Purpose: The security market comprises the buy and sell of the securities based on the demand. The security markets instruments comprise debt, equity, hybrid securities, and derivatives.
Theoretical framework:The investors perceive the adoption of the security market as necessary to examine the pre-investment in the security market.
Design/methodology/approach: With the implementation and incorporation of security features, the performance of the security market is improved.
Research, Practical & Social implications:The analysis is based on the Indian security market scenario through data collection from 300 respondents. The data were collected through primary data collection in quantitative and qualitative approaches.
Findings:The examination is based on the evaluation of the framed hypothesis based on the demographic profile of the investors in the security market.
Originality/value: The findings concluded that the risk associated with the security investments was subjected to security risk.
Establishing and management of social relationships among huge amount of users has been provided by the emerging communication medium called online social networks (OSNs). The attackers have attracted because of the rapid increasing of OSNs and the large amount of its subscriber’s personal data. Then they pretend to spread malicious activities, share false news and even stolen personal data. Twitter is one of the biggest networking platforms of micro blogging social networks in which daily more than half a billion tweets are posted most of that are malware activities. Analyze, who are encouraging threats in social networks is need to classify the social networks profiles of the users. Traditionally, there are different classification methods for detecting the fake profiles on the social networks that needed to improve their accuracy rate of classification. Thus machine learning algorithms are focused in this paper. Therefore detection of fake profiles on twitter using hybrid Support Vector Machine (SVM) algorithm is proposed in this paper. The machine learning based hybrid SVM algorithm is used in this for classification of fake and genuine profiles of Twitter accounts and applied the dimension reduction techniques, feature selection and bots. Less number of features is used in the proposed hybrid SVM algorithm and 98% of the accounts are correctly classified with proposed algorithm.
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