Elhadj / Nanne / Koubaa Artificial Intelligence and Its Practical Applications in the Digital Economy

Proceedings of the International Conference on Artificial Intelligence and its Practical Applications in the Age of Digital Transformation 2024, Volume 2
1. Auflage 2024
ISBN: 978-3-031-71429-0
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 304 Seiten

Reihe: Lecture Notes in Networks and Systems

Artificial Intelligence (AI) technologies hold immense promise for developing countries by offering innovative solutions to longstanding challenges. By leveraging AI in health care, education, economic development, infrastructure, and resource management, these countries can potentially leapfrog traditional development stages and improve the quality of life for their populations. However, it's essential to approach AI deployment with ethical considerations to ensure that the technology serves the best interests of these communities and thus to maximize the expected benefits.

The I2COMSAPP'24 "International Conference on Artificial Intelligence and its Applications in the Age of Digital Transformation" aims to provide an excellent opportunity to gather experts, researchers, practitioners, and innovators from various fields to explore the latest advancements, challenges, and practical implementations of artificial intelligence and machine learning (ML) technologies. Moreover, it aims to foster knowledge sharing, collaboration, and networking among professionals who are driving responsible and innovative use of AI and leveraging real-world applications for the betterment of society and industries.

Zielgruppe

Research

Autoren/Hrsg.

Elhadj, Yahya Mohamed

Nanne, Mohamedade Farouk

Koubaa, Anis

Meziane, Farid

Deriche, Mohamed

Inhaltsverzeichnis

The prediction of the wind speed and the solar irradiation in the Sahel using the Artificial neural networks case study site of Nouakchott.- Deep learning for smart grid application addressing data scarcity challenges and enhancing load forecasting efficiency.- Enhancing Advanced Time-Series Forecasting of Electric Energy Consumption based on RNN augmented with LSTM Techniques.