SUDIP KUMAR ADHIKARI

@kgec.edu.in

Assistant Professor of Department of Computer Science & Engineering
KALYANI GOVERNMENT ENGINEERING COLLEGE, KALYANI

SUDIP KUMAR ADHIKARI


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https://researchid.co/sudipadhikari
Dr. Sudip Kumar Adhikari passed B. Tech in Computer Science & Engineering from Vidyasagar
University in 2002. He obtained the M.E. and Ph.D. degree in Computer Science & Engineering from Jadavpur University. He had more than 19 years of teaching experiences. He had published nearly eighteen research papers in reputed International Journals and Conferences. His research interest includes Medical image processing, Pattern recognition, Artificial intelligence, Soft Computing. He is a senior member of IEEE and member of Institute of Engineers. He is currently an assistant professor in Computer Science & Engineering Department of Cooch Behar Government Engineering College, Cooch Behar.

EDUCATION

B.Tech, M.E., PhD

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Engineering, Artificial Intelligence, Computer Vision and Pattern Recognition, Human-Computer Interaction
21

Scopus Publications

532

Scholar Citations

9

Scholar h-index

9

Scholar i10-index

Scopus Publications

  • Programmable Optically Variable Resistors: Automating the Design and Measurement of Transistor Biasing Circuits
    S. Ray, A. Acharyya, A. Sarkar, P. Das, R. Das, T. Halder, A. Maji, S. B. R. Chowdhury, S. R. Hossain, A. Gain, S. S. Mondal, S. Mondal, S. K. Adhikari
    Journal of Circuits Systems and Computers, 2025
    This paper presents a solution for implementing basic electronic circuits related to the limited availability of precise resistor values. Traditional fixed-value resistors often cause deviations between desired and actual outputs. To address this, a programmable optically variable resistor (POVR) device is introduced, offering a wide range of resistance settings by adjusting input voltage. The POVR uses an optically coupled light-emitting diode (LED) and light-dependent resistor (LDR) pair to modulate resistance by varying LED light intensity, minimizing output deviations in circuit design. Additionally, the authors develop a POVR-enabled transistor bias automation system (POVR-ETBAS) for automatic resistance measurement and circuit optimization. This system provides automated circuit design and measurement functionalities. Demonstrations on common emitter (CE) mode BJT self-bias circuits show promising results in achieving desired resistance values and accurate measurements.
  • FedProx-IoT: Federated Workload Prediction with On-Demand Retraining
    Mrinal Kanti Mahato, Bikash Choudhury, Sudip Kumar Adhikari
    Proceedings of the International Conference on Research in Computational Intelligence and Communication Networks Icrcicn, 2025
    The growing density of connected devices has intensified the demand for scalable and low-latency network infrastructures, where accurate workload prediction plays a crucial role in ensuring efficient resource utilization and proactive service management. However, most existing solutions underutilize the synergistic potential of cloud-edge resource cooperation. This paper presents a federated workload prediction framework for dynamic IoT environments using lightweight 2D-CNN models at edge nodes. A FedProx-based on-demand retraining strategy is introduced to address data heterogeneity and trigger model updates only when local prediction accuracy degrades. The hierarchical three-tier architecture, which spans access devices, edge gateways and cloud servers, facilitates distributed learning with coordinated model updates. Extensive simulations analyze the impact of prediction and retraining buffer lengths on accuracy, convergence stability, training time and communication overhead. The results show that longer prediction buffers improve stability and accuracy, whereas shorter buffers provide faster adaptability. Compared with an existing workload prediction strategy, ELASTIC, the proposed approach achieves comparable accuracy while significantly reducing retraining and communication costs, providing a practical and resource-efficient solution for cloud-edge IoT workload forecasting.
