Emerging Trends of Deep Learning in AI: Challenges and Methodologies
A special issue of AI (ISSN 2673-2688).
Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 9118
Special Issue Editors
2. Deep Learning & Data Science Division, Capacloud AI, Kolkata 711103, India
Interests: computational material science; computational mechanics; condensed matter physics; computer vision; deep learning; generative adversarial networks; object detection; brain–machine interface
Special Issue Information
Dear Colleagues,
The last decade has seen the increasingly important, even dominant, application of deep learning (DL) in the field of various applications. Conventional machine learning methods have been the focus of intense investigations for years; however, they have limited capabilities, are biased to dataset selection, and are faced with an overwhelming challenge to integrate large, heterogeneous data sources. On the other hand, recent advancements in deep learning architectures, coupled with high-performance computing, have demonstrated significant breakthroughs in dealing with complexities by radically changing research methodologies toward a data-oriented approach.
This Special Issue encourages authors, from academia and industry, to submit new research results about positioning and navigation models based on machine learning for complex systems. The Special Issue topics include but are not limited to the following:
- Artificial neural networks;
- Convolutional neural networks;
- Recurrent neural network;
- Deep reinforcement learning;
- Generative adversarial network;
- Attention-based transformer;
- Computer vision;
- Vision transformer;
- Object detection;
- Image segmentation;
- Brain–machine interface.
Dr. Arunabha Mohan Roy
Dr. Jayabrata Bhaduri
Guest Editors
Manuscript Submission Information
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Keywords
- deep learning
- machine learning
- artificial intelligence
- big data
- complex system
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