Adaptive Decision Making Across Industries with AI and Machine Learning: Frameworks, Challenges, and Innovations
A special issue of Computers (ISSN 2073-431X).
Deadline for manuscript submissions: 31 October 2025 | Viewed by 32
Special Issue Editor
Special Issue Information
Dear Colleagues,
Artificial intelligence (AI) and machine learning (ML) are transforming decision-making processes across various industries, from manufacturing and supply chain management to healthcare and finance. By enabling data-driven insights, predictive analytics, and automation, AI-driven adaptive decision making enhances efficiency, reduces risks, and improves overall operational performance. However, challenges such as model reliability, ethical considerations, interpretability, and real-time adaptability remain critical in industrial applications. This Special Issue aims to explore cutting-edge AI and ML methodologies that drive adaptive decision making across industries. We invite original research articles, review papers, and case studies that address the advancements, challenges, and practical implementations of AI-driven decision systems. Submissions focused on optimization techniques, forecasting, decision making under uncertainty, real-time analytics, risk management, and ethical AI applications in industry are particularly encouraged.
Topics of interest include, but are not limited to, the following:
- AI-driven predictive analytics for industrial decision making;
- Optimization and reinforcement learning in industrial applications;
- Ethical considerations and fairness in AI-driven decisions;
- Real-time adaptive decision models in manufacturing and logistics;
- AI in supply chain resilience and risk management;
- Human-AI collaboration in industrial automation;
- Scalable AI solutions for dynamic industrial environments;
- Trust, explainability, and transparency in AI-based decision systems.
Dr. Samiul Islam
Guest Editor
Manuscript Submission Information
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Keywords
- artificial intelligence
- deep learning in Industry 5.0
- optimization
- reinforcement learning
- automation
- predictive analytics
- decision science
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