Huge amount of Healthcare data are produced every day from the various health care sectors. The accumulated data can be effectively analyzed to identify people's risk from chronic diseases. The process of predicting the presence or absence of the disease and also to diagnosing the various disease using the historical medical data is known as Health Care Analytics. Health care analytics will improve patient care and also the harness practice of medical practitioner. The feature selection is considered as a core aspect of the machine learning which hugely contribute towards the performance of the machine learning model. In this paper symmetry based feature subset selection is proposed to select the optimal features from the Health care data which contribute towards the prediction outcome. The Multilayer perceptron algorithm(MLP) used as a classifier which will predict the outcome by using the features which are selected from the Symmetry-based feature subset selection technique. The chronic disease dataset Diabetes, Cancer, Breast Cancer, and HeartDisease data set accumulated from UCI repository is used to conduct the experiment. The experimental results demonstrate that the proposed hybrid combination of feature selection technique and the multilayer perceptron outperforms in accuracy compare to the existing approaches.
Chat is one of the most effective and efficient forms of communication over the internet that offers an instantaneous transmission of text based messages from sender to the receiver. In our day to day life we had come across chat system many times. It may be either personal chat or confidential chat. The objective of this project is to maximize the security given to the chat messages. This security is provided by encrypting the message while sending, displaying as well as storing the messages. Smart chat allows the user to chat conveniently preserving the confidentiality of the messages on both the sender and the receiver side, hence allowing them to communicate their private messages in public. We implemented using our own symmetric algorithm called Rounded Box Encryption algorithm. We are planning to implement this project by using java programming language and Microsoft SQL with Visual Studio .Net Platform. This system can be used in organization, military application and in other applications where the secrete message to be exchanged and we also implement help to each other concept using data mining.
Sentiment analysis plays a major role in e-commerce and social media these days. Due to the increasing growth of social media, a huge number of peoples and users send their reviews through the Internet and several other sources. Analyzing this data is challenging in today's life. In this paper new normalization based feature selection method is proposed and the topic of interest here is to select the relevant features and perform the classification of the data and find the accuracy. Stability of the data is considered as the most important challenge in analyzing the sentiments. In this paper investigating the sentiments and selecting the relevant features from the data set places a major role. The aim is to work with the vector-based feature selection and check the classification performance using recurrent networks. In this paper, text mining depends on feature retrieval methods to improve accuracy and propose a single matrix normalization method to reduce the dimensions. The proposed method performs data preprocessing or sentiment classification and features reduction to improve accuracy. The proposed method achieves better accuracy than the N-gram feature selection method. The experimental results show that the proposed method has better accuracy than other traditional feature selection approaches and that the proposed method can decrease the implementation time.
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