检索结果-内蒙古大学图书馆

作者： Nguyen, Van Du Truong, Hai Bang Merayo, Mercedes G. Nguyen, Ngoc Thanh Division of Knowledge and System Engineering for ICT Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City Viet Nam Viet Nam Department Sistemas Informáticos y Computación Universidad Complutense de Madrid Spain Faculty of Computer Science and Management Wroclaw University of Science and Technology Wyb. Wyspianskiego 27 Wroclaw50-370 Poland Faculty of Information Technology Nguyen Tat Thanh University Ho Chi Minh city Viet Nam

Recently, the use of the wisdom of crowds (WoC) for finding solutions to a wide range of real-life problems has dramatically expanded. Prior studies have revealed that diversity, independence, decentralization, and aggregation are the determinants of collective wisdom. However, these findings are often based on the so-called point estimates - single values are used as the representations of individual predictions on the task of estimating unknown quantities or predicting outcomes of future events. In some situations, interval values, which are often called interval estimates, can be used for such representations. Accordingly, one can provide an individual prediction in the form of an interval value including a lower and an upper bounds. Taking into account this kind of representation, in this paper, we present a case study in which collectives of randomly selected predictions can outperform those of most accurate predictions. Then, we evaluate the WoC level by taking into account diversity and cardinality. The computational experiments have indicated that diversity is positively related to collective wisdom. Finally, we discuss some related theoretical and practical implications for further research. © 2019 - IOS Press and the authors. All rights reserved.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Erratum to: Large circuit models: opportunities and challenges

引用

SCIENCE CHINA Information Sciences 2024年第11期67卷 1-1页

作者： Lei CHEN Yiqi CHEN Zhufei CHU Wenji FANG Tsung-Yi HO Ru HUANG Yu HUANG Sadaf KHAN Min LI Xingquan LI Yu LI Yun LIANG Jinwei LIU Yi LIU Yibo LIN Guojie LUO Hongyang PAN Zhengyuan SHI Guangyu SUN Dimitrios TSARAS Runsheng WANG Ziyi WANG Xinming WEI Zhiyao XIE Qiang XU Chenhao XUE Junchi YAN Jun YANG Bei YU Mingxuan YUAN Evangeline F.Y. YOUNG Xuan ZENG Haoyi ZHANG Zuodong ZHANG Yuxiang ZHAO Hui-Ling ZHEN Ziyang ZHENG Binwu ZHU Keren ZHU Sunan ZOU Huawei Noah's Ark Lab Hong Kong 999077 China School of Integrated Circuits Peking University Beijing 100871 China Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo 315211 China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong 999077 China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong 999077 China School of Integrated Circuits Southeast University Nanjing 210096 China Huawei HiSilicon Shenzhen 518129 China Peng Cheng Laboratory Shenzhen 518052 China School of Computer Science Peking University Beijing 100871 China School of Microelectronics State Key Laboratory of Integrated Chips and System Fudan University Shanghai 200433 China School of Artificial Intelligence Shanghai Jiao Tong University Shanghai 200240 China

来源：评论

学校读者我要写书评

暂无评论

Reviewing continual learning from the perspective of human-level intelligence

arXiv

引用

arXiv 2021年

作者： Chang, Yifan Li, Wenbo Peng, Jian Tang, Bo Kang, Yu Lei, Yinjie Gui, Yuanmiao Zhu, Qing Liu, Yu Li, Haifeng The State Key Laboratory of Fire Science Department of Automation and Institute of Advanced Technology University of Science and Technology of China Hefei China Institute of Intelligent Machines Chinese Academy of Sciences Hefei230031 China The Department of Electrical and Computer Engineering Mississippi State University United States College of Electronics and Information Engineering Sichuan University Chengdu China Faculty of Geosciences and Environmental Engineering The Southwest Jiaotong University Chengdu China Institute of Remote Sensing and Geographic Information System Peking Universit Beijing China School of Geosciences and Info-Physics Central South University South Lushan Road Changsha410083 China

Humans continual learning (CL) ability is closely related to Stability Versus Plasticity Dilemma that describes how humans achieve ongoing learning capacity and preservation for learned information. The notion of CL has always been present in artificial intelligence (AI) since its births. This paper proposes a comprehensive review of CL. Different from previous reviews that mainly focus on the catastrophic forgetting phenomenon in CL, this paper surveys CL from a more macroscopic perspective based on the Stability Versus Plasticity mechanism. Analogous to biological counterpart, "smart" AI agents are supposed to i) remember previously learned information (information retrospection);ii) infer on new information continuously (information prospection:);iii) transfer useful information (information transfer), to achieve high-level CL. According to the taxonomy, evaluation metrics, algorithms, applications as well as some open issues are then introduced. Our main contributions concern i) rechecking CL from the level of artificial general intelligence;ii) providing a detailed and extensive overview on CL topics;iii) presenting some novel ideas on the potential development of CL. © 2021, CC BY.

