Faculty Publications
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Publications by NITK Faculty
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Item Accurate Estimation for Stability of Slope and Partition Over Old Underground Coal Workings Using Regression-Based Algorithms(Springer Science and Business Media Deutschland GmbH, 2022) Dorthi, K.; Kumar, A.; Ram Chandar, K.R.Numerical modeling simulation has found to be best solution for predicting slope and partition stability over old underground coal workings. But it has taken huge time to complete a single simulation model. In this regard, machine learning-based framework is used to predict the stability of old galleries. A case study is taken up in opencast mine and simulation is carried out using numerical model and machine learning-based framework. Framework has shown an overall accuracy of 94–95% for different slope and partition stability. Framework shows a speedup of 2366 × against numerical simulator. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Machine Learning and Thresholding Approach for Defects Classification in Solar Panels(Springer Science and Business Media Deutschland GmbH, 2025) Abhishek, G.H.; Kumar, A.; Kashyap, Y.This research addresses critical aspects of solar photovoltaic (PV) system maintenance and monitoring to ensure sustained performance. Emphasizing solar panel reliability, the study employs image processing, clustering algorithms, and machine learning (K-Means, Naive Bayes) to detect and categorize factors impacting efficiency, such as dust accumulation and sunlight exposure. The developed system facilitates comprehensive assessment and classification, enhancing operational lifespan. Demonstrating versatility, the project incorporates alternative feature extraction and interactive threshold selection, ensuring adaptability to diverse scenarios. Experimental validation, including hotspot detection in thermal images, underscores the robustness of the proposed methodology, contributing significantly to solar panel monitoring and maintenance advancements. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.Item Classification of Lateral Driving Events Using Vehicle Response Signals for Profiling Driving Behaviors(Springer Science and Business Media Deutschland GmbH, 2025) Arichandran, R.; Kumar, A.; Krishnakar, H.S.; Mohan, M.Classification of driving events is crucial in profiling driving behaviors, which could significantly increase road safety. In several studies classifying driving events, drivers were asked to perform hard turns and lane changes. However, these represented simulated situations much different from real-life scenarios. The main aim of this study was to classify lateral driving events (turn) and non-events from naturalistic driving data in actual driving conditions. A stretch of 8 km state highway was identified as the study road, and the data were collected using 8 drivers. The acceleration and gyroscope data were collected using Inertial Measurement Unit (IMU) sensors with a frequency of approximately 200 Hz with the timestamps. A dashboard camera was fixed to capture the driver’s view with timestamps. The start and end times of the turns (left turn and right turn) and non-events were manually marked using the timestamps in the recorded videos. The total count of marked events and non-events was 1246, and their start and end times were used to label the driving events in the IMU sensor data. These labeled driving events were split into the train (934 driving events) and test (312 driving events) datasets. The Hidden Markov Model (HMM) algorithm was adopted to create classification models for the driving events. HMM models were developed using the training dataset for various features, such as lateral and longitudinal acceleration. The accuracy of these models was then compared to a test dataset. The models achieved 96.09% and 95.1% accuracy in classifying turns and non-events using data from the gyroscope’s y-axis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.Item LBF-NoC: Learning-Based Framework to Predict Performance, Power and Area for Network-On-Chip Architectures(World Scientific, 2022) Kumar, A.; Talawar, B.Extensive large-scale data and applications have increasing requests for high-performance computations which is fulfilled by Chip Multiprocessors (CMP) and System-on-Chips (SoCs). Network-on-Chips (NoCs) emerged as the reliable on-chip communication framework for CMPs and SoCs. NoC architectures are evaluated based on design parameters such as latency, area, and power. Cycle-accurate simulators are used to perform the design space exploration of NoC architectures. Cycle-accurate simulators become slow for interactive usage as the NoC topology size increases. To overcome these limitations, we employ a Machine Learning (ML) approach to predict the NoC simulation results within a short span of time. LBF-NoC: Learning-based framework is proposed to predict performance, power and area for Direct and Indirect NoC architectures. This provides chip designers with an efficient way to analyze various NoC features. LBF-NoC is modeled using distinct ML regression algorithms to predict overall performance of NoCs considering different synthetic traffic patterns. The performance metrics of five different (Mesh, Torus, Cmesh, Fat-Tree and Flattened Butterfly) NoC architectures can be analyzed using the proposed LBF-NoC framework. BookSim simulator is employed to validate the results. Various architecture sizes from 2×2 to 45×45 are used in the experiments considering various virtual channels, traffic patterns, and injection rates. The prediction error of LBF-NoC is 6% to 8%, and the overall speedup