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Electronics
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Fuzzy Logic and Artificial Intelligence: Emerging Techniques in AI Applications
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Fuzzy Logic and Artificial Intelligence: Emerging Techniques in AI Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 15 May 2024 | Viewed by 4996

Special Issue Editors

Dr. Pei-Chun Lin

E-Mail Website
Guest Editor
Department of Information Engineering and Computer Science, Feng Chia University, Taichung City, Taiwan
Interests: fuzzy statistics; statistical modeling; Kansei engineering; robot computing; big data analytics
Dr. Nureize Arbaiy

E-Mail Website
Guest Editor
Faculty of Computer Science & Information Technology, University Tun Hussein Onn Malaysia (UTHM), Batu Pahat 86400, Johor, Malaysia
Interests: fuzzy systems; fuzzy regression; multicriteria decision making; fuzzy time series
Prof. Dr. Patrick Hung

E-Mail Website
Guest Editor
Faculty of Business and Information Technology, Ontario Tech University, Oshawa, ON, Canada
Interests: social robots; human–robot interactions; smart toy; robotic computing; services computing; security and privacy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are inviting submissions for a Special Issue to be published in the journal of Electronics on the subject area, “Fuzzy Logic and Artificial Intelligence: Emerging Techniques in AI Applications”.

In recent years, artificial intelligence (AI) technologies have been used and are popular in many fields, such as e-commerce, fraud prevention, education, autonomous vehicles, robotics, marketing, chatbots, etc. Nowadays, our life is full of AI applications, but many uncertain situations are hard to explain and design for AI applications. Theory and applications have become a matter of interest and a source of concern for academics, various industries, and governments. In particular, computational intelligence algorithms have proven to be quite effective and useful when it comes to overcoming decision-making issues. Fuzzy logic, one of the AI branches, offers a method for simulating linguistic uncertainty and is being employed to support cognitive and intellectual processes. Fuzzy logic facilitates decision making by removing the need for intricate mathematical computations, making it simple to consider various factors and deal with uncertainties.

This Special Issue serves as a collection that will bring together all recently developed fuzzy logic and fuzzy-set-based artificial intelligence approaches, and that will encourage further developments in this crucial area. Through concepts and computations with membership functions, fuzzy sets can provide an effective paradigm for the accurate understanding of natural language and establish successful connections to human intellect. The topics of interest for publication include, but are not limited to:

  • Fuzzy modeling;
  • Hybrid fuzzy models;
  • Fuzzy decision making;
  • Fuzzy forecasting;
  • Fuzzy control;
  • Vagueness and fuzzy logic;
  • Fuzzy statistics;
  • Fuzzy statistical approaches;
  • Fuzzy set theorem’s applications;
  • Uncertain environments in AI (power systems, geographic information systems, etc.);
  • Uncertainty in artificial intelligence of things (AIoT);
  • Vague information analysis (e-commerce, human–robot interactions, etc.);
  • Kansei engineering (soft computing, cognitive computing, etc.);
  • Fuzzy algorithms on neural networks (CNN, ANN, deep learning, genetic algorithms, etc.);
  • Fuzzy logic in machine learning (big data analysis, data mining, information retrieval, etc.).

Dr. Pei-Chun Lin
Dr. Nureize Arbaiy
Prof. Dr. Patrick Hung
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 modeling
  • vagueness and fuzzy logic
  • fuzzy statistical approaches
  • fuzzy decision making
  • fuzzy algorithms in AI

Published Papers (3 papers)

