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Electronics
Special Issues
Recent Trends in Intelligent Systems
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Recent Trends in Intelligent Systems

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

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 20864

Special Issue Editors

Prof. Dr. Jesús Ángel Román Gallego

E-Mail Website
Guest Editor
Escuela Politécnica Superior de Zamora, University of Salamanca, Av. Requejo 33, C.P. 49022 Zamora, Spain
Interests: swarm systems; artificial intelligence; computer engineering; service-oriented architectures; expert systems
Special Issues, Collections and Topics in MDPI journals
Dr. Claudio Savaglio

E-Mail Website
Guest Editor
ICAR-CNR, 90146 Palermo, Italy
Interests: internet of things; smart objects; software agents
Special Issues, Collections and Topics in MDPI journals
Dr. María-Luisa Pérez-Delgado

E-Mail Website
Guest Editor
Department of Science and Automation, Universidad de Salamanca, Av. Requejo 33, C.P., 49022 Zamora, Spain
Interests: swarm systems; artificial intelligence; computer engineering; service-oriented architectures; expert systems
Special Issues, Collections and Topics in MDPI journals
Dr. Roberto García Martín

E-Mail Website
Guest Editor
Department of Mechanical Engineering, University of Salamanca, 49029 Zamora, Spain
Interests: artificial intelligence; machine learning; data science; bioinformatics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. José Escuadra Burrieza

E-Mail Website
Guest Editor
Computer Science and Automation Department, University of Salamanca, Av. Requejo 33, C.P. 49022 Zamora, Spain
Interests: algorithms optimization; machine learning; expert systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

Nowadays, intelligent systems are present everywhere. Powerful machines and existing communications allow our lives to be directed in many ways by these systems. This ranges from machines to intelligent software agents with the ability to learn, plan, and solve complex problems.

Machine learning, deep learning, data mining, and any other discipline of artificial intelligence, in addition to service-oriented architectures, provide these intelligent systems with great potential and the ability to be deployed in any type of device.

The main purpose of this Special Issue is to attract high-quality articles detailing recent research as well as review articles on recent developments in intelligent systems. Topics relevant to this Special Issue may include (but are not limited to):

  • Distributed intelligent systems
  • Image processing
  • Organizational systems
  • Expert systems
  • IoT
  • Swarm systems
  • Intelligent virtual educational systems

Dr. Jesús Ángel Román Gallego
Dr. Claudio Savaglio
Dr. María-Luisa Pérez-Delgado
Dr. Roberto García Martín
Prof. José Escuadra Burrieza
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

  • Distributed intelligent systems
  • Image processing
  • Organizational systems
  • Expert systems
  • IoT
  • Swarm systems
  • Intelligent virtual educational systems

Published Papers (5 papers)

