2024 8th International Conference on Electronic Information Technology and Computer Engineering
Speakers of EITCE 2022
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Prof. Xiangrong Liu

Xiamen University, China

Experience: Professor Xiangrong Liu, Department of Computer Science and Technology of Xiamen University. He is doctoral supervisor. He graduated from the Department of Biomedical Engineering of Huazhong University of Science and Technology in 2000 with a bachelor's degree in engineering; In 2007, he graduated from the Department of Control Science and Engineering of Huazhong University of Science and Technology with a doctorate degree in engineering; From 2007 to 2009, he worked as a postdoctoral fellow in the Department of Computer Science, School of Information, Peking University; In 2009, he joined the Department of Computing, School of Information, Xiamen University; From 2014 to 2015, he visited the University of Illinois at Champaign for electronic and computer engineering. His research interests include computational intelligence, computing theory, data mining, biological information processing, mobile and micro sensor technology, etc. In recent years, he has published more than 40 papers in SCI, including Bioinformatics、Briefings in Bioinformatics、Advanced Optical Materials、IEEE Transactions on Nanobioscience、Information  Science.He has been a reviewer of IEEE Transactions on Nanobioscience, NeuroComputing, Chinese Science and other journals for many times, and was invited to serve as a member of the procedure committee of international conferences such as BICTA and ACMC. He has presided over many national and enterprise scientific research projects, including national key research projects, National Natural Science Foundation of China, and subprojects of the 13th Five Year National Marine Economic Innovation and Development Regional Demonstration Project. He has won the first prize of the Provincial Natural Science Award, the second prize of the Provincial Science and Technology Progress Award, and the second prize of the Natural Science Award of the Ministry of Education, respectively. He has also won honorary titles such as Suzhou Excellent Science and Technology Township Leader and Xiamen Key Talent.

Title: When Deep Graph Learning Meets Drug Discovery

Abstract: Diseases have seriously threatened the survival and development of human beings since ancient times. Drug therapy is the main means of treating diseases gradually formed by human beings in the process of practice and exploration. Drug discovery is the process of discovering potential new drugs, involving multiple disciplines such as biology, chemistry, and pharmacology, with long cycles, huge costs, and low success rates. The application of deep learning methods to assist drug discovery can effectively speed up drug development, facilitate the acquisition of molecules with relevant activities, and reduce the cost and time of drug development. Developing more efficient algorithms can help scientists and researchers find symptomatic drugs faster. I will report some research progress and achievements of our team in artificial intelligence-assisted drug research and development over these years. Specifically, it includes the application cases of artificial intelligence in the following important issues: 1) Drug reposition; 2) cancer drug response prediction; 3) drug combination prediction, etc.

Reporter information: Xiangrong Liu, PhD is the Professor and Chair of Department of Computer Science, School of Informatics in Xiamen University. His research interests are computational intelligence, computational theory, data mining, biological information processing, mobile and micro-sensing technology, etc. In recent years, he has published more than 40 papers.He hosted a number of national and corporate scientific research projects including the National Key Research Program, the National Natural Science Foundation of China, and the thirteenth Five-Year National Marine Economic Innovation and Development Regional Demonstration Project. And he won the first prize of the Provincial Natural Science Award and the Provincial Science and Technology Progress Award and the second prize of the Ministry of Education's Natural Science Award.

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Prof. Seifedine Kadry

Noroff University College, Norway (IET Fellow; IETE Fellow; IACSIT Fellow)

Experience: Professor Seifedine Kadry has a Bachelor degree in 1999 from Lebanese University, MS degree in 2002 from Reims University (France) and EPFL (Lausanne), PhD in 2007 from Blaise Pascal University (France), HDR degree in 2017 from Rouen University. At present his research focuses on Data Science, education using technology, system prognostics, stochastic systems, and applied mathematics. He is an ABET program evaluator for computing, and ABET program evaluator for Engineering Tech. He is a Fellow of IET, Fellow of IETE, and Fellow of IACSIT. He is a distinguish speaker of IEEE Computer Society.

