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Applied Sciences
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Soft Sensors Based on Deep Neural Networks
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Soft Sensors Based on Deep Neural Networks

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 7927

Special Issue Editors

Dr. Patrik Kamencay

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Guest Editor
Department of Multimedia and Information-Communication Technologies, University of Zilina, 010 26 Zilina, Slovakia
Interests: image segmentation; image analysis; feature extraction; computer vision; pattern recognition; digital image processing; object recognition; classification algorithms; image processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Róbert Hudec

E-Mail Website
Guest Editor
Department of multimedia and information-communication technologies, FEIT, University of Zilina, Univerzitna 8215/1, 01026 Zilina, Slovakia
Interests: neural network; machine learning; deep learning; computer vision; image processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Milan Dado

E-Mail Website
Guest Editor
Faculty of Electrical Engineering and Information Technology, University of Zilina, 01026 Zilina, Slovakia
Interests: smart systems and services; photonic sensors; sensor networks; IoT; optical communication systems; smart cities

Special Issue Information

Dear Colleagues,

Soft sensors estimate unmeasured variables using computational models. For this purpose, machine learning (ML), deep learning (DL) and artificial intelligence (AI) are turning out to be effective procedures. From this, deep learning (DL), as a type of data-driven approach, shows its great potential in many fields, as well as in areas of soft sensor sensing.

This Special Issue welcomes papers that cover the development, validation and application of sensors and sensor networks using one deep learning-based model (DLM) or a combination of several DLMs. It attempts to bring together all the latest developments in the area of “soft sensors based on deep neural networks”. It aims at promoting the recent advances in this research field while highlighting the main real-world challenges.

We invite investigators to contribute original as well as review articles on research and development in areas of smart sensor technologies using deep learning (DL). These include solutions that are designed for smart devices, and solutions that are not directly designed for such use but have the potential for it. Potential topics include, but are not limited to, the following:

  • Deep learning-based computer vision;
  • Sensors and sensor networks for intelligent sensing;
  • Sensor data processing and fusion;
  • Deep neural networks in safety-critical applications;
  • Pattern recognition using sensor networks;
  • Multimodal sensors enabling smart IoT;
  • Smart cities and smart environments;
  • Artificial intelligence for intelligent solutions;
  • New trends and applications for intelligent systems;
  • Developing new models for multimodal deep learning;
  • Big data for intelligent sensor systems;
  • Software/hardware design and solutions for intelligent systems;
  • Intelligent autonomous systems.

Dr. Patrik Kamencay
Prof. Dr. Róbert Hudec
Prof. Dr. Milan Dado
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. Applied Sciences is an international peer-reviewed open access semimonthly 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

  • intelligent sensor systems
  • sensor networks
  • neural network
  • deep learning
  • artificial intelligence
  • big data
  • intelligent systems

Published Papers (3 papers)

