Dr. Kumar Gaurav

Dr. Kumar Gaurav is a dedicated academician and researcher with a Ph.D. in Computer Science and Engineering from NIT Jamshedpur and M.Tech from NIT Patna. His expertise lies in reliability analysis of safety-critical systems, with a special focus on fault monitoring in nuclear power plants. His research contributions include developing an Intelligent Fault Monitoring Framework, integrating Fuzzy Fault Tree Analysis, and utilizing Neutrosophic logic with Petri Nets to enhance the reliability of safety-critical systems.

Dr. Gaurav brings extensive teaching experience, having guided students in Database Management Systems, Software Engineering, Software Quality Assurance and Software Project Management. His commitment to academic excellence and research-driven teaching fosters an innovative learning environment for students.

EDUCATION

Ph.D - NIT Jamshedpur
M.Tech - NIT Patna
B.Tech - DCE
All the three degrees are from the department of Computer Science and Engineering of the respective public colleges/universities.

RESEARCH INTERESTS

Database Management Systems, Design and Analysis of Algorithms, Software Engineering and Reliability Engineering.

FUTURE PROJECTS

Scopus Publications

Enhanced fuzzy fault tree with OWA operator with 2-level distributive function for analysing reliability in NPP with a case study
Kumar Gaurav, Binod Kumar Singh, and Vinay Kumar
Informa UK Limited



Application of image processing in agriculture
Dipra Mitra, Kumar Gaurav, Pallab Banerjee, and Sudeshna Sani
CRC Press



Intelligent fault monitoring and reliability analysis in safety–critical systems of nuclear power plants using SIAO-CNN-ORNN
Kumar Gaurav, Binod Kumar Singh, and Vinay Kumar
Springer Science and Business Media LLC



Predictive Modeling and Dynamic Analysis of Price Trends in eCommerce using ML Technique
Pallab Banerjee, Vishal Prasad, Kanika Thakur, Dipra Mitra, Kumar Gaurav, and Soumen Kanrar
IEEE



Implementing Newly Proposed Dynamic Tree Structure: Development, Integration, and Optimization
Pallab Banerjee, Sweety Kumari, Biresh Kumar, Kanika Thakur, Kumar Gaurav, and Purushottam Kumar
IEEE
An innovative data structure called a “dynamic tree” has important uses in algorithmic time complexity optimization. It creates well-organized trees by modifying the properties of several single-node trees, therefore preserving the forest structure. This structure is essential to improving the effectiveness of many algorithms. In this work, I particularly address the “Maximum Flow method,” a crucial subtopic in the larger field of dynamic trees. One basic method for solving network flow issues is the Maximum Flow Algorithm, but it frequently runs into issues with time complexity. My research attempts to overcome these obstacles by using dynamic trees to enhance the efficiency of the algorithm. This work reduces the time complexity of network flow problems, leading to more effective solutions. The findings have potential implications in a variety of computing and real-world scenarios.


A Proposed Method to Translate Code Blended Web based Text in English Language




Design and Implementation of Real Time Image Retrieval System for De-Noising Space Images in Chandrayaan 3 Lunar Mission Using Autoencoders
Prince Shubham, Pallab Banerjee, Kanika Thakur, Biresh Kumar, Dipta Mitra, and Kumar Gaurav
IEEE
In the context of the Chandrayaan 3 Lunar Mission, this research paper introduces a real-time image retrieval and denoising system powered by autoencoders, designed to tackle the challenge of noisy space imagery. Leveraging advanced deep learning techniques, our system employs autoencoders to extract essential features from the noisy lunar images and subsequently retrieves and integrates similar, noise-free reference images from a comprehensive database. By doing so, it achieves real-time denoising, ensuring that the mission’s acquired lunar images are of high quality, thus facilitating more accurate scientific analysis. The paper details the architecture of the autoencoder-based denoising system, its training process using a meticulously curated lunar image dataset, and its seamless integration into the mission’s image processing pipeline. Experimental results underscore the system’s remarkable noise reduction capabilities, thereby playing a pivotal role in enhancing the Chandrayaan 3 mission’s scientific contributions, ultimately advancing our understanding of the lunar environment and bolstering the success of lunar exploration endeavours.


Dependability Analysis of a System Using State-Space Modeling Techniques: A Systematic Review
Kumar Gaurav, Vinay Kumar, and Binod Kumar Singh
Institute of Electrical and Electronics Engineers (IEEE)



Consumer satisfaction rating system using sentiment analysis
Kumar Gaurav and Prabhat Kumar
Springer International Publishing



Facebook like: Past, present and future
Kumar Gaurav, Akash Sinha, Jyoti Prakash Singh, and Prabhat Kumar
Springer Singapore

RECENT SCHOLAR PUBLICATIONS