Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.5
2.4
Journals
Mathematics
Special Issues
Advanced Computational Intelligence
share announcement

Advanced Computational Intelligence

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Computational and Applied Mathematics".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 3339

Special Issue Editors

Dr. Laura Cornejo-Bueno

E-Mail Website
Guest Editor
Department of Signal Processing and Communications, Universidad de Alcalá, 28805 Alcalá de Henares, Spain
Interests: different algorithms and techniques belonging to artificial intelligence field and their application to different problems in science and engineering; especially in renewable energy problems, meteorology and climatology
Dr. Jorge Pérez-Aracil

E-Mail Website
Guest Editor
Department of Signal Processing and Communications, Universidad de Alcalá, 28805 Alcalá de Henares, Spain
Interests: application of artificial intelligence techniques; specially optimization; machine learning; deep learning, renewable energy problems; climatology; structural design; vibration control

Special Issue Information

Dear Colleagues,

We are pleased to invite contributions to this Special Issue entitled “Advanced Computational Intelligence”.

For decades, different computational techniques and, above all, machine learning, have had a major impact on the research sector. This has been caused by the generalist nature of these techniques, which allows their application in different fields.

Nowadays, numerous advances are made in the field of computational intelligence. Researchers are exploiting the capacity of algorithms for approaching a wide range of problems, which may have a great socio-economic impact on the population.

This Special Issue welcomes proposals that provide new approaches and techniques, belonging to the branch of computational intelligence, to tackle problems of different topics, especially those real cases that can be of great social interest.

Dr. Laura Cornejo-Bueno
Dr. Jorge Pérez-Aracil
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. Mathematics 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 2600 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

  • artificial intelligence
  • machine learning
  • data analysis
  • time series

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

20 pages, 3898 KiB  
Article
Neural Networks with Transfer Learning and Frequency Decomposition for Wind Speed Prediction with Missing Data
by Xiaoou Li and Yingqin Zhu
Mathematics 2024, 12(8), 1137; https://doi.org/10.3390/math12081137 - 10 Apr 2024
Viewed by 318
Abstract
This paper presents a novel data-driven approach for enhancing time series forecasting accuracy when faced with missing data. Our proposed method integrates an Echo State Network (ESN) with ARIMA (Autoregressive Integrated Moving Average) modeling, frequency decomposition, and online transfer learning. This combination specifically [...] Read more.
This paper presents a novel data-driven approach for enhancing time series forecasting accuracy when faced with missing data. Our proposed method integrates an Echo State Network (ESN) with ARIMA (Autoregressive Integrated Moving Average) modeling, frequency decomposition, and online transfer learning. This combination specifically addresses the challenges missing data introduce in time series prediction. By using the strengths of each technique, our framework offers a robust solution for handling missing data and achieving superior forecasting accuracy in real-world applications. We demonstrate the effectiveness of the proposed model through a wind speed prediction case study. Compared to the existing methods, our approach achieves significant improvement in prediction accuracy, paving the way for more reliable decisionmaking in wind energy operations and management. Full article
(This article belongs to the Special Issue Advanced Computational Intelligence)
►▼ Show Figures

Figure 1

15 pages, 454 KiB  
Article
Community Detection in Multiplex Networks Using Orthogonal Non-Negative Matrix Tri-Factorization Based on Graph Regularization and Diversity
by Yuqi Yang, Shanshan Yu, Baicheng Pan, Chenglu Li and Man-Fai Leung
Mathematics 2024, 12(8), 1124; https://doi.org/10.3390/math12081124 - 09 Apr 2024
Viewed by 398
Abstract
In recent years, community detection has received increasing interest. In network analysis, community detection refers to the identification of tightly connected subsets of nodes, which are called “communities” or “groups”, in the network. Non-negative matrix factorization models are often used to solve the [...] Read more.
In recent years, community detection has received increasing interest. In network analysis, community detection refers to the identification of tightly connected subsets of nodes, which are called “communities” or “groups”, in the network. Non-negative matrix factorization models are often used to solve the problem. Orthogonal non-negative matrix tri-factorization (ONMTF) exhibits significant potential as an approach for community detection within multiplex networks. This paper explores the application of ONMTF in multiplex networks, aiming to detect both shared and exclusive communities simultaneously. The model decomposes each layer within the multiplex network into two low-rank matrices. One matrix corresponds to shared communities across all layers, and the other to unique communities within each layer. Additionally, graph regularization and the diversity of private communities are taken into account in the algorithm. The Hilbert Schmidt Independence Criterion (HSIC) is used to constrain the independence of private communities. The results prove that ONMTF effectively addresses community detection in multiplex networks. It also offers strong interpretability and feature extraction capabilities. Therefore, it is an advanced method for community detection in multiplex networks. Full article
(This article belongs to the Special Issue Advanced Computational Intelligence)
►▼ Show Figures

