The paper proposes a fuzzy logic based food recommendation with the concepts of BMI (Body Mass Index), age, recommended nutrients and income. In Bangladesh, most of the people are suffering from malnutrition as they have no clear idea about food nutrition and the case is worse in the rural area due to low income of people are living there. We have developed a fuzzy model that recommends addition or reduction of food items in daily food habit that meets nutritional needs and budget limits of rural people in the context of Bangladesh. Since people have variations in their food intake, we have focused mostly in their nutritional imbalances to find the appropriate food groups that can be suggested. Local food items have been checked and divided into low and high cost categories. By fuzzifying different parameters, a recommended food list is presented that will consider both the income and preference level of rural people.

The paper demonstrates the use of clustering to find different sensitive seismic zones and time series for the earthquake hazard prediction. Anticipating seismic activities using previous history data is obtained by applying hierarchical, k-means and density based clustering. Data is collected first and then clustered. Finally, the clustered data is used to obtain the different seismic zones on map. On the top of that data is used in linear regression to build a predictive model for forecasting upcoming earthquakes’ magnitudes for different regions in and nearby areas of Bangladesh.

This analysis has focused on the dissimilarity of diseases caused by malnutrition in different districts of Bangladesh. Among the 64 districts, there is no single one found where people have grown proper nutritional food habit. Low income and less knowledge are the triggering factors and the case is worse in the rural areas. In this research, a distributed enumerating framework for large data set is processed in big data models. Fuzzy logic has the ability to model the nutrition problem, in the way helping people to calculate the suitability between food calories and user’s profile. A Map Reduce-based K-nearest neighbor (mrK-NN) classifier has been applied in this research in order to classify data. We have designed a balanced model applying fuzzy logic and big data analysis on Hadoop concerning food habit, food nutrition and disease, especially for the rural people.