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Research

32 pages, 65765 KiB  
Article
Fostering Fuzzy Logic in Enhancing Pedestrian Safety: Harnessing Smart Pole Interaction Unit for Autonomous Vehicle-to-Pedestrian Communication and Decision Optimization
by Vishal Chauhan, Chia-Ming Chang, Ehsan Javanmardi, Jin Nakazato, Pengfei Lin, Takeo Igarashi and Manabu Tsukada
Electronics 2023, 12(20), 4207; https://doi.org/10.3390/electronics12204207 - 11 Oct 2023
Cited by 1 | Viewed by 1627
Abstract
In autonomous vehicles (AVs), ensuring pedestrian safety within intricate and dynamic settings, particularly at crosswalks, has gained substantial attention. While AVs perform admirably in standard road conditions, their integration into unique environments like shared spaces devoid of traditional traffic infrastructure control presents complex [...] Read more.
In autonomous vehicles (AVs), ensuring pedestrian safety within intricate and dynamic settings, particularly at crosswalks, has gained substantial attention. While AVs perform admirably in standard road conditions, their integration into unique environments like shared spaces devoid of traditional traffic infrastructure control presents complex challenges. These challenges involve issues of right-of-way negotiation and accessibility, particularly in “naked streets”. This research delves into an innovative smart pole interaction unit (SPIU) with an external human–machine interface (eHMI). Utilizing virtual reality (VR) technology to evaluate the SPIU efficacy, this study investigates its capacity to enhance interactions between vehicles and pedestrians at crosswalks. The SPIU is designed to communicate the vehicles’ real-time intentions well before arriving at the crosswalk. The study findings demonstrate that the SPIU significantly improves secure decision making for pedestrian passing and stops in shared spaces. Integrating an SPIU with an eHMI in vehicles leads to a substantial 21% reduction in response time, greatly enhancing the efficiency of pedestrian stops. Notable enhancements are observed in unidirectional (one-way) and bidirectional (two-way) scenarios, highlighting the positive impact of the SPIU on interaction dynamics. This work contributes to AV–pedestrian interaction and underscores the potential of fuzzy-logic-driven solutions in addressing complex and ambiguous pedestrian behaviors. Full article
(This article belongs to the Special Issue Fuzzy Logic and Artificial Intelligence: Emerging Techniques in AI Applications)
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16 pages, 5534 KiB  
Article
Semantic Positioning Model Incorporating BERT/RoBERTa and Fuzzy Theory Achieves More Nuanced Japanese Adverb Clustering
by Eric Odle, Yun-Ju Hsueh and Pei-Chun Lin
Electronics 2023, 12(19), 4185; https://doi.org/10.3390/electronics12194185 - 09 Oct 2023
Viewed by 916
Abstract
Japanese adverbs are difficult to classify, with little progress made since the 1930s. Now in the age of large language models, linguists need a framework for lexical grouping that incorporates quantitative, evidence-based relationships rather than purely theoretical categorization. We herein address this need [...] Read more.
Japanese adverbs are difficult to classify, with little progress made since the 1930s. Now in the age of large language models, linguists need a framework for lexical grouping that incorporates quantitative, evidence-based relationships rather than purely theoretical categorization. We herein address this need for the case of Japanese adverbs by developing a semantic positioning approach that incorporates large language model embeddings with fuzzy set theory to achieve empirical Japanese adverb groupings. To perform semantic positioning, we (i) obtained multi-dimensional embeddings for a list of Japanese adverbs using a BERT or RoBERTa model pre-trained on Japanese text, (ii) reduced the dimensionality of each embedding by principle component analysis (PCA), (iii) mapped the relative position of each adverb in a 3D plot using K-means clustering with an initial cluster count of n=3, (iv) performed silhouette analysis to determine the optimal cluster count, (v) performed PCA and K-means clustering on the adverb embeddings again to generate 2D semantic position plots, then finally (vi) generated a centroid distance matrix. Fuzzy set theory informs our workflow at the embedding step, where the meanings of words are treated as quantifiable vague data. Our results suggest that Japanese adverbs optimally cluster into n=4 rather than n=3 groups following silhouette analysis. We also observe a lack of consistency between adverb semantic positions and conventional classification. Ultimately, 3D/2D semantic position plots and centroid distance matrices were simple to generate and did not require special hardware. Our novel approach offers advantages over conventional adverb classification, including an intuitive visualization of semantic relationships in the form of semantic position plots, as well as a quantitative clustering “fingerprint” for Japanese adverbs that express vague language data as a centroid distance matrix. Full article
(This article belongs to the Special Issue Fuzzy Logic and Artificial Intelligence: Emerging Techniques in AI Applications)
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16 pages, 1852 KiB  
Article
Bridging the Gap between Medical Tabular Data and NLP Predictive Models: A Fuzzy-Logic-Based Textualization Approach
by Chérubin Mugisha and Incheon Paik
Electronics 2023, 12(8), 1848; https://doi.org/10.3390/electronics12081848 - 13 Apr 2023
Viewed by 1657
Abstract
The increasing use of electronic health records (EHRs) generates a vast amount of data, which can be leveraged for predictive modeling and improving patient outcomes. However, EHR data are typically mixtures of structured and unstructured data, which presents two major challenges. While several [...] Read more.
The increasing use of electronic health records (EHRs) generates a vast amount of data, which can be leveraged for predictive modeling and improving patient outcomes. However, EHR data are typically mixtures of structured and unstructured data, which presents two major challenges. While several studies have focused on using machine learning models to predict patient outcomes, these models often require data to be in a structured format, which may lead to the loss of important information. On the other hand, unstructured data, such as narrative reports, can be noisy and challenging for natural language processing applications and interoperability. Therefore, there is a need to bridge the gap between structured EHR data and NLP-based predictive models. In this paper, we propose a fuzzy-logic-based pipeline that generates medical narratives from structured EHR data and evaluates its performance in predicting patient outcomes. The pipeline includes a feature selection operation and a reasoning and inference function that generates medical narratives. We then extensively evaluate the generated narratives using transformer-based NLP models for a patient-outcome-prediction task. We furthermore assess the interpretability of the generated text using Shapley values. Our approach has demonstrated comparable performance to the benchmark baseline models with an F1-score of 93.7%, while exhibiting slightly improved results in terms of recall. The model demonstrated proficiency in the preservation of information and interpretability inherited from nuanced and structured narratives. To the best of our knowledge, this is the first study to demonstrate the ability to transform tabular data into text to apply NLP for a prediction task. Full article
(This article belongs to the Special Issue Fuzzy Logic and Artificial Intelligence: Emerging Techniques in AI Applications)
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