Department of Computer Science and Technology
The University of Cambridge
Building multimodality digital patient twin using graph neural networks
I will first introduce graph neural networks. In this talk I will focus on how to build a multimodality digital patient twin using graph representation and considering physiological (cardiovascular), clinical (inflammation) and molecular variables (multi-omics and genetics). I will consider different pathologies such as inflammation and immuno senescence through the use of neural graph ODEs. I will discuss how this approach could also use neural concepts explainers, logic and attention and keep the clinician in the loop to avoid excessive automatisation. I will conclude with some insights into more advanced neural networks including sheaf neural networks and hypergraph neural networks for the development of digital patient twins utilising multi-sources multimodal information.
Pietro Lio' received the PhD degree in complex systems and non linear dynamics from the School of Informatics, dept of Engineering, University of Firenze, Italy and the PhD degree in theoretical genetics from the University of Pavia, Italy. He is currently a professor of computational biology with the Department of Computer Science and Technology, University of Cambridge and a member of the Artificial Intelligence Group. He is also a member of the Cambridge Centre for AI in medicine. His research interests include systems biology, multi omic, and clnical data integration methodology, developing artificial intelligence and computational biology models to detect biomarkers for diseases complexity and address personalised, and precision medicine.
Scientist and former Director
Indian Statistical Institute
Granular Data Mining in Video Analytics: Shallow to Deep Learning
The talk first describes the –
The application demonstrates the roles of different kinds of granules, rough lower approximation, and various information measures. Granules considered range from crisp, overlapping, 1-d color, 2-d spatio-color and 3-d spatio-temporal to regular shape and arbitrary shape. Concept of rough lower approximation in temporal domain provides an initial estimate of the object model in unsupervised tracking, while that of lower-upper approximations in granular level, as used in designing a neighborhood rough filter, estimates the location and color model of objects for handling overlapping and occlusion.
The third part deals with the significance of deep learning (DL) in object recognition and tracking in video. Explaining the relevance of GrC in DL in reducing the computation time, the talk presents a new deep architecture, named G-RCNN (granulated region proposal neural nets). This is an improved version of the Fast RCNN and Faster RCNN for extracting RoIs (regions of interest) by incorporating spatio-temporal granulation in a deep CNN. G-RCNN accepts videos directly as input. Spatio-color granules enable extraction of less number but more representative RoI-pixels of object regions, and thereby make the G-RCNN superior in real-time detection speed and accuracy during both training and testing.
Several examples and results are provided to explain the aforesaid concepts. The talk concludes mentioning the challenging issues and future directions of research including some cautions.
Sankar K Pal (www.isical.ac.in/~sankar) is currently a National Science Chair, Govt. of India. He is a Distinguished Scientist and former Director of Indian Statistical Institute (ISI), a former Distinguished Professor of Indian National Science Academy, and a former Chair Professor of Indian National Academy of Engineering. He founded the Machine Intelligence Unit and the Center for Soft Computing Research: A National Facility in the Institute in Calcutta. He received a Ph.D. in Radio Physics and Electronics from the University of Calcutta in 1979, and another Ph.D. in Electrical Engineering along with DIC from Imperial College, University of London in 1982. He joined his Institute ISI in 1975 as a CSIR Senior Research Fellow where he became a Full Professor in 1987, Distinguished Scientist in 1998 and the Director for the term 2005-10. He was the first Computer scientist and someone outside Statistics and Mathematics to become the Director of the Indian Statistical Institute since its inception in 1931.
He worked at the University of California, Berkeley and the University of Maryland, College Park in 1986-87; the NASA Johnson Space Center, Houston, Texas in 1990-92 & 1994; and in US Naval Research Laboratory, Washington DC in 2004. Since 1997 he has served as a Distinguished Visitor of IEEE Computer Society (USA) for the Asia-Pacific Region, and held several visiting positions in Italy, Poland, Hong Kong and Australian universities.
Prof. Pal is a Fellow of the IEEE, the Academy of Sciences for the Developing World (TWAS), International Association for Pattern recognition, International Association of Fuzzy Systems, Asia-Pacific Artificial Intelligence Association, and all the four National Academies for Science/Engineering in India. He is a Member of the European Academy of Sciences and Arts. He has coauthored twenty-one books and about five ndred research publications in the areas of Pattern Recognition and Machine Learning, Image Processing, Data Mining and Web Intelligence, Soft Computing, Neural Nets, Genetic Algorithms, Fuzzy Sets, Rough Sets, Cognitive Machine and Bioinformatics. He introduced the Soft Computing concept and research in India. He visited forty-five countries as a Keynote/ Invited speaker or an academic visitor.
