About the Journal

Aim

The Journal of Artificial Intelligence, Information in Engineering and Management Science (JAIIEMS) is dedicated to advancing cutting-edge research, innovation, and practical applications at the intersection of artificial intelligence, information sciences, engineering disciplines, and management science.

With AI emerging as a transformative force across all sectors, JAIEMS aims to provide an interdisciplinary platform where scientists, engineers, managers, policymakers, and industry practitioners can share original ideas, theoretical developments, empirical research, and real-world case studies.

The journal’s mission is to bridge the gap between AI technology and its strategic deployment in engineering systems and organizational decision-making, ensuring knowledge dissemination that drives sustainable development, technological competitiveness, and societal benefit.

Scope

JAIEMS invites high-quality, original contributions—including research articles, review papers, technical notes, case studies, conceptual frameworks, and applied research—from a broad spectrum of disciplines. The journal’s coverage includes, but is not limited to, the following areas:

1. Artificial Intelligence Core Technologies

• Machine learning, deep learning, reinforcement learning
• Natural language processing (NLP), text mining, and conversational AI
• Computer vision, pattern recognition, and image/video analytics
• Generative AI, large language models (LLMs), and multimodal AI
• Explainable AI (XAI), interpretable models, and AI ethics
• AI for real-time, edge, and cloud computing environments

2. Information Science and Data Engineering

• Big data analytics, data warehousing, and knowledge discovery
• Data governance, security, and privacy in AI ecosystems
• Information retrieval systems and semantic web technologies
• Ontologies, linked data, and knowledge graphs
• Blockchain for secure data management and transactions
• Human–computer interaction (HCI) and usability engineering

3. AI in Engineering Disciplines

Mechanical & Manufacturing Engineering

• AI in design optimization, product lifecycle management, and predictive maintenance
• Robotics, autonomous systems, and industrial automation
• AI in additive manufacturing and digital twins

Civil & Environmental Engineering

• AI in structural health monitoring and smart infrastructure
• Geospatial analytics, remote sensing, and GIS-based decision-making
• AI in sustainable urban development and disaster resilience

Electrical & Electronics Engineering

• AI in power systems optimization, load forecasting, and smart grids
• AI for IoT-enabled devices, sensor networks, and embedded systems
• AI in control systems, signal processing, and telecommunications

Computer & Software Engineering

• AI-driven software engineering, testing, and quality assurance
• Cybersecurity and intrusion detection systems using AI
• AI in edge computing and distributed architectures

Chemical & Process Engineering

• AI in process control, simulation, and optimization
• Predictive analytics in chemical manufacturing and safety monitoring

Aerospace & Automotive Engineering

• AI in navigation, flight control, and autonomous vehicles
• Intelligent transportation systems and traffic management

4. AI in Management Science and Business Applications

• AI-enabled decision support systems (DSS) and business intelligence
• Supply chain analytics, logistics optimization, and inventory forecasting
• AI in finance, risk assessment, and fraud detection
• HR analytics, talent management, and organizational behavior modeling
• Marketing analytics, customer segmentation, and consumer behavior prediction
• Strategic management with AI-driven insights

5. Sector-Specific and Societal Applications

• AI in healthcare and biomedical engineering
• AI for agriculture, precision farming, and food supply chains
• AI in education, adaptive learning, and knowledge management
• AI for environmental monitoring, energy efficiency, and climate modeling
• AI in public policy, e-governance, and smart city solutions
• Ethics, fairness, and responsible AI adoption across sectors

6. Emerging Trends and Interdisciplinary Studies

• Hybrid human–AI collaboration models
• Socio-economic impact assessments of AI deployment
• AI integration with Industry 4.0 and 5.0 paradigms
• Multidisciplinary AI frameworks combining engineering, management, and social sciences
• Cross-border and cross-sector AI policy framework

• Development of AI standards and best practices for engineering and management applications
• Benchmark datasets, performance metrics, and evaluation methodologies
• International regulations, compliance, and AI governance policies
• Ethical, legal, and societal implications (ELSI) of AI adoption
• Intellectual property rights and AI-generated content management
• Safety certification and validation processes for AI-based systems

7. Standards, Benchmarking, and Regulatory Frameworks

• Development of AI standards and best practices for engineering and management applications
• Benchmark datasets, performance metrics, and evaluation methodologies
• International regulations, compliance, and AI governance policies
• Ethical, legal, and societal implications (ELSI) of AI adoption
• Intellectual property rights and AI-generated content management
• Safety certification and validation processes for AI-based systems