RECOMMENDATION SYSTEM FOR BLOOD AND ORGAN DONATION FOR THE HOSPITAL MANAGEMENT SYSTEM

Big data analytics is nowadays a growing field where real time applications developed. Among the various applications, recommender system application playing vital role in recommending the services and products to the end users. In this paper we developed online blood bank and organ donation information system for hospitals in case of emergencies. As this plays a major role in saving lives, it is necessary to maintain the database for all the related information about the blood banks and the organ donation. Making this process simpler by creating MySQL database and using geo-location information and haversine algorithm for distance calculation and TOPSIS algorithm (Technique for Order of Preference by Similarity to Ideal Solution) for ranking the blood banks. The RVD algorithm (Regular Voluntary Donor) is used to select donors based on satisfy the condition. The availability of organs is displayed as pop up message with the time and its details are displayed

RECOMMENDATION SYSTEM FOR BLOOD AND ORGAN DONATION FOR THE HOSPITAL MANAGEMENT SYSTEM

Big data analytics is nowadays a growing field where real time applications developed. Among the various applications, recommender system application playing vital role in recommending the services and products to the end users. In this paper we developed online blood bank and organ donation information system for hospitals in case of emergencies. As this plays a major role in saving lives, it is necessary to maintain the database for all the related information about the blood banks and the organ donation. Making this process simpler by creating MySQL database and using geo-location information and haversine algorithm for distance calculation and TOPSIS algorithm (Technique for Order of Preference by Similarity to Ideal Solution) for ranking the blood banks. The RVD algorithm (Regular Voluntary Donor) is used to select donors based on satisfy the condition. The availability of organs is displayed as pop up message with the time and its details are displayed

A Survey to Identify an Efficient Classification Algorithm for Heart Disease Prediction

Classification is one of the prominent data mining techniques. The objective of the classification algorithms is to place the data in the appropriate class. Data mining plays a vital role in medical diagnosis. The aim of this paper is to identify an efficient classification algorithm for cardiovascular disease prediction. The efficiency of each classification algorithm is expressed using two parameters namely accuracy and Root Mean Square Error (RMSE). From our experimental analysis, we infer that iterative classifier optimizer algorithm results in higher accuracy

A Survey to Identify an Efficient Classification Algorithm for Heart Disease Prediction

Classification is one of the prominent data mining techniques. The objective of the classification algorithms is to place the data in the appropriate class. Data mining plays a vital role in medical diagnosis. The aim of this paper is to identify an efficient classification algorithm for cardiovascular disease prediction. The efficiency of each classification algorithm is expressed using two parameters namely accuracy and Root Mean Square Error (RMSE). From our experimental analysis, we infer that iterative classifier optimizer algorithm results in higher accuracy

A COMPARATIVE STUDY ON HEART DISEASE ANALYSIS USING CLASSIFICATION TECHNIQUES

As it is modern era where people use computers more for work and other purposes physical activities are reduced. Due to work pressure they are not worrying about food habits. This results in introduction of junk food. These junk foods in turn results in many health issues. Major issue is heart disease. It is the major cause of casualty all over the world. Prediction of such heart disease is a tough task. But Countless mining approaches overcome this difficulty. Nowadays data mining techniques play’s an important role in many fields such as business application, stock market analysis, e-commerce, medical field and many more. Previously many techniques like Bayesian classification, decision tree and many more are employed for heart disease prediction. In this proposal we are going to do a comparative study on three algorithms

NEIGHBORHOOD-BASED APPROACH OF COLLABORATIVE FILTERING TECHNIQUES FOR BOOK RECOMMENDATION SYSTEM

Recommendation System or Recommender System help the user to predict the "rating" or "preference" a user would give to an item. Recommender systems in general helps the users to find content, products, or services (such as digital products, books, music, movie, TV programs, and web sites) by combining and analyzing suggestions from other users, which mean rating from various people, and users. These recommendation systems use analytic technology to calculate the results that a user is willing to purchase, and the users will receive recommendations to a product of their interest. The aim of the System is to provide a recommendation based on users likes or reviews or ratings. Recommendation system comprises of content based and collaborative based filtering techniques. In this paper, collaborative based filtering has been used to get the expected outcome. The expected outcome has been achieved through collaborative filtering with the help of correlation techniques which in turn comprises of Pearson correlation, cosine similarity, Kendall’ s Tau correlation, Jaccard similarity, Spearman Rank Correlation, Mean-squared distance, etc. This paper tells about which similarity metrics such us Pearson correlation (PC), constrained Pearson correlation (CPC), spearman rank correlation (SRC) which is good in the context of book recommendation system and then applied with neighborhood algorithm

A COMPARATIVE STUDY ON HEART DISEASE ANALYSIS USING CLASSIFICATION TECHNIQUES

As it is modern era where people use computers more for work and other purposes physical activities are reduced. Due to work pressure they are not worrying about food habits. This results in introduction of junk food. These junk foods in turn results in many health issues. Major issue is heart disease. It is the major cause of casualty all over the world. Prediction of such heart disease is a tough task. But Countless mining approaches overcome this difficulty. Nowadays data mining techniques play’s an important role in many fields such as business application, stock market analysis, e-commerce, medical field and many more. Previously many techniques like Bayesian classification, decision tree and many more are employed for heart disease prediction. In this proposal we are going to do a comparative study on three algorithms

Hybrid Sentiment Classification of Reviews Using Synonym Lexicon and Word embedding

Sentiment analysis is used in extract some useful information from the given set of documents by using Natural Language Processing (NLP) techniques. These techniques have wide scope in various fields which are dealing with huge amount of data link e-commerce, business and market analysis, social media and review impact of products and movies. Sentiment analysis can be applied over these data for finding the polarity of the data like positive, neutral or negative automatically or many complex sentiments like happiness, sad, anger, joy, etc. for a particular product and services based on user reviews. Sentiment analysis not only able to find the polarity of the reviews. Sentiment analysis utilizes machine learning algorithms with vectorization techniques based on textual documents to train the classifier models. These models are later used to perform sentiment analysis on the given dataset of particular domain on which the classifier model is trained. Vectorization is done for text document by using word embedding based and hybrid vectorization. The proposed methodology focus on fast and accurate sentiment prediction with higher confidence value over the dataset in both Tamil and English

A Modified Polybius Square Based Approach for Enhancing Data Security

Digital communication is the prominent technique used by various organizations for information exchange. It replaces the traditional methods with the help of internet and its related technologies. There is a chance to retrieve the contents of the transmitted message from the unsecure communication medium. The biggest challenge is to deploy a suitable mechanism for secure communication. Cryptography plays a dominant role in the information security domain. This paper proposes a modified Polybius square based approach for efficient key generation. New key is obtained from the original key by performing three different operations on modified Polybius square namely Square ring rotation, Square reversal and Transpose. From the security analysis it can be inferred that the proposed approach generates an efficient key

A Modified Polybius Square Based Approach for Enhancing Data Security

Digital communication is the prominent technique used by various organizations for information exchange. It replaces the traditional methods with the help of internet and its related technologies. There is a chance to retrieve the contents of the transmitted message from the unsecure communication medium. The biggest challenge is to deploy a suitable mechanism for secure communication. Cryptography plays a dominant role in the information security domain. This paper proposes a modified Polybius square based approach for efficient key generation. New key is obtained from the original key by performing three different operations on modified Polybius square namely Square ring rotation, Square reversal and Transpose. From the security analysis it can be inferred that the proposed approach generates an efficient key