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Application of Artificial Intelligence in Industry and Medicine

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A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Advanced Digital and Other Processes".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 20383

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Special Issue Editors

Prof. Dr. Yo-Ping Huang

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Guest Editor

1. Department of Electrical Engineering, National Penghu University of Science and Technology, Penghu 88046, Taiwan
2. Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan
3. Department of Computer Science and Information Engineering, National Taipei University, New Taipei City 23741, Taiwan
4. Department of Information and Communication Engineering, Chaoyang University of Technology, Taichung 41349, Taiwan
Interests: AI; IoT; healthcare systems design; AI in defect detection and classification

Prof. Dr. Yue-Shan Chang

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Guest Editor

Department of Computer Science and Information Engineering, National Taipei University, New Taipei City 23741, Taiwan
Interests: application of artificial intelligence in environmental pollution forecasting, big data analysis, ensemble learning, and information fusion

Prof. Dr. Hung-Chi Chu

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Guest Editor

Department of Information and Communication Engineering, Chaoyang University of Technology, Taichung 41349, Taiwan
Interests: computer network; internet of things; artificial intelligence

Special Issue Information

Dear Colleagues,

Through several waves of ups and downs in the past decades, artificial intelligence (AI) has evolved into a must-have new technology or tool in various domains. Furthermore, with the advent of powerful GPU, AI-related research or AI-based applications have sprouted in every corner of the world. Originating from pure network connectivity, the Internet of Things (IoT) has become a structure that can collect every piece of data from physical devices, daily activities, images, or videos into a data reservoir. As a result, tons of data are automatically generated into an enterprise database in a single day. This creates research opportunities on integrating AI, IoT, big data, and cloud or edge computing, to improve the quality of industrial production or medical service.

Applications of AI algorithms, models, or techniques play important roles and can be found everywhere, including widespread usage in industry and medical systems for tasks such as locating and detecting scratches or defects in product surface, printed circuit board manufacturing, monitoring rehabilitation progress for patients with Parkinson’s disease or stroke, autonomous moving and planning of service robots in healthcare, and short-term or long-term prediction of air quality in certain areas. Furthermore, AI can be integrated with other techniques, such as Internet of things, big data, cloud computing, and edge computing to become powerful tools for industry and medicine domains. Still, practical and successful applications of AI to meet the industry and medical requirements is a long journey and many challenges remain to be resolved. However, the use of AI is the key to success in industry and medicine fields and domains.

This Special Issue focuses on the applications of artificial intelligence in industry and medicine. Topics of interest for publication include but are not limited to the following:

Artificial intelligence applied to scratch/defect detection and classification (e.g., product surface scratch detection, defect types classification, transfer learning models for cross-platform applications)
Artificial intelligence applied to healthcare system design (e.g., rehabilitation systems design, fuzzy logic, neural networks, and multi-criteria decision making in evaluating exercises performance)
Artificial intelligence applied to robotics (e.g., route control, service robot control in warehouses, medical robots in measuring physiological data from people under self-quarantine or in quarantine hotel)
Artificial intelligence applied to drone flight control (e.g., multi-drone control, target identification, and recognition)
Artificial intelligence applied to autonomous cars control (e.g., driving planning and operation, smooth control)
Artificial intelligence applied to PM2.5 or PM10 prediction (e.g., short-term and long-term air quality prediction, alert)
Case study of AI or IoT in industry or medicine applications (preferred topics that have been successful implemented in industry or healthcare)

Prof. Dr. Yo-Ping Huang
Prof. Dr. Yue-Shan Chang
Prof. Dr. Hung-Chi Chu
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 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

AI in scratch/defect detection and classification
AI in healthcare systems design
AI in industrial or service robotics
AI in drone flight control and target recognition
AI in autonomous cars planning and operation
AI in PM2.5 or PM10 prediction
case study of AI or IoT in industry applications
case study of AI or IoT in medicine applications
other topics related to successful applications of AI

Published Papers (9 papers)

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Research

13 pages, 457 KiB

Open AccessArticle

Machine Learning Methods to Identify Predictors of Psychological Distress

by Yang Chen, Xiaomei Zhang, Lin Lu, Yinzhi Wang, Jiajia Liu, Lei Qin, Linglong Ye, Jianping Zhu, Ben-Chang Shia and Ming-Chih Chen

