Worldwide research shows that millions of lives lost per year because of heart disease. The healthcare sector produces massive volumes of data on heart disease that are sadly not used to locate secret knowledge for successful decision making. One of the most important aspects at this moment is detecting heart disease at an early stage. Researchers have applied distinct techniques to the UCI Machine Learning heart disease dataset. Many researchers have tried to apply some complex techniques to this dataset, where detailed studies are still missing. In this paper, Principal Component Analysis (PCA) has been used to reduce attributes. Apart from a Hybrid genetic algorithm (HGA) with k-means used for final clustering. Typically, the k-means method is using for clustering the data. This type of clustering can get stuck in the local optima because this method is heuristic. We used the Hybrid Genetic Algorithm (HGA) for data clustering to avoid this problem. Our proposed method can predict early heart disease with an accuracy of 94.06%.

In this present era, sentiment analysis is considered as one of the most rapidly growing fields of computer science study. It is a text mining technique which is automated and determines the emotion of a text. A text can be divided into many emotions using sentiment analysis. Since there are some studies on emotion analysis in the Bangla language, it is regarded as a key research area in the field of analyzing Bangla language. This paper works with five different emotions and those are Happy, Sad, Angry, Surprise and Excited. Apart from these emotions our paper also deals with two categories, such as Abusive and Religious. We proposed a method of machine learning technique which is the SVM algorithm to extract these five individual emotions from Bangla text.

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