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Processes
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Recent Advances in Machine Learning and Applications
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Recent Advances in Machine Learning and Applications

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 (30 June 2022) | Viewed by 77116

Special Issue Editor

Prof. Dr. Chien-Chih Wang

E-Mail Website
Guest Editor
Department of Industrial Engineering and Management, Ming Chi University of Technology, New Taipei City 24301, Taiwan
Interests: machine learning and AI applications; process quality control and engineering optimization; machine vision and inspection
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

With the rapid advancement of digital technologies of cyber-physical systems, the high-dimensionality, noise contamination, incompleteness, inconsistency, and massive amounts of data from the ubiquity of the Internet of Things (IoT) have posed significant challenges for academic and industrial applications. Artificial intelligence models based on machine learning (ML) are used in data analytics and process optimization, which play significant roles in many research directions. Since 2012, various machine learning technologies have been quick to develop and have proven to be of substantial practical value in a diversity of application domains. Such technology has solved numerous complex industrial problems that have existed in the AI community for many years, such as predictive maintenance, process optimization, task scheduling, quality improvement, supply and demand forecasting, defect detection, vibration signal recognition, and many more. Machine learning is one of the liveliest areas of discussion and is central in current process technological developments. To review recent advances in machine learning, this Special Issue on "Recent Advances in Machine Learning and Applications" will focus on publishing high-quality original research studies that address challenges in the broad area of optimization and artificial intelligence in-process applications. Topics include but are not limited to the following:

  • ML models and applications for predictive maintenance, quality control, and process optimization
  • ML models and applications for smart manufacturing process monitoring and control
  • ML models and application for intelligent manufacturing diagnostics, prognostics, and asset health management
  • ML models and applications for scheduling and supply chain management
  • ML models and applications for robotics and human–machine interaction
  • ML algorithms and approaches to handling big data, data imbalance, uncertainty, data fusion, etc.

Prof. Dr. Chien-Chih Wang
Guest Editor

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. Processes 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 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

  • Smart manufacturing process monitoring, quality control, and process optimization 
  • Intelligent manufacturing diagnostics, prognostics, and asset health management 
  • Intelligent scheduling and supply chain management 
  • Intelligent risk management and anomaly management detection 
  • Smart robotics and human–machine interaction 
  • Digital transformation through advances in artificial intelligence 
  • Case study and innovation-decision for traditional industry and small and medium enterprises 
  • ML algorithms and approaches to handling big data, data imbalance, uncertainty, data fusion, etc.

Published Papers (24 papers)

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Editorial

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4 pages, 173 KiB  
Editorial
Special Issue on Recent Advances in Machine Learning and Applications
by Chien-Chih Wang
Processes 2022, 10(11), 2411; https://doi.org/10.3390/pr10112411 - 16 Nov 2022
Viewed by 1195
Abstract
Digital technologies for cyber-physical systems are rapidly advancing, and the ubiquity of the Internet of Things (IoT) has created significant challenges for academic, industrial, and service applications due to high dimensionality, noise contamination, incompleteness, inconsistency, and massive amounts of data [...] Full article
(This article belongs to the Special Issue Recent Advances in Machine Learning and Applications)

