Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Nair, V.

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    Glowworm swarm based informative attribute selection using support vector machines for simultaneous feature selection and classification
    (Springer Verlag service@springer.de, 2015) Gurav, A.; Nair, V.; Gupta, U.; Jayaraman, J.
    In this paper, we propose a hybrid filter-wrapper algorithm, GSO-Infogain, for simultaneous feature selection for improved classification accuracy. GSO-Infogain employs Glowworm-Swarm Optimization(GSO) algorithm with Support Vector Machine(SVM) as its internal learning algorithm and utilizes feature ranking based on information gain as a heuristic. The GSO algorithm randomly generates a population of worms, each of which is a candidate subset of features. The fitness of each candidate solution, which is evaluated using Support Vector Machine, is encoded within its luciferin value. Each worm probabilistically moves towards the worm with the highest luciferin value in its neighbourhood. In the process, they explore the feature space and eventually converge to the global optimum. We have evaluated the performance of the hybrid algorithm for feature selection on a set of cancer datasets. We obtain a classification accuracy in the range 94-98% for these datasets, which is comparable to the best results from other classification algorithms. We further tested the robustness of GSO-Infogain by evaluating its performance on the CoEPrA training and test datasets. GSO-Infogain performs well in this experiment too by giving similar prediction accuracies on the training and test datasets thus indicating its robustness. © Springer International Publishing Switzerland 2015.
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    Formal Specification and Verification of Time-Sensitive Drone Systems using TLA+: A Case Study
    (Institute of Electrical and Electronics Engineers Inc., 2024) Surya, A.; Ayush, V.; Thakur, V.; Nair, V.; Das, M.; Mohan, B.R.
    This research paper presents a detailed analysis of time sensitivity in drone system operations, exploring the critical impact of temporal factors on their performance and reliability using Temporal Logic of Action (TLA+), primarily aiming to enhance the reliability and safety of drone systems. The study addresses the critical need to rigorously model complex drone behaviors while considering their interactions with the environment to identify and rectify potential safety hazards and system flaws. It introduces a new dimension by emphasizing the temporal aspect in critical systems, providing a dynamic perspective on system reliability. This research introduces a real-time module to accommodate commonly used time patterns, responding to the growing demand for time-sensitive evaluations in mission-critical systems. © 2024 IEEE.