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Electronics
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Advancements in Cross-Disciplinary AI: Theory and Application
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Advancements in Cross-Disciplinary AI: Theory and Application

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

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 46331

Special Issue Editors

Prof. Dr. Yanping Zhang

E-Mail Website
Guest Editor
Department of Computer Science, Gonzaga University, Spokane, WA 99258, USA
Interests: computer networks; bio-computing; computer security; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Zhifeng Xiao

E-Mail Website
Guest Editor
Department of Computer Science and Software Engineering, Penn State Erie, The Behrend College, Erie, PA 16563, USA
Interests: interdisciplinary AI; cybersecurity; accountable systems
Special Issues, Collections and Topics in MDPI journals
Dr. Jianjun Yang

E-Mail Website
Guest Editor
Department of Computer Science, University of North Georgia, Oakwood, GA 30566, USA
Interests: computer networks; management information systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advances in artificial intelligence (AI) have revolutionized all industries and greatly changed people’s daily lives. Intelligent systems supported by AI can learn from massive data, structured or unstructured, open domain or domain-specific, at a scale we have never seen before. A broad spectrum of disciplines have embraced AI, producing numerous exciting applications across various technological fields, such as smart city, transportation, health care, finance, criminal justice, and many others.

This Special Issue aims to collect high-quality research papers presenting theoretical and applied research findings for intelligent systems and their cross-disciplinary applications. Topics include but are not limited to:

  • AI in cybersecurity;
  • Emerging applications in natural language processing;
  • AI in the internet of things;
  • AI in finance;
  • AI in smart transportation and smart cities;
  • AI in health care;
  • AI in bioinformatics;
  • AI in education.

Dr. Yanping Zhang
Dr. Zhifeng Xiao
Dr. Jianjun Yang
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

  • artificial intelligence
  • intelligent systems
  • natural language processing
  • cybersecurity
  • smart cities
  • bioinformatics
  • health care
  • education

Related Special Issue

Published Papers (7 papers)

