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Trends of Machine Learning in Multidisciplinary Engineering Processes
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Trends of Machine Learning in Multidisciplinary Engineering Processes

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Process Control and Monitoring".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 24774

Special Issue Editors

Prof. Dr. Adel Ali Ahmed

E-Mail Website
Guest Editor
Information Technology Department, King Abdulaziz University, Rabigh,25729, Saudi Arabia
Interests: machine learning; security; mobile sensor networks; internet of things; 5G networks technology; mobile ad-hoc networks; VANET; location tracking and mobile IPv6
Dr. AbdulRahman Alsewari

E-Mail Website
Guest Editor
School of Computing and Digital Technology, Faculty of Computing, Engineering and the Built Environment, Birmingham City University, Birmingham B4 7XG, UK
Interests: artificial intelligence; combinatorial testing; optimisation algorithms
Special Issues, Collections and Topics in MDPI journals
Dr. Yousef Fazea

E-Mail Website
Guest Editor
Department of Computer Science and Electrical Engineering, Marshall University, 1 John Marshall Drive, Huntington, WV 25755, USA
Interests: high computing performance; next-generation computing and telecommunication; digital communication networks; high-speed networks
Special Issues, Collections and Topics in MDPI journals
Dr. Waleed Ali

E-Mail Website
Guest Editor
Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Rabigh 25729, Saudi Arabia
Interests: machine learning techniques and evolutionary optimization algorithms, and their applications in cybersecurity and healthcare data analysis, intelligent web caching, intelligent web pre-fetching, web usage mining, web pages predictions, web users clustering and analysis, intelligent phishing website detection using machine learning, and intelligent android malware detection using machine learning

Special Issue Information

Dear Colleagues,

Recently, machine learning (ML) has become the core of artificial intelligence (AI) and data science. The new trends of multidisciplinary engineering processes are how to make the machines imitate intelligent human behaviour with a high performance of accuracy, less latency and cost, high productivity/throughput, and emulate human behaviour to solve a complex task. Therefore, 97% of future companies are using or planning to use ML in multidisciplinary engineering processes. It has been demonstrated that ML has been found throughout science, technology and industry, leading to more evidence-based decision-making across many walks of life, including engineering, healthcare, communication and networking, cybersecurity, nanotechnology, biomedicine, manufacturing, education, sustainable agriculture, financial modelling, data governance, policing, and marketing.

This Special Issue on “Trends of Machine Learning in Multidisciplinary Engineering Processes” aims to attract high-quality paper submissions to provide a cohesive and novelty perspective on ML methods that will be integrated in multidisciplinary engineering processes to resolve the challenges in the industrialization and communications. Topics of interest include, but are not limited to:

  • The development of new ML algorithms/applications in manufacturing processes.
  • The development of new networking-based ML algorithms/applications.
  • The development of new security-based ML algorithms/applications.
  • The development of new healthcare-based ML algorithms/applications.
  • The development of new educational-based ML algorithms/applications.
  • The development of new sustainable agriculture-based ML algorithms/applications.
  • The development of new education-based ML algorithms/applications.

Prof. Dr. Adel Ali Ahmed
Dr. AbdulRahman Alsewari
Dr. Yousef Fazea
Dr. Waleed Ali
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. 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

  • manufacturing processes
  • networks
  • cybersecurity
  • Internet of Things (IoT)
  • machine learning
  • healthcare
  • educational processes
  • sustainable agriculture
  • biomedicine
  • chemical processes

Published Papers (10 papers)