He has received the 1990 S.S. Bhatnagar Prize (which is the most coveted award for a scientist in India), 2013 Padma Shri (one of the highest civilian awards) by the President of India and many prestigious awards in India and abroad including the 1999 G.D. Birla Award, 1998 Om Bhasin Award, 1993 Jawaharlal Nehru Fellowship, 2000 Khwarizmi International Award from the President of Iran, 2000-2001 FICCI Award, 1993 Vikram Sarabhai Research Award, 1993 NASA Tech Brief Award (USA), 1994 IEEE Trans. Neural Networks Outstanding Paper Award, 1995 NASA Patent Application Award (USA), 1997 IETE-R.L. Wadhwa Gold Medal, 2001 INSA-S.H. Zaheer Medal, 2005-06 Indian Science Congress-P.C. Mahalanobis Birth Centenary Gold Medal from the Prime Minister of India for Lifetime Achievement, 2007 J.C. Bose Fellowship of the Government of India, 2013 INAE Chair Professorship, 2015 DAE Raja Ramanna Fellowship, 2015 INAE-S.N. Mitra Award, 2017 INSA-Jawaharlal Nehru Birth Centenary Lecture Award, 2018 INSA Distinguished Professorial Chair, 2020 National Science Chair, Govt. of India, and 2021 AICTE Distinguished Chair Professor.
Prof. Pal has served/serving in editorial board of ~30 internationally well-known scientific journals in computer science and engineering including several IEEE Transactions. His Google Scholar h-index is 79 with 35,000+ total citations.
University of Twente,
Towards Ever-present Augmented Reality
We present various views on the future use of augmented reality in public spaces. The views address enhanced walking, social activity, appropriation of public spaces, and futuristic social aspects of future outdoor augmented reality. Although we will also refer to handheld augmented reality, the focus is on more natural ways of experiencing augmented reality through optical see-through eyewear (smart glasses or lenses) that allow the experience of ever-present vision and audio-based augmented reality.
Anton Nijholt is interested in non-traditional human-computer interaction issues. These issues include irrational behavior, deception, food, and humor. They are included in research on entertainment computing, augmented reality, brain-computer interfacing, multimodal interaction, affective interaction, and modelling interactions in smart environments, including human-human interaction, human-robot interaction, human-virtual agent interaction, and playable cities. He has been program chair or general chair of the main international conferences of affective computing (ACII), entertainment computing (ACE, INTETAIN, ICEC), virtual agents (IVA), faces & gestures (FG), and some others. He organised many workshops on related topics, such as multisensorial augmented reality, humor engineering, human-food interaction, playable cities, and brain-computer interfacing. Recent edited books are "Playable Cities: The City as a Digital Playground", "Making Smart Cities more Playable", and "Brain Art: Brain-Computer Interfaces for Artistic Expression".
Nijholt held positions at various universities in Belgium and the Netherlands. He acted as supervisor for about fifty Ph.D. students. During some years Nijholt was scientific advisor of Philips Research, Eindhoven. He has been research-fellow at McMaster University (Canada), the Netherlands Institute for Advanced Study in the Humanities and Social Sciences, the Imagineering Institute in Malaysia, and member of Microsoft's Technical Leadership Advisory Board. Nijholt is Chief Editor of the section Human-Media Interaction of Frontiers in Psychology and Frontiers in Computer Science, Springer Book Series Editor Gaming Media and Social Effects, and many editorial boards. In preparation are two edited books (on playful augmented reality and a second book on brain art) that are planned to appear late 2022 or early 2023.
