Twitter is a renowned microblogging site that allows users to interact using tweets and it has almost reached 206 million daily active users by the second quarter of 2021. The ratio of Twitter bots has risen in tandem with their popularity. Bot detection is critical for combating misinformation and protecting the credibility of online disclosures. Current bot detection approaches rely on the Twitosphere’s topological structure, ignoring the heterogeneity among the profiles. Moreover, most techniques incorporate supervised learning, which depends strongly on large-scale training sets. Therefore, to overcome these issues, we proposed a novel entropy-based framework to detect correlated bots leveraging only user behavior. Specifically, real-time data of users is collected and their online behaviors are modeled as DNA sequences. We then determine the probability distribution of DNA sequences and compute relative entropy to evaluate the distance between the distributions. Accounts with entropy values less than a fixed threshold represent bots. Extensive experiments conducted in real-time Twitter data prove that the proposed detection technique outperforms state-of-the-art approaches with precision = 0.9471, recall = 0.9682, F1 score = 0.9511, and accuracy = 0.9457.

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Deoxyribonucleic acid called DNA is the smallest fundamental unit that bears the genetic instructions of a living organism. It is used in the up growth and functioning of all known living organisms. Current DNA sequencing equipment creates extensive heaps of genomic data. The Nucleotide databases like GenBank, size getting 2 to 3 times larger annually. The increase in genomic data outstrips the increase in storage capacity. Massive amount of genomic data needs an effectual depository, quick transposal and preferable performance. To reduce storage of abundant data and data storage expense, compression algorithms were used. Typical compression approaches lose status while compressing these sequences. However, novel compression algorithms have been introduced for better compression ratio. The performance is correlated in terms of compression ratio; ratio of the capacity of compressed file and compression/decompression time; time taken to compress/decompress the sequence. In the proposed work, the input DNA sequence is compressed by reconstructing the sequence into varied formats. Here the input DNA sequence is subjected to bit reduction. The binary output is converted to hexadecimal format followed by encoding. Thus, the compression ratio of the biological sequence is improved.