2022 2nd International Conference on Artificial Intelligence, Robotics, and Communication (ICAIRC 2022)
Speakers
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Prof. Bing Shi

Wuhan University of Technology, China


Research Area: Artificial Intelligence


Title: 

Matching and Pricing in Shared Transportation


Abstract: 


Nowadays, bike-sharing and ride-sharing, as typical spatial crwodsourcing tasks, have attracted a lot of attentions. In such spatial crowdsourcing tasks, task matching, worker dispatching and pricing are key challenges. In this talk, we will discuss these challenges, and proposal some reinforcement learning and game theory based approaches to solve these issues. We also run extensive experiments to show the effectiveness of the proposed approaches.




Bio:

Bing Shi, Professor, Hubei Chutian student, vice president of computer college. He graduated from the Department of computer science and technology of Nanjing University with a bachelor's degree and a master's degree. He graduated from the school of electronics and computer of the University of Southampton with a doctor's degree. He also engaged in postdoctoral research in the University of Southampton. Dr. Shi Bing is a member of IEEE, ACM and CCF. He is mainly engaged in the research of artificial intelligence and multi-agent systems. He has published more than 20 papers in CCF recommended conferences and journals. He is an authoritative multi-agent system conference, AAMAS 2017, AAMAS 2018, AAMAS 2019, He is a member of AAMAS 2020 procedure Committee, and is responsible for reviewing manuscripts of several journals at the same time.



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Prof. Fenghua Huang

Yango University, China  


Research Area:

Machine Learning, Big Data, Remote Sensing Application


Bio:

Fenghua Huang(Prof./Ph.D/postdoctor) is the vice dean of College of Artificial Intelligence, Yango University(China),director of the UERCSDMA(University Engineering Research Center for Spatial Data Mining and Application) in Fujian Province(China),director of the Institute of spatial data mining in Yango University, master supervisor in the fields of computer technology ,Surveying and Mapping Engineering in Fuzhou University(China).He was a visiting scholar of the University of North Carolina (UNC) in USA during 2017 and 2018 and awarded as the excellent teacher of Fujian Province in China(2015-2017) in 2017.He is the formal member of IEEE(Institute of Electrical and Electronics Engineers),CCF(China Computer Federation) and CIE(Chinese Institute of Electronics).In 2015, Prof.Huang was selected into the cultivation program for outstanding young scientific research talents of Fujian universities. In 2016, he was selected into the new century excellent talents support program of Fujian universities and the overseas high-end visiting scholar program for outstanding discipline leaders of Fujian undergraduate universities. He is the peer reviewer of some international journals,such as IEEE Access,ACM International Conference Proceedings Series,et al.

The main research interests of Prof.Huang include machine learning, spatial data mining, bid data and remote sensing image processing. In the last decade, He has presided more than 10 research projects funded by the governments and enterprises, including 1 project funded by National Natural Science Foundation (another one was participated), 1 project funded by China Postdoctoral Science Foundation project, 1 project funded by Natural Science Foundation of Fujian Province(China), 1 project funded by Fujian Social Science Planning Foundation, 2 projects funded by the education and scientific research foundation for young and middle-aged teachers in Fujian Province(China),2 projects funded by enterprises. He has published more than 30 papers in the related journals and conferences, including 15 papers indexed by SCI/EI and 4 papers indexed by CSCD. In addtion, He has obtained the authorizaiton of 8 national utility model patents and published 3 monographs and textbooks.The total amount of scientific research funds is nearly 2 million yuan (including the funds from Yango University).






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Prof. Nagaraja G.S

Professor and Associate Dean, IEEE Senior Member, Dept of CSE, RV College of Engineering, India


Research Area:

Computer Architecture and its applications, Computer Networks, Networks Management, Storage Optimization, Cloud computing, High Performance Computing, Protocol Design and Multimedia Communications


Title: 

Advanced future Technologies and its applications in Industries


Abstract: 

