Conference Papers
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Item Comparative Analysis of Modern Mobile Operating Systems(Institute of Electrical and Electronics Engineers Inc., 2021) Chandrashekar, A.; Kumar, P.V.; Chandavarkar, B.R.The importance of smartphones has grown exponentially since their emergence. Smartphones have fundamentally modified the way people live by allowing them to easily access information and communicate with one another. With this meteoric rise, the operating systems run by these mobile devices have also come a long way in terms of their functionalities and their features. The operating system plays a vital and essential role in the use of these devices and cannot be overlooked. The operating system acts as the foundation of the device. The quality of the operating system directly impacts the quality of the device and also determines the usability of the device. A wide range of mobile operating systems, each with its own set of characteristics and features, currently exist in the market. This paper looks into some of the popular operating systems used in mobile devices and aims to compare and evaluate their different characteristics like architecture, security, and other attributes. This paper also analyzes a few of the advantages and disadvantages of these operating systems. © 2021 IEEE.Item fastText-Based Siamese Network for Hindi Semantic Textual Similarity(Springer Science and Business Media Deutschland GmbH, 2025) Chandrashekar, A.; Rushad, M.; Nambiar, A.; Rashmi, V.; Koolagudi, S.G.Semantic textual similarity is a measurement of the degree of similarity or equivalence between two sentences semantically. Semantic sentence pairs have the ability to substitute text from each other and retain their meaning. Various rule-based and machine learning models have gained quick prominence in the field, especially in a language like English, where there is an abundance of lexical tools and resources. However, other languages like Hindi have not seen much improvement in state-of-the-art methods and models to evaluate semantic similarity of text data. This paper proposes a fastText-based Siamese neural network architecture to evaluate the semantic equivalency between a Hindi sentence pair. The pair is scored on a scale of 0–5, where 0 indicates least similar and 5 indicates most similar. The corpus contains a combination of two datasets containing manually scored sentence pairs. The performance parameters used to evaluate this approach are model accuracy and model loss over a training period of multiple epochs. The proposed architecture incorporates a fastText-based embedding layer and a bi-directional Long Short Term Memory layer to achieve a similarity score. The proposed architecture can extract semantic and various global features of the text to determine a similarity score. This model achieves an accuracy of 85.5% on a compiled Hindi-Hindi sentence pair dataset, which is a considerable improvement over existing rule and supervise-based systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.