Manish Kumar | Digital Twin | Excellence in Privacy-Preserving Technologies

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Assist Prof Dr. Manish Kumar | Digital Twin | Excellence in Privacy-Preserving Technologies

Research Professor at Seoul National University of Science and Technology, South Korea

Assist Prof. Dr. Manish Kumar is a distinguished academic and researcher specializing in neural networks, IoT security, and signal processing. He earned his Ph.D. in Electrical and Electronics Engineering from Birla Institute of Technology, Mesra, Ranchi, where he developed adaptive filters for denoising medical images using nature-inspired neural network models. Dr. Kumar has extensive teaching and research experience, having served as a Research Professor at Seoul National University of Science & Technology and an Assistant Professor at Mody University of Science & Technology in Rajasthan, India. His work focuses on IoT security, machine learning, and biomedical engineering, with numerous publications in high-impact journals. In addition, Dr. Kumar holds a granted patent for “Vitro: Virtual Trial Room” and has contributed to various international projects. His expertise spans across coding, sensor systems, signal acquisition, and advanced neural network applications.

Profile:

Education:

Assist Prof. Dr. Manish Kumar holds a Ph.D. in Electrical and Electronics Engineering from Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India. He completed his doctoral research between 2014 and 2018, focusing on the development of adaptive filters based on nature-inspired neural network models for denoising medical images, earning a CGPA of 7.25. His Ph.D. was officially awarded on October 11, 2018. Additionally, he has undergone various professional training and certifications in fields such as TensorFlow, machine learning, and deep learning, further complementing his academic expertise.

Professional Experience:

Assist Prof. Dr. Manish Kumar has accumulated extensive professional experience in academia and research. He is currently a Research Professor in the Department of Computer Science & Engineering at Seoul National University of Science & Technology, a role he has held since November 2022. In this position, he conducts research on IoT Security and High-Performance Computing (HPC) performance. From June 2019 to November 2022, he served as an Assistant Professor in the Department of Electronics & Biomedical Engineering at Mody University of Science & Technology, Rajasthan, where he taught various subjects and pursued research in the fields of artificial intelligence and biomedical engineering. Prior to this, Dr. Kumar worked at the Indian Institute of Technology, Patna, where he conducted research on machine learning-based fault prediction. His efforts were supported by a fellowship from the Department of Science & Technology, India. During his Ph.D. at Birla Institute of Technology, Mesra, Ranchi (2014-2018), he focused on the development of adaptive filters based on neural networks and nature-inspired techniques for medical image denoising, leading to multiple published research articles. Additionally, he has experience teaching as a guest faculty at the University Polytechnic, BIT Mesra, and served as an intern at the Central Scientific Instrument Organization (CSIR-CSIO), where he worked on the design of sensors for infant condition monitoring systems. Dr. Kumar’s professional journey reflects his expertise in IoT security, machine learning, artificial intelligence, and signal processing, along with a strong foundation in teaching and technical research.

Research Interests:

Assist Prof. Dr. Manish Kumar’s research interests lie at the intersection of advanced technologies and healthcare applications. His primary focus is on Internet of Things (IoT) security, particularly in developing robust mechanisms to safeguard connected devices in healthcare systems. He is also deeply engaged in high-performance computing (HPC) performance optimization, which is crucial for processing large datasets generated in medical imaging and IoT environments. Dr. Kumar is keen on exploring machine learning and artificial intelligence techniques, especially in predictive analytics for fault prediction and data-driven decision-making in medical applications. His work involves the development of adaptive filters based on nature-inspired neural network models for denoising medical images, contributing to enhanced diagnostic accuracy and patient outcomes. Additionally, Dr. Kumar is interested in sensor systems and signal acquisition, focusing on innovative solutions for real-time monitoring and analysis in biomedical contexts. Through his research, he aims to address critical challenges in healthcare technology, emphasizing the importance of privacy-preserving methods in data management and processing.