Professor of Computer Science and Engineering
Kyung Hee University,
Yongin, Republic of Korea
AI for Aerial and Space Networking
The conceptualization on 6G's vision and enabling technologies have recently gained attention in both academics and industry. Artificial intelligence (AI) represents one of the biggest emerging opportunities in technology, especially for tasks such as wireless resource management, networking planning, and power saving. Aerial supporting networking becomes promising technology for enabling computation-oriented communications (COC) applications such as virtual and augmented reality (VR and AR), real-time monitoring, and surveillance. In 5G and upcoming 6G cellular networks, reconfigurable intelligent surface (RIS) has perceived a prodigious interest to develop a new communication infrastructure by utilizing unnamed aerial vehicle (UAV) with RIS due to flexibility, line-of-sight (LOS) transmission, spectral efficiency enhancement, and cost-effectiveness. To guarantee last-mile internet connectivity, the next generation of networking has made satellite communication possible. Thus, the low earth orbit (LEO) satellite system assures future networking to manage the space-air-sea (SAS) communication system. This talk will cover the role of AI in managing and controlling the aerial and space-supported networking for future internet and its services.
Choong Seon Hong (Senior Member, IEEE) received the B.S. and M.S. degrees in electronic engineering from Kyung Hee University, Seoul, South Korea, in 1983 and 1985, respectively, and the Ph.D. degree from Keio University, Tokyo, Japan, in 1997. In 1988, he joined KT, Gyeonggi-do, South Korea, where he was involved in broadband networks as a member of the Technical Staff. Since 1993, he has been with Keio University. He was with the Telecommunications Network Laboratory, KT, as a Senior Member of Technical Staff and as the Director of the Networking Research Team until 1999. Since 1999, he has been a Professor with the Department of Computer Science and Engineering, Kyung Hee University. His research interests include future Internet, intelligent edge computing, network management, and network security.
Dr. Hong is a member of the Association for Computing Machinery (ACM), the Institute of Electronics, Information and Communication Engineers (IEICE), the Information Processing Society of Japan (IPSJ), the Korean Institute of Information Scientists and Engineers (KIISE), the Korean Institute of Communications and Information Sciences (KICS), the Korean Information Processing Society (KIPS), and the Open Standards and ICT Association (OSIA). He has served as the General Chair, the TPC Chair/Member, or an Organizing Committee Member of international conferences, such as the Network Operations and Management Symposium (NOMS), International Symposium on Integrated Network Management (IM), Asia-Pacific Network Operations and Management Symposium (APNOMS), End-to-End Monitoring Techniques and Services (E2EMON), IEEE Consumer Communications and Networking Conference (CCNC), Assurance in Distributed Systems and Networks (ADSN), International Conference on Parallel Processing (ICPP), Data Integration and Mining (DIM), World Conference on Information Security Applications (WISA), Broadband Convergence Network (BcN), Telecommunication Information Networking Architecture (TINA), International Symposium on Applications and the Internet (SAINT), and International Conference on Information Networking (ICOIN). He was an Associate Editor of the IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT and the IEEE JOURNAL OF COMMUNICATIONS AND NETWORKS and an Associate Editor for the International Journal of Network Management and an Associate Technical Editor of the IEEE Communications Magazine, and guest editor of IEEE Network Journal. He currently serves as an Associate Editor for the International Journal of Network Management and Future Internet Journal.
Dept. of Electrical, Electronic and Systems Engineering
Universiti Kebangsaan Malaysia (UKM), Malaysia
Microwave Based Stroke Diagnosis Solutions with Metamaterial Based Planar Antenna
Uberization of healthcare allow patients to instantly access and tracking their health as well as their rehabilitation performance at clinical environment. Not only patient knowing their health condition at their fingertips, but the health practitioner can also understand and prescribed next level of intervention program to the patients. The last two decades has proven that not only stroke remains top three health burden worldwide, the age of first stroke occurrence has become younger (54.5 – 62.2 years) compared to 75.2 years old in the last decade. Incomplete intervention reporting would be difficult for the physiotherapist to evaluate its effectiveness later.
Current imaging technologies like computed axial tomography (CAT), magnetic resonance imaging (MRI), positron emission tomography (PET) and ultrasound CAT are commonly used to monitor hemorrhagic stroke. However, CAT, MRI and PET scan are not available outside the hospital environment due to their large and bulky structure. Moreover, they are characteristics with invasive and elevated cost. However, to avoid death or possibility of disabled it is necessary to monitor stroke immediate after onset of symptoms so that proper rehabilitation process can be initiated. The difficulties motivate us to develop a new simplified portable imaging system that is noninvasive and less expansive and will be able to provide medical facility to the patients on onset of the stroke symptoms. Therefore, researcher continue to search for accurate, safe, reliable, non-ionizing, portable and cost-effective imaging systems, which will be helpful to guide clinicians and therapists in individualizing further rehabilitation intervention.