Research

Jump to: Editorial

15 pages, 2982 KiB  
Article
An Intelligent Gender Classification System in the Era of Pandemic Chaos with Veiled Faces
by Jawad Rasheed, Sadaf Waziry, Shtwai Alsubai and Adnan M. Abu-Mahfouz
Processes 2022, 10(7), 1427; https://doi.org/10.3390/pr10071427 - 21 Jul 2022
Cited by 11 | Viewed by 2372
Abstract
In the world of chaos, the pandemic has driven individuals around the globe to wear face masks for preventing the virus’s transmission, however, this has made it difficult to determine the gender of the person wearing a mask. Gender information is part of [...] Read more.
In the world of chaos, the pandemic has driven individuals around the globe to wear face masks for preventing the virus’s transmission, however, this has made it difficult to determine the gender of the person wearing a mask. Gender information is part of soft biometrics, which provides extra information about a person’s identification, thus, identifying a gender based on a veiled face is among the urgent challenges that must be advocated for in the next decade. Therefore, this study exploited various pre-trained deep learning networks (DenseNet121, DenseNet169, ResNet50, ResNet101, Xception, InceptionV3, MobileNetV2, EfficientNetB0, and VGG16) to analyze the effect of the mask while identifying the gender using facial images of human beings. The study comprises two strategies. First, the experimental part involves the training of models using facial images with and without masks, while the second strategy considers images with masks only, to train the pre-trained models. Experimental results reveal that DenseNet121 and Xception networks performed well for both strategies. Besides this, the Inception network outperformed all others by attaining 98.75% accuracy for the first strategy, whereas EfficientNetB0 performed well for the second strategy by securing 97.27%. Moreover, results suggest that facemasks evidently impact the performance of state-of-the-art pre-trained networks for gender classification. Full article
(This article belongs to the Special Issue Recent Advances in Machine Learning and Applications)
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21 pages, 904 KiB  
Article
A Healthcare Quality Assessment Model Based on Outlier Detection Algorithm
by Nawaf Alharbe, Mohamed Ali Rakrouki and Abeer Aljohani
Processes 2022, 10(6), 1199; https://doi.org/10.3390/pr10061199 - 16 Jun 2022
Cited by 3 | Viewed by 1943
Abstract
With the extremely rapid growth of data in various industries, big data is gradually recognized and valued by people. Medical big data, which can best reflect the significance of big data value, has also received attention from various parties. In Saudi Arabia, healthcare [...] Read more.
With the extremely rapid growth of data in various industries, big data is gradually recognized and valued by people. Medical big data, which can best reflect the significance of big data value, has also received attention from various parties. In Saudi Arabia, healthcare quality assessment is mostly based on human experience and basic statistical methods. In this paper, we proposed a healthcare quality assessment model based on medical big data in a region of Saudi Arabia, which integrated traditional evaluation methods and machine learning based techniques. Healthcare data has been accurate and effective after noise processing, and the outliers could reflect certain medical quality information. An improved k-nearest neighbors (KNN) algorithm has been proposed and its time complexity have been reduced to be more suitable for big data processing. An outlier indicator has been established based on statistical methods and the improved KNN algorithm. Experimental results showed that the proposed approach has good potential for detecting hospitals with financial fraud and poor-quality medical care. Full article
(This article belongs to the Special Issue Recent Advances in Machine Learning and Applications)
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19 pages, 4892 KiB  
Article
Development of an Empathy-Centric Counseling Chatbot System Capable of Sentimental Dialogue Analysis
by Amy J. C. Trappey, Aislyn P. C. Lin, Kevin Y. K. Hsu, Charles V. Trappey and Kevin L. K. Tu
Processes 2022, 10(5), 930; https://doi.org/10.3390/pr10050930 - 08 May 2022
Cited by 11 | Viewed by 4809
Abstract
College students encounter various types of stresses in school due to schoolwork, personal relationships, health issues, and future career concerns. Some students are susceptible to the strikes of failures and are inexperienced with or fearful of dealing with setbacks. When these negative emotions [...] Read more.
College students encounter various types of stresses in school due to schoolwork, personal relationships, health issues, and future career concerns. Some students are susceptible to the strikes of failures and are inexperienced with or fearful of dealing with setbacks. When these negative emotions gradually accumulate without resolution, they can cause long-term negative effects on students’ physical and mental health. Some potential health problems include depression, anxiety, and disorders such as eating disorders. Universities commonly offer counseling services; however, the demand often exceeds the counseling capacities due to limited numbers of counsellors/psychologists. Thus, students may not receive immediate counseling or treatments. If students are not treated, some repercussions may lead to severe abnormal behavior and even suicide. In this study, combining immersive virtual reality (VR) technique with psychological knowledge base, we developed a VR empathy-centric counseling chatbot (VRECC) that can complementarily support troubled students when counsellors cannot provide immediate support. Through multi-turn (verbal or text) conversations with the chatbot, the system can demonstrate empathy and give therapist-like responses to the users. During the study, more than 120 students were required to complete a questionnaire and 34 subjects with an above-median stress level were randomly drawn for the VRECC experiment. We observed decreasing average stress level and psychological sensitivity scores among subjects after the experiment. Although the system did not yield improvement in life-impact scores (e.g., behavioral and physical impacts), the significant outcomes of lowering stress level and psychological sensitivity have given us a very positive outlook for continuing to integrate VR, AI sentimental natural language process, and counseling chatbot for advanced VRECC research in helping students improve their psychological well-being and life quality at schools. Full article
(This article belongs to the Special Issue Recent Advances in Machine Learning and Applications)
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17 pages, 1586 KiB  
Article
An Empirical Investigation to Understand the Issues of Distributed Software Testing amid COVID-19 Pandemic
by Abdullah Alharbi, Md Tarique Jamal Ansari, Wael Alosaimi, Hashem Alyami, Majid Alshammari, Alka Agrawal, Rajeev Kumar, Dhirendra Pandey and Raees Ahmad Khan
Processes 2022, 10(5), 838; https://doi.org/10.3390/pr10050838 - 24 Apr 2022
Cited by 9 | Viewed by 1789
Abstract
Generally, software developers make errors during the distributed software development process; therefore, software testing delay is a significant concern. Some of the software mistakes are minor, but others may be costly or harmful. Since things can still go wrong—individuals encounter mistakes from time [...] Read more.
