Important Dates

Special Session & Workshop Proposals	May 30, 2019
Paper Submission Deadline	August 15, 2019 August 30, 2019
Author Notification	September 5, 2019 September 15, 2019
Last version of accepted papers	Octorber 02, 2019
Author registration	Octorber 05, 2019
Conference Dates	December 19-20, 2019
Early registration	Octorber 05, 2019

Conference Program

CONFERENCE PROGRAM

Accepted papers

ACCEPTED PAPERS FOR CONFERENCE PROCEEDING

Michele Della Ventura, Speech Assessment Based on Entropy and Similarity Measures
Satyam Paul and Magnus Lofstrand, Discrete Time Sliding Mode Control of Milling Chatter
Vinh Truong Hoang and Mai Bui Thuy Huynh, Fusing of Deep Learning, Transfer Learning and GAN for Breast Cancer Histopathological Image Classification
Jiancheng Lyu, Jack Xin and Yifeng Yu, Computing Residual Diffusivity by Adaptive Basis Learning via Super-Resolution Deep Neural Networks
Jiancheng Lyu and Spencer Sheen, A Channel-Pruned and Weight-Binarized Convolutional Neural Network for Keyword Spotting
Thanh-Nghi Do, The-Phi Pham, Nguyen-Khang Pham, Huu-Hoa Nguyen, Karim Tabia and Salem Benferhat, Stacking of SVMs for classifying intangible cultural heritage images
Orchida Dianita, Thomas Djorgie and Muhammad Kusumawan Herliansyah, Improvement of Production Layout in the Furniture Industry in Indonesia with the Concept of Group Technology
Chien Pham Van and Giang Nguyen-Van, Assessment of the water area in the lowland region of the Mekong River using MODIS EVI time series
Phuong Nguyen-Thanh, Duc Van-Tien, Tan Le-Nhat, Thanh-Tan Mai and Khuong Nguyen-An, An Intensive Empirical Study of Machine Learning Algorithms for Predicting Vietnamese Stock Prices
Oanh Tran, Attention-based biLSTMs for Understanding Students Learning Experiences on Social Media
Nga Ly-Tu, Qui Vo-Phu and Thuong Le-Tien, Using Support Vector Machine to Monitor Behaviour of an Object based WSN System
Thi Thu Hong Phan, Emilie Poisson Caillault and Andre Bigand, eDTWBI: effective imputation method for univariate time series
Thi Thanh Luu Le and Trong Hieu Tran, Belief merging for Possibilistic belief bases
Chu Anh My, Duong Xuan Bien, Nguyen Van Cong and Le Chi Hieu, New Feed Rate Optimization Formulation in a Parametric Domain for 5-Axis Milling Robots
Ha Duyen Trung and Nguyen Tai Hung, Opensource Based IoT Platform and LoRa Communications with Edge Device Calibration for Real-time Monitoring Systems
Duc Manh Nguyen, A Combination of CMAES-APOP algorithm and quasi-Newton method
Quang-Vinh Dang, Reinforcement Learning in Stock Trading
Duc Quynh Tran, A new efficient algorithm for maximizing the pro?t and the compactness in land use planning problem
Hue Chi Lam, Hanyu Gu and Yakov Zinder, A Genetic Algorithm Approach for Scheduling Trains Maintenance under Uncertainty
Phuoc-Hung Vo, Thai-Son Nguyen, Van-Thanh Huynh, Thanh-C Vo and Thanh-Nghi Do, Secure and Robust Watermarking Scheme in Frequency Domain Using Chaotic Logistic Map Encoding
Thi-Lich Nghiem and Thi-Toan Nghiem, Applying MASI algorithm to improve the classification performance of imbalanced data in fraud detection
Tatiana Zarodnyuk, Aleksander Gornov, Anton Anikin and Pavel Sorokovikov, Numerical technologies for investigating optimal control problems with free right-hand end of trajectories
Bach Tran and Hoai An Le Thi, Deep Clustering with Spherical Distance in Latent Space
Dinh Tuyen Hoang, Botambu Collins, Hyojeon Yoon, Ngoc-Thanh Nguyen and Dosam Hwang, A Survey on forecasting models for preventing terrorism
Van Thien Hoang, Dang Hung Kiet, Vu Van Giang and Le Hoang Thai, Palmprint Recognition Using Discriminant Local Line Directional Representation
Thi Thoi Tran, Delphine Sinoquet, Sebastien Da Veiga and Marcel Mongeau, An adapted derivative-free optimization method for an optimal design application with mixed binary and continuous variables
Thuong-Cang Phan, Anh-Cang Phan, Thi-To-Quyen Tran and Ngoan-Thanh Trieu, Efficient Processing of Recursive Joins on Large-Scale Datasets in Spark
Vinh Thanh Ho, Hoai An Le Thi and Tao Pham Dinh, DCA with successive DC decomposition for convex piecewise-linear fitting
Quang-Vu Nguyen and Hai-Bang Truong, An improvement of applying multi-objective optimization algorithm into higher order mutation testing
Hung Son Nguyen and Sinh Hoa Nguyen, Learning Rough Set based Classifiers using Boolean Kernels
Vuong Le Luong, Thuan Nguyen Quang and Quynh Tran Duc, A New Solution Method for a Mean-Risk Mixed Integer Nonlinear Program in Transportation Network Protection
Quang Thuan Nguyen and Duc Anh Nguyen, A Novel Approach for Travel Time Optimization in Single-track Railway Networks
Thi-Ngan Pham, Quang-Thuy Ha, Minh-Chau Nguyen and Tri-Thanh Nguyen, A probability-based close domain metric in lifelong learning for multi-label classification
Thanh Hai Nguyen, Sergiu Carpov and Vincent Herbert, Homomorphic Encryption-based Privacy – Aware Design for Collaborative Information Sharing in Cybersecurity Cloud Platform
Anh Son Ta, Hoai An Le Thi and Dinh Tao Pham, Solving Efficient Target-Oriented Scheduling in Directional Sensor Networks by DCA
Andre Dembele, Babacar Mbaye Ndiaye, Guy Degla and Adam Ouorou, A triple stabilized bundle method for constrained nonconvex nonsmooth optimization

