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Impact and Influence of Artificial Intelligence Technology and Computing
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Impact and Influence of Artificial Intelligence Technology and Computing

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

Deadline for manuscript submissions: closed (10 October 2023) | Viewed by 11402

Special Issue Editors

Dr. Mohd Nor Akmal Khalid

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Guest Editor
Research Center for Entertainment Science, School of Information Science, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi 923-1211, Ishikawa, Japan
Interests: artificial intelligence; games informatics; search algorithm; information dynamics; decision-support system
Special Issues, Collections and Topics in MDPI journals
Dr. Hanhan Maulana

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Guest Editor
Department of Informatics Engineering and Computer Science, Universitas Komputer Indonesia (UNIKOM), Jawa Barat 40132, Indonesia
Interests: information technology; augmented reality; game informatics
Dr. Masnizah Mohd

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Guest Editor
Faculty of Information Science and Technology (FTSM), Universiti Kebangsaan Malaysia (UKM), 43600 Bangi, Malaysia
Interests: information retrieval; natural language processing (NLP); cyber intelligence
Prof. Dr. Nursuriati Jamil

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Guest Editor
Faculty of Computer and Mathematical Science, Universiti Technology MARA, Shah Alam 40450, Malaysia
Interests: speech and image processing; multimedia information retrieval; machine learning
Prof. Dr. Hiroyuki Iida

E-Mail Website1 Website2
Guest Editor
School of Information Science, Japan Advanced Institute of Science and Technology, Ishikawa 923-1211, Japan
Interests: artificial intelligence; games; search algorithm; information dynamics; entertainment science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue will present extended versions of selected papers presented at the 10th ASEAN Workshop on Information Science and Technology (AWIST2022). Initiated in 2014, this annual event has been the main forum for exchanging information and preliminary research results on the development of information science and technology along with their applications in addressing real-world problems.

The workshop provides platforms for academics of different backgrounds in various areas of AI to share their lessons learned from the technological and computational standpoints of AI adoption. While AI development has been influential for several decades, its recent impacts from information and technological standpoints are yet to be widely adopted in several critical areas, such as learning, health, well-being, security and decision making.

AI techniques and development, including search, natural language processing, data mining and analytics, fuzzy logic, machine learning and decision-support system, have been abundantly introduced to solve various problems. This Special Issue will highlight the impacts and influences on the successful application of AI in many domains. Authors of invited papers should be aware that the final submitted manuscript must provide a minimum of 50% new content and not exceed 30% copy/paste from the proceedings paper.

Dr. Mohd Nor Akmal Khalid
Dr. Hanhan Maulana
Dr. Masnizah Mohd
Prof. Dr. Nursuriati Jamil
Prof. Dr. Hiroyuki Iida
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

  • health, healthcare, entertainment, well-being, accessibility and digitalization
  • environment, society, acceptance, sustainability, cybersecurity and awareness
  • information, knowledge and education management
  • pedagogy support system, learning and instructional design
  • human–computer interface, interface design, application areas and case studies

Published Papers (6 papers)