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Research

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12 pages, 3125 KiB  
Article
Deep Learning Model of Sleep EEG Signal by Using Bidirectional Recurrent Neural Network Encoding and Decoding
by Ziyang Fu, Chen Huang, Li Zhang, Shihui Wang and Yan Zhang
Electronics 2022, 11(17), 2644; https://doi.org/10.3390/electronics11172644 - 24 Aug 2022
Cited by 7 | Viewed by 1919
Abstract
Electroencephalogram (EEG) is a signal commonly used for detecting brain activity and diagnosing sleep disorders. Manual sleep stage scoring is a time-consuming task, and extracting information from the EEG signal is difficult because of the non-linear dependencies of time series. To solve the [...] Read more.
Electroencephalogram (EEG) is a signal commonly used for detecting brain activity and diagnosing sleep disorders. Manual sleep stage scoring is a time-consuming task, and extracting information from the EEG signal is difficult because of the non-linear dependencies of time series. To solve the aforementioned problems, in this study, a deep learning model of sleep EEG signal was developed using bidirectional recurrent neural network (BiRNN) encoding and decoding. First, the input signal was denoised using the wavelet threshold method. Next, feature extraction in the time and frequency domains was realized using a convolutional neural network to expand the scope of feature extraction and preserve the original EEG feature information to the maximum extent possible. Finally, the time-series information was mined using the encoding–decoding module of the BiRNN, and the automatic discrimination of the sleep staging of the EEG signal was realized using the SoftMax function. The model was cross-validated using Fpz-Cz single-channel EEG signals from the Sleep-EDF dataset for 19 nights, and the results demonstrated that the proposed model can achieve a high recognition rate and stability. Full article
(This article belongs to the Special Issue Advancements in Cross-Disciplinary AI: Theory and Application)
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11 pages, 682 KiB  
Article
Assessing the Impact of AI Solutions’ Ethical Issues on Performance in Managerial Accounting
by Anca Antoaneta Vărzaru
Electronics 2022, 11(14), 2221; https://doi.org/10.3390/electronics11142221 - 16 Jul 2022
Cited by 5 | Viewed by 6390
Abstract
In the contemporary, constantly changing business environment characterized by globalization, openness, and competitiveness, implementing different processes of new information technologies has become a competitive advantage. The field of managerial accounting is a successful example of the implementation of artificial intelligence in operations and [...] Read more.
In the contemporary, constantly changing business environment characterized by globalization, openness, and competitiveness, implementing different processes of new information technologies has become a competitive advantage. The field of managerial accounting is a successful example of the implementation of artificial intelligence in operations and the decision-making process based on accounting information. However, ethical issues within managerial accounting and those added through the implementation of artificial intelligence need to be addressed carefully. In this paper, the main objective is to investigate these ethical issues regarding the perception of accountants on the usefulness, efficiency, and effectiveness of implementing artificial intelligence in managerial accounting. To investigate these effects, we conducted a study based on a questionnaire among 396 accountants in Romania who use various artificial intelligence solutions in their activities in managerial accounting. The results of structural equation modeling showed that the ethical issues of autonomy, responsibility, and trust significantly influence the perceived usefulness and the performance of artificial intelligence solutions. The research concludes that artificial intelligence solutions solve many ethical issues in managerial accounting. Still, through their design and application, artificial intelligence solutions can create other ethical problems specific to managerial accounting and business ethics. Therefore, despite all the barriers and reluctance of professionals, artificial intelligence will substantially impact managerial accounting in the years to come. Full article
(This article belongs to the Special Issue Advancements in Cross-Disciplinary AI: Theory and Application)
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22 pages, 11420 KiB  
Article
Traffic Sign Based Point Cloud Data Registration with Roadside LiDARs in Complex Traffic Environments
by Zheyuan Zhang, Jianying Zheng, Yanyun Tao, Yang Xiao, Shumei Yu, Sultan Asiri, Jiacheng Li and Tieshan Li
Electronics 2022, 11(10), 1559; https://doi.org/10.3390/electronics11101559 - 13 May 2022
Cited by 7 | Viewed by 2278
Abstract
The intelligent road is an important component of the intelligent vehicle infrastructure cooperative system, the latest development of intelligent transportation systems. As an advanced sensor, Light Detection and Ranging (LiDAR) has gradually been used to collect high-resolution micro-traffic data on the roadside of [...] Read more.
The intelligent road is an important component of the intelligent vehicle infrastructure cooperative system, the latest development of intelligent transportation systems. As an advanced sensor, Light Detection and Ranging (LiDAR) has gradually been used to collect high-resolution micro-traffic data on the roadside of intelligent roads. Furthermore, a fusion of multiple LiDARs has become a current hot spot to extend the data collection range and improve detection accuracy. This paper focuses on point cloud registration in a complex traffic environment and proposes a three-dimensional (3D) registration method based on traffic signs and prior knowledge of traffic scenes. Traffic signs with their reflective films are used as reference targets to register 3D point cloud data from roadside LiDARs. The proposed method consists of a vertical registration and a horizontal registration. For the vertical registration, we propose a panel rotation algorithm to rotate the initial point cloud to register it vertically, converting the 3D point cloud registration into a two-dimensional (2D) rigid body transformation. For the vertical registration, our system registers traffic signs from different LiDARs. Our method has been verified in some actual scenarios. Compared with previous methods, the proposed method is automatic and does not need to search reference targets manually. Furthermore, it is suitable for actual engineering use and can be applied to sparse point cloud data from LiDAR with few beams, realizing point cloud registration of large disparity. Full article
(This article belongs to the Special Issue Advancements in Cross-Disciplinary AI: Theory and Application)
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19 pages, 5840 KiB  
Article
Cascade Parallel Random Forest Algorithm for Predicting Rice Diseases in Big Data Analysis
by Lei Zhang, Lun Xie, Zhiliang Wang and Chen Huang
Electronics 2022, 11(7), 1079; https://doi.org/10.3390/electronics11071079 - 29 Mar 2022
Cited by 4 | Viewed by 1961
Abstract
Experts in agriculture have conducted considerable work on rice plant protection. However, in-depth exploration of the plant disease problem has not been performed. In this paper, we find the trend of rice diseases by using the cascade parallel random forest (CPRF) algorithm on [...] Read more.
Experts in agriculture have conducted considerable work on rice plant protection. However, in-depth exploration of the plant disease problem has not been performed. In this paper, we find the trend of rice diseases by using the cascade parallel random forest (CPRF) algorithm on the basis of relevant data analysis in the recent 20 years. To confront the problems of high dimensions and imbalanced data distributions in agricultural data. The proposed method diminishes the dimensions and the negative effect of imbalanced data by cascading several random forests. For experimental evaluation, we utilize the Spark platform to analyze botanic data from several provinces of China in the past 20 years. Results for the CPRF model of plant diseases that affect rice yield, as well as results for samples by using random forest, CRF, and Spark-MLRF are presented, and the accuracy of CPRF is 96.253%, which is higher than that of the other algorithms. These results indicate that the CPRF and the utilization of big data analysis are beneficial in solving the problem of plant diseases. Full article
(This article belongs to the Special Issue Advancements in Cross-Disciplinary AI: Theory and Application)
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21 pages, 13065 KiB  
Article
Impacts of GPS Spoofing on Path Planning of Unmanned Surface Ships
by Jia Wang, Yang Xiao, Tieshan Li and C. L. Philip Chen
Electronics 2022, 11(5), 801; https://doi.org/10.3390/electronics11050801 - 4 Mar 2022
Cited by 12 | Viewed by 3069
Abstract
The Artificial Potential Field (APF) method is a classical path planning method for unmanned ships, relying on Global Positioning System (GPS) positioning information for path planning. Unfortunately, once the path planning algorithm uses inaccurate or even fake data, it will lead to ship [...] Read more.
The Artificial Potential Field (APF) method is a classical path planning method for unmanned ships, relying on Global Positioning System (GPS) positioning information for path planning. Unfortunately, once the path planning algorithm uses inaccurate or even fake data, it will lead to ship collision, grounding, or deviation from the course, causing severe economic losses and causing significant security risks to other sailing ships. This paper aims to study the impacts of GPS spoofing on the path planning of unmanned ships. We propose a GPS attack and study GPS spoofing of path planning based on the APF method for an unmanned ship by a low-cost software-defined radio, which causes the unmanned ship to deviate from the course. Our simulation tests show that this method has significant impacts on the path planning results of the APF method. Full article
(This article belongs to the Special Issue Advancements in Cross-Disciplinary AI: Theory and Application)
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14 pages, 2553 KiB  
Article
Two-Branch Attention Learning for Fine-Grained Class Incremental Learning
by Jiaqi Guo, Guanqiu Qi, Shuiqing Xie and Xiangyuan Li
Electronics 2021, 10(23), 2987; https://doi.org/10.3390/electronics10232987 - 1 Dec 2021
Cited by 5 | Viewed by 1607
Abstract
As a long-standing research area, class incremental learning (CIL) aims to effectively learn a unified classifier along with the growth of the number of classes. Due to the small inter-class variances and large intra-class variances, fine-grained visual categorization (FGVC) as a challenging visual [...] Read more.
As a long-standing research area, class incremental learning (CIL) aims to effectively learn a unified classifier along with the growth of the number of classes. Due to the small inter-class variances and large intra-class variances, fine-grained visual categorization (FGVC) as a challenging visual task has not attracted enough attention in CIL. Therefore, the localization of critical regions specialized for fine-grained object recognition plays a crucial role in FGVC. Additionally, it is important to learn fine-grained features from critical regions in fine-grained CIL for the recognition of new object classes. This paper designs a network architecture named two-branch attention learning network (TBAL-Net) for fine-grained CIL. TBAL-Net can localize critical regions and learn fine-grained feature representation by a lightweight attention module. An effective training framework is proposed for fine-grained CIL by integrating TBAL-Net into an effective CIL process. This framework is tested on three popular fine-grained object datasets, including CUB-200-2011, FGVC-Aircraft, and Stanford-Car. The comparative experimental results demonstrate that the proposed framework can achieve the state-of-the-art performance on the three fine-grained object datasets. Full article
(This article belongs to the Special Issue Advancements in Cross-Disciplinary AI: Theory and Application)
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Review