PROCEEDINGS

Prospective authors of papers are invited to submit contributions for presentations at ICCSAMA 2019. The submissions should present the results of original research or innovative practical applications relevant to conference topics.

The conference language is English.

The conference proceedings will be published in the series Advances in Intelligent Systems and Computing of Springer-Verlag and indexed by indexed by ISI Proceedings, DBLP, Ulrich’s, EI-Compendex, SCOPUS, Zentralblatt Math, MetaPress, Springerlink. All submissions should follow the LNCS/LNAI style and not exceed 12 pages.

Invited Lectures

PLENARY LECTURE

Professor Aharon Ben-Tal
Technion – Israel Institute of Technology

Title: Robust Optimization: the need, the challenge, the success

Abstract:
The Need: Optimization problems are affected by uncertainties, either due to inaccuracy in measurements, lack of timely information on values of parameters (which incurs estimation errors), and limitation on executing accurately a computed solution (causing implementation errors). Due to these uncertainties a solution may divert from the expected optimality, and even worse; can lead to infeasibility. Consequently, there is a great need to come up with a method that can address the above difficulties.
The challenge is to introduce a methodology which on one hand will not impose on the user of the optimization the need to provide full information on the uncertainties, which he cannot deliver, and on the other hand will not impose on the optimizer the need to solve intractable optimization problems, in particular dynamic (multi-stage) ones and Chance Constrained problems. Robust Optimization (RO) is an attempt to meet these challenges. The talks will explain and demonstrate how this is achieved. We will present in particular examples in Antenna design and in Signal processing.
The success: RO created a new branch of optimization theory; this is evidence by huge number of publications addressing topics in RO. What is even more impressive is the large number of diverse applications of RO (As of November 2019, (Google Scholar lists over 144,000,000 items when one clicks “application of robust optimization”!).

