Journal of Human Intelligence and Artificial Innovation

Journal of Human Intelligence and Artificial Innovation (JHIAI) is an international, peer-reviewed, open-access journal dedicated to advancing research at the intersection of human intelligence and artificial intelligence. The journal aims to provide a platform for innovative studies that explore how human cognition and artificial systems interact, collaborate, and co-evolve in various domains. JHIAI publishes high-quality original research, systematic reviews, and scholarly articles that contribute to both theoretical and practical developments in intelligent systems. The journal emphasizes interdisciplinary approaches, integrating computer science, cognitive science, data science, and social perspectives to better understand the role of AI in enhancing human capabilities. The scope of the journal includes emerging topics such as human-centered artificial intelligence, human–AI interaction, and hybrid intelligence systems, where collaboration between humans and intelligent machines becomes increasingly essential in solving complex problems. JHIAI applies a rigorous double-blind peer-review process to ensure the quality, integrity, and academic contribution of all published articles.

Publication Frequency

Biannual

2 issues / year

Access Model

Open Access

Free for all readers

ISSN

—

Review Type

Double-Blind

Peer reviewed

Aims & Scope

Journal of Human Intelligence and Artificial Innovation publishes original research, systematic reviews, and scholarly reports that make significant contributions to the advancement of human-centered intelligence and artificial innovation, particularly in the design, development, and application of intelligent technologies.

Journal Scope

Artificial Intelligence

Research on intelligent systems, knowledge representation, reasoning, planning, and AI applications across domains including healthcare, education, and industry.

Machine Learning (ML)

Studies on supervised, unsupervised, and reinforcement learning algorithms, model optimization, generalization, and real-world predictive modeling applications.

Deep Learning (DL)

Advances in neural network architectures including CNNs, RNNs, transformers, and generative models applied to complex pattern recognition and data-driven tasks.

Natural Language Processing (NLP)

Computational approaches to language understanding, text generation, sentiment analysis, machine translation, and conversational AI systems.

Computer Vision (CV)

Methods for image and video analysis, object detection, scene understanding, facial recognition, and visual reasoning in automated and intelligent systems.

Human–Computer Interaction (HCI)

Investigation of user experience, interface design, human-centered AI, accessibility, and the cognitive and social dimensions of human interaction with intelligent systems.

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