Special Issue "Entropy in Soft Computing and Machine Learning Algorithms"

A special issue of Entropy (ISSN 1099-4300).

Deadline for manuscript submissions: 30 November 2021.

Special Issue Editors

Dr. Diego Oliva
E-Mail Website
Guest Editor
Depto. de Ciencias Computacionales, Universidad de Guadalajara, CUCEI, Av. Revolución 1500, Guadalajara, Jalisco, Mexico
Interests: metaheuristic algorithms; bioinspired computation; image processing; machine learning; optimization
Special Issues, Collections and Topics in MDPI journals
Dr. Salvador Miguel Hinojosa Cervantes
E-Mail
Guest Editor
Depto. de Ciencias Computacionales, Universidad de Guadalajara, CUCEI, Av. Revolución 1500, Guadalajara, Jalisco, Mexico
Interests: image processing; bioinspired computation; multi-objective optimization; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Soft computing and machine learning algorithms are used in different fields of science and technology. They are important tools designed to solve complex real-life problems under uncertainty.

Entropy is a powerful tool that has changed the analysis of information. The use of entropy has been extended in soft computing and machine learning methodologies, from measuring uncertainty to exploring and exploiting search spaces in optimization. Different kinds of entropy are used depending on what is required. Moreover, it is necessary to use soft computing and machine learning methods to provide accurate solutions to complex problems in the information era. Hybrid algorithms are also important; they merge skills from different approaches and make decisions based on different rules to accurately explore the possible solutions.

Since the fields of soft computing and machine algorithms are constantly growing, following all the different branches in which entropy is used is complicated. Considering the above, this Special Issue aims to present the latest advances in soft computing and machine learning algorithms that employ or solve problems where entropy is included. It also seeks to include literature reviews and surveys on related topics.

Dr. Diego Oliva
Dr. Salvador Miguel Hinojosa Cervantes
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 papers will be 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. Entropy is an international peer-reviewed open access monthly 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 1800 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

  • Metaheuristics
  • Bioinspired algorithms
  • Cross entropy
  • Shannon entropy
  • Fuzzy entropy
  • Machine learning
  • Neural networks
  • Swarm optimization
  • Evolutionary computation
  • Fuzzy logic
  • Genetic algorithms
  • Deep learning

Published Papers (7 papers)

