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Call for Reviewers
- Last Date (25-11-2023)
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Call for Special Session
- Last Date (25-11-2023)
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Special Session

Future 6G Wireless Network Technologies with Artificial Intelligence and IoT for Sustainable Earth and Healthcare    View

Frontiers in Machine Learning: Unveiling Cyber Physical Systems    View

New Age Computing Techniques and Technologies in Computational Intelligence and Pattern Recognition.    View

Multidisciplinary Pattern Recognition and Intelligence with Generative AI.    View

Convergence of Edge Computing, Blockchain, and Computational Intelligence in IoT: Advances, Challenges, and Applications    View

Optimal Deep Neural Networks architectures via Evolutionary Algorithms    View

Invited Speakers

Prof. KC Santosh,
Chair, Department of Computer Science, The University of South Dakota

Title: Human-in-the-loop machine learning – how big data is big to begin with?
Teaser of the talk: click here
Abstract: How big does the dataset need to be for a machine learning scientist to begin with? Can we afford to wait years to gather data and train models, risking the possibility that there won't be enough people left to test them on? Yes, isn't this scenario reminiscent of the challenges faced during the Covid-19 pandemic? And, future epidemics is inevitable. Isn't there a pressing need for human-in-the-loop machine learning to ensure we effectively serve the population? Moreover, when we have massive datasets like those in Amazon, Meta, Google, and Tesla, shouldn't we also consider the carbon footprint associated with them? Yes, no doubt - AI for good, but how about carbon footprint?
I would entice you with real-world applied AI use cases with such aforementioned curiosities - come and join me.
Biography: KC Santosh – a highly accomplished AI expert – is the chair of the Department of Computer Science and the founder/director of the Applied AI research lab at the University of South Dakota. He is also served the National Institutes of Health as a research fellow and LORIA Research Center as a postdoctoral research scientist, in collaboration with industrial partner, ITESOFT, France. He earned his PhD in Computer Science - Artificial Intelligence from INRIA Nancy Grand East Research Center (France). With funding exceeding $2 million from sources like DOD, NSF, and SDBOR, he has authored 10 books (e.g., AI, Ethical Issues, and Explainability) and over 250 peer-reviewed research articles, including IEEE TPAMI. He serves as associate editor for esteemed journals such as IEEE Transactions on AI, Int. J of Machine Learning & Cybernetics, and Int. J of Pattern Recognition & Artificial Intelligence. Prof. Santosh, founder of AI programs at USD, has significantly boosted graduate enrollment by over 3,000% in just three years, establishing USD as a leader in AI within South Dakota. More information: https://kc-santosh.org/..

Prof Dr. Tarik Ahmed Rashid,
Principal Fellow for the Higher Education Authority (PFHEA-UK)

Title: Revolutionizing Medical Diagnosis and Treatment: The Impact of Machine Learning and Optimization
Abstract: Computer science is rapidly reshaping the landscape of healthcare with cutting-edge machine learning and optimization methodologies. This talk will delve into the transformative applications of these techniques, focusing on advancements in medical image analysis, disease prediction, and personalized treatment design. We'll explore how deep learning algorithms excel at disease classification, surpassing traditional methods in tasks such as tumor detection and tissue abnormality identification. Furthermore, we'll examine novel optimization strategies for drug development, accelerating the search for effective therapies while minimizing costs and side effects. The presentation will highlight the immense potential of these computational approaches to revolutionize patient care, offering earlier diagnoses, more accurate treatment decisions, and ultimately, improved health outcomes.
Biography: Dr. Tarik Ahmed Rashid is a Principal Fellow for the Higher Education Authority (PFHEA-UK) and a professor in the Department of Computer Science and Engineering at the University of Kurdistan Hewlêr (UKH), Iraq. His areas of research cover the fields of Artificial Intelligence, Nature Inspired Algorithms, Swarm Intelligence, Computational Intelligence, Machine Learning, and Data Mining. He is a member of (IEEE, Machine Intelligence Research Labs). He has journal editorial experience as an editor/board member and acted as a Keynote conference speaker in several conferences, conference chairing, conference program committee member, etc. It is worth mentioning that our team has designed several single and multi-objective optimization algorithms, such as FDO, CDDO, DSO, ANA, FOX, LPB, ECA*, and iECA*.
It is noteworthy that Professor Tarik Ahmed Rashid is on the prestigious Stanford University list of the World's Top 2% of Scientists for the years 2021, 2022, and 2023. The ranking has been performed with the condition of 44 criteria.
Tarik is also on the list of top 10 researchers in the Al-Ayen Iraqi Researchers Ranking (2022). AIR-Ranking 2022 is a national ranking organized by Al-Ayen University. The ranking has been performed with the condition of 24 criteria.