is 5000× to 5500× with respect to BookSim simulator. © 2022 World Scientific Publishing Company.Item Effect of dry torrefaction pretreatment of the microwave-assisted catalytic pyrolysis of biomass using the machine learning approach(Elsevier Ltd, 2022) Ramesh, R.; Suriapparao, D.V.; Sankar Rao, C.S.; Sridevi, V.; Kumar, A.This study employs the Leave-One-Out cross-validation approach to build a machine-learning model using polynomial regression to predict pyro product yield through microwave-assisted pyrolysis of sawdust over KOH catalyst and graphite powder a susceptor. The determination of coefficient (R2) validates the developed models. All the developed models achieved a high prediction accuracy with R2 > 0.93, which signifies that the experimental values are in good agreement with the predicted one. The dependence of the catalyst loading and pretreatment temperature on dominating process parameters such as heating rate, pyrolysis temperature, susceptor thermal energy, and pyro products, namely bio-oil, biochar, and biogas, are explored. The yield of biochar is reduced; however, bio-oil and biogas are enhanced as the catalyst loading increased. On the other hand, increasing the temperature of pretreated sawdust decreased bio-oil and biogas yields while increasing biochar yields. Further, microwave conversion efficiency, and susceptor thermal energy increased with increased catalyst quantity and pretreatment temperatures of sawdust. It was observed that the average heating rate was increased by increasing the catalyst quantity while maintaining the same pyrolysis time until pretreatment temperatures of 150 °C were reached, after which the heating rate dropped due to the continuous microwave energy input to the system. © 2022 Elsevier LtdItem Exploring and understanding the microwave-assisted pyrolysis of waste lignocellulose biomass using gradient boosting regression machine learning model(Elsevier Ltd, 2024) Sinha, S.; Sankar Rao, C.; Kumar, A.; Venkata Surya, D.; Basak, T.The production of bio-oil is a complex process influenced by various parameters. Optimizing these parameters can significantly enhance bio-oil yield, thus improving process efficiency. This study aims to develop a predictive model for bio-oil yield using the Gradient Boosting Regression (GBR) technique. It also seeks to identify the key factors affecting bio-oil yield and determine the optimal conditions for maximizing production. The GBR model was constructed using data collected from the literature. The model's performance was evaluated based on its determination coefficients for training and testing datasets. Optimization studies were conducted to identify the best conditions for bio-oil production. The GBR model demonstrated high precision, with determination coefficients of 0.983 and 0.913 for the training and testing datasets, respectively, indicating its effectiveness in predicting bio-oil yield. The optimal conditions for maximizing bio-oil yield were identified as 20 min of pyrolysis time, a temperature of 771 °C, and 524W of microwave power. The two-way PDP analysis provided valuable insights into the interactive effects of temperature with other factors, enhancing the understanding of the dynamics of the bio-oil production process. This study not only identifies the most impactful variables for bio-oil yield but also offers critical guidance for optimizing the production process. © 2024 Elsevier LtdItem Human-in-the-Loop Data Analytics for Classifying Fatal Mining Accident Causes Using Natural Language Processing and Machine Learning Techniques(Springer Science and Business Media Deutschland GmbH, 2025) Sharma, A.; Kumar, A.; Vardhan, H.; Mangalpady, A.; Mandal, B.B.; Senapati, A.; Akhil, A.; Saini, S.Mining remains one of the most hazardous industries globally, marked by frequent fatalities resulting from complex operational risks. While accident investigation reports hold valuable insights for improving safety practices, the manual coding of fatality narratives remains labor-intensive, inconsistent, and impractical for large datasets. Although natural language processing (NLP) and machine learning (ML) techniques have gained traction for automating the analysis of safety narratives in other high-risk industries, their application to mining accident data, particularly within the Indian context, remains limited. Addressing this gap, the present study proposes a ML framework for the semi-automated classification of fatal accident causes from unstructured text narratives reported by the Directorate General of Mines Safety (DGMS) between 2016 and 2022. A total of 401 fatal accident descriptions were pre-processed and vectorized using Bag-of-Words, TF-IDF, and Word2Vec techniques, followed by model evaluation across multiple algorithms. A semi-automated classification scheme was developed to balance efficiency with expert oversight, where high-confidence predictions were assigned automatically and uncertain cases were flagged for manual review. Logistic regression combined with TF-IDF unigram features achieved the highest performance, with an F1 score of 0.78 and an accuracy of 0.81. Overall, the developed framework successfully auto-coded 68.75% of cases with 94% accuracy, 0.93 recall, and 0.91 precision. Word cloud visualizations were also employed to capture dominant words associated with different cause categories. The proposed framework offers a practical and operationally feasible solution for assigning fatality causes in the mining sector, contributing to active safety management, surveillance, and policy formulation. © Society for Mining, Metallurgy & Exploration Inc. 2025.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