Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.3
3.4
Journals
Sensors
Special Issues
Artificial Intelligence for Sensor Data Analysis
sensors-logo
Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Artificial Intelligence for Sensor Data Analysis

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 9591

Special Issue Editors

Dr. Alberto Cano

E-Mail Website
Guest Editor
Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA
Interests: data streams; concept drift; multi-label learning; imbalanced learning
Special Issues, Collections and Topics in MDPI journals
Dr. Sylvio Barbon Junior

E-Mail Website
Guest Editor
Department of Engineering and Architecture, University of Trieste, Trieste, Italy
Interests: stream mining; process mining; automatic machine learning

Special Issue Information

Dear Colleagues,

The increasing amount of real-time data provided by smaller and cheaper sensors has increased the interest of the research community in methods of analyzing large collections of sensor data. This Special Issue welcomes original research works in the area of sensor data analysis and processing using machine learning, data science, and artificial intelligence techniques.

Dr. Alberto Cano
Dr. Sylvio Barbon Junior
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. Sensors 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 2600 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

  • sensor data
  • machine learning
  • deep learning
  • big data
  • data streams
  • internet of things
  • intelligent systems

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

22 pages, 2874 KiB  
Article
Dynamic Adaptation Attack Detection Model for a Distributed Multi-Access Edge Computing Smart City
by Nouf Saeed Alotaibi, Hassan Ibrahim Ahmed and Samah Osama M. Kamel
Sensors 2023, 23(16), 7135; https://doi.org/10.3390/s23167135 - 12 Aug 2023
Cited by 1 | Viewed by 1193
Abstract
The internet of things (IoT) technology presents an intelligent way to improve our lives and contributes to many fields such as industry, communications, agriculture, etc. Unfortunately, IoT networks are exposed to many attacks that may destroy the entire network and consume network resources. [...] Read more.
The internet of things (IoT) technology presents an intelligent way to improve our lives and contributes to many fields such as industry, communications, agriculture, etc. Unfortunately, IoT networks are exposed to many attacks that may destroy the entire network and consume network resources. This paper aims to propose intelligent process automation and an auto-configured intelligent automation detection model (IADM) to detect and prevent malicious network traffic and behaviors/events at distributed multi-access edge computing in an IoT-based smart city. The proposed model consists of two phases. The first phase relies on the intelligent process automation (IPA) technique and contains five modules named, specifically, dataset collection and pre-processing module, intelligent automation detection module, analysis module, detection rules and action module, and database module. In the first phase, each module composes an intelligent connecting module to give feedback reports about each module and send information to the next modules. Therefore, any change in each process can be easily detected and labeled as an intrusion. The intelligent connection module (ICM) may reduce the search time, increase the speed, and increase the security level. The second phase is the dynamic adaptation of the attack detection model based on reinforcement one-shot learning. The first phase is based on a multi-classification technique using Random Forest Trees (RFT), k-Nearest Neighbor (K-NN), J48, AdaBoost, and Bagging. The second phase can learn the new changed behaviors based on reinforced learning to detect zero-day attacks and malicious events in IoT-based smart cities. The experiments are implemented using a UNSW-NB 15 dataset. The proposed model achieves high accuracy rates using RFT, K-NN, and AdaBoost of approximately 98.8%. It is noted that the accuracy rate of the J48 classifier achieves 85.51%, which is lower than the others. Subsequently, the accuracy rates of AdaBoost and Bagging based on J48 are 98.9% and 91.41%, respectively. Additionally, the error rates of RFT, K-NN, and AdaBoost are very low. Similarly, the proposed model achieves high precision, recall, and F1-measure high rates using RFT, K-NN, AdaBoost, and Bagging. The second phase depends on creating an auto-adaptive model through the dynamic adaptation of the attack detection model based on reinforcement one-shot learning using a small number of instances to conserve the memory of any smart device in an IoT network. The proposed auto-adaptive model may reduce false rates of reporting by the intrusion detection system (IDS). It can detect any change in the behaviors of smart devices quickly and easily. The IADM can improve the performance rates for IDS by maintaining the memory consumption, time consumption, and speed of the detection process. Full article
(This article belongs to the Special Issue Artificial Intelligence for Sensor Data Analysis)
►▼ Show Figures

Figure 1

17 pages, 5030 KiB  
Article
Modelling and Predictive Monitoring of Business Processes under Uncertainty with Reinforcement Learning
by Alexandros Bousdekis, Athanasios Kerasiotis, Silvester Kotsias, Georgia Theodoropoulou, Georgios Miaoulis and Djamchid Ghazanfarpour
Sensors 2023, 23(15), 6931; https://doi.org/10.3390/s23156931 - 3 Aug 2023
Cited by 1 | Viewed by 1446
Abstract
The analysis of business processes based on their observed behavior recorded in event logs can be performed with process mining. This method can discover, monitor, and improve processes in various application domains. However, the process models produced by typical process discovery methods are [...] Read more.
The analysis of business processes based on their observed behavior recorded in event logs can be performed with process mining. This method can discover, monitor, and improve processes in various application domains. However, the process models produced by typical process discovery methods are difficult for humans to understand due to their high complexity (the so-called “spaghetti-like” process models). Moreover, these methods cannot handle uncertainty or perform predictions because of their deterministic nature. Recently, researchers have been developing predictive approaches for running business cases of processes. This paper focuses on developing a predictive business process monitoring approach using reinforcement learning (RL), which has been successful in other contexts but not yet explored in this area. The proposed approach is evaluated in the banking sector through a use case. Full article
(This article belongs to the Special Issue Artificial Intelligence for Sensor Data Analysis)
►▼ Show Figures