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Research

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14 pages, 3903 KiB  
Article
Energy Consumption Optimization Strategy of Hybrid Vehicle Based on NSGA-II Genetic Algorithm
by Xuanxuan Wang, Wujun Ji and Yun Gao
Processes 2023, 11(6), 1735; https://doi.org/10.3390/pr11061735 - 06 Jun 2023
Cited by 3 | Viewed by 1457
Abstract
Hybrid electric vehicles (HEVs) have certain advantages over internal combustion engines in terms of energy consumption and emission performance. However, the transmission system parameters are uncertain. The low matching between the engine and the power transmission system makes it a big problem to [...] Read more.
Hybrid electric vehicles (HEVs) have certain advantages over internal combustion engines in terms of energy consumption and emission performance. However, the transmission system parameters are uncertain. The low matching between the engine and the power transmission system makes it a big problem to improve the efficiency of hybrid vehicles. Therefore, the multi-objective optimization design of hybrid vehicles is studied. The transmission system parameters of hybrid vehicles are analyzed from the objective function, decision variables, and constraints. The NSGA-II algorithm with elite strategy is introduced to realize the optimal selection of parameters and formulation of energy consumption optimization strategy. The results showed that the multi-objective optimization algorithm could adjust the position of the working point of the engine and improve the efficiency by more than 10%. There was an average difference of 2.15% after the improvement in the fuel consumption of four-gear vehicles. The fuel consumption per 100 km decreases by more than 3%. The maximum climbing gradient of the whole vehicle was 33.9%. The power factor of the direct gear of the maximum power factor increases by 15% after the improvement. The multi-objective energy consumption optimization design of hybrid vehicles proposed in the study can effectively improve the economic and dynamic performance of the whole vehicle and reduce fuel consumption. It provides a reference for the optimization of the hybrid vehicle transmission system. Full article
(This article belongs to the Special Issue Trends of Machine Learning in Multidisciplinary Engineering Processes)
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16 pages, 4444 KiB  
Article
Real-Time Steel Surface Defect Detection with Improved Multi-Scale YOLO-v5
by Ling Wang, Xinbo Liu, Juntao Ma, Wenzhi Su and Han Li
Processes 2023, 11(5), 1357; https://doi.org/10.3390/pr11051357 - 28 Apr 2023
Cited by 11 | Viewed by 2546
Abstract
Steel surface defect detection is an important issue when producing high-quality steel materials. Traditional defect detection methods are time-consuming and uneconomical and require manually designed prior information or extra supervisors. Surface defects have different representations and features at different scales, which make it [...] Read more.
Steel surface defect detection is an important issue when producing high-quality steel materials. Traditional defect detection methods are time-consuming and uneconomical and require manually designed prior information or extra supervisors. Surface defects have different representations and features at different scales, which make it challenging to automatically detect the locations and defect types. This paper proposes a real-time steel surface defect detection technology based on the YOLO-v5 detection network. In order to effectively explore the multi-scale information of the surface defect, a multi-scale explore block is especially developed in the detection network to improve the detection performance. Furthermore, the spatial attention mechanism is also developed to focus more on the defect information. Experimental results show that the proposed network can accurately detect steel surface defects with approximately 72% mAP and satisfies the real-time speed requirement. Full article
(This article belongs to the Special Issue Trends of Machine Learning in Multidisciplinary Engineering Processes)
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19 pages, 5854 KiB  
Article
Fuzzy Harmony Search Technique for Cyber Risks in Industry 4.0 Wireless Communication Networks
by Zhifeng Diao and Fanglei Sun
Processes 2023, 11(3), 951; https://doi.org/10.3390/pr11030951 - 20 Mar 2023
Viewed by 1170
Abstract
Industry 4.0 houses diverse technologies including wireless communication and shared networks for internal and external operations. Due to the wireless nature and remote operability, the exposure to security threats is high. Cyber risk detection and mitigation are prominent for secure industrial operations and [...] Read more.
