Professor Nikola K Kasabov is a Life Fellow of IEEE, Fellow of the Royal Society of New Zealand, Fellow of the INNS College of Fellows, DVF of the Royal Academy of Engineering UK. He has Doctor Honoris Causa from Obuda University, Budapest. He is the Founding Director of KEDRI and Professor Emeritus at the School of Engineering, Computing and Mathematical Sciences at Auckland University of Technology, New Zealand. He is also a Visiting Professor at IICT Bulgarian Academy of Sciences and Dalian University, China and Honorary professor at the University of Auckland. Kasabov is Past President of the Asia Pacific Neural Network Society (APNNS) and the International Neural Network Society (INNS). He has been a chair and a member of several technical committees of IEEE Computational Intelligence Society and Distinguished Lecturer of IEEE (2012-2014). He is Editor of Springer Handbook of Bio-Neuroinformatics, EiC of Springer Series of Bio-and Neuro-systems and co-EiC of the Springer journal Evolving Systems. He is Associate Editor of several other journals. Kasabov holds MSc in computer engineering and PhD in mathematics from TU Sofia, Bulgaria. His main research interests are in the areas of neural networks, intelligent informat ion systems, soft computing, bioinformatics, neuroinformatics. He has published more than 700 publications, highly cited internationally. He has extensive academic experience at various academic and research organisations in Europe and Asia, including: TU Sofia Bulgaria; University of Essex UK; University of Otago, NZ; Shanghai Jiao Tong University and CASIA Beijing; ETH/University of Zurich. Kasabov has received a number of awards, among them: INNS Ada Lovelace Meritorious Service Award; NN journal Best Paper Award for 2016; APNNA ‘Outstanding Achievements Award’; INNS Gabor Award for ‘Outstanding contributions to engineering applications of neural networks’; EU Marie Curie Fellowship; Bayer Science Innovation Award; APNNA Excellent Service Award; RSNZ Science and Technology Medal; 2015 AUT NZ Medal; Medal “Bacho Kiro” and Honorary Citizen of Pavlikeni, Bulgaria; Fellow and Honorary Member of the New Zealand-, the Bulgarian-, the Greek- and the Scottish Societies for Computer Science and Information Technologies. More information of Prof. Kasabov can be found in: https://academics.aut.ac.nz/nkasabov.
Prof. Ir. Dr. Nor Ashidi received the B. Eng. Degree in Electrical and Electronic Engineering with First Class Honors in 1999 and the PhD degree in Electronic Engineering (majoring in Image Processing and Artificial Neural Network) in 2003 from Universiti Sains Malaysia (USM). He is currently a Professor at the School of Electrical and Electronic Engineering, USM. His research interests include intelligent systems, image processing, machine learning, deep learning and medical image processing. As of September 2024, he has published more than 195, 238 and 317 articles indexed in WoS-ISI (H-index 35), SCOPUS (H-index 42) and Google Scholar (H-Index 51) respectively. Due to his outstanding achievement in research, he gained recognition, both national and internationally. He was recognized as top 2% researcher in category – Citation Impact in Single Calendar Years 2020, 2021, 2022, 2023 and 2024 by Stanford University USA - Elsevier and Top Research Scientist Malaysia (TRSM) by Akademi Sains Malaysia (ASM) in 2020.
She is a professor at the Faculty of Computing, Universiti Technologi Malaysia (UTM). She received her B.Sc. in Software Engineering from Sheffield Hallam University, UK, and her M.Sc. and Ph.D. in the field of Software Engineering from UTM. She has served as an academic administrator at UTM, since 2009 and currently she is Deputy Dean (Academic and Student Affairs) at Faculty of Computing, UTM. Her research areas are software engineering and computing education. Most of her research projects are focused to the domain of educational robotics, computational thinking, healthcare system and real‐time embedded system application.