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Research

21 pages, 4902 KiB  
Article
SAgric-IoT: An IoT-Based Platform and Deep Learning for Greenhouse Monitoring
by Juan Contreras-Castillo, Juan Antonio Guerrero-Ibañez, Pedro C. Santana-Mancilla and Luis Anido-Rifón
Appl. Sci. 2023, 13(3), 1961; https://doi.org/10.3390/app13031961 - 02 Feb 2023
Cited by 19 | Viewed by 3264
Abstract
The Internet of Things (IoT) and convolutional neural networks (CNN) integration is a growing topic of interest for researchers as a technology that will contribute to transforming agriculture. IoT will enable farmers to decide and act based on data collected from sensor nodes [...] Read more.
The Internet of Things (IoT) and convolutional neural networks (CNN) integration is a growing topic of interest for researchers as a technology that will contribute to transforming agriculture. IoT will enable farmers to decide and act based on data collected from sensor nodes regarding field conditions and not purely based on experience, thus minimizing the wastage of supplies (seeds, water, pesticide, and fumigants). On the other hand, CNN complements monitoring systems with tasks such as the early detection of crop diseases or predicting the number of consumable resources and supplies (water, fertilizers) needed to increase productivity. This paper proposes SAgric-IoT, a technology platform based on IoT and CNN for precision agriculture, to monitor environmental and physical variables and provide early disease detection while automatically controlling the irrigation and fertilization in greenhouses. The results show SAgric-IoT is a reliable IoT platform with a low packet loss level that considerably reduces energy consumption and has a disease identification detection accuracy and classification process of over 90%. Full article
(This article belongs to the Special Issue Soft Sensors Based on Deep Neural Networks)
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18 pages, 1822 KiB  
Article
Tool for Parsing Important Data from Web Pages
by Martina Radilova, Patrik Kamencay, Robert Hudec, Miroslav Benco and Roman Radil
Appl. Sci. 2022, 12(23), 12031; https://doi.org/10.3390/app122312031 - 24 Nov 2022
Viewed by 1562
Abstract
This paper discusses the tool for the main text and image extraction (extracting and parsing the important data) from a web document. This paper describes our proposed algorithm based on the Document Object Model (DOM) and natural language processing (NLP) techniques and other [...] Read more.
This paper discusses the tool for the main text and image extraction (extracting and parsing the important data) from a web document. This paper describes our proposed algorithm based on the Document Object Model (DOM) and natural language processing (NLP) techniques and other approaches for extracting information from web pages using various classification techniques such as support vector machine, decision tree techniques, naive Bayes, and K-nearest neighbor. The main aim of the developed algorithm was to identify and extract the main block of a web document that contains the text of the article and the relevant images. The algorithm on a sample of 45 web documents of different types was applied. In addition, the issue of web pages, from the structure of the document to the use of the Document Object Model (DOM) for their processing, was analyzed. The Document Object Model was used to load and navigation of the document. It also plays an important role in the correct identification of the main block of web documents. The paper also discusses the levels of natural language. These methods of automatic natural language processing help to identify the main block of the web document. In this way, the all-textual parts and images from the main content of the web document were extracted. The experimental results show that our method achieved a final classification accuracy of 88.18%. Full article
(This article belongs to the Special Issue Soft Sensors Based on Deep Neural Networks)
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18 pages, 7065 KiB  
Article
Convolutional Neural Network for Measurement of Suspended Solids and Turbidity
by Daniela Lopez-Betancur, Ivan Moreno, Carlos Guerrero-Mendez, Tonatiuh Saucedo-Anaya, Efrén González, Carlos Bautista-Capetillo and Julián González-Trinidad
Appl. Sci. 2022, 12(12), 6079; https://doi.org/10.3390/app12126079 - 15 Jun 2022
Cited by 7 | Viewed by 2253
Abstract
The great potential of the convolutional neural networks (CNNs) provides novel and alternative ways to monitor important parameters with high accuracy. In this study, we developed a soft sensor model for dynamic processes based on a CNN for the measurement of suspended solids [...] Read more.
The great potential of the convolutional neural networks (CNNs) provides novel and alternative ways to monitor important parameters with high accuracy. In this study, we developed a soft sensor model for dynamic processes based on a CNN for the measurement of suspended solids and turbidity from a single image of the liquid sample to be measured by using a commercial smartphone camera (Android or IOS system) and light-emitting diode (LED) illumination. For this, an image dataset of liquid samples illuminated with white, red, green, and blue LED light was taken and used to train a CNN and fit a multiple linear regression (MLR) by using different color lighting, we evaluated which color gives more accurate information about the concentration of suspended particles in the sample. We implemented a pre-trained AlexNet model, and an MLR to estimate total suspended solids (TSS), and turbidity values in liquid samples based on suspended particles. The proposed technique obtained high goodness of fit (R2 = 0.99). The best performance was achieved using white light, with an accuracy of 98.24% and 97.20% for TSS and turbidity, respectively, with an operational range of 0–800 mgL−1, and 0–306 NTU. This system was designed for aquaculture environments and tested with both commercial fish feed and paprika. This motivates further research with different aquatic environments such as river water, domestic and industrial wastewater, and potable water, among others. Full article
(This article belongs to the Special Issue Soft Sensors Based on Deep Neural Networks)
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