Industry 4.0 houses diverse technologies including wireless communication and shared networks for internal and external operations. Due to the wireless nature and remote operability, the exposure to security threats is high. Cyber risk detection and mitigation are prominent for secure industrial operations and planned outcomes. In addition, the system faces the threat of intelligence attacks, security standards issues, privacy concerns and scalability problems. The cyber risk related research problems influence overall data transmission in industry wireless communication networks. For augmenting communication security through cyber risk detection, this article introduces an Explicit Risk Detection and Assessment Technique (ERDAT) for cyber threat mitigation in the industrial process. A fuzzy harmony search algorithm powers this technique for identifying the risk and preventing its impact. The harmony search algorithm mimics the adversary impact using production factors such as process interruption or halting and production outcome. The search performs a mimicking operation for a high objective function based on production output for the admitted plan. The fuzzy operation admits the above factors for identifying the cyber impacting risk, either for its impacts or profitable outcome. In this process, the fuzzy optimization identifies the maximum or minimum objective output targeted for either outcome or risk interrupts, respectively. The fuzzy threshold is identified using a mediated acceptable range, computed as the ratio between minimum and maximum, mimicking occurrences between the risk and scheduled production outcomes. Therefore, the mimicking crossing or falling behind the threshold for the interruption/halting or production, respectively, are identified as risks and their source is detected. The detection communication source is disconnected from the industrial process for preventing further adversary impacts. The introduced system achieves 8.52% high-risk detection, 12.5% fewer outcome interrupts, 8.3% fewer halted schedules, 8.08% less interrupt span, and 7.94% less detection time compared to traditional methods. Full article
(This article belongs to the Special Issue Trends of Machine Learning in Multidisciplinary Engineering Processes)
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19 pages, 3895 KiB  
Article
B-LIME: An Improvement of LIME for Interpretable Deep Learning Classification of Cardiac Arrhythmia from ECG Signals
by Talal A. A. Abdullah, Mohd Soperi Mohd Zahid, Waleed Ali and Shahab Ul Hassan
Processes 2023, 11(2), 595; https://doi.org/10.3390/pr11020595 - 16 Feb 2023
Cited by 5 | Viewed by 2503
Abstract
Deep Learning (DL) has gained enormous popularity recently; however, it is an opaque technique that is regarded as a black box. To ensure the validity of the model’s prediction, it is necessary to explain its authenticity. A well-known locally interpretable model-agnostic explanation method [...] Read more.
Deep Learning (DL) has gained enormous popularity recently; however, it is an opaque technique that is regarded as a black box. To ensure the validity of the model’s prediction, it is necessary to explain its authenticity. A well-known locally interpretable model-agnostic explanation method (LIME) uses surrogate techniques to simulate reasonable precision and provide explanations for a given ML model. However, LIME explanations are limited to tabular, textual, and image data. They cannot be provided for signal data features that are temporally interdependent. Moreover, LIME suffers from critical problems such as instability and local fidelity that prevent its implementation in real-world environments. In this work, we propose Bootstrap-LIME (B-LIME), an improvement of LIME, to generate meaningful explanations for ECG signal data. B-LIME implies a combination of heartbeat segmentation and bootstrapping techniques to improve the model’s explainability considering the temporal dependencies between features. Furthermore, we investigate the main cause of instability and lack of local fidelity in LIME. We then propose modifications to the functionality of LIME, including the data generation technique, the explanation method, and the representation technique, to generate stable and locally faithful explanations. Finally, the performance of B-LIME in a hybrid deep-learning model for arrhythmia classification was investigated and validated in comparison with LIME. The results show that the proposed B-LIME provides more meaningful and credible explanations than LIME for cardiac arrhythmia signal data, considering the temporal dependencies between features. Full article
