Prof. Cyrus F Nourani

Akdmkrd-DAI-TU Berlin, Germany

Speech Title: Generative Visual AI Processes with a visual commonsense deductive processor

Abstract:A visual multiagent diagrammatic reasoning system with abstract models, predictive visual analytics based on a visual virtual tree-based functional deductive system called Morph Gentzen which was developed by the author since 1997. Context abstractions with categorical linguistics, agent languages, and MetaContextual Reasoning are newer areas encompassed since the Morph Gentzen computing logic by this author since 1997. Reflecting on what was accomplished over the years, this brief is a glimpse on the techniques that bring forth computable AI world knowledge representable with generic model diagrams, characterized with a minimal family of generalized Skolem functions. The functions may correspond to objects defining shapes and depicting pictures. The process is instantiated on tableau sequents with logical deductive completeness on the sequent models that are proved to have compactness properties.
Biography: Cyrus F. Nourani, PhD, has a national and international reputation in computer science, artificial intelligence, mathematics, virtual haptic computation, enterprise modeling, decision theory, data sciences, predictive analytics economic games, information technology, and management science. In recent years he has been engaged as a research professor at Simon Frasier University in Burnaby, British Columbia, Canada, and at the Technical University of Berlin, Germany, and has been working on research projects in Germany, Sweden, and France. He has many years of experience in the design and implementation of computing systems. Dr. Nourani’s academic experience includes faculty positions at the University of Michigan-Ann Arbor, the University of Pennsylvania, the University of Southern California, UCLA, MIT, and the University of California, Santa Barbara. He was a visiting professor at Edith Cowan University, Perth, Australia, and a lecturer of Management Science and IT at the University of Auckland, New Zealand. Dr. Nourani has taught AI to the Los Angeles aerospace industry and has worked in many R&D and commercial ventures. He has written and coauthored several books. He has over 400 publications in computing science, mathematics, and management science, and he has written several books and has edited several volumes on additional topics, such as pure mathematics; AI, EC, and IT management science; decision trees; and predictive economics game modeling. In 1987, he founded Ventures for computing R&D and was a consultant for such clients such as System Development Corporation (SDC), the US Air Force Space Division, and GE Aerospace. Dr. Nourani has designed and developed AI robot planning and reasoning systems at Northrop Research and Technology Center, Palos Verdes, California. He also has comparable AI, software, and computing foundations and R&D experience at GTE Research Labs. Dr. Nourani commenced his university degrees at MIT, where he became interested in algebraic semantics. That was pursued with a world-renowned category theorist at the University of California and Oxford University. Dr. Nourani’s dissertation on computing models and categories proved to have pure mathematics foundations developments that were published from his postdoctoral times in US and Europe publications.



Prof. Umesh C. Pati

National Institute of Technology, India

Speech Title: Video-based Loitering Detection System (LDS) using Deep Learning (DL) Techniques

