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Computers
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Artificial Intelligence in Control
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Artificial Intelligence in Control

A special issue of Computers (ISSN 2073-431X).

Deadline for manuscript submissions: 30 September 2025 | Viewed by 10129

Special Issue Editors

Dr. Mads Sloth Vinding

E-Mail Website
Guest Editor
Center of Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, DK-8200 Aarhus, Denmark
Interests: deep learning; optimal control; magnetic resonance imaging; pulse design
Dr. Ivan Maximov

E-Mail Website
Guest Editor
Department of Health and Functioning, Western Norway University of Applied Sciences, 5063 Bergen, Norway
Interests: diffusion MRI; machine learning; mathematical modelling
Dr. Christoph Stefan Aigner

E-Mail Website
Guest Editor
Physikalisch-Technische Bundesanstalt (PTB), 38116 Braunschweig and 10587 Berlin, Germany
Interests: medical engineering; biomedical signal processing; magnetic resonance imaging; imaging; biomedical engineering

Special Issue Information

Dear Colleagues,

Dynamic systems that can be controlled by humans or computers (or by a combination) exist in many situations ranging from, e.g., transportation, fabrication, and trade to medical imaging, engineering, and physics experiments. Depending on the application, control decisions typically depend on the dynamic system state, its objective, and variable external conditions, but also on control and state limits, and various costs. This is typically gathered in an overall performance/cost functional or an optimization problem, and then optimized with optimal control or other optimizers by computers. Depending on the performance ambition and the amount of information in play, the optimization problem can easily grow and become impractical, especially if the result is needed in real time, e.g., for autonomous cars, if external conditions may rapidly change, or in specialized situations tailored to hospital patients in for treatment, etc. In such cases, conventional optimization methods may not meet our expectations. Artificial intelligence has already proven useful and a potential candidate to steer some control systems, and it certainly solves many other tasks we hand to computers. This Special Issue welcomes original research and review articles on all aspects of artificial intelligence in control systems. Topics of interest include but are not limited to the following areas:

  • Medical imaging and treatment
  • (Quantum) optimal control
  • Control systems (PID, fuzzy logic, etc.).

Dr. Mads Sloth Vinding
Dr. Ivan Maximov
Dr. Christoph Stefan Aigner
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. Computers is an international peer-reviewed open access monthly 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 1800 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

  • deep learning
  • neural networks
  • artificial intelligence
  • (quantum) optimal control
  • (nuclear) magnetic resonance (imaging)
  • medical imaging
  • controls systems

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Published Papers (5 papers)

