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CIPR PROGRAM SCHEDULE

PAPER PRESENTATION TECHNICAL SCHEDULE

Full Paper Submission Open:
25th October, 2020

Full Paper Submission Deadline :
25th January, 2021 30th January, 2021

Acceptance Notification :
5th February, 2021 15th February, 2021

Final Camera Ready Paper :
20th February, 2021

Registration :
05th to 20th February, 2021

Date(s) of Conference :
24th & 25th April, 2021

SPEAKERS

w Prof. KC Santosh, PhD
Chair & Associate Professor Department of Computer Science
The University of South Dakota
Editor-In-Chief, IJSIP
Associate Editor, IJMLC, IEEE Access, IJACI, PRR
Senior Member, IEEE
Web (personal): http://kc-santosh.org
Title of the Talk: Data for medical imaging tools: #COVID19; how big is big?


wProf. Ashish Ghosh, Fellow: NASI, WAST
Machine Intelligence Unit
ISI, Kolkata, India








w Prof. Oscar Castillo, Ph.D., D.Sc.
Research Chair of Graduate Studies
Tijuana Institute Technology (SNI Level 3)
Web page: www.hafsamx.org/castillo







wDr. Alireza Souri, Department of Computer Engineering, Halic University, Beyoglu, Istanbul, Turkey, alirezasouri@halic.edu.tr
Title: Computational Intelligence in Internet of Things (IoT)

Abstract: The advent of the Internet of Things (IoT) has made it possible to provide innovative services and applications. However, the massive amount of data generated by sensors poses new challenges that are still to be adequately addressed. In addition, the current trend towards intelligent Internet services and applications poses new requirements for the networking infrastructure, such as scalability, robustness and configuration flexibility. On the other hand, Computational intelligence (CI) or soft computing focuses on the ability of natural (and potentially artificial) agents to behave intelligently. It encompasses the theory, design, application, and development of biologically and linguistically motivated computational paradigms combining elements of learning, adaptation, evolution and fuzzy logic. At revealing contributions of Computational Intelligence (Neural Networks, Machine Learning, Data Mining, Fuzzy Logic and Evolutionary Computation) applied to Internet infrastructure, architectures, protocols, services and applications. These algorithms tend to approximate and generalize the answer to very hard problems which leads to very low-robust solutions. The primary objective of CI is to supplement natural intelligence to produce human-competitive results in IoT environments. In this talk, I will describe the current status, the trend, and the future directions of CI in the IoT. I will share our vision of future AI-based collaborative IoT applications, with the design of a framework that supports cross-node resource management, distributed intelligence and advanced cloud-edge computing.
http://scholar.google.com/citations?user=LZTMrOUAAAAJ&hl=en
https://www.researchgate.net/profile/Alireza_Souri2
https://publons.com/researcher/1336275/alireza-souri/


wDr. Noor Zaman Jhanjhi
Associate Prof. Dr NOOR ZAMAN
School of Computer Science and Engineering SCE,
Taylor’s University SDN BHD,
Subang Jaya, Selangor, 47500, Malaysia
+60 - 133791193
noorzaman.jhanjhi@taylors.edu.my;
drnzamanj@gmail.com & noorzaman650@hotmail.com;
https://expert.taylors.edu.my/cv/noorzaman.jhanjhi
https://www.linkedin.com/in/noorzaman
https://www.researchgate.net/profile/Noor_Zaman3


w DR. V. VIJAYA SARADHI
ASSOCIATE PROFESSOR
Department of Computer Science and Engineering,
Indian Institute of Technology Guwahati, India

Title: "Visualization of Unstructured Sports Data - An Example of Cricket Text Commentary".
Abstract: Sports data visualization focuses on the use of structured data, such as box score data and tracking data. Unstructured data sources pertaining to sports are available in various places such as blogs, social media posts, and online news articles. Sports visualization methods either not fully exploited the information present in these sources or the proposed visualizations through the use of these sources did not augment to the body of sports visualization methods. We propose the use of unstructured data, namely cricket text commentary for visualization. The short text commentary data is used for constructing individual players strength and weakness rules. A computationally feasible definition for players strength/weakness rule construction is proposed. A visualization method for the constructed rules is presented. Temporal changes in the constructed rules are extracted, and an equivalent visualization method is presented. In addition, players having similar strength/weakness rules is computed and visualized. We demonstrate the usefulness of text commentary in visualization by analysing the strengths and weaknesses of cricket players using more than one million short text commentaries. We validate the constructed rules through two validation methods. The collected data, source code, and obtained results on more than 500 players are made publicly available.




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