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Recent Advances in Fuzzy Deep Learning for Uncertain Medicine Data
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Recent Advances in Fuzzy Deep Learning for Uncertain Medicine Data

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Fuzzy Sets, Systems and Decision Making".

Deadline for manuscript submissions: 31 May 2024 | Viewed by 5137

Special Issue Editors

Dr. Naeem Jan

E-Mail Website
Guest Editor
Department of Software, Korea National University of Transportation, Chungju 27469, Republic of Korea
Interests: graph theory; artificial intelligence; fuzzy sets and generalizations; fuzzy algebra; theoretical computer science
Dr. Jeonghwan Gwak

E-Mail Website
Guest Editor
Department of Software, Korea National University of Transportation, Chungju 27469, Republic of Korea
Interests: computer vision; machine/deep learning; applications in visual surveillance and healthcare
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Fuzzy and deep learning are two powerful theories within the field of machine learning. Fuzzy logic is a well-developed mathematical theory that provides various powerful methods capable of dealing with the vagueness contained in the data, and thus, suggests solutions that work well. On the other hand, deep learning is a subfield of machine learning that uses artificial neural networks with many layers to learn from vast amounts of data. It is particularly effective in handling unstructured and complex data such as images, speech, and natural language processing. Deep learning has achieved remarkable success in several applications, such as computer vision, speech recognition, medical diagnoses, and natural language processing.

Fuzzy logic and deep learning can be combined to create more powerful and robust models that can handle both structured and unstructured data. For example, a fuzzy deep learning model can be used to predict customer behavior in an e-commerce platform, where the inputs are a mix of structured data, such as demographic information, medical data, and unstructured data, such as clickstream and search queries. The model can then be used to personalize recommendations and improve the overall user experience.

This Special Issue is centered around the integration of fuzzy and deep learning techniques for medical applications, with a specific focus on the development of an efficient and effective integrated model, algorithm, and system. The aim is to improve the reasoning and intelligent monitoring, control, and treatment of uncertain medical data in the context of epidemic outbreaks.

The goal of this Special Issue is to bring together advanced works in these research areas, including the latest research, development, and practical experiences. Additionally, this Special Issue will address current issues, review accomplishments, and assess future directions and challenges in this field. Our intention is to provide a comprehensive platform for knowledge sharing and collaboration among experts in this area.

The potential topics are given below, but this list is not limited to only these:

  • Fuzzy logic and disease diagnosis;
  • Fuzzy deep learning models;
  • Fuzzy logic and medical data;
  • Fuzzy deep neural networks;
  • Fuzzy inference system;
  • Fuzzy clustering;
  • Fuzzy medical imaging;
  • Electronic health records;
  • Fuzzy expert systems;
  • Fuzzy systems and healthcare analytics.

Dr. Naeem Jan
Dr. Jeonghwan Gwak
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. Mathematics 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

  • fuzzy logic and disease diagnosis
  • fuzzy deep learning models
  • fuzzy logic and medical data
  • fuzzy deep neural networks
  • fuzzy inference system
  • fuzzy clustering
  • fuzzy medical imaging
  • electronic health records
  • fuzzy expert systems
  • fuzzy systems and healthcare analytics

Published Papers (4 papers)