Generally, software developers make errors during the distributed software development process; therefore, software testing delay is a significant concern. Some of the software mistakes are minor, but others may be costly or harmful. Since things can still go wrong—individuals encounter mistakes from time to time—there is a need to double-check any software we develop in a distributed environment. The current global pandemic, COVID-19, has exacerbated and generated new challenges for IT organizations. Many issues exist for distributed software testing that prevent the achievement of successful and timely risk reduction when several of the mechanisms on which testing is based are disrupted. The environment surrounding COVID-19 is quickly evolving on a daily basis. Moreover, the pandemic has exposed or helped to develop flaws in production systems, which obstruct software test completion. Although some of these issues were urgent and needed to be evaluated early during the distributed software development process, this paper attempts to capture the details that represent the current pandemic reality in the software testing process. We used a Fuzzy TOPSIS-based multiple-criteria decision-making approach to evaluate the distributed software testing challenges. The statistical findings show that data insecurity is the biggest challenge for successful distributed software testing. Full article
(This article belongs to the Special Issue Recent Advances in Machine Learning and Applications)
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15 pages, 3242 KiB  
Article
Predictive Classifier for Cardiovascular Disease Based on Stacking Model Fusion
by Jimin Liu, Xueyu Dong, Huiqi Zhao and Yinhua Tian
Processes 2022, 10(4), 749; https://doi.org/10.3390/pr10040749 - 13 Apr 2022
Cited by 13 | Viewed by 3335
Abstract
The etiology of cardiovascular disease is still an unsolved world problem, and high morbidity, disability, and mortality are the main characteristics of cardiovascular diseases. There is, therefore, a need for effective and rapid early prediction of likely outcomes in patients with cardiovascular disease [...] Read more.
The etiology of cardiovascular disease is still an unsolved world problem, and high morbidity, disability, and mortality are the main characteristics of cardiovascular diseases. There is, therefore, a need for effective and rapid early prediction of likely outcomes in patients with cardiovascular disease using artificial intelligence (AI) techniques. The Internet of Things (IoT) is becoming a catalyst for enhancing the capabilities of AI applications. Data are collected through IoT sensors and analyzed and predicted using machine learning (ML). Existing traditional ML models do not handle data inequities well and have relatively low model prediction accuracy. To address this problem, considering the data observation mechanism and training methods of different algorithms, this paper proposes an ensemble framework based on stacking model fusion, from Support Vector Machines (SVM), K-Nearest Neighbor (KNN), Logistic Regression (LR), Random Forest (RF), Extra Tree (ET), Gradient Boosting Decision Tree (GBDT), XGBoost, LightGBM, CatBoost, and Multilayer Perceptron (MLP) (10 classifiers to select the optimal base learners). In order to avoid the overfitting phenomenon generated by the base learners, we use the Logistic Regression (LR) simple linear classifier as the meta learner. We validated the proposed algorithm using a fused Heart Dataset from several UCI machine learning repositories and another publicly available Heart Attack Dataset, and compared it with 10 single classifier models. The experimental results show that the proposed stacking classifier outperforms other classifiers in terms of accuracy and applicability. Full article
(This article belongs to the Special Issue Recent Advances in Machine Learning and Applications)
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24 pages, 1646 KiB  
Article
Technology Trend Forecasting and Technology Opportunity Discovery Based on Text Mining: The Case of Refrigerated Container Technology
by Yansen Wang, Lijie Feng, Jinfeng Wang, Huadong Zhao and Peng Liu
Processes 2022, 10(3), 551; https://doi.org/10.3390/pr10030551 - 11 Mar 2022
Cited by 4 | Viewed by 2291
Abstract
How to detect potential useful technical information hidden in patents and scientific papers is crucial for technology trend forecasting and potential research opportunities. Using the text mining method to extract hidden technical information is helpful in assisting strategic decision-making and predicting the tendency [...] Read more.
How to detect potential useful technical information hidden in patents and scientific papers is crucial for technology trend forecasting and potential research opportunities. Using the text mining method to extract hidden technical information is helpful in assisting strategic decision-making and predicting the tendency of technological development. This study proposes a framework to combine patent analysis and scientific paper analysis to predict technology trends and discover technology opportunities. By mining the hidden technical information, we compared an analysis of patents and scientific papers to reveal the technological development trajectory and future development trends and discover potential technological innovation opportunities. First, we extracted the knowledge contained in patents and scientific papers by text mining. Then, we cleaned and coded the data through natural language processing. We used the Latent Dirichlet Allocation topic model to cluster topics and list the multi-dimensional hierarchical structure diagram. We compared and analyzed results, combined them with expert knowledge, and drew a technology roadmap to predict future development trends and discover technological opportunities. Finally, we used refrigerated container technology as an example for validation. Results showed that the framework proposed in this study can directly and comprehensively display the development trend of technology and discover potential technological innovation opportunities to verify the effectiveness of the framework. The proposed method also provides an effective reference and inspiration for future research. Full article
(This article belongs to the Special Issue Recent Advances in Machine Learning and Applications)
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16 pages, 1561 KiB  
Article
An Empirical Performance Analysis of the Speak Correct Computerized Interface
by Kamal Jambi, Hassanin Al-Barhamtoshy, Wajdi Al-Jedaibi, Mohsen Rashwan and Sherif Abdou
Processes 2022, 10(3), 487; https://doi.org/10.3390/pr10030487 - 28 Feb 2022
Cited by 1 | Viewed by 1920
Abstract
The way in which people speak reveals a lot about where they are from, where they were raised, and also where they have recently lived. When communicating in a foreign language or second language, accents from one’s first language are likely to emerge, [...] Read more.