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24 pages, 2011 KiB  
Review
Human-Computer Interaction in Customer Service: The Experience with AI Chatbots—A Systematic Literature Review
by Luminița Nicolescu and Monica Teodora Tudorache
Electronics 2022, 11(10), 1579; https://doi.org/10.3390/electronics11101579 - 15 May 2022
Cited by 49 | Viewed by 27399
Abstract
Artificial intelligence (AI) conversational agents (CA) or chatbots represent one of the technologies that can provide automated customer service for companies, a trend encountered in recent years. Chatbot use is beneficial for companies when associated with positive customer experience. The purpose of this [...] Read more.
Artificial intelligence (AI) conversational agents (CA) or chatbots represent one of the technologies that can provide automated customer service for companies, a trend encountered in recent years. Chatbot use is beneficial for companies when associated with positive customer experience. The purpose of this paper is to analyze the overall customer experience with customer service chatbots in order to identify the main influencing factors for customer experience with customer service chatbots and to identify the resulting dimensions of customer experience (such as perceptions/attitudes and feelings and also responses and behaviors). The analysis uses the systematic literature review (SLR) method and includes a sample of 40 publications that present empirical studies. The results illustrate that the main influencing factors of customer experience with chatbots are grouped in three categories: chatbot-related, customer-related, and context-related factors, where the chatbot-related factors are further categorized in: functional features of chatbots, system features of chatbots and anthropomorphic features of chatbots. The multitude of factors of customer experience result in either positive or negative perceptions/attitudes and feelings of customers. At the same time, customers respond by manifesting their intentions and/or their behaviors towards either the technology itself (chatbot usage continuation and acceptance of chatbot recommendations) or towards the company (buying and recommending products). According to empirical studies, the most influential factors when using chatbots for customer service are response relevance and problem resolution, which usually result in positive customer satisfaction, increased probability for chatbots usage continuation, product purchases, and product recommendations. Full article
(This article belongs to the Special Issue Advancements in Cross-Disciplinary AI: Theory and Application)
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