Skills:

Assist Prof. Dr. Manish Kumar possesses a diverse skill set that encompasses both technical and academic expertise. His strong foundation in research is complemented by his proficiency in coding and technical writing, which allows him to effectively communicate complex ideas and findings. Dr. Kumar is well-versed in a variety of programming languages, including C and C++, and has hands-on experience with several machine learning frameworks such as TensorFlow, PyTorch, and Keras. His technical skills extend to software and tools like LabView, OpenCV, Google Colab, Scikit-learn, and Latex, enabling him to implement and document his research effectively. In addition to his coding skills, Dr. Kumar has a robust understanding of IoT security, sensor systems, and signal acquisition and processing. He is also experienced in the field of accreditation work, specifically with the Institution of Engineering and Technology (IET). His teaching experience includes subjects like Digital Signal Processing, Image Processing, and Artificial Neural Networks, showcasing his ability to convey complex concepts to students at various academic levels. Overall, Dr. Kumar’s blend of technical acumen, research capabilities, and teaching proficiency positions him as a valuable asset in the field of computer science and engineering.

Conclusion:

With his deep technical expertise in neural networks, IoT security, and machine learning, combined with his prolific research output and practical experience in securing sensitive data, Dr. Manish Kumar is well-positioned to excel in research for privacy-preserving technologies. His innovative work and forward-thinking approach align perfectly with the needs of this cutting-edge field.

Publication Top Noted:

Comparative Analysis of Classification Methods with PCA and LDA for Diabetes

  • Authors: V.K.D. Choubey, M. Kumar, V. Shukla, S. Tripathi
  • Journal: Current Diabetes Review
  • Year: 2020
  • Citations: 95

Cat swarm optimization based functional link artificial neural network filter for Gaussian noise removal from computed tomography images

  • Authors: M. Kumar, S.K. Mishra, S.S. Sahu
  • Journal: Applied Computational Intelligence and Soft Computing
  • Year: 2016
  • Article ID: 6304915
  • Citations: 26

Functional Link Convolutional Neural Network for the Classification of Diabetes Mellitus

  • Authors: S.K. Jangir, M. Kumar, D.K. Choubey, M. Verma
  • Journal: International Journal of Numerical Methods in Biomedical Engineering
  • Year: 2021
  • Citations: 25

A comprehensive review on nature inspired neural network based adaptive filter for eliminating noise in medical images

  • Authors: M. Kumar, S.K. Mishra
  • Journal: Current Medical Imaging
  • Year: 2020
  • Volume: 16(4)
  • Pages: 278-287
  • Citations: 17

Teaching learning based optimization-functional link artificial neural network filter for mixed noise reduction from magnetic resonance image

  • Authors: M. Kumar, S.K. Mishra
  • Journal: Bio-Medical Materials and Engineering
  • Year: 2017
  • Volume: 28(6)
  • Pages: 643-654
  • Citations: 17

GRU-based Digital Twin Framework for Data Allocation and Storage in IoT-enabled Smart Home Networks

  • Authors: S.K. Singh, M. Kumar, S. Tanwar, J.H. Park
  • Journal: Future Generation Computer Systems
  • Year: 2023
  • Citations: 16

Feature importance score-based functional link artificial neural networks for breast cancer classification

  • Authors: S. Singh, S.K. Jangir, M. Kumar, M. Verma, S. Kumar, T.S. Walia, S.M.M. Kamal
  • Journal: BioMed Research International
  • Year: 2022
  • Citations: 13

Jaya based functional link multilayer perceptron adaptive filter for Poisson noise suppression from X-ray images

  • Authors: M. Kumar, S.K. Mishra
  • Journal: Multimedia Tools and Applications
  • Year: 2018
  • Volume: 77(18)
  • Pages: 24405-24425
  • Citations: 13

Jaya-FLANN based adaptive filter for mixed noise suppression from ultrasound images

  • Authors: Manish Kumar, Sudhansu Kumar Mishra
  • Journal: Biomedical Research
  • Year: 2017
  • Volume: 28(9)
  • Pages: 4159-4164
  • Citations: 13