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Research

11 pages, 752 KiB  
Article
Prediction of Heatwave Using Advanced Soft Computing Technique
by Ratnakar Das, Jibitesh Mishra, Pradyumna Kumar Pattnaik and Muhammad Mubashir Bhatti
Information 2023, 14(8), 447; https://doi.org/10.3390/info14080447 - 07 Aug 2023
Viewed by 945
Abstract
At present, there is no suitable instrument available to simulate modeling the thermal performance of various areas of our states due to its complicated meteorological behavior. To accurately predict a heatwave, we studied the research gaps and current ongoing research on the prediction [...] Read more.
At present, there is no suitable instrument available to simulate modeling the thermal performance of various areas of our states due to its complicated meteorological behavior. To accurately predict a heatwave, we studied the research gaps and current ongoing research on the prediction of heatwaves. For the accurate prediction of a heatwave, we considered two soft computing concepts, (a) Rough Set Theory (RST) and (b) Support Vector Machine (SVM). All the ongoing research on the prediction of heatwaves is based on future predictions with an error margin. All the available techniques use a particular pattern of heatwave data, and these methods do not apply to vague data. This paper used an innovative RST and SVM technique, which can be applied to vague and imprecise datasets to produce the best outcomes. RST is helpful in finding the most significant attributes that will be alarming in the future. This analysis identifies the heat wave as the most prominent characteristic among various meteorological data. SVM is responsible for the future prediction of heat waves, which includes various parameters. By further classification of heatwaves, we found that a lack of greenery will increase the heatwave in the future. Although the survey was conducted based on a sampling distribution, we expect this result to represent the population as we collected our sample in a heterogeneous environment. These outcomes are validated using a statistical method. Full article
(This article belongs to the Special Issue Impact and Influence of Artificial Intelligence Technology and Computing)
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14 pages, 2115 KiB  
Article
Semi-Supervised Model for Aspect Sentiment Detection
by Zohreh Madhoushi, Abdul Razak Hamdan and Suhaila Zainudin
Information 2023, 14(5), 293; https://doi.org/10.3390/info14050293 - 16 May 2023
Cited by 1 | Viewed by 1142
Abstract
Advancements in text representation have produced many deep language models (LMs), such as Word2Vec and recurrent-based LMs. However, there are scarce works that focus on detecting implicit sentiments with a small amount of labelled data because there are many different review areas. Deep [...] Read more.
Advancements in text representation have produced many deep language models (LMs), such as Word2Vec and recurrent-based LMs. However, there are scarce works that focus on detecting implicit sentiments with a small amount of labelled data because there are many different review areas. Deep learning techniques are suitable to automate the representation learning process. Hence, we proposed a semi-supervised aspect-based sentiment analysis (ABSA) model for online review to predict explicit and implicit sentiment in three domains (laptop, restaurant, and hotel). The datasets of this study, S1 and S2, were obtained from a standard SemEval online competition and Amazon review datasets. The proposed models outperform the previous baseline models regarding the F1-score of aspect category detection and accuracy of sentiment detection. This study finds more relevant aspects and accurate sentiment for ABSA by developing more stable and robust models. The accuracy of sentiment detection is 84.87% in the restaurant domain on the first dataset. For the second dataset, the proposed method achieved 84.43% in the laptop domain, 85.21% in the restaurant domain, and 85.57% in the hotel domain. The novelty is the proposed new semi-supervised model for aspect sentiment detection with embedded aspect inspired by the encoder–decoder architecture in the neural machine translation (NMT) model. Full article
(This article belongs to the Special Issue Impact and Influence of Artificial Intelligence Technology and Computing)
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13 pages, 1991 KiB  
Article
Deep Learning Pet Identification Using Face and Body
by Elham Azizi and Loutfouz Zaman
Information 2023, 14(5), 278; https://doi.org/10.3390/info14050278 - 08 May 2023
Cited by 1 | Viewed by 3397
Abstract
According to the American Humane Association, millions of cats and dogs are lost yearly. Only a few thousand of them are found and returned home. In this work, we use deep learning to help expedite the procedure of finding lost cats and dogs, [...] Read more.
According to the American Humane Association, millions of cats and dogs are lost yearly. Only a few thousand of them are found and returned home. In this work, we use deep learning to help expedite the procedure of finding lost cats and dogs, for which a new dataset is collected. We applied transfer learning methods on different convolutional neural networks for species classification and animal identification. The framework consists of seven sequential layers: data preprocessing, species classification, face and body detection with landmark detection techniques, face alignment, identification, animal soft biometrics, and recommendation. We achieved an accuracy of 98.18% on species classification. In the face identification layer, 80% accuracy was achieved. Body identification resulted in 81% accuracy. When using body identification in addition to face identification, the accuracy increased to 86.5%, with a 100% chance that the animal would be in our top 10 recommendations of matching. By incorporating animals’ soft biometric information, the system can identify animals with 92% confidence. Full article
(This article belongs to the Special Issue Impact and Influence of Artificial Intelligence Technology and Computing)
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19 pages, 2606 KiB  
Article
Target Positioning and Tracking in WSNs Based on AFSA
by Shu-Hung Lee, Chia-Hsin Cheng, Chien-Chih Lin and Yung-Fa Huang
Information 2023, 14(4), 246; https://doi.org/10.3390/info14040246 - 18 Apr 2023
Cited by 3 | Viewed by 1092
Abstract