Brief bio:
Aharon Ben-Tal is a Professor of Operations Research Management at the Technion � Israel Institute of Technology. He received his Ph.D. in Applied Mathematics from Northwestern University in 1973. He has been a Visiting Professor at the University of Michigan, University of Copenhagen, Delft University of Technology, MIT and CWI Amsterdam, Columbia and NYU. His interests are in Continuous Optimization, particularly nonsmooth and large-scale problems, conic and robust optimization, as well as convex and nonsmooth analysis. In recent years the focus of his research is on optimization problems affected by uncertainty. In the last 20 years he has devoted much effort to engineering applications of optimization methodology and computational schemes. Some of the algorithms developed in the MINERVA Optimization Center are in use by Industry (Medical Imaging, Aerospace). He has published more than 135 papers in professional journals and co-authored three books. Prof. Ben-Tal was Dean of the Faculty of Industrial Engineering and Management at the Technion (1989-1992) and (2011-2014). He served in the editorial board of all major OR/Optimization journals. He gave numerous plenary and keynote lectures in international conferences.

In 2007 Professor Ben-Tal was awarded the EURO Gold Medal – the highest distinction of Operations Research within Europe.

In 2009 he was named Fellow of INFORMS.

In 2015 he was named Fellow of SIAM.

In 2016 he was awarded by INFORMS the Khchiyan prize for Lifetime Achievement in the area of Optimization.

In 2017, the Operation Research Society of Israel (ORSIS) awarded him the Lifetime Achievement Prize.

As of September 2018 his work has over 22,900 citations (Google scholar).

PLENARY LECTURE

Professor Melvyn Sim
National University of Singapore, Business school

Title: Robust Stochastic Optimization

Abstract: We present a new distributionally robust optimization model called the robust stochastic optimization (RSO), which unifies both scenario-tree based stochastic linear optimization and distributionally robust optimization in a practicable framework that can be solved using the state-of-the-art commercial optimization solvers. The model of uncertainty incorporates both discrete and continuous random variables, typically assumed in scenario-tree based stochastic linear optimization and distributionally robust optimization respectively. To address the non-anticipativity of recourse decisions, we introduce the event-wise recourse adaptations, which integrate the scenario-tree adaptation originating from stochastic linear optimization and the affine adaptation popularized in distributionally robust optimization. Our proposed event-wise ambiguity set is rich enough to capture traditional statistic-based ambiguity sets with convex generalized moments, mixture distribution, f-divergence, Wasserstein (Kantorovich-Rubinstein) metric, and also inspire machine-learning- based ones using techniques such as K-means clustering, and classification and regression trees. Several interesting RSO models, including optimizing over the Hurwicz criterion and two-stage problems over Wasser- stein ambiguity sets, are provided. We develop a new algebraic modeling package, RSOME to facilitate the implementation of RSO models. This is a joint work with Zhi Chen and Peng Xiong.

Brief bio:
Dr. Melvyn Sim is Professor and Provost’s Chair at the Department of Analytics & Operations, NUS (National University of Singapore) Business School. He holds PhD in Operations Research, June 2004 Massachusetts Institute of Technology, Cambridge MA Thesis: Robust Optimization Advisor: Dimitris J. Bertsimas, MIT His research interests fall broadly under the categories of decision making and optimization under uncertainty with applications ranging from finance, supply chain management, healthcare to engineered systems. He is one of the active proponents of Robust Optimization and has given invited talks in this field at international conferences. Dr. Sim won second places in the 2002 and 2004 George Nicholson best student paper competition and first place in the 2007 Junior Faculty Interest Group (JFIG) best paper competition. He is also the recipient of the 2009 NUS outstanding young researcher award. Dr. Sim serves as an associate editor for Operations Research, Management Science and Mathematical Programming Computations.

