研究者学術情報データベース

（最終更新日 : 2022-10-12 19:05:43）

ジェンラフルコマル
ＪＡＩＮＲＡＨＵＬＫＵＭＡＲ
JAIN RAHUL KUMAR

所属情報理工学部情報理工学科

職名助教

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

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

研究テーマ
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.

現在の専門分野
知覚情報処理, 知能情報学, ソフトコンピューティング, 生命、健康、医療情報学

論文
1.	2022/10/28	A multi-head pseudo nodes based spatial–temporal graph convolutional network for emotion perception from GAIT │ Neurocomputing │ 511,437--447頁 (共著)
2.	2022/09/27	Unsupervised Domain Adaptation Approach for Liver Tumor Detection in Multi-phase CT Images │ 228,61--78頁 (共著)
3.	2022/09/08	Unsupervised Domain Adaptation Using Adversarial Learning and Maximum Square Loss for Liver Tumors Detection in Multi-phase CT Images │ ,1536--1539頁 (共著)
4.	2022/07/06	Unsupervised Domain Adaptation with Adversarial Learning for Liver Tumors Detection in Multi-phase CT Images │ 308,149--159頁 (共著)
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) (共著)
全件表示（10件）

学会発表
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

メールアドレス

科研費研究者番号
80962555

外部研究者ID
ORCID ID
0000-0002-0768-2193