(This article belongs to the Special Issue Trends of Machine Learning in Multidisciplinary Engineering Processes)
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22 pages, 1686 KiB  
Article
Hybrid Filter and Genetic Algorithm-Based Feature Selection for Improving Cancer Classification in High-Dimensional Microarray Data
by Waleed Ali and Faisal Saeed
Processes 2023, 11(2), 562; https://doi.org/10.3390/pr11020562 - 12 Feb 2023
Cited by 9 | Viewed by 2733
Abstract
The advancements in intelligent systems have contributed tremendously to the fields of bioinformatics, health, and medicine. Intelligent classification and prediction techniques have been used in studying microarray datasets, which store information about the ways used to express the genes, to assist greatly in [...] Read more.
The advancements in intelligent systems have contributed tremendously to the fields of bioinformatics, health, and medicine. Intelligent classification and prediction techniques have been used in studying microarray datasets, which store information about the ways used to express the genes, to assist greatly in diagnosing chronic diseases, such as cancer in its earlier stage, which is important and challenging. However, the high-dimensionality and noisy nature of the microarray data lead to slow performance and low cancer classification accuracy while using machine learning techniques. In this paper, a hybrid filter-genetic feature selection approach has been proposed to solve the high-dimensional microarray datasets problem which ultimately enhances the performance of cancer classification precision. First, the filter feature selection methods including information gain, information gain ratio, and Chi-squared are applied in this study to select the most significant features of cancerous microarray datasets. Then, a genetic algorithm has been employed to further optimize and enhance the selected features in order to improve the proposed method’s capability for cancer classification. To test the proficiency of the proposed scheme, four cancerous microarray datasets were used in the study—this primarily included breast, lung, central nervous system, and brain cancer datasets. The experimental results show that the proposed hybrid filter-genetic feature selection approach achieved better performance of several common machine learning methods in terms of Accuracy, Recall, Precision, and F-measure. Full article
(This article belongs to the Special Issue Trends of Machine Learning in Multidisciplinary Engineering Processes)
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23 pages, 5260 KiB  
Article
An Intelligent Early Flood Forecasting and Prediction Leveraging Machine and Deep Learning Algorithms with Advanced Alert System
by Israa M. Hayder, Taief Alaa Al-Amiedy, Wad Ghaban, Faisal Saeed, Maged Nasser, Ghazwan Abdulnabi Al-Ali and Hussain A. Younis
Processes 2023, 11(2), 481; https://doi.org/10.3390/pr11020481 - 05 Feb 2023
Cited by 9 | Viewed by 3047
Abstract
Flood disasters are a natural occurrence around the world, resulting in numerous casualties. It is vital to develop an accurate flood forecasting and prediction model in order to curb damages and limit the number of victims. Water resource allocation, management, planning, flood warning [...] Read more.
Flood disasters are a natural occurrence around the world, resulting in numerous casualties. It is vital to develop an accurate flood forecasting and prediction model in order to curb damages and limit the number of victims. Water resource allocation, management, planning, flood warning and forecasting, and flood damage mitigation all benefit from rain forecasting. Prior to recent decades’ worth of research, this domain demonstrated to be promising prospects in time series prediction tasks. Therefore, the main aim of this study is to build a forecasting model based on the exponential smoothing-long-short term memory (ES-LSTM) structure and recurrent neural networks (RNNs) for predicting hourly precipitation seasons; and classify the precipitation using an artificial neural network (ANN) model and decision tree (DT) algorithm. We employ the dataset from the Australian commonwealth office of meteorology named Historical Daily Weather dataset to test the effectiveness of the proposed model. The findings showed that the ES-LSTM and RNN had achieved 3.17 and 6.42 in terms of mean absolute percentage error (MAPE), respectively. Meanwhile, the ANN and DT models obtained a prediction accuracy rate of 96.65% and 84.0%, respectively. Finally, the outcomes revealed that ES-LSTM and ANN had achieved the best results compared to other models. Full article