The way in which people speak reveals a lot about where they are from, where they were raised, and also where they have recently lived. When communicating in a foreign language or second language, accents from one’s first language are likely to emerge, giving an individual a ‘strange’ accent. This is a great and challenging problem. Not particularly, because it is a part of one’s personality that they do not have to give up. It is only challenging when pronunciation causes a disruption in communication between an individual and the individuals with whom they are speaking. Making oneself understandable is the goal of perfecting English pronunciations. Many people require their pronunciation to be perfect, such as those individuals working in the healthcare industry, where it is rather critical that each term be read precisely. Speak Correct offers each of its users a service that assists them with any English pronunciation concerns that may arise. Some of the pronunciation improvements will only apply to a specific customer’s dictionary; however, in some cases, the modifications can be applied to the standard dictionary as well, benefiting our whole customer base. Speak Correct is a computerized linguist interface that can assist its users in many different places around the world with their English pronunciation issues due to Saudi or Egyptian accents. In this study, the authors carry out an empirical investigation of the Speak Correct computerized interface to assess its performance. The results of this research reveal that Speak Correct is highly effective at delivering pronunciation correction. Full article
(This article belongs to the Special Issue Recent Advances in Machine Learning and Applications)
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24 pages, 4016 KiB  
Article
A Combined Text-Based and Metadata-Based Deep-Learning Framework for the Detection of Spam Accounts on the Social Media Platform Twitter
by Atheer S. Alhassun and Murad A. Rassam
Processes 2022, 10(3), 439; https://doi.org/10.3390/pr10030439 - 22 Feb 2022
Cited by 18 | Viewed by 3115
Abstract
Social networks have become an integral part of our daily lives. With their rapid growth, our communication using these networks has only increased as well. Twitter is one of the most popular networks in the Middle East. Similar to other social media platforms, [...] Read more.
Social networks have become an integral part of our daily lives. With their rapid growth, our communication using these networks has only increased as well. Twitter is one of the most popular networks in the Middle East. Similar to other social media platforms, Twitter is vulnerable to spam accounts spreading malicious content. Arab countries are among the most targeted, possibly due to the lack of effective technologies that support the Arabic language. In addition, as a complex language, Arabic has extensive grammar rules and many dialects that present challenges when extracting text data. Innovative methods to combat spam on Twitter have been the subject of many current studies. This paper addressed the issue of detecting spam accounts in Arabic on Twitter by collecting an Arabic dataset that would be suitable for spam detection. The dataset contained data from premium features by using Twitter premium API. Data labeling was conducted by flagging suspended accounts. A combined framework was proposed based on deep-learning methods with several advantages, including more accurate, faster results while demanding less computational resources. Two types of data were used, text-based data with a convolution neural networks (CNN) model and metadata with a simple neural networks model. The output of the two models combined identified accounts as spam or not spam. The results showed that the proposed framework achieved an accuracy of 94.27% with our combined model using premium feature data, and it outperformed the best models tested thus far in the literature. Full article
(This article belongs to the Special Issue Recent Advances in Machine Learning and Applications)
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11 pages, 541 KiB  
Article
Forecasting the 10.7-cm Solar Radio Flux Using Deep CNN-LSTM Neural Networks
by Junqi Luo, Liucun Zhu, Kunlun Zhang, Chenglong Zhao and Zeqi Liu
Processes 2022, 10(2), 262; https://doi.org/10.3390/pr10020262 - 28 Jan 2022
Cited by 5 | Viewed by 2357
Abstract
Predicting the time series of 10.7-cm solar radio flux is a challenging task because of its daily variability. This paper proposed a non-linear method, a convolutional and recurrent neural network combined model to achieve end-to-end F10.7 forecasts. The network consists of a one-dimensional [...] Read more.
Predicting the time series of 10.7-cm solar radio flux is a challenging task because of its daily variability. This paper proposed a non-linear method, a convolutional and recurrent neural network combined model to achieve end-to-end F10.7 forecasts. The network consists of a one-dimensional convolutional neural network and a long short-term memory network. The CNN network extracted features from F10.7 original data, then trained the feature signals in the long short-term memory network, and outputted the predicted values. The F10.7 daily data during 2003–2014 are used for the testing set. The mean absolute percentage error values of approximately 2.04%, 2.78%, and 4.66% for 1-day, 3-day, and 7-day forecasts, respectively. The statistical results of evaluating the root mean square error, spearman correlation coefficient shows a superior effect as a whole for the 1–27 days forecast, compared with the ordinary single neural network and combination models. Full article
(This article belongs to the Special Issue Recent Advances in Machine Learning and Applications)
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16 pages, 776 KiB  
Article
Semi-Natural and Spontaneous Speech Recognition Using Deep Neural Networks with Hybrid Features Unification
by Ammar Amjad, Lal Khan and Hsien-Tsung Chang
Processes 2021, 9(12), 2286; https://doi.org/10.3390/pr9122286 - 20 Dec 2021
Cited by 11 | Viewed by 2677
Abstract
Recently, identifying speech emotions in a spontaneous database has been a complex and demanding study area. This research presents an entirely new approach for recognizing semi-natural and spontaneous speech emotions with multiple feature fusion and deep neural networks (DNN). A proposed framework extracts [...] Read more.
Recently, identifying speech emotions in a spontaneous database has been a complex and demanding study area. This research presents an entirely new approach for recognizing semi-natural and spontaneous speech emotions with multiple feature fusion and deep neural networks (DNN). A proposed framework extracts the most discriminative features from hybrid acoustic feature sets. However, these feature sets may contain duplicate and irrelevant information, leading to inadequate emotional identification. Therefore, an support vector machine (SVM) algorithm is utilized to identify the most discriminative audio feature map after obtaining the relevant features learned by the fusion approach. We investigated our approach utilizing the eNTERFACE05 and BAUM-1s benchmark databases and observed a significant identification accuracy of 76% for a speaker-independent experiment with SVM and 59% accuracy with, respectively. Furthermore, experiments on the eNTERFACE05 and BAUM-1s dataset indicate that the suggested framework outperformed current state-of-the-art techniques on the semi-natural and spontaneous datasets. Full article
(This article belongs to the Special Issue Recent Advances in Machine Learning and Applications)
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17 pages, 4758 KiB  
Article
