Dr. Sujit Kumar

@gecbanka.org

Assistant Professor, Electrical Engineering
Government Engineering College Banka, Science Technology and Technical Education Department, Bihar Engineering University, Patna, Bihar, India

Dr. Sujit Kumar


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https://researchid.co/sujit0412
29

Scopus Publications

740

Scholar Citations

13

Scholar h-index

16

Scholar i10-index

Scopus Publications

  • A dual-condition cost-sensitive optimization framework for intelligent bearing fault diagnosis
    Sujit Kumar, Bam Bahadur Sinha, Pranab Das
    Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2026
  • A robust deep learning framework for bearing fault diagnosis using residual shrinkage networks and signal denoising
    Sujit Kumar, Bam Bahadur Sinha
    International Journal of Dynamics and Control, 2026
  • Intelligent analysis of voltage stability enhancement in radial distribution networks considering distributed generation and composite load modeling
    Sujit Kumar, Amit Kumar, Bam Bahadur Sinha
    International Journal of Electrical Engineering and Education, 2026
    This paper presents an intelligent analytical framework for enhancing voltage stability in radial distribution networks through optimal distributed generation (DG) placement and composite load modeling. A novel voltage stability index (VSI) is formulated to identify weak buses, while a fuzzy inference system integrates VSI, power-loss reduction, and feeder distance to determine the most suitable DG location. The optimal DG capacity is derived using a quadratic curve-fitting method that minimizes real-power losses. The proposed approach is validated on the IEEE 10-bus test system using MATLAB/PSAT simulations. Results reveal that placing a Type-I DG of approximately 2.6 MW at Bus 10 increases the minimum bus voltage from 0.895 p.u. to 0.971 p.u., reduces total real-power losses by 64.2%, and improves the voltage stability margin by 41.7% compared to the base case. The fuzzy–analytical hybrid approach demonstrates robust performance under composite load conditions and provides an interpretable, computationally efficient strategy for planning DG integration in modern distribution networks.
  • An intelligent SCNNBN-TBiG hybrid model for casting defect classification
    Pranay Kumar, Gaurav Kumar, Sujit Kumar, Bam Bahadur Sinha
    Australian Journal of Mechanical Engineering, 2026
    Casting is a fundamental manufacturing process for producing components with complex geometries; however, surface and subsurface defects continue to compromise product reliability andproduction efficiency. To support automated and consistent quality inspection, this paper presents a hybrid deep learning framework termed SCNNBN – TBiG for intelligent casting defect classification. The proposed approach integrates stacked convolutional neural networks with batch normalisation to extract stable and discriminative spatial features, followed by a Transformer encoder that captures long-range contextual relationships through multi-head self-attention. The resulting representations are compressed using global average pooling and subsequently analysed by stacked bidirectional gated recurrent unit layers to model sequential dependencies within the learned feature space. The framework is evaluated on a publicly available industrial casting image dataset comprising 7,348 samples under both defective and non-defective categories. Experimental results demonstrate that the proposed model achieves a testing accuracy of 99.44%, outperforming several existing deep learning and hybrid architectures. The findings confirm that the synergistic integration of spatial, global, and sequential feature learning provides a robust and efficient solution for high-precision industrial quality inspection.
  • Hybrid CNN–Transformer–GRU framework with improved residual shrinkage network and DCCS optimisation for steel surface defect classification
    Brajesh Kumar, Sunil Kumar Singh, Sujit Kumar, Bam Bahadur Sinha
    Australian Journal of Mechanical Engineering, 2026
