Researchers, consultants, progressive circle of readers from premier education bodies, Academicians and leading corporate etc.
Inspired by nature and with the ever growing abundance of data, computer science technologists, engineers and scientists devised several algorithms to make the computer intelligent as humans, to draw inferences, to reveal hidden patterns, to make decisions, to visualize the data, to recognize and distinguish, to listen, to construct visual models and to control unmanned systems with decision support, etc. i-manager’s Journal on Artificial Intelligence and Machine Learning aims in bringing the researches in the advancements of nature inspired and bionic algorithms, supervised and unsupervised machine learning, with applications in education, healthcare, aviation, unmanned vehicles, machine design, design and modelling, industrial automation, industrial safety, military and warfare applications, night vision and guidance, logistics, safety and security applications, disaster rescue, animation series, recommender systems in online business, personalized learning and career guidance systems, etc. This Journal will bridge the academicians, researchers and entrepreneurs with the corporates focused on product development.
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We follow stringent publication ethics, and plagiarized papers are not published, and are withdrawn at any stage of the publishing process. Plagiarism is not limited to the Results and Discussion sections; it can involve any part of the manuscript, including figures and tables, in which material is copied from another publication without attestation, reference, or permission.
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The Journal follows a double blind peer-review process. The submitted articles / research papers are reviewed by Professors/Educators in Engineering fields.
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This Journal is owned and managed by i-manager’s Educational Society. The Journal is self-supported by academic subscriptions and royalty from academic databases.
Computational Neural Networks and Artificial Neural Networks (ANN)
Deep Learning and Neural Networks with Keras
Supervised and Unsupervised Learning
Natural Language Processing, Understanding and Generation (NLP, NLU & NLG)
Bias-Variance Trade Off
Classification, Clustering, Regression and Association
Gradient Descent Algorithm
Support Vector Machines (SVM)
Evolutionary Algorithms and Algorithm Development for Improved AI and ML
Generative Adversarial Networks
Biomimetic and Evolutionary Techniques
Deep Learning with Biomimicry
Deep Learning with TensorFlow
AI Initiatives and Ethical Standards