(This article belongs to the Special Issue Trends of Machine Learning in Multidisciplinary Engineering Processes)
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28 pages, 5577 KiB  
Article
Deep Transfer Learning Techniques-Based Automated Classification and Detection of Pulmonary Fibrosis from Chest CT Images
by Asif Hassan Syed, Tabrej Khan and Sher Afzal Khan
Processes 2023, 11(2), 443; https://doi.org/10.3390/pr11020443 - 01 Feb 2023
Cited by 3 | Viewed by 3140
Abstract
Pulmonary Fibrosis (PF) is a non-curable chronic lung disease. Therefore, a quick and accurate PF diagnosis is imperative. In the present study, we aim to compare the performance of the six state-of-the-art Deep Transfer Learning techniques to classify patients accurately and perform abnormality [...] Read more.
Pulmonary Fibrosis (PF) is a non-curable chronic lung disease. Therefore, a quick and accurate PF diagnosis is imperative. In the present study, we aim to compare the performance of the six state-of-the-art Deep Transfer Learning techniques to classify patients accurately and perform abnormality localization in Computer Tomography (CT) scan images. A total of 2299 samples comprising normal and PF-positive CT images were preprocessed. The preprocessed images were split into training (75%), validation (15%), and test data (10%). These transfer learning models were trained and validated by optimizing the hyperparameters, such as the learning rate and the number of epochs. The optimized architectures have been evaluated with different performance metrics to demonstrate the consistency of the optimized model. At epoch 26, using an optimized learning rate of 0.0000625, the ResNet50v2 model achieved the highest training and validation accuracy (training = 99.92%, validation = 99.22%) and minimum loss (training = 0.00428, validation = 0.00683) for CT images. The experimental evaluation on the independent testing data confirms that optimized ResNet50v2 outperformed every other optimized architecture under consideration achieving a perfect score of 1.0 in each of the standard performance measures such as accuracy, precision, recall, F1-score, Mathew Correlation Coefficient (MCC), Area under the Receiver Operating Characteristic (ROC-AUC) curve, and the Area under the Precision recall (AUC_PR) curve. Therefore, we can propose that the optimized ResNet50v2 is a reliable diagnostic model for automatically classifying PF-positive patients using chest CT images. Full article
(This article belongs to the Special Issue Trends of Machine Learning in Multidisciplinary Engineering Processes)
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19 pages, 632 KiB  
Article
Tail Prediction for Heterogeneous Data Center Clusters
by Sharaf Malebary, Sami Alesawi and Hao Che
Processes 2023, 11(2), 407; https://doi.org/10.3390/pr11020407 - 30 Jan 2023
Viewed by 1253
Abstract
Service providers need to meet their service level objectives (SLOs) to ensure better client experiences. Predicting tail sojourn times of applications is an essential step to combat long tail latency. Therefore, as an attempt to further unravel the power of our prediction model, [...] Read more.
Service providers need to meet their service level objectives (SLOs) to ensure better client experiences. Predicting tail sojourn times of applications is an essential step to combat long tail latency. Therefore, as an attempt to further unravel the power of our prediction model, new study scenarios for heterogeneous environments will be introduced in this research by using either of two methods: white- or black-box solutions. This research presents several techniques for modeling clusters of inhomogeneous nodes. Those techniques are recognized as heterogeneous fork-join queuing networks (HFJQNs). Moreover, included in the research is a nested-event-based simulation model, borrowing help from multi-core technologies. This model adopts the multiprocessing technique to take part in its design to enable different architectural designs for all computing nodes. This novel implementation of the simulation model is believed to be the next logical step for research studies targeting heterogeneous clusters in addition to the several provided scenarios. Experimental results confirm that even with the existence of such heterogeneous conditions, the tail latency can be predicted at high-load regions with an approximated relative error of less than 15%. Full article
(This article belongs to the Special Issue Trends of Machine Learning in Multidisciplinary Engineering Processes)
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Review