    Automated inspection of steel surface defects is an important task in intelligent manufacturing systems to ensure product quality and reduce manual inspection efforts. This study proposes a hybrid deep learning framework for accurate classification of metal surface defects by integrating convolutional feature extraction, residual shrinkage learning, transformer-based attention modelling, and recurrent sequence analysis. Initially, greyscale defect images from the NEU metal surface defect dataset are processed using a convolutional neural network (CNN) combined with Batch Normalisation and Improved Residual Shrinkage Network (IRSN) blocks to extract noise-resilient and discriminative local features. The IRSN mechanism performs adaptive soft thresholding to suppress noise and enhance relevant defect patterns. Transformer encoder with multi-head attention learns long-range relationships among extracted features. In parallel, a Gated Recurrent Unit (GRU) branch models sequential dependencies in the feature representation. The outputs from both branches are fused and passed through fully connected layers for multi-class defect classification. A Dual-Condition Cost-Sensitive (DCCS) optimisation strategy is applied to automatically tune hyperparameters. The proposed model attains an accuracy of 93.06% and 98.40% on the NEU metal surface defect dataset and Severstal Steel Defect Detection dataset, respectively. The proposed model underscores an improvement of around 0.06% and 1.2% over the recent benchmark model.
  • A compact corrugated double-tapered slot Vivaldi antenna with resonator-based interference suppression and AZIM-enhanced gain
    Manoj Bhaskar, Chetan Barde, Prakash Ranjan, Sujit Kumar, Himanshu Kumar Shekhar, Bam Bahadur Sinha
    International Journal of Microwave and Wireless Technologies, 2026
    This paper presents the design and experimental validation of a compact corrugated double-tapered slot Vivaldi antenna (DTSVA) for ultra-wideband (UWB) applications. The proposed antenna provides efficient impedance matching over a wide frequency range of 1–20 GHz. To suppress electromagnetic interference from coexisting narrowband systems, triple band-notched characteristics are realized using spatially distributed resonant elements, including a complementary split-ring resonator (CSRR) and U-shaped slots etched beneath different sections of the feed network, enabling effective rejection of WiMAX (3.3–3.8 GHz), WLAN (5.15–5.85 GHz), and X-band uplink (7.9–8.4 GHz) signals. To enhance forward radiation without increasing the antenna size, corrugated edges and an anisotropic zero-index metamaterial (AZIM) unit cell are integrated within the tapered slot region. Consequently, the proposed antenna achieves a maximum peak realized gain of 12.5 dBi, corresponding to an improvement of up to 3.8 dBi compared to the reference DTSVA. Simulated and measured results show good agreement in terms of impedance bandwidth, radiation characteristics, realized gain, and group delay. Owing to its compact size, enhanced gain, stable time-domain performance, and effective interference suppression, the proposed antenna is well suited for portable indoor and outdoor UWB applications, including imaging, radar sensing, and material characterization.
  • Enhanced Fault Diagnosis of Rolling Bearings with Noise Filtering and Neural Networks
    Sujit Kumar, Bam Bahadur Sinha
    Journal of Vibration Engineering and Technologies, 2025
  • Optimized fault detection in bearings of rotating machines via batch normalization-integrated bidirectional gated recurrent unit networks
    Sujit Kumar, Manish Kumar, Chetan Barde, Prakash Ranjan
    Iaes International Journal of Artificial Intelligence, 2025
    <p><span lang="EN-US">Motor is commonly used in industrial applications. Although motors are frequently found to have bearing problems, this causes a serious safety risk to industrial production. Traditionally, fault diagnostics methods often required only signal processing techniques and are ineffective. To overcome this problem, deep learning (DL) has been recently developed rapidly and achieved remarkable results in fault diagnosis. The intelligent fault diagnosis and classification of rolling bearing faults based on ensemble empirical mode decomposition (EEMD) and batch normalization (BN), principal component analysis (PCA) based stacked bidirectional-gated recurrent unit (Bi-GRU) neural network, is proposed in this paper. BN is introduced to improve the fast convergence of gated recurrent unit (GRU). EEMD is applied to eliminate the noise interference from the vibrational signal, and then important features are selected using the correlation coefficient value. Next, PCA is utilized for dimensionality reduction to retain only the essential. Finally, the BN based stacked Bi-GRU model is developed to classify faults based on extracted features. The proposed model correctly classifies the different types of faults in real operating conditions and also compared with existing techniques.</span></p>