关键词： Petroleum reservoir evaluation

来源：评论

学校读者我要写书评

暂无评论

Room-Temperature Charge-to-Spin Conversion from Quasi-2D Electron Gas at SrTiO3-Based Interfaces

引用

physica status solidi (RRL) – Rapid Research Letters 2023年第6期17卷

作者： Utkarsh Shashank Angshuman Deka Chen Ye Surbhi Gupta Rohit Medwal Rajdeep Singh Rawat Hironori Asada X. Renshaw Wang Yasuhiro Fukuma Department of Physics and Information Technology Faculty of Computer Science and System Engineering Kyushu Institute of Technology 680-4 Kawazu Iizuka 820-8502 Japan Birck Nanotechnology Center School of Electrical and Computer Engineering Purdue University West Lafayette IN 47907 USA School of Physical and Mathematical Sciences Nanyang Technological University 637616 Singapore Natural Sciences and Science Education National Institute of Education Nanyang Technological University 637616 Singapore Department of Physics Indian Institute of Technology Kanpur Kanpur 208016 India Graduate School of Sciences and Technology for Innovation Yamaguchi University 2-16-1 Tokiwadai Ube 755-8611 Japan School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore Research Center for Neuromorphic AI hardware Kyushu Institute of Technology Kitakyushu 808-0196 Japan

来源：评论

学校读者我要写书评

暂无评论

Epileptic seizures detection using deep learning techniques: A review

arXiv

引用

arXiv 2020年

作者： Shoeibi, Afshin Khodatars, Marjane Ghassemi, Navid Jafari, Mahboobeh Moridian, Parisa Alizadehsani, Roohallah Panahiazar, Maryam Khozeimeh, Fahime Zare, Assef Hosseini-Nejad, Hossein Khosravi, Abbas Atiya, Amir F. Aminshahidi, Diba Hussain, Sadiq Rouhani, Modjtaba Nahavandi, Saeid Acharya, Udyavara Rajendra K. N. Toosi University of Technology Tehran*** Iran The Computer Engineering Department Ferdowsi University of Mashhad Mashhad*** Iran The Mashhad Branch Islamic Azad University Mashhad91735413 Iran Electrical and Computer Engineering Faculty Semnan University Semnan*** Iran The Faculty of Engineering Science and Research Branch Islamic Azad University Tehran*** Iran Deakin University Waurn PondsVIC3217 Australia The Institute for Computational Health Sciences School of Medicine University of California San FranciscoCA94143 United States Faculty of Electrical Engineering Gonabad Branch Islamic Azad University Gonabad*** Iran The Faculty of Electrical and Computer Engineering K. N. Toosi University of Technology Tehran*** Iran The Department of Computer Engineering Faculty of Engineering Cairo University Cairo12613 Egypt System Administrator at Dibrugarh University Assam 786004 India The Department of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore599494 Singapore The Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore599489 Singapore The Department of Bioinformatics and Medical Engineering Taichung City41354 Taiwan

A variety of screening approaches have been proposed to diagnose epileptic seizures, using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities. Artificial intelligence encompasses a variety of areas, and one of its branches is deep learning (DL). Before the rise of DL, conventional machine learning algorithms involving feature extraction were performed. This limited their performance to the ability of those handcrafting the features. However, in DL, the extraction of features and classification are entirely automated. The advent of these techniques in many areas of medicine, such as in the diagnosis of epileptic seizures, has made significant advances. In this study, a comprehensive overview of works focused on automated epileptic seizure detection using DL techniques and neuroimaging modalities is presented. Various methods proposed to diagnose epileptic seizures automatically using EEG and MRI modalities are described. In addition, rehabilitation systems developed for epileptic seizures using DL have been analyzed, and a summary is provided. The rehabilitation tools include cloud computing techniques and hardware required for implementation of DL algorithms. The important challenges in accurate detection of automated epileptic seizures using DL with EEG and MRI modalities are discussed. The advantages and limitations in employing DL-based techniques for epileptic seizures diagnosis are presented. Finally, the most promising DL models proposed and possible future works on automated epileptic seizure detection are delineated. © 2020, CC BY-NC-ND.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

The simulation models for human pedestrian movement of a departure process in an airport

The simulation models for human pedestrian movement of a dep...