Speech Title: Women in Software Engineering Education: Competency and Diversity
In 1990, Mary Shaw emphasized the importance of systematic methods, empirical evidence, and engineering principles in software engineering to enhance quality and reliability. These principles influence modern trends like Agile methodologies, microservices, cloud computing, and AI‐driven development, advancing the field towards disciplined practice. This presentation links Shawʹs ideas with these trends to underscore the critical importance of software engineering education in developing core competencies and preparing future engineers. Additionally, nurturing female talent in software engineering brings the benefits of diversity, driving innovation and addressing industry challenges.
Focusing on Malaysian female students in engineering and technology programs, this talk will use a case study from Universiti Teknologi Malaysiaʹs software engineering program to analyze trends in female participation. It will highlight the competencies of female students, providing insights into womenʹs representation in software engineering education. By showcasing their contributions and potential, this presentation aims to inspire and advocate for an inclusive and supportive environment for women in software engineering, ultimately contributing to a more diverse and innovative industry.
Prof. MOHAMED BAHAJ is a Full Professor in the Department of Mathematics and Computer Sciences from the University Hassan 1st Faculty of Sciences & Technologies Settat Morocco. He has published over 130 peer-reviewed papers. His research interests focus on Artificial Intelligence, Human-Computer Interaction, Information Systems, Deep Learning, Business Intelligence, Internet of Things, Big Data Analysis, Intelligent Systems, Ontologies Engineering, Scientific Computing.
He served as a reviewer at many reputed journals of Elsevier (Expert Systems with Applications Journal, SoftwareX Journal, Big Data Research Journal, Applied Soft Computing Journal, Knowledge-Based Systems Journal, Information Systems Journal, Information Sciences Journal, Computer & Security Journal, Journal of King Saud University - Computer and Information Sciences, Journal of Computer Science Review, Journal of Informatics in Medicine Unlocked).
He has supervised several PhD theses in Computer Sciences & in Applied Mathematics. He chaired many international conferences (Indexed Scopus, Web of Sciences, Springer). He also attended a series of workshops, seminars and discussion forums for Academic Development on Software and Research.
Speech Title: Artificial Intelligence: Deep Learning and Next-Gen Approaches for Conversational Agents
Today’s AI systems can interact with users, discern their requirements, understand their needs, map their preferences, learn patterns in human conversation, and recommend an appropriate line of action with minimal or no human intervention and coherent responses.
We aim in this presentation to foster open advanced existing conversational AI platforms and share the latest advancements in Chabot communication and deep Learning. Specifically to assess near-human capabilities in conversational agents.
Chatbots are based on the NLP tasks, which contain, Optical Character Recognition, Speech Recognition, Speech Segmentation, Text-To-Speech and also NLP applications Text Summarization, Machine Translation, Natural Language Understanding (NLU), Natural Language Generation (NLG), Question Answering, Text-To-Image generation.
We will cover topics ranging from concepts of variants of autoencoder architectures to basic innovations of GANs/ Attention Mechanisms and Transformers in the context of AI-Powered Chatbot Architecture and show their limitations and newer varieties.
We outline how the approaches from RNNs LSTMs, Encoder-Decoder Convolutional LSTM, GANs, DCGANs, SAGAN, Transformer (The Self-Attention Mechanism) can leverage and build Deep learning-based Conversational AI.
A federated or a hybrid approach leverages the strengths and mitigates the weaknesses of both the latest technologies in conversational agents and deep Learning tools
The presentation intends also to explore, create strategic value and improve performances (Environment sensing and Data acquisition, Data analysis for detection and prediction, Real-time analysis for decision support system, Use Case Development).
The architecture of these models are scalable and layered in such a way to provide necessary refined chatbots: Enhancing Agility and Adaptability.
This Presentation also focuses on assessing the latest programming technologies extensively used in Deep Learning models/AI-Powered Chatbot Architecture, which serve as substantial and pivotal criteria for evaluating diverse performance compliance needs.
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