Academic and Professional Experience

1. Head, Department of Analytics & Operations, 2017 – Present
2. Professor and Provost’s Chair, April 2016 – Present
3. Courtesy appointment at Industrial and Systems Engineering, Jan 2016 – Present
4. Deputy Director, NUS Global Asian Institute, Aug 2012 – Present
5. Professor, Jan 2012 – Present
6. Dean’s Chair, July 2009 – 2012
7. Deputy Head, Decision Sciences, May 2009 – July 2011.
8. Associate Professor (with tenure), Decision Sciences, July 2008 – Dec 2011
9. NUS Risk Management Institute Affiliated Researcher , 2007 – 2016
10. Fellow, Singapore-MIT-Alliance, 2004 – 2008
11. Assistant Professor , Decision Sciences, NUS, 2004 – 2008
12. Senior Tutor , Decision Sciences, NUS, 2000 – 2004
13. Research Engineer , Singapore Ministry of Defense, 1997 – 1999

Area of Expertise

1. Optimization and under Uncertainty
2. Modeling and Optimization of Operations/Supply chains/Healthcare systems

PLENARY LECTURE

Professor Martin J. Wainwright
Department of Statistics, UC Berkeley, Berkeley, USA
Department of EECS, UC Berkeley, Berkeley, USA

Title: Randomized algorithms for big data: From optmization to machine learning

Abstract: Large-scale data sets are now ubiquitous throughout engineering, science and technology, and they present a number of interesting challenges at the interface between machine learning and optimization. In this talk, we discuss the use of randomized dimensionality reduction techniques, also known as sketching, for quickly obtaining approximate solutions to large-scale optimization problems that arise in machine learning and statistics. We first show how sketching allows for much faster solution of constrained quadratic problems, and how the sketch dimension can be adapted to the intrinsic dimension of the solution space. We then show how these ideas lead to a faster, randomized version of the Newton algorithm with provable guarantees.
Based on joint work with Mert Pilanci, Stanford University

Brief bio: Martin Wainwright joined the faculty at University of California at Berkeley in Fall 2004, and is currently a Chancellor’s Professor with a joint appointment between the Department of Statistics and the Department of Electrical Engineering and Computer Sciences. He received his Bachelor’s degree in Mathematics from University of Waterloo, Canada, and his Ph.D. degree in Electrical Engineering and Computer Science (EECS) from Massachusetts Institute of Technology (MIT), for which he was awarded the George M. Sprowls Prize from the MIT EECS department in 2002. He is interested in high-dimensional statistics, information theory and statistics, and statistical machine learning. He has received an Alfred P. Sloan Foundation Research Fellowship (2005), IEEE Best Paper Awards from the Signal Processing Society (2008) and Communications Society (2010); the Joint Paper Award from IEEE Information Theory and Communication Societies (2012); a Medallion Lecturer (2013) of the Institute for Mathematical Statistics; a Section Lecturer at the International Congress of Mathematicians (2014); and the COPSS Presidents’ Award in Statistics (2014). He is currently serving as an Associate Editor for the Annals of Statistics, Journal of Machine Learning Research, Journal of the American Statistical Association, and Journal of Information and Inference.

PLENARY LECTURE

Professor Jack Xin
University of California Irvine, USA

Title: Nonconvex non-smooth optimization methods for reducing complexity of deep neural networks

Abstract: We discuss nonconvex optimization problems arising in training quantized and sparsified deep neural networks. Such networks have much lower memory and inference costs than their full precision counterparts. The training aims to maintain full precision network performance. Quantization restricts the network weights to discrete values such as {1,-1} up to a scalar multiplier or piecewise constant activation functions. Sparsification refers to network weights being sparse or groupwise sparse. The mathematical and algorithmic challenge is to reconcile the continuous nature of stochastic gradient descent and the discreteness in quantization or sparsification so that the training process is convergent and efficient. We show computational results on large image data sets as well as theoretical analysis on model problems.