引用

2018 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2018

作者： Bakar, Noor Akma Abu Fauzan, Anis Izzati Ab Majid, Mazlina Abdul Allegra, Mario Fakhreldin, M. Faculty of Computer System and Software Engineering Universiti Malaysia Pahang Pahang Malaysia Italian National Research Council CNR Institute for Educational Technology ITD Palermo Italy Faculty of Computer Science and Information Technology Jazan University Saudi Arabia

ISBN: (纸本)9781538692073

The emergence of simulating human movement and behaviour rose in order to imitate and replace human interference with this complex demeanour. It is so diverse and uncontrollable to the extent it is almost impossible to undergo exhaustive experiments. Hence, simulating this model is crucial. Especially in designing a public area where human density is too much concentration and the need for properly planned traffic or human movement is very much needed. For that, simulations and modelling are always the best options for investigating human behaviour. In this research work, the aim is to investigate how effective and to what extent the Agent-Based Simulation (ABS), Discrete Event Simulation (DES) and hybrid of both give benefits to the society adequately. Its expected results will be an improved and better version of human pedestrian movement in the airport of the departure process simulation model. © 2018 IEEE.

关键词： Discrete event simulation

来源：评论

学校读者我要写书评

暂无评论

Comparing CART and C5.0 algorithm performance of human development index 3

Comparing CART and C5.0 algorithm performance of human devel...

引用

3rd International Conference on Informatics and Computing, ICIC 2018

作者： Assiroj, Priati Warnars, Harco Leslie Hendric Spits Fauzi, Ahmad Dept. of Information System Faculty of Technology and Computer Science Buana Perjuangan University Karawang Indonesia Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Dept. of Computer Science Faculty of Technology and Computer Science Buana Perjuangan University Karawang Indonesia

ISBN: (纸本)9781538669204

Human Development Index (HDI) is a reference to how far the administration can develops their people. The development of a territory is related to a prosperity of the people. This research is focused on Human Development Index in West Java, a Province of Indonesia. Research identifies variables which are influence and very important to HDI and compares the model of CART and C5.0 algorithm based on HDI concept that has developed by United Nation (UN). Research uses 14 variables which are commonly used for measuring HDI and found that there are two variables that most influencing to HDI, MYS-index and EDU-Index. By knowing the MYS and EDU-Index research can predict and classify each territory to its HDI. Research could be conducted to predict HDI in the other territory. The results evaluated by measuring the accuracy and confusion matrix, 91.35 for CART algorithm and 90.80 for C5.0 algorithm. © 2018 IEEE.

关键词： Algorithm Classification Comparison EDU-Index Human Development Index (HDI) MYS-Index Territory

来源：评论

学校读者我要写书评

暂无评论

Noise bias compensation for reconstructed HDR images in two layer coding

IEEJ Transactions on Electronics, Information and Systems

引用

IEEJ Transactions on Electronics, Information and systems 2019年第12期139卷 1442-1451页

作者： Minewaki, Sayaka Hamzah, Fairoza Amira Binti Yoshida, Taichi Iwahashi, Masahiro Kiya, Hitoshi Department of Computer Science and Engineering National Institute of Technology Yuge College 1000 Kamijima-cho Ochi-gun Ehime794-2593 Japan Kyoto College of Graduate Studies for Informatics 7 Tanakamonzen-cho Sakyo-ku Kyoto606-8225 Japan Department of Computer and Network Engineering University of Electro-Communications 1-5-1 Chofugaoka Chofu Tokyo182-8585 Japan Department of Electrical Electronics and Information Engineering Nagaoka University of Technology 1603-1 Kamitomioka Nagaoka Niigata940-2188 Japan Department of Information and Communication Systems Faculty of System Design Tokyo Metropolitan University 6-6 Asahigaoka Hino Tokyo191-0065 Japan

