AI and Digital Transformations: Future Trends and Challenges

• Samir Anter: Hassan II University of Casablanca, Morocco.
  Email: samir.anter@fstm.ac.ma

• Mly Hafid El Yazidi: Mohammed V university in Rabat, Morocco.
  Email: my-hafid.elyazidi@um5.ac.ma

• Ahmed Oussous, Hassan II University, Casablanca, Morocco
• Zakaria Boulouard, Hassan II University, Casablanca, Morocco
• Ahmed Zellou, Mohammed V University, Rabat, Morocco
• Karim Doumi, Mohammed V University, Rabat, Morocco
• Ali Choukri, Ibn Tofail V University, Kenitra, Morocco
• Mohamed Amnai, Ibn Tofail V University, Kenitra, Morocco
• Mohammed El ghmari, Sidi Mohammed Ben Abdellah University, Fes, Morocco
• Lahbib Ajallouda, Ibn Zohr University, Agadir, Morocco
• Jamal Bakkas, Cadi Ayyad University, Marrakech, Morocco

This session delves into the forefront of digital transformations, artificial intelligence (AI), and data science, offering insights into emerging trends and confronting challenges. It serves as a meeting ground for researchers, practitioners, and industry experts to exchange knowledge and experiences, exploring the profound impact of these domains on our present and future digital landscape.

Participants will gain:

• Insight into Innovations: Discover the latest advancements in AI, machine learning, and data science.
• Understanding of Digital Transformation: Learn from real-world case studies on how organizations are leveraging digital technologies for enhanced efficiency and competitiveness.
• Exploration of Big Data: Explore the tools and techniques driving big data analytics and its role in shaping AI and data science solutions.
• Insightful Discussions: Engage in discussions on key challenges and opportunities in AI, data science, and digital transformation initiatives.
• Foresight into Future Scenarios: Gain prospective insights into the future directions of AI, data science, and digital transformations.

This session offers a unique opportunity to comprehend the evolving landscape of AI and digital transformations, equipping participants with the knowledge and networking opportunities needed to navigate the complexities of tomorrow’s digital world.

Topics to be discussed in this special session include (but are not limited to) the following:

• Machine learning algorithms
• Big data analytics methods
• Deep neural networks
• Natural language processing and language modeling
• Recommendation systems
• Predictive analytics
• Knowledge extraction from unstructured data
• Privacy-preserving data mining techniques
• Social network analysis and complex network modeling
• Real-time data processing
• Model interpretability in AI
• Federated learning and collaborative learning
• Probabilistic machine learning
• Reinforcement learning
• Clustering and data grouping methods
• Anomaly detection in data
• AI applications in healthcare and medicine
• Personalized recommendation systems
• Sentiment analysis and opinion mining in social media
• Ethical AI and social responsibility

Researchers and practitioners are invited to submit papers for this special theme session on AI and Digital Transformations: Future Trends and Challenges on or before September 10^th, 2024 (Extended Deadline).

All submissions must be original and may not be under review by another publication.

All papers should be submitted

electronically through the easychair submission system as mentioned below:

The paper prior to submission should be checked for plagiarism from licensed plagiarism softwares like Turnitin/iAuthenticate etc. The similarity content should not exceed 20%.

INTERESTED AUTHORS SHOULD CONSULT THE CONFERENCE’S GUIDELINES FOR MANUSCRIPT SUBMISSIONS at https://icactce-conf.com/call-for-papers-2/.

All submitted papers will be reviewed on a double-blind, peer review basis.

All the accepted papers (after double blinded peer review) will be published within the ICACTCE’24 Proceedings (SCOPUS Indexed).

NOTE: While submitting paper in this special session, please specify AI and Digital Transformations: Future Trends and Challenges at the top (above paper title) of the first page of your paper.