Browsing by Author "Samanta, B."

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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
Land-use land-cover changes in east singbhum mineralized zone: A case study
(2011) Kashinath, P.; Deb, D.; Vardhan, H.; Aruna, M.; Samanta, B.
Remote sensing images and techniques are widely used for environmental monitoring, climate changes, forest management and for water resource management. In the present work, identification of land-use land-cover (LULC) changes was studied based on Landsat Satellite (MSS) and IRS Satellite (LISS-III) images by Maximum Likelihood Classification (MLC) method. The study finds that the areas of water bodies and dense forest have decreased by more than 11 % and 6 %, respectively, while area covered by vegetation and habitats have increased by 16 % and 5 %, respectively. It was also found that dense forest was increased by 30 % around Norwapahar mine site area.
Land-use land-cover changes in east singbhum mineralized zone: A case study
(2011) Kashinath, P.; Deb, D.; Vardhan, H.; Mangalpady, M.; Samanta, B.
Remote sensing images and techniques are widely used for environmental monitoring, climate changes, forest management and for water resource management. In the present work, identification of land-use land-cover (LULC) changes was studied based on Landsat Satellite (MSS) and IRS Satellite (LISS-III) images by Maximum Likelihood Classification (MLC) method. The study finds that the areas of water bodies and dense forest have decreased by more than 11 % and 6 %, respectively, while area covered by vegetation and habitats have increased by 16 % and 5 %, respectively. It was also found that dense forest was increased by 30 % around Norwapahar mine site area.