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Applied Sciences
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Advanced Computing and Neural Networks Applied in Learning Systems
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Advanced Computing and Neural Networks Applied in Learning Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 August 2023) | Viewed by 5928

Special Issue Editor

Dr. Laia Subirats

E-Mail Website
Guest Editor
1. Eurecat - Centre Tecnològic de Catalunya, 08005 Barcelona, Spain
2. ADaS Lab, Universitat Oberta de Catalunya, 08018 Barcelona, Spain
Interests: artificial intelligence; e-learning; intelligent tutoring systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

At present, the use of advanced computing techniques and artificial intelligence is a cornerstone of learning systems. They can be applied to several learning domains, such as intelligent tutoring systems, gamification, the improvement of learning difficulties such as dyslexia, and adaptive learning systems, among others.

The goal of this Special Issue is to cover topics in this field. The following types of papers will be considered:

  • Research papers: analytical papers of new research;
  • Case studies: real-world applications, evaluation of industrial solutions, and lessons learned in putting solutions into practice;
  • Reviews, surveys, and tutorials: analysis of the state of the art.

Dr. Laia Subirats
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. Applied Sciences 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

  • AI-based learning systems
  • decision support systems applied to education
  • intelligent systems to improve learning difficulties
  • neural networks and gamification in education
  • industrial cases of AI applied to education
  • intelligent tutoring systems

Published Papers (4 papers)

