Digitalization, Information Technology and Social Development

A special issue of Telecom (ISSN 2673-4001).

Deadline for manuscript submissions: 31 March 2025 | Viewed by 2666

Special Issue Editor


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Guest Editor
Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Narutowicza 11/12, 80-233 Gdansk, Poland
Interests: audio; broadcasting; coding; compression; digitization; mobile technologies; multimedia; positioning; signal processing; speech processing; video; wireless communication
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Special Issue Information

Dear Colleagues,

Currently, we live in a digital society filled with cutting-edge ICT solutions. Mobile devices, apps, e-services, etc., are available at any time and everywhere. As we speak, more and more branches of both business and governance are migrating to the digital domain. Digitization itself is present in the broadcasting industry such as radio and television, as well as various multimedia-based services. With the aid of mobile devices, applications, and related technologies, we desire to stay healthy or monitor the parameters of our relatives, including young people and the elderly, as well as ourselves. Nowadays, some technologies seem to be limited only to use by their creators.

However, there are still parts of our planet that suffer from digital division. Access to the latest modern-day solutions may be limited because of numerous factors, including access to education, labor markets, etc. With the aid of academics and professionals, the quality of life of such individuals can be improved.

In this Special Issue, we invite the scientific community to publish works focused on digitalization, information technology, and social development.

Original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Bridging the digital divide;
  • Broadcasting and communication systems;
  • E-business, e-commerce, and e-governance;
  • E-health and telemedicine;
  • Media digitization;
  • Quality of life;
  • Smart cities, schools, and education.

I look forward to receiving your contributions.

Dr. Przemysław Falkowski-Gilski
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. Telecom is an international peer-reviewed open access quarterly 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 1200 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

  • broadcasting
  • communication systems
  • digital divide
  • e-business
  • e-commerce
  • e-governance
  • e-health
  • media digitization
  • quality of life
  • smart cities
  • smart education
  • smart schools
  • telemedicine

Published Papers (2 papers)

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Research

19 pages, 1474 KiB  
Article
Bi-GRU-APSO: Bi-Directional Gated Recurrent Unit with Adaptive Particle Swarm Optimization Algorithm for Sales Forecasting in Multi-Channel Retail
by Aruna Mogarala Guruvaya, Archana Kollu, Parameshachari Bidare Divakarachari, Przemysław Falkowski-Gilski and Hirald Dwaraka Praveena
Telecom 2024, 5(3), 537-555; https://doi.org/10.3390/telecom5030028 - 1 Jul 2024
Viewed by 403
Abstract
In the present scenario, retail sales forecasting has a great significance in E-commerce companies. The precise retail sales forecasting enhances the business decision making, storage management, and product sales. Inaccurate retail sales forecasting can decrease customer satisfaction, inventory shortages, product backlog, and unsatisfied [...] Read more.
In the present scenario, retail sales forecasting has a great significance in E-commerce companies. The precise retail sales forecasting enhances the business decision making, storage management, and product sales. Inaccurate retail sales forecasting can decrease customer satisfaction, inventory shortages, product backlog, and unsatisfied customer demands. In order to obtain a better retail sales forecasting, deep learning models are preferred. In this manuscript, an effective Bi-GRU is proposed for accurate sales forecasting related to E-commerce companies. Initially, retail sales data are acquired from two benchmark online datasets: Rossmann dataset and Walmart dataset. From the acquired datasets, the unreliable samples are eliminated by interpolating missing data, outlier’s removal, normalization, and de-normalization. Then, feature engineering is carried out by implementing the Adaptive Particle Swarm Optimization (APSO) algorithm, Recursive Feature Elimination (RFE) technique, and Minimum Redundancy Maximum Relevance (MRMR) technique. Followed by that, the optimized active features from feature engineering are given to the Bi-Directional Gated Recurrent Unit (Bi-GRU) model for precise retail sales forecasting. From the result analysis, it is seen that the proposed Bi-GRU model achieves higher results in terms of an R2 value of 0.98 and 0.99, a Mean Absolute Error (MAE) of 0.05 and 0.07, and a Mean Square Error (MSE) of 0.04 and 0.03 on the Rossmann and Walmart datasets. The proposed method supports the retail sales forecasting by achieving superior results over the conventional models. Full article
(This article belongs to the Special Issue Digitalization, Information Technology and Social Development)
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20 pages, 5844 KiB  
Article
Smart Detection System of Safety Hazards in Industry 5.0
by Stavroula Bourou, Apostolos Maniatis, Dimitris Kontopoulos and Panagiotis A. Karkazis
Telecom 2024, 5(1), 1-20; https://doi.org/10.3390/telecom5010001 - 22 Dec 2023
Cited by 2 | Viewed by 1531
Abstract
Safety management is a priority to guarantee human-centered manufacturing processes in the context of Industry 5.0, which aims to realize a safe human–machine environment based on knowledge-driven approaches. The traditional approaches for safety management in the industrial environment include staff training, regular inspections, [...] Read more.
Safety management is a priority to guarantee human-centered manufacturing processes in the context of Industry 5.0, which aims to realize a safe human–machine environment based on knowledge-driven approaches. The traditional approaches for safety management in the industrial environment include staff training, regular inspections, warning signs, etc. Despite the fact that proactive measures and procedures have exceptional importance in the prevention of safety hazards, human–machine–environment coupling requires more sophisticated approaches able to provide automated, reliable, real-time, cost-effective, and adaptive hazard identification in complex manufacturing processes. In this context, the use of virtual reality (VR) can be exploited not only as a means of human training but also as part of the methodology to generate synthetic datasets for training AI models. In this paper, we propose a flexible and adjustable detection system that aims to enhance safety management in Industry 5.0 manufacturing through real-time monitoring and identification of hazards. The first stage of the system contains the synthetic data generation methodology, aiming to create a synthetic dataset via VR, while the second one concerns the training of AI object detectors for real-time inference. The methodology is evaluated by comparing the performance of models trained on both real-world data from a publicly available dataset and our generated synthetic data. Additionally, through a series of experiments, the optimal ratio of synthetic and real-world images is determined for training the object detector. It has been observed that even with a small amount of real-world data, training a robust AI model is achievable. Finally, we use the proposed methodology to generate a synthetic dataset of four classes as well as to train an AI algorithm for real-time detection. Full article
(This article belongs to the Special Issue Digitalization, Information Technology and Social Development)
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