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Artificial Intelligence and Neural Networks at the Intersection of Society,...
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Artificial Intelligence and Neural Networks at the Intersection of Society, Business, and Science

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 182573

Special Issue Editor

Dr. W. G. Will Zhao

E-Mail Website
Guest Editor
Faculty of Business Administration at the Lakehead University, Ontario, Canada
Interests: machine learning; data analysis; social AI; social robotics

Special Issue Information

Dear Colleagues,

Advances in artificial intelligence (AI) and machine learning (ML), especially those powered by research in artificial neural networks (ANNs), are changing people’s lives in profound ways, including in education, employment, entertainment, health care, and transportation. Artificial intelligence has also presented itself as an unprecedented opportunity for companies to design intelligent products and novel services and invent new business models and even organizational forms. It is no surprise that researchers in computer science, electrical and computer engineering, management, social studies of technology, linguistics, and education are showing strong interest in AI, ML, and ANNs, while, at the same time, interdisciplinary research is gaining greater momentum.

This Special Issue seeks to gather intra-field and cross-field insights on the new tasks, methods, and applications of AI, ML, and ANNs. We welcome submissions that represent the latest advances in ML, deep learning, and ANN research, as well as those that examine AI and its applications from the perspectives of management, social science, and the humanities. In order to foster cross-field discussions and debates around AI, we particularly encourage contributions from interdisciplinary teams.

Topics of interest therefore include, but are not limited to, the following:

  • New approaches to ANNs;
  • Deep-learning-based intelligent information processing;
  • Big-data-based intelligent optimization;
  • ML-based data mining and data augmentation;
  • Business applications of AI in strategy, organizational change, retail, marketing, finance, accounting, and human resources;
  • Social implications of AI, e.g., health, education, media, disaster monitoring, and public policy;
  • Critical studies of AI.

All papers submitted prior to the submission deadline will undergo the journal’s standard peer-review procedure. If accepted, the papers will be published in open access format in a timely fashion.

Dr. W. G. Will Zhao
Dr. Yimin 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. Information is an international peer-reviewed open access monthly 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 1600 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

  • Machine Learning
  • Deep Learning
  • Artificial Neural Networks
  • Big Data
  • Data Augmentation
  • Business AI
  • Social AI
  • Interdisciplinary Research

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

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15 pages, 11481 KiB  
Article
Pear Defect Detection Method Based on ResNet and DCGAN
by Yan Zhang, Shiyun Wa, Pengshuo Sun and Yaojun Wang
Information 2021, 12(10), 397; https://doi.org/10.3390/info12100397 - 28 Sep 2021
Cited by 33 | Viewed by 3546
Abstract
To address the current situation, in which pear defect detection is still based on a workforce with low efficiency, we propose the use of the CNN model to detect pear defects. Since it is challenging to obtain defect images in the implementation process, [...] Read more.
To address the current situation, in which pear defect detection is still based on a workforce with low efficiency, we propose the use of the CNN model to detect pear defects. Since it is challenging to obtain defect images in the implementation process, a deep convolutional adversarial generation network was used to augment the defect images. As the experimental results indicated, the detection accuracy of the proposed method on the 3000 validation set was as high as 97.35%. Variant mainstream CNNs were compared to evaluate the model’s performance thoroughly, and the top performer was selected to conduct further comparative experiments with traditional machine learning methods, such as support vector machine algorithm, random forest algorithm, and k-nearest neighbor clustering algorithm. Moreover, the other two varieties of pears that have not been trained were chosen to validate the robustness and generalization capability of the model. The validation results illustrated that the proposed method is more accurate than the commonly used algorithms for pear defect detection. It is robust enough to be generalized well to other datasets. In order to allow the method proposed in this paper to be applied in agriculture, an intelligent pear defect detection system was built based on an iOS device. Full article
(This article belongs to the Special Issue Artificial Intelligence and Neural Networks at the Intersection of Society, Business, and Science)
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14 pages, 447 KiB  
Article
Talent Competitiveness Evaluation of the Chongqing Intelligent Industry Based on Using the Entropy TOPSIS Method
by Xianhang Xu, Mohd Anuar Arshad and Arshad Mahmood
Information 2021, 12(8), 288; https://doi.org/10.3390/info12080288 - 21 Jul 2021
Cited by 4 | Viewed by 2409
Abstract
Talent is the foundation of industrial development, based on the movement process and the role of the talent competitiveness cycle. This study uses the Entropy TOPSIS method to evaluate the talent competitiveness of Chongqing in comparison to Beijing, Tianjin, Shanghai, Jiangsu, Zhejiang, Guangdong, [...] Read more.
Talent is the foundation of industrial development, based on the movement process and the role of the talent competitiveness cycle. This study uses the Entropy TOPSIS method to evaluate the talent competitiveness of Chongqing in comparison to Beijing, Tianjin, Shanghai, Jiangsu, Zhejiang, Guangdong, Sichuan, and Shaanxi from five dimensions of human resources, talent contribution, talent investment, development support, and development environment. The research shows that the talent competitiveness of the intelligent industry in Chongqing ranks eighth as compared to other eight provinces and cities. The result shows that there is a big gap in human resources, talent contribution and talent investment compared with the developed areas, while development support and environment level were found to be relatively backward. It is suggested to strengthen the policy implementation from the aspects of high-end talent introduction, investment in higher education, industrial ecological system and talent development environment. Full article
(This article belongs to the Special Issue Artificial Intelligence and Neural Networks at the Intersection of Society, Business, and Science)
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Review

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42 pages, 2038 KiB  
Review
Investigating the Influence of Artificial Intelligence on Business Value in the Digital Era of Strategy: A Literature Review
by Nikolaos-Alexandros Perifanis and Fotis Kitsios
Information 2023, 14(2), 85; https://doi.org/10.3390/info14020085 - 2 Feb 2023
Cited by 93 | Viewed by 175291
Abstract
For organizations, the development of new business models and competitive advantages through the integration of artificial intelligence (AI) in business and IT strategies holds considerable promise. The majority of businesses are finding it difficult to take advantage of the opportunities for value creation [...] Read more.
For organizations, the development of new business models and competitive advantages through the integration of artificial intelligence (AI) in business and IT strategies holds considerable promise. The majority of businesses are finding it difficult to take advantage of the opportunities for value creation while other pioneers are successfully utilizing AI. On the basis of the research methodology of Webster and Watson (2020), 139 peer-reviewed articles were discussed. According to the literature, the performance advantages, success criteria, and difficulties of adopting AI have been emphasized in prior research. The results of this review revealed the open issues and topics that call for further research/examination in order to develop AI capabilities and integrate them into business/IT strategies in order to enhance various business value streams. Organizations will only succeed in the digital transformation alignment of the present era by precisely adopting and implementing these new, cutting-edge technologies. Despite the revolutionary potential advantages that AI capabilities may promote, the resource orchestration, along with governance in this dynamic environment, is still complex enough and in the early stages of research regarding the strategic implementation of AI in organizations, which is the issue this review aims to address and, as a result, assist present and future organizations effectively enhance various business value outcomes. Full article
(This article belongs to the Special Issue Artificial Intelligence and Neural Networks at the Intersection of Society, Business, and Science)
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