About the Journal

About the Journal

Insights into Intelligent Information Systems (I-IIS) is an interdisciplinary, peer-reviewed open-access journal dedicated to advancing the understanding, design, and application of intelligent systems that transform data into actionable knowledge. The journal aims to bridge the gap between theoretical advancements in artificial intelligence (AI), machine learning (ML), and data science with their practical implementation in real-world information systems. By fostering innovation and collaboration across academia, industry, and policymaking, I-IIS seeks to address emerging challenges in data-driven decision-making, automation, and adaptive technologies while emphasizing ethical, transparent, and human-centric solutions.

Scope

The journal welcomes original research, reviews, case studies, and methodological breakthroughs in the following areas:

1. Core Technologies

  • Machine learning, deep learning, and neural networks for intelligent systems.
  • Natural language processing, knowledge representation, and semantic technologies.
  • Big data analytics, data mining, and predictive modeling.
  • Explainable AI (XAI), trustworthy AI, and algorithmic fairness.
  • Human-AI collaboration, interactive systems, and adaptive interfaces.

2. Applications and Systems

  • Intelligent information systems in healthcare, finance, education, logistics, smart cities, and environmental science.
  • AI-driven decision support systems, recommendation engines, and automation tools.
  • IoT-enabled systems, cyber-physical systems, and edge computing for real-time intelligence.
  • Generative AI, multimodal systems, and intelligent content creation.

3. Sociotechnical Perspectives

  • Ethical implications, privacy, and security in intelligent systems.
  • Policy frameworks, governance, and regulatory challenges for AI adoption.
  • Human-computer interaction, user experience, and societal impact of intelligent technologies.

4. Emerging Trends

  • Integration of quantum computing, blockchain, or neuromorphic architectures with intelligent systems.
  • Sustainability-aware AI and green computing practices.
  • Cross-disciplinary innovations that redefine the future of information systems.