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Research

19 pages, 7837 KiB  
Article
Evaluating the Impact of Filtering Techniques on Deep Learning-Based Brain Tumour Segmentation
by Sofia Rosa, Verónica Vasconcelos and Pedro J. S. B. Caridade
Computers 2024, 13(9), 237; https://doi.org/10.3390/computers13090237 - 18 Sep 2024
Viewed by 752
Abstract
Gliomas are a common and aggressive kind of brain tumour that is difficult to diagnose due to their infiltrative development, variable clinical presentation, and complex behaviour, making them an important focus in neuro-oncology. Segmentation of brain tumour images is critical for improving diagnosis, [...] Read more.
Gliomas are a common and aggressive kind of brain tumour that is difficult to diagnose due to their infiltrative development, variable clinical presentation, and complex behaviour, making them an important focus in neuro-oncology. Segmentation of brain tumour images is critical for improving diagnosis, prognosis, and treatment options. Manually segmenting brain tumours is time-consuming and challenging. Automatic segmentation algorithms can significantly improve the accuracy and efficiency of tumour identification, thus improving treatment planning and outcomes. Deep learning-based segmentation tumours have shown significant advances in the last few years. This study evaluates the impact of four denoising filters, namely median, Gaussian, anisotropic diffusion, and bilateral, on tumour detection and segmentation. The U-Net architecture is applied for the segmentation of 3064 contrast-enhanced magnetic resonance images from 233 patients diagnosed with meningiomas, gliomas, and pituitary tumours. The results of this work demonstrate that bilateral filtering yields superior outcomes, proving to be a robust and computationally efficient approach in brain tumour segmentation. This method reduces the processing time by 12 epochs, which in turn contributes to lowering greenhouse gas emissions by optimizing computational resources and minimizing energy consumption. Full article
(This article belongs to the Special Issue Artificial Intelligence in Control)
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21 pages, 5377 KiB  
Article
Detecting Breast Tumors in Tomosynthesis Images Utilizing Deep Learning-Based Dynamic Ensemble Approach
by Loay Hassan, Adel Saleh, Vivek Kumar Singh, Domenec Puig and Mohamed Abdel-Nasser
Computers 2023, 12(11), 220; https://doi.org/10.3390/computers12110220 - 30 Oct 2023
Cited by 1 | Viewed by 2528
Abstract
Digital breast tomosynthesis (DBT) stands out as a highly robust screening technique capable of enhancing the rate at which breast cancer is detected. It also addresses certain limitations that are inherent to mammography. Nonetheless, the process of manually examining numerous DBT slices per [...] Read more.
Digital breast tomosynthesis (DBT) stands out as a highly robust screening technique capable of enhancing the rate at which breast cancer is detected. It also addresses certain limitations that are inherent to mammography. Nonetheless, the process of manually examining numerous DBT slices per case is notably time-intensive. To address this, computer-aided detection (CAD) systems based on deep learning have emerged, aiming to automatically identify breast tumors within DBT images. However, the current CAD systems are hindered by a variety of challenges. These challenges encompass the diversity observed in breast density, as well as the varied shapes, sizes, and locations of breast lesions. To counteract these limitations, we propose a novel method for detecting breast tumors within DBT images. This method relies on a potent dynamic ensemble technique, along with robust individual breast tumor detectors (IBTDs). The proposed dynamic ensemble technique utilizes a deep neural network to select the optimal IBTD for detecting breast tumors, based on the characteristics of the input DBT image. The developed individual breast tumor detectors hinge on resilient deep-learning architectures and inventive data augmentation methods. This study introduces two data augmentation strategies, namely channel replication and channel concatenation. These data augmentation methods are employed to surmount the scarcity of available data and to replicate diverse scenarios encompassing variations in breast density, as well as the shapes, sizes, and locations of breast lesions. This enhances the detection capabilities of each IBTD. The effectiveness of the proposed method is evaluated against two state-of-the-art ensemble techniques, namely non-maximum suppression (NMS) and weighted boxes fusion (WBF), finding that the proposed ensemble method achieves the best results with an F1-score of 84.96% when tested on a publicly accessible DBT dataset. When evaluated across different modalities such as breast mammography, the proposed method consistently attains superior tumor detection outcomes. Full article
(This article belongs to the Special Issue Artificial Intelligence in Control)
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21 pages, 8749 KiB  
Article
The SARS-CoV-2 Virus Detection with the Help of Artificial Intelligence (AI) and Monitoring the Disease Using Fractal Analysis
by Mihai-Virgil Nichita, Maria-Alexandra Paun, Vladimir-Alexandru Paun and Viorel-Puiu Paun
Computers 2023, 12(10), 213; https://doi.org/10.3390/computers12100213 - 21 Oct 2023
Viewed by 1745
Abstract
This paper introduces an AI model designed for the diagnosis and monitoring of the SARS-CoV-2 virus. The present artificial intelligence (AI) model founded on the machine learning concept was created for the identification/recognition, keeping under observation, and prediction of a patient’s clinical evaluation [...] Read more.