Prof Richi Nayak,
Queensland University of Technology, Brisbane

Title: Leveraging Generative AI for Knowledge Discovery Applications
Abstract: The emergence of ChatGPT has sparked excitement regarding the potential impact of Generative AI on various industries and society at large. Generative AI encompasses a range of techniques based on deep learning and neural networks to create original content such as text, images, music, and even human-like conversations. In this talk, I will delve into the workings of Generative AI and some applications that unleash the potential across industries. This seminar aims to equip attendees with the knowledge and critical thinking necessary to effectively engage with Generative AI in an ever-evolving landscape
Biography: Prof Richi Nayak is a Professor of computer science and the Deputy Director of the Centre for Data Science at Queensland University of Technology in Brisbane. Her research centers around exploring artificial intelligence, data mining, and machine learning theories for addressing real-world challenges. She has collaborated with various international, national, and government agencies, offering consultancy in the domain of data science. She has authored over 250 refereed publications in this area. In recognition of her exemplary contributions to the field of Data Analytics, she received the 2016 Women in Technology Infotech Outstanding Achievement Award.

Prof. Debasis Samanta,
Department of Computer Science and Engineering Indian Institute of Technology Kharagpur, India

Title: Hands-Free and Touch-Free Interaction: Human Computer Interaction Augmented with Brain Computer Interface
Abstract: In our contemporary existence, the indispensability of computers is undeniable. The evolution of computers, from their initial cumbersome presence spanning multiple rooms devoid of display units to today's sleek, palm-sized devices with sophisticated screens, represents a remarkable journey. Throughout this transformation, the pivotal concept has been interaction—the dynamic relationship between humans and machines. We've witnessed a progression through generations of interactions, from command typing to GUI Windows and currently, touch-enabled interfaces. However, as we navigate this technological landscape, fundamental questions emerge: What lies beyond our current interactions? Can we accommodate all user types? Is it conceivable to achieve touch-free interactions that transcend the need for physical movement? Is this concept a mere myth or a tangible reality? Does the burgeoning computational intelligence and pattern recognition technology possess the maturity to bridge this gap? This keynote speech aims to unravel these inquiries and shed light on the trajectory of future interactions in the realm of computing.
Biography: Prof. Debasis Samanta - received his B. Tech. from Science College, Calcutta University, M. Tech. from Jadavpur University Kolkata (Gold Medal), and PhD from Indian Institute of Technology Kharagpur all in Computer Science & Engineering. Currently, he is a Professor in the Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur.  Prof.  Samanta is actively working in the field of Human-Computer Interaction and Cyber Physical Security. He has developed efficient interaction mechanisms for people with special needs such as motor-impaired people and people with other disabilities, illiterate people, etc. Further, he has developed a multi-modal interaction technique, text entry mechanisms in Indian languages, which are new of their kind to bridge the digital divide. Currently, he is working toward the development of next-generation hands-free and touch-free interaction mechanisms using brain-computer interfaces. He and his research team are in the process of developing BCI-HCI lab in IIT Kharagpur, a unique lab facility in India. He is the author of 5 books, more than 90 journals, and 130 conference papers of international repute. He is currently an Honorary Member of the Editorial Board of the International Journal of Biosciences and Technology, USA, and a member of the Editorial Board of the International Journal of Communication Networks and Distributed Systems, U.K.  He is the recipient of “Best Author of the Year Award” by the Computer Society of India, the Best Paper awards by the 8th ADCOM Conference (2012), the 7th International Conference on Data Management, Analytics and Innovation (2023), the 8th International Conference of Computer Vision and Image Processing (CVIP-2023), etc. He is also the recipient of the prestigious Microsoft Valued Professional award (2017) by Microsoft, USA, and the Author of the Best Selling Book (for the years 2012 and 2019) by Prentice Hall of India, New Delhi..

