Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.1
2.0
Journals
Automation
Special Issues
Intelligent Automation: Bridging Artificial Intelligence and Automation
share announcement

Intelligent Automation: Bridging Artificial Intelligence and Automation

A special issue of Automation (ISSN 2673-4052). This special issue belongs to the section "Intelligent Control and Machine Learning".

Deadline for manuscript submissions: 30 December 2025 | Viewed by 1374

Special Issue Editors

Prof. Dr. João Miguel da Costa Sousa

E-Mail Website
Guest Editor
Department of Mechanical Engineering, IDMEC, Instituto Superior Tecnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal
Interests: computational intelligence and fuzzy systems; intelligent data analysis; smart industry; applications in energy and healthcare
Special Issues, Collections and Topics in MDPI journals
Dr. Susana Vieira

E-Mail Website
Guest Editor
Departamento de Engenharia Mecânica, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal
Interests: artificial intelligence; soft computing; feature selection; fuzzy modelling; optimization; metaheuristics; computational intelligence; knowledge data discovery
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Intelligent automation (IA) represents the convergence of artificial intelligence and traditional automation technologies to create systems that can learn, adapt, and execute complex tasks with minimal human intervention.

IA systems incorporate feedback loops that continuously refine performance. Machine learning algorithms analyze outcome data—such as processing times or throughput—and adjust decision parameters over time. Their self-optimizing nature means that IA deployments grow increasingly efficient and accurate, unlocking higher levels of productivity and customer service. By adding machine learning, natural language processing (NLP), and computer vision to classic automation, IA can handle tasks that traditional systems cannot deal with.

Research in intelligent automation must also address ethical considerations around data privacy, ensure transparency in AI-driven decisions, and maintain robust changes in automated systems. When implemented thoughtfully, IA becomes a powerful bridge between AI’s cognitive strengths and automation’s precision, transforming manual processes into agile, intelligent workflows—laying the groundwork for truly autonomous systems. These cognitive capabilities allow for IA systems to learn from experience and adapt to new data. For example, predictive analytics applied to sensor data can forecasting equipment failure, enabling maintenance before breakdown.

Intelligent automation can be practical across many sectors. For instance, in manufacturing and logistics, artificial intelligence and robotic systems support predictive maintenance, quality control, or warehouse optimization. In healthcare, artificial intelligence, NLP and vision systems can accelerate diagnostics and patient triage.

These examples highlight IA’s potential impact in diverse fields and its relevance to complex systems engineering and research.

However, implementing IA poses significant challenges. Robust data governance (ensuring data quality, privacy, and security) is critical for trustworthy AI. Transparency and explainability are active research directions, since opaque models hinder auditing. Adaptability is also essential: IA systems must adjust to new conditions. Overall, IA represents a significant scientific advance in integrating AI with automation, promising efficiency gains while raising issues of data governance, model transparency, and ethical human–machine integration.

This Special Issue on intelligent automation welcome contributions across a large spectrum of areas of AI in automation, e.g., AI in control, AI in optimization, AI in manufacturing systems, AI in process automation and monitoring, AI in energy systems, AI in the healthcare industry, and any other area that may be compassed by the scope of the Special Issue topic.

Prof. Dr. João Miguel da Costa Sousa
Dr. Susana Vieira
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Automation is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • inteligent automation
  • artificial intelligence (AI)
  • AI in control
  • AI in optimization algorithms
  • AI in manufacturing systems
  • AI in process automation and monitoring
  • AI in energy systems
  • AI in the healthcare industry

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

26 pages, 2009 KB  
Article
Tool Wear Prediction Using Machine-Learning Models for Bone Drilling in Robotic Surgery
by Shilpa Pusuluri, Hemanth Satya Veer Damineni and Poolan Vivekananda Shanmuganathan
Automation 2025, 6(4), 59; https://doi.org/10.3390/automation6040059 - 16 Oct 2025
Viewed by 242
Abstract
Bone drilling is a widely encountered process in orthopedic surgeries and keyhole neuro surgeries. We are developing a sensor-integrated smart end-effector for drilling for robotic surgical applications. In manual surgeries, surgeons assess tool wear based on experience and force perception. In this work, [...] Read more.
Bone drilling is a widely encountered process in orthopedic surgeries and keyhole neuro surgeries. We are developing a sensor-integrated smart end-effector for drilling for robotic surgical applications. In manual surgeries, surgeons assess tool wear based on experience and force perception. In this work, we propose a machine-learning (ML)-based tool condition monitoring system based on multi-sensor data to preempt excessive tool wear during drilling in robotic surgery. Real-time data is acquired from the six-component force sensor of a collaborative arm along with the data from the temperature and multi-axis vibration sensor mounted on the bone specimen being drilled upon. Raw data from the sensors may have noises and outliers. Signal processing in the time- and frequency-domain are used for denoising as well as to obtain additional features to be derived from the raw sensory data. This paper addresses the challenging problem of identification of the most suitable ML algorithm and the most suitable features to be used as inputs to the algorithm. While dozens of features and innumerable machine learning and deep learning models are available, this paper addresses the problem of selecting the most relevant features, the most relevant AI models, and the optimal hyperparameters to be used in the AI model to provide accurate prediction on the tool condition. A unique framework is proposed for classifying tool wear that combines machine learning-based modeling with multi-sensor data. From the raw sensory data that contains only a handful of features, a number of additional features are derived using frequency-domain techniques and statistical measures. Using feature engineering, we arrived at a total of 60 features from time-domain, frequency-domain, and interaction-based metrics. Such additional features help in improving its predictive capabilities but make the training and prediction complicated and time-consuming. Using a sequence of techniques such as variance thresholding, correlation filtering, ANOVA F-test, and SHAP analysis, the number of features was reduced from 60 to the 4 features that will be most effective in real-time tool condition prediction. In contrast to previous studies that only examine a small number of machine learning models, our approach systematically evaluates a wide range of machine learning and deep learning architectures. The performances of 47 classical ML models and 6 deep learning (DL) architectures were analyzed using the set of the four features identified as most suitable. The Extra Trees Classifier (an ML model) and the one-dimensional Convolutional Neural Network (1D CNN) exhibited the best prediction accuracy among the models studied. Using real-time data, these models monitored the drilling tool condition in real-time to classify the tool wear into three categories of slight, moderate, and severe. Full article
(This article belongs to the Special Issue Intelligent Automation: Bridging Artificial Intelligence and Automation)
►▼ Show Figures