  • Analysis of System Response, Energy Savings, and Fault Detection in a Weather and Traffic-Adaptive Smart Lighting System
    Tanumay Halder, Arnab Gain, Sudip Kumar Adhikari, Biswanath Roy
    Ssrg International Journal of Electrical and Electronics Engineering, 2024
    In the era of smart lighting systems, it is essential to save energy without sacrificing the quality of light. The main aim of this research work is to develop a wireless LED street lighting system that incorporates smart control techniques to maintain appropriate light levels over street surfaces in response to variations in object speed and to improve visibility during foggy conditions. In this control scheme, the main microcontroller senses the daylight level and collects data on fog from a third-party weather API. If fog is detected, the system adjusts the LEDs from cool white to warm white to improve visibility. Additionally, the system detects the presence of objects or pedestrians; if an object is present, it increases brightness depending on the speed of the object up to 100% of LED light. Otherwise, it reduces the LED brightness to 25%. The system also includes fault detection by monitoring the lamp voltage and current to determine if any LED light is faulty, providing timely maintenance alerts. This smart control system ensures energy efficiency and optimal lighting conditions, adapting dynamically to both environmental and situational changes, thereby enhancing safety and visibility on the streets.
  • Cocoa-Net: Performance Analysis on Classification of Cocoa Beans Using Structural Image Feature
    Chandrajit Pal, Samikshan Das, Amitava Akuli, Sudip Kumar Adhikari, Aniruddha Dey
    Informatica Slovenia, 2024
    The process of cocoa hybridization produces new types that have unique chemical properties that impact the manufacturing of chocolate yet are resistant to a number of plant illnesses. Image analysis is a valuable tool for visually differentiating between cocoa beans, deep learning (DL) has become the standard way for image processing. Nevertheless, these techniques necessitate a substantial quantity of data and meticulous hyperparameter adjustment. In this paper, we compare machine learning and deep learning models because it takes a lot of images to cover the wide range of agricultural products. Specifically, we extract features from images using a series of image processing techniques, and then we use both traditional machine learning methods (KNN, Decision tree, SVM, and Random Forest) and Convolutional Neural Networks (proposed Cocoa-Net and RESNET 50) to classify the cocoa beans into four categories: large, medium, small, and rejected. Since each method offers strong classification performance and has potential for use in the classification of food, they were all chosen. To test these methods, a dataset including 200 samples of fragmented images was utilised. Studies that compare various approaches are also carried out. Two optimisation techniques: Univariate Selection and Feature Importance are used to optimise the retrieved features before the machine learning deep learning models are trained. The Adam optimizer is used to optimise the proposed Cocoa-Net model. K-fold cross validation is used to assess trained models, and mean cross validation scores are then computed for performance analysis. The empirical result show that, the proposed Cocoa-Net model predicts with the highest mean accuracy score of 0.83 overall, while the Random Forest Classifier score of 0.75.
  • Medical image analysis using swarm intelligence: A survey
    Sudip Kumar Adhikari, Prasenjit Dey, Sourav De, Shouvik Paul
    Recent Trends in Swarm Intelligence Enabled Research for Engineering Applications, 2024
  • Internet of Things-Based Machine Learning in Healthcare: Technology and Applications
    Prasenjit Dey, Sudip Kumar Adhikari, Sourav De, Indrajit Kar
    Internet of Things Based Machine Learning in Healthcare Technology and Applications, 2024
  • Applications of Internet of Things and Machine Learning Technologies in Healthcare
    Prasenjit Dey, Sudip Kumar Adhikari, Sourav De, Rudranath Banerjee
    Internet of Things Based Machine Learning in Healthcare Technology and Applications, 2024
    This study meticulously examines the transformative implications stemming from the integration of machine learning (ML) and the Internet of Things (IoT) within the healthcare domain. Representing a shift from traditional paper-based records to a dynamic, data-centric industry, this convergence ushers in a paradigm shift in disease diagnosis, treatment modalities, and patient care. The analytical prowess of ML, complemented by IoT's seamless data aggregation, opens novel avenues for healthcare optimization. ML algorithms exhibit unparalleled precision in identifying anomalies within medical images, facilitating timelier interventions, while predictive analytics enhance the potential for personalized treatment plans, fostering clinical efficacy and cost-effectiveness. Simultaneously, the advent of IoT-enabled wearable devices propels continuous patient monitoring and real-time data collection, laying the foundation for proactive healthcare strategies. The integration of ML and IoT is particularly conspicuous in remote patient monitoring, where real-time data analysis elevates patient care and streamlines operational burdens on healthcare professionals. Beyond individual-focused applications, this amalgamation extends to predictive analytics, introducing a sophisticated approach to resource allocation, staff scheduling, and holistic optimization of healthcare delivery. This interdisciplinary confluence at the intersection of the Internet of Medical Things (IoMT) and ML promises revolutionary advancements, paving the way for diagnostic precision, personalized treatment modalities, and overarching optimization of healthcare systems on the horizon.
  • Local contextual information and Gaussian function induced fuzzy clustering algorithm for brain MR image segmentation and intensity inhomogeneity estimation
    Nabanita Mahata, Sayan Kahali, Sudip Kumar Adhikari, Jamuna Kanta Sing
    Applied Soft Computing Journal, 2018
  • Convolution of 3D Gaussian surfaces for volumetric intensity inhomogeneity estimation and correction in 3D brain MR image data
    Sayan Kahali, Sudip Kumar Adhikari, Jamuna Kanta Sing
    Iet Computer Vision, 2018
    Magnetic resonance (MR) imaging technique has become indispensable in image‐guided diagnosis and clinical research. However, present MR image acquisition leads to a slow varying intensity inhomogeneity (IIH) in MR image data. This study presents a novel technique based on convolution of three‐dimensional (3D) Gaussian surfaces, which is denoted as ‘Co3DGS’, for volumetric IIH estimation and correction for 3D brain MR image data. A 3D Gaussian surface is approximated using local voxel gradients on each tissue volume corresponding to grey matter, white matter and cerebrospinal fluid of the 3D brain MR image data and then convolved to partially estimate the IIH, which is subsequently removed from the image data. The above processes are repeated until there is no such significant change in the voxel gradients. The Co3DGS technique has been tested on both synthetic and in‐vivo human 3D brain MR image data of different pulse sequences. The empirical results both in qualitatively and quantitatively, which include coefficient of joint variation, index of variation, index of joint variation, index of class separability and root mean square error, collectively demonstrate that the Co3DGS efficiently estimates and removes the IIH from the 3D brain MR image data and stands superior to some state‐of‐the‐art methods.