Researchers are trying to find alternatives to conventional diagnosis systems to the microwave-based healthcare diagnosis solution. Microwave signal contrast between the electrical characteristics of human tissues can easily be distinguished by microwave antenna sensors. In microwave imaging, one or more antenna sensors receive the radiated and scattered power. Microwave-based portable medical diagnosis tools have the potential to save lives by utilizing microwave sensor antennas that perform well. The scope of this talk is to highlight the research work performed by the speaker on planar antenna technologies which is suitable for healthcare solution. The effect of ground plane in the designing of UWB planar antenna is discussed and a new technique for bandwidth enhancement is presented. Metamaterial can successfully miniature the antenna size which is a prime requisite of modern portable communication devices. Microwave imaging (MI) is an ideal candidate for the stroke and head tumour detection. The difference between the electrical properties is identified with the microwave sensors. In this MI system, the power is radiated over an antenna sensor, and another one or pair of sensor receives the scattered power. The scattered signals are further processed to detect the unwanted malignant tissues. The ultra-wideband has the advantage of deep penetration and higher resolution features. In this talk, I will discuss metamaterial loaded patch antenna focusing on imaging sensing capabilities and present exciting new ways of capturing backscattered signal from the phantom to see the imaging property. The presentation also introduces the basic principles of metamaterial loaded patch antennas prototype development and the integration to microwave imaging system to evaluate the unwanted tumor detection in the head tissue.
MOHAMMAD TARIQUL ISLAM (Senior Member, IEEE) is currently a Professor with the Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia (UKM) and a Visiting Professor with the Kyushu Institute of Technology, Japan. He is the author and coauthor of about 600 research journal articles, nearly 250 conference articles, and a few book chapters on various topics related to antennas, metamaterials, and microwave imaging with 23 inventory patents ﬁled. Thus far, his publications have been cited 10,000 times and his H-index is 45 (Source: Scopus). His Google scholar citation is 15,700 and H-index is 54. He was a recipient of more than 40 research grants from the Malaysian Ministry of Science, Technology and Innovation, Ministry of Education, UKM research grant, international research grants from Japan, Saudi Arabia and Kuwait. His research interests include communication antenna design, metamaterial, satellite antennas, and microwave imaging. Dr. Islam has been serving as an Executive Committee member for IEEE AP/MTT/EMC Malaysia Chapter, since 2019-2020, the Chartered Professional Engineer (CEng), a fellow of IET, U.K., and a Senior Member of IEICE, Japan. He received several International Gold Medal awards, a Best Invention in Telecommunication Award for his research and innovation, and best researcher awards at UKM. He was a recipient of 2018, 2019 and 2020 IEEE AP/MTT/EMC Malaysia Chapter, Excellent Award. He also won the best innovation award and the Best Researcher award by UKM, in different years. He was a recipient of Publication Award from Malaysian Space Agency, in several years. He has supervised about 50 Ph.D. theses, 30 M.Sc. theses, and has mentored more than 10 postdocs and Visiting scholars. He has developed the Antenna Measurement Laboratory which includes antenna design and measurement facility till 40 GHz. He was an Associate Editor of IET Electronics Letter. He also serves as the Guest Editor, SENSORS journal, and an Associate Editor for IEEE ACCESS.
Interactive Systems for Social Inclusion
School of Science & Technology
Nottingham Trent University, UK
Some uses of machine learning to support students with learning disabilities and autism.
This presentation will cover the case for applying Artificial Intelligence Tools for Education (AIEd) with students with Intellectual Disability and Autism, where schools are receiving more diverse students in their classrooms requiring diverse teaching. Approaches that address the real issue of teachers not having enough capacity to attend to each child’s individual learning needs, or to support their best behavioural outcomes in class are called for, to ensure that all students are supported to develop their full academic and social potential. Approaches to using eXplainable AI (XAI) will be covered, as are the reasons for pursuing such methods in light of the vulnerabilities of such students.
