Journal Objectives

This journal serves as an authoritative platform for researchers, policymakers, and industry professionals, integrating AI advancements with national and international research landscapes. The journal aims to:

Ø  Advance AI research methodologies across computational science, policy design, cybersecurity, and research analytics.

Ø  Promote AI-driven knowledge structuring for enhanced scientific data organization and retrieval.

Ø  Foster interdisciplinary discussions on AI’s societal, regulatory, and technological implications.

Ø  Bridge AI innovations with real-world applications in business, governance, cybersecurity, and research ecosystems.

Ø  Development the interaction of artificial intelligence with terminology, linguistics, and the creation of knowledge organization systems

The Scope of the journal

General points

This journal serves as a comprehensive platform for pioneering research in artificial intelligence (AI), covering theoretical, applied, and interdisciplinary advancements across information science, technology management, linguistics, cyberspace security, social information systems, and knowledge organization. The journal emphasizes cutting-edge AI methodologies, including machine learning, deep learning, natural language processing, and AI-driven automation, in alignment with the five main mission areas of IRANDOC.

Specific points

1. Artificial Intelligence in Information Technology Research

Exploring AI applications in IT systems, electronic commerce, and technology-driven management, information processing, decision-making, and business transformation. Relevant themes include:

Ø  AI in IT Management: Optimizing governance, risk assessment, and workflow automation using AI-driven predictive analytics and decision-support systems.

Ø  AI in Information Systems: Developing AI-powered data management frameworks, intelligent databases, and adaptive information retrieval for knowledge-based computing.

Ø  AI in Electronic Business: Enhancing automated trading, personalized recommendations, fraud detection, and AI-based customer behavior analytics in e-commerce ecosystems.

2. Artificial Intelligence in Information Science Research

Investigating AI’s role in knowledge representation, semantic processing, scientometric analysis, and computational linguistics, information structuring and knowledge storage and dissemination. Key areas include:

Ø  AI in Knowledge Organization Systems (KOSs): AI-enhanced ontology learning, automated taxonomy development, and intelligent information indexing.

Ø  AI in Computational Linguistics: Advancements in natural language understanding, machine translation, sentiment analysis, and AI-driven discourse analysis.

Ø  AI in Scientometrics and Information Analytics: Utilizing AI for academic impact assessment, citation prediction models, research trend analysis, and automated literature synthesis.

3. Artificial Intelligence in Society and Information Studies

Assessing AI’s impact on policy-making, ethical frameworks, societal information ecosystems, data privacy, algorithmic fairness, and AI-governance structures. Topics include:

Ø  AI in Information Policy: Examining AI-driven regulation frameworks, digital sovereignty, and automated governance models.

Ø  AI in Information Ethics and Law: Addressing bias mitigation, intellectual property challenges, and AI’s ethical responsibilities in automated decision-making.

Ø  AI in Social Information Studies: Investigating public discourse modeling, misinformation detection, and societal adaptation to AI-driven knowledge dissemination.

4. Artificial Intelligence in IT and Cyberspace Security

Exploring AI’s role in cybersecurity, threat mitigation, intelligent infrastructure management, AI-powered cyber defense and risk prediction models. Critical themes include:

Ø  AI in IT Planning and Design: Developing adaptive software architectures, automated system configurations, and AI-optimized network designs.

Ø  AI in Information Security: AI-driven anomaly detection, penetration testing automation, and threat intelligence modeling.

Ø  AI in Cyberspace Security: Implementing AI-assisted cryptographic protocols, machine learning-based intrusion detection, and autonomous security response mechanisms.

5. Artificial Intelligence in Science & Technology Information Systems

Addressing AI’s role in knowledge preservation, data management, research dissemination, scientific knowledge-sharing networks and automated research indexing systems. Core areas include:

Ø  AI in Information Registration & Provision Management: AI-powered metadata extraction, automated document indexing, and semantic search for digital archives.

Ø  AI in Information Organization & Analysis: Utilizing machine learning for predictive knowledge management, automated bibliometric analysis, and AI-assisted classification frameworks.

Ø  AI in Information Preservation & Dissemination: AI-enhanced digitization, long-term archiving solutions, and research accessibility improvements.