Photoplethysmography Analysis with Duffing–Holmes Self-Synchronization Dynamic Errors and 1D CNN-Based Classifier for Upper Extremity Vascular Disease Screening
by Pi-Yun Chen, Zheng-Lin Sun, Jian-Xing Wu, Ching-Chou Pai, Chien-Ming Li, Chia-Hung Lin and Neng-Sheng Pai
Processes 2021, 9(11), 2093; https://doi.org/10.3390/pr9112093 - 22 Nov 2021
Cited by 5 | Viewed by 1536
Abstract
Common upper limb peripheral artery diseases (PADs) are atherosclerosis, embolic diseases, and systemic diseases, which are often asymptomatic, and the narrowed arteries (stenosis) will gradually reduce blood flow in the right or left upper limbs. Upper extremity vascular disease (UEVD) and atherosclerosis are [...] Read more.
Common upper limb peripheral artery diseases (PADs) are atherosclerosis, embolic diseases, and systemic diseases, which are often asymptomatic, and the narrowed arteries (stenosis) will gradually reduce blood flow in the right or left upper limbs. Upper extremity vascular disease (UEVD) and atherosclerosis are high-risk PADs for patients with Type 2 diabetes or with both diabetes and end-stage renal disease. For early UEVD detection, a fingertip-based, toe-based, or wrist-based photoplethysmography (PPG) tool is a simple and noninvasive measurement system for vital sign monitoring and healthcare applications. Based on time-domain PPG analysis, a Duffing–Holmes system with a master system and a slave system is used to extract self-synchronization dynamic errors, which can track the differences in PPG morphology (in amplitudes (systolic peak) and time delay (systolic peak to diastolic peak)) between healthy subjects and PAD patients. In the preliminary analysis, the self-synchronization dynamic errors can be used to evaluate risk levels based on the reflection index (RI), which includes normal condition, lower PAD, and higher PAD. Then, a one-dimensional convolutional neural network is established as a multilayer classifier for automatic UEVD screening. The experimental results indicated that the self-synchronization dynamic errors have a positive correlation with the RI (R2 = 0.6694). The K-fold cross-validation is used to verify the performance of the proposed classifier with recall (%), precision (%), accuracy (%), and F1 score. Full article
(This article belongs to the Special Issue Recent Advances in Machine Learning and Applications)
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14 pages, 5576 KiB  
Article
A Comparative Study of Linear, Random Forest and AdaBoost Regressions for Modeling Non-Traditional Machining
by G. Shanmugasundar, M. Vanitha, Robert Čep, Vikas Kumar, Kanak Kalita and M. Ramachandran
Processes 2021, 9(11), 2015; https://doi.org/10.3390/pr9112015 - 11 Nov 2021
Cited by 33 | Viewed by 2953
Abstract
Non-traditional machining (NTM) has gained significant attention in the last decade due to its ability to machine conventionally hard-to-machine materials. However, NTMs suffer from several disadvantages such as higher initial cost, lower material removal rate, more power consumption, etc. NTMs involve several process [...] Read more.
Non-traditional machining (NTM) has gained significant attention in the last decade due to its ability to machine conventionally hard-to-machine materials. However, NTMs suffer from several disadvantages such as higher initial cost, lower material removal rate, more power consumption, etc. NTMs involve several process parameters, the appropriate tweaking of which is necessary to obtain economical and suitable results. However, the costly and time-consuming nature of the NTMs makes it a tedious and expensive task to manually investigate the appropriate process parameters. The NTM process parameters and responses are often not linearly related and thus, conventional statistical tools might not be enough to derive functional knowledge. Thus, in this paper, three popular machine learning (ML) methods (viz. linear regression, random forest regression and AdaBoost regression) are employed to develop predictive models for NTM processes. By considering two high-fidelity datasets from the literature on electro-discharge machining and wire electro-discharge machining, case studies are shown in the paper for the effectiveness of the ML methods. Linear regression is observed to be insufficient in accurately mapping the complex relationship between the process parameters and responses. Both random forest regression and AdaBoost regression are found to be suitable for predictive modelling of NTMs. However, AdaBoost regression is recommended as it is found to be insensitive to the number of regressors and thus is more readily deployable. Full article
(This article belongs to the Special Issue Recent Advances in Machine Learning and Applications)
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19 pages, 4376 KiB  
Article
Attention-Based STL-BiLSTM Network to Forecast Tourist Arrival
by Mohd Adil, Jei-Zheng Wu, Ripon K. Chakrabortty, Ahmad Alahmadi, Mohd Faizan Ansari and Michael J. Ryan
Processes 2021, 9(10), 1759; https://doi.org/10.3390/pr9101759 - 30 Sep 2021
Cited by 14 | Viewed by 3247
Abstract
Tourism makes a significant contribution to the economy of almost every country, so accurate demand forecasting can help in better planning for the government and a range of stakeholders involved in the tourism industry and can aid economic sustainability. Machine learning models, and [...] Read more.
Tourism makes a significant contribution to the economy of almost every country, so accurate demand forecasting can help in better planning for the government and a range of stakeholders involved in the tourism industry and can aid economic sustainability. Machine learning models, and in particular, deep neural networks, can perform better than traditional forecasting models which depend mainly on past observations (e.g., past data) to forecast future tourist arrivals. However, search intensities indices (SII) indicators have recently been included as a forecasting model, which significantly enhances forecasting accuracy. In this study, we propose a bidirectional long short-term memory (BiLSTM) neural network to forecast the arrival of tourists along with SII indicators. The proposed BiLSTM network can remember information from left to right and right to left, which further adds more context for forecasting in memory as compared to a simple long short- term memory (LSTM) network that can remember information only from left to right. A seasonal and trend decomposition using the Loess (STL) approach is utilized to decompose time series tourist arrival data suggested by previous studies. The resultant approach, called STL-BiLSTM, decomposes time series into trend, seasonality, and residual. The trend provides the general direction of the overall data. Seasonality is a regular and predictable pattern which re-occurs at fixed time intervals, and residual is a random fluctuation that is something which cannot be forecast. The proposed BiLSTM network achieves better accuracy than the other methods considered under the current study. Full article
(This article belongs to the Special Issue Recent Advances in Machine Learning and Applications)
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13 pages, 839 KiB  
Article
Imbalanced Fault Diagnosis of Rotating Machinery Based on Deep Generative Adversarial Networks with Gradient Penalty
by Junqi Luo, Liucun Zhu, Quanfang Li, Daopeng Liu and Mingyou Chen