  • An Advanced Deep Learning Approach for Intelligent Casting Defect Detection
    Pranay Kumar, Sujit Kumar, Gaurav Kumar
    National Power Electronics Conference Npec 2025, 2025
    Casting is one of the most essential processes in manufacturing industries; however, defects in cast components remain a persistent challenge, leading to reduced product quality, increased production costs, and customer dissatisfaction. Traditional inspection techniques are often time-consuming, subjective, and error-prone, thereby emphasizing the need for intelligent and automated defect detection methods. In this study, a deep learning framework is developed for the automated detection of casting defects. The proposed approach utilizes a stacked Convolutional Neural Network (CNN) architecture to extract discriminative low-level spatial features from casting surface images. To improve the model's learning efficiency and stability, Batch Normalization (BN) layers are integrated after convolutional operations. This normalization process helps maintain consistent activation distributions, accelerates convergence during training, and minimizes the risk of overfitting, thereby enhancing the model's generalization capability. The model was trained and validated using a publicly available Kaggle dataset containing 7,348 casting images, systematically divided into defective and non-defective categories. Experimental results indicate that the proposed network achieved a testing accuracy of 96.92 %, along with strong precision, recall, and F1-score metrics. These results confirm the reliability and robustness of the model in identifying casting defects with high confidence. Overall, the proposed deep learning-based approach provides an efficient and accurate solution for automated visual inspection, contributing to improved quality assurance and productivity in industrial manufacturing processes.
  • A Hybrid Deep Learning Framework with Stacked CNN, Batch Normalization, and Transformer Encoder for Casting Defect Detection
    Pranay Kumar, Sujit Kumar, Gaurav Kumar
    2025 IEEE 6th Global Conference for Advancement in Technology Gcat 2025, 2025
    Industry relies heavily on casting as a fundamental manufacturing process; however, defects in cast products remain a significant challenge, often resulting in compromised quality, increased costs, and reduced customer satisfaction. Traditional inspection techniques are largely manual, time-consuming, and prone to inconsistencies, highlighting the need for intelligent and automated defect detection systems. This paper proposes a deep learning-based framework for intelligent casting defect detection. A stacked Convolutional Neural Network (CNN) is employed to extract discriminative low-level spatial features from casting images, while Batch Normalization (BN) is integrated to stabilize training, accelerate convergence, and mitigate over-fitting. To capture global contextual information and complex feature dependencies, a Transformer module is incorporated into the architecture. Dense layers are further utilized to enhance classification accuracy and ensure effective generalization to unseen data. The model is trained and validated on a Kaggle dataset containing 7,348 casting images, categorized into defective and non-defective classes. Experimental results demonstrate that the proposed framework achieves a testing accuracy of 97.76%, along with high precision, recall, and F1-score, confirming its robustness and reliability. Overall, the approach provides an efficient solution for automated visual inspection of castings, thereby supporting improved quality control and operational efficiency in manufacturing industries.
  • Condition Based Monitoring of Rolling Bearing by Naive Bayes Classifier
