About the Journal
Artificial Intelligence, Machine Learning, and Autonomous Systems (AMLAS) is a peer-reviewed, open-access scholarly journal committed to advancing frontier research in intelligent computation, self-learning models, and autonomous technologies. AMLAS serves as an interdisciplinary platform uniting academics, industry experts, and policymakers to share original research and practical breakthroughs in AI-driven systems. The journal publishes cutting-edge work on machine learning algorithms, robotics, autonomous agents, adaptive control systems, and ethical AI deployment. It further emphasizes innovations in computer vision, reinforcement learning, human-machine collaboration, and context-aware automation. AMLAS values academic rigor, reproducibility, and societal relevance—delivering impactful content that catalyzes responsible tech adoption. With a global perspective and quarterly frequency, AMLAS maintains strict double-blind peer review standards and aims to cultivate a diverse body of knowledge shaping the future of autonomy. The journal stands at the convergence of intelligence, automation, and accountability in the age of transformative digital systems.
Journal Name: Artificial Intelligence, Machine Learning, and Autonomous Systems (AMLAS)
ISSN: 2991-0045
Impact Factor: 6.6 (By ResearchBib)
Journal Initials: AMLAS
Research Scope: Artificial Intelligence, Machine Learning, Robotics, Reinforcement Learning, Autonomous Vehicles, Human-Machine Interaction, AI Ethics, Intelligent Control Systems, Computer Vision
Publication Mode: Digital (On this Website)
Frequency: Annual (1 Volume per Year)
Launch Year: 2017
Review Mode: Double Blind Peer Review
Plagiarism Allowed: 10% (as per Turnitin)
Coverage: Worldwide
Language: English
Current Issue
We proudly present this issue of the Artificial Intelligence, Machine Learning, and Autonomous Systems (AMLAS) journal, showcasing peer-reviewed contributions from global researchers at the forefront of intelligent systems. This volume includes novel methodologies, applied models, and interdisciplinary insights across AI, ML, and automation. From adaptive algorithms to autonomous agents, each paper embodies the journal’s mission to accelerate responsible and impactful technological advancement. AMLAS continues to be a platform where critical scientific discourse meets real-world relevance—empowering developers, scholars, and engineers to shape the future of human-aligned, ethically grounded autonomous systems.