Dr. Maheshkumar H. Kolekar,
IIT, Patna

Title: Generative AI for intelligent transportation systems under different lighting conditions
Abstract: As urban environments present varying lighting scenarios, ranging from backlight to low-light or nighttime conditions, traditional intelligent transportation systems (ITS) may face challenges in accurately perceiving and responding to dynamic traffic situations. Generative AI, leveraging advanced deep learning techniques, offers a promising solution to adapt and optimize transportation systems in real-time. We have delved into the development and implementation of generative AI models specifically tailored for addressing the challenges posed by different lighting conditions. By training on extensive datasets that encompass a spectrum of lighting scenarios, the models aim to improve the robustness and reliability of transportation systems. We have also discussed the potential benefits of such AI-driven enhancements, including improved object detection, enhanced road safety, and optimized traffic flow. Furthermore, we touched upon the implications of this research for urban planning, traffic management, and the overall efficiency of intelligent transportation systems. As cities continue to evolve, the integration of generative AI in transportation systems stands as a pivotal advancement to ensure adaptability and performance under diverse lighting conditions, contributing to safer and more efficient urban mobility.
Biography: Dr. Maheshkumar H. Kolekar, FIETE, - is working as Associate Professor in Dept of Electrical Engg at Indian Institute of Technology Patna, India. He received the Ph.D. degree in Electronics and Electrical Communication Engg from the IIT Kharagpur in 2007. During 2008 to 2009, he was a Post-Doctoral Research Fellow with the Department of Computer Science, University of Missouri, Columbia, USA where he worked on intelligent video surveillance systems. During May to July 2017, he worked as DAAD fellow in Technical University Berlin where he worked on EEG signal analysis using machine learning and deep learning. He has authored a book titled, “Intelligent Video Surveillance Systems: An Algorithmic Approach”, CRC Press, Taylor and Francis Group, (2018). He served as a Head, Dept of Electrical Engg, IIT Patna in 2013 for one year and Head of the Center (HoC) for Advanced Systems Engineering, IIT Patna during 2014 to 2016 for two years. He served as Professor-in-charge, National Knowledge Network of IIT Patna during August 2017 to Sept 2019. Presently, he is working as Institute PhD Coordinator, IIT Patna since Sept 2022. He has successfully completed R and D project sponsored by Principal Scientific Advisor to Govt of India on abnormal human activity recognition. He is serving as Editor, IETE Journal of Research since 2022. His name is appeared continuously three years in top 2 % Scientist in the world in the area of Artificial Intelligence and Image Processing list released by Stanford University, 2021, 2022 and 2023..

Dr. Saroj K. Meher,
Indian Statistical Institute, India

Title: Domain Adaptation in pattern classification tasks
Abstract: One effective tactic used in pattern recognition and classification tasks is domain adaptation. Transferring knowledge from a source domain, where a large amount of labeled data is available, to a target domain, where training data is limited, is its main goal. Novel methodologies have been developed in the framework of supervised domain adaptation (SDA), which is especially helpful when there are few labeled samples available in the target domain. The center transfer loss (CTL), intended for deep learning (DL) models, is one such technique. CTL uses a single-stream input based on mini-batch training, in contrast to conventional SDA techniques that depend on paired training samples. It improves feature discriminability and aligns the domain, two crucial functions. Unlike other methods, CTL does away with the requirement for a hyperparameter in order to balance these functionalities. In domain adaptation challenges, extensive studies show that CTL performs better than contemporary state-of-the-art techniques. To summarise, domain adaptation serves as a bridge across domains, enabling models to efficiently generalise even in situations when the destination domain has insufficient labeled data.
Biography: Saroj K. Meher is an Associate Professor of the Systems Science and Informatics Unit at the Indian Statistical Institute, Bangalore Centre. He received a B.Sc. degree in Physics (Honours) from Sambalpur University in 1990, an M.Sc. degree in Physics with Electronics Specialization, and a Ph.D. degree in Science from the National Institute of Technology, Rourkela in 1997 and 2003, respectively.
He worked as a Senior Research Scientist at Research & Development Units of various Industries in India for about three years. He was awarded many times for his excellent contribution to various critical projects. He worked as a Post Doctoral Fellow and Visiting Assistant Professor at Indian Statistical Institute, Kolkata, in 2005-2006 and 2009-2010. He received the Sir. J. C. Bose memorial award of the Institute of Electronics and Telecommunication Engineers, India in 2003 and Orissa Young Scientist award for research in Electronic Sciences & Technology for 2003. He is an Senior Member of IEEE since 2011.
His current research interest includes Image processing and analysis, including remote sensing imagery, Pattern Recognition, Granular Computing, Domain Adaptation, Advarsarial Machine Learning. He has contributed about 80 research papers in well known and prestigious archival journals, internationally refereed conferences, and edited monograph volumes. One GRANTED US patent also goes to Dr. Saroj’s credit in the year 2015.