Advancements in Artificial Intelligence, Robotics and communication has ensured the use of them in future industrial environment. The usage of AI technologies: natural language processing in speech recognition, computer vision for face recognition and machine learning-deep learning for data analysis has and will improve the industry. The 5G communication is the basis for developing next generation communications e.g. autonomous vehicles, intelligent transportation systems and Internet of things. The machine learning is useful for society and social benefits. It performs regression and classification tasks by recognizing the hidden patterns in features of huge data. It can handle big datasets, real-time data and has high computational power. The accumulation of big data via Internet-of-Things (IoT) technology has led to the rapid growth of information retrieval and analysis techniques such as AI. Big data is the foundation of smart factories where everything is conducted intelligently and in an automated fashion during every cycle of the manufacturing process. AI consists of ML which is based on pattern recognition from structured, semi-structured and unstructured data. It is useful for repetitive tasks. Robots needs to cope up with two challenges: learning control policies so that it can change the way when encountered with an obstacle; learning complex and high dimensional changing aspects to understand the image signals or GPS signals. It needs to recognize an object to identify the object of interest nearby. AI combined with 6G will improve multiple aspects of industry. The AI can enable the interconnection of whole industry through intelligent network systems. When AI combines with new robotics technologies, it will enable the high speed, accuracy, reliability, security, safety, avoids breakdowns and maintenance expenses. The use of 3D and thermal vision in robotics and combination of AI technologies with robotics, enabled robots to perform complex tasks e.g. drone automatically returning home when battery low or autonomous navigation in warehouse. This improves social care by robots in healthcare, chatbot used to aid and guide people. Robots are used for assembling products, handling dangerous materials, spray-painting, cutting and polishing, inspection of products. The number of robots used in tasks as diverse as cleaning sewers, detecting bombs and performing intricate surgery is increasing steadily, and will continue to grow in coming years.


Bio:

Dr. Nagaraja G.S obtained his Graduate degree in Computer Science and Engineering, Post Graduate degree in Master of Engineering and Doctoral degree in Computer Science and Engineering. Dr Nagaraja G.S, is presently working as Professor and Associate Dean in the department of Computer Science and Engineering, R.V. College of Engineering, Bengaluru-59. He has 27+ years of Teaching and 18+Years of Research experience in computer network and its related domains as-well. His research interests include Computer Organization, Computer Architecture and its applications, Computer Networks, Networks Management, Storage Optimization, Wireless Networks, Cloud computing, Parallel processing, High Performance Computing, Routing and Switching, Protocol Design and Multimedia Communications. Currently Dr Nagaraja is teaching PG/UG students, guided 08 PhD students and supervising 05 Research scholars under VTU. Completed a major research project sanctioned by the University Grant Commission Titled "Effective Multimedia Information retrieval using Indexing Technique" during the tenure of 2012-2015. Completed a Research project “Solar Ironing Cart “sanctioned by National Institute of advanced studies IIsc, Bangalore collaboratively with EEE Department for the academic year-2020. Completed a Collaborative development project on Silkworm Seed production sanctioned by Central Silk Board-2021. He has published 07 papers in book chapter, 67 papers in an International Journals, presented 37 papers in an International conferences and 22 papers in National conferences. He has delivered many technical talks in different engineering colleges with the theme of CCNA modules, Research Methodology, Computer Communications, Mobile App Design and Development, Routing and Switching, Cloud Computing, Cyber Security, 5G Security issues and Technologies, IoT Protocols, Research challenges in Cloud Security and trust management, Cyber security and  Writing Quality papers.

He has involved in PG-NBA, BoS, BOE member for autonomous colleges under VTU. Presently working as BoS VTU member for NIE Mysuru. He is an active member / reviewer of IEEE, ISTE, CSI and RVCE-CISCO academy. Also involved in many activities of IEEE, ISTE and RVCE-CISCO academy. Chaired many International and National Conferences in Karnataka with different Conference themes. He has organized many workshops and  International Conferences held at RVCE with the theme of Computational System for information technology for Sustainable Solutions (CSITSS-SMAC) Social Media, Mobility, Analytics, and Cloud Computing from 2016 onwards. Dr Nagaraja G.S worked as a General Chair for the IEEE Conference on Computational Systems for Information Technology for Sustainable Solution CSITSS-2016. Technical Chair of IEEE International Conference on Computational Systems for Information Technology for Sustainable Solution for the years, CSITSS-2017, CSITSS-2019 and CSITSS-2021. Technical Committee member for the IEEE International Conference on Computational Systems for Information Technology for Sustainable Solution for the years, CSITSS-2018 and CSITSS-2022. Also working as Conference lead for the IEEE Conference on Data Decision and Systems (ICDDS-2022). Worked as IEEE-Computer Society ExeCom Member for the year 2020 and 2021. Presently working as Vice Chair of IEEE Computer Society, Bengaluru Chapter. Worked as reviewer for many IEEE Conferences and Journals as-well.