Processes 2021, 9(10), 1751; https://doi.org/10.3390/pr9101751 - 30 Sep 2021
Cited by 13 | Viewed by 1992
Abstract
In practical industrial application, the fault samples collected from rotating machinery are frequently unbalanced, which will create difficulties when it comes to diagnosis. Besides, the variation of working conditions and noise factors will further reduce the diagnosis’s accuracy and stability. Considering the above [...] Read more.
In practical industrial application, the fault samples collected from rotating machinery are frequently unbalanced, which will create difficulties when it comes to diagnosis. Besides, the variation of working conditions and noise factors will further reduce the diagnosis’s accuracy and stability. Considering the above problems, we established a model based on deep Wasserstein generative adversarial network with gradient penalty (DWGANGP). In this model, the unbalanced fault data set will first be trained by the sample generation network to generate synthetic samples, which will be used to restore the balance. A one-dimensional convolutional neural network with a specific structure is then used as the fault diagnosis network to classify the reconstructed equilibrium samples. The experimental results show that the proposed sample generation network can generate high-quality synthetic samples under highly imbalanced data, and the diagnostic network has a fast training convergence. Compared to the combination methods of support vector machines, back propagation neural network and deep belief network, our method has a 74% average accuracy in all unbalanced experimental conditions, which has 64%, 69% and 87% averages leading, respectively. Full article
(This article belongs to the Special Issue Recent Advances in Machine Learning and Applications)
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23 pages, 3241 KiB  
Article
Interterminal Truck Routing Optimization Using Cooperative Multiagent Deep Reinforcement Learning
by Taufik Nur Adi, Hyerim Bae and Yelita Anggiane Iskandar
Processes 2021, 9(10), 1728; https://doi.org/10.3390/pr9101728 - 27 Sep 2021
Cited by 7 | Viewed by 2689
Abstract
Many ports worldwide continue to expand their capacity by developing a multiterminal system to catch up with the global containerized trade demand. However, this expansion strategy increases the demand for container exchange between terminals and their logistics facilities within a port, known as [...] Read more.
Many ports worldwide continue to expand their capacity by developing a multiterminal system to catch up with the global containerized trade demand. However, this expansion strategy increases the demand for container exchange between terminals and their logistics facilities within a port, known as interterminal transport (ITT). ITT forms a complex transportation network in a large port, which must be managed efficiently given the economic and environmental implications. The use of trucks in ITT operations leads to the interterminal truck routing problem (ITTRP), which has been attracting increasing attention from researchers. One of the objectives of truck routing optimization in ITT is the minimization of empty-truck trips. Selection of the transport order (TO) based on the current truck location is critical in minimizing empty-truck trips. However, ITT entails not only transporting containers between terminals operated 24 h: in cases where containers need to be transported to a logistics facility within operating hours, empty-truck trip cost (ETTC) minimization must also consider the operational times of the transport origin and destination. Otherwise, truck waiting time might be incurred because the truck may arrive before the opening time of the facility. Truck waiting time seems trivial, but it is not, since thousands of containers move between locations within a port every day. So, truck waiting time can be a source of ITT-related costs if it is not managed wisely. Minimization of empty-truck trips and truck waiting time is considered a multiobjective optimization problem. This paper proposes a method of cooperative multiagent deep reinforcement learning (RL) to produce TO truck routes that minimize ETTC and truck waiting time. Two standard algorithms, simulated annealing (SA) and tabu search (TS) were chosen to assess the performance of the proposed method. The experimental results show that the proposed method represents a considerable improvement over the other algorithms. Full article
(This article belongs to the Special Issue Recent Advances in Machine Learning and Applications)
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17 pages, 2844 KiB  
Article
Solving the Problem of Class Imbalance in the Prediction of Hotel Cancelations: A Hybridized Machine Learning Approach
by Mohd Adil, Mohd Faizan Ansari, Ahmad Alahmadi, Jei-Zheng Wu and Ripon K. Chakrabortty
Processes 2021, 9(10), 1713; https://doi.org/10.3390/pr9101713 - 24 Sep 2021
Cited by 12 | Viewed by 3763
Abstract
The cancelation of bookings puts a considerable strain on management decisions in the case of the hospitability industry. Booking cancelations restrict precise predictions and are thus a critical tool for revenue management performance. However, in recent times, thanks to the availability of considerable [...] Read more.
The cancelation of bookings puts a considerable strain on management decisions in the case of the hospitability industry. Booking cancelations restrict precise predictions and are thus a critical tool for revenue management performance. However, in recent times, thanks to the availability of considerable computing power through machine learning (ML) approaches, it has become possible to create more accurate models to predict the cancelation of bookings compared to more traditional methods. Previous studies have used several ML approaches, such as support vector machine (SVM), neural network (NN), and decision tree (DT) models for predicting hotel cancelations. However, they are yet to address the class imbalance problem that exists in the prediction of hotel cancelations. In this study, we have shortened this gap by introducing an oversampling technique to address class imbalance problems, in conjunction with machine learning algorithms to better predict hotel booking cancelations. A combination of the synthetic minority oversampling technique and the edited nearest neighbors (SMOTE-ENN) algorithm is proposed to address the problem of class imbalance. Class imbalance is a general problem that occurs when classifying which class has more examples compared to others. Our research has shown that, after addressing the class imbalance problem, the performance of a machine learning classifier improves significantly. Full article
(This article belongs to the Special Issue Recent Advances in Machine Learning and Applications)
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30 pages, 730 KiB  
Article
Multiresolution Forecasting for Industrial Applications
by Quirin Stier, Tino Gehlert and Michael C. Thrun
Processes 2021, 9(10), 1697; https://doi.org/10.3390/pr9101697 - 22 Sep 2021
Cited by 2 | Viewed by 2913
Abstract
The forecasting of univariate time series poses challenges in industrial applications if the seasonality varies. Typically, a non-varying seasonality of a time series is treated with a model based on Fourier theory or the aggregation of forecasts from multiple resolution levels. If the [...] Read more.
The forecasting of univariate time series poses challenges in industrial applications if the seasonality varies. Typically, a non-varying seasonality of a time series is treated with a model based on Fourier theory or the aggregation of forecasts from multiple resolution levels. If the seasonality changes with time, various wavelet approaches for univariate forecasting are proposed with promising potential but without accessible software or a systematic evaluation of different wavelet models compared to state-of-the-art methods. In contrast, the advantage of the specific multiresolution forecasting proposed here is the convenience of a swiftly accessible implementation in R and Python combined with coefficient selection through evolutionary optimization which is evaluated in four different applications: scheduling of a call center, planning electricity demand, and predicting stocks and prices. The systematic benchmarking is based on out-of-sample forecasts resulting from multiple cross-validations with the error measure MASE and SMAPE for which the error distribution of each method and dataset is estimated and visualized with the mirrored density plot. The multiresolution forecasting performs equal to or better than twelve comparable state-of-the-art methods but does not require users to set parameters contrary to prior wavelet forecasting frameworks. This makes the method suitable for industrial applications. Full article
(This article belongs to the Special Issue Recent Advances in Machine Learning and Applications)
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16 pages, 2323 KiB  
Article
Improving Sports Outcome Prediction Process Using Integrating Adaptive Weighted Features and Machine Learning Techniques
by Chi-Jie Lu, Tian-Shyug Lee, Chien-Chih Wang and Wei-Jen Chen
Processes 2021, 9(9), 1563; https://doi.org/10.3390/pr9091563 - 01 Sep 2021
Cited by 5 | Viewed by 3923
Abstract
Developing an effective sports performance analysis process is an attractive issue in sports team management. This study proposed an improved sports outcome prediction process by integrating adaptive weighted features and machine learning algorithms for basketball game score prediction. The feature engineering method is [...] Read more.
Developing an effective sports performance analysis process is an attractive issue in sports team management. This study proposed an improved sports outcome prediction process by integrating adaptive weighted features and machine learning algorithms for basketball game score prediction. The feature engineering method is used to construct designed features based on game-lag information and adaptive weighting of variables in the proposed prediction process. These designed features are then applied to the five machine learning methods, including classification and regression trees (CART), random forest (RF), stochastic gradient boosting (SGB), eXtreme gradient boosting (XGBoost), and extreme learning machine (ELM) for constructing effective prediction models. The empirical results from National Basketball Association (NBA) data revealed that the proposed sports outcome prediction process could generate a promising prediction result compared to the competing models without adaptive weighting features. Our results also showed that the machine learning models with four game-lags information and adaptive weighting of power could generate better prediction performance. Full article
(This article belongs to the Special Issue Recent Advances in Machine Learning and Applications)
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13 pages, 2298 KiB  
Article
Real-Time Process Monitoring Based on Multivariate Control Chart for Anomalies Driven by Frequency Signal via Sound and Electrocardiography Cases
by Chih-Hung Jen and Chien-Chih Wang
Processes 2021, 9(9), 1510; https://doi.org/10.3390/pr9091510 - 26 Aug 2021
Cited by 2 | Viewed by 1295
Abstract
Recent developments in network technologies have led to the application of cloud computing and big data analysis to industrial automation. However, the automation of process monitoring still has numerous issues that need to be addressed. Traditionally, offline statistical processes are generally used for [...] Read more.
Recent developments in network technologies have led to the application of cloud computing and big data analysis to industrial automation. However, the automation of process monitoring still has numerous issues that need to be addressed. Traditionally, offline statistical processes are generally used for process monitoring; thus, problems are often detected too late. This study focused on the construction of an automated process monitoring system based on sound and vibration frequency signals. First, empirical mode decomposition was combined with intrinsic mode functions to construct different sound frequency combinations and differentiate sound frequencies according to anomalies. Then, linear discriminant analysis (LDA) was adopted to classify abnormal and normal sound frequency signals, and a control line was constructed to monitor the sound frequency. In a case study, the proposed method was applied to detect abnormal sounds at high and low frequencies, and a detection accuracy of over 90% was realized. In another case study, the proposed method was applied to analyze electrocardiography signals and was similarly able to identify abnormal situations. Thus, the proposed method can be applied to real-time process monitoring and the detection of abnormalities with high accuracy in various situations. Full article
(This article belongs to the Special Issue Recent Advances in Machine Learning and Applications)
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26 pages, 4680 KiB  
Article
End-to-End Control Chart Pattern Classification Using a 1D Convolutional Neural Network and Transfer Learning
by Chuen-Sheng Cheng, Ying Ho and Tzu-Cheng Chiu
Processes 2021, 9(9), 1484; https://doi.org/10.3390/pr9091484 - 24 Aug 2021
Cited by 9 | Viewed by 3045
Abstract
Control charts are an important tool in statistical process control (SPC). They have been commonly used for monitoring process variation in many industries. Recognition of non-random patterns is an important task in SPC. The presence of non-random patterns implies that a process is [...] Read more.
Control charts are an important tool in statistical process control (SPC). They have been commonly used for monitoring process variation in many industries. Recognition of non-random patterns is an important task in SPC. The presence of non-random patterns implies that a process is affected by certain assignable causes, and some corrective actions should be taken. In recent years, a great deal of research has been devoted to the application of machine learning (ML) based approaches to control chart pattern recognition (CCPR). However, there are some gaps that hinder the application of the CCPR methods in practice. In this study, we applied a control chart pattern recognition method based on an end-to-end one-dimensional convolutional neural network (1D CNN) model. We proposed some methods to generate datasets with high intra-class diversity aiming to create a robust classification model. To address the data scarcity issue, some data augmentation operations suitable for CCPR were proposed. This study also investigated the usefulness of transfer learning techniques for the CCPR task. The pre-trained model using normally distributed data was used as a starting point and fine-tuned on the unknown non-normal data. The performance of the proposed approach was evaluated by real-world data and simulation experiments. Experimental results indicate that our proposed method outperforms the traditional machine learning methods and could be a promising tool to effectively classify control chart patterns. The results and findings of this study are crucial for the further realization of smart statistical process control. Full article