Title: Leukemia Segmentation and Classification: A Comprehensive Survey

Abstract: Blood is made up of leukocytes (WBCs), erythrocytes (RBCs), and thrombocytes. The ratio of blood cancer diseases is increasing rapidly, among which leukemia is one of the most famous cancers which may lead to death. Leukemia cancer is initiated by the unnecessary growth of immature WBCs present in the sponge tissues of bone marrow. It is generally analyzed by etiologists by perceiving slides of blood smear images under a microscope. The morphological features and blood cells count facilitated the etiologists to detect leukemia. Due to the late detection and expensive instruments used for leukemia analysis, the death rate has risen significantly. The fluorescence-based cell sorting technique and manual recounts using a hemocytometers are error-prone and imprecise. The proposed methods using image processing methodologies are utilized to examine the microscopic blood smears to detect the occurrence of leukemia precisely and rapidly. The aim is to bring out information from the various procedures in the related areas which help to select enormous features from WBCs and to create a system that will specifically detect, classify and segment blood disease.


Prof. Yanchun Zhang

Peng Cheng Laboratory & Guangzhou University,China

Experience: Yanchun Zhang is currently Professor at Guangzhou University and Chief Scientist at New Cyber Research Department of Pengcheng Laboratory, China.  He is also Emeritus Professor at Victoria University.Dr Zhang obtained a PhD degree in Computer Science from The University of Queensland in 1991. His research interests include databases, data mining, web services and e-health. He has published over 400 research papers in these areas, and supervised over 40 PhDs and post doctors in completion.  Dr. Zhang is a founding editor and editor-in-chief of World Wide Web Journal (Springer) and Health Information Science and Systems Journal (Springer).  He speaks regularly at international conferences in the areas of data engineering / data science and health informatics. He has served as an expert panel member for various international funding agencies including National Natural Science Foundation of China, Australian Research Council, the Royal Society of New Zealand’s Marsden Fund, Medical Research Council of United Kingdom and NHMRC of Australia on Built Environment and Prevention Research.

Title:Smart Medicine: Medical Big Data / AI with Innovative Applications in Patient Monitoring, Disease Diagnosis, Prediction and Health Management

Abstract: Due to the recent development or maturation of database, data storage, data capturing, and sensor technologies, huge medical and health data have been generated at hospitals and medical organizations at unprecedented speed. Those data are a very valuable resource for improving health delivery, health care and decision making and better risk analysis and diagnosis. Health care and medical service is now becoming more data-intensive and evidence-based since electronic health records are used to track individuals' and communities' health information (particularly changes). These substantially motivate and advance the emergence and the progress of data-centric health data and knowledge management research and practice.

In this talk, we will introduce several innovative data mining techniques and case studies to address the challenges encountered in e-health and medical big data. This includes techniques and development on medical data streams, correlation analysis, abnormally detection and risk predictions with patient monitoring and aging care applications. 

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Prof. Chun-Wei Lin(Jerry)

Western Norway University of Applied Sciences, Norway (IET Fellow; ACM Distinguished Member)

Experience: Jerry Chun-Wei Lin received his Ph.D. from the Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan in 2010. He is currently a full Professor with the Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, Norway. He has published more than 500+ research articles in refereed journals (with 60+ ACM/IEEE transactions journals) and international conferences (IEEE ICDE, IEEE ICDM, PKDD, PAKDD), 16 edited books, as well as 33 patents (held and filed, 3 US patents). His research interests include data mining and analytics, natural language processing (NLP), soft computing, IoTs, bioinformatics, artificial intelligence/machine learning, and privacy preserving and security technologies. He is the Editor-in-Chief of the International Journal of Data Science and Pattern Recognition, the Associate Editor for IEEE TNNLS, IEEE TCYB, IEEE TDSC, INS, JIT, AIHC, IJIMAI, HCIS, IDA, PlosOne, IEEE Access, and the Guest Editor for several IEEE/ACM journals such as IEEE TFS, IEEE TII, IEEE TIST, IEEE JBHI, ACM TMIS, ACM TOIT, ACM TALLIP, and ACM JDIQ. He has recognized as the most cited Chinese Researcher respectively in 2018, 2019, 2020, and 2021 by Scopus/Elsevier. He is the Fellow of IET (FIET), ACM Distinguished Member (Scientist), and IEEE Senior Member.

