Speakers Gallery

Keynote Speakers

2023 International Conference on Internet of Things and Cloud Computing Technology (IoTCCT 2023)

Prof. Wanyang Dai, Nanjing University, China

Biography

Wanyang Dai is a Distinguished Professor in Nanjing University, Chief Scientist in Su Xia Control Technology. He is the current President & CEO of U.S. based (Blockchain & Quantum-Computing) SIR Forum, President of Jiangsu Probability & Statistical Society, Chairman of Jiangsu BigData-Blockchain and Smart Information Special Committee. He received his Ph.D. in mathematics and systems & industrial engineering from Georgia Institute of Technology in USA. He was an MTS and principal investigator in U.S. based AT&T Bell Labs (currently Nokia Bell Labs) with some project won “Technology Transfer” now called cloud system. He was the Chief Scientist in DepthsData Digital Economic Research Institute. He published numerous influential papers in big name journals including Quantum Information Processing, Operations Research, Operational Research, Queueing Systems, Computers & Mathematics with Applications, Communications in Mathematical Sciences, and Journal of Computational and Applied Mathematics. He received various academic awards and has presented over 50 keynote/plenary speeches in IEEE/ACM, big data and cloud computing, quantum computing and communication technology, computational and applied mathematics, biomedical engineering, mathematics & statistics, and other international conferences. He has been serving as IEEE/ACM conference chairs, editors-in-chief and editorial board members for various international journals ranging from artificial intelligence, machine learning, data science, wireless communication, pure mathematics & statistics to their applications.

Title

Quantum-cloud computing based policy computing for IoT via FL & big model

Abstract

Based on quantum-cloud computing, we establish a big model system for IoT to conduct policy computing via blockchained federated learning (FL), which appears or will appear in 5G or 6G supported IoV, etc. The design and analysis of an optimal policy computing algorithm for smart contracts within the blockchain will be the focus. Inside the system, each order associated with a demand may simultaneously require multiple service items from different suppliers and the corresponding arrival rate may depend on blockchain history data represented by a long-range dependent stochastic process. The optimality of the computed dynamic policy on maximizing the expected infinite-horizon discounted profit is proved concerning both demand and supply rate controls with dynamic pricing and sequential packaging scheduling in an integrated fashion. Our policy is a pathwise oriented one and can be easily implemented online. The effectiveness of our optimal policy is supported by simulation comparisons.

Prof. Yong Wang, Guilin University of Electronic Technology, China 

Title

Next-Generation Software-Defined Storage Systems: Performance Optimization, Reliability Assurance, and Large-Scale Deployment

Prof. Bao Feng, Guilin University of Aerospace Technology, China (IEEE Senior Member)

Biography

Feng Bao, Professor of Guilin Institute of Aerospace Technology, IEEE Senior member, PhD from South China University of Technology, Master Supervisor of Guilin University of Electronic Technology, Member of Medical Robotics and Artificial Intelligence Branch of Guangdong Institute of Biomedical Engineering. His research interests include brain-like computing, hybrid intelligent algorithms and intelligent rehabilitation apparatus. He has published more than 30 articles in renowned journals at domestic and foreign level (Cancers, European Radiology, Cancer Imaging, Acta Automatica Sinica, Chinese Journal of Scientific Instrument, etc.), granted 3 National invention patent, led 1project of the National Natural Science Foundation of China, and host 2 provincial or ministerial science foundation projects.

Title

Overview of Radiomics Techniques Based on Transfer Learning

Abstract

Radiomics is a technique that utilizes AI technology to transform medical images into exploitable data, extracting quantitative tumor image features to facilitate clinical decision-making. Currently, radiomics data is characterized by small sample sizes, and applying deep learning methods may lead to issues such as overfitting. To address these challenges, we have conducted research in the field of transfer learning, specifically in the areas of natural images, pathological images, and probabilistic distribution adaptation for multi-source transfer. By transferring knowledge from various source domains to the target domain tasks, we aim to enhance the diagnostic performance of radiomics models on small sample data. Additionally, to tackle the problem of data silos, we have explored federated learning, a strategy where multiple institutions collaborate by sharing only model parameters rather than data, to train a global model and improve its diagnostic performance. Furthermore, to enhance the interpretability of deep learning methods in the field of medical imaging, we have employed various visualization techniques, combining medical expertise with image analysis to provide clinical explanations for discriminative regions, uncover clinically meaningful radiomics features, and improve the accuracy of computer-aided diagnosis.

Prof. Nagaraja G.S, R V College of Engineering, India (IEEE Senior Member)

Biography

His research interests include computer networks, networks management, wireless networks, cloud computing, Internet of things, computer architecture, storage optimization and multimedia communications. Supervised 08 PhD Students in the domain of computer networks and allied research areas. Presently supervising 06 Research scholars. He has published more than 150 research articles in referred International Journal / Conferences. Completed a major research project sanctioned by the University Grant Commission titled a. "Effective Multimedia Information retrieval using Indexing Technique" b. “Solar Ironing Cart “sanctioned by the National Institute of advanced studies IIsc, collaboratively with EEE Department for the academic year-2020. c. Collaborative development project on Silkworm Seed production sanctioned by Central Silk Board-2021. He has been involved in a number of conferences, workshops in various capacities such as General chair, Technical chair, Co-chair and technical programme committee /   organizing member. ISTE presented a National Award "Rajarambapu Patil" for promising engineering teacher for the creative work done in technical education. Cisco presented an advanced level instructor excellence recognition (2013, 2015) for his contributions RVCE-CISCO networking academy. 