Brief bio: Jack Xin is Chancellor’s Professor of Mathematics at UC Irvine. He received his Ph.D in applied mathematics at Courant Institute, New York University in 1990. He was a postdoctoral fellow at Berkeley and Princeton in 1991 and 1992. He was assistant and associate professor of mathematics at the University of Arizona from 1991 to 1999. He has been professor of mathematics at the University of Texas at Austin (1999-2005), and UC Irvine since 2005. His research interests include applied analysis, computational methods and their applications in multi-scale problems, nonconvex optimization, and data science. He authored over a hundred twenty journal papers and two Springer books. He is a fellow of the Guggenheim Foundation, and the American Mathematical Society. He is Editor-in-Chief of Society of Industrial and Applied Mathematics Interdisciplinary Journal Multi-scale Modeling and Simulation (MMS).

SEMI-PLENARY LECTURE

Professor Takahito Kuno
University of Tsukuba, Japan

Title: Global optimization of a class of DC functions over a polytope

Abstract:
It is known that every twice continuously differentiable function can be represented as the sum of a convex function and a separable concave function. To find the global minimum of such a class of DC functions over a polytope, we extend the rectangular branch-and- bound algorithm for separable concave minimization and try to improve the bounding process. We also report some numerical results, which indicate our algorithm is rather promising.

Brief bio: From 1988 to 1991, Takahito Kuno worked at TIT as an assistant professor, and then moved to University of Tsukuba in 1991, and now He is now professor in Faculty of Engineering, Information and Systems, University of Tsukuba. His research interest is in Global optimization of multiextremal nonconvex functions, and he is a coeditor of Journal of Global Optimization, Optimization Letters, and SN Operations Research forum.

Program Committee

Program Co-chairs:

Le Thi Hoai An, University of Lorraine, France.
Pham Dinh Tao, National Institute for Applied Sciences, Rouen, France
Nguyen Ngoc Thanh, Wroclaw University of Technology, Poland.
Members
Bui Alain, University of Versailles Saint-Quentin-en-Yvelines, France.
Bui Minh Phong, Eotvos Lorand University, Hungary
Do Nam Hoai, Budapest University of Technology and Economics, Hungary.
Do Van Tien, Budapest University of Technology and Economics, Hungary.
Do Thanh Nghi, Can Tho University, Vietnam.
Ha Quang Thuy, Vietnam National University, Vietnam.
Hain Ferenc, Budapest College of Communications and Business, Hungary.
Ho Vinh Thanh, University of Lorraine, France.
Le Chi Hieu, University of Greenwich, UK.
Le Hoai Minh, University of Lorraine, France.
Le Nguyen Thinh, Humboldt Universitat zu Berlin, Germany.
Nguyen Anh Linh, Warsaw University, Poland.
Nguyen Canh Nam, Hanoi University of Science and Technology, Vietnam.
Nguyen Benjamin, University of Versailles Saint-Quentin-en-Yvelines, France.
Nguyen Duc Cuong, School of Computer Science & Engineering of International University, Vietnam.
Nguyen Duc Khuong, IPAG Business School, Paris, France
Nguyen Duc Manh, Hanoi National University of Education, Vietnam.
Nguyen Giang, Slovak Academy of Sciences, Slovakia.
Nguyen Hung Son, Warsaw University, Poland.
Nguyen Luu Lan Anh, Eotvos Lorand University, Hungary.
Nguyen Manh Cuong, Hanoi University of Industry, Vietnam.
Nguyen Quang Thuan, International School, Vietnam National University, Hanoi, Vietnam.
Nguyen Thanh Binh, International Institute for Applied Systems Analysis (IIASA), Austria.
Nguyen Thanh Thuy, National University of Hanoi, Vietnam.
Nguyen Van Sinh, International University, Vietnam National University HCM, Vietnam
Nguyen Viet Hung, Laboratory of Computer Sciences, Paris 6, France.
Pham Cong Duc, University of Pau and Pays de l’Adour, France.
Pham Dinh Tao, National Institute for Applied Sciences, Rouen, France.
Phan Duong Hieu, Université Paris 8, France.
Pham Duc Truong, University of Birmingham, UK.
Pham Hoang, Rutgers, The State University of New Jersey, United States
Pham Ngoc Anh, Posts and Telecommunications Institute of Technology, Vietnam.
Pham Viet Nga, Vietnam National University of Agriculture, Vietnam.
Phan Duy Nhat, University of Lorraine, France.
Ta Thong Vinh, Budapest University of Technology and Economics, Hungary.
Ta Anh Son, Hanoi University of Science and Technology, Vietnam.
Tran Duc Quynh, Vietnam National University of Agriculture, Vietnam.
Tran Dinh Viet, Slovak Academy of Sciences, Slovakia.
Tran Gia Phuoc, University of Wuerzburg Am Hubland, Germany
Tran Hoai Linh, Hanoi University of Science and Technology, Vietnam.
Tran Thi Thuy, FPT University, Vietnam.
Trinh Anh Tuan, Budapest University of Technology and Economics, Hungary.
Truong Trong Tuong, Cergy-Pontoise University, France.
Yi-Shuai Niu, Shanghai Jiao Tong University, China.

