2022 3rd International Symposium on Computer Engineering and Intelligent Communications(ISCEIC 2022)

Date Venue: September 16-18, 2022 in Xian, China

Submission Deadline: September 14

Index : EI Compendex, Scopus

Website: http://isceic.org/

 

【Introduction】

ISCEIC 2022 will be held in September 16-18, 2022 in Xian, China. ISCEIC 2022 is a not-to-be-missed opportunity that distills the most current knowledge on a rapidly advancing discipline in one conference. Focused on hot research topics and difficulties in Computer and Communication, ISCEIC 2022 will explore cutting-edge Computer and Communication Technology, share typical study cases and encourage scientific innovation in this field. Renowned experts and academics in relevant fields will be invited to this conference to deliver keynote speeches, present reports and exchange ideas with attendees on research advancements and challenges.

We are looking forward to meeting you in the very beautiful city Xian, China, during August 19-21, 2022. Definitely, ISCEIC 2022 will provide you a pleasant experience, new contacts and happy stay in Xian.

【Keynote Speakers】

Prof.Qixin Cao,

Shanghai Jiao Tong University, China

Title: Research on ubiquitous medical robotics

Abstract: Medical robots as innovative and intelligent medical devices can both meet the demand for quality medical services and cope with the shortage of medical resources brought about by an aging society. It has therefore attracted the attention of governments, capital and research sectors. However, the high technological threshold, long R&D cycle and the need for continuous capital investment have become bottlenecks in the development and application of medical robots. For this reason, our team has proposed the concept of ubiquitous medical robotics, i.e., based on ubiquitous robotics, we have introduced robotic functional component technology and IoT technology to develop intelligent medical equipment on traditional surgical instruments and medical equipment. Our lab has developed corresponding intelligent medical robots based on robot-assisted surgical system and vital sign monitoring for smart elderly rehabilitation. Thus, the development cost and period of medical robots are greatly reduced. It plays a role in throwing a brick for the application and popularization of medical robots.

Experience: Qixin Cao, a professor of Shanghai Jiao Tong University, doctoral supervisor, member of National Robot Standardization Technical Committee (SAC/TC591), standing committee member of Intelligent Robot Professional Committee of Chinese Association for Artificial Intelligence, advisory committee member of Robot Competition Working Committee of Chinese Association of Automation, standing committee member of Artificial Intelligence Branch of Chinese Society for Agricultural Machinery, member of the Standing Committee of The Chinese Research Hospital Society for Medical And Industrial Transformation and Health Industry Integration branch, visiting professor of Miyazaki University and visiting professor of The University of Electro-Communications. His research interests include machine vision, ubiquitous robotics, mobile robotics, medical robotics and agricultural robotics. He has published more than 120 EI&SCI papers and obtained more than 90 national invention patents. He has won one Second prize of National Science and Technology Progress Award, one First prize of Science and Technology Award of Chinese Society for Artificial Intelligence, five Provincial and Ministerial Science and Technology awards, and two Second prizes of National Teaching Achievements; Guided students to win the National University Students "Challenge Cup" Special prize; three First prizes for Provincial and Ministerial Teaching Achievements. In 2017 and 2019, I guided my students to win the gold medal and bronze award in the graduation Design Competition of "Star Cup" of China Machinery Industry Excellent Engineers Education Alliance.


Dr. M.Vijayalakshmi

Thiagarajar College of Engineering

Title:  IP TRACEBACK: Denial of Service Deterrent
Abstract: Securing the Internet and its services is recognized as one of the  most challenging research problems. Amongst the threats imposed  on the Internet, Distributed Denial of Service (DDoS) attack has  occurred recurrently with a severe impact on the economy of the  organization. Despite the fact that the security experts propose  numerous stupendous solutions to mitigate DDoS attack, it has  continued to prevail over a decade. The stateless nature of Internet  and the destination oriented routing have encouraged the attackers  to forge the source IP addresses of the attack packets. This  complicates the forensic investigations and countermeasures  against DDoS attack. Identifying the origin of the attack is an important and essential step towards deterrence  and countermeasures against these attacks. IP traceback is the  concept of identifying the source of the packet, even if the IP  address is spoofed. Several efficient IP traceback schemes were  proposed with remarkable results in the past. However, they either  require huge storage at the routers or require numerous packets to  traceback the attack path. Further, most of the marking based  traceback schemes are not backward compatible. This thesis  focuses on exploring these issues and proposes a feasible solution to identify the origin of Direct Distributed Denial of Service (DDoS) attack. Securing the Internet  and its services is recognized as one of the most challenging research problems. Amongst the threats imposed on  the Internet, Distributed Denial of Service (DDoS) attack has  occurred recurrently with a severe impact on the economy of the  organization. Despite the fact that the security experts propose  numerous stupendous solutions to mitigate DDoS attack, it has  continued to prevail over a decade. The stateless nature of Internet  and the destination oriented routing have encouraged the attackers  to forge the source IP addresses of the attack packets. This  complicates the forensic investigations and countermeasures  against DDoS attack. Identifyi ng the origin of the attack is an  important and essential step towards deterrence and  countermeasures against these attacks. IP traceback is the  concept of identifying the source of the packet, even if the IP  address is spoofed. Several efficient IP traceback schemes were  proposed with remarkable results in the past. However, they either  require huge storage at the routers or require numerous packets to  traceback the attack path. Further, most of the marking based  traceback schemes are not backward compatible. This speech  focuses on exploring these issues and discussing feasible solution  to identify the origin of Direct Distributed Denial of Service (DDoS) attack.

