Intelligent Information Technology

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

Deadline for manuscript submissions: 31 May 2024 | Viewed by 16438

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Guest Editor
Department of Computer Science and Information Engineering, National Taipei University of Technology (Taipei Tech), Taipei 10608, Taiwan
Interests: big data management and processing; uncertain data management; data science; data management over edge computing; spatial data processing; data streams; ad hoc and sensor networks; location-based services
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Special Issue Information

Dear Colleagues,

This Special Issue will collect extended versions of selected papers presented at the 7th International Conference on Intelligent Information Technology (ICIIT 2022).

The ICIIT conference series has been held annually to provide an interactive forum for researchers from academia, industry, or government worldwide to present and discuss cutting-edge technology related to information on intelligence and applications. For ICIIT 2022, we would like to make the conference more diverse by including advances in information technology and more related topics. 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.

Prof. Dr. Chuan-Ming Liu
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. 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

  • information systems
  • information processes
  • information theory
  • information application
  • communication theory and techniques
  • artificial intelligence
  • information security
  • internet technologies
  • wireless networks
  • big data management and processing

Published Papers (7 papers)

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Research

15 pages, 2015 KiB  
Article
Gated Convolution and Stacked Self-Attention Encoder–Decoder-Based Model for Offline Handwritten Ethiopic Text Recognition
by Direselign Addis Tadesse, Chuan-Ming Liu and Van-Dai Ta
Information 2023, 14(12), 654; https://doi.org/10.3390/info14120654 - 9 Dec 2023
Viewed by 1371
Abstract
Offline handwritten text recognition (HTR) is a long-standing research project for a wide range of applications, including assisting visually impaired users, humans and robot interactions, and the automatic entry of business documents. However, due to variations in writing styles, visual similarities between different [...] Read more.
Offline handwritten text recognition (HTR) is a long-standing research project for a wide range of applications, including assisting visually impaired users, humans and robot interactions, and the automatic entry of business documents. However, due to variations in writing styles, visual similarities between different characters, overlap between characters, and source document noise, designing an accurate and flexible HTR system is challenging. The problem becomes serious when the algorithm has a low learning capacity and when the text used is complex and has a lot of characters in the writing system, such as Ethiopic script. In this paper, we propose a new model that recognizes offline handwritten Ethiopic text using a gated convolution and stacked self-attention encoder–decoder network. The proposed model has a feature extraction layer, an encoder layer, and a decoder layer. The feature extraction layer extracts high-dimensional invariant feature maps from the input handwritten image. Using the extracted feature maps, the encoder and decoder layers transcribe the corresponding text. For the training and testing of the proposed model, we prepare an offline handwritten Ethiopic text-line dataset (HETD) with 2800 samples and a handwritten Ethiopic word dataset (HEWD) with 10,540 samples obtained from 250 volunteers. The experiment results of the proposed model on HETD show a 9.17 and 13.11 Character Error Rate (CER) and Word Error Rate (WER), respectively. However, the model on HEWD shows an 8.22 and 9.17 CER and WER, respectively. These results and the prepared datasets will be used as a baseline for future research. Full article
(This article belongs to the Special Issue Intelligent Information Technology)
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12 pages, 2607 KiB  
Article
CRI-Based Smart Lighting System That Provides Characteristics of Natural Light
by Seung-Taek Oh and Jae-Hyun Lim
Information 2023, 14(12), 628; https://doi.org/10.3390/info14120628 - 23 Nov 2023
Viewed by 1233
Abstract
Natural light continuously changes its correlated color temperature (CCT) from sunrise to sunset, providing the best color reproducibility and healthy light. In the lighting field, efforts have been made to improve the Color Rendering Index (CRI) to provide light quality at the same [...] Read more.
Natural light continuously changes its correlated color temperature (CCT) from sunrise to sunset, providing the best color reproducibility and healthy light. In the lighting field, efforts have been made to improve the Color Rendering Index (CRI) to provide light quality at the same level as natural light. A unique light source technology that mixes and controls multiple LED light sources with different spectral or CCT characteristics or provides a high color rendering index has been introduced. However, the characteristics of natural light, which provide high CRI light while changing color temperature every moment, could not be reproduced as they were. Therefore, in this paper, we propose a CRI-based smart lighting system that reproduces natural light characteristics, provides light with high color reproducibility, and maintains homeostasis even under the changing environment of natural light CCT. After extracting the CCT for each day from the characteristics of measured natural light, the light with the highest CRI under the CCT condition for each hour was provided through a CRI-based CCT matching algorithm. Performance evaluation was conducted for four-channel LED experimental lighting. For each clear and cloudy day, daily natural light was reproduced with a light quality higher than average CRI 98 within the MAE range of CCT 6.78 K. Full article