  • A fuzzy clustering algorithm with local contextual information and Gaussian function for simultaneous brain MR image segmentation and intensity inhomogeneity estimation
    Nabanita Mahata, Sayan Kahali, Jamuna Kanta Sing, Sudip Kumar Adhikari
    Proceedings 2017 2nd International Conference on Man and Machine Interfacing Mami 2017, 2018
    In this paper, we present a fuzzy clustering algorithm by integrating local contextual information and a Gaussian function for simultaneous brain MR image segmentation and intensity inhomogeneity estimation. For each pixel, a local contextual information is integrated due to highly correlation between the image pixels and used to define its fuzzy membership function to belong into a tissue type. Whereas, a Gaussian surface is fitted over each tissue region using the local image gradients to estimate the intensity inhomogeneity (IIH). In doing so, we have introduced global and local membership functions for each pixel. The combined IIH is iteratively removed from the image and the final segmentation result is obtained based on the global membership values. The simulation results on brain MR images show its superiority over other fuzzy-based clustering algorithms.
  • A two-stage fuzzy multi-objective framework for segmentation of 3D MRI brain image data
    Sayan Kahali, Sudip Kumar Adhikari, Jamuna Kanta Sing
    Applied Soft Computing Journal, 2017
  • 3D MRI brain image segmentation: A two-stage framework
    Sayan Kahali, Sudip Kumar Adhikari, Jamuna Kanta Sing
    Communications in Computer and Information Science, 2017
  • On estimation of bias field in MRI images: polynomial vs Gaussian surface fitting method
    Sayan Kahali, Sudip Kumar Adhikari, Jamuna Kanta Sing
    Journal of Chemometrics, 2016
  • Bias field estimation and segmentation of MRI images using a Spatial Fuzzy C-means algorithm
    Sudip Kumar Adhikari, Jamuna Kanta Sing, Dipak Kumar Basu
    2016 2nd International Conference on Control Instrumentation Energy and Communication Ciec 2016, 2016
  • On estimation of bias field in MRI images
    Jamuna Kanta Sing, Sudip Kumar Adhikari, Sayan Kahali
    2015 IEEE International Conference on Computer Graphics Vision and Information Security Cgvis 2015, 2016
  • A nonparametric method for intensity inhomogeneity correction in MRI brain images by fusion of Gaussian surfaces
    Sudip Kumar Adhikari, Jamuna Kanta Sing, Dipak Kumar Basu, Mita Nasipuri, Punam Kumar Saha
    Signal Image and Video Processing, 2015
  • A modified fuzzy C-means algorithm using scale control spatial information for MRI image segmentation in the presence of noise
    Jamuna Kanta Sing, Sudip Kumar Adhikari, Dipak Kumar Basu
    Journal of Chemometrics, 2015
  • Conditional spatial fuzzy C-means clustering algorithm for segmentation of MRI images
    Sudip Kumar Adhikari, Jamuna Kanta Sing, Dipak Kumar Basu, Mita Nasipuri
    Applied Soft Computing Journal, 2015
  • A spatial fuzzy C-means algorithm with application to MRI image segmentation
    Sudip Kumar Adhikari, Jamuna Kanta Sing, Dipak Kumar Basu, Mita Nasipuri
    Icapr 2015 2015 8th International Conference on Advances in Pattern Recognition, 2015
  • Conditional spatial fuzzy C-means clustering algorithm with application in MRI image segmentation
    Sudip Kumar Adhikari, Jamuna Kanta Sing, Dipak Kumar Basu, Mita Nasipuri
    Advances in Intelligent Systems and Computing, 2015
  • Segmentation of MRI brain images by incorporating intensity inhomogeneity and spatial information using probabilistic fuzzy c-means clustering algorithm
    Sudip Kumar Adhikari, J. K. Sing, D. K. Basu, M. Nasipuri, P. K. Saha
    Proceedings of the 2012 International Conference on Communications Devices and Intelligent Systems Codis 2012, 2012