    Sujit Kumar, Alka Kumari, Durgesh Nandani, Manish Kumar
    Lecture Notes in Electrical Engineering, 2025
  • Design and Performance Analysis of a Compact Nanogon-Shaped UWB Printed Monopole Antenna with Circular Arc Balun
    Manoj Bhaskar, Chetan Barde, Prakash Ranjan, Nawneet Kumar, Sujit Kumar
    2025 International Conference on Information Implementation and Innovation in Technology I2itcon 2025, 2025
  • Satellite Image Classification Using Convolutional Neural Networks
    Manoj Bhaskar, Chetan Barde, Prakash Ranjan, Nawneet Kumar, Sujit Kumar
    2025 IEEE 1st International Conference on Smart and Sustainable Developments in Electrical Engineering Ssdee 2025, 2025
  • Condition based monitoring of rolling bearing of rotating machines using intelligent fault classification
    Sujit Kumar, Manish Kumar, Chetan Barde, Prakash Ranjan, Shalu Priya, Divya Sri
    International Journal of System Assurance Engineering and Management, 2025
  • Rolling Bearing Fault Classification: Multinomial Logistic Regression Approach for Enhanced Efficiency
    Sujit Kumar, Manish Kumar, Ravi Kumar, Pawan Kumar, Ayush Kumar, Priyanshu Raj, Divyanshu Kumar, Sumant Kumar, Santosh Kumar
    Lecture Notes in Electrical Engineering, 2025
  • Efficient Fault Classification in Rotating Machinery Using the K-Nearest Neighbor Algorithm
    Sujit Kumar, Alka Kumari, Shalu Priya, Nisha Bharti, Durgesh Nandani, Manish Kumar
    2024 IEEE International Conference on Smart Power Control and Renewable Energy Icspcre 2024, 2024
  • Combinational Framework for Classification of Bearing Faults in Rotating Machines
    Sujit Kumar, D. Ganga
    Journal of Computing and Information Science in Engineering, 2023
  • Classification of Rolling Bearing Fault Based on Long Short Term Memory Neural Network
    Sujit Kumar, D. Ganga
    2023 2nd International Conference for Innovation in Technology Inocon 2023, 2023
  • Intelligent Fault Classification of Motor Bearing by using Support Vector Machine
    Sujit Kumar
    2022 IEEE Global Conference on Computing Power and Communication Technologies Globconpt 2022, 2022
  • Multi Classification of Rolling Bearing Conditions based on Random Forest Classifier Model
    Sujit Kumar
    2022 International Conference on Futuristic Technologies Incoft 2022, 2022
  • Intelligent Bearing Fault Diagnosis and Classification based on Support Vector Machine
    Sujit Kumar
    2021 2nd Global Conference for Advancement in Technology Gcat 2021, 2021
  • Motor Bearing Faults Detection and Classification based on Convolutional Neural Network and Support Vector Machine: A Comparative Study
    Sujit Kumar
    2021 2nd Global Conference for Advancement in Technology Gcat 2021, 2021
  • Dynamic analysis of an open-loop proportional valve controlled hydrostatic drive
    K Dasgupta, Sanjoy K Ghoshal, Sujit Kumar, J Das
    Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering, 2019
  • Dynamic analysis of a hydro-motor drive system using priority valve
    Sujit Kumar, K Dasgupta, SK Ghoshal, J Das
    Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering, 2019
  • Fault diagnosis and prognosis of a hydro-motor drive system using priority valve
    Sujit Kumar, Kabir Dasgupta, Sanjoy K. Ghoshal
    Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2019
  • Comparative Study for Steady-State Operation of IAG in Stand-Alone mode using MATLAB and Typhoon HIL
    Vineet P. Chandran, Sujit Kumar, Priya Singh Bhakar
    Proceedings 2018 3rd International Conference on Internet of Things Smart Innovation and Usages Iot Siu 2018, 2018
  • Determination of the optimum steady-state performance of an open-loop and a closed-loop valve-controlled hydro-motor drive: a design approach
    Sujit Kumar, K. Dasgupta, J. Das
    Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2018
  • Characterization and tracking control of a nonlinear electrohydraulic valve-cylinder system
    J Das, Santosh Kr Mishra, RamaShankar Paswan, Ajit Kumar, Sujit Kumar, R Saha, S Mookherjee
    Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering, 2016
  • Hydraulic circuit in damper:An overview
    Ramashankar Paswan, Jayanta Das, N. Kumar, Ajit Kumar, Santosh Kumar Mishra, Kumar Sujit
    Applied Mechanics and Materials, 2014

RECENT SCHOLAR PUBLICATIONS

  • Hybrid CNN–Transformer–GRU framework with improved residual shrinkage network and DCCS optimisation for steel surface defect classification
    BB Brajesh Kumar sunil Kumar Singh, Sujit Kumar & Bam bahdur Sinha
    australian journal of mechanical engineering, 1-26 , 2026
    2026
  • A dual-condition cost-sensitive optimization framework for intelligent bearing fault diagnosis
    S Kumar, D Bam, Bahadur, Sinha, Pranab
    Journal of the Brazilian Society of Mechanical Sciences and Engineering 48 … , 2026
    2026
    Citations: 1
  • An intelligent SCNNBN-TBiG hybrid model for casting defect classification
    P Kumar, G Kumar, S Kumar, BB Sinha
    Australian Journal of Mechanical Engineering, 1-19 , 2026
    2026
    Citations: 1
  • A hybrid deep learning framework for intelligent casting defect detection and quality assurance in manufacturing
    P Kumar, G Kumar, S Kumar, BB Sinha
    Australian Journal of Multi-Disciplinary Engineering, 1-18 , 2026
    2026
  • A robust deep learning framework for bearing fault diagnosis using residual shrinkage networks and signal denoising: S. Kumar et al.
    S Kumar, BB Sinha
    International Journal of Dynamics and Control 14 (3), 105 , 2026
    2026
    Citations: 1
  • An Advanced Deep Learning Approach for Intelligent Casting Defect Detection
    P Kumar, S Kumar, G Kumar
    2025 12th National Power Electronics Conference (NPEC), 1-6 , 2025
    2025
  • Condition based monitoring of rolling bearing of rotating machines using intelligent fault classification
    SK Manish Kumar, Chetan Barde, Prakash Ranjan, Shalu Priya, Divya Sri
    International Journal of System Assurance Engineering and Management , 2025
    2025
  • A Hybrid Deep Learning Framework with Stacked CNN, Batch Normalization, and Transformer Encoder for Casting Defect Detection
    P Kumar, S Kumar, G Kumar
    2025 IEEE 6th Global Conference for Advancement in Technology (GCAT), 1-6 , 2025
    2025
  • Optimized fault detection in bearings of rotating machines via batch normalization-integrated bidirectional gated recurrent unit networks
    PR Sujit Kumar, Manish Kumar, Chetan Barde
    IAES International Journal of Artificial Intelligence (IJ-AI) 14 (4), 3334~3342 , 2025
    2025
    Citations: 2
  • Enhanced fault diagnosis of rolling bearings with noise filtering and neural networks
    S Kumar, BB Sinha
    Journal of Vibration Engineering & Technologies 13 (6), 411 , 2025
    2025
    Citations: 8
  • Design Registration: AI Based Finance Forecasting Device
    MB Meenakshi Rathi, Sujit Kumar, Amit Kumar
    IN Patent Design No.: 455537-001 , 2025
    2025
  • Design and Performance Analysis of a Compact Nanogon-Shaped UWB Printed Monopole Antenna with Circular Arc Balun
    M Bhaskar, C Barde, P Ranjan, N Kumar, S Kumar
    2025 International Conference on Information, Implementation, and Innovation … , 2025
    2025
  • Rainfall Runoff and Flood Plain Inundation Modeling of the Kharkai River, India using HEC-HMS and HEC-RAS
    A Verma, S Kumar, PK Parhi
    Journal of Water Management Modeling 33 , 2025
    2025
  • Satellite Image Classification Using Convolutional Neural Networks
    M Bhaskar, C Barde, P Ranjan, N Kumar, S Kumar
    2025 IEEE 1st International Conference on Smart and Sustainable Developments … , 2025
    2025
  • Enhancing Road Safety with Smart Infrastructure: Development and Implementation of Advanced Zebra Crossings and Speed Breaking
    MZ Sujit Kumar, Deeba Ashique, Alka Raj
    International Journal For Research in Applied Science and Engineering … , 2024
    2024
  • Condition Based Monitoring of Rolling Bearing by Naive Bayes Classifier
    S Kumar, A Kumari, D Nandani, M Kumar
    International Conference on Electrical and Electronics Engineering, 589-599 , 2024
    2024
  • Rolling Bearing Fault Classification: Multinomial Logistic Regression Approach for Enhanced Efficiency
    S Kumar, M Kumar, R Kumar, P Kumar, A Kumar, P Raj, D Kumar, ...
    International Conference on Electrical and Electronics Engineering, 329-338 , 2024
    2024
    Citations: 1
  • Efficient Fault Classification in Rotating Machinery Using the K-Nearest Neighbor Algorithm
    S Kumar, A Kumari, S Priya, N Bharti, D Nandani, M Kumar
    2024 IEEE International Conference on Smart Power Control and Renewable … , 2024
    2024
    Citations: 1
  • Crop water requirement of rice in different agroclimatic zones of Jharkhand
    S Kumar, R Kumar, MK Singh, S Yadav, PK Parhi, A Bardhan
    Journal of Agrometeorology 26 (2), 233-237 , 2024
    2024
    Citations: 3
  • Combinational framework for classification of bearing faults in rotating machines
    S Kumar, D Ganga
    Journal of Computing and Information Science in Engineering 24 (2), 021012 , 2024
    2024
    Citations: 10