Abstract: Intelligent video surveillance systems (IVSS) are widely used in security applications to detect potential crimes and suspicious activities in the early stage for smart city applications. Usually, suspicious activity such as loitering often leads to crime activities such as vandalism, terrorist attacks, bank robbery, pickpocketing, stealing and drug-dealing activity. Loitering can be defined as the act of staying in a sensitive place or public place for a protracted duration or for a period of time longer than a given time threshold. Detection of the loitering in real-time from the enormous amount of video surveillance data by the human operator is an inefficient, erroneous, and tedious job. The timely detection and intimation of the loitering of an individual in a particular geographical area can help in preventing various crime activities. Hence, a deep-learning-based Loitering Detection System (LDS) with re-identification (ReID) capability over a multicamera network is proposed. The proposed LDS mainly comprises of object detection and tracking, loitering detection, feature extraction, camera switching, and re-identification of the loiterer. The person is detected using YOLO and tracked using Simple Online Real-time Tracking with a deep association matrix (DeepSORT). From the trajectory analysis, once the time and displacement thresholds are satisfied, the person is treated as a loiterer. When the loiterer moves from one camera to another, then the algorithm is switched to the appropriate camera feed as per the proposed camera switching algorithm to minimize the computational cost. Subsequently, the loiterer is reidentified in the switched camera feed by comparing the features of the loiterer extracted by the MobileNets with those of the other detected persons based on the triplet loss criteria. The proposed system provides an enhanced accuracy of 96 % on an average fps of 33 (without ReID) and 81.5 % at an average fps of 30 (with ReID).
Biography:Dr. Umesh C. Pati is a Full Professor at the Department of Electronics and CommunicationEngineering, National Institute of Technology (NIT), Rourkela. He has obtained his B.Tech.Degree in Electrical Engineering from National Institute of Technology (NIT), Rourkela,Odisha. He received both M.Tech. and Ph.D. degrees in Electrical Engineering withspecialization in Instrumentation and Image Processing, respectively, from the Indian Instituteof Technology (IIT), Kharagpur.
His current areas of interest are Internet of Things (IoT), Industrial Automation,Instrumentation Systems, Artificial Intelligence, Image/Video Processing, Computer Vision,and Medical Imaging. He has authored/edited two books and published more than 100 articlesin the peer-reviewed international journals as well as conference proceedings. Dr. Pati has filed2 Indian patents. He has served as a reviewer in a wide range of reputed international journalsand conferences. He also has guest-edited special issues of Cognitive Neurodynamics and theInternational Journal of Signal and Imaging System Engineering. He has delivered manyKeynote/Invited talks in India as well as abroad. Besides other sponsored projects, he iscurrently associated with a high-value IMPRINT project, “Intelligent Surveillance DataRetriever (ISDR) for Smart City Applications,” which is an initiative of the Ministry ofEducation (formerly the Ministry of Human Resource Development) and Ministry of Housingand Urban Affairs, Govt. of India.
He has visited countries like the USA, Australia, Italy, Austria, Singapore, Mauritius,etc., in connection with research collaboration and paper presentation. He was also an academicvisitor to the Department of Electrical and Computer Engineering, San Diego State University,USA, and the Institute for Automation, University of Leoben, Austria. He is a Senior memberof IEEE, Fellow of The Institution of Engineers (India), Fellow of The Institution of Electronicsand Telecommunication Engineers (IETE), and life member of various professional bodies likeMIR Labs (USA), The Indian Society for Technical Education, Instrument Society of India,Computer Society of India, and Odisha Bigyan Academy. His biography has been included inthe 32nd edition of MARQUIS Who’s Who in the World 2015.



Prof. Loc Nguyen

Sunflower Soft Company, Vietnam

Speech Title: Adversarial Variational Autoencoders to extend and improve generative model

Abstract: Generative artificial intelligence (GenAI) has been developing with many incredible achievements like ChatGPT and Bard. Deep generative model (DGM) is a branch of GenAI, which is preeminent in generating raster data such as image and sound due to strong points of deep neural network (DNN) in inference and recognition. The built-in inference mechanism of DNN, which simulates and aims to synaptic plasticity of human neuron network, fosters generation ability of DGM which produces surprised results with support of statistical flexibility. Two popular approaches in DGM are Variational Autoencoders (VAE) and Generative Adversarial Network (GAN). Both VAE and GAN have their own strong points although they share and imply underline theory of statistics as well as incredible complex via hidden layers of DNN when DNN becomes effective encoding/decoding functions without concrete specifications. In this research, I try to unify VAE and GAN into a consistent and consolidated model called Adversarial Variational Autoencoders (AVA) in which VAE and GAN complement each other, for instance, VAE is good at generator by encoding data via excellent ideology of Kullback-Leibler divergence and GAN is a significantly important method to assess reliability of data which is realistic or fake. In other words, AVA aims to improve accuracy of generative models, besides AVA extends function of simple generative models. In methodology this research focuses on combination of applied mathematical concepts and skillful techniques of computer programming in order to implement and solve complicated problems as simply as possible.
Biography: Loc Nguyen is an independent scholar from 2017. He holds Master degree in Computer Science from University of Science, Vietnam in 2005. He holds PhD degree in Computer Science and Education at Ho Chi Minh University of Science in 2009. His PhD dissertation was honored by World Engineering Education Forum (WEEF) and awarded by Standard Scientific Research and Essays as excellent PhD dissertation in 2014. He holds Postdoctoral degree in Computer Science from 2013, certified by Institute for Systems and Technologies of Information, Control and Communication (INSTICC) by 2015. Now he is interested in poetry, computer science, statistics, mathematics, education, and medicine. He serves as reviewer, editor, speaker, and lecturer in a wide range of international journals and conferences from 2014. He is volunteer of Statistics Without Borders from 2015. He was granted as Mathematician by London Mathematical Society for Postdoctoral research in Mathematics from 2016. He is awarded as Professor by Scientific Advances and Science Publishing Group from 2016. He was awarded Doctorate of Statistical Medicine by Ho Chi Minh City Society for Reproductive Medicine (HOSREM) from 2016. He was awarded and glorified as contributive scientist by International Cross-cultural Exchange and Professional Development-Thailand (ICEPD-Thailand) from 2021 and by Eudoxia Research University USA (ERU) and Eudoxia Research Centre India (ERC) from 2022. He has published 92 papers and preprints in journals, books, conference proceedings, and preprint services. He is author of 5 scientific books. He is author and creator of 9 scientific and technological products.



Assoc. Prof. Mohammed M. Bait-Suwailam

Sultan Qaboos University, Oman

Speech Title: Impact of Open Datasets on Objects Detection and Tracking

Abstract: Open datasets play a major role in advancing artificial intelligence and machine learning algorithms, especially in the detection and tracking of small objects and features, in fields including but not limited to healthcare, autonomous vehicular systems, consumer electronics among others. Although datasets from real-world experiments can significantly help in the prediction and tracking of objects, such datasets have certain limitations, due to the nature of experimental setups along with high cost of experimental preparation and data labeling and segmentation. Thus, the use of synthetic datasets applied to various real-world problems can be advantageous to fit many realistic problems. In this talk, the impact of datasets generation, size and scalability will be discussed and addressed to some problems of interest. Some suggested measures to alleviate the encountered challenges under various environmental conditions will be addressed.
Biography: Mohammed M. Bait-Suwailam (Senior Member, IEEE) received the B.Eng. degree in Electrical and Computer Engineering from Sultan Qaboos University, Muscat, Oman, in 2001, the MSc. degree in electrical and computer engineering from Dalhousie University, Halifax, NS, Canada, in 2004, and the Ph.D. degree in Electrical and Computer Engineering from the University of Waterloo, in 2011. From 2018 to 2019, he spent his sabbatical research leave at the School of Electronic Engineering and Computer Science, Queen Mary University of London, London, U.K. He is currently an Associate Professor with the Department of Electrical and Computer Engineering, Sultan Qaboos University. He is also working as the Director of Communication and Information Research Center, Sultan Qaboos University. He has authored/co-authored more than 60 refereed journals and conference papers. His research interests include antenna theory and design, metamaterials, EMI/EMC, electromagnetic energy harvesting and flexible sensors for healthcare applications, deployment of artificial intelligence and remote sensing for food inspection and renewable energy solutions. Dr. M. Bait-Suwailam was the recipient of several scholarships and awards, including the Best Paper Award from The Research Council of Oman in 2017 and the Best Teacher Award from Sultan Qaboos University in 2015. He is also serving as an Associate Editor for IEEE Access and the Journal of Engineering Research.



Assoc. Prof. Renjith V Ravi

M.E.A Engineering College, India

Speech Title: The Role of Explainable Artificial Intelligence (XAI) in Cybersecurity: Building Trust and Transparency in Visible Data Protection

Abstract: Machine learning (ML) models, especially deep learning systems, have made significant progress in various areas of image security. However, due to the complex nature of these models, there are often difficulties in understanding their decisions, leading to a lack of trust and transparency. This talk discusses the importance of XAI in the context of image security and explores ways to build trustworthy and transparent ML-based security systems. Briefly, this lecture examines the principles, techniques, and applications of XAI in image security. Emphasis will be placed on XAI's ability to increase interpretability, improve human-AI collaboration, and build trust in security settings.