RECENT SCHOLAR PUBLICATIONS

  • FedProx-IoT: Federated Workload Prediction with On-Demand Retraining
    MK Mahato, B Choudhury, SK Adhikari
    2025 Seventh International Conference on Research in Computational … , 2026
    2026
  • Programmable optically variable resistors: Automating the design and measurement of transistor biasing circuits
    S Ray, A Acharyya, A Sarkar, P Das, R Das, T Halder, A Maji, ...
    Journal of Circuits, Systems and Computers 34 (10), 2550219 , 2025
    2025
    Citations: 8
  • Cocoa-Net: Performance Analysis on Classification of Cocoa Beans Using Structural Image Feature
    C Pal, S Das, A Akuli, SK Adhikari, A Dey
    Informatica 48 (12) , 2024
    2024
    Citations: 6
  • Analysis of System Response, Energy Savings, and Fault Detection in a Weather and Traffic-Adaptive Smart Lighting System
    T Halder, A Gain, SK Adhikari, B Roy
    SSRG International Journal of Electrical and Electronics Engineering 11 (6 … , 2024
    2024
  • Internet of Things-Based Machine Learning in Healthcare Technology and Applications
    P Dey, SK Adhikari, S De, I Kar
    Internet of Things-Based Machine Learning in Healthcare: Technology and … , 2024
    2024
    Citations: 11
  • Applications of Internet of Things and Machine Learning Technologies in Healthcare
    P Dey, SK Adhikari, S De, R Banerjee
    Internet of Things-Based Machine Learning in Healthcare Technology and … , 2024
    2024
    Citations: 11
  • Medical image analysis using swarm intelligence: A survey
    SK Adhikari, P Dey, S De, S Paul
    Recent Trends in Swarm Intelligence Enabled Research for Engineering … , 2024
    2024
    Citations: 4
  • Performance analysis of healthcare information in big data NoSql platform
    SS Mondal, S Mondal, SK Adhikari
    Doctoral Symposium on Intelligence Enabled Research, 235-247 , 2022
    2022
    Citations: 2
  • Quality Analysis of the Ganges River Water Utilizing Machine Learning Technologies
    P Dey, SK Adhikari, A Gain, S Koner
    Doctoral Symposium on Intelligence Enabled Research, 11-20 , 2022
    2022
  • Applications of big data in various fields: a survey
    SS Mondal, S Mondal, SK Adhikari
    Doctoral Symposium on Intelligence Enabled Research, 221-233 , 2022
    2022
    Citations: 5
  • Local contextual information and Gaussian function induced fuzzy clustering algorithm for brain MR image segmentation and intensity inhomogeneity estimation
    N Mahata, S Kahali, SK Adhikari, JK Sing
    Applied Soft Computing 68, 586-596 , 2018
    2018
    Citations: 64
  • Convolution of 3D Gaussian surfaces for volumetric intensity inhomogeneity estimation and correction in 3D brain MR image data
    S Kahali, SK Adhikari, JK Sing
    IET Computer Vision 12 (3), 288-297 , 2018
    2018
    Citations: 6
  • A fuzzy clustering algorithm with local contextual information and Gaussian function for simultaneous brain MR image segmentation and intensity inhomogeneity estimation
    N Mahata, S Kahali, JK Sing, SK Adhikari
    2017 2nd International conference on man and machine interfacing (MAMI), 1-6 , 2017
    2017
    Citations: 1
  • A two-stage fuzzy multi-objective framework for segmentation of 3D MRI brain image data
    S Kahali, SK Adhikari, JK Sing
    Applied Soft Computing 60, 312-327 , 2017
    2017
    Citations: 34
  • 3D MRI brain image segmentation: A two-stage framework
    S Kahali, SK Adhikari, JK Sing
    International Conference on Computational Intelligence, Communications, and … , 2017
    2017
    Citations: 3
  • On estimation of bias field in MRI images: polynomial vs Gaussian surface fitting method
    S Kahali, SK Adhikari, JK Sing
    Journal of Chemometrics 30 (10), 602-620 , 2016
    2016
    Citations: 17
  • Bias field estimation and segmentation of MRI images using a Spatial Fuzzy C-means algorithm
    SK Adhikari, JK Sing, DK Basu
    2016 2nd International Conference on Control, Instrumentation, Energy … , 2016
    2016
    Citations: 4
  • On estimation of bias field in MRI images
    JK Sing, SK Adhikari, S Kahali
    2015 IEEE International Conference on Computer Graphics, Vision and … , 2015
    2015
    Citations: 8
  • Conditional spatial fuzzy C-means clustering algorithm for segmentation of MRI images
    SK Adhikari, JK Sing, DK Basu, M Nasipuri
    Applied soft computing 34, 758-769 , 2015
    2015
    Citations: 247
  • Conditional spatial fuzzy c-means clustering algorithm with application in MRI image segmentation
    SK Adhikari, JK Sing, DK Basu, M Nasipuri
    Information Systems Design and Intelligent Applications: Proceedings of … , 2015
    2015
    Citations: 8