MOST CITED SCHOLAR PUBLICATIONS

  • Epoxidation of styrene over a titanium silicate molecular sieve TS1 using dilute H2O2 as oxidizing agent
    SB Kumar, SP Mirajkar, GCG Pais, P Kumar, R Kumar
    Journal of catalysis 156 (1), 163-166 , 1995
    1995
    Citations: 201
  • Para selective chlorination of toluene with an L-zeolite catalyst
    AP Singh, SB Kumar
    Applied Catalysis A: General 126 (1), 27-38 , 1995
    1995
    Citations: 62
  • Genetic diversity, population structure and association analysis in linseed ( Linum usitatissimum L.)
    Chandrawati, N Singh, R Kumar, S Kumar, PK Singh, VK Yadav, ...
    Physiology and molecular biology of plants 23 (1), 207-219 , 2017
    2017
    Citations: 49
  • Biopesticides—An alternative and eco-friendly source for the control of pests in agricultural crops
    S Prabha, A Yadav, A Kumar, A Yadav, HK Yadav, S Kumar, RS Yadav, ...
    Plant Arch 16 (2), 902-906 , 2016
    2016
    Citations: 47
  • Capturing agro-morphological variability for tolerance to terminal heat and combined heat–drought stress in landraces and elite cultivar collection of wheat
    S Kumar, H Kumar, V Gupta, A Kumar, CM Singh, M Kumar, AK Singh, ...
    Frontiers in Plant Science 14, 1136455 , 2023
    2023
    Citations: 43
  • Selective para-chlorination of toluene using zeolite catalysts
    AP Singh, SB Kumar, A Paul, A Raj
    Journal of Catalysis 147 (1), 360-363 , 1994
    1994
    Citations: 39
  • Pulses production and productivity: Status, potential and way forward for enhancing farmers income
    AS Yadav, S Kumar, N Kumar, H Ram
    Int J Curr Microbiol App Sci 8 (4), 2315-2322 , 2019
    2019
    Citations: 36
  • Specificity of α-amylase and trypsin inhibitor proteins in wheat against insect pests
    S Priya, S Kumar, N Kaur, AK Gupta
    New Zealand Journal of Crop and Horticultural Science 41 (1), 49-56 , 2013
    2013
    Citations: 28
  • Convenient synthesis of crystalline microporous transition metal silicates using complexing agents
    R Kumar, A Raj, SB Kumar, P Ratnasamy
    Studies in Surface Science and Catalysis 84, 109-116 , 1994
    1994
    Citations: 28
  • Effect of alkali activator ratio on mechanical properties of GGBS based geopolymer concrete
    S Kumar, PD Gautam, BSC Kumar
    International Journal of Innovative Technology and Exploring Engineering 8 … , 2019
    2019
    Citations: 27
  • Genetic variability and interrelationship among morphological and yield traits in linseed (Linum usitatissimum L.)
    N Singh, KV Yadav, R Kumar, S Kumar, HK Yadav
    Genetika 48 (3), 881-892 , 2016
    2016
    Citations: 24
  • Reduction of blast-induced ground vibration and utilization of explosive energy using low-density explosives for environmentally sensitive areas
    S Kumar, AK Mishra
    Arabian Journal of Geosciences 13 (14), 655 , 2020
    2020
    Citations: 18
  • Importance of molecular marker in linseed (Linum usitatissimum) genome analysis-A review.
    S Prabha, A Yadav, HK Yadav, S Kumar, R Kumar
    Crop Research (0970-4884) 52 , 2017
    2017
    Citations: 13
  • Heterosis study in Okra [Abelmoschus esculentus (L.) Moench] genotypes for pod yield attributes
    S Kumar, AK Singh, H Yadav, A Verma
    Journal of Applied and Natural science 9 (2), 774-779 , 2017
    2017
    Citations: 12
  • Data aggregation using spatial and temporal data correlation
    S Kumar, S Kumar
    2015 International Conference on Futuristic Trends on Computational Analysis … , 2015
    2015
    Citations: 11
  • Combinational framework for classification of bearing faults in rotating machines
    S Kumar, D Ganga
    Journal of Computing and Information Science in Engineering 24 (2), 021012 , 2024
    2024
    Citations: 10
  • Mapping QTLs for Alternaria blight in Linseed ( Linum usitatissimum L.)
    N Singh, R Kumar, S Kumar, PK Singh, HK Yadav
    3 Biotech 11 (2), 91 , 2021
    2021
    Citations: 9
  • Enhanced fault diagnosis of rolling bearings with noise filtering and neural networks
    S Kumar, BB Sinha
    Journal of Vibration Engineering & Technologies 13 (6), 411 , 2025
    2025
    Citations: 8
  • Classification of rolling bearing fault based on long short term memory neural network
    S Kumar, D Ganga
    2023 2nd International Conference for Innovation in Technology (INOCON), 1-5 , 2023
    2023
    Citations: 8
  • Implementation of Flexigel™ bulk system: a case study of West Bokaro Colliery, Tata Steel Limited
    S Kumar, P Ranjan, AK Mishra, I Ahmad, A Rai, P Singha, G Kumar
    7th Asian Mining Congress and Exhibition, Kolkata, India , 2017
    2017
    Citations: 7