Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.7
2.9
Journals
Electronics
Special Issues
Advances in Fuzzy and Intelligent Systems
share announcement

Advances in Fuzzy and Intelligent Systems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (15 December 2023) | Viewed by 11676

Special Issue Editors

Dr. Mohammad Bagher Dowlatshahi

E-Mail Website
Guest Editor
Department of Computer Engineering, Faculty of Engineering, Lorestan University, Khorramabad 68151-44316, Iran
Interests: intelligent systems; fuzzy system; machine learning; data mining; pattern recognition; swarm intelligence; multi-criteria decision making
Dr. Yue Wu

E-Mail Website
Guest Editor
Department of Computer Science and Technology, Xidian University, Xi'an 710071, China
Interests: computer vision; image processing and pattern recognition; theory and applications of computational intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,  

The development of fuzzy and intelligent systems has spanned across various areas, such as software engineering, robotics, finance, industry, space technologies, education, home appliances, health, communication, security, military, aviation, energy, and many other engineering and technology dimensions. Many extended models are improving the practicality and effectiveness of fuzzy and intelligent systems in engineering and technology. They offer a more powerful system to analyze uncertainty and extract knowledge from big heterogeneous data. This Special Issue aims to provide a platform for researchers to discuss research, developments, and innovations in fuzzy and intelligent systems in engineering and technology. The topics of interest include, but are not limited to, the following:

  • Fuzzy system and fuzzy inference;
  • Expert systems and uncertain decision making;
  • Machine learning and deep learning;
  • Bioinformatics;Data mining, Social media mining;
  • Evolutionary and swarm computation;
  • Multi-criteria decision making;
  • Intelligent information processing;
  • Intelligent software engineering;
  • Recommender systems, Other topics related to intelligent systems.

We look forward to receiving your contributions.

Dr. Mohammad Bagher Dowlatshahi
Dr. Yue Wu
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. Electronics 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

  • fuzzy sets
  • fuzzy system
  • data mining
  • machine learning
  • deep learning
  • swarm intelligence
  • evolutionary computation
  • recommender systems
  • multi-criteria decision making
  • software engineering

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

20 pages, 8846 KiB  
Article
Advanced Fuzzy Sets and Genetic Algorithm Optimizer for Mammographic Image Enhancement
by Anastasios Dounis, Andreas-Nestor Avramopoulos and Maria Kallergi
Electronics 2023, 12(15), 3269; https://doi.org/10.3390/electronics12153269 - 29 Jul 2023
Cited by 2 | Viewed by 1169
Abstract
A well-researched field is the development of Computer Aided Diagnosis (CADx) Systems for the benign-malignant classification of abnormalities detected by mammography. Due to the nature of the breast parenchyma, there are significant uncertainties about the shape and geometry of the abnormalities that may [...] Read more.
A well-researched field is the development of Computer Aided Diagnosis (CADx) Systems for the benign-malignant classification of abnormalities detected by mammography. Due to the nature of the breast parenchyma, there are significant uncertainties about the shape and geometry of the abnormalities that may lead to an inaccurate diagnosis. These same uncertainties give mammograms a fuzzy character that is essential to the application of fuzzy processing. Fuzzy set theory considers uncertainty in the form of a membership function, and therefore fuzzy sets can process imperfect data if this imperfection originates from vagueness and ambiguity rather than randomness. Fuzzy contrast enhancement can improve edge detection and, by extension, the quality of related classification features. In this paper, classical (Linguistic hedges and fuzzy enhancement functions), advanced fuzzy sets (Intuitionistic fuzzy set (ΙFS), Pythagorean fuzzy set (PFS), and Fermatean fuzzy sets (FFS)), and a Genetic Algorithm optimizer are proposed to enhance the contrast of mammographic features. The advanced fuzzy sets provide better information on the uncertainty of the membership function. As a result, the intuitionistic method had the best overall performance, but most of the techniques could be used efficiently, depending on the problem that needed to be solved. Linguistic methods could provide a more manageable way of spreading the histogram, revealing more extreme values than the conventional methods. A fusion technique of the enhanced mammography images with Ordered Weighted Average operators (OWA) achieves a good-quality final image. Full article
(This article belongs to the Special Issue Advances in Fuzzy and Intelligent Systems)
►▼ Show Figures