MOST CITED SCHOLAR PUBLICATIONS

  • Conditional spatial fuzzy C-means clustering algorithm for segmentation of MRI images
    SK Adhikari, JK Sing, DK Basu, M Nasipuri
    Applied soft computing 34, 758-769 , 2015
    2015
    Citations: 247
  • Local contextual information and Gaussian function induced fuzzy clustering algorithm for brain MR image segmentation and intensity inhomogeneity estimation
    N Mahata, S Kahali, SK Adhikari, JK Sing
    Applied Soft Computing 68, 586-596 , 2018
    2018
    Citations: 64
  • A modified fuzzy C‐means algorithm using scale control spatial information for MRI image segmentation in the presence of noise
    JK Sing, SK Adhikari, DK Basu
    Journal of Chemometrics 29 (9), 492-505 , 2015
    2015
    Citations: 41
  • A two-stage fuzzy multi-objective framework for segmentation of 3D MRI brain image data
    S Kahali, SK Adhikari, JK Sing
    Applied Soft Computing 60, 312-327 , 2017
    2017
    Citations: 34
  • A spatial fuzzy C-means algorithm with application to MRI image segmentation
    SK Adhikari, JK Sing, DK Basu, M Nasipuri
    2015 Eighth International Conference on Advances in Pattern Recognition … , 2015
    2015
    Citations: 30
  • A nonparametric method for intensity inhomogeneity correction in MRI brain images by fusion of Gaussian surfaces
    SK Adhikari, JK Sing, DK Basu, M Nasipuri, PK Saha
    Signal, Image and Video Processing 9 (8), 1945-1954 , 2015
    2015
    Citations: 19
  • On estimation of bias field in MRI images: polynomial vs Gaussian surface fitting method
    S Kahali, SK Adhikari, JK Sing
    Journal of Chemometrics 30 (10), 602-620 , 2016
    2016
    Citations: 17
  • Segmentation of MRI brain images by incorporating intensity inhomogeneity and spatial information using probabilistic fuzzy c-means clustering algorithm
    SK Adhikari, JK Sing, DK Basu, M Nasipuri, PK Saha
    2012 International Conference on Communications, Devices and Intelligent … , 2012
    2012
    Citations: 14
  • Internet of Things-Based Machine Learning in Healthcare Technology and Applications
    P Dey, SK Adhikari, S De, I Kar
    Internet of Things-Based Machine Learning in Healthcare: Technology and … , 2024
    2024
    Citations: 11
  • Applications of Internet of Things and Machine Learning Technologies in Healthcare
    P Dey, SK Adhikari, S De, R Banerjee
    Internet of Things-Based Machine Learning in Healthcare Technology and … , 2024
    2024
    Citations: 11
  • Programmable optically variable resistors: Automating the design and measurement of transistor biasing circuits
    S Ray, A Acharyya, A Sarkar, P Das, R Das, T Halder, A Maji, ...
    Journal of Circuits, Systems and Computers 34 (10), 2550219 , 2025
    2025
    Citations: 8
  • On estimation of bias field in MRI images
    JK Sing, SK Adhikari, S Kahali
    2015 IEEE International Conference on Computer Graphics, Vision and … , 2015
    2015
    Citations: 8
  • Conditional spatial fuzzy c-means clustering algorithm with application in MRI image segmentation
    SK Adhikari, JK Sing, DK Basu, M Nasipuri
    Information Systems Design and Intelligent Applications: Proceedings of … , 2015
    2015
    Citations: 8
  • Cocoa-Net: Performance Analysis on Classification of Cocoa Beans Using Structural Image Feature
    C Pal, S Das, A Akuli, SK Adhikari, A Dey
    Informatica 48 (12) , 2024
    2024
    Citations: 6
  • Convolution of 3D Gaussian surfaces for volumetric intensity inhomogeneity estimation and correction in 3D brain MR image data
    S Kahali, SK Adhikari, JK Sing
    IET Computer Vision 12 (3), 288-297 , 2018
    2018
    Citations: 6
  • Applications of big data in various fields: a survey
    SS Mondal, S Mondal, SK Adhikari
    Doctoral Symposium on Intelligence Enabled Research, 221-233 , 2022
    2022
    Citations: 5
  • Medical image analysis using swarm intelligence: A survey
    SK Adhikari, P Dey, S De, S Paul
    Recent Trends in Swarm Intelligence Enabled Research for Engineering … , 2024
    2024
    Citations: 4
  • Bias field estimation and segmentation of MRI images using a Spatial Fuzzy C-means algorithm
    SK Adhikari, JK Sing, DK Basu
    2016 2nd International Conference on Control, Instrumentation, Energy … , 2016
    2016
    Citations: 4
  • 3D MRI brain image segmentation: A two-stage framework
    S Kahali, SK Adhikari, JK Sing
    International Conference on Computational Intelligence, Communications, and … , 2017
    2017
    Citations: 3
  • Performance analysis of healthcare information in big data NoSql platform
    SS Mondal, S Mondal, SK Adhikari
    Doctoral Symposium on Intelligence Enabled Research, 235-247 , 2022
    2022
    Citations: 2