Research Area: Network Security, IoT, Blockchain

Prof. Hongming Chen

Zhejiang Ocean University,China

Title: Interior comfort prediction and optimization for carbon peaking
Abstract:The factors affecting the human comfort interior of buildings are very complex, and scientists have conducted many thermal comfort types of research. The predicted mean vote (PMV) model is one of the most widespread models. In existing research, most of the methods to calculate PMV are simplified calculations such that it loses some information and affects the calculation of PMV value and comfort results. Moreover, the PMV model only focuses on thermal comfort, interior comfort effects not only includes thermal comfort. For this reason, the multi-factor interior comfort research based on the internet of things(IoT) and machine learning is carried out in this research.

  The structure of this system is composed of three layers: execution layer, transmission layer, and decision layer. The execution layer mainly includes collects, controls, and changes the interior environment, temperature, humidity, illumination, color temperature, air velocity, etc.; The transmission layer transmits collected data and transfer these data to the data center through the master controller SAM3X8E, and each subject will contain online questionnaire data with 18 attributes; After aggregating the data, we get a dataset of 18000 rows*18 columns, and after data cleaning and data transformation we get a dataset of 17561 rows*14 columns. The decision layer uses these data for machine learning and feedback.

  In this research, BP neural network, decision tree, optimization decision tree, and random forest algorithms are used to learn and predict models. Simultaneously, decision trees, optimization decision trees, and random forests are visualized using Pythagorean trees and Pythagorean forests.

  Based on the results of the learning and prediction in our four models, we obtain their confusion matrices and performance metrics and calculate their recall, precision, accuracy, and F1 values. The accuracy of the random forest model can reach 78.6%. Thus we conclude that the interior comfort of multi-factors can be predicted in advance, and then our research can provide some reference to the national or industry standards.

Experience: He is currently a professor with the School of Information Engineering, Zhejiang Ocean University. He is also a long term part-time professor with the School of Electronic Information, Wuhan University. Director of the Laboratory of Marine Aerospace integrated intelligent Internet of things, was selected into the " Changjiang Scholars Programme of China " of the Ministry of Education". A total of more than 70 academic papers have been published, of which more than 20 papers have been searched by the SCI/EI database, 5 patents have been authorized, 2 monographs have been published, and more than 40 public academic exchange reports have been held.

Distinguished Professor Philippe Fournier-Viger

Shenzhen University,China

Title: Advances and challenges for the automatic discovery of interesting patterns in data

Abstract: Intelligent systems can play an important role in various domains such as for factory automation, education, and the management of telecommunication and computer networks. To build intelligent systems, high-quality data is generally required. Moreover, these systems can also yield large amounts of data such usage logs, alarm logs, images, videos, and data collected from sensors. Due to the large volumes of data, managing the data generated by intelligent systems to gain insights and improve these systems is thus a key challenge. It is also desirable to be able to extract information or models from data that are easily understandable by humans. Based on these objectives, this talk will discuss the use of data mining algorithms for discovering interesting and useful patterns in data generated from intelligent systems.

The talk will first briefly review early study on designing algorithms for identifying frequent patterns and how can be used for instance to identify frequent alarms or faults in telecommunication networks. Then, an overview of recent challenges and advances will be presented to identify other types of interesting patterns in more complex data. Topics that will be discussed include high utility patterns, locally interesting patterns, and periodic patterns. Lastly, the SPMF open-source software will be mentioned and opportunities related to the combination of pattern mining algorithms with traditional artificial intelligence techniques for intelligent systems will be discussed.

Experience: Philippe Fournier-Viger (Ph.D) is a Canadian researcher, distinguished professor at Shenzhen University (China). Five years after completing his Ph.D., he came to China and became full professor at the Harbin Institute of Technology (Shenzhen), after obtaining a title of national talent from the National Science Foundation of China. He has published more than 350 research papers related to data mining, intelligent systems and applications, which have received more than 10,000 citations (H-Index 51). He is editor-in-chief of Data Science and Pattern Recognition and former associate editor-in-chief of the Applied Intelligence journal (SCI, Q1). He is the founder of the popular SPMF data mining library, offering more than 230 algorithms, cited in more than 1,000 research papers. He is a co-founder of the UDML, PMDB and MLiSE series of workshops held at the ICDM, PKDD and KDD conferences.

Prof. Qinmin Yang

Zhejiang University, China

Title: Coming soon......
Abstract: Coming soon......