Title:Utility-Oriented Techniques, Modeling, and Analytics  

Abstract: As a large amount of data is collected daily from individuals, businesses, and other organizations or applications, various algorithms have been developed to identify interesting and useful patterns in data that meet a set of requirements specified by a user. The main purpose of data analysis and data mining is to find new, potentially useful patterns that can be used in real-world applications. For example, analyzing customer transactions in a retail store can reveal interesting patterns in customer buying behavior that can then be used for decision-making. In recent years, the demand for utility-oriented pattern mining and analytics has increased because it can discover more useful and interesting information than basic binary-based pattern mining approaches, which have been used in many domains and applications, e.g., cross-marketing, e-commerce, finance, medical and biomedical applications. In this talk, I will first highlight the benefits of using the utility-oriented pattern mining and analytics compared to the past studies (e.g., association rule/frequent itemset mining). I will then provide a general overview of the state of the art in utility-oriented pattern mining and analytic techniques according to three main categories (i.e., data level, constraint level, and application level). Several techniques and modeling on different aspects (levels) of utility-oriented pattern mining will be presented and reviewed. 


Prof. Dong Xu

University of Missouri-Columbia, USA (AAAS Fellow; AIMBE Fellow)

Experience: Dong Xu is Curators’ Distinguished Professor in the Department of Electrical Engineering and Computer Science, with appointments in the Christopher S. Bond Life Sciences Center and the Informatics Institute at the University of Missouri-Columbia. He obtained his Ph.D. from the University of Illinois, Urbana-Champaign in 1995 and did two years of postdoctoral work at the US National Cancer Institute. He was a Staff Scientist at Oak Ridge National Laboratory until 2003 before joining the University of Missouri, where he served as Department Chair of Computer Science during 2007-2016 and Director of Information Technology Program during 2017-2020. Over the past 30 years, he has conducted research in many areas of computational biology and bioinformatics, including single-cell data analysis, protein structure prediction and modeling, protein post-translational modifications, protein localization prediction, computational systems biology, biological information systems, and bioinformatics applications in human, microbes, and plants. His research since 2012 has focused on the interface between bioinformatics and deep learning. He has published more than 400 papers with more than 21,000 citations and an H-index of 74, according to Google Scholar. He was elected to the rank of American Association for the Advancement of Science (AAAS) Fellow in 2015 and American Institute for Medical and Biological Engineering (AIMBE) Fellow in 2020.

Title:Neural relational inference from molecular dynamics simulations of proteins

Abstract: Molecular dynamics (MD) simulation provides a powerful computational approach to studying proteins. However, current MD simulations cannot reach the time scales of most biological processes. The advent of deep learning made it possible to evaluate spatially short and long-range communications from MD trajectories to understand protein dynamics. For this purpose, we adapted a neural relational inference model using an encoder-decoder graph neural network architecture to infer latent interactions between residues. The model can predict free energy changes upon mutations more accurately than other methods. We applied our method to study protein allostery, a biological process facilitated by spatially long-range intra-protein communication, whereby ligand binding or amino acid mutation at a distant site affects the active site remotely. This model successfully learned the long-range interactions and pathways that mediate the allosteric communications between remote sites in the Pin1, SOD1, and MEK1 systems. Furthermore, it can discover allosteric patterns in early MD simulation trajectories before significant conformational changes.