About this Research Topic
Artificial intelligence (AI) technologies are rapidly evolving to address the industrial and manufacturing markets for a more reliable and sustainable future. Industrial AI has been applied to enhance performance, reduce energy consumption, optimize manufacturing and operations processes, mitigate business risk, and create more efficient products. Industrial AI can be integrated with existing digital technologies, to create a revolution in industrial intelligent solutions. Moreover, AI technologies can be effectively embedded in industrial products or business services to achieve faster service, lower cost, more reliable products, and profit. Thus, investment in industrial AI technologies and tools can save businesses lead to millions of dollars in savings.
In the Industry 4.0, computer vision enables vehicles to safely stay on the road safely and avoid accidents. In manufacturing, predictive and preventive maintenance based on machine learning, (including deep learning, neural networks, and reinforcement learning), enables the prediction of failures before they occur, saving repair cost and time and extending asset lifecycles. Other industrial AI applications include forecasting, decision-support systems, scheduling, optimized solutions, root cause analysis, and data analysis. Another aspect is the interaction of industrial AI with the surroundings, and cognitive technologies, such as language recognition, audio, cameras, natural language processing, virtual personal assistants, and asset identification. Modelling cyber-manufacturing systems are is another application of industrial AI, which combines machine learning and optimization algorithms. Healthcare management applications of industrial AI include patient data analysis, intelligent endoscopic devices, diagnostic systems, and the modeling of health equipment. In the aviation industry, data-driven AI has an essential function in providing insight into engineering systems provide insights into engineering systems through managing aircraft and analyzing flight data to provide safety assurance and avoid risks. While industrial AI presents opportunities to transform industry and the economy, some applications may threaten jobs and raise ethical and social issues. With the industry 4.0 paradigm, some challenges facing industrial AI in industry are data, speed, reliability, and interpretability. Some limitations of machines’ capability, along with issues of accuracy, validity, complexity, and interpretation of results, may affect the performance and reliability of intelligent industrial systems. The development of sophisticated industrial AI technologies are is necessary to deal with these challenges., and it offers significant benefits.
This special issue aims to share innovative theories, practices, and approaches, and integrate digital technologies with industrial AI techniques to reveal the issues associated with AI-driven industrial applications.
Potential topics include, but are not limited to, the following:
• Artificial intelligence and machine learning in industrial applications
• Emerging computational intelligence for complex industrial systems
• Methods and approaches to implement real-time soft computing for industrial AI applications
• Industrial AI-driven big-data analysis
• Hybrid methods, including evolutionary computation, optimization, machine learning, and AI algorithms
• IoT, blockchain, and networking in industrial AI
• Ethical computational intelligence for industrial automation (e.g., e-factories, smart production lines, and robotics)
• Smart industrial rule-based applications;
• Industrial AI-driven vision, inference, asset identification, audio, and natural language processing
• Modelling of large-scale systems and cyber-manufacturing systems with machine learning and optimization algorithms.
In the Industry 4.0, computer vision enables vehicles to safely stay on the road safely and avoid accidents. In manufacturing, predictive and preventive maintenance based on machine learning, (including deep learning, neural networks, and reinforcement learning), enables the prediction of failures before they occur, saving repair cost and time and extending asset lifecycles. Other industrial AI applications include forecasting, decision-support systems, scheduling, optimized solutions, root cause analysis, and data analysis. Another aspect is the interaction of industrial AI with the surroundings, and cognitive technologies, such as language recognition, audio, cameras, natural language processing, virtual personal assistants, and asset identification. Modelling cyber-manufacturing systems are is another application of industrial AI, which combines machine learning and optimization algorithms. Healthcare management applications of industrial AI include patient data analysis, intelligent endoscopic devices, diagnostic systems, and the modeling of health equipment. In the aviation industry, data-driven AI has an essential function in providing insight into engineering systems provide insights into engineering systems through managing aircraft and analyzing flight data to provide safety assurance and avoid risks. While industrial AI presents opportunities to transform industry and the economy, some applications may threaten jobs and raise ethical and social issues. With the industry 4.0 paradigm, some challenges facing industrial AI in industry are data, speed, reliability, and interpretability. Some limitations of machines’ capability, along with issues of accuracy, validity, complexity, and interpretation of results, may affect the performance and reliability of intelligent industrial systems. The development of sophisticated industrial AI technologies are is necessary to deal with these challenges., and it offers significant benefits.
This special issue aims to share innovative theories, practices, and approaches, and integrate digital technologies with industrial AI techniques to reveal the issues associated with AI-driven industrial applications.
Potential topics include, but are not limited to, the following:
• Artificial intelligence and machine learning in industrial applications
• Emerging computational intelligence for complex industrial systems
• Methods and approaches to implement real-time soft computing for industrial AI applications
• Industrial AI-driven big-data analysis
• Hybrid methods, including evolutionary computation, optimization, machine learning, and AI algorithms
• IoT, blockchain, and networking in industrial AI
• Ethical computational intelligence for industrial automation (e.g., e-factories, smart production lines, and robotics)
• Smart industrial rule-based applications;
• Industrial AI-driven vision, inference, asset identification, audio, and natural language processing
• Modelling of large-scale systems and cyber-manufacturing systems with machine learning and optimization algorithms.
Keywords: Computational intelligence, artificial intelligence, machine learning, optimization, IoT, deep neural network, real applications in engineering, cyber-manufacturing system, industrial automation
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