Processes 2022, 10(5), 1030; https://doi.org/10.3390/pr10051030 - 22 May 2022

Cited by 4 | Viewed by 2230

Abstract

As people pay ever-increasing attention to the problems caused by psychological stress, research on its influencing factors becomes crucial. This study analyzed the Health Information National Trends Survey (HINTS, Cycle 3 and Cycle 4) data (N = 5484) and assessed the outcomes using descriptive statistics, Chi-squared tests, and t-tests. Four machine learning algorithms were applied for modeling: logistic regression (linear), random forests (RF) (ensemble), the artificial neural network (ANN) (nonlinear), and gradient boosting (GB) (ensemble). The samples were randomly assigned to a 50% training set and a 50% validation set. Twenty-six preselected variables from the databases were used in the study as predictors, and the four models identified twenty predictors of psychological distress. The essence of this paper is a binary classification problem of judging whether an individual has psychological distress based on many different factors. Therefore, accuracy, precision, recall, F1-score, and AUC were used to evaluate the model performance. The logistic regression model selected predictors by forward selection, backward selection, and stepwise regression; variable importance values were used to identify predictors in the other three machine learning methods. Of the four machine learning models, the ANN exhibited the best predictive effect (AUC = 73.90%). A range of predictors of psychological distress was identified by combining the four machine learning models, which would help improve the performance of the existing mental health screening tools. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Industry and Medicine)

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16 pages, 3808 KiB

Open AccessArticle

Face Verification Based on Deep Learning for Person Tracking in Hazardous Goods Factories

by Xixian Huang, Xiongjun Zeng, Qingxiang Wu, Yu Lu, Xi Huang and Hua Zheng

Processes 2022, 10(2), 380; https://doi.org/10.3390/pr10020380 - 17 Feb 2022

Cited by 6 | Viewed by 1544

Abstract

Person tracking in hazardous goods factories can provide a significant improvement in security and safety. This article proposes a face verification model which can be used to record travel paths for staff or related persons in the factory. As face images are captured from the dynamic crowd at entrance–exit gates of workshops, face verification is challenged by polymorphic faces, poor illumination and changing of a person’s pose. To adapt to this situation, a new face verification model is proposed, which is composed of two advanced deep learning neural network models. Firstly, MTCNN (Multi-Task Cascaded Convolutional Neural Network) is used to construct a face detector. Based on the SphereFace-20 network model, we have reconstructed a convolutional network architecture with the embedded Batch Normalization elements and the optimized network parameters. The new model, which is called the MDCNN, is used to extract efficient face features. A set of specific processing algorithms is used in the model to process polymorphic face images. The multi-view faces and various types of face images are used to train the models. The experimental results have demonstrated that the proposed model outperforms most existing methods on benchmark datasets such as the Labeled Faces in the Wild (LFW) and YouTube Face (YTF) datasets without multi-view (accuracy is 99.38% and 94.30%, respectively) and the CNBC/FERET datasets with multi-view (accuracy is 94.69%). Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Industry and Medicine)

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17 pages, 6840 KiB

Open AccessArticle

Hybrid Sleep Stage Classification for Clinical Practices across Different Polysomnography Systems Using Frontal EEG

by Cheng-Hua Su, Li-Wei Ko, Jia-Chi Juang and Chung-Yao Hsu

Processes 2021, 9(12), 2265; https://doi.org/10.3390/pr9122265 - 16 Dec 2021

Cited by 2 | Viewed by 2390

Abstract

Automatic bio-signal processing and scoring have been a popular topic in recent years. This includes sleep stage classification, which is time-consuming when carried out by hand. Multiple sleep stage classification has been proposed in recent years. While effective, most of these processes are trained and validated against a singular set of data in uniformed pre-processing, whilst in a clinical environment, polysomnography (PSG) may come from different PSG systems that use different signal processing methods. In this study, we present a generalized sleep stage classification method that uses power spectra and entropy. To test its generality, we first trained our system using a uniform dataset and then validated it against another dataset with PSGs from different PSG systems. We found that the system achieved an accuracy of 0.80 and that it is highly consistent across most PSG records. A few samples of NREM3 sleep were classified poorly, and further inspection showed that these samples lost crucial NREM3 features due to aggressive filtering. This implies that the system’s effectiveness can be evaluated by human knowledge. Overall, our classification system shows consistent performance against PSG records that have been collected from different PSG systems, which gives it high potential in a clinical environment. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Industry and Medicine)

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12 pages, 31759 KiB

Open AccessArticle

Machine Learning Models for the Classification of CK2 Natural Products Inhibitors with Molecular Fingerprint Descriptors

by Yuting Liu, Mengzhou Bi, Xuewen Zhang, Na Zhang, Guohui Sun, Yue Zhou, Lijiao Zhao and Rugang Zhong