Steering Committee

Prof. Le Thi Hoai An, Lorraine University, France (Co-chair)
Prof. Nguyen Ngoc Thanh, Wroclaw University of Technology, Poland (Co-chair)
Prof. Bui Alain, Université de Versailles-St-Quentin-en-Yvelines, France
Prof. Bui Minh Phong, Eotvos Lorand University, Hungary
Prof. Do Van Tien, Budapest University of Technology and Economics, Hungary
Prof. Dosam Hwang, Yeungnam University, Korea
Prof. Pham Dinh Tao, National Institute for Applied Sciences, Rouen, France
Prof. Pham Duc Truong, University of Birmingham, UK
Prof. Pham Hoang, Rutgers, The State University of New Jersey, United States
Assoc. Prof. Nguyen Anh Linh, Warsaw University, Poland
Prof. Nguyen Dinh Chau, AGH University of Science and Technology, Poland
Prof. Nguyen Hung Son, Warsaw University, Poland
Dr. Tran Dinh Viet, Slovak Academy of Sciences, Slovakia
Prof. Tran Gia Phuoc, University of Wuerzburg Am Hubland, Germany

Special Issues

The ICCSAMA 2019 invites proposals for Workshops & Special Sessions to be held during the conference. They intend to provide researchers in focused areas the opportunity to present and discuss their work, as well as to offer a forum for interaction among a broader community of researchers. A Special Session or Workshop will consist of a group of papers in a sub-discipline of Computer Science, Applied Mathematics and Applications related to the main topics of ICCSAMA 2019. The papers will be required to meet the same standards as ICCSAMA 2019 papers and will be published in the conference proceedings, in a bound volume by Springer-Verlag in their Advances in Inttelligent Systems and Computing series. All the Special Sessions will be centralized as tracks in the same conference submission and reviewing system as the regular papers.

Please send the Workshop / Special Session proposals to Prof. Le Thi Hoai An with the following information:

1. Title & acronym of the special session or workshop
2. Brief profiles of special session or workshop organizers
3. General description of the special session or workshop scope
4. List of topics
5. Proposed Session Program Committee (to be invited)

The organizers will be responsible for the advertisement and promotion of the special session and the conference including the Special Session & Workshop webpage preparation. The management of papers review will be achieved by Special Session & Workshop Committees, using the Conference System (a separate EasyChair track will be provided for each Special Session & Workshop). The organizers are responsible for managing the review process. All the reviews should be submitted through the ICCSAMA 2019 conference system. Each paper should obtain at least two reviews. The final decision as for the acceptance will be taken by the ICCSAMA 2019 Program Committee based on recommendations provided by Special Session & Workshop organizers.

