Keyword is the important item in a document that provides efficient access to the content of a document. In the Existing system, Synonym, Homonym, Hyponymy and Polysemy problems were solved from only trained extracted keywords in the meeting transcripts. Synonym problem means different words which have similar meaning they are grouped and single keyword is extracted. Hyponymy problem means one word denoting subclass that is considered and super class keyword is extracted. Homonym means a word which can have two or more different meanings.. A Polysemy means word with different, but related senses. Hidden topics from meeting transcripts can be found using LDA model. MaxEnt classifier is used for extracting keywords and topics which will be used for information retrieval Training the keyword from the dataset is separately needed for all the problems, it is not an automatic one .In this proposed frame work, a dataset has been designed to solve the above mentioned four problems automatically.

Patient medical data are stored as Electronic Health Records (EHRs) in the cloud for decentralized clinical access. Information related to a patient’s health, diagnosis, and medication is vital for which individual privacy and security are vital considerations. This article introduces a Two-Phased Privacy Preserving Security Scheme (TP3SS) for EHR stored in clouds. The proposed scheme offers secure access control and attribute-based encryption for privacy-preserving and preventing data falsification. Secure access control is achieved by establishing mutual key-dependent smart contracts between the user, doctors and the EHR storage. The key authentication is provided using record-related attribute encryption that is valid within the contract period. The access and key validity are confined to the smart contract allocated interval by verifying the user identity. Here validity verification and access confinement are pursued using ledge-stored user information. The validation occurs for ensuring the EHR and user attributes are mapped together in the current and previous smart contract access sessions. In the record management process, Hyperledger fabric blockchain is used for preventing internal computation complexities. Similarly, the attribute that is inferred by the Hyperledger fabric blockchain in the current access session is alone used for a key generation; used for accessing and sharing the records. This process prevents the entry of adversary access and improves the security level under controlled complexity.

Sentiment classification aims to detect information such as opinions, explicit , implicit feelings expressed in text. The most existing approaches are able to detect either explicit expressions or implicit expressions of sentiments in the text separately. In this proposed framework it will detect both Implicit and Explicit expressions available in the meeting transcripts. It will classify the Positive, Negative, Neutral words and also identify the topic of the particular meeting transcripts by using fuzzy logic. This paper aims to add some additional features for improving the classification method. The quality of the sentiment classification is improved using proposed fuzzy logic framework .In this fuzzy logic it includes the features like Fuzzy rules and Fuzzy C-means algorithm.The quality of the output is evaluated using the parameters such as precision, recall, f-measure. Here Fuzzy C-means Clustering technique measured in terms of Purity and Entropy. The data set was validated using 10-fold cross validation method and observed 95% confidence interval between the accuracy values .Finally, the proposed fuzzy logic method produced more than 85 % accurate results and error rate is very less compared to existing sentiment classification techniques.