In wireless sensor networks (WSNs), the target positioning and tracking are very important topics. There are many different methods used in target positioning and tracking, for example, angle of arrival (AOA), time of arrival (TOA), time difference of arrival (TDOA), and received signal [...] Read more.
In wireless sensor networks (WSNs), the target positioning and tracking are very important topics. There are many different methods used in target positioning and tracking, for example, angle of arrival (AOA), time of arrival (TOA), time difference of arrival (TDOA), and received signal strength (RSS). This paper uses an artificial fish swarm algorithm (AFSA) and the received signal strength indicator (RSSI) channel model for indoor target positioning and tracking. The performance of eight different method combinations of fixed or adaptive steps, the region segmentation method (RSM), Hybrid Adaptive Vision of Prey (HAVP) method, and a Dynamic AF Selection (DAFS) method proposed in this paper for target positioning and tracking is investigated when the number of artificial fish is 100, 72, 52, 24, and 12. The simulation results show that using the proposed HAVP total average positioning error is reduced by 96.1%, and the positioning time is shortened by 26.4% for the target position. Adopting HAVP, RSM, and DAFS in target tracking, the positioning time can be greatly shortened by 42.47% without degrading the tracking success rate. Full article
(This article belongs to the Special Issue Impact and Influence of Artificial Intelligence Technology and Computing)
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16 pages, 3201 KiB  
Article
A Super-Efficient TinyML Processor for the Edge Metaverse
by Arash Khajooei, Mohammad (Behdad) Jamshidi and Shahriar B. Shokouhi
Information 2023, 14(4), 235; https://doi.org/10.3390/info14040235 - 10 Apr 2023
Cited by 3 | Viewed by 2126
Abstract
Although the Metaverse is becoming a popular technology in many aspects of our lives, there are some drawbacks to its implementation on clouds, including long latency, security concerns, and centralized infrastructures. Therefore, designing scalable Metaverse platforms on the edge layer can be a [...] Read more.
Although the Metaverse is becoming a popular technology in many aspects of our lives, there are some drawbacks to its implementation on clouds, including long latency, security concerns, and centralized infrastructures. Therefore, designing scalable Metaverse platforms on the edge layer can be a practical solution. Nevertheless, the realization of these edge-powered Metaverse ecosystems without high-performance intelligent edge devices is almost impossible. Neuromorphic engineering, which employs brain-inspired cognitive architectures to implement neuromorphic chips and Tiny Machine Learning (TinyML) technologies, can be an effective tool to enhance edge devices in such emerging ecosystems. Thus, a super-efficient TinyML processor to use in the edge-enabled Metaverse platforms has been designed and evaluated in this research. This processor includes a Winner-Take-All (WTA) circuit that was implemented via a simplified Leaky Integrate and Fire (LIF) neuron on an FPGA. The WTA architecture is a computational principle in a neuromorphic system inspired by the mini-column structure in the human brain. The resource consumption of the WTA architecture is reduced by employing our simplified LIF neuron, making it suitable for the proposed edge devices. The results have indicated that the proposed neuron improves the response speed to almost 39% and reduces resource consumption by 50% compared to recent works. Using our simplified neuron, up to 4200 neurons can be deployed on VIRTEX 6 devices. The maximum operating frequency of the proposed neuron and our spiking WTA is 576.319 MHz and 514.095 MHz, respectively. Full article
(This article belongs to the Special Issue Impact and Influence of Artificial Intelligence Technology and Computing)
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24 pages, 2462 KiB  
Article
An Improved Co-Training and Generative Adversarial Network (Diff-CoGAN) for Semi-Supervised Medical Image Segmentation
by Guoqin Li, Nursuriati Jamil and Raseeda Hamzah
Information 2023, 14(3), 190; https://doi.org/10.3390/info14030190 - 17 Mar 2023
Cited by 1 | Viewed by 1641
Abstract
Semi-supervised learning is a technique that utilizes a limited set of labeled data and a large amount of unlabeled data to overcome the challenges of obtaining a perfect dataset in deep learning, especially in medical image segmentation. The accuracy of the predicted labels [...] Read more.
Semi-supervised learning is a technique that utilizes a limited set of labeled data and a large amount of unlabeled data to overcome the challenges of obtaining a perfect dataset in deep learning, especially in medical image segmentation. The accuracy of the predicted labels for the unlabeled data is a critical factor that affects the training performance, thus reducing the accuracy of segmentation. To address this issue, a semi-supervised learning method based on the Diff-CoGAN framework was proposed, which incorporates co-training and generative adversarial network (GAN) strategies. The proposed Diff-CoGAN framework employs two generators and one discriminator. The generators work together by providing mutual information guidance to produce predicted maps that are more accurate and closer to the ground truth. To further improve segmentation accuracy, the predicted maps are subjected to an intersection operation to identify a high-confidence region of interest, which reduces boundary segmentation errors. The predicted maps are then fed into the discriminator, and the iterative process of adversarial training enhances the generators’ ability to generate more precise maps, while also improving the discriminator’s ability to distinguish between the predicted maps and the ground truth. This study conducted experiments on the Hippocampus and Spleen images from the Medical Segmentation Decathlon (MSD) dataset using three semi-supervised methods: co-training, semi-GAN, and Diff-CoGAN. The experimental results demonstrated that the proposed Diff-CoGAN approach significantly enhanced segmentation accuracy compared to the other two methods by benefiting on the mutual guidance of the two generators and the adversarial training between the generators and discriminator. The introduction of the intersection operation prior to the discriminator also further reduced boundary segmentation errors. Full article
(This article belongs to the Special Issue Impact and Influence of Artificial Intelligence Technology and Computing)
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