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In recent times, sentiment analysis research has gained wide popularity. That situation is caused by the nature of online applications that allow users to express their opinions on events, services, or products through social media applications such as Twitter, Facebook, and Amazon. This paper proposes a novel sentiment classification method according to the Fuzzy rule-based system (FRBS) with crow search algorithm (CSA). FRBS is used to classify the polarity of sentences or documents, and the CSA is employed to optimize the best output from the fuzzy logic algorithm. The FRBS is applied to extract the sentiment and classify its polarity into negative, neutral, and positive. Sometimes, the outputs of the FRBS must be enhanced, especially since many variables are present and the rules between them overlap. For such cases, the CSA is used to solve this limitation faced by FRBS to optimize the outputs of FRBS and achieve the best result. We compared the performance of our proposed model with different machine learning algorithms, such as SVM, maximum entropy, boosting, and SWESA. We tested our model on three famous data sets collected from Amazon, Yelp, and IMDB. Experimental results demonstrated the effectiveness of the proposed model and achieved competitive performance in terms of accuracy, recall, precision, and the F–score.

The Real-time Transport Protocol (RTP) is a media transport protocol. RTP is primarily designed to satisfy of the needs for multimedia transmission [1]. The Secure Real-time Transport Protocol (SRTP) provides confidentiality, message authentication, and replay protection for RTP traffic. However, there are risks of weak message authentication in SRTP Protocol [2]. With a weak Message authentication code it is easy for attacker to modify the SRTP packets. The main purpose of this paper is to propose an alternative scheme to provide a stronger authentication and integrity. The proposed scheme is implemented using Microsoft open source project for conference. The implementation is tested using StsGui NIST Statistical suite and Cryptool software [3] for security and Wireshark [4] for performance. The test results show that the proposed modification enhances the security with minor effect on the quality of service (QoS).

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