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

Reversible Data Hiding scheme using modified Histogram Shifting in Encrypted Images for Bio-medical images

Existing Least Significant Bit (LSB) steganography system is less robust and the stego-images can be corrupted easily by attackers. To overcome these problems Reversible data hiding (RDH) techniques are used. RDH is an efficient way of embedding confidential message into a cover image. Histogram expansion and histogram shifting are effective techniques in reversible data hiding. The embedded message and cover images can be extracted without any distortion. The proposed system focuses on implementation of RDH techniques for hiding data in encrypted bio-medical images without any loss. In the proposed techniques the bio-medical data are embedded into cover images by reversible data hiding technique. Histogram expansion and histogram shifting have been used to extract cover image and bio- medical data. Each pixel is encrypted by public key of Paillier cryptosystem algorithm. The homomorphic multiplication is used to expand the histogram of the image in encrypted domain. The histogram shifting is done based on the homomorphic addition and adjacent pixel difference in the encrypted domain. The message is embedded into the host image pixel difference. On receiving encrypted image with additional data, the receiver using his private key performs decryption. As a result, due to histogram expansion and histogram shifting embedded message and the host image can be recovered perfectly. The embedding rate is increased in host image than in existing scheme due to adjacency pixel difference

Reversible Data Hiding scheme using modified Histogram Shifting in Encrypted Images for Bio-medical images

Existing Least Significant Bit (LSB) steganography system is less robust and the stego-images can be corrupted easily by attackers. To overcome these problems Reversible data hiding (RDH) techniques are used. RDH is an efficient way of embedding confidential message into a cover image. Histogram expansion and histogram shifting are effective techniques in reversible data hiding. The embedded message and cover images can be extracted without any distortion. The proposed system focuses on implementation of RDH techniques for hiding data in encrypted bio-medical images without any loss. In the proposed techniques the bio-medical data are embedded into cover images by reversible data hiding technique. Histogram expansion and histogram shifting have been used to extract cover image and bio- medical data. Each pixel is encrypted by public key of Paillier cryptosystem algorithm. The homomorphic multiplication is used to expand the histogram of the image in encrypted domain. The histogram shifting is done based on the homomorphic addition and adjacent pixel difference in the encrypted domain. The message is embedded into the host image pixel difference. On receiving encrypted image with additional data, the receiver using his private key performs decryption. As a result, due to histogram expansion and histogram shifting embedded message and the host image can be recovered perfectly. The embedding rate is increased in host image than in existing scheme due to adjacency pixel difference

SECURE DEDUP WITH ENCRYPTED DATA

Cloud computing is one of the way of service provision over the internet today. Cloud computing is the developing a next level from the last decades. One of the drawbacks, cloud storage is a privacy security at the CSP. So, the chunks users stored by the encrypted data for the purpose of security. Cloud storage vendors which allow to decreases chunked data and more efficient storage saver. One of the best techniques is deduplication, duplicate data is stored only once .In this paper, propose a checksum algorithm for distributing objects to agents, in a way that improves our chances of identifying a leaker. We evaluate its performance based on effective and efficient storage level. Its support data access control and revocation at the same time

Securing Web Applications from malware attacks using hybrid feature extraction

In this technological era, many of the applications are taking the utilization of services of internet in order to cater to the needs of its users. With the rise in number of internet users, there's a substantial inflation within the internet attacks. Because of this hike, Web Services give rise to new security threats. One among the major concerns is the susceptibility of the internet services for cross site scripting (XSS). More than three fourths of the malicious attacks are contributed by XSS. This article primarily focuses on detection and exploiting XSS vulnerabilities. Generally, improper sanitization of input results in these type of susceptibilities. This article primarily focuses on fuzzing, and brute forcing parameters for XSS vulnerability. In addition, we've mentioned the planned framework for contradicting XSS vulnerability

VALIDATING EFFECTIVE RESUME BASED ON EMPLOYER’S INTEREST WITH RECOMMENDATION SYSTEM

In current technological world, recruitment process of corporate has evolved to the greater extent. Both the candidates and the recruiters prefer resumes to be submitted as an e-document. Validating those resumes manually is not much flexible and effective and time saving. The team requires more man power to scrutinize the resumes of the candidates. The aim of our work is to help the recruiters to find the most appropriate resume that match all their requirements. The system allows the recruiter to post his/her requirement as query, and the system will recommend the relevant resume by calculating the similarity between the query and the resume using Vector Space Model (VSM)

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

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

Securing Web Applications from malware attacks using hybrid feature extraction

In this technological era, many of the applications are taking the utilization of services of internet in order to cater to the needs of its users. With the rise in number of internet users, there's a substantial inflation within the internet attacks. Because of this hike, Web Services give rise to new security threats. One among the major concerns is the susceptibility of the internet services for cross site scripting (XSS). More than three fourths of the malicious attacks are contributed by XSS. This article primarily focuses on detection and exploiting XSS vulnerabilities. Generally, improper sanitization of input results in these type of susceptibilities. This article primarily focuses on fuzzing, and brute forcing parameters for XSS vulnerability. In addition, we've mentioned the planned framework for contradicting XSS vulnerability