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Assoc. Prof. Suraiya Parveen

Department of Computer Science and Engineering School of Engineering Science and Technology Jamia Hamdard, New Delhi, India


Research Area:

Artificial Intelligence, Soft Computing, Algorithms, Semantic Technology, Data Structures, Big Data Analytics, Software Engineering, Machine Learning


Title: 

Semantic Similarity Measures: Ontology based Perspective


Abstract: 

Semantic Web is an expansion of the web which makes World Wide Web more useful  and powerful. The Internet has become a blessing to our daily lives. It has become amazingly efficient because of the growing popularity of smart phones. The Semantic Web is considered as the next generation web of data. Existing web is a web of documents. Humans need to read, understand, analyze and extract the information available from World Wide Web. Semantic Web envisages all this to be done by computers. Data available on the Internet shall be used vastly by simple use of software and less human involvement. The vision of Tim Berner Lee will explore new possibilities for web search and applications. The Semantic Web is an attempt to enhance current web so that computers can utilize, exchange and connect the information presented on the web, which can minimize the limitations of present Internet. The idea is to upgrade the present web to a huge knowledge based system for humans to extract required information automatically. The focus of Semantic Web is to share, collaborate, and manipulate data available on the Internet and to get useful information at a click. The Semantic Web is a collaborative effort led by the World Wide Web Consortium (W3C). It is a mission that should provide a common framework that allows data to be shared and reused across applications, enterprises, and community boundaries.

“Semantic Similarity relates to computing the conceptual similarity between terms which are not lexicographically similar.” “Map two terms to ontology and compute their relationship in that ontology.”

We are living in the age of big data and data analytics. The data is so huge that to extract relevant information from digital repositories has become very difficult. The information retrieval techniques use similarity measures to match the user queries and the document information. To extract accurate and precision information, a highly sophisticated information tools are required. Another way to achieve accuracy in information retrieval is by using ontology. The design of ontology is such that it models concepts and their relationship within a domain.

The objective of this work is to quantify the similarity in ontological concepts of a single ontology. This quantification of semantic similarity can help not only in information retrieval but in other applications such as semantic search, semantic retrieval and semantic clustering. The traditional keywords search technique matches the keywords with the content of the document and these techniques do not reflect the meaning or relatedness. Hence, relevance and accuracy of the retrieved documents is less. Another important application of semantic similarity measurement is in cluster analysis.

The semantic search is combination of document and data search. For semantic search or semantic retrieval, the search keyword is expanded with the help of ontology and these terms are incorporated with the original queries. Semantic similarity is a measure of distance between the concepts. It shows that the quantitative relationship between the terms of the domain ontology. Semantic search and other applications use domain ontology to quantify the relationship between the two terms. Semantic similarity is a suitable measure for quantifying the relationship between concepts or terms. Semantic similarity, with the support of ontology and metadata provides strong background support for semantic search. Semantic retrieval results in data as well as document retrieval. In the present scenario of data analytics and big data, web repositories are full of heterogeneous and variable data. The semantic technology can help resolve heterogeneity and many other such issues. Ontology makes existing search engines more intelligent. A large amount of data is available in the Resource Description Framework (RDF) format and metadata is being embedded with documents to make semantic search possible. The semantic retrieval includes searching the data along with documents.  Semantic search explores the meanings of the query in different situations to capture user’s intent. It includes data in addition to documents to make information retrieval more efficient. The data in structured format can have a better impact on search. Interoperability, a key technique in Semantic Web, allows sharing the data across the web.  By using semantic similarity, search engine can search concepts or terms that are conceptually linked to the keywords, instead of matching the keyword with documents only. Similarity is a generic term which means some comparison, connection or relationship between two different entities or objects. In technical terms, the similarity has become very important for computation, retrieval, cluster analysis and many other applications. In the domain of wild animals, all carnivorous animals have a good amount of similarities and all herbivorous animals are also similar to each other. But, there is a lesser degree of similarity between carnivorous and herbivorous animals. Similarity can be used for classifications, grouping objects etc. It becomes very useful as the same functions can be applied to similar objects, group and cluster. Extraction and cluster analysis of a knowledge-base also uses similarity. It is also used in studying behavior pattern, reactions, diagnosis, problem solving and analysis. If the degree of similarity between two objects is known, it becomes more valuable.

 

Bio: 

Dr. Suraiya  Parveen received her Bachelor of  Engineering degree from Jamia Millia Islamia, New Delhi  and her Master of  Technology from GGS Indraprastha  University, New Delhi and her Ph.D in Computer Science and Engineering from Jamia Hamdard, New Delhi.  She is an teaching  in the Department of Computer Science and Engineering at Hamdard University, New Delhi. Her research interests include Information Retrieval, Data Structure and Semantic Technology.