Journal of Artificial Intelligence and Deep Learning aims to advance the field of artificial intelligence (AI) and deep learning by providing a leading platform for the dissemination of high-quality research and innovative applications. Our goal is to foster the development of new methodologies, theoretical insights, and practical solutions that push the boundaries of what AI and deep learning can achieve. We seek to facilitate interdisciplinary collaboration and drive forward the impact of these technologies across various domains.

All the manuscripts published by Journal of Artificial Intelligence and Deep Learning undergo rapid, quality and quick  review processing by eminent editorial and review team maintaining high standards and ethics of publishing.

The scholarly content published online will be freely available to every reader anywhere in the world to read, download, copy, reuse and distribute, provided that the original work is properly cited.

Authors may submit their valuable work either via the online submission form or via email to the editor’s office at submissions@asterpublications.com

Scope

Journal of Artificial Intelligence and Deep Learning encompasses a wide range of topics related to AI and deep learning, including but not limited to:

Fundamentals of AI and Deep Learning: Theoretical advancements, mathematical foundations, and algorithmic developments in AI and deep learning.

Neural Networks: Research on architectures, training techniques, and optimization methods for neural networks, including convolutional, recurrent, and transformer networks.

Machine Learning: Studies on supervised, unsupervised, and reinforcement learning algorithms, and their integration with deep learning techniques.

Natural Language Processing (NLP): Innovations in text analysis, language modeling, sentiment analysis, and conversational agents.

Computer Vision: Research on image and video analysis, object detection, recognition, and generative models.

AI Ethics and Safety: Exploration of ethical considerations, bias mitigation, transparency, and the societal impact of AI technologies.

Applications and Case Studies: Practical implementations of AI and deep learning in various fields such as healthcare, finance, robotics, autonomous systems, and more.

Emerging Trends: Cutting-edge topics such as explainable AI, federated learning, and novel architectures and training paradigms.

The journal welcomes original research articles, review papers, short communications, and case studies that contribute to the ongoing advancement of AI and deep learning. We aim to provide a forum for discussing both fundamental theories and applied research, with a focus on the practical implications and future directions of these rapidly evolving fields.

Key Topics

• Automated Reasoning
• Knowledge representation
• Automated planning and scheduling
• Machine learning
• Natural language processing
• Computer vision
• Robotics
• General intelligence, or strong AI
• Expert systems
• Neural networks
• Fuzzy logic systems
• Data Mining
• Game theory and Strategic planning.
• Constraint processing
• Heuristic search
• Intelligent interfaces
• Cognitive aspects of AI
• Intelligent robotics
• Multiagent systems
• Image and speech recognition

Journal of Artificial Intelligence and Deep Learning follows the Committee on Publication Ethics (COPE) Guidelines for issues of fraud, image manipulation, and duplicate publication.