Call for Special Sessions

Please send the Workshop / Special Session proposals to Prof. Le Thi Hoai An with the following information:

Call For Paper

CALL FOR PAPER

ICAMCS2024

THE INTERNATIONAL CONFERENCE ON

APPLIED MATHEMATICS AND COMPUTER SCIENCE

in honor of Professors Pham Dinh Tao and Le Thi Hoai An

for the 40th birthday of DC programming and DCA

December 20-21, 2024

Hanoi, Vietnam

The International Conference on Applied Mathematics and Computer Science (ICAMCS2024), in honor of Professors Pham Dinh Tao and Le Thi Hoai An for the 40th birthday of DC programming and DCA, will be held on December 20-21, 2024 in Hanoi, Vietnam. ICAMCS2024 brings together leading academic scientists, researchers and scholars to discuss and share their newest research results in all aspects of Computer Science, Applied Mathematics and Applications. The scope of the conference includes all aspects of theory, methodology and applications of Computer Sciences and Applied Mathematics. Contributions to the links between these two domains as well as the solution of real-life problems are particularly appreciated.

ICAMCS2024 is organized by Vietnam National University Hanoi- International School; Hanoi University of Science and Technology, Vietnam; Hanoi University of Industry, Vietnam in collaboration with University of Lorraine, France; and Institute of Optimization CTOPTIMAL, Vietnam.

The conference proceedings will be published in a Springer book series and indexed by ISI Proceedings, DBLP, Ulrich’s, EI-Compendex, SCOPUS, Zentralblatt Math, MetaPress, Springerlink. Best papers presented at the conference will be planned for publication in SCI journals.

MAIN TOPICS:

The conference invites all researchers and practitioners working in Computer Science, Applied Mathematics to submit original works for consideration. The main topics are (but not limited to):

Mathematical programming and optimization: theory, methods and software
DC programming and DCA
Operations research and decision making
Optimal control
Data science
Computational intelligence
Computer vision and image processing
Natural langguage processing
Numerical analysis
Machine learning: theory, systems and applications
Information security

The practical aspects of the previous fields cover a large number of areas such as:

Biology and bioinformatics
Communication systems
Economy and finance
Engineering systems, mechanic, manufacturing and robotic systems
Medicine and health care
Network security
Transport logistics, supply chain management, production planning and scheduling
Social network
Web intelligence and interaction

STEERING COMMITTEE:

Prof. Le Thi Hoai An, University of Lorraine, France (Chair)

Prof. Pham Dinh Tao, National Institute for Applied Sciences, Rouen, France

Assoc. Prof. Dr. Le Trung Thanh, VNU-International School, Vietnam (Co-chair)

PROGRAM COMMITTEE:

Prof. Le Thi Hoai An, University of Lorraine, France (Chair)

Prof. Pham Dinh Tao, National Institute for Applied Sciences, Rouen, France

Prof. Le Hoai Minh, University of Lorraine, France

Prof. Huynh Van Ngai, Quy Nhon University, Vietnam

Prof. Aharon Ben-Tal Technion – Israel Institute of Technology, Israel

Dr. Nguyen Canh Nam, Hanoi University of Science and Technology, Vietnam

Dr. Frédéric Dambreville, ONERA, France

Prof. Nguyen Hung Son, Warsaw University, Poland

Prof. Adnan Yassine, University of Le Havre, France

Dr. Nguyen Trong Phuc, University of Transport and Communications, Vietnam

Assoc. Prof. Ho Vinh Thanh, university of Limoges, France

Dr. Nguyen Nhu Tuan, The Government Cipher Committee, Vietnam

Prof. Pham Hoang, Rutgers, The State University of New Jersey, United States

Dr. Ta Minh Thuy, The State Bank of Vietnam

Prof. Sergiy Butenko, Texas A&M University of Engineering, USA

Dr. Nguyen Thi Bich Thuy, VNU- University of Science, Vietnam

Prof. Masao Fukushima Nanzan University, Japan

Dr. Tran Thi Thuy, FPT University, Vietnam

Prof. Lyes Benyoucef, University of Aix-Marseille, France

Dr. Nguyen Duc Manh, Hanoi National University of Education, Vietnam

Prof. Ngoc Thanh Nguyen, Wroclaw University of Science and Technology, Poland

Prof. Panos Pardalos, University of Florida, USA

Dr. Nguyen Thi Minh Tam, Posts and Telecommunications Institute of Technology, Vietnam

