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About the Journal
Journal DOI: 10.33140/AMLAI
Editorial Panel View Editorial Board
Jiayan Xu , MS
Associate, The Grainger College of Engineering United States
Qiong Chen
Transportation Planning and Management Navigation College, Jimei University, China
Wenhui Zhu , PhD
Research Assistant, School of Computing and Augmented Intelligence Arizona State University, Tempe, United States
Jayesh Soni , PhD
PostDoc, Computer Science Florida International University, Miami, USA
Advances in Machine Learning & Artificial Intelligence journal aims to publish the most-advanced and rigorous scientific research related to the basic science and clinical aspects of Robotics, AI, and Mechatronics. Artificial Intelligence (AI) is that the branch of computer sciences that emphasizes the event of intelligent machines, thinking and dealing like humans. Artificial intelligence (AI) makes it possible for machines to go looking out from experience, accommodate new inputs, and perform human-like tasks. Artificial Intelligence can:
- Bring analytics to industries and domains where it’s currently underutilized.
- Improve the performance of existing analytic technologies, like computer vision and statistical analysis.
- Break down economic barriers, including language and translation barriers.
- Give us better vision, better understanding, better memory and far more
Submit Paper
Submit manuscript at www.opastpublishers.com/journal/advances-in-machine-learning-artificial-intelligence/manuscript-submission or send as an e-mail attachment to the Editorial Office at info@opastpublishers.com
Editorial Board Member Registration
If you feel like to be a part of Advances in Machine Learning & Artificial Intelligence as Editor, Please register at https://www.opastpublishers.com/journal/advances-in-machine-learning-artificial-intelligence/editor-registration (or) send an email to info@opastpublishers.com
Journal key Highlights
Recent Articles
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Covariance Eigenanalysis for Direction Finding with Analytic Ray-Traced Steering Vectors
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Convolutional Neural Networks with Fuzzy-Based Modelling: A Framework for Disease Detection in Cocoa Crops
Olaoluwa Adekoye, Abiodun Muyideen Mustapha* and Daberechi Okorie