Figure 1

16 pages, 481 KiB  
Article
The Generalization of Non-Negative Matrix Factorization Based on Algorithmic Stability
by Haichao Sun and Jie Yang
Electronics 2023, 12(5), 1147; https://doi.org/10.3390/electronics12051147 - 27 Feb 2023
Cited by 2 | Viewed by 964
Abstract
The Non-negative Matrix Factorization (NMF) is a popular technique for intelligent systems, which can be widely used to decompose a nonnegative matrix into two factor matrices: a basis matrix and a coefficient one, respectively. The main objective of NMF is to ensure that [...] Read more.
The Non-negative Matrix Factorization (NMF) is a popular technique for intelligent systems, which can be widely used to decompose a nonnegative matrix into two factor matrices: a basis matrix and a coefficient one, respectively. The main objective of NMF is to ensure that the operation results of the two matrices are as close to the original matrix as possible. Meanwhile, the stability and generalization ability of the algorithm should be ensured. Therefore, the generalization performance of NMF algorithms is analyzed from the perspective of algorithm stability and the generalization error bounds are given, which is named AS-NMF. Firstly, a general NMF prediction algorithm is proposed, which can predict the labels for new samples, and then the corresponding loss function is defined further. Secondly, the stability of the NMF algorithm is defined according to the loss function, and two generalization error bounds can be obtained by employing uniform stability in the case where U is fixed and it is not fixed under the multiplicative update rule. The bounds numerically show that its stability parameter depends on the upper bound on the module length of the input data, dimension of hidden matrix and Frobenius norm of the basis matrix. Finally, a general and stable framework is established, which can analyze and measure generalization error bounds for the NMF algorithm. The experimental results demonstrate the advantages of new methods on three widely used benchmark datasets, which indicate that our AS-NMF can not only achieve efficient performance, but also outperform the state-of-the-art of recommending tasks in terms of model stability. Full article
(This article belongs to the Special Issue Advances in Fuzzy and Intelligent Systems)
►▼ Show Figures

Figure 1

11 pages, 2071 KiB  
Article
Evaluating the Performance of Fuzzy-PID Control for Lane Recognition and Lane-Keeping in Vehicle Simulations
by Moveh Samuel, Khalid Yahya, Hani Attar, Ayman Amer, Mahmoud Mohamed and Tajudeen Adeleke Badmos
Electronics 2023, 12(3), 724; https://doi.org/10.3390/electronics12030724 - 01 Feb 2023
Cited by 5 | Viewed by 1974
Abstract
This study presents the use of a vision-based fuzzy-PID lane-keeping control system for the simulation of a single-track bicycle model. The lane-keeping system (LKS) processes images to identify the lateral deviation of the vehicle from the desired reference track and generates a steering [...] Read more.
This study presents the use of a vision-based fuzzy-PID lane-keeping control system for the simulation of a single-track bicycle model. The lane-keeping system (LKS) processes images to identify the lateral deviation of the vehicle from the desired reference track and generates a steering control command to correct the deviation. The LKS was compared to other lane-keeping control methods, such as Ziegler–Nichols proportional derivative (PD) and model predictive control (MPC), in terms of response time and settling time. The fuzzy-PID controller had the best performance, with fewer oscillations and a faster response time compared to the other methods. The PD controller was not as robust under various conditions due to changing parameters, while the MPC was not accurate enough due to similar reasons. However, the fuzzy-PID controller showed the best performance, with a maximum lateral deviation of 2 cm, a settling time of 12 s, and Kp and Kd values of 0.01 and 0.06, respectively. Overall, this work demonstrates the potential of using fuzzy-PID control for effective lane recognition and lane-keeping in vehicles. Full article
(This article belongs to the Special Issue Advances in Fuzzy and Intelligent Systems)
►▼ Show Figures