Dr. Nguyen Manh Cuong, Hanoi University of Industry, Vietnam

Dr. Pham Viet Nga, Vietnam Maritime University, Vietnam

Assoc. Prof. Dr. Nguyen Ha Nam, Electric Power University, Vietnam

Dr. Chu Dinh Toi, VNU- International School, Vietnam

Assoc. Prof. Tran Thi Oanh, VNU- International School, Vietnam

Dr. Le Duc Thinh, VNU- International School, Vietnam

Dr. Nguyen Quang Thuan, VNU- International School, Vietnam

Dr. Tran Duc Quynh, VNU- International School, Vietnam

Dr. Ta Anh Son, Hanoi University of Science and Technology, Vietnam

Dr. Nguyen Van Vinh, Dalat University, Vietnam

Prof. Jack Xin, University of California, Irvine, USA

Prof. Stephane Canu, National Institute for Applied Sciences-Rouen, France

Prof. Emilio Carrizosa, University of Sevilla, Spain

Dr. Nguyen Ngoc Linh, VNU- International School, Vietnam

Asso. Prof. Dr. Do Thanh Nghi, Can Tho University, Vietnam

Prof. Yaroslav D. Sergeyev, University of Calabria, Italy

Dr. Yann Guermeur, CNRS, France

Dr. Le Xuan Hai, VNU- International School, Vietnam

Dr. Yi-Shuai Niu, Beijing Institute of Mathematical Sciences and Applications – BIMSA, China

Prof. Joaquim Judice, University Coimbra, Portugal

Prof. Claude Sammut, University of New South Wales, Sydney, Australia

Prof. Melvyn Sim, National University of Singapore, Singapore

Assoc. Prof. Dr. Trung Hieu Tran, University of Cranfield, UK

Prof. Mau-Nam Nguyen, Portland State University, USA

Prof. Nguyen Thoi Trung, Van Lang University, Vietnam

Prof. Viet-Hung Nguyen, University of Clermont Auvergne, France

Dr. Vo Xuan Thanh, Institute of Optimization CTOPTIMAL, Vietnam

Dr. Pham Thi Hoai, Hanoi University of Science and Technology, Vietnam

Dr. Nguyen Tuyet Trinh, Academy of Cryptography Techniques, Vietnam

Dr Lưu Hoang Phuc Hau, University of Helsinki, Finland

Dr. Nguyen Van Thien, Hanoi University of Industry, Vietnam

Dr. Dang Trong Hop, Hanoi University of Industry, Vietnam

Prof. Vu Hoang Linh, VNU- University of Science, Vietnam

Prof. Le Hoai Bac, VNUHCM – University of Science, Vietnam

IMPORTANT DATES

Conference dates:	December 20-21, 2024
Paper Submission Deadline:	September 1, 2024
Author Notification:	September 29, 2024
Last version of accepted papers:	October 6, 2024
Author registration:	October 6, 2024

SUBMISSION FEE: US$ 350

We look forward to receiving your abstract and/or full paper in response to the call and are happy to respond to inquiries from interested parties.

Your questions may be addressed to our e-mail colloque-icamcs2024@vnuis.edu.vn

For more information, please come to our website: https://iccsama2024.isvnu.vn/

Your participation in this effort to produce research papers will contribute the success of ICAMCS2024.

Sincerely,

Assoc. Prof. Le Trung Thanh

Rector, International School

Vietnam National University, Hanoi, Vietnam.