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Research

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17 pages, 2275 KiB  
Article
Designing an Intelligent Virtual Educational System to Improve the Efficiency of Primary Education in Developing Countries
by Vidal Alonso-Secades, Alfonso-José López-Rivero, Manuel Martín-Merino-Acera, Manuel-José Ruiz-García and Olga Arranz-García
Electronics 2022, 11(9), 1487; https://doi.org/10.3390/electronics11091487 - 06 May 2022
Cited by 3 | Viewed by 3164
Abstract
Incorporating technology into virtual education encourages educational institutions to demand a migration from the current learning management system towards an intelligent virtual educational system, seeking greater benefit by exploiting the data generated by students in their day-to-day activities. Therefore, the design of these [...] Read more.
Incorporating technology into virtual education encourages educational institutions to demand a migration from the current learning management system towards an intelligent virtual educational system, seeking greater benefit by exploiting the data generated by students in their day-to-day activities. Therefore, the design of these intelligent systems must be performed from a new perspective, which will take advantage of the new analytical functions provided by technologies such as artificial intelligence, big data, educational data mining techniques, and web analytics. This paper focuses on primary education in developing countries, showing the design of an intelligent virtual educational system to improve the efficiency of primary education through recommendations based on reliable data. The intelligent system is formed of four subsystems: data warehousing, analytical data processing, monitoring process and recommender system for educational agents. To illustrate this, the paper contains two dashboards that analyze, respectively, the digital resources usage time and an aggregate profile of teachers’ digital skills, in order to infer new activities that improve efficiency. These intelligent virtual educational systems focus the teaching–learning process on new forms of interaction on an educational future oriented to personalized teaching for the students, and new evaluation and teaching processes for each professor. Full article
(This article belongs to the Special Issue Recent Trends in Intelligent Systems)
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8 pages, 950 KiB  
Article
Nondominated Policy-Guided Learning in Multi-Objective Reinforcement Learning
by Man-Je Kim, Hyunsoo Park and Chang Wook Ahn
Electronics 2022, 11(7), 1069; https://doi.org/10.3390/electronics11071069 - 28 Mar 2022
Cited by 2 | Viewed by 1879
Abstract
Control intelligence is a typical field where there is a trade-off between target objectives, and researchers in this field have longed for artificial intelligence that achieves the target objectives. Multi-objective deep reinforcement learning was sufficient to satisfy this need. In particular, multi-objective deep [...] Read more.
Control intelligence is a typical field where there is a trade-off between target objectives, and researchers in this field have longed for artificial intelligence that achieves the target objectives. Multi-objective deep reinforcement learning was sufficient to satisfy this need. In particular, multi-objective deep reinforcement learning methods based on policy optimization are leading the optimization of control intelligence. However, multi-objective reinforcement learning has difficulties when finding various Pareto optimals of multi-objectives due to the greedy nature of reinforcement learning. We propose a method of policy assimilation to solve this problem. This method was applied to MO-V-MPO, one of preference-based multi-objective reinforcement learning, to increase diversity. The performance of this method has been verified through experiments in a continuous control environment. Full article
(This article belongs to the Special Issue Recent Trends in Intelligent Systems)
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27 pages, 17857 KiB  
Article
Convolutional Neural Networks Used to Date Photographs
by Jesús-Ángel Román-Gallego, María-Luisa Pérez-Delgado and Sergio Vicente San Gregorio
Electronics 2022, 11(2), 227; https://doi.org/10.3390/electronics11020227 - 12 Jan 2022
Cited by 1 | Viewed by 1621
Abstract
Nowadays, the information provided by digital photographs is very complete and very relevant in different professional fields, such as scientific or forensic photography. Taking this into account, it is possible to determine the date when they were taken, as well as the type [...] Read more.
Nowadays, the information provided by digital photographs is very complete and very relevant in different professional fields, such as scientific or forensic photography. Taking this into account, it is possible to determine the date when they were taken, as well as the type of device that they were taken with, and thus be able to locate the photograph in a specific context. This is not the case with analog photographs, which lack any information regarding the date they were taken. Extracting this information is a complicated task, so classifying each photograph according to the date it was taken is a laborious task for a human expert. Artificial intelligence techniques make it possible to determine the characteristics and classify the images automatically. Within the field of artificial intelligence, convolutional neural networks are one of the most widely used methods today. This article describes the application of convolutional neural networks to automatically classify photographs according to the year they were taken. To do this, only the photograph is used, without any additional information. The proposed method divides each photograph into several segments that are presented to the network so that it can estimate a year for each segment. Once all the segments of a photograph have been processed, a general year for the photograph is calculated from the values generated by the network for each of its segments. In this study, images taken between 1960 and 1999 were analyzed and classified using different architectures of a convolutional neural network. The computational results obtained indicate that 44% of the images were classified with an error of less than 5 years, 20.25% with a marginal error between 5 and 10 years, and 35.75% with a higher marginal error of more than 10 years. Due to the complexity of the problem, the results obtained are considered good since 64.25% of the photographs were classified with an error of less than 10 years. Another important result of the study carried out is that it was found that the color is a very important characteristic when classifying photographs by date. The results obtained show that the approach given in this study is an important starting point for this type of task and that it allows placing a photograph in a specific temporal context, thus facilitating the work of experts dedicated to scientific and forensic photography. Full article
(This article belongs to the Special Issue Recent Trends in Intelligent Systems)
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17 pages, 4146 KiB  
Article
Comparative Analysis of Performance between Multimodal Implementation of Chatbot Based on News Classification Data Using Categories
by Prasnurzaki Anki, Alhadi Bustamam and Rinaldi Anwar Buyung
Electronics 2021, 10(21), 2696; https://doi.org/10.3390/electronics10212696 - 04 Nov 2021
Cited by 11 | Viewed by 2280
Abstract
In the modern era, the implementation of chatbot can be used in various fields of science. This research will focus on the application of sentence classification using the News Aggregator Dataset that is used to test the model against the categories determined to [...] Read more.
In the modern era, the implementation of chatbot can be used in various fields of science. This research will focus on the application of sentence classification using the News Aggregator Dataset that is used to test the model against the categories determined to create the chatbot program. The results of the chatbot program trial by multimodal implementation applied four models (GRU, Bi-GRU, 1D CNN, 1D CNN Transpose) with six variations of parameters to produce the best results from the entire trial. The best test results from this research for the chatbot program using the 1D CNN Transpose model are the best models with detailed characteristics in this research, which produces an accuracy value of 0.9919. The test results on both types of chatbot are expected to produce sentence prediction results and precise and accurate detection results. The stages in making the program are explained in detail; therefore, it is hoped that program users can understand not only how to use the program by entering an input and receiving program output results that are explained in more detail in each sub-topic of this study. Full article
(This article belongs to the Special Issue Recent Trends in Intelligent Systems)
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Review

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42 pages, 823 KiB  
Review
Cloud and Edge Computing-Based Computer Forensics: Challenges and Open Problems
by Vijay Prakash, Alex Williams, Lalit Garg, Claudio Savaglio and Seema Bawa
Electronics 2021, 10(11), 1229; https://doi.org/10.3390/electronics10111229 - 21 May 2021
Cited by 15 | Viewed by 10377
Abstract
In recent years, there has been a dramatic change in attitude towards computers and the use of computer resources in general. Cloud and Edge computing have emerged as the most widely used technologies, including fog computing and the Internet of Things (IoT). There [...] Read more.
In recent years, there has been a dramatic change in attitude towards computers and the use of computer resources in general. Cloud and Edge computing have emerged as the most widely used technologies, including fog computing and the Internet of Things (IoT). There are several benefits in exploiting Cloud and Edge computing paradigms, such as lower costs and higher efficiency. It provides data computation and storage where data are processed, enables better data control, faster understanding and actions, and continuous operation. However, though these benefits seem to be appealing, their effects on computer forensics are somewhat undesirable. The complexity of the Cloud and Edge environments and their key features present many technical challenges from multiple stakeholders. This paper seeks to establish an in-depth understanding of the impact of Cloud and Edge computing-based environmental factors. Software and hardware tools used in the digital forensic process, forensic methods for handling tampered sound files, hidden files, image files, or images with steganography, etc. The technical/legal challenges and the open design problems (such as distributed maintenance, multitasking and practicality) highlight the various challenges for the digital forensics process. Full article
(This article belongs to the Special Issue Recent Trends in Intelligent Systems)
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