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Electronics
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Selected Papers from Young Researchers in Computer Science & Engineering
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Selected Papers from Young Researchers in Computer Science & Engineering

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 September 2024 | Viewed by 2190

Special Issue Editors

Dr. João M. L. P. Caldeira

E-Mail Website
Guest Editor
1. Polytechnic Institute of Castelo Branco, Av. Pedro Álvares Cabral No 12, 6000-084 Castelo Branco, Portugal
2. Instituto de Telecomunicações, Rua Marquês d’Ávila e Bolama, 6201-001 Covilhã, Portugal
Interests: mobility support for wireless sensor networks; Internet of Things; smart cities; smart farming
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Vasco N. G. J. Soares

E-Mail Website
Guest Editor
1. Polytechnic Institute of Castelo Branco, Av. Pedro Álvares Cabral No 12, 6000-084 Castelo Branco, Portugal
2. Instituto de Telecomunicações, Rua Marquês d’Ávila e Bolama, 6201-001 Covilhã, Portugal
Interests: vehicular networks; delay/disruption-tolerant networks; Internet of Things; smart cities; smart farming
Special Issues, Collections and Topics in MDPI journals
Dr. Bruno Bogaz Zarpelão

E-Mail Website
Guest Editor
Computer Science Department, State University of Londrina (UEL), Londrina 86057-970, Brazil
Interests: security analytics; intrusion detection; Internet of Things
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The advancement of the state-of-the-art in Computer Science & Engineering is achieved by disseminating new discoveries and results obtained in the work developed by its researchers.

This Special Issue aims to promote and disseminate works developed by young researchers in several areas covered by computer science and engineering, such as Internet of Things, Industry 4.0, smart cities, smart grids, smart agriculture, cloud computing, edge computing, fog computing, artificial intelligence, machine learning, deep learning, blockchain, deep tech, distributed computing, emotional systems, Fintech and intelligent textiles, among other technologies in this field.

Prof. Dr. João M. L. P. Caldeira
Prof. Dr. Vasco N. G. J. Soares
Dr. Bruno Bogaz Zarpelão
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. Electronics 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

  • Internet of Things
  • Industry 4.0
  • artificial intelligence
  • blockchain
  • cloud computing
  • edge computing
  • smart cities
  • smart agriculture
  • machine learning
  • intelligent textiles

Published Papers (2 papers)

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Research

17 pages, 6139 KiB  
Article
Multi-Convolutional Neural Network-Based Diagnostic Software for the Presumptive Determination of Non-Dermatophyte Molds
by Mina Milanović, Suzana Otašević, Marina Ranđelović, Andrea Grassi, Claudia Cafarchia, Mihai Mares and Aleksandar Milosavljević
Electronics 2024, 13(3), 594; https://doi.org/10.3390/electronics13030594 - 31 Jan 2024
Viewed by 533
Abstract
Based on the literature data, the incidence of superficial and invasive non-dermatophyte mold infection (NDMI) has increased. Many of these infections are undiagnosed or misdiagnosed, thus causing inadequate treatment procedures followed by critical conditions or even mortality of the patients. Accurate diagnosis of [...] Read more.
Based on the literature data, the incidence of superficial and invasive non-dermatophyte mold infection (NDMI) has increased. Many of these infections are undiagnosed or misdiagnosed, thus causing inadequate treatment procedures followed by critical conditions or even mortality of the patients. Accurate diagnosis of these infections requires complex mycological analyses and operator skills, but simple, fast, and more efficient mycological tests are still required to overcome the limitations of conventional fungal diagnostic procedures. In this study, software has been developed to provide an efficient mycological diagnosis using a trained convolutional neural network (CNN) model as a core classifier. Using EfficientNet-B2 architecture and permanent slides of NDM isolated from patient’s materials (personal archive of Prof. Otašević, Department of Microbiology and Immunology, Medical Faculty, University of Niš, Serbia), a multi-CNN model has been trained and then integrated into the diagnostic tool, with a 93.73% accuracy of the main model. The Grad-CAM visualization model has been used for further validation of the pattern recognition of the model. The software, which makes the final diagnosis based on the rule of the major method, has been tested with images provided by different European laboratories, showing an almost faultless accuracy with different test images. Full article
(This article belongs to the Special Issue Selected Papers from Young Researchers in Computer Science & Engineering)
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14 pages, 2152 KiB  
Article
Enhanced Real-Time Maintenance Management Model—A Step toward Industry 4.0 through Lean: Conveyor Belt Operation Case Study
by David Mendes, Pedro D. Gaspar, Fernando Charrua-Santos and Helena Navas
Electronics 2023, 12(18), 3872; https://doi.org/10.3390/electronics12183872 - 13 Sep 2023
Cited by 1 | Viewed by 1101
Abstract
Conveyor belts (CBs) are widely used for the continuous transport of bulk materials. CBs must be extremely reliable due to the cost associated with their failure in continuous production systems. Thus, it is highly relevant in terms of maintenance and planning to find [...] Read more.
Conveyor belts (CBs) are widely used for the continuous transport of bulk materials. CBs must be extremely reliable due to the cost associated with their failure in continuous production systems. Thus, it is highly relevant in terms of maintenance and planning to find solutions to reduce the existing stoppages from these assets. In this sense, it is essential to monitor and collect real-time data from this piece of equipment. This work presents a case study, where a model that combines the Lean Philosophy, Total Productive Maintenance (TPM), and the enabling technologies of Industry 4.0 is applied to a CB. The proposed model monitors the CB and provides data on its operation, which, using the calculation of indicators, allows a more accurate and thorough view and evaluation, contributing to improving and supporting decision making by those responsible for maintenance. The data collected by the sensor help those responsible for maintenance and production, in the readjustment of more accurate and optimized planning, programming, and execution, supporting decision making in these areas. During the field test of a two-hour monitoring period (10 a.m. to 12 p.m.), the model identified six stoppages, resulting in approximately 88.6% of operational time for the conveyor. The field test showed that this model can result in more accurate maintenance decision making than conventional approaches. This research also contributes to the advancement of electronics and industrial automation sectors by empowering companies to transform maintenance methodologies. The potential of this approach and its implications for enhanced productivity and overall performance are therefore highlighted. Full article
(This article belongs to the Special Issue Selected Papers from Young Researchers in Computer Science & Engineering)
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