Prof. Debi Prosad Dogra,
IIT, Bhubaneswar

Title: Applications of AI for Visual Surveillance
Abstract: Rapid advancement in computational hardware has opened up various possibilities to tackle difficult problems with the help of artificial intelligence (AI) and machine learning (ML). During the last decade or so, AI and ML guided automated systems have started replacing human dependent systems in services including surveillance, industrial processes, agriculture, healthcare, etc. Computer vision and visual surveillance are two of the areas that are possibly receiving the highest dividend from the emergence of AI and ML. This talk summarizes the state-of-art AI and visual surveillance systems that are primarily built with the help of supervised as well as unsupervised ML algorithms. The focus is mainly given to the services ranging from computer vision guided smart transportation, public place surveillance, and monitoring.
Biography: Prof Debi Prosad Dogra received Ph.D. from IIT Kharagpur in 2012, M.Tech. from IIT Kanpur in 2003, and B.Tech. from HIT Haldia in 2001 all in Computer Sc. & Engineering. Earlier, he worked with Haldia Institute of Technology as a faculty during 2003-2006 and ETRI, South Korea as a researcher during 2006-2007. He also worked as research group leader in Samsung Research Institute Noida during 2011-2013 before joining IIT Bhubaneswar in 2013. He has published more than 130 research papers in international conferences/journals and patents in areas including applications of AI and ML in visual surveillance, intelligent transportation systems, computer vision, augmented reality, healthcare analysis, multi-media analysis etc. He is a senior member of IEEE. He is currently officiating as the Treasurer of IEEE Bhubaneswar Subsection. Dr Dogra is an editor of SNCS and JMIS journals. Presently, Dr Dogra is heading the IIT Bhubaneswar Research and Entrepreneurship Park, a section-8 company of IIT Bhubaneswar. More about Dr. Dogra can be found at https://www.iitbbs.ac.in/profile.php/dpdogra/

Dr. Pradipta Roy
Scientist F, Integrated Test Range Chandipur

Title: Real Time Video Tracker: Evolution from classical to learning based schemes
Abstract: Video tracker is key component in tasks like surveillance, tracking of flight vehicles for military and civilian applications, traffic monitoring, activity recognition and many more. Video tracker algorithms have evolved over the years in spatial , temporal and spatio-temporal domain covering the task of object detection, event detection, motion estimation and movement prediction of the target. There are many varieties of classical algorithms available based on target and background contrast, feature correlation , Kalman filter etc. With the advent of machine and deep learning techniques there are few tracking algorithms coming up based on target detection and classification. Consequently, the video tracker hardware also changed the platform from primitive processors to dedicated embedded processors, from FPGAs to SoCs and nowadays very popular GPUs. In this talk, all these aspects of algorithmic and architectural evolution of the state of the art trackers will be discussed with special emphasis on parallel algorithms and architectures for real time tracking.
Biography: Dr. Pradipta Roy received his B.Tech from Jadavpur University and his MTech And PhD from IIT, Kharagpur in Electronics Engineering. He is presently working as Scientist F in Integrated Test Range Chandipur. His has published more than 30 papers in reputed International Journal and Conferences. He is also reviewer of three IEEE Transaction and Journals. He has also organized national and international conferences as Technical Chair. His research interests are Computer Vision, VLSI architecture, Machine and Deep learning etc. He is senior member of IEEE. He has received Young Scientist award from the then President DR. A P J Abdul Kalam.