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Sensors in Machine Intelligence and Soft Computing
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Sensors in Machine Intelligence and Soft Computing

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

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 4556

Special Issue Editors

Prof. Dr. Debnath Bhattacharyya

E-Mail Website
Guest Editor
Computer Science and Engineering, K L Deemed to be University, Vaddeswaram, India
Interests: sensor network; image processing; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yu-Chen Hu

E-Mail Website
Guest Editor
Department of Computer Science and Information Management, Providence University, Taichung 43301, Taiwan
Interests: computer vision; digital forensics; information hiding; image and signal processing; data compression; information security; computer network; deep learning; bioinformatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Today, the amount of data that is generated by both humans and machines (IoT, sensors, etc.) outpaces our ability to absorb, interpret, and make complex decisions based on these data. Artificial intelligence and machine intelligence form the basis for all computer learning and are the future of all complex decision making where networking (WSN) cannot be avoided. Important uses of artificial intelligence and machine intelligence are medical science, air transport, business (banking and finance), business decision making, gaming, space, data analytics, etc.

This Special Issue aims for the rapid dissemination of research results relevant to the targeted artificial intelligence (AI), machine intelligence, image processing, pattern recognition, computer vision, and networking communities. The scope encompasses research areas, including agents and multi-agent systems, automated reasoning, constraint processing and searching, knowledge representation, machine learning, planning, scheduling, computer vision, and uncertainty/approximation. This Special Issue also provides a premier interdisciplinary platform for researchers, practitioners, and educators to promote the most recent innovations, trends, and concerns, as well as practical challenges encountered, alongside solutions adopted in the fields of machine intelligence, soft computing, and networking. This is a purely target- and research-oriented Special Issue.

Prof. Dr. Debnath Bhattacharyya
Prof. Dr. Yu-Chen Hu
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

  • wireless sensors
  • wearable sensors
  • sensor devices
  • image and signal sensing
  • IoT
  • AIoT
  • medical imaging
  • sensing principles
  • pattern recognition
  • data analytics
  • computer vision

Published Papers (2 papers)

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Research

17 pages, 6080 KiB  
Article
Soft-Computing-Based Estimation of a Static Load for an Overhead Crane
by Tom Kusznir and Jaroslaw Smoczek
Sensors 2023, 23(13), 5842; https://doi.org/10.3390/s23135842 - 23 Jun 2023
Viewed by 987
Abstract
Payload weight detection plays an important role in condition monitoring and automation of cranes. Crane cells and scales are commonly used in industrial practice; however, when their installation to the hoisting equipment is not possible or costly, an alternative solution is to derive [...] Read more.
Payload weight detection plays an important role in condition monitoring and automation of cranes. Crane cells and scales are commonly used in industrial practice; however, when their installation to the hoisting equipment is not possible or costly, an alternative solution is to derive information about the load weight indirectly from other sensors. In this paper, a static payload weight is estimated based on the local strain of a crane’s girder and the current position of the trolley. Soft-computing-based techniques are used to derive a nonlinear input–output relationship between the measured signals and the estimated payload mass. Data-driven identification is performed using a novel variant of genetic programming named grammar-guided genetic programming with sparse regression, multi-gene genetic programming, and subtractive fuzzy clustering method combined with the least squares algorithm on experimental data obtained from a laboratory overhead crane. A comparative analysis of the methods showed that multi-gene genetic programming and grammar-guided genetic programming with sparse regression performed similarly in terms of accuracy and both performed better than subtractive fuzzy clustering. The novel approach was able to find a more parsimonious model with its direct implantation having a lower execution time. Full article
(This article belongs to the Special Issue Sensors in Machine Intelligence and Soft Computing)
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15 pages, 2818 KiB  
Article
An Electro-Oculogram (EOG) Sensor’s Ability to Detect Driver Hypovigilance Using Machine Learning
by Suganiya Murugan, Pradeep Kumar Sivakumar, C. Kavitha, Anandhi Harichandran and Wen-Cheng Lai
Sensors 2023, 23(6), 2944; https://doi.org/10.3390/s23062944 - 08 Mar 2023
Cited by 1 | Viewed by 2250
Abstract
Driving safely is crucial to avoid death, injuries, or financial losses that can be sustained in an accident. Thus, a driver’s physical state should be monitored to prevent accidents, rather than vehicle-based or behavioral measurements, and provide reliable information in this regard. Electrocardiography [...] Read more.
Driving safely is crucial to avoid death, injuries, or financial losses that can be sustained in an accident. Thus, a driver’s physical state should be monitored to prevent accidents, rather than vehicle-based or behavioral measurements, and provide reliable information in this regard. Electrocardiography (ECG), electroencephalography (EEG), electrooculography (EOG), and surface electromyography (sEMG) signals are used to monitor a driver’s physical state during a drive. The purpose of this study was to detect driver hypovigilance (drowsiness, fatigue, as well as visual and cognitive inattention) using signals collected from 10 drivers while they were driving. EOG signals from the driver were preprocessed to remove noise, and 17 features were extracted. ANOVA (analysis of variance) was used to select statistically significant features that were then loaded into a machine learning algorithm. We then reduced the features by using principal component analysis (PCA) and trained three classifiers: support vector machine (SVM), k-nearest neighbor (KNN), and ensemble. A maximum accuracy of 98.7% was obtained for the classification of normal and cognitive classes under the category of two-class detection. Upon considering hypovigilance states as five-class, a maximum accuracy of 90.9% was achieved. In this case, the number of detection classes increased, resulting in a reduction in the accuracy of detecting more driver states. However, with the possibility of incorrect identification and the presence of issues, the ensemble classifier’s performance produced an enhanced accuracy when compared to others. Full article
(This article belongs to the Special Issue Sensors in Machine Intelligence and Soft Computing)
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