Figure 1

17 pages, 6432 KB  
Article
An AI-Enabled System for Automated Plant Detection and Site-Specific Fertilizer Application for Cotton Crops
by Arjun Chouriya, Peeyush Soni, Abhilash K. Chandel and Ajay Kumar Patel
Automation 2025, 6(4), 53; https://doi.org/10.3390/automation6040053 - 8 Oct 2025
Viewed by 418
Abstract
Typical fertilizer applicators are often restricted in performance due to non-uniformity in distribution, required labor and time intensiveness, high discharge rate, chemical input wastage, and fostering weed proliferation. To address this gap in production agriculture, an automated variable-rate fertilizer applicator was developed for [...] Read more.
Typical fertilizer applicators are often restricted in performance due to non-uniformity in distribution, required labor and time intensiveness, high discharge rate, chemical input wastage, and fostering weed proliferation. To address this gap in production agriculture, an automated variable-rate fertilizer applicator was developed for the cotton crop that is based on deep learning-initiated electronic control unit (ECU). The applicator comprises (a) plant recognition unit (PRU) to capture and predict presence (or absence) of cotton plants using the YOLOv7 recognition model deployed on-board Raspberry Pi microprocessor (Wale, UK), and relay decision to a microcontroller; (b) an ECU to control stepper motor of fertilizer metering unit as per received cotton-detection signal from the PRU; and (c) fertilizer metering unit that delivers precisely metered granular fertilizer to the targeted cotton plant when corresponding stepper motor is triggered by the microcontroller. The trials were conducted in the laboratory on a custom testbed using artificial cotton plants, with the camera positioned 0.21 m ahead of the discharge tube and 16 cm above the plants. The system was evaluated at forward speeds ranging from 0.2 to 1.0 km/h under lighting levels of 3000, 5000, and 7000 lux to simulate varying illumination conditions in the field. Precision, recall, F1-score, and mAP of the plant recognition model were determined as 1.00 at 0.669 confidence, 0.97 at 0.000 confidence, 0.87 at 0.151 confidence, and 0.906 at 0.5 confidence, respectively. The mean absolute percent error (MAPE) of 6.15% and 9.1%, and mean absolute deviation (MAD) of 0.81 g/plant and 1.20 g/plant, on application of urea and Diammonium Phosphate (DAP), were observed, respectively. The statistical analysis showed no significant effect of the forward speed of the conveying system on fertilizer application rate (p > 0.05), thereby offering a uniform application throughout, independent of the forward speed. The developed fertilizer applicator enhances precision in site-specific applications, minimizes fertilizer wastage, and reduces labor requirements. Eventually, this fertilizer applicator placed the fertilizer near targeted plants as per the recommended dosage. Full article
(This article belongs to the Special Issue Intelligent Automation: Bridging Artificial Intelligence and Automation)
►▼ Show Figures

Figure 1

12 pages, 2228 KB  
Article
Intelligent Fault Diagnosis of Gas Pressure Regulator Based on AE-GWO-SVM Algorithm
by Shunyuan Hu, Zhixiang Dai, Keqing Zhang, Jingjian Liu, Qing Wen, Yanhua Qiu and Weidong Li
Automation 2025, 6(3), 46; https://doi.org/10.3390/automation6030046 - 17 Sep 2025
Viewed by 415
Abstract
A pressure regulator is essential for pressure control in a gas transmission system. The traditional maintenance approaches for pressure regulators involve equipment disassembly that disrupts normal production. In response, this paper proposes a support vector machine (SVM) model improved by the grey wolf [...] Read more.
A pressure regulator is essential for pressure control in a gas transmission system. The traditional maintenance approaches for pressure regulators involve equipment disassembly that disrupts normal production. In response, this paper proposes a support vector machine (SVM) model improved by the grey wolf optimization algorithm (GWO) and autoencoder (AE), i.e., the AE-GWO-SVM model. It achieves intelligent fault diagnosis on pressure regulators based on operation data collected by the SCADA system. Firstly, a number of actual pressure regulator faults are counted, and it is found that internal and external leakages are key problems faced by pressure regulators. To address the limited fault data available in field practice, a dataset of pressure regulator faults is constructed using numerical simulation. Additionally, 18 new features are obtained through feature combination, and an improved distance evaluation method (IDE) is utilized to select highly correlated features as input for the machine learning model. Furthermore, an autoencoder (AE) is employed to overcome the interference from abnormal data, which significantly enhances the fault identification process of GWO-SVM. To verify the performance of the AE-GWO-SVM model, an experimental platform for pressure regulators is designed and constructed. Compared with conventional SVM, the accuracy of the AE-GWO-SVM model increases from 60.4% to 86.7%, indicating its strong diagnosis capability for pressure regulator faults. Full article
(This article belongs to the Special Issue Intelligent Automation: Bridging Artificial Intelligence and Automation)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop