Faculty Publications
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Publications by NITK Faculty
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Item ELBA-NoC: Ensemble learning-based accelerator for 2D and 3D network-on-chip architectures(Inderscience Publishers, 2020) Kumar, A.; Talawar, B.Network-on-chips (NoCs) have emerged as a scalable alternative to traditional bus and point-to-point architectures, it has become highly sensitive as the number of cores increases. Simulation is one of the main tools used in NoC for analysing and testing new architectures. To achieve the best performance vs. cost trade-off, simulators have become an essential tool. Software simulators are too slow for evaluating large scale NoCs. This paper presents a framework which can be used to analyse overall performance of 2D and 3D NoC architectures which is fast and accurate. This framework is named as ensemble learning-based accelerator (ELBA-NoC) which is built using random forest regression algorithm to predict parameters of NoCs. On 2D, 3D NoC architectures, ELBA-NoC was tested and the results obtained were compared with extensively used Booksim NoC simulator. The framework showed an error rate of less than 5% and an overall speedup of up to 16 K×. © © 2020 Inderscience Enterprises Ltd.Item An uncertainty-aware decision support system: Integrating text narratives and conformal prediction for trustworthy accident code classification(Institution of Chemical Engineers, 2025) Kumar, A.; Senapati, A.; Upadhyay, R.; Chatterjee, S.; Bhattacherjee, A.; Samanta, B.It is imperative to assign accident classification codes to the Mine Safety and Health Administration (MSHA) accident data for effective data analysis and risk assessment. Although trained personnel are capable of performing this task, the manual process is both time-consuming and resource-intensive. Automating the classification process with machine learning (ML) algorithms promises to expedite code assignment. However, ML predictions typically lack uncertainty metrics. This study proposes an uncertainty-aware hierarchical classification framework that assists human experts in efficiently and accurately assigning accident codes. Several text representation techniques combined with different ML algorithms were employed within a hierarchical architecture to assign classification codes. Low-frequency codes were consolidated into a single category, with a primary classifier distinguishing between these and a secondary classifier further classifying the grouped categories. Regularized Adaptive Prediction Sets (RAPS) was integrated to quantify uncertainty. Highly confident predictions yielding single-class sets were automatically classified, whereas multi-class sets were flagged for manual review. Primary Classifier with XGBoost with word2vec text representation achieved the best performance, with 95.12 % coverage, 37.02 % single-class prediction sets at 96.11 % accuracy, and an average prediction set size of 2.39. Whereas the secondary classifier, a logistic regression model with TF-IDF representation, yielded 96.19 % coverage, an average set size of 1.80, and 53.66 % single-class prediction sets with 98.90 % accuracy. Additionally, sensitivity analysis determined that a 95 % coverage guarantee offers the best trade-off between prediction set size and coverage. The framework effectively integrates conformal prediction to quantify uncertainty and aid human experts in improving the decision-making process in safety management. Although the framework is broadly applicable across different sectors, it needs to be retrained on domain-specific data for effective use. © 2025 The Institution of Chemical Engineers