Researcher Database

（Last updated : 2022-10-12 19:05:43）

JAIN RAHUL KUMAR

Department / Course College of Information Science and Engineering Department of Information Science and Engineering

Title / Position Assistant Professor

Academic background
1.	2019/04/01～2022/03/30	Object Detection using Deep Learning │ Graduate School of Information Science and Engineering │ Ritsumeikan University, Shiga, Japan │ Graduated │ Doctor of Engineering
2.	2013/07/01～2015/06/30	Computer Applications │ Department of Computer Science and Applications │ Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, India │ Graduated │ Master of Computer Applications

Business career
1.	2022/04/01	Assistant Professor │ College of Information Science and Engineering │ Ritsumeikan University

Subject of research
1.	Nowadays, Deep Learning-based (DL) frameworks dominate the top AI-based technology in many academic and industrial areas. DL based frameworks have become the leading techniques for various computer vision tasks such as image-classification, image-segmentation, object-detection, human pose-estimation. These computer vision tasks are tremendous useful in various applications in the modern era. The basic objective of my research is to develop effective Deep Learning-Based AI system that may be useful in various fields. Since the advancement of cutting-edge hardware devices like Jetson Nano and Raspberry-Pi, Deep Learning (DL) frameworks have become very famous and are being used everywhere. DL frameworks has immense potential for development of state-of-the-art AI-based computer vision technologies in many fields. I also aim to practically implement the novel Deep Learning approaches for real-world applications in collaboration with AI based Start-up.

Present specialized field
Perceptual information processing, Intelligent informatics, Soft computing, Life, health and medical informatics

Papers
1.	2022/10/28	A multi-head pseudo nodes based spatial–temporal graph convolutional network for emotion perception from GAIT │ Neurocomputing │ 511,pp.437--447 (Co-authored)
2.	2022/09/27	Unsupervised Domain Adaptation Approach for Liver Tumor Detection in Multi-phase CT Images │ 228,pp.61--78 (Co-authored)
3.	2022/09/08	Unsupervised Domain Adaptation Using Adversarial Learning and Maximum Square Loss for Liver Tumors Detection in Multi-phase CT Images │ ,pp.1536--1539 (Co-authored)
4.	2022/07/06	Unsupervised Domain Adaptation with Adversarial Learning for Liver Tumors Detection in Multi-phase CT Images │ 308,pp.149--159 (Co-authored)
5.	2021/10/15	LogoNet: A Robust Layer-Aggregated Dual-Attention Anchorfree Logo Detection Framework with an Adversarial Domain Adaptation Approach │ Applied Sciences │ 11 (20) (Co-authored)
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Academic conference presentation
1.	2022/07/08	Unsupervised Domain Adaptation for Liver Tumor Detection in Multi-Phase CT images Using Adversarial Learning with Maximum Square Loss
2.	2021/12/16	Unsupervised Logo Detection Using Adversarial Learning from Synthetic to Real Images

E-Mail Address

Department laboratory expense researcher number
80962555

External Researcher ID
ORCID ID
0000-0002-0768-2193