David is a highly experienced project manager (€4M as Principal Investigator), and over 100 high quality journal and conference publications. He is Chair of the International Conference on Disability, Virtual Reality and Associated Technology. He is Co-Investigator on the EPSRC The Internet of Soft Things Project; PI on the EU H2020 MaTHiSiS and No One Left Behind projects; and PI on the Erasmus DiversAsia and AI-TOP projects. His research focusses on the development and evaluation of enabling technologies for the cognitive and physical rehabilitation of users within the real world, and promotion of their mental wellbeing. He is Associate Editor for Frontiers: Virtual Reality in Medicine (https://www.frontiersin.org/journals/virtual-reality/sections/virtual-reality-in-medicine).
Department of Computer Science and Engineering,
Bangladesh University of Engineering and Technology, Bangladesh
Prediction based on biological sequences (where Machine Learning meets Life Sciences) (IEEE Distinguish Lecture)
Due to the rapid development of fast sequencing technologies, we now have tremendous amount data on different biological sequences. For example, the number of sequence-known proteins has grown exponentially in recent years. On the contrary, the biochemical experiments to learn the attributes of proteins are expensive and time consuming. A large gap thus exists between the number of sequence-known proteins and that of attribute-known proteins. To catch up, researchers have started to rely on state of the art computational intelligence based methods (e.g., Machine Learning) to predict different attributes of proteins and other biological sequences.
In this lecture, we will discuss Machine Learning based methods for a number of prediction tasks in the domain of life sciences. We will discuss predictors that have been developed based on a machine learning based framework where the features are extracted from the primary sequence only. Overall, our research empirically asserts the natural belief that the functional and structural information of a biological sequence are intrinsically encoded within its primary sequence. This assertion culminates in generalizing a framework for sequence based feature extraction and selection that can be applied to any prediction problem in life sciences
Dr. Sohel Rahman is a Professor of CSE, BUET. He had worked as a Visiting Research Fellow of King’s College London, UK during 2008-2011 and again as a Visiting Senior Research Fellow there during 2014-15. He is a Senior Member of both IEEE and ACM. He is also a Peer-review College Member of EPSRC, UK. He was a recipient of Commonwealth Scholarship, Commonwealth Fellowship, ACU Titular Fellowship, University College London-Big Data Institute visiting grant, London Mathematical Society Visiting Grant etc. He is also a recipient of the Bangladesh Academy of Sciences Gold Medal and UGC Award. He has led projects funded by British Council, Microsoft, UGC-World Bank, ICT Division and BUET. He has so far published more than one hundred peer-reviewed journal papers. Dr. Rahman is an Academic Editor of PLOS One, Associate Editor of BMC Research Notes and had edited special issues as guest editors in different journals. He has also served as Program Committee members in a number of conference series. Dr.Rahman regularly writes reviews at Mathematical Review and ACM Computing Review. He is currently an ACM Distinguished Speaker and IEEE CS Distinguished Speaker. Recently, he has been elected as a Fellow of Bangladesh Academy of Sciences.
Department of Computer Science,
The University of Alabama at Birmingham, USA
Cyber Security in the 21st century: A Data-Driven AI-based Approach Towards a Secure Digital Life.
The world of cyber security continues to evolve every day. As we enter the third decade of the 21st century, the scope and extent of cybercrimes have increased significantly. Each year, billions of dollars are lost to cybercrimes including Phishing, Smishing, Ransomware, and bank fraud. To address these emerging challenges, our traditional approach towards cyber security must also change. A data-driven, AI-based approach combined with years of cyber security research can help us address these challenges and save humanity from harm in the digital world. This talk examines the challenges from cybercrime, state-of-the-art data-driven and AI-based solutions, and open problems.
Ragib Hasan, Ph.D., is a tenured Associate Professor at the Department of Computer Science at the University of Alabama at Birmingham. Prior to joining UAB in 2011, He received his Ph.D. and M.S. in Computer Science from the University of Illinois at Urbana Champaign and was an NSF/CRA Computing Innovation Fellow at the Department of Computer Science, Johns Hopkins University. Hasan explores research on cloud security, the Internet of Things, digital forensics, big data, fog computing, mobile malware security, and secure provenance. He is the founder of the Secure and Trustworthy Computing Lab (SECRETLab) at UAB. Hasan is the author of more than 125 peer reviewed articles in top journals and conferences. His research is supported by the National Science Foundation (NSF), the National Institutes for Health (NIH), Department of Homeland Security (DHS), the Office of Naval Research (ONR), Facebook Inc., Google Inc., and Amazon Inc. He is a 2014 awardee of the prestigious NSF CAREER Award from the National Science Foundation for his work on cloud security. He is also the PI of multiple NSF grants and the Co-PI of an NIH grant. For his research, he has won a best paper award, a best poster award, and a runner up to the best demo award from top IEEE conferences.