Title

IOT based Sensors and Protocols in SMART CITY APPLICATIONS

Abstract

Sensor technology has benefited people's daily lives in practically every domain where they have been used. Sensors are tiny low computing devices which sense and gather signals from the source and then develop the solution correspondingly. There are a variety of sources that can be employed, comprising light, temperature, motion and pressure among others. Intelligent sensor techniques are used in a broad array of uses in lifestyle, medical, fitness, production, and everyday life. Sensors are an important component of IoT (Internet of Things) growth as these are not the typical kinds that translate physical parameters into electronic signals. To serve a technically and commercially feasible role in the IoT context the sensor devices have to expand into something highly robust. The smart city applications make use of different sensors and protocols to provide different services.

Invited Speaker

Prof. KANNIMUTHU SUBRAMANIYAM, Anna University, India (IEEE Senior Member)

Biography

Kannimuthu Subramaniyam is currently working as Professor in the Department of Computer Science and Engineering at Karpagam College of Engineering, Coimbatore, Tamil Nadu, India. He is a Head of Center of Excellence in Algorithms. He is an IBM Certified Cybersecurity Analyst. He did PhD in Computer Science and Engineering at Anna University, Chennai. He did his M.E (CSE) and B.Tech (IT) at Anna University, Chennai. He has more than 16 years of teaching and industrial experience. He is the recognized supervisor of Anna University, Chennai. Three PhD candidates are completed their research under his guidance. He is now guiding 7 PhD Research Scholars. He has published 60 research articles in various International Journals. He published 2 books ("Artificial Intelligence" & “LinkedList Demystified-A Placement Perspective” and 3 Book Chapters (WOS / Scopus Indexed). He is acting as mentor / consultant for DeepLearning.AI, Hubino, MaxByte Technologies and Dhanvi Info Tech, Coimbatore. He is the expert member for AICTE Student learning Assessment Project (ASLAP). He has presented a number of papers in various National and International conferences. He has visited more than 100 Engineering colleges / Universities and delivered more than 150 Guest Lectures on various topics. He is the reviewer for 50 Journals and 3 Books. He has successfully completed the consultancy project through Industry-Institute Interaction for ZF Wind Power Antwerpen Ltd., Belgium. He has received funds from CSIR, DRDO and ISRO to conduct workshops and seminars. He has completed more than 610 Certifications (41 Specializations and 4 Professional Certifications) in Coursera, Hackerrank and NPTEL on various domains. He has guided a number of research-oriented as well as application-oriented projects organized by well-known companies like IBM. He is actively involving in setting up lab for Cloud Computing, Big Data Analytics, Open-Source Software, Internet Technologies etc., His research interests include Artificial Intelligence, Data Structures and Algorithms, Machine Learning, Big Data Analytics, Virtual Reality & Blockchain. One of his research works is incorporated SPMF Open-Source Data Mining Tool. Source: http://www.philippe-fournier-viger.com/spmf/index.php?link=algorithms.php. He Conferred   Second Best Team in NLP Challenge as part of FIRE 2019 conference. He secured first Position in NLP Challenge as part of FIRE 2018 Conference.

Title

DISTRIBUTED DEEP LEARNING: CHALLENGES AND OPPORTUNITIES

Abstract

The software organizations have been anticipating a new trend of distributed software development in recent years. When processing units or entities are dispersed throughout computer networks and connected, distributed software systems are crucial. The development of computer hardware systems has significantly increased the efficiency of computer systems. A subtype of machine learning called distributed deep learning includes simultaneously training deep neural networks on a number of different devices. Distributed deep learning is a type of machine learning in which deep neural networks are taught concurrently across several machines. Deep classical learning can be difficult and computationally taxing to implement on a single machine. By spreading out the work among numerous computers, training times can be significantly reduced, allowing for rapid model creation and experimentation. The popularity of distributed deep learning has increased recently as a result of the growth of big data and the need to quickly analyze enormous volumes of data.Deep learning approaches are desperately needed to deal with the issue of managing vast resources in distributed systems. Machine learning (ML) offers a method for resolving crucial issues with distributed computing. Deep learning applies a number of processing components or agents to each distributed system resource. The fundamental method for deep learning is data parallelism. The main concerns when implementing machine learning and deep learning techniques are data security and privacy. Cloud platforms like Amazon Web Services, Microsoft Azure, and Google Cloud Platform offer distributed deep learning services, allowing academics and data scientists to use distributed computing for their deep learning workloads. In this keynote talk, an overview about the opportunities HPC can provide to enhance Deep Learning research will be given. In particular, distribution strategies for the training of Deep Neural Networks and challenges one faces when trying to implement them is discussed. Finally, an outline of how we tackle these challenges within our distributed Deep Learning framework will be discussed.