http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : ( Ruihui Mu ) (검색결과 3 건)
- 무료
- 기관 내 무료
- 유료
-
The Research of Network Security Based on Cognitive Radio
Ruihui Mu,Junwei Li 보안공학연구지원센터 2016 International Journal of Security and Its Applicat Vol.10 No.6
Firstly, we are given the security problems faced by a detailed analysis of the cognitive radio networks, and introduces the related basic cognitive radio network problems. Then, based on the difference between the cognitive radio network and the existing wireless network, which analyzes security and artificial intelligence, dynamic spectrum access and discussion. It concluded for the safety of cross-layer design.
-
Auxiliary Stacked Denoising Autoencoder based Collaborative Filtering Recommendation
( Ruihui Mu ),( Xiaoqin Zeng ) 한국인터넷정보학회 2020 KSII Transactions on Internet and Information Syst Vol.14 No.6
In recent years, deep learning techniques have achieved tremendous successes in natural language processing, speech recognition and image processing. Collaborative filtering(CF) recommendation is one of widely used methods and has significant effects in implementing the new recommendation function, but it also has limitations in dealing with the problem of poor scalability, cold start and data sparsity, etc. Combining the traditional recommendation algorithm with the deep learning model has brought great opportunity for the construction of a new recommender system. In this paper, we propose a novel collaborative recommendation model based on auxiliary stacked denoising autoencoder(ASDAE), the model learns effective the preferences of users from auxiliary information. Firstly, we integrate auxiliary information with rating information. Then, we design a stacked denoising autoencoder based collaborative recommendation model to learn the preferences of users from auxiliary information and rating information. Finally, we conduct comprehensive experiments on three real datasets to compare our proposed model with state-of-the-art methods. Experimental results demonstrate that our proposed model is superior to other recommendation methods.
-
A Review of Deep Learning Research
( Ruihui Mu ),( Xiaoqin Zeng ) 한국인터넷정보학회 2019 KSII Transactions on Internet and Information Syst Vol.13 No.4
With the advent of big data, deep learning technology has become an important research direction in the field of machine learning, which has been widely applied in the image processing, natural language processing, speech recognition and online advertising and so on. This paper introduces deep learning techniques from various aspects, including common models of deep learning and their optimization methods, commonly used open source frameworks, existing problems and future research directions. Firstly, we introduce the applications of deep learning; Secondly, we introduce several common models of deep learning and optimization methods; Thirdly, we describe several common frameworks and platforms of deep learning; Finally, we introduce the latest acceleration technology of deep learning and highlight the future work of deep learning.
ScienceON
KISS연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