(This article belongs to the Special Issue Intelligent Information Technology)
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15 pages, 21230 KiB  
Article
Data Augmentation Method for Pedestrian Dress Recognition in Road Monitoring and Pedestrian Multiple Information Recognition Model
by Huiyong Wang, Liang Guo, Ding Yang and Xiaoming Zhang
Information 2023, 14(2), 125; https://doi.org/10.3390/info14020125 - 15 Feb 2023
Viewed by 1289
Abstract
Road intelligence monitoring is an inevitable trend of urban intelligence, and clothing information is the main factor to identify pedestrians. Therefore, this paper establishes a multi-information clothing recognition model and proposes a data augmentation method based on road monitoring. First, we use Mask [...] Read more.
Road intelligence monitoring is an inevitable trend of urban intelligence, and clothing information is the main factor to identify pedestrians. Therefore, this paper establishes a multi-information clothing recognition model and proposes a data augmentation method based on road monitoring. First, we use Mask R-CNN to detect the clothing category information in the monitoring; then, we transfer the mask to the k-means cluster to obtain the color and finally obtain the clothing color and category. However, the monitoring scene and dataset are quite different, so a data augmentation method suitable for road monitoring is designed to improve the recognition ability of small targets and occluded targets. The small target mAP (mean average precision) recognition ability is improved by 12.37% (from 30.37%). The method of this study can help find relevant passers-by in the actual monitoring scene, which is conducive to the intelligent development of the city. Full article
(This article belongs to the Special Issue Intelligent Information Technology)
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20 pages, 1059 KiB  
Article
Toward Efficient Similarity Search under Edit Distance on Hybrid Architectures
by Madiha Khalid, Muhammad Murtaza Yousaf and Muhammad Umair Sadiq
Information 2022, 13(10), 452; https://doi.org/10.3390/info13100452 - 26 Sep 2022
Viewed by 2290
Abstract
Edit distance is the most widely used method to quantify similarity between two strings. We investigate the problem of similarity search under edit distance. Given a collection of sequences, the goal of similarity search under edit distance is to find sequences in the [...] Read more.
Edit distance is the most widely used method to quantify similarity between two strings. We investigate the problem of similarity search under edit distance. Given a collection of sequences, the goal of similarity search under edit distance is to find sequences in the collection that are similar to a given query sequence where the similarity score is computed using edit distance. The canonical method of computing edit distance between two strings uses a dynamic programming-based approach that runs in quadratic time and space, which may not provide results in a reasonable amount of time for large sequences. It advocates for parallel algorithms to reduce the time taken by edit distance computation. To this end, we present scalable parallel algorithms to support efficient similarity search under edit distance. The efficiency and scalability of the proposed algorithms is demonstrated through an extensive set of experiments on real datasets. Moreover, to address the problem of uneven workload across different processing units, which is mainly caused due to the significant variance in the size of the sequences, different data distribution schemes are discussed and empirically analyzed. Experimental results have shown that the speedup achieved by the hybrid approach over inter-task and intra-task parallelism is 18 and 13, respectively. Full article
(This article belongs to the Special Issue Intelligent Information Technology)
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23 pages, 2823 KiB  
Article
Modification of the DIBR and MABAC Methods by Applying Rough Numbers and Its Application in Making Decisions
by Duško Tešić, Marko Radovanović, Darko Božanić, Dragan Pamucar, Aleksandar Milić and Adis Puška
Information 2022, 13(8), 353; https://doi.org/10.3390/info13080353 - 25 Jul 2022
Cited by 9 | Viewed by 1914
Abstract
This study considers the problem of selecting an anti-tank missile system (ATMS). The mentioned problem is solved by applying a hybrid multi-criteria decision-making model (MCDM) based on two methods: the DIBR (Defining Interrelationships Between Ranked criteria) and the MABAC (Multi-Attributive Border Approximation area [...] Read more.
