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Aims & Scope

Aims & Scope

Frontiers in Emerging Artificial Intelligence and Machine Learning (FEAIML)

eISSN: 2978-3704 | International Peer-Reviewed Journal | Open Access

About the Journal

Frontiers in Emerging Artificial Intelligence and Machine Learning (FEAIML) is an international, peer-reviewed, open-access journal committed to publishing high-quality, impactful scholarly research that advances knowledge across foundational AI, deep learning algorithms, computer vision, natural language processing, intelligent robotics, and cross-disciplinary machine learning applications. The journal encourages interdisciplinary collaboration and welcomes novel research that bridges mathematical and theoretical rigor with real-world computational systems, contributing to technological innovation and ethical AI deployment in dynamic global environments.

Aim of the Journal

The primary aim of FEAIML is to provide an international, peer-reviewed platform for computer scientists, AI engineers, data specialists, academicians, and technology policy experts to publish original, ethical, and evidence-based research. The journal strives to:

  • Promote scientific excellence, algorithmic correctness, and methodological rigor in machine learning architectures and artificial intelligence systems.

  • Support technological innovation, automated decision-making frameworks, and scalable deep learning deployments across complex digital ecosystems.

  • Encourage interdisciplinary research connecting emerging AI paradigms with healthcare, robotics, cybersecurity, autonomous vehicles, and sustainable computing.

  • Contribute to the continuous advancement of artificial intelligence and machine learning literature for the benefit of the global scientific and industrial community.

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Scope of the Journal

The journal welcomes Original Research Articles, Review Articles, Applied Technical Case Studies, Computational & Algorithmic Models, Systematic Reviews, Short Communications, and Industrial Perspectives covering a broad range of topics. The scope outlined below is illustrative rather than exhaustive:

  • Foundational AI & Machine Learning: Supervised, Unsupervised, Semi-Supervised, Self-Supervised, and Reinforcement Learning, Mathematical Foundations of Machine Learning, Statistical Learning Theory, Optimization Algorithms, and Meta-Learning.

  • Deep Learning & Neural Architectures: Convolutional Neural Networks (CNNs), Recurrent & LSTM Networks, Transformer Models, Graph Neural Networks (GNNs), Generative Adversarial Networks (GANs), Diffusion Models, and Deep Reinforcement Learning.

  • Computer Vision & Visual Computing: Image Recognition and Classification, Object Detection and Segmentation, Video Analytics, 3D Computer Vision, Biometrics, Augmented/Virtual Reality, and Medical Image Analysis.

  • Natural Language Processing & Speech Analysis: Large Language Models (LLMs), Machine Translation, Sentiment Analysis, Dialogue Systems, Text Mining, Knowledge Graphs, Information Retrieval, and Automatic Speech Recognition (ASR).

  • Robotics, Autonomous Systems & Edge AI: Intelligent Control Systems, Multi-Agent Systems, Swarm Intelligence, Embedded AI, Edge Computing Optimization, Autonomous Vehicles, and Drone Navigation.

  • Trustworthy, Explainable & Responsible AI: Explainable AI (XAI), Fairness, Accountability, and Transparency (FAT), AI Safety, Adversarial Robustness, Privacy-Preserving Machine Learning, Federated Learning, and AI Ethics.

  • Cross-Disciplinary AI Applications: AI in Healthcare & Bioinformatics, Computational Finance (FinTech), Smart Cities, Cybersecurity Threat Detection, Quantum Machine Learning, Cyber-Physical Systems, and Industrial Automation.

Interdisciplinary Research Focus

FEAIML strongly encourages interdisciplinary research that integrates advanced machine learning with internet of things (IoT), cloud/edge infrastructure, software engineering, environmental sustainability, and industrial automation. Manuscripts offering cross-functional insights, benchmark dataset evaluations, open-source code repositories, and practical computational solutions are particularly welcomed.

Article Types Accepted

  • Original Research Articles: Empirical quantitative, experimental, computational, or theoretical machine learning studies.

  • Review Articles & Systematic Literature Reviews: Critical, comprehensive evaluations of state-of-the-art literature and emerging AI models.

  • Applied Case Studies & Computational/Conceptual Models: Real-world industrial implementations, system engineering evaluations, and theoretical frameworks.

  • Short Communications & Technical Notes: Concise reports on novel computational tools, benchmark dataset releases, or urgent perspectives.

  • Letters to the Editor & Perspectives: Expert opinions on emerging trends, ethical guidelines, and future directions in AI.

Open Access & APC Principles

FEAIML is dedicated to full Open Access publishing under Creative Commons licensing:

  • Immediate Global Access: All published research is immediately available to read, download, and share without subscription barriers.

  • Post-Acceptance APC: To cover open access production, copyediting, neural network layout rendering, Crossref DOI registration, and digital archiving, an Article Processing Charge (APC) applies only after a manuscript receives formal editorial acceptance.

  • Submission Freedom: No fees are requested during manuscript submission or peer review.

  • Editorial Independence: APC processing is completely separated from double-blind peer review decisions to ensure publication ethics.

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