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> JAIN RAHUL KUMAR
(最終更新日 : 2023-08-03 19:27:12)
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JAIN RAHUL KUMAR
JAIN RAHUL KUMAR
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情報理工学部 情報理工学科
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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.
2023/07/28
A Boundary-Enhanced Liver Segmentation Network for Multi-Phase CT Images with Unsupervised Domain Adaptation │ Bioengineering │ 10(8) (899) (共著)
2.
2023/03/31
Unsupervised Logo Detection Using Adversarial Learning From Synthetic to Real Images │ IEEE Transactions on Emerging Topics in Computational Intelligence │ ,1-14頁 (共著)
3.
2023/06/21
A Spatial-Temporal Graph Convolutional Networks-based Approach for the OpenPack Challenge 2022 │ IEEE Annual Conference on Pervasive Computing and Communications Workshops (PerCom) │ (共著)
4.
2023/05/31
An Efficient Dual-Pooling Channel Attention Module for Weakly-Supervised Liver Lesion Classification on CT Images │ International KES Conference on Innovation in Medicine and Healthcare │ (共著)
5.
2022/09/27
Unsupervised Domain Adaptation Approach for Liver Tumor Detection in Multi-phase CT Images │ 228,61--78頁 (共著)
全件表示(14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