Figure 1

20 pages, 748 KiB  
Article
A Method for Perception and Assessment of Semantic Textual Similarities in English
by Omar Zatarain, Jesse Yoe Rumbo-Morales, Silvia Ramos-Cabral, Gerardo Ortíz-Torres, Felipe d. J. Sorcia-Vázquez, Iván Guillén-Escamilla and Juan Carlos Mixteco-Sánchez
Mathematics 2023, 11(12), 2700; https://doi.org/10.3390/math11122700 - 14 Jun 2023
Viewed by 965
Abstract
This research proposes a method for the detection of semantic similarities in text snippets; the method achieves an unsupervised extraction and comparison of semantic information by mimicking skills for the identification of clauses and possible verb conjugations, the selection of the most accurate [...] Read more.
This research proposes a method for the detection of semantic similarities in text snippets; the method achieves an unsupervised extraction and comparison of semantic information by mimicking skills for the identification of clauses and possible verb conjugations, the selection of the most accurate organization of the parts of speech, and similarity analysis by a direct comparison on the parts of speech from a pair of text snippets. The method for the extraction of the parts of speech in each text exploits a knowledge base structured as a dictionary and a thesaurus to identify the possible labels of each word and its synonyms. The method consists of the processes of perception, debiasing, reasoning and assessment. The perception module decomposes the text into blocks of information focused on the elicitation of the parts of speech. The debiasing module reorganizes the blocks of information due to the biases that may be produced in the previous perception. The reasoning module finds the similarities between blocks from two texts through analyses of similarities on synonymy, morphological properties, and the relative position of similar concepts within the texts. The assessment generates a judgement on the output produced by the reasoning as the averaged similarity assessment obtained from the parts of speech similarities of blocks. The proposed method is implemented on an English language version to exploit a knowledge base in English for the extraction of the similarities and differences of texts. The system implements a set of syntactic and logical rules that enable the autonomous reasoning that uses a knowledge base regardless of the concepts and knowledge domains of the latter. A system developed with the proposed method is tested on the “test” dataset used on the SemEval 2017 competition on seven knowledge bases compiled from six dictionaries and two thesauruses. The results indicate that the performance of the method increases as the degree of completeness of concepts and their relations increase, and the Pearson correlation for the most accurate knowledge base is 77%. Full article
(This article belongs to the Special Issue Advanced Computational Intelligence)
►▼ Show Figures

Figure 1

22 pages, 576 KiB  
Article
New Probabilistic, Dynamic Multi-Method Ensembles for Optimization Based on the CRO-SL
by Jorge Pérez-Aracil, Carlos Camacho-Gómez, Eugenio Lorente-Ramos, Cosmin M. Marina, Laura M. Cornejo-Bueno and Sancho Salcedo-Sanz
Mathematics 2023, 11(7), 1666; https://doi.org/10.3390/math11071666 - 30 Mar 2023
Cited by 2 | Viewed by 1066
Abstract
In this paper, new probabilistic and dynamic (adaptive) strategies for creating multi-method ensembles based on the coral reef optimization with substrate layers (CRO-SL) algorithm are proposed. CRO-SL is an evolutionary-based ensemble approach that is able to combine different search procedures for a single [...] Read more.
In this paper, new probabilistic and dynamic (adaptive) strategies for creating multi-method ensembles based on the coral reef optimization with substrate layers (CRO-SL) algorithm are proposed. CRO-SL is an evolutionary-based ensemble approach that is able to combine different search procedures for a single population. In this work, two different probabilistic strategies to improve the algorithm are analyzed. First, the probabilistic CRO-SL (PCRO-SL) is presented, which substitutes the substrates in the CRO-SL population with tags associated with each individual. Each tag represents a different operator which will modify the individual in the reproduction phase. In each generation of the algorithm, the tags are randomly assigned to the individuals with similar probabilities, obtaining this way an ensemble that sees more intense changes with the application of different operators to a given individual than CRO-SL. Second, the dynamic probabilistic CRO-SL (DPCRO-SL) is presented, in which the probability of tag assignment is modified during the evolution of the algorithm, depending on the quality of the solutions generated in each substrate. Thus, the best substrates in the search process will be assigned higher probabilities than those which showed worse performance during the search. The performances of the proposed probabilistic and dynamic ensembles were tested for different optimization problems, including benchmark functions and a real application of wind-turbine-layout optimization, comparing the results obtained with those of existing algorithms in the literature. Full article
(This article belongs to the Special Issue Advanced Computational Intelligence)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop