Keynote Speakers
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Professor Faïez Gargouri
Computer Science Professor at the Higher Institute of computer science and multimedia University of Sfax, Tunisia
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Professor Faïez Gargouri is a computer science Professor at the Higher Institute of computer science and multimedia at the University of Sfax, Tunisia (www.isimsf.rnu.tn), where he was the Head (from 2014 to 2020 and from 2007 to 2011). He was also the Director of the Multimedia, InfoRmation Systems and Advanced Computing Laboratory (www.miracl.rnu.tn) (from 2016 to 2020 and from 2011 to 2014). He was the Vice President of the University of Sfax, Tunisia (www.univ-sfax.tn), from 2020 to 2024 in charge of training offers, pedagogy and employability and it’s representative into the Scientific Council of the Tunisia Virtual University. He is still the President of the University of Sfax quality committee, coordinator of its PAQ-DGSU project and the founder of the PaqNews, a jornal specialized in the quality projects (DGSU and DGSU). He is also a scientific consultant representing Tunisia to the European Union in the field of information and communication technologies since September 2020. Faiez Gargouri is also involved in many international projects (Tempus, Erasmus, Prima, Twining, CMCU, PRC, UTIC…). Faïez Gargouri obtained his Master’s degree in Computer Science from the Paris 6 University (1990) and a PhD from the Paris 5 University (1995). In 2002, he obtained an Habilitation Universitaire en Informatique from the University of Tunis (Tunisia). His research interest focuses on different information systems’ fields, such as, Design, Quality Measurement, Verification, Data Warehousing, Multimedia, Knowledge Management and Ontology. He published more than 400 papers in journals and conferences and more than ten books (pedagogical or conference proceedings). He is member of the Scientific and Steering committees of major international conferences and jornals. He is namely one of the founding fathers of the JFO conference (French Workshops on Ontologies) and ASD (Conference on decisional systems). In 2024 he founded with other Tunisian all over the word colleagues, TunAISia: the Tunisia Chapter of the Association for Information Systems
(https://www.linkedin.com/company/tunaisia/). Faiez Gargouri is the founding of the scientific association AIG (Association Informatique de Gestion), President of the APPAD (Association du Patrimoine et des Proprietaires des Anciennes Demeures) association and he is a Chevalier de l’ordre national du mérite in the Education and Science fields (Décret numéro 2009 – 2195 du 16 juillet 2009).
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Professor Björn W. Schuller
Full Professor of Artificial Intelligence and the Head of GLAM at Imperial College London/UK
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Professor Björn W. Schuller received his diploma, doctoral degree, habilitation, and Adjunct Teaching Professor in Machine Intelligence and Signal Processing all in EE/IT from TUM in Munich/Germany where he has been called as Full Professor of Health Informatics. He is also Full Professor of Artificial Intelligence and the Head of GLAM at Imperial College London/UK, Full Professor and Chair of Embedded Intelligence for Health Care and Wellbeing at the University of Augsburg/Germany, co-founding CEO and current CSO of audEERING – an Audio Intelligence company based near Munich and in Berlin/Germany, amongst other Professorships and Affiliations. Previous stays include Full Professor at the University of Passau/Germany, Key Researcher at Joanneum Research in Graz/Austria, and the CNRS-LIMSI in Orsay/France. He is a Fellow of the IEEE and Golden Core Awardee of the IEEE Computer Society, Fellow of the BCS, Fellow of the ELLIS, Fellow of the ISCA, Fellow and President-Emeritus of the AAAC, Elected Full Member Sigma Xi, and Senior Member of the ACM. He (co-)authored 1,200+ publications (50,000+ citations, h- index=100+), is Field Chief Editor of Frontiers in Digital Health and was Editor in Chief of the IEEE Transactions on Affective Computing amongst manifold further commitments and service to the community. Currently, he was awarded IEEE Signal Processing Society Distinguished Lecturer 2024.
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Title: From R2-D2 to Samantha: Artificial Emotional Intelligence Evolved
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Professor Mohamed BAKHOUYA
International University of Rabat, Morocco
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Professor Mohamed BAKHOUYA is a professor of computer science at the International University of Rabat. He obtained his HDR from UHA-France in 2013 and his PhD from UTBM-France in 2005. He has more than ten years experiences in participating and working in sponsored ICT projects. He was EiC of IJARAS journal and also serves as a guest editor of a number of international journals, e.g.,ACM Trans. on Autonomous and Adaptive Systems, Product Development Journal, Concurrency and Computation: Practice and Experience, FGCS, and MICRO. He has published several papers in international journals, books, and conferences. His research interests include various aspects related to the design, validation, and implementation of distributed and adaptive systems, architectures, and protocols.
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Title: Blockchain-based IoT Platforms for smart Farming Systems
Abstract: Recent advances in pervasive technologies, such as wireless ad hoc networks and wearable sensor devices, allow the connection of everyday things to the Internet, commonly denoted as Internet of Things (IoT). IoT is seen as an enabler to the development of intelligent and context-aware services and applications. These services could dynamically react to the environment changes and users’ preferences. The main aim is to make users’ life more comfortable according to their locations, current requirements, and on-going activities. However, handling dynamic and frequent context changes is a difficult task without a real-time event/data acquisition and processing platform. Big data, WSN, and IoT technologies have been recently proposed for timely gathering and analysing information (i.e., data, events) streams. In this talk, we shed more light on the potential of these technologies for continuous and real-time data monitoring and processing in different real-case applications (e.g, Healthcare, energy efficient building, smart grid).