Experience: Qinmin Yang received the Bachelor's degree in Electrical Engineering from Civil Aviation University of China, Tianjin, China in 2001, the Master of Science Degree in Control Science and Engineering from Institute of Automation, Chinese Academy of Sciences, Beijing, China in 2004, and the Ph.D. degree in Electrical Engineering from the University of Missouri-Rolla, MO USA, in 2007. From 2007 to 2008, he was a Post-doctoral Research Associate at University of Missouri-Rolla. From 2008 to 2009, he was a system engineer with Caterpillar Inc. From 2009 to 2010, he was a Post-doctoral Research Associate at University of Connecticut. Since 2010, he has been with the State Key Laboratory of Industrial Control Technology, the College of Control Science and Engineering, Zhejiang University, China, where he is currently a professor. He has also held visiting positions in University of Toronto and Lehigh University.

【Invited  Speaker】

Assoc. Prof. Jiawei Xu, Wenzhou  University, China

Title: Coming soon......
Abstract: Coming soon......

Experience: Coming soon......

【Publication】

*All full paper submissions to the ISCEIC 2022 could be written in English and will be sent to at least two reviewers and evaluated based on originality, technical or research content or depth, correctness, relevance to conference, contributions, and readability.

*All accepted papers of ISCEIC 2022 will be published in the conference proceedings, which will be submitted to EI Compendex, Scopus for indexing.

 

【Call for Papers】(Include but not limited to the following topics)

【Computer Engineering and Technology】

Cloud Computing

Computational Linguistics

Data Analysis

Operating System

Database Management System

Computer Program

Logic Programming

Software Design

Software Engineering

Machine Learning

TF Spectrum Analysis & Wavelet

Higher Order Spectral Analysis

Hardware Implementation for Signal Processing

Speech and Audio Coding

Speech Synthesis & Recognition

Image Processing & Understanding

Computer Vision & VR

Multimedia & Human-computer Interaction

Statistic Learning & Pattern Recognition

AI & Neural Networks

SP for Internet and Wireless Communications 

 

【Communication Technology】

Antenna and Propagation

Bioelectronics and Biosensor

DSP Algorithm and Architecture

Embedded Systems

Green Communication

Communication Theory and Information Theory

Microwave Communication

Optoelectronics and Optical Communication

Wireless / Mobile Communication and Technology

Satellite Communications

Communication Signal Processing

Digital Signal Processing

Intelligent Communication

5G Communication Technology

Pattern Analysis and Classification

VLSI Design

Wireless Sensor Network

Information Theory and Coding Theory

Pattern Recognition and Image Processing

Information Technology and Application

Communication Systems

 

【Submission and Attending】

#Submission#

1.Submission Method: Please submit your paper(word+pdf) to Online Submission System or English Submission System: https://www.ais.cn/attendees/index/NBJQQU

2.Paper Template: The manuscript should be written in accordance with the standard of template. Paper Template( Download the Paper Template: https://www.ais.cn/attendees/material/NBJQQU)

3.The paper should not be less than 4 pages in length and not exceed 12 pages.

Note: All submitted articles should report original, previously unpublished research results, experimental or theoretical. Articles submitted to the conference should meet these criteria and must not be under consideration for publication elsewhere. Authors need to check the duplicates at their own expense through the query system, and the papers suspected of plagiarism will not be published and will be published on the conference homepage.We firmly believe that ethical conduct is the most essential virtual of any academic. Hence any act of plagiarism is a totally unacceptable academic misconduct and cannot be tolerated.

 

#Attending#

1.How to Attending: You can register for the conference through the online registration system：https://www.ais.cn/attendees/index/NBJQQU

2.Join as Listener: ISCEIC 2022 is an unmissable conference. It is a good chance and an effective plateform to meet many renowned experts and researchers in the filed of latest academic research. You are welcome to attend this great event.

3.Join as Presenter: If you are interested in giving presentation on conference, without publishing your paper in the proceeding, you can choose to attend ISCEIC 2022 as a Presenter. As presenter, you need to submit the abstract and title of your presentation before register, the presentation will not be published.

 

【Registration】

1.Author (Regular) (4-6 Pages)：3200RMB (550 USD) / Paper

2.Manuscript numbers ≥ 3: 2900RMB (450 USD) / Paper

3.Extra Pages (Begin at Page 7)：300 RMB (50 USD) / Extra Page

4.Attendees without a Submission：1200 RMB (180 USD) / Person

5.Attendees without a Submission (Groups）：1000 RMB (150 USD) / Person（≥ 3 Person）

6.Purchase Extra Proceedings/Journal copies: 500 RMB (75 USD) /book

(Note: Team submissions can enjoy more discounts. Please consult the conference secretary for more details.)

 

【Contact Us】

Conference Secretary: Mickey Liu

QQ: 2489647082

Phone number/WeChat: 18024032582

Website: http://isceic.org

E-mail: ISCEIC@126.com