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首页 > 商务会议 > IT互联网会议 > ICAMDS 2019第四届应用数学与数据科学国际会议(西安) 更新时间:2019-05-20T09:24:22

ICAMDS 2019第四届应用数学与数据科学国际会议(西安)
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ICAMDS 2019第四届应用数学与数据科学国际会议(西安) 已过期

会议时间:2019-05-29 08:00至 2019-05-31 18:00结束

会议地点: 西安  详细地址会前通知  

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        会议介绍

        会议内容 主办方介绍


        ICAMDS 2019第四届应用数学与数据科学国际会议(西安)

        ICAMDS 2019第四届应用数学与数据科学国际会议(西安)宣传图

        2019第四届应用数学数据科学国际会议(ICAMDS 2019)为广大从事应用数学与数据科学等相关领域的研究学者、专家提供交流平台。会议组委会诚邀全球相关领域的学者、专家参加此次国际会议,就应用数学与数据科学相关热点问题进行探讨、交流,共同促进全球应用数学与数据科学的发展。

        2019第四届应用数学与数据科学国际会议将于2019年5月29-31日在中国西安召开。西安,古称长安,陕西省省会。西安是举世闻名的世界四大文明古都之一,居中国古都之首,是中国历史上建都时间最长、建都朝代最多、影响力最大的都城。作为华夏文明的发源地,西安的历史悠久,文化的积淀非常厚重,它是著名的丝绸之路的起点。西汉时期,汉武帝派遣张骞出使西域,正式开辟了以长安为起点,联结欧亚大陆的通道“丝绸之路”。“西安文物甲天下”,深厚的历史文化积淀和浩瀚的文物古迹遗存使西安享有“天然历史博物馆”的美称。有诸如秦始皇兵马俑、大雁塔、西安古城墙、华清池等著名旅游景点。

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        会议日程

        (最终日程以会议现场为准)


        5月29日 11:00-17:00 报到
        5月30日 09:00-12:00 特邀嘉宾演讲
        12:00-14:00 午餐
        14:00-17:30 口头报告
        18:30-20:00 晚宴
        5月31日 08:00-17:00 西安一日游

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        会议嘉宾

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        ICAMDS2019演讲嘉宾信息如下:

        ICAMDS 2019第四届应用数学与数据科学国际会议(西安)

        Dr. Zainidin Eshkuvatov, Associate Professor

        Faculty of Science and Technology, Universiti Sains Islam Malaysia, Malaysia

        Biography: Dr. Zainidin K. Eshkuvatov was born in 18 February 1966, Samarkand, Uzbekistan. He received his B.SC. degree and MSc degree in Mathematics from Tashkent State University in 1988, Tashkent, Uzbekistan. His Ph.D. degree in Mathematics and Physics on computational mathematics field, from National University of Uzbekistan. He has 27 years (11 years at the University of World Economy and Diplomacy (UWED), Tashkent Uzbekistan, 11 years in University Putra Malaysia (UPM), Malaysia and 5 years in University Sains Islam Malaysia (USIM)) experience in teaching of variaty of Mathematics subjects. Currently, He is the Associate Professor of Faculty of Science and Technology, Universiti Sains Islam Malaysia (USIM), Negeri Sembilan, Malaysia. His research interests are approximation of onedimentional and multy dimentional regular and singular integrals, singular and hypersingular integral equations and integro-differential equations of linear and nonlinear types as well as fractional integro-differential equations.

        Topic: Four Types of Solution of Hypersingular Integral Equations of the First Kind

        Abstract: In this note, we consider a hypersingular integral equations (HSIEs) of the first kind on the interval [-1, 1] with the assumption that kernel of the hypersingular integral is constant on the diagonal of the domain D = [1, -1] × [-1, 1]. Projection method together with Chebyshev polynomials of the first, second, third and forth kinds are used to find bounded, unbounded and semi-bounded solutions of HSIEs respectively. Exact calculations of hypersingular and singular integrals for Chebyshev polynomials allow us to obtain high accurate approximate solution. Gauss-Chebyshev quadrature with Gauss-Lobotto nodes are presented as the high accurate computation of regular kernel integrals. Existence of inverse of hypersingular integral operator leads to the convergence of the proposed method in the case of bounded and unbounded solution. Norm convergence are obtained in Hilbert space. Many examples are provided to verify the validity and accuracy of the proposed method. Comparisons with other methods are also given. Numerical examples reveal that approximate solutions are exact if solution of HSIEs is of the polynomial forms with corresponding weights. It is worth to note that proposed method works well for large value of node points and errors are drastically decreases. SPU times are also shown to present effectiveness of the method and less complexity computations.

