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This research works on high symbolic Bengali text and transforms it into corresponding less symbolic English complying with the transliteration method. The Huffman-based approaches serve to compress retaining the original quality of the data. On the other hand, faster encoding and decoding is the most sophisticated sphere in data compression. We propose an adjacent distance array, a novel data structure based on the Huffman principle for encoding and decoding the character of transliterated text. The encoding and decoding algorithms have been explained for the introduced modus operandi and juxtaposed with conventional Huffman-based algorithms. Our research is outdoing than any regular Huffman-based algorithms, concentrating on the speed of the encoding and decoding manner discovered after estimating all decisions.

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The short message service (SMS) is a wireless medium of transmission that allows you to send brief text messages. Cell phone devices have an uttermost SMS capacity of 1,120 bits in the traditional system. Moreover, the conventional SMS employs seven bits for each character, allowing the highest 160 characters for an SMS text message to be transmitted. This research demonstrated that an SMS message could contain more than 200 characters by representing around five bits each, introducing a data structure, namely, adjacent distance array (ADA) using the Huffman principle. Allowing the concept of lossless data compression technique, the proposed method of the research generates character's codeword utilising the standard Huffman. However, the ADA encodes the message by putting the ASCII value distances of all characters, and decoding performs by avoiding the whole Huffman tree traverse, which is the pivotal contribution of the research to develop an effective SMS compression technique for personal digital assistants (PDAs). The encoding and decoding processes have been discussed and contrasted with the conventional SMS text message system, where our proposed ADA technique performs outstandingly better from every aspect discovered after evaluating all outcomes.

Requirements Prioritization (RP) is very indispensable and laborious phase in the course of requirement management of software engineering. Numerous research works have been conducted in the prioritization of small size requirements. However, problems are said to occur while considering large set software project requirements. In order to address the issue, in this paper we present the novel method called the Interdependency-aware Qubit and BrownRoost Rank (IQ-BR) method to prioritize the huge number of requirements. Optimization is a model that identifies the optimal requirements from a set of probable functions with respect to their attributes or requirements. Quantum Optimization is the familiar optimization algorithms is used in the IQ-BR. The novelty of the work lies in the use of the Interdependency-aware Qubit Requirement Selection algorithm and BrownBoost Rank Requirement Prioritization Learning model. An Interdependency-aware Qubit Requirement Selection algorithm is used to address the requirements prioritization issues to handle volatile and interdependencies among requirements during RP. With the optimal requirement selection results, BrownBoost Rank Requirement Prioritization Learning is finally applied to rank the requirements based on the BrownBoost Rank function. The proposed IQ-BR and existing methods are discussed with different factors such as requirement prioritization accuracy, requirement prioritization time, true positive rate and false-positive rate with respect to different functional and non-functional requirements. The observed results show superior performance of our proposed IQ-BR method when compared to state-of-the-art methods.