This paper introduces an AI model designed for the diagnosis and monitoring of the SARS-CoV-2 virus. The present artificial intelligence (AI) model founded on the machine learning concept was created for the identification/recognition, keeping under observation, and prediction of a patient’s clinical evaluation infected with the CoV-2 virus. The deep learning (DL)-initiated process (an AI subset) is punctually prepared to identify patterns and provide automated information to healthcare professionals. The AI algorithm is based on the fractal analysis of CT chest images, which is a practical guide to detecting the virus and establishing the degree of lung infection. CT pulmonary images, delivered by a free public source, were utilized for developing correct AI algorithms with the aim of COVID-19 virus observation/recognition, having access to coherent medical data, or not. The box-counting procedure was used with a predilection to determine the fractal parameters, the value of the fractal dimension, and the value of lacunarity. In the case of a confirmation, the analysed image is used as input data for a program responsible for measuring the degree of health impairment/damage using fractal analysis. The support of image scans with computer tomography assistance is solely the commencement part of a correctly established diagnostic. A profiled software framework has been used to perceive all the details collected. With the trained AI model, a maximum accuracy of 98.1% was obtained. This advanced procedure presents an important potential in the progress of an intricate medical solution to pulmonary disease evaluation. Full article
(This article belongs to the Special Issue Artificial Intelligence in Control)
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13 pages, 617 KiB  
Article
Comparison of Automated Machine Learning (AutoML) Tools for Epileptic Seizure Detection Using Electroencephalograms (EEG)
by Swetha Lenkala, Revathi Marry, Susmitha Reddy Gopovaram, Tahir Cetin Akinci and Oguzhan Topsakal
Computers 2023, 12(10), 197; https://doi.org/10.3390/computers12100197 - 29 Sep 2023
Cited by 6 | Viewed by 2565
Abstract
Epilepsy is a neurological disease characterized by recurrent seizures caused by abnormal electrical activity in the brain. One of the methods used to diagnose epilepsy is through electroencephalogram (EEG) analysis. EEG is a non-invasive medical test for quantifying electrical activity in the brain. [...] Read more.
Epilepsy is a neurological disease characterized by recurrent seizures caused by abnormal electrical activity in the brain. One of the methods used to diagnose epilepsy is through electroencephalogram (EEG) analysis. EEG is a non-invasive medical test for quantifying electrical activity in the brain. Applying machine learning (ML) to EEG data for epilepsy diagnosis has the potential to be more accurate and efficient. However, expert knowledge is required to set up the ML model with correct hyperparameters. Automated machine learning (AutoML) tools aim to make ML more accessible to non-experts and automate many ML processes to create a high-performing ML model. This article explores the use of automated machine learning (AutoML) tools for diagnosing epilepsy using electroencephalogram (EEG) data. The study compares the performance of three different AutoML tools, AutoGluon, Auto-Sklearn, and Amazon Sagemaker, on three different datasets from the UC Irvine ML Repository, Bonn EEG time series dataset, and Zenodo. Performance measures used for evaluation include accuracy, F1 score, recall, and precision. The results show that all three AutoML tools were able to generate high-performing ML models for the diagnosis of epilepsy. The generated ML models perform better when the training dataset is larger in size. Amazon Sagemaker and Auto-Sklearn performed better with smaller datasets. This is the first study to compare several AutoML tools and shows that AutoML tools can be utilized to create well-performing solutions for the diagnosis of epilepsy via processing hard-to-analyze EEG timeseries data. Full article
(This article belongs to the Special Issue Artificial Intelligence in Control)
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14 pages, 3883 KiB  
Article
Model and Fuzzy Controller Design Approaches for Stability of Modern Robot Manipulators
by Shabnom Mustary, Mohammod Abul Kashem, Mohammad Asaduzzaman Chowdhury and Jia Uddin
Computers 2023, 12(10), 190; https://doi.org/10.3390/computers12100190 - 23 Sep 2023
Cited by 1 | Viewed by 1756
Abstract
Robotics is a crucial technology of Industry 4.0 that offers a diverse array of applications in the industrial sector. However, the quality of a robot’s manipulator is contingent on its stability, which is a function of the manipulator’s parameters. In previous studies, stability [...] Read more.
Robotics is a crucial technology of Industry 4.0 that offers a diverse array of applications in the industrial sector. However, the quality of a robot’s manipulator is contingent on its stability, which is a function of the manipulator’s parameters. In previous studies, stability has been evaluated based on a small number of manipulator parameters; as a result, there is not much information about the integration/optimal arrangement/combination of manipulator parameters toward stability. Through Lagrangian mechanics and the consideration of multiple parameters, a mathematical model of a modern manipulator is developed in this study. In this mathematical model, motor acceleration, moment of inertia, and deflection are considered in order to assess the level of stability of the ABB Robot manipulator of six degrees of freedom. A novel mathematical approach to stability is developed in which stability is correlated with motor acceleration, moment of inertia, and deflection. In addition to this, fuzzy logic inference principles are employed to determine the status of stability. The numerical data of different manipulator parameters are verified using mathematical approaches. Results indicated that as motor acceleration increases, stability increases, while stability decreases as moment of inertia and deflection increase. It is anticipated that the implementation of these findings will increase industrial output. Full article
(This article belongs to the Special Issue Artificial Intelligence in Control)
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