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28 pages, 3706 KiB  
Review
Multimodal Age and Gender Estimation for Adaptive Human-Robot Interaction: A Systematic Literature Review
by Hussain A. Younis, Nur Intan Raihana Ruhaiyem, Ameer A. Badr, Alia K. Abdul-Hassan, Ibrahim M. Alfadli, Weam M. Binjumah, Eman A. Altuwaijri and Maged Nasser
Processes 2023, 11(5), 1488; https://doi.org/10.3390/pr11051488 - 15 May 2023
Cited by 3 | Viewed by 2233
Abstract
Identifying the gender of a person and his age by way of speaking is considered a crucial task in computer vision. It is a very important and active research topic with many areas of application, such as identifying a person, trustworthiness, demographic analysis, [...] Read more.
Identifying the gender of a person and his age by way of speaking is considered a crucial task in computer vision. It is a very important and active research topic with many areas of application, such as identifying a person, trustworthiness, demographic analysis, safety and health knowledge, visual monitoring, and aging progress. Data matching is to identify the gender of the person and his age. Thus, the study touches on a review of many research papers from 2016 to 2022. At the heart of the topic, many systematic reviews of multimodal pedagogies in Age and Gender Estimation for Adaptive were undertaken. However, no current study of the theme concerns connected to multimodal pedagogies in Age and Gender Estimation for Adaptive Learning has been published. The multimodal pedagogies in four different databases within the keywords indicate the heart of the topic. A qualitative thematic analysis based on 48 articles found during the search revealed four common themes, such as multimodal engagement and speech with the Human-Robot Interaction life world. The study touches on the presentation of many major concepts, namely Age Estimation, Gender Estimation, Speaker Recognition, Speech recognition, Speaker Localization, and Speaker Gender Identification. According to specific criteria, they were presented to all studies. The essay compares these themes to the thematic findings of other review studies on the same topic such as multimodal age, gender estimation, and dataset used. The main objective of this paper is to provide a comprehensive analysis based on the surveyed region. The study provides a platform for professors, researchers, and students alike, and proposes directions for future research. Full article
(This article belongs to the Special Issue Trends of Machine Learning in Multidisciplinary Engineering Processes)
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27 pages, 1749 KiB  
Review
Deep Learning Based Methods for Molecular Similarity Searching: A Systematic Review
by Maged Nasser, Umi Kalsom Yusof and Naomie Salim
Processes 2023, 11(5), 1340; https://doi.org/10.3390/pr11051340 - 26 Apr 2023
Cited by 2 | Viewed by 2640
Abstract
In rational drug design, the concept of molecular similarity searching is frequently used to identify molecules with similar functionalities by looking up structurally related molecules in chemical databases. Different methods have been developed to measure the similarity of molecules to a target query. [...] Read more.
In rational drug design, the concept of molecular similarity searching is frequently used to identify molecules with similar functionalities by looking up structurally related molecules in chemical databases. Different methods have been developed to measure the similarity of molecules to a target query. Although the approaches perform effectively, particularly when dealing with molecules with homogenous active structures, they fall short when dealing with compounds that have heterogeneous structural compounds. In recent times, deep learning methods have been exploited for improving the performance of molecule searching due to their feature extraction power and generalization capabilities. However, despite numerous research studies on deep-learning-based molecular similarity searches, relatively few secondary research was carried out in the area. This research aims to provide a systematic literature review (SLR) on deep-learning-based molecular similarity searches to enable researchers and practitioners to better understand the current trends and issues in the field. The study accesses 875 distinctive papers from the selected journals and conferences, which were published over the last thirteen years (2010–2023). After the full-text eligibility analysis and careful screening of the abstract, 65 studies were selected for our SLR. The review’s findings showed that the multilayer perceptrons (MLPs) and autoencoders (AEs) are the most frequently used deep learning models for molecular similarity searching; next are the models based on convolutional neural networks (CNNs) techniques. The ChEMBL dataset and DrugBank standard dataset are the two datasets that are most frequently used for the evaluation of deep learning methods for molecular similarity searching based on the results. In addition, the results show that the most popular methods for optimizing the performance of molecular similarity searching are new representation approaches and reweighing features techniques, and, for evaluating the efficiency of deep-learning-based molecular similarity searching, the most widely used metrics are the area under the curve (AUC) and precision measures. Full article
(This article belongs to the Special Issue Trends of Machine Learning in Multidisciplinary Engineering Processes)
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