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Invited Speaker
Mohamed Bahaj, University Hassan 1st Faculty of Sciences & Technologies Settat Morocco, Morocco
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.