(This article belongs to the Special Issue Recent Advances in Machine Learning and Applications)
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16 pages, 2239 KiB  
Article
IP Analytics and Machine Learning Applied to Create Process Visualization Graphs for Chemical Utility Patents
by Amy J. C. Trappey, Charles V. Trappey, Chih-Ping Liang and Hsin-Jung Lin
Processes 2021, 9(8), 1342; https://doi.org/10.3390/pr9081342 - 30 Jul 2021
Cited by 2 | Viewed by 3311
Abstract
Researchers must read and understand a large volume of technical papers, including patent documents, to fully grasp the state-of-the-art technological progress in a given domain. Chemical research is particularly challenging with the fast growth of newly registered utility patents (also known as intellectual [...] Read more.
Researchers must read and understand a large volume of technical papers, including patent documents, to fully grasp the state-of-the-art technological progress in a given domain. Chemical research is particularly challenging with the fast growth of newly registered utility patents (also known as intellectual property or IP) that provide detailed descriptions of the processes used to create a new chemical or a new process to manufacture a known chemical. The researcher must be able to understand the latest patents and literature in order to develop new chemicals and processes that do not infringe on existing claims and processes. This research uses text mining, integrated machine learning, and knowledge visualization techniques to effectively and accurately support the extraction and graphical presentation of chemical processes disclosed in patent documents. The computer framework trains a machine learning model called ALBERT for automatic paragraph text classification. ALBERT separates chemical and non-chemical descriptive paragraphs from a patent for effective chemical term extraction. The ChemDataExtractor is used to classify chemical terms, such as inputs, units, and reactions from the chemical paragraphs. A computer-supported graph-based knowledge representation interface is developed to plot the extracted chemical terms and their chemical process links as a network of nodes with connecting arcs. The computer-supported chemical knowledge visualization approach helps researchers to quickly understand the innovative and unique chemical or processes of any chemical patent of interest. Full article
(This article belongs to the Special Issue Recent Advances in Machine Learning and Applications)
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19 pages, 3377 KiB  
Article
On the Application of ARIMA and LSTM to Predict Order Demand Based on Short Lead Time and On-Time Delivery Requirements
by Chien-Chih Wang, Chun-Hua Chien and Amy J. C. Trappey
Processes 2021, 9(7), 1157; https://doi.org/10.3390/pr9071157 - 02 Jul 2021
Cited by 23 | Viewed by 5917
Abstract
Suppliers are adjusting from the order-to-order manufacturing production mode toward demand forecasting. In the meantime, customers have increased demand uncertainty due to their own considerations, such as end-product demand frustration, which leads to suppliers’ inaccurate demand forecasting and inventory wastes. Our research applies [...] Read more.
Suppliers are adjusting from the order-to-order manufacturing production mode toward demand forecasting. In the meantime, customers have increased demand uncertainty due to their own considerations, such as end-product demand frustration, which leads to suppliers’ inaccurate demand forecasting and inventory wastes. Our research applies ARIMA and LSTM techniques to establish rolling forecast models, which greatly improve accuracy and efficiency of demand and inventory forecasting. The forecast models, developed through historical data, are evaluated and verified by the root mean squares and average absolute error percentages in the actual case application, i.e., the orders of IC trays for semiconductor production plants. The proposed ARIMA and LSTM are superior to the manufacturer’s empirical model prediction results, with LSTM exhibiting enhanced performance in terms of short-term forecasting. The inventory continued to decline significantly after two months of model implementation and application. Full article
(This article belongs to the Special Issue Recent Advances in Machine Learning and Applications)
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14 pages, 489 KiB  
Article
HCRNNIDS: Hybrid Convolutional Recurrent Neural Network-Based Network Intrusion Detection System
by Muhammad Ashfaq Khan
Processes 2021, 9(5), 834; https://doi.org/10.3390/pr9050834 - 10 May 2021
Cited by 117 | Viewed by 9017
Abstract
Nowadays, network attacks are the most crucial problem of modern society. All networks, from small to large, are vulnerable to network threats. An intrusion detection (ID) system is critical for mitigating and identifying malicious threats in networks. Currently, deep learning (DL) and machine [...] Read more.
Nowadays, network attacks are the most crucial problem of modern society. All networks, from small to large, are vulnerable to network threats. An intrusion detection (ID) system is critical for mitigating and identifying malicious threats in networks. Currently, deep learning (DL) and machine learning (ML) are being applied in different domains, especially information security, for developing effective ID systems. These ID systems are capable of detecting malicious threats automatically and on time. However, malicious threats are occurring and changing continuously, so the network requires a very advanced security solution. Thus, creating an effective and smart ID system is a massive research problem. Various ID datasets are publicly available for ID research. Due to the complex nature of malicious attacks with a constantly changing attack detection mechanism, publicly existing ID datasets must be modified systematically on a regular basis. So, in this paper, a convolutional recurrent neural network (CRNN) is used to create a DL-based hybrid ID framework that predicts and classifies malicious cyberattacks in the network. In the HCRNNIDS, the convolutional neural network (CNN) performs convolution to capture local features, and the recurrent neural network (RNN) captures temporal features to improve the ID system’s performance and prediction. To assess the efficacy of the hybrid convolutional recurrent neural network intrusion detection system (HCRNNIDS), experiments were done on publicly available ID data, specifically the modern and realistic CSE-CIC-DS2018 data. The simulation outcomes prove that the proposed HCRNNIDS substantially outperforms current ID methodologies, attaining a high malicious attack detection rate accuracy of up to 97.75% for CSE-CIC-IDS2018 data with 10-fold cross-validation. Full article
(This article belongs to the Special Issue Recent Advances in Machine Learning and Applications)
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