This study considers the problem of selecting an anti-tank missile system (ATMS). The mentioned problem is solved by applying a hybrid multi-criteria decision-making model (MCDM) based on two methods: the DIBR (Defining Interrelationships Between Ranked criteria) and the MABAC (Multi-Attributive Border Approximation area Comparison) methods. The methods are modified by applying rough numbers, which present a very suitable area for considering uncertainty following decision-making processes. The DIBR method is a young method with a simple mathematical apparatus which is based on defining the relation between ranked criteria, that is, adjacent criteria, reducing the number of comparisons. This method defines weight coefficients of criteria, based on the opinion of experts. The MABAC method is used to select the best alternative from the set of the offered ones, based on the distance of the criteria function of every observed alternative from the border approximate area. The paper has two main innovations. With the presented decision-making support model, the ATMS selection problem is raised to a higher level, which is based on a proven mathematical apparatus. In terms of methodology, the main innovation is successful application of the rough DIBR method, which has not been treated in this way in the literature so far. Additionally, an analysis of the literature related to the research problem as well as to the methods used is carried out. After the application of the model, the sensitivity analysis of the output results of the presented model to the change of the weight coefficients of criteria is performed, as well as the comparison of the results of the presented model with other methods. Finally, the proposed model is concluded to be stable and multi-criteria decision-making methods can be a reliable tool to help decision makers in the selection process. The presented model has the potential of being applied in other case studies as it has proven to be a good means for considering uncertainty. Full article
(This article belongs to the Special Issue Intelligent Information Technology)
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11 pages, 560 KiB  
Article
On the Malleability of Consumer Attitudes toward Disruptive Technologies: A Pilot Study of Cryptocurrencies
by Horst Treiblmaier and Evgeny Gorbunov
Information 2022, 13(6), 295; https://doi.org/10.3390/info13060295 - 10 Jun 2022
Cited by 3 | Viewed by 2316
Abstract
The digital transformation of core marketing activities substantially impacts relations between consumers and companies. Novel technologies are usually complex, making their underlying functionality as well as the desirable and undesirable implications hard to grasp for ordinary consumers. Cryptocurrencies are a prominent yet controversial [...] Read more.
The digital transformation of core marketing activities substantially impacts relations between consumers and companies. Novel technologies are usually complex, making their underlying functionality as well as the desirable and undesirable implications hard to grasp for ordinary consumers. Cryptocurrencies are a prominent yet controversial and poorly understood example of an innovation that may transform companies’ future marketing activities. In this study, we investigate how easily consumers’ attitudes toward cryptocurrencies can be shaped by splitting a convenience sample of 100 consumers into two equal groups and exposing them to true, but biased, information about cryptocurrencies (including market forecasts), respectively, highlighting either the advantages or disadvantages of the technology. We subsequently found a significant difference in the trust, security and risk perceptions between the two groups; specifically, more positive attitudes pertaining to trust, security, risk and financial gains prevailed in the group exposed to positively-skewed information, while perceptions regarding trust, risk and the sustainability of cryptocurrencies were weaker among the group exposed to negatively-skewed information. These findings reveal some important insights into how easily consumer attitudes toward new technologies can be shaped through the presentation of lopsided information and call for further in-depth research in this important yet under-researched field. Full article
(This article belongs to the Special Issue Intelligent Information Technology)
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14 pages, 552 KiB  
Article
Digital Transformation in Healthcare 4.0: Critical Factors for Business Intelligence Systems
by Fotis Kitsios and Nikolaos Kapetaneas
Information 2022, 13(5), 247; https://doi.org/10.3390/info13050247 - 12 May 2022
Cited by 11 | Viewed by 4547
Abstract
The health sector is one of the most knowledge-intensive and complicated globally. It has been proven repeatedly that Business Intelligence (BI) systems in the healthcare industry can help hospitals make better decisions. Some studies have looked at the usage of BI in health, [...] Read more.
The health sector is one of the most knowledge-intensive and complicated globally. It has been proven repeatedly that Business Intelligence (BI) systems in the healthcare industry can help hospitals make better decisions. Some studies have looked at the usage of BI in health, but there is still a lack of information on how to develop a BI system successfully. There is a significant research gap in the health sector because these studies do not concentrate on the organizational determinants that impact the development and acceptance of BI systems in different organizations; therefore, the aim of this article is to develop a framework for successful BI system development in the health sector taking into consideration the organizational determinants of BI systems’ acceptance, implementation, and evaluation. The proposed framework classifies the determinants under organizational, process, and strategic aspects as different types to ensure the success of BI system deployment. Concerning practical implications, this paper gives a roadmap for a wide range of healthcare practitioners to ensure the success of BI system development. Full article
(This article belongs to the Special Issue Intelligent Information Technology)
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