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Research

46 pages, 10425 KiB  
Article
A Bidirectional Arabic Sign Language Framework Using Deep Learning and Fuzzy Matching Score
by Mogeeb A. A. Mosleh, Adel Assiri, Abdu H. Gumaei, Bader Fahad Alkhamees and Manal Al-Qahtani
Mathematics 2024, 12(8), 1155; https://doi.org/10.3390/math12081155 - 11 Apr 2024
Viewed by 588
Abstract
Sign language is widely used to facilitate the communication process between deaf people and their surrounding environment. Sign language, like most other languages, is considered a complex language which cannot be mastered easily. Thus, technology can be used as an assistive tool to [...] Read more.
Sign language is widely used to facilitate the communication process between deaf people and their surrounding environment. Sign language, like most other languages, is considered a complex language which cannot be mastered easily. Thus, technology can be used as an assistive tool to solve the difficulties and challenges that deaf people face during interactions with society. In this study, an automatic bidirectional translation framework for Arabic Sign Language (ArSL) is designed to assist both deaf and ordinary people to communicate and express themselves easily. Two main modules were intended to translate Arabic sign images into text by utilizing different transfer learning models and to translate the input text into Arabic sign images. A prototype was implemented based on the proposed framework by using several pre-trained convolutional neural network (CNN)-based deep learning models, including the DenseNet121, ResNet152, MobileNetV2, Xception, InceptionV3, NASNetLarge, VGG19, and VGG16 models. A fuzzy string matching score method, as a novel concept, was employed to translate the input text from ordinary people into appropriate sign language images. The dataset was constructed with specific criteria to obtain 7030 images for 14 classes captured from both deaf and ordinary people locally. The prototype was developed to conduct the experiments on the collected ArSL dataset using the utilized CNN deep learning models. The experimental results were evaluated using standard measurement metrics such as accuracy, precision, recall, and F1-score. The performance and efficiency of the ArSL prototype were assessed using a test set of an 80:20 splitting procedure, obtaining accuracy results from the highest to the lowest rates with average classification time in seconds for each utilized model, including (VGG16, 98.65%, 72.5), (MobileNetV2, 98.51%, 100.19), (VGG19, 98.22%, 77.16), (DenseNet121, 98.15%, 80.44), (Xception, 96.44%, 72.54), (NASNetLarge, 96.23%, 84.96), (InceptionV3, 94.31%, 76.98), and (ResNet152, 47.23%, 98.51). The fuzzy matching score is mathematically validated by computing the distance between the input and associative dictionary words. The study results showed the prototype’s ability to successfully translate Arabic sign images into Arabic text and vice versa, with the highest accuracy. This study proves the ability to develop a robust and efficient real-time bidirectional ArSL translation system using deep learning models and the fuzzy string matching score method. Full article
(This article belongs to the Special Issue Recent Advances in Fuzzy Deep Learning for Uncertain Medicine Data)
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15 pages, 336 KiB  
Article
On Primal Soft Topology
by Tareq M. Al-shami, Zanyar A. Ameen, Radwan Abu-Gdairi and Abdelwaheb Mhemdi
Mathematics 2023, 11(10), 2329; https://doi.org/10.3390/math11102329 - 16 May 2023
Cited by 11 | Viewed by 1471
Abstract
In a soft environment, we investigated several (classical) structures such as ideals, filters, grills, etc. It is well known that these structures are applied to expand abstract concepts; in addition, some of them offer a vital tool to address some practical issues, especially [...] Read more.
In a soft environment, we investigated several (classical) structures such as ideals, filters, grills, etc. It is well known that these structures are applied to expand abstract concepts; in addition, some of them offer a vital tool to address some practical issues, especially those related to improving rough approximation operators and accuracy measures. Herein, we contribute to this line of research by presenting a novel type of soft structure, namely “soft primal”. We investigate its basic properties and describe its behaviors under soft mappings with the aid of some counterexamples. Then, we introduce three soft operators (·)⋄, Cl⋄ and (·)□ inspired by soft primals and explore their main characterizations. We show that Cl⋄ satisfies the soft Kuratowski closure operator, which means that Cl⋄ generates a unique soft topology we call a primal soft topology. Among other obtained results, we elaborate that the set of primal topologies forms a natural class in the lattice of topologies over a universal set and set forth some descriptions for primal soft topology under specific types of soft primals. Full article
(This article belongs to the Special Issue Recent Advances in Fuzzy Deep Learning for Uncertain Medicine Data)
22 pages, 1209 KiB  
Article
Assessment of Structural Systems to Design Earthquake Resistance Buildings by Employing Multi-Attribute Decision-Making Method Based on the Bipolar Complex Fuzzy Dombi Prioritized Aggregation Operators
by Zhiping Xu, Ubaid ur Rehman, Tahir Mahmood, Jabbar Ahmmad and Yun Jin
Mathematics 2023, 11(10), 2226; https://doi.org/10.3390/math11102226 - 09 May 2023
Cited by 1 | Viewed by 988
Abstract
An earthquake is a natural phenomenon that occurs when two tectonic plates in the earth’s crust move against each other. This movement creates seismic waves that can cause the ground to shake, sometimes resulting in damage to buildings and infrastructure. It is important [...] Read more.
An earthquake is a natural phenomenon that occurs when two tectonic plates in the earth’s crust move against each other. This movement creates seismic waves that can cause the ground to shake, sometimes resulting in damage to buildings and infrastructure. It is important to be prepared for earthquakes, particularly if you live in an area that is at high risk for seismic activity. This includes having an emergency kit, knowing how to shut off utilities, having a plan in place for what to do in the event of an earthquake, and most importantly, constructing earthquake resistance buildings. The assessment and the ranking of structural systems to design earthquake resistance buildings is a MADM (multi-attribute decision-making) dilemma. Consequently, in this script, we initiate the method of MADM under the bipolar complex fuzzy (BCF) information. For this method, we devise BCF Dombi prioritized averaging (BCFDPA), BCF Dombi prioritized weighted averaging (BCFDPWA), BCF Dombi prioritized geometric (BCFDPG), and BCF Dombi prioritized weighted geometric (BCFDPPWG) operators by utilizing the Dombi aggregation operator (AO) with BCF information. After that, by using artificial data, we assess the structural systems to design earthquake resistance buildings with the assistance of the invented method of MADM. To exhibit the dominancy and supremacy of the elaborated work, the advantages, sensitive examination, graphical representation, and comparative study are described in this script. Full article
(This article belongs to the Special Issue Recent Advances in Fuzzy Deep Learning for Uncertain Medicine Data)
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25 pages, 1831 KiB  
Article
Connectivity Status of Intuitionistic Fuzzy Graph and Its Application to Merging of Banks
by Jayanta Bera, Kinkar Chandra Das, Sovan Samanta and Jeong-Gon Lee
Mathematics 2023, 11(8), 1949; https://doi.org/10.3390/math11081949 - 20 Apr 2023
Cited by 5 | Viewed by 1482
Abstract
Intuitionistic fuzzy graph theory is used to represent ambiguous networks, such as financial and social networks. The connectivity of such networks has a significant role in analyzing the network characteristics. This study investigates the connectivity status of vertices in an intuitionistic fuzzy graph. [...] Read more.
Intuitionistic fuzzy graph theory is used to represent ambiguous networks, such as financial and social networks. The connectivity of such networks has a significant role in analyzing the network characteristics. This study investigates the connectivity status of vertices in an intuitionistic fuzzy graph. Few properties have been established. Some areas of applications are shown for merging different banks and finding the central affected nodes by any infectious diseases. Full article
(This article belongs to the Special Issue Recent Advances in Fuzzy Deep Learning for Uncertain Medicine Data)
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