Figure 1

31 pages, 1481 KiB  
Article
Application of Machine Learning Algorithms for Tool Condition Monitoring in Milling Chipboard Process
by Agata Przybyś-Małaczek, Izabella Antoniuk, Karol Szymanowski, Michał Kruk and Jarosław Kurek
Sensors 2023, 23(13), 5850; https://doi.org/10.3390/s23135850 - 23 Jun 2023
Cited by 6 | Viewed by 1824
Abstract
In this article, we present a novel approach to tool condition monitoring in the chipboard milling process using machine learning algorithms. The presented study aims to address the challenges of detecting tool wear and predicting tool failure in real time, which can significantly [...] Read more.
In this article, we present a novel approach to tool condition monitoring in the chipboard milling process using machine learning algorithms. The presented study aims to address the challenges of detecting tool wear and predicting tool failure in real time, which can significantly improve the efficiency and productivity of the manufacturing process. A combination of feature engineering and machine learning techniques was applied in order to analyze 11 signals generated during the milling process. The presented approach achieved high accuracy in detecting tool wear and predicting tool failure, outperforming traditional methods. The final findings demonstrate the potential of machine learning algorithms in improving tool condition monitoring in the manufacturing industry. This study contributes to the growing body of research on the application of artificial intelligence in industrial processes. In conclusion, the presented research highlights the importance of adopting innovative approaches to address the challenges of tool condition monitoring in the manufacturing industry. The final results provide valuable insights for practitioners and researchers in the field of industrial automation and machine learning. Full article
(This article belongs to the Special Issue Artificial Intelligence for Sensor Data Analysis)
►▼ Show Figures

Figure 1

16 pages, 3697 KiB  
Article
Hybrid Domain Consistency Constraints-Based Deep Neural Network for Facial Expression Recognition
by Xiaoliang Zhu, Junyi Sun, Gendong Liu, Chen Shen, Zhicheng Dai and Liang Zhao
Sensors 2023, 23(11), 5201; https://doi.org/10.3390/s23115201 - 30 May 2023
Viewed by 1272
Abstract
Facial expression recognition (FER) has received increasing attention. However, multiple factors (e.g., uneven illumination, facial deflection, occlusion, and subjectivity of annotations in image datasets) probably reduce the performance of traditional FER methods. Thus, we propose a novel Hybrid Domain Consistency Network (HDCNet) based [...] Read more.
Facial expression recognition (FER) has received increasing attention. However, multiple factors (e.g., uneven illumination, facial deflection, occlusion, and subjectivity of annotations in image datasets) probably reduce the performance of traditional FER methods. Thus, we propose a novel Hybrid Domain Consistency Network (HDCNet) based on a feature constraint method that combines both spatial domain consistency and channel domain consistency. Specifically, first, the proposed HDCNet mines the potential attention consistency feature expression (different from manual features, e.g., HOG and SIFT) as effective supervision information by comparing the original sample image with the augmented facial expression image. Second, HDCNet extracts facial expression-related features in the spatial and channel domains, and then it constrains the consistent expression of features through the mixed domain consistency loss function. In addition, the loss function based on the attention-consistency constraints does not require additional labels. Third, the network weights are learned to optimize the classification network through the loss function of the mixed domain consistency constraints. Finally, experiments conducted on the public RAF-DB and AffectNet benchmark datasets verify that the proposed HDCNet improved classification accuracy by 0.3–3.84% compared to the existing methods. Full article
(This article belongs to the Special Issue Artificial Intelligence for Sensor Data Analysis)
►▼ Show Figures

Figure 1

13 pages, 4906 KiB  
Article
Deep Learning-Based ADHD and ADHD-RISK Classification Technology through the Recognition of Children’s Abnormal Behaviors during the Robot-Led ADHD Screening Game
by Wonjun Lee, Sanghyub Lee, Deokwon Lee, Kooksung Jun, Dong Hyun Ahn and Mun Sang Kim
Sensors 2023, 23(1), 278; https://doi.org/10.3390/s23010278 - 27 Dec 2022
Cited by 2 | Viewed by 2730
Abstract
Although attention deficit hyperactivity disorder (ADHD) in children is rising worldwide, fewer studies have focused on screening than on the treatment of ADHD. Most previous similar ADHD classification studies classified only ADHD and normal classes. However, medical professionals believe that better distinguishing the [...] Read more.
Although attention deficit hyperactivity disorder (ADHD) in children is rising worldwide, fewer studies have focused on screening than on the treatment of ADHD. Most previous similar ADHD classification studies classified only ADHD and normal classes. However, medical professionals believe that better distinguishing the ADHD–RISK class will assist them socially and medically. We created a projection-based game in which we can see stimuli and responses to better understand children’s abnormal behavior. The developed screening game is divided into 11 stages. Children play five games. Each game is divided into waiting and game stages; thus, 10 stages are created, and the additional waiting stage includes an explanation stage where the robot waits while explaining the first game. Herein, we classified normal, ADHD–RISK, and ADHD using skeleton data obtained through games for ADHD screening of children and a bidirectional long short-term memory-based deep learning model. We verified the importance of each stage by passing the feature for each stage through the channel attention layer. Consequently, the final classification accuracy of the three classes was 98.15% using bi-directional LSTM with channel attention model. Additionally, the attention scores obtained through the channel attention layer indicated that the data in the latter part of the game are heavily involved in learning the ADHD–RISK case. These results imply that for ADHD–RISK, the game is repeated, and children’s attention decreases as they progress to the second half. Full article
(This article belongs to the Special Issue Artificial Intelligence for Sensor Data Analysis)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop