertain and unpredictable, which may have devastating effects on the agriculture production. India being an agricultural country, most of the farmers largely rely on monsoons and agricultural production is weather dependent. The environmental factors like temperature, humidity, moisture, precipitation and many other parameters keep on changing rapidly and unpredictably. This unpredictable nature, variability of climatic or weather conditions makes the life of farmers quite miserable as they are unable to take proper decisions at the right time. Thus, it is the need of the hour to have a real-time, local weather station which would keep the farmers informed well in advance about the prevailing weather conditions so that they can take appropriate decisions at the right time and save their crops from loss. Precision Agriculture (PA) is an art of using the latest available technologies in the agriculture domain so as to make traditional agriculture more profitable and sustainable while reducing the wastage of resources. The penetration of internet into India is very deep and very fast, especially due to the Jio mania by Reliance Jio Infocomm Limited last year, high speed internet is now possible even in rural areas. This paper proposes a IoT based real-time local weather station for PA, that would provide farmers a means of automizing their agricultural practices (irrigation, fertilization, harvesting) at the right time. The proposed system would also aid the farmers to carry out the agricultural tasks on real-time bases, which in turn helps them to use the agricultural resources in efficient way and at the time when needed by the crops. The proposed weather system is a small step towards the development of PA system considering the Indian scenarios.

To determine the main factors associated with road traffic accidents is one of main objectives of accident data analysis. Due heterogeneity nature of road accident data makes analysis tricky. To overcome heterogeneity of data partitioning is used. The proposed method uses k-means clustering method as the main task of segmentation of road accident data. Further, association rule mining is applied to discover the situations related with the occurrence of the whole data set and the occurrence of clusters recognized by the k-means clustering algorithm. The combined result of k-means clustering and association rule produces major information.

Recent developments in digital image and Internet technology help the common users to easily produce illegal copies of the images. In order to solve the copyright protection problems of the image, several watermarking schemes have been widely used. Very few watermarking schemes have been proposed for defining the copyrights of color image. To resolve the copyright protection problem of color image, we propose an effective, robust and imperceptible color image watermarking scheme. This scheme embeds the watermark into cover image in (Red, Green, Blue) RGB space. The combinations of Discrete Wavelet Transformation (DWT) and Singular Value Decomposition (SVD) of Blue channel is used to embed the watermark. The singular values of different subband coefficients of Blue channel are modified using different scaling factors to embed the singular values of the watermark. The copy of the watermark is embedded into four subband coefficients which is very difficult to remove or destroy. The combinations of DWT and SVD increases the security, robustness and imperceptibility of the scheme.