        ICAMDS 2019第四届应用数学与数据科学国际会议(西安)

        Dr. En-Bing Lin , Professor

        Department of Mathematics, Central Michigan University, USA

        Biography: Dr. En-Bing Lin is Professor of Mathematics at Central Michigan University, USA. He is a former mathematics department chair at the University of Toledo and Central Michigan University. He has taught and visited at several institutions including Massachusetts Institute of Technology, University of Wisconsin-Milwaukee, University of California, Riverside, University of Toledo, UCLA, and University of Illinois at Chicago. He received his Ph. D. in Mathematics from Johns Hopkins University. His research interests include Data Analysis, Image Processing, Applied and Computational Mathematics, Wavelet Analysis and Applications, and Mathematical Physics. He has supervised a number of graduate and undergraduate students. Dr. Lin serves on the editorial boards of several mathematics and computational journals. He has served on several academic committees of regional and national associations. He has organized several special sessions at regional IEEE conference and American Mathematical Society national and regional meetings. He received Central Michigan University Distinguished Service Award and many research, travel and education grants.

        Topic: Incomplete Information Systems and Big Data Analytics

        Abstract: We provide an overview of Big Data Analysis and current trends of data analytics to process information systems. Rough Set Theory (RST), which was introduced by Pawlak in 1982 to deal with data analysis based on approximation methods in information systems. RST is a novel approach to cope with imperfect data analysis as well. From the viewpoint of RST, it is usually to represent a data set of an information system as a table. We discuss the decision tables that some attribute values are lost or incomplete. We show how we pass from classical rough set theory to variable precision generalized rough set theory. RST has many applications in many different areas, such as engineering, environment, banking, medicine, bioinformatics, pattern recognition, data mining, machine learning and others. RST is intrinsically a study of equivalence relations on the universe (a set of objects). In fact, rough sets can be used to represent ambiguity, vagueness and general uncertainty. Given some relations between objects in the set, we can construct lower and upper approximations of the objects. We can also use some advanced computing technologies to determine lower and upper approximations and find several properties of the characteristics of objects within RST, as well as to extend RST to generalized RST. We point out the connection with fuzzy sets, fuzzy binary relations and multiple universal sets. We will also mention some examples and applications.

        ICAMDS 2019第四届应用数学与数据科学国际会议(西安)

        Dr. Feng-Jen Yang, Associate Professor

        Computer Science Department, Florida Polytechnic University, USA

        Biography: Dr. Feng-Jen Yang received the B.E. degree in Information Engineering from Feng Chia University, Taichung, Taiwan, in 1989, the M.S. degree in Computer Science from California State University, Chico, California, in 1995, and the Ph.D. degree in Computer Science from Illinois Institute of Technology, Chicago, Illinois, in 2001, respectively. Currently, he is teaching and conducting researches at the Florida Polytechnic University. Besides the currently academic career, he also has highly regarded prior research experiences. He once was a research assistant at the Chung Shan Institute of Science and Technology (CSIST), Taoyuan, Taiwan, from 1989 to 1993, as well as an engineer at the Industrial Technology Research Institute (ITRI), Hsinchu, Taiwan, from 1995 to 1996. His research areas include Artificial Intelligence, Machine Learning, Database Management Systems, Data Communications, and Software Engineering. He had also been awarded with research grants from ACM SIGCSE and Seed Grants from Florida Polytechnic University.

        Topic: The Implementation of Probabilistic Inferencing and Classification in Python Programming Language

        Abstract: Our daily life is full of uncertainty, it is almost inevitable that we have to strive for performing informed behaviors despite the uncertain environment that we are living in. As a result, many intelligent systems are counting on probabilistic inference to support for decisions, hypotheses, forecasts and diagnoses. In this talk, we will go through the mathematic rationale as well as the design and implementation of a probabilistic inference engine. This inference engine is well modeled and implemented by using object-oriented approach. It can be easily plugged into future systems to speed up the development life cycle.

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