Browsing by Author "Rushad, M."

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    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.
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    Machine Learning Based Propagation Loss Module for Enabling Digital Twins of Wireless Networks in ns-3
    (Association for Computing Machinery, 2022) Almeida, E.N.; Rushad, M.; Kota, S.R.; Nambiar, A.; Harti, H.L.; Gupta, C.; Waseem, D.; Santos, G.; Fontes, H.; Campos, R.; Tahiliani, M.P.
    The creation of digital twins of experimental testbeds allows the validation of novel wireless networking solutions and the evaluation of their performance in realistic conditions, without the cost, complexity and limited availability of experimental testbeds. Current trace-based simulation approaches for ns-3 enable the repetition and reproduction of the same exact conditions observed in past experiments. However, they are limited by the fact that the simulation setup must exactly match the original experimental setup, including the network topology, the mobility patterns and the number of network nodes. In this paper, we propose the Machine Learning based Propagation Loss (MLPL) module for ns-3. Based on network traces collected in an experimental testbed, the MLPL module estimates the propagation loss as the sum of a deterministic path loss and a stochastic fast-fading loss. The MLPL module is validated with unit tests. Moreover, we test the MLPL module with real network traces, and compare the results obtained with existing propagation loss models in ns-3 and real experimental results. The results obtained show that the MLPL module can accurately predict the propagation loss observed in a real environment and reproduce the experimental conditions of a given testbed, enabling the creation of digital twins of wireless network environments in ns-3. © 2022 ACM.
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    Resource-Aware Cryptography: An Analysis of Lightweight Cryptographic Primitives
    (Springer, 2022) Rushad, M.; Nambiar, A.; Chandavarkar, B.R.
    With the increased use of highly constrained interconnected devices in various areas like healthcare, sensor networks, the internet of things (IoT), etc., the need for security and proper encryption of data in such devices has grown as well. To ease the integration of security into these devices, it is reasonable to standardize some primitives. Despite there being several cryptographic algorithms to safely encrypt data, they are mainly designed for heavier use and they not only consume a lot of energy, but also require a lot of processing power and space. To provide secure data transfer and storage while allowing implementation on smaller, constrained devices, various lightweight cryptographic primitives have been developed and implemented. In this paper, we first describe the constraints that must be followed to claim a lightweight design, then we analyze the design as well as security and cryptanalysis of some recently proposed state of the art primitives that are competing to be chosen as industry standards. Finally, we also compare the performance of each of these primitives when implemented on microcontrollers and categorize them according to their design, functionalities, claimed security and respective performance. Finally, we present various insights and observations based on our study. © The Author(s).