Figure 1

14 pages, 2588 KiB  
Article
A Novel Framework for Classification of Different Alzheimer’s Disease Stages Using CNN Model
by Gowhar Mohi ud din dar, Avinash Bhagat, Syed Immamul Ansarullah, Mohamed Tahar Ben Othman, Yasir Hamid, Hend Khalid Alkahtani, Inam Ullah and Habib Hamam
Electronics 2023, 12(2), 469; https://doi.org/10.3390/electronics12020469 - 16 Jan 2023
Cited by 12 | Viewed by 2567
Abstract
Background: Alzheimer’s, the predominant formof dementia, is a neurodegenerative brain disorder with no known cure. With the lack of innovative findings to diagnose and treat Alzheimer’s, the number of middle-aged people with dementia is estimated to hike nearly to 13 million by the [...] Read more.
Background: Alzheimer’s, the predominant formof dementia, is a neurodegenerative brain disorder with no known cure. With the lack of innovative findings to diagnose and treat Alzheimer’s, the number of middle-aged people with dementia is estimated to hike nearly to 13 million by the end of 2050. The estimated cost of Alzheimer’s and other related ailments is USD321 billion in 2022 and can rise above USD1 trillion by the end of 2050. Therefore, the early prediction of such diseases using computer-aided systems is a topic of considerable interest and substantial study among scholars. The major objective is to develop a comprehensive framework for the earliest onset and categorization of different phases of Alzheimer’s. Methods: Experimental work of this novel approach is performed by implementing neural networks (CNN) on MRI image datasets. Five classes of Alzheimer’s disease subjects are multi-classified. We used the transfer learning determinant to reap the benefits of pre-trained health data classification models such as the MobileNet. Results: For the evaluation and comparison of the proposed model, various performance metrics are used. The test results reveal that the CNN architectures method has the following characteristics: appropriate simple structures that mitigate computational burden, memory usage, and overfitting, as well as offering maintainable time. The MobileNet pre-trained model has been fine-tuned and has achieved 96.6 percent accuracy for multi-class AD stage classifications. Other models, such as VGG16 and ResNet50 models, are applied tothe same dataset whileconducting this research, and it is revealed that this model yields better results than other models. Conclusion: The study develops a novel framework for the identification of different AD stages. The main advantage of this novel approach is the creation of lightweight neural networks. MobileNet model is mostly used for mobile applications and was rarely used for medical image analysis; hence, we implemented this model for disease detection andyieldedbetter results than existing models. Full article
(This article belongs to the Special Issue Advances in Fuzzy and Intelligent Systems)
►▼ Show Figures

Figure 1

21 pages, 5269 KiB  
Article
The Role of ML, AI and 5G Technology in Smart Energy and Smart Building Management
by Tehseen Mazhar, Muhammad Amir Malik, Inayatul Haq, Iram Rozeela, Inam Ullah, Muhammad Abbas Khan, Deepak Adhikari, Mohamed Tahar Ben Othman and Habib Hamam
Electronics 2022, 11(23), 3960; https://doi.org/10.3390/electronics11233960 - 29 Nov 2022
Cited by 13 | Viewed by 4222
Abstract
With the help of machine learning, many tasks can be automated. The use of computers and mobile devices in “intelligent” buildings may make tasks such as controlling the indoor climate, monitoring security, and performing routine maintenance much easier. Intelligent buildings employ the Internet [...] Read more.
With the help of machine learning, many tasks can be automated. The use of computers and mobile devices in “intelligent” buildings may make tasks such as controlling the indoor climate, monitoring security, and performing routine maintenance much easier. Intelligent buildings employ the Internet of Things to establish connections among the many components that make up the structure. As the notion of the Internet of Things (IoT) gains attraction, smart grids are being integrated into larger networks. The IoT is an integral part of smart grids since it enables beneficial services that improve the experience for everyone inside and individuals are protected because of tried-and-true life support systems. The reason for installing Internet of Things gadgets in smart structures is the primary focus of this investigation. In this context, the infrastructure behind IoT devices and their component units is of the highest concern. Full article
(This article belongs to the Special Issue Advances in Fuzzy and Intelligent Systems)
►▼ 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