Advances on Explainable Artificial Intelligence and related Mathematical Modeling
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Organizer(s): |
Name:
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Affiliation:
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Country:
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Massimiliano Ferrara
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University Mediterranea of Reggio Calabria
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Italy
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Introduction:
| | Machine Learning Models, Deep learning neural networks, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and Generative Adversarial Networks (GANs), have achieved remarkable success in various fields, such as computer vision and natural language processing and forecasting issues. Their effectiveness also extends to real-world applications in healthcare, business and autonomous vehicles, where they excel at analyzing and utilizing complex data and information.
Although they achieve remarkable success, the complicated architectures and inherent opacity of deep learning neural networks and machine learning supervised often pose a challenge to full mastery and limit their use in important applications, especially in interdisciplinary domains. To address this problem and broaden the scope of research, we are expanding the focus of our collection to include the successful applications of deep learning neural networks. With this expansion, we aim to highlight their efficiency in extracting valuable insights from complex data and information to improve understanding and utilization in different contexts. We sincerely invite researchers and experts in the field of Explainable AI to contribute their original research papers to this Special Session
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