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Research

13 pages, 622 KiB  
Article
CAM-NAS: An Efficient and Interpretable Neural Architecture Search Model Based on Class Activation Mapping
by Zhiyuan Zhang, Zhan Wang and Inwhee Joe
Appl. Sci. 2023, 13(17), 9686; https://doi.org/10.3390/app13179686 - 27 Aug 2023
Viewed by 855
Abstract
Artificial intelligence (AI) has made rapid progress in recent years, but as the complexity of AI models and the need to deploy them on multiple platforms gradually increases, the design of network model structures for specific platforms becomes more difficult. A neural network [...] Read more.
Artificial intelligence (AI) has made rapid progress in recent years, but as the complexity of AI models and the need to deploy them on multiple platforms gradually increases, the design of network model structures for specific platforms becomes more difficult. A neural network architecture search (NAS) serves as a solution to help experts discover new network structures that are suitable for different tasks and platforms. However, traditional NAS algorithms often consume time and many computational resources, especially when dealing with complex tasks and large-scale models, and the search process can become exceptionally time-consuming and difficult to interpret. In this paper, we propose a class activation graph-based neural structure search method (CAM-NAS) to address these problems. Compared with traditional NAS algorithms, CAM-NAS does not require full training of submodels, which greatly improves the search efficiency. Meanwhile, CAM-NAS uses the class activation graph technique, which makes the searched models have better interpretability. In our experiments, we tested CAM-NAS on an NVIDIA RTX 3090 graphics card and showed that it can evaluate a submodel in only 0.08 seconds, which is much faster than traditional NAS methods. In this study, we experimentally evaluated CAM-NAS using the CIFAR-10 and CIFAR-100 datasets as benchmarks. The experimental results show that CAM-NAS achieves very good results. This not only proves the efficiency of CAM-NAS, but also demonstrates its powerful performance in image classification tasks. Full article
(This article belongs to the Special Issue Advanced Computing and Neural Networks Applied in Learning Systems)
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17 pages, 3252 KiB  
Article
Early Prediction of Students’ Performance Using a Deep Neural Network Based on Online Learning Activity Sequence
by Xiao Wen and Hu Juan
Appl. Sci. 2023, 13(15), 8933; https://doi.org/10.3390/app13158933 - 03 Aug 2023
Cited by 1 | Viewed by 958
Abstract
Predicting students’ performance is one of the most important issues in educational data mining. In this study, a method for representing students’ partial sequence of learning activities is proposed, and an early prediction model of students’ performance is designed based on a deep [...] Read more.
Predicting students’ performance is one of the most important issues in educational data mining. In this study, a method for representing students’ partial sequence of learning activities is proposed, and an early prediction model of students’ performance is designed based on a deep neural network. This model uses a pre-trained autoencoder to extract latent features from the sequence in order to make predictions. The experimental results show that: (1) compared with demographic features and assessment scores, 20% and wholly online learning activity sequences can achieve a classifier accuracy of 0.5 and 0.84, respectively, which can be used for an early prediction of students’ performance; (2) the proposed autoencoder can extract latent features from the original sequence effectively, and the accuracy of the prediction can be improved more than 30% by using latent features; (3) after using distance-based oversampling on the imbalanced training datasets, the end-to-end prediction model achieves an accuracy of more than 80% and has a better performance for non-major academic grades. Full article
(This article belongs to the Special Issue Advanced Computing and Neural Networks Applied in Learning Systems)
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19 pages, 1962 KiB  
Article
Performance Predictions of Sci-Fi Films via Machine Learning
by Amjed Al Fahoum and Tahani A. Ghobon
Appl. Sci. 2023, 13(7), 4312; https://doi.org/10.3390/app13074312 - 29 Mar 2023
Cited by 4 | Viewed by 2460
Abstract
The films teenagers watch have a significant influence on their behavior. After witnessing a film starring an actor with a particular social habit or personality trait, viewers, particularly youngsters, may attempt to adopt the actor’s behavior. This study proposes an algorithm-based technique for [...] Read more.
The films teenagers watch have a significant influence on their behavior. After witnessing a film starring an actor with a particular social habit or personality trait, viewers, particularly youngsters, may attempt to adopt the actor’s behavior. This study proposes an algorithm-based technique for predicting the market potential of upcoming science fiction films. Numerous science fiction films are released annually, and working in the film industry is both profitable and delightful. Before the film’s release, it is necessary to conduct research and make informed predictions about its success. In this investigation, different machine learning methods written in MATLAB are examined to identify and forecast the future performance of movies. Using 14 methods for machine learning, it was feasible to predict how individuals would vote on science fiction films. Due to their superior performance, the fine, medium, and weighted KNN algorithms were given more consideration. In comparison to earlier studies, the KNN-adopted methods displayed greater precision (0.89–0.93), recall (0.88–0.92), and accuracy (90.1–93.0%), as well as a rapid execution rate, more robust estimations, and a shorter execution time. These tabulated statistics illustrate that the weighted KNN method is effective and trustworthy. If several KNN algorithms targeting specific viewer behavior are logically coupled, the film business and its global expansion can benefit from precise and consistent forecast outcomes. This study illustrates how prospective data analytics could improve the film industry. It is possible to develop a model that predicts a film’s success, effect, and social behavior by assessing features that contribute to its success based on historical data. Full article
(This article belongs to the Special Issue Advanced Computing and Neural Networks Applied in Learning Systems)
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12 pages, 665 KiB  
Article
A Comparative Study for Some Mathematical Models of Epidemic Diseases with Application to Strategic Management
by I. K. Youssef and M. H. M. Hassan
Appl. Sci. 2022, 12(24), 12639; https://doi.org/10.3390/app122412639 - 09 Dec 2022
Cited by 3 | Viewed by 1133
Abstract
A local performance of the SIR model on actual data is introduced. A good approximation of the SIR model parameters in Saudi Arabia during a period of 275 days (the first of April 2020 to the end of December 2020) is determined. The [...] Read more.
A local performance of the SIR model on actual data is introduced. A good approximation of the SIR model parameters in Saudi Arabia during a period of 275 days (the first of April 2020 to the end of December 2020) is determined. The parameters are estimated from the recorded data and used to predict the values in the next subsequent period. The performance of the standard fourth order Runge–Kutta method is considered for the classical SIR models over different periods. A comparison of the recorded data and the predicted values during the considered period illustrated the effectiveness of the treatment. The mathematical properties and initial conditions are considered within the estimated parameter values. It is shown that lockdown and social distance attitudes effectively controlled the spread of the disease. The maximum number of daily active infected cases is 63,026, and occurs in July and this agrees with the calculated values. To make the graphs representable, we considered a fixed closed population, the effective sample during the considered period of size N = 400,000 only (represents only 1% of the overall population susceptible, this must be associated, with great thanks, to the authorities in KSA). Full article
(This article belongs to the Special Issue Advanced Computing and Neural Networks Applied in Learning Systems)
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