Processes 2021, 9(11), 2074; https://doi.org/10.3390/pr9112074 - 19 Nov 2021

Cited by 4 | Viewed by 1890

Abstract

Casein kinase 2 (CK2) is considered an important target for anti-cancer drugs. Given the structural diversity and broad spectrum of pharmaceutical activities of natural products, numerous studies have been performed to prove them as valuable sources of drugs. However, there has been little study relevant to identifying structural factors responsible for their inhibitory activity against CK2 with machine learning methods. In this study, classification studies were conducted on 115 natural products as CK2 inhibitors. Seven machine learning methods along with six molecular fingerprints were employed to develop qualitative classification models. The performances of all models were evaluated by cross-validation and test set. By taking predictive accuracy(CA), the area under receiver operating characteristic (AUC), and (MCC)as three performance indicators, the optimal models with high reliability and predictive ability were obtained, including the Extended Fingerprint-Logistic Regression model (CA = 0.859, AUC = 0.826, MCC = 0.520) for training test andPubChem fingerprint along with the artificial neural model (CA = 0.826, AUC = 0.933, MCC = 0.628) for test set. Meanwhile, the privileged substructures responsible for their inhibitory activity against CK2 were also identified through a combination of frequency analysis and information gain. The results are expected to provide useful information for the further utilization of natural products and the discovery of novel CK2 inhibitors. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Industry and Medicine)

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14 pages, 1779 KiB

Open AccessArticle

The Process and Platform for Predicting PM_2.5 Inhalation and Retention during Exercise

by Hui-Chin Wu, Ai-Lun Yang, Yue-Shan Chang, Yu-Hsiang Chang and Satheesh Abimannan

Processes 2021, 9(11), 2026; https://doi.org/10.3390/pr9112026 - 12 Nov 2021

Viewed by 1565

Abstract

In recent years, people have been increasingly concerned about air quality and pollution since a number of studies have proved that air pollution, especially PM_2.5 (particulate matter), can affect human health drastically. Though the research on air quality prediction has become a mainstream research field, most of the studies focused only on the prediction of urban air quality and pollution. These studies did not predict the actual impact of these pollutants on people. According to the researchers’ best knowledge, the amount of polluted air inhaled by people and the amount of polluted air that remains inside their body are two important factors that affect their health. In order to predict the quantity of PM_2.5 inhaled by people and what they have retained in their body, a process and a platform have been proposed in the current research work. In this research, the experimental process is as follows: (1) First, a personalized PM_2.5 sensor is designed and developed to sense the quantity of PM_2.5 around people. (2) Then, the Bruce protocol is applied to collect the information and calculate the relationship between heart rate and air intake under different activities. (3) The amount of PM_2.5 retained in the body is calculated in this step using the International Commission on Radiological Protection (ICRP) air particle retention formula. (4) Then, a cloud platform is designed to collect people’s heart rate under different activities and PM_2.5 values at respective times. (5) Finally, an APP is developed to show the daily intake of PM_2.5. The result reveals that the developed app can show a person’s daily PM_2.5 intake and retention in a specific population. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Industry and Medicine)

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21 pages, 3906 KiB

Open AccessFeature PaperArticle

From Human-Human to Human-Machine Cooperation in Manufacturing 4.0

by Lydia Habib, Marie-Pierre Pacaux-Lemoine, Quentin Berdal and Damien Trentesaux

Processes 2021, 9(11), 1910; https://doi.org/10.3390/pr9111910 - 26 Oct 2021

Cited by 7 | Viewed by 2360

Abstract

Humans are currently experiencing the fourth industrial revolution called Industry 4.0. This revolution came about with the arrival of new technologies that promise to change the way humans work and interact with each other and with machines. It aims to improve the cooperation between humans and machines for mutual enrichment. This would be done by leveraging human knowledge and experience, and by reactively balancing some complex or complicated tasks with intelligent systems. To achieve this objective, methodological approaches based on experimental studies should be followed to ensure a proper evaluation of human-machine system design choices. This paper proposes an experimental study based on a platform that uses an intelligent manufacturing system made up of mobile robots, autonomous shuttles using the principle of intelligent products, and manufacturing robots in the context of Manufacturing 4.0. Two experiments were conducted to evaluate the impact of teamwork human-machine cooperation, performance, and workload of the human operator. The results showed a lower level of participants’ assessment of time demand and physical demand in teamwork conditions. It was also found that the team working improves the subjective human operator Know-how-to-cooperate when controlling the autonomous shuttles. Moreover, the results showed that in addition to the work organization, other personal parameters, such as the frequency of playing video games could affect the performance and state of the human operator. They raised the importance of further analysis to determine cooperative patterns in a group of humans that can be adapted to improve human-machine cooperation. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Industry and Medicine)