We propose a two-layer coding method for HDR images with noise bias compensation (NBC). Although the mean of image noise is assumed to be zero originally, it becomes nonzero (that is, noise bias is present) after tone mapping, because of the nonlinearity of tone mapping. In the conventional two-layer coding, the reconstructed LDR image is decoded using a lossy encoder of a base layer that includes noise;the noise is included in an HDR image that is inverse tone mapped from a reconstructed LDR image with noise bias. We aim to improve the quality of reconstructed HDR images by compensating for noise bias. NBC classifies pixels in a noisy image into several subsets according to the pixel value after tone mapping, and compensates for the pixel value in each subset with a compensation value determined in advance. The compensation values are determined by using all pixel values in the LDR image that is input to the base layer, in addition to those in the HDR image that is inversely tone mapped from the reconstructed LDR image. We perform NBC following inverse tone mapping for the reconstructed LDR image. Experimental results show the effectiveness of the proposed method. © 2019 The Institute of Electrical Engineers of Japan.

关键词： Image denoising

来源：评论

学校读者我要写书评

暂无评论

Analysis of smart city environment by artificial intelligent techniques

引用

AIP Conference Proceedings 2023年第1期2831卷

作者： Sujay Mugaloremutt Jayadeva A. K. Gnanasekar Prashant Sunagar N. Harshith Sahil Sanjeev Salvi Ashok Kumar Department of Health System Management Studies JSS Academy of Higher Education & Research (Faculty of Management Studies) Mysuru-570015 India Department of Electronics & Communication Engineering Rajalakshmi Institute of Technology Chembarambakkam Chennai Tamil Nadu-600124 India Department of Civil Engineering M.S. Ramaiah Institute of Technology MSR Nagar Bengaluru Karnataka-560054 India Department of Civil Engineering Pimpri Chinchwad College of Engineering and Research Ravet Pune Maharashtra-412101 India Department of Computer Science Banasthali Vidyapith Banasthali Rajasthan-304022 India

The expectation of artificial intelligence is happened to realize in such a way that it essentially and most peculiarly supports the sustainable enhancement of fore coming smart environment surrounding the cities. The contribution towards the different developing fields of artificial intelligence have been made in determining the transformation towards traditional cities with conventional facilities into fully built smart environmental cities. The knowledge of producing better standards of living by rebuilding technology into daily activities of human is the primary requirements of the smart city emergence with esteemed technologies. In this proposed study, the concepts are enlightened towards few enhanced and developed technologies and identification of solutions to different problems that has been faced by people due to absence of digital environment. It illustrates certain problems that has been related to the smart city infrastructure, security, public safety, which lends suitable solutions for the denoted problems. It not only creates the emphasis on the technique of artificial intelligence but also on the Internet of Things (IoT) applications, pattern recognitions, deep and machine learning, analytics of big data and infrastructures associated with cloud computing in establishing the development as a completely equipped smart city.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Biometric authentication integrated with wireless communication malicious activity detection in a cyber physical system-based Fintech banking

引用

Optik 2023年 272卷

作者： Almuhannad Sulaiman Alorfi Saud Yonbawi Sultan Alahmari Abdullaev Abror Bozorboevich Mahendran Arumugam Pham Quang Huy Department of Information System Faculty of Computing and Information Technology King Abdulaziz University City Rabigh Saudi Arabia Department of Software Engineering College Of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia King Abdul Aziz City for Science and Technology Riyadh Saudi Arabia The Department of Acconting Economic Analysis and Audit Tashkent Institute of Finance Tashkent Uzbekistan Center for Transdisciplinary Research Saveetha Dental College Saveetha institute of Medical and Technical Science Chennai India University of Economics Ho Chi Minh City (UEH) Vietnam

Recently, biometric technology has been extensively embedded in mobile devices to enhance security of mobile devices. With rise of financial technology (FinTech) that employs mobile applications as well as devices as promotional platforms, biometrics has a significant role in strengthening the detection of this privacy application. This manuscript offers the design of salp swarm optimization with auto-encoder based biometric authentication (SSOAE-BMA) model for the recognition of abnormal activities in the Fintech banking applications based on wireless communication. The major aim of the SSOAE-BMA model lies in the proper authentication of persons via biometric matching process. Initially, the presented SSOAE-BMA model makes use of stacked ResNet-50 model for deriving feature vectors. Next, the SSOAE-BMA model utilizes AE for biometric verification and the performance of the AE model is adjusted using the Social Spider Optimization (SSO) Algorithm which in turn enhances the recognition outcomes. To demonstrate the improved performance of SSOAE-BMA model, a series of simulations were carried out. The experimental outcomes signified the enhancements of the SSOAE-BMA model over existing models.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：