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Artificial Intelligence and Sustainable Digital Transformation
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Artificial Intelligence and Sustainable Digital Transformation

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (15 November 2022) | Viewed by 10207

Special Issue Editor

Dr. Yi-Jen Mon

E-Mail Website
Guest Editor
Department of Electronic Engineering, Ming-Chuan University, Taoyuan City 333, Taiwan
Interests: fuzzy logic control; AI, deep learning; CNN; intelligent control system; automotive safety systems and embedded system design
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the course of the development of artificial intelligence (AI), research on strategies and algorithms was more prevalent in the early years. More recently, due to the rapid development of hardware, such as CPU, GPU, TPU, memory, solid-state drives, etc., performance is far better than it was 10 years ago. Therefore, model research related to AI has now become the mainstream, and we are no longer limited by the computational burden of hardware, allowing us to boldly use AI model applications. At present, AI has received more and more attention from government agencies, business circles and academia. Whether is the application is in engineering, society, the economy, politics, etc., artificial intelligence can provide excellent results. For example, robotics, self-driving autonomous vehicles, videogames, chess, prediction and recognition technologies, etc., have all benefited from AI and are in mass production on the marketM. In particular, sustainable development for the integration of the Internet of Things (IOT) and big data for making deeper applications have attracted the attention and work of many researchers. The purpose of the call for papers for this Special Issue is to broaden the research scope of artificial intelligence in various academic circles, including, but not limited to, the following scopes:

  1. Artificial intelligence control;
  2. Image processing and classification;
  3. Reinforcement learning;
  4. Deep learning;
  5. Examples of successful applications of artificial intelligence;
  6. AI Prediction;
  7. AI learning strategies and algorithms;
  8. AI model construction;
  9. Research on AI games: chess, videogames, etc.;
  10. Research on neural networks;
  11. Fuzzy logic research;
  12. Research on decision-making systems;
  13. Market economic forecasting;
  14. Other theoretical developments or applied research on AI.

Proposed manuscripts are welcome and will be reviewed according to Sustainability’s review process.

Submission Deadline: 31 July 2022.

Prof. Dr. Yi-Jen Mon
Guest Editor

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. Sustainability 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

  • AI
  • control
  • image
  • deep learning
  • machine learning
  • AI sustainability
  • etc.

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Published Papers (2 papers)

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Research

19 pages, 2757 KiB  
Article
Sentiment Analysis of Consumer Reviews Using Deep Learning
by Amjad Iqbal, Rashid Amin, Javed Iqbal, Roobaea Alroobaea, Ahmed Binmahfoudh and Mudassar Hussain
Sustainability 2022, 14(17), 10844; https://doi.org/10.3390/su141710844 - 31 Aug 2022
Cited by 46 | Viewed by 7880
Abstract
Internet and social media platforms such as Twitter, Facebook, and several blogs provide various types of helpful information worldwide. The increased usage of social media and e-commerce websites is constantly generating a massive volume of data about image/video, sound, text, etc. The text [...] Read more.
Internet and social media platforms such as Twitter, Facebook, and several blogs provide various types of helpful information worldwide. The increased usage of social media and e-commerce websites is constantly generating a massive volume of data about image/video, sound, text, etc. The text among these is the most significant type of unstructured data, requiring special attention from researchers to acquire meaningful information. Recently, many techniques have been proposed to obtain insights from these data. However, there are still challenges in dealing with the text of enormous size; therefore, accurate polarity detection of consumer reviews is an ongoing and exciting problem. Due to this, it is challenging to derive exact meanings from the textual data from consumer reviews, comments, tweets, posts, etc. Previously, a reasonable amount of work has been conducted to simplify the extraction of exact meanings from these data. A unique technique that includes data gathering, preprocessing, feature encoding, and classification utilizing three long short-term memory variations is presented to address sentiment analysis problems. Analysing appropriate data collection, preprocessing, and classification is crucial when interpreting such data. Different textual datasets were used in the studies to gauge the importance of the suggested models. The proposed technique of predicting sentiments shows better, or at least comparable, results with less computational complexity. The outcome of this work shows the significant importance of sentiment analysis of consumer reviews and social media content to obtain meaningful insights. Full article
(This article belongs to the Special Issue Artificial Intelligence and Sustainable Digital Transformation)
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14 pages, 3501 KiB  
Article
Vision Robot Path Control Based on Artificial Intelligence Image Classification and Sustainable Ultrasonic Signal Transformation Technology
by Yi-Jen Mon
Sustainability 2022, 14(9), 5335; https://doi.org/10.3390/su14095335 - 28 Apr 2022
Cited by 4 | Viewed by 1552
Abstract
The unsupervised algorithm of artificial intelligence (AI), named ART (Adaptive Resonance Theory), is used to first roughly classify an image, that is, after the image is processed by the edge filtering technology, the image window is divided into 25 square areas of 5 [...] Read more.
The unsupervised algorithm of artificial intelligence (AI), named ART (Adaptive Resonance Theory), is used to first roughly classify an image, that is, after the image is processed by the edge filtering technology, the image window is divided into 25 square areas of 5 rows and 5 columns, and then, according to the location of the edge of the image, it determines whether the robot should go straight (represented by S), turn around (represented by A), stop (T), turn left (represented by L), or turn right (represented by R). Then, after sustainable ultrasonic signal acquisition and transformation into digital signals are completed, the sustainable supervised neural network named SGAFNN (Supervised Gaussian adaptive fuzzy neural network) will perform an optimal path control that can accurately control the traveling speed and turning of the robot to avoid hitting walls or obstacles. Based on the above, this paper proposes the use of the ART operation after image processing to judge the rough direction, followed by the use of the ultrasonic signal to carry out the sustainable development of artificial intelligence and to carry out accurate speed and direction SGAFNN control to avoid obstacles. After simulation and practical evaluations, the proposed method is proved to be feasible and to exhibit good performance. Full article
(This article belongs to the Special Issue Artificial Intelligence and Sustainable Digital Transformation)
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