Applied Artificial Intelligence

Scalable Artificial Intelligence for Real World Intelligent Manufacturing Systems

Machine Learning and Deep Learning are reshaping the design, deployment, and evaluation of intelligent real-world systems across industry, healthcare, transportation, energy, education, agriculture, business, public administration, and smart infrastructure. Recent progress in computer vision, natural language processing, multimodal learning, anomaly detection, reinforcement learning, edge intelligence, and generative AI has enabled systems that can perceive complex environments, learn from heterogeneous data, support decisions, optimize processes, and adapt to changing operational conditions. However, many AI models still face barriers when transferred from controlled laboratory settings to practical environments. These barriers include data scarcity, domain shift, noisy sensors, limited annotation, model opacity, latency constraints, computational cost, safety requirements, and trust in automated decisions. This Article Collection seeks high quality contributions that advance the theory, methodology, engineering, and evaluation of scalable AI systems for real world applications.

This topic is important because applied AI is moving from isolated algorithmic development toward integrated, reliable, and deployable systems. Across modern sectors, organizations need AI models that are accurate, interpretable, robust, resource efficient, and compatible with operational workflows. In manufacturing, AI enables defect inspection, predictive maintenance, robotic perception, process monitoring, and quality assurance. In healthcare, AI supports diagnosis, medical image analysis, patient monitoring, rehabilitation robotics, and clinical decision support. In transportation and smart cities, AI contributes to anomaly prediction, traffic intelligence, safety monitoring, and infrastructure assessment. In education and administration, AI improves adaptive learning, workflow automation, document intelligence, and decision analytics. The next generation of applied AI research must therefore combine algorithmic novelty with system level validation, human oversight, ethical deployment, and measurable real-world impact.

This Article Collection welcomes original research, review articles, comparative evaluations, benchmark studies, case studies, and application focused contributions, subject to the article types accepted by the journal.

Topics of interest include, but are not limited to:

• Deep Learning and Machine Learning for real world decision systems
• Computer Vision for inspection, monitoring, robotics, healthcare, agriculture, and infrastructure
• Multimodal AI using image, text, sensor, audio, video, depth, thermal, and time series data anomaly detection, defect recognition, fault diagnosis, predictive maintenance, and risk prediction
• Edge AI, lightweight neural networks, model compression, quantization, and real time inference
• Transfer learning, domain adaptation, self-supervised learning, and few shot learning
• Explainable AI, trustworthy AI, uncertainty estimation, fairness, privacy, and human centered evaluation
• Reinforcement learning and optimization for control, planning, and automation
• Generative AI for data augmentation, simulation, digital twins, and intelligent content systems
• Rigorous evaluations of AI systems in operational, industrial, clinical, educational, administrative, and service environments

Keywords: Deep Learning, Computer Vision, Intelligent Manufacturing, Applied Artificial Intelligence, Real World AI Deployment

Manuscript Submissions:

Manuscript submission is open until 31st March 2027.

All manuscripts submitted to this Article Collection will undergo desk assessment and peer-review as part of our standard editorial process. Manuscripts which do not fall within the scope of the journal will be rejected.

This journal applies the Taylor & Francis' Open Data Sharing Policy. All authors will be required to make their data and materials supporting their results or analyses presented in their paper freely available.

To submit your papers to this Article Collection, please:

1. Check "Yes" for the question, "Are you submitting your paper for a specific special issue or article collection?"
2. Select the relevant Article Collection from the drop-down menu under the question, "Scalable Artificial Intelligence for Real World Intelligent Manufacturing Systems"
3. Enter your Discount Code on the Final Page (Services) in the “Apply Discount Code” box on the right-hand side of the screen, otherwise this will not be applied for their submission, and you would be subject to the journal’s Full APC Fee

We are able to offer a 10% Discount to all authors, and have a limited number of 20% Discount codes only available for early submissions. It should be noted that discount codes must be entered in at the point of submission as they cannot be applied retroactively, nor can these be combined as only the higher valued discount would be applicable.

Please contact Christopher Montgomery, Commissioning Editor regarding details on obtaining your discount codes, and with any other queries for this Article Collection.

Article Collection Guest Advisors

Dr. Riaz Waqar is an Associate Professor at Nanfang College Guangzhou. His research focuses on robotic vision, deep learning, intelligent manufacturing and smart healthcare. He has published several papers and has developed applied AI systems for defect detection, medical classification, patients’ emotion recognition, traffic intelligence, and smart industrial automation.