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Professor Hamed Taherdoost
University Canada West
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Professor. Hamed Taherdoost is faculty member of University Canada West. He holds PhD of Computer Science and Master of Information Security. He has over 20 years of experience in both industry and academic sectors. He has worked at international companies from Cyprus, the UK, Malta, Iran, Malaysia, and Canada and has been highly involved in development of several projects in different industries including oil and gas, healthcare, transportation, and information technology, holding positions as varied as Project Manager, R&D Manager, Tech Lead, and CTO. He has spent the last nine years helping start-ups to grow by implementing new projects and business lines. Apart from his experience in industry, he also has some achievements in academia. He has been a lecturer in three different parts of the world, Southeast Asia, the Middle East, and North America. Besides, he has organized and chaired numerous workshops, conferences, and conference sessions respectively, and has delivered speeches as chief guest and keynote speaker. Moreover, he is the editorial, reviewer, and advisory board member of some authentic peer-reviewed journals publishing with Taylor & Francis, Springer, Emerald, Elsevier, MDPI, EAI, & IGI Publishing, and Inderscience. Hamed has been an active multidisciplinary researcher and R&D specialist involved in several academic and industrial research projects. He has been working with researchers from various disciplines and has been actively engaged in different research studies. His research achievements also include winning several best paper awards and outstanding reviewer awards. His views on science and technology have been published in top-ranked scientific publishers such as Elsevier, Springer, Emerald, IEEE, IGI Global, Inderscience, Taylor and Francis and Dr. Hamed has published over 160 scientific articles in authentic peer-reviewed international journals and conference proceedings (h-index = 31; i10-index = 52; May 2022), ten book chapters as well as eight books in the field of technology and research methodology. He was the finalist for the Innovation in Teaching of Research Methodology Excellence Awards at ECRM, UK in 2022 and was nominated as the finalists in Southeast Asian Startup Awards in 2020 by Global Startup Awards and has been listed on the Stanford-Elsevier list of World’s top 2% of scientist by August 2021. He is a Certified Cyber Security Professional and Certified Graduate Technologist. He is senior member of IEEE, IAEEEE, IASED, and IEDRC and WGM of IFIP TC11 - Assurance and Information Security Management, and member of CSIAC), ACT-IAC, and AASHE. Currently, he is involved in several multidisciplinary research projects, including studying innovation in information technology, blockchain and cybersecurity, and technology acceptance.
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Title: Threat Intelligence and Machine Learning: A Powerful Combination for Cybersecurity
Abstract: Machine learning and threat intelligence combined provide a potent cybersecurity tool. While unstructured data can be analyzed with machine learning, threat intelligence entails gathering and evaluating data to foresee new assaults. Additionally, risk exposure assessment, alert management, raw data analysis, and cyber threat intelligence can all benefit from machine learning. It is imperative that each customer concentrates on the threat landscape that pertains to them, as the majority of the threat landscape is unimportant to most firms. By automatically generating a customized threat profile and making it easier for analysts to enrich that threat profile by providing them with AI-based natural language processing capabilities, the threat environment may be made more personalized. The question of whether open-source intelligence can be successfully incorporated into a practical method that reliably categorizes cyber threat intelligence can also be answered using machine learning. Machine learning and rule-based algorithms are used in the processing pipeline of the threat intelligence machine to convert unstructured data from open, technical sources into organized, useful intelligence. To strengthen cybersecurity, machine learning can also be utilized to visualize trends in CTI data. In summary, this speech discusses how threat intelligence and machine learning together can offer a strong basis for artificial intelligence (AI) solutions that can safeguard companies from online attacks.
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Professor Imad Zeroual
Moulay Ismail University, Morocco
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Prof Imad Zeroual is an Associate Professor of Computer Science at Moulay Ismail University in Meknes and is affiliated with the Business Intelligence and Systems Modeling research team at the Faculty of Sciences and Techniques in Errachidia. His research interests encompass artificial intelligence, with a particular focus on natural language processing, machine learning, information retrieval/extraction, and language teaching/learning. Prof. Zeroual has been invited as a guest speaker at various national and international research institutions, including KU Leuven (Katholieke Universiteit Leuven) and ICESCO (Islamic World Educational, Scientific and Cultural Organization). Additionally, he is the founding president of the Moroccan Center for Information Literacy (MorCIL) and a professional member of the International Association for Educators and Researchers (IAER) based in London, UK. He serves as an associate editor and reviewer for several esteemed journals within the artificial intelligence field. Furthermore, Prof. Zeroual has participated as an organizing and program committee member in numerous international conferences and symposiums, such as ICAISE, CoNLP4LTL, ACLing, and ICALP.
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Title: The Era of Large Language Models
Abstract: Large Language Models (LLMs) have emerged as one of the most significant advancements in artificial intelligence and natural language processing. These models are trained on vast datasets to understand and generate human-like text. They analyze patterns in language, enabling them to perform various tasks, such as text completion, translation, summarization, and even complex question-answering. One of the key features of LLMs is their ability to generate coherent and contextually relevant responses. By leveraging architectures like transformers, they can capture the nuances of language and understand context, which allows them to produce high-quality text that often mimics human writing. This capability opens up numerous applications across various fields, including customer service (through chatbots), content creation, education, and even programming assistance. However, LLMs are not without challenges. Issues such as bias in training data, the potential for generating misleading or harmful information, and concerns around privacy and security are critical considerations. Researchers and developers are actively working on strategies to mitigate these concerns, such as fine-tuning models, implementing better filtering mechanisms, and establishing ethical guidelines. The future of LLMs looks promising, with ongoing improvements in algorithms and computing power. As technology evolves, we can expect even more sophisticated applications that harness the power of LLMs, making them an integral part of our daily interactions with technology.
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