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Processes
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Challenges in Machine Learning, Artificial Intelligence, Wireless Sensor Networks and...
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Challenges in Machine Learning, Artificial Intelligence, Wireless Sensor Networks and Smart Cities

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 November 2022) | Viewed by 20594

Special Issue Editors

Dr. Pablo Chamoso

E-Mail Website
Guest Editor
BISITE Research Group, University of Salamanca, Calle Espejo sn, 24.2, 37007 Salamanca, Spain
Interests: smart cities; machine learning; IoT
Special Issues, Collections and Topics in MDPI journals
Dr. Guillermo Hernández

E-Mail Website
Guest Editor
Air Institute, IoT Digital Innovation Hub, 37188 Salamanca, Spain
Interests: machine learning; deep learning; natural language processing; visual analytics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Paulo Novais

E-Mail Website
Guest Editor
Algoritmi Center, Campus of Gualtar, 4710-057 Braga, Portugal
Interests: ambient intelligence; ambient assisted living; conflict resolution; behavioral analysis; intelligent tutors
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

All sectors that are directly related to information technologies (IT) are experiencing strong growth. Every year they continue to gain more interest, with new challenges and new solutions that were previously unimaginable. This Special Issue aims to show how new trends in these fields can transform and innovate processes and services in areas such as smart cities or the Internet of Things.

This Special Issue encourages original and high-quality submissions, with both applied and theoretical research approaches, related (but not limited) to the following topics:

  • Machine learning;
  • Deep learning;
  • Artificial intelligence;
  • Smart cities;
  • Internet of Things;
  • Wireless sensor networks;
  • Visual analytics;
  • Ambient intelligence;
  • Robotics;
  • Natural language processing.

Dr. Pablo Chamoso
Dr. Guillermo Hernández
Prof. Dr. Paulo Novais
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

  • machine learning
  • deep learning
  • artificial intelligence
  • smart cities
  • Internet of Things
  • wireless sensor networks

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

Published Papers (6 papers)

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Research

12 pages, 3604 KiB  
Article
Approach to the Technical Processes of Incorporating Sustainability Information—The Case of a Smart City and the Monitoring of the Sustainable Development Goals
by Javier Parra-Domínguez, Raúl López-Blanco and Francisco Pinto-Santos
Processes 2022, 10(8), 1651; https://doi.org/10.3390/pr10081651 - 19 Aug 2022
Cited by 2 | Viewed by 1875
Abstract
Currently, the concern for achieving and fulfilling the Sustainable Development Goals (SDGs) is a constant in advanced societies. The scientific community and various organisations are working on obtaining an information system that will make it possible to offer the necessary value to this [...] Read more.
Currently, the concern for achieving and fulfilling the Sustainable Development Goals (SDGs) is a constant in advanced societies. The scientific community and various organisations are working on obtaining an information system that will make it possible to offer the necessary value to this type of sustainability information. The article aims to incorporate criteria on the technology used in the reporting system, specifically in collecting the different types of data and generating other interfaces. The methods described here are carried out on a specific case study, a Smart City, showing the different types of data that exist and the possible interfaces that allow objective monitoring of the achievement of the SDGs. It is, therefore, a descriptive study of a process whose results are the establishment of criteria concerning the different data sources as well as the generation of a set of interfaces that motivate the monitoring that can be carried out in a specific city to observe its compliance and deviations from critical values, for example, environmental. The main conclusions of this research establish the importance of incorporating and sizing the technology needed to develop the criteria for monitoring the SDGs. There is a need for convergence between the correct, objective and universal provision of this type of sustainability information and the technology used for the collection and presentation of the data. Full article
(This article belongs to the Special Issue Challenges in Machine Learning, Artificial Intelligence, Wireless Sensor Networks and Smart Cities)
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10 pages, 401 KiB  
Article
Software Refactoring Prediction Using SVM and Optimization Algorithms
by Mohammed Akour, Mamdouh Alenezi and Hiba Alsghaier
Processes 2022, 10(8), 1611; https://doi.org/10.3390/pr10081611 - 15 Aug 2022
Cited by 6 | Viewed by 2583
Abstract
Test suite code coverage is often used as an indicator for test suite capability in detecting faults. However, earlier studies that have explored the correlation between code coverage and test suite effectiveness have not addressed this correlation evolutionally. Moreover, some of these works [...] Read more.
Test suite code coverage is often used as an indicator for test suite capability in detecting faults. However, earlier studies that have explored the correlation between code coverage and test suite effectiveness have not addressed this correlation evolutionally. Moreover, some of these works have only addressed small sized systems, or systems from the same domain, which makes the result generalization process unclear for other domain systems. Software refactoring promotes a positive consequence in terms of software maintainability and understandability. It aims to enhance the software quality by modifying the internal structure of systems without affecting their external behavior. However, identifying the refactoring needs and which level should be executed is still a big challenge to software developers. In this paper, the authors explore the effectiveness of employing a support vector machine along with two optimization algorithms to predict software refactoring at the class level. In particular, the SVM was trained in genetic and whale algorithms. A well-known dataset belonging to open-source software systems (i.e., ANTLR4, JUnit, MapDB, and McMMO) was used in this study. All experiments achieved a promising accuracy rate range of between 84% for the SVM–Junit system and 93% for McMMO − GA + Whale + SVM. It was clear that added value was gained from merging the SVM with two optimization algorithms. All experiments achieved a promising F-measure range between the SVM–Antlr4 system’s result of 86% and that of the McMMO − GA + Whale + SVM system at 96%. Moreover, the results of the proposed approach were compared with the results from four well known ML algorithms (NB-Naïve, IBK-Instance, RT-Random Tree, and RF-Random Forest). The results from the proposed approach outperformed the prediction performances of the studied MLs. Full article
(This article belongs to the Special Issue Challenges in Machine Learning, Artificial Intelligence, Wireless Sensor Networks and Smart Cities)
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15 pages, 1625 KiB  
Article
Intelligent Model for Power Cells State of Charge Forecasting in EV
by Víctor López, Esteban Jove, Francisco Zayas Gato, Francisco Pinto-Santos, Andrés José Piñón-Pazos, Jose-Luis Casteleiro-Roca, Hector Quintian and Jose Luis Calvo-Rolle
Processes 2022, 10(7), 1406; https://doi.org/10.3390/pr10071406 - 19 Jul 2022
Cited by 7 | Viewed by 2658
Abstract
In electric vehicles and mobile electronic devices, batteries are one of the most critical components. They work by using electrochemical reactions that have been thoroughly investigated to identify their behavior and characteristics at each operating point. One of the fascinating aspects of batteries [...] Read more.
In electric vehicles and mobile electronic devices, batteries are one of the most critical components. They work by using electrochemical reactions that have been thoroughly investigated to identify their behavior and characteristics at each operating point. One of the fascinating aspects of batteries is their complicated behavior. The type of power cell reviewed in this study is a Lithium Iron Phosphate LiFePO4 (LFP). The goal of this study is to develop an intelligent model that can forecast the power cell State of Charge (SOC). The dataset used to create the model comprises all the operating points measured from an actual system during a capacity confirmation test. Regression approaches based on Deep Learning (DL), such as Long Short-Term Memory networks (LSTM), were evaluated under different model configurations and forecasting horizons. Full article
(This article belongs to the Special Issue Challenges in Machine Learning, Artificial Intelligence, Wireless Sensor Networks and Smart Cities)
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21 pages, 8328 KiB  
Article
Identification Method for Cone Yarn Based on the Improved Faster R-CNN Model
by Hangxing Zhao, Jingbin Li, Jing Nie, Jianbing Ge, Shuo Yang, Longhui Yu, Yuhai Pu and Kang Wang
Processes 2022, 10(4), 634; https://doi.org/10.3390/pr10040634 - 24 Mar 2022
Cited by 9 | Viewed by 2591
Abstract
To solve the problems of high labor intensity, low efficiency, and frequent errors in the manual identification of cone yarn types, in this study five kinds of cone yarn were taken as the research objects, and an identification method for cone yarn based [...] Read more.
To solve the problems of high labor intensity, low efficiency, and frequent errors in the manual identification of cone yarn types, in this study five kinds of cone yarn were taken as the research objects, and an identification method for cone yarn based on the improved Faster R-CNN model was proposed. In total, 2750 images were collected of cone yarn samples in real of textile industry environments, then data enhancement was performed after marking the targets. The ResNet50 model with strong representation ability was used as the feature network to replace the VGG16 backbone network in the original Faster R-CNN model to extract the features of the cone yarn dataset. Training was performed with a stochastic gradient descent approach to obtain an optimally weighted file to predict the categories of cone yarn. Using the same training samples and environmental settings, we compared the method proposed in this paper with two mainstream target detection algorithms, YOLOv3 + DarkNet-53 and Faster R-CNN + VGG16. The results showed that the Faster R-CNN + ResNet50 algorithm had the highest mean average precision rate for the five types of cone yarn at 99.95%, as compared with the YOLOv3 + DarkNet-53 algorithm with a mean average precision rate that was 2.24% higher and the Faster R-CNN + VGG16 algorithm with a mean average precision that was 1.19% higher. Regarding cone yarn defects, shielding, and wear, the Faster R-CNN + ResNet50 algorithm can correctly identify these issues without misdetection occurring, with an average precision rate greater than 99.91%. Full article
(This article belongs to the Special Issue Challenges in Machine Learning, Artificial Intelligence, Wireless Sensor Networks and Smart Cities)
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12 pages, 2987 KiB  
Article
A Cotton High-Efficiency Water-Fertilizer Control System Using Wireless Sensor Network for Precision Agriculture
by Chanchan Du, Lixin Zhang, Xiao Ma, Xiaokang Lou, Yongchao Shan, He Li and Runmeng Zhou
Processes 2021, 9(10), 1693; https://doi.org/10.3390/pr9101693 - 22 Sep 2021
Cited by 17 | Viewed by 3222
Abstract
Scientific researchers have applied newly developed technologies, such as sensors and actuators, to different fields, including environmental monitoring, traffic management, and precision agriculture. Using agricultural technology to assist crop fertilization is an important research innovation that can not only reduce the workload of [...] Read more.
Scientific researchers have applied newly developed technologies, such as sensors and actuators, to different fields, including environmental monitoring, traffic management, and precision agriculture. Using agricultural technology to assist crop fertilization is an important research innovation that can not only reduce the workload of farmers, but also reduce resource waste and soil pollution. This paper describes the design and development of a water-fertilizer control system based on the soil conductivity threshold. The system uses a low-cost wireless sensor network as a data collection and transmission tool and transmits the data to the decision support system. The decision support system considers the change in soil electrical conductivity (EC) and moisture content to guide the application of water-fertilizer, and then improves the fertilization accuracy of the water-fertilizer control system. In the experiment, the proposed water-fertilizer control system was tested, and it was concluded that, compared with the existing traditional water-fertilizer integration control system, the amount of fertilizer used by the system was reduced by 10.89% on average, and it could save 0.76–0.87 tons of fertilizer throughout the whole growth period of cotton. Full article
(This article belongs to the Special Issue Challenges in Machine Learning, Artificial Intelligence, Wireless Sensor Networks and Smart Cities)
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16 pages, 3194 KiB  
Article
Production Flow Analysis in a Semiconductor Fab Using Machine Learning Techniques
by Ivan Kristianto Singgih
Processes 2021, 9(3), 407; https://doi.org/10.3390/pr9030407 - 24 Feb 2021
Cited by 11 | Viewed by 5823
Abstract
In a semiconductor fab, wafer lots are processed in complex sequences with re-entrants and parallel machines. It is necessary to ensure smooth wafer lot flows by detecting potential disturbances in a real-time fashion to satisfy the wafer lots’ demands. This study aims to [...] Read more.
In a semiconductor fab, wafer lots are processed in complex sequences with re-entrants and parallel machines. It is necessary to ensure smooth wafer lot flows by detecting potential disturbances in a real-time fashion to satisfy the wafer lots’ demands. This study aims to identify production factors that significantly affect the system’s throughput level and find the best prediction model. The contributions of this study are as follows: (1) this is the first study that applies machine learning techniques to identify important real-time factors that influence throughput in a semiconductor fab; (2) this study develops a test bed in the Anylogic software environment, based on the Intel minifab layout; and (3) this study proposes a data collection scheme for the production control mechanism. As a result, four models (adaptive boosting, gradient boosting, random forest, decision tree) with the best accuracies are selected, and a scheme to reduce the input data types considered in the models is also proposed. After the reduction, the accuracy of each selected model was more than 97.82%. It was found that data related to the machines’ total idle times, processing steps, and machine E have notable influences on the throughput prediction. Full article
(This article belongs to the Special Issue Challenges in Machine Learning, Artificial Intelligence, Wireless Sensor Networks and Smart Cities)
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