Global Technical Director
Accelerating Industry 4.0 in Manufacturing through Data and AI
Industry 4.0 is revolutionizing the way that goods are produced and distributed. Manufacturers all over the world are looking to expedite their return on invested capital (ROIC) and I4.0 is seen as the key enabler. The technologies within the I4.0 ecosystem help to manage and optimize manufacturing processes, supply chain, and logistics, with the aim of delivering real-time insights for quicker and smarter decision making. The key drivers for I4.0 are digitization, big data, analytics, and AI. This keynote will address current industry trends in deploying data and AI solutions, to drive I4.0 adoption and success. Current challenges in the industry will also be addressed, from the existence of siloed data on legacy systems to the inability to deploy analytics and AI at scale, and more importantly, how the industry is overcoming these challenges through recent innovations. This talk will also cover the concepts of the Data Lakehouse platform as the underlying core data platform to deliver on I4.0 objectives.
Dr. Bala Amavasai is the Global Technical Director for manufacturing and logistics at Databricks. He has over 25 years of experience in the data and AI space. Prior to Databricks, Bala was the Head of AI at Stanley Black & Decker. Bala is currently serving as the 2021/22 Chair for the IEEE SMC Society in the UK & Ireland. He also serves as an industrial advisor to the EPSRC (Science/Engineering Research Council) Centre for Doctoral Training on AI, Machine Learning, and Advanced Computing. He has several patents in the smart products and machine learning area. Bala received his Ph.D. in machine learning from the University of Sheffield, UK, in 2000.
Co-Founder and Chairman
ScITech Consulting & Solutions
Human genome research in BCSIR, Bangladesh in context of SARS-COV-2
Mutation drives viral evolution and genome variability, thereby enabling the virus to evade host immune response and develop drug resistance over time. The genomic mutation of SARS-CoV-2 may be influenced by the local-to-local climate, transmission, disease manifestation, treatment methods and vaccine strategies; therefore, local genomic surveillance is essential. This study’s objectives were to characterize genomic variations, evolution and mutation rate, and the overall character of SARS-CoV-2 collected from Bangladesh's eight administrative divisions. To investigate the genetic diversity, genomes of SARS-CoV-2 strains were sequenced by genomic Research Lab, BCSIR. We observed differences in the Bangladesh dominant lineage relative to the Wuhan-1 genome sequence. The differences occurring in samples are the signature differences for this lineage; they include differences that are characteristic of clade derived from a European lineage. A difference at base 1163 seems to be characteristic of the Bangladesh dominant lineage; we use this difference in conjunction with epidemiological information to differentiate another incursion event that we identify as the West Bengal lineage enabling us to understand the establishment of the Bangladesh dominant lineage. The difference at 1163, a missense difference is causing a change in orf1ab (I300F). We estimated the rate for evolution to be 24.6 substitution site per year, while the mutation rate was 6.7x10-4 per nucleotide site per cell infection. The sequences from several samples are unrelated to the major lineages in Bangladesh and show several incursions of SARS-CoV-2 into Bangladesh early in the pandemic, with one lineage eventually becoming dominant.
Dr. M. Samiruzzaman is a visiting research associate at King's College London. He completed his bachelor’s degree in CSE from BUET in 2003. Later he studied in postgraduate level at University of Oxford and completed his PhD from Kings College London. He has been working for UK government and different agencies in UK including City of London Corporation. He is the co-founder and chairman of Rokkhi and ScITech in Bangladesh. He was the proposer and the principal consultant of first human genome lab at BCSIR, Bangladesh. He is now the principal consultant of Next Generation Human Genome Centre at National Institute of Biotechnology, Savar.
International Conference on Machine Intelligence and Emerging Technologies 2022 (MIET 2022) is going to organize by Noakhali Science and Technology University, Sonapur, Noakhali, 3814, Bangladesh. MIET focuses on theoretical, practical, state-of-art applications, and research challenges in the field of artificial intelligence and associated emerging technologies.