Protected stored data as well as transfer in this virtual environment have been a significant thing since this world wide web has been used for information exchange. The need for data security rises as the level of personal data exchanged on the web is becoming more susceptible. To protect information from malicious use as well as alteration, services like confidential information but also data integrity have been needed. So many traditional cryptographic methods have been proposed by numerous studies throughout recent times to maintain multimedia data communicated over public networks. The chapter proposes a novel keyless picture encryption algorithm focused on a chaotic map. Almost every picture element is encoded by shuffling pixel values, which would be measured by an adapted cat map. In this suggested technique, steganography is used to transfer keyless encrypted information using a cover picture with encrypted information inserted in picture, audio, and video files.
With the emergence of social media platforms, most people have changed their way of interacting. Perhaps, sharing day-to-day lifestyle updates is a trend substantially influenced by microblogging sites, specifically Twitter, Facebook, Instagram, and many more. Moreover, text and messages are the most preferred way for such interactions. Twitter is one of the most commonly used microblogging tools that enable people to express their thoughts, opinions, emotions, happiness, sadness, excitement, ideas, mental stress, and so on. Hence, the sentiment prediction furnished by such textual data becomes a complex and challenging task. In this research, the authors proposed a hybridization of the convolutional neural network and bi-directional long short-term memory model (named ConvBidirectional-LSTM), which aims to better the categorization of sentiments of text data. Then, this proposed hybrid ConvBidirectional-LSTM model is compared with the existing state-of-the-art models, GloVe-based CNN-LSTM and Hierarchical Bi-LSTM (HeBiLSTM) models models. Furthermore, the performance of the proposed hybrid ConvBidirectional-LSTM model is evaluated on the US airline dataset using various performance parameters like accuracy, precision, recall, and
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score. The proposed model outperformed the existing state-of-the-art models with an accuracy rate of 93.25% in sentiment prediction.
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