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14 pages, 1637 KiB

Open AccessArticle

Estimating Relaxation Time and Fractionality Order Parameters in Fractional Non-Fourier Heat Conduction Using Conjugate Gradient Inverse Approach in Single and Three-Layer Skin Tissues

by Piran Goudarzi, Awatef Abidi, Seyed Abdollah Mansouri Mehryan, Mohammad Ghalambaz and Mikhail A. Sheremet

Processes 2021, 9(11), 1877; https://doi.org/10.3390/pr9111877 - 21 Oct 2021

Cited by 2 | Viewed by 1186

Abstract

In this work, the relaxation parameter (τ) and fractionality order (α) in the fractional single phase lag (FSPL) non-Fourier heat conduction model are estimated by employing the conjugate gradient inverse method (CGIM). Two different physics of skin tissue are chosen as the studied cases; single and three-layer skin tissues. Single-layer skin is exposed to laser radiation having the constant heat flux of Q_in. However, a heat pulse with constant temperature is imposed on the three-layer skin. The required inputs for the inverse problem in the fractional diffusion equation are chosen from the outcomes of the dual phase lag (DPL) theory. The governing equations are solved numerically by utilizing implicit approaches. The results of this study showed the efficiency of the CGIM to estimate the unknown parameters in the FSPL model. In fact, obtained numerical results of the CGIM are in excellent compatibility with the FSPL model. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Industry and Medicine)

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17 pages, 5156 KiB

Open AccessFeature PaperArticle

Imbalance Modelling for Defect Detection in Ceramic Substrate by Using Convolutional Neural Network

by Yo-Ping Huang, Chun-Ming Su, Haobijam Basanta and Yau-Liang Tsai

Processes 2021, 9(9), 1678; https://doi.org/10.3390/pr9091678 - 18 Sep 2021

Cited by 3 | Viewed by 1742

Abstract

The complexity of defect detection in a ceramic substrate causes interclass and intraclass imbalance problems. Identifying flaws in ceramic substrates has traditionally relied on aberrant material occurrences and characteristic quantities. However, defect substrates in ceramic are typically small and have a wide variety of defect distributions, thereby making defect detection more challenging and difficult. Thus, we propose a method for defect detection based on unsupervised learning and deep learning. First, the proposed method conducts K-means clustering for grouping instances according to their inherent complex characteristics. Second, the distribution of rarely occurring instances is balanced by using augmentation filters. Finally, a convolutional neural network is trained by using the balanced dataset. The effectiveness of the proposed method was validated by comparing the results with those of other methods. Experimental results show that the proposed method outperforms other methods. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Industry and Medicine)

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28 pages, 6720 KiB

Open AccessArticle

AI Landing for Sheet Metal-Based Drawer Box Defect Detection Using Deep Learning (ALDB-DL)

by Ruey-Kai Sheu, Lun-Chi Chen, Mayuresh Sunil Pardeshi, Kai-Chih Pai and Chia-Yu Chen

Processes 2021, 9(5), 768; https://doi.org/10.3390/pr9050768 - 27 Apr 2021

Cited by 5 | Viewed by 3162

Abstract

Sheet metal-based products serve as a major portion of the furniture market and maintain higher quality standards by being competitive. During industrial processes, while converting a sheet metal to an end product, new defects are observed and thus need to be identified carefully. Recent studies have shown scratches, bumps, and pollution/dust are identified, but orange peel defects present overall a new challenge. So our model identifies scratches, bumps, and dust by using computer vision algorithms, whereas orange peel defect detection with deep learning have a better performance. The goal of this paper was to resolve artificial intelligence (AI) as an AI landing challenge faced in identifying various kinds of sheet metal-based product defects by ALDB-DL process automation. Therefore, our system model consists of multiple cameras from two different angles to capture the defects of the sheet metal-based drawer box. The aim of this paper was to solve multiple defects detection as design and implementation of Industrial process integration with AI by Automated Optical Inspection (AOI) for sheet metal-based drawer box defect detection, stated as AI Landing for sheet metal-based Drawer Box defect detection using Deep Learning (ALDB-DL). Therefore, the scope was given as achieving higher accuracy using multi-camera-based image feature extraction using computer vision and deep learning algorithm for defect classification in AOI. We used SHapley Additive exPlanations (SHAP) values for pre-processing, LeNet with a (1 × 1) convolution filter, and a Global Average Pooling (GAP) Convolutional Neural Network (CNN) algorithm to achieve the best results. It has applications for sheet metal-based product industries with improvised quality control for edge and surface detection. The results were competitive as the precision, recall, and area under the curve were 1.00, 0.99, and 0.98, respectively. Successively, the discussion section presents a detailed insight view about the industrial functioning with ALDB-DL experience sharing. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Industry and Medicine)

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