Biography: Dr Renjith V Ravi is presently employed as Associate Professor and Head of the Department of Electronics and Communication Engineering and Coordinator of the Post Graduate Programmes at MEA Engineering College, Kerala, India. He possesses B.Tech. degree in Electronics and Communication Engineering in, M.E. degree in Embedded System Technology and Ph.D. in Electronics and Communication Engineering. He is a member of the panel of academic auditors of APJ Abdul Kalam Technological University, Kerala and had conducted external academic auditing in various affiliated institutions under the same University. He had published several research articles in SCIE and Scopus indexed journals, Edited books and international conferences inside and outside the country. He is an academy graduate and academy mentor in Web of Science and a certified peer reviewer from Elsevier Academy. He has been serving as a reviewer for various SCIE and Scopus indexed journals from IEEE, ACM, Springer, Elsevier, Taylor & Francis, IET, Inderscience, World Scientific, IOS Press De-Gruyter and IGI Global. He has been published five edited books and currently editing one edited book from renowned international publishers. He got granted one patent, one industrial design and two copyrights. He had been awarded several outstanding achievement and outstanding service awards, and several best paper awards from international Conferences. He is a Fellow of IETE and member of IE, ISTE, CRSI, IACSIT, IAENG, SDIWC and senior member of SCIEI and SAISE and a chartered engineer certified by the Institution of Engineers (India). He has been served as the Program Committee member, Session Chair as well as reviewer of several National and International conferences conducted in India and abroad. His research areas include Image Cryptography, Image Processing, Machine Learning, Internet of Things Etc. He is currently focusing his research in the area of secure image communication using image cryptography.
ORCID: https://orcid.org/0000-0001-9047-3220
SCOPUS: https://www.scopus.com/authid/detail.uri?authorId=57200193505
Publon : https://publons.com/researcher/3344582/renjith-v-ravi



Assoc. Prof. Pavel Loskot

ZJU-UIUC Institute, China

Speech Title: Quantifying Uncertainty via Conformal Predictions

Abstract:In many scenarios, it is useful to understand how good the estimated or predicted values are, especially when the observations are very noisy. One option is to evaluate the parameter likelihood or even posterior distribution. This may, however, be problematic when more sophisticated machine learning methods such as deep neural networks are used. On the other hand, conformal prediction is a simple and model-agnostic method for obtaining credible or confidence bounds very likely containing the true values. The uncertainty bounds can be also used for other machine learning tasks such as measuring the model uncertainty or deciding how likely it is that the sample comes from a training distribution. In this talk, we will introduce conformal predictions, outline how they are related to quantile regression, then discuss their key statistical properties, and finally explain how conformal predictions can be used in machine learning.
Biography: Pavel Loskot joined the ZJU-UIUC Institute, Haining, China, in January 2021 as Associate Professor after 14 years being the Senior Lecturer at Swansea University in the UK. He obtained his PhD degree in Wireless Communications from the University of Alberta in Canada, and the MSc and BSc degrees in Radioelectronics and Biomedical Electronics, respectively, from the Czech Technical University of Prague in the Czech Republic. In the past 25 years, he was involved in numerous collaborative research and development projects, and also held a number of consultancy contracts with industry. Pavel Loskot is a Senior Member of the IEEE, a Fellow of the Higher Education Academy in the UK, and the Recognized Research Supervisor of the UK Council for Graduate Education. His current research interests focus on mathematical and probabilistic modeling, statistical signal processing and classical machine learning for multi-sensor data in biomedicine, computational molecular biology, and wireless communications.



Assoc. Prof. Bambang Leo Handoko

Bina Nusantara University of Indonesia, Indonesia

Speech Title: Enhancing Fraud Prevention: Exploring the Interplay of Internal Control System, Organizational Culture, Internal Audit Roles and Online Whistleblowing Mechanisms

Abstract: The purpose of this study is to determine whether internal control systems, organizational cultures, internal audit roles, and online whistleblowing systems significantly affect fraud prevention. The study uses quantitative methodology. Data was collected through an online questionnaire employing a Likert Scale. Structural Equation Modeling (SEM) with Partial Least Squares (PLS) was used as the analytical technique, using SmartPLS 4.0 software. The study included 95 employees in the position of sales supervisor at a retail company. The results indicate that internal control systems, organizational culture, internal audit roles, and online whistleblowing platforms all significantly contribute to fraud prevention.
Biography: Bambang Leo Handoko, academics and practitioners in the field of accounting, specialty in Auditing. Experience as auditor in public accounting firm, internal auditor for corporation and auditor for securing vital objects of National Police Headquarters. He is an expert in financial audit, cryptocurrencies, financial technology, stock market and e-business. He has had many international publications in reputable journals and proceeding with high index from many citation and acknowledgement from international researchers. He had won a lot of research grant from institution and government. Currently work as Subject Content Coordinator Auditing in Accounting Department, School of Accounting, Bina Nusantara University of Indonesia. He also technical committee in many reputable journal and conference. He is also reviewer for many of Elsevier Journal and professional member of world class reputable research organization, Association of Computer Machinery (ACM).



Assoc. Prof. Moirangthem Marjit Singh

North Eastern Regional Institute of Science & Technology (NERIST),India

Speech Title: Unsupervised Machine Learning based Techniques for NIDs

Abstract:This invited talk is targeted to explore various unsupervised Machine Learning (uML) based techniques that are developed for Network Intrusion Detection systems (NIDs). The supervised Machine Learning(sML) techniques are used to detect known attacks. To detect unknown and zero-day attacks, the uML techniques are used. The talk will investigate on some of the recent uML based techniques developed for NIDs indicating issues and challenges. The talk will delve into elucidating the research landscape where uML based techniques are applied for NIDs. At the end of the talk, appropriate problem and solution domains will also be discussed briefly.
Biography: Dr. Moirangthem Marjit Singh is currently an Associate Professor in Computer Science & Engineering Department at North Eastern Regional Institute of Science & Technology (NERIST), Arunachal Pradesh,India. He received B.Tech. and M.Tech. in Computer Science & Engineering degrees from NERIST and was awarded Gold Medal for securing top position in M.Tech. He received his PhD (Engineering) degree in computer Science and Engineering from University of Kalyani, West Bengal,India. He was the Head of the Department of Computer Science and Engineering, NERIST during 2018 to 2022. He was also the founder Honorary Joint Secretary of the Institution of Engineers, Arunachal Pradesh State Centre, India during 2019-2021. Dr. Marjit is a Fellow of IETE New Delhi, India and Fellow of the Institutions of Engineers (India) and the senior member IEEE, USA. Dr. Marjit was honoured with “Academic Excellence Award” by Taylor’s University, Malaysia in recognition of his outstanding academic performance on 13 September 2023 at Taylor’s University in association International Conference on Evolutionary Artificial Intelligence (ICEAI 2023). He was awarded the IE(I) Young Engineers Award 2014–2015 from the Computer Engineering Division, Institution of Engineers, India. He received the Best Paper Awards at international conferences namely the ICEAI 2023(held at Taylors’ University, Malaysia) and the ICACCT 2016, (held at APIIT, India) published by springer.
Dr. Marjit secured First Position in X and Second Position in XII Examinations conducted by CBSE, New Delhi, India, amongst the candidates sent up from Jawahar Navodaya Vidyalayas (JNVs) of North Eastern region states of India, in 1995 and 1997, respectively. He was awarded the Gold Medal for getting top position in the M.Tech.(CSE) at NERIST in 2010 He has more than 20 years of teaching and research experience. He has published several research papers in journals and conferences of repute. He has organized/associated with several technical conferences held in India and abroad. His research interests include mobile adhoc networks, wireless sensor networks, network security, AI, machine learning, and deep learning.


Dr. Chiagoziem Chima Ukwuoma

Oxford Brooks University, Sino-British Collaborative Education, Chengdu University of Technology, China

Speech Title: Towards the Explainability of the security concerns of Machine Learning models in Renewable Energy Production

Abstract: The integration of machine learning (ML) models in renewable energy production systems presents significant opportunities for optimization and efficiency. However, the adoption of these models also introduces complex security concerns that demand comprehensive understanding and mitigation strategies. Using some examples from his work on security concerns of machine learning models, Dr. Chima will illustrate the pivotal aspect of explainability in elucidating the potential vulnerabilities, threats, and challenges associated with ML models deployed in renewable energy production. Moreover, Dr. Chima will explore explainable AI (XAI) techniques as a means to enhance transparency and interpretability, thereby fostering trust and reliability in the decision-making processes of these models.
Biography: Dr. Chima received a Bachelor of Engineering degree in Mechanical Engineering (Automotive Technology) from the Federal University of Technology Owerri (FUTO) Nigeria and a Master of Science degree in Software Engineering from the University of Electronics Science and Technology of China respectively. He was awarded a Doctor of Philosophy degree in Software Engineering from the University of Electronics Science and Technology of China. Dr. Chima currently is a Senior Lecturer/Senior Researcher at Oxford Brooks University, Sino-British Collaborative Education, Chengdu University of Technology, China. He has published over 70 peer-reviewed papers in the field as well as served as an academic judge for the United States Academic Decathlon & Pentathlon (USAD & USAP) China, National Economics Challenge (NEC) 2019 till date. He is a recipient of the University of Electronic Science and Technology of China Full Scholarship for Masters Research Program, the Chinese Government Scholarship for Doctoral Research Program, and the Centre for West African Studies of UESTC Doctoral Research fund.


© Copyright 2017-2024 ACMLC All rights reserved.