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Research

Article
Belief and Possibility Belief Interval-Valued N-Soft Set and Their Applications in Multi-Attribute Decision-Making Problems
Entropy 2021, 23(11), 1498; https://doi.org/10.3390/e23111498 - 13 Nov 2021
Viewed by 310
Abstract
In this research article, we motivate and introduce the concept of possibility belief interval-valued N-soft sets. It has a great significance for enhancing the performance of decision-making procedures in many theories of uncertainty. The N-soft set theory is arising as an effective mathematical [...] Read more.
In this research article, we motivate and introduce the concept of possibility belief interval-valued N-soft sets. It has a great significance for enhancing the performance of decision-making procedures in many theories of uncertainty. The N-soft set theory is arising as an effective mathematical tool for dealing with precision and uncertainties more than the soft set theory. In this regard, we extend the concept of belief interval-valued soft set to possibility belief interval-valued N-soft set (by accumulating possibility and belief interval with N-soft set), and we also explain its practical calculations. To this objective, we defined related theoretical notions, for example, belief interval-valued N-soft set, possibility belief interval-valued N-soft set, their algebraic operations, and examined some of their fundamental properties. Furthermore, we developed two algorithms by using max-AND and min-OR operations of possibility belief interval-valued N-soft set for decision-making problems and also justify its applicability with numerical examples. Full article
(This article belongs to the Special Issue Entropy in Soft Computing and Machine Learning Algorithms)
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Article
A Nighttime Vehicle Detection Method with Attentive GAN for Accurate Classification and Regression
Entropy 2021, 23(11), 1490; https://0-doi-org.brum.beds.ac.uk/10.3390/e23111490 - 11 Nov 2021
Viewed by 231
Abstract
Vehicle detection plays a vital role in the design of Automatic Driving System (ADS), which has achieved remarkable improvements in recent years. However, vehicle detection in night scenes still has considerable challenges for the reason that the vehicle features are not obvious and [...] Read more.
Vehicle detection plays a vital role in the design of Automatic Driving System (ADS), which has achieved remarkable improvements in recent years. However, vehicle detection in night scenes still has considerable challenges for the reason that the vehicle features are not obvious and are easily affected by complex road lighting or lights from vehicles. In this paper, a high-accuracy vehicle detection algorithm is proposed to detect vehicles in night scenes. Firstly, an improved Generative Adversarial Network (GAN), named Attentive GAN, is used to enhance the vehicle features of nighttime images. Then, with the purpose of achieving a higher detection accuracy, a multiple local regression is employed in the regression branch, which predicts multiple bounding box offsets. An improved Region of Interest (RoI) pooling method is used to get distinguishing features in a classification branch based on Faster Region-based Convolutional Neural Network (R-CNN). Cross entropy loss is introduced to improve the accuracy of classification branch. The proposed method is examined with the proposed dataset, which is composed of the selected nighttime images from BDD-100k dataset (Berkeley Diverse Driving Database, including 100,000 images). Compared with a series of state-of-the-art detectors, the experiments demonstrate that the proposed algorithm can effectively contribute to vehicle detection accuracy in nighttime. Full article
(This article belongs to the Special Issue Entropy in Soft Computing and Machine Learning Algorithms)
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Article
Boosting COVID-19 Image Classification Using MobileNetV3 and Aquila Optimizer Algorithm
Entropy 2021, 23(11), 1383; https://0-doi-org.brum.beds.ac.uk/10.3390/e23111383 - 22 Oct 2021
Viewed by 420
Abstract
Currently, the world is still facing a COVID-19 (coronavirus disease 2019) classified as a highly infectious disease due to its rapid spreading. The shortage of X-ray machines may lead to critical situations and delay the diagnosis results, increasing the number of deaths. Therefore, [...] Read more.
Currently, the world is still facing a COVID-19 (coronavirus disease 2019) classified as a highly infectious disease due to its rapid spreading. The shortage of X-ray machines may lead to critical situations and delay the diagnosis results, increasing the number of deaths. Therefore, the exploitation of deep learning (DL) and optimization algorithms can be advantageous in early diagnosis and COVID-19 detection. In this paper, we propose a framework for COVID-19 images classification using hybridization of DL and swarm-based algorithms. The MobileNetV3 is used as a backbone feature extraction to learn and extract relevant image representations as a DL model. As a swarm-based algorithm, the Aquila Optimizer (Aqu) is used as a feature selector to reduce the dimensionality of the image representations and improve the classification accuracy using only the most essential selected features. To validate the proposed framework, two datasets with X-ray and CT COVID-19 images are used. The obtained results from the experiments show a good performance of the proposed framework in terms of classification accuracy and dimensionality reduction during the feature extraction and selection phases. The Aqu feature selection algorithm achieves accuracy better than other methods in terms of performance metrics. Full article
(This article belongs to the Special Issue Entropy in Soft Computing and Machine Learning Algorithms)
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Article
F-Divergences and Cost Function Locality in Generative Modelling with Quantum Circuits
Entropy 2021, 23(10), 1281; https://0-doi-org.brum.beds.ac.uk/10.3390/e23101281 (registering DOI) - 30 Sep 2021
Viewed by 596
Abstract
Generative modelling is an important unsupervised task in machine learning. In this work, we study a hybrid quantum-classical approach to this task, based on the use of a quantum circuit born machine. In particular, we consider training a quantum circuit born machine using [...] Read more.
Generative modelling is an important unsupervised task in machine learning. In this work, we study a hybrid quantum-classical approach to this task, based on the use of a quantum circuit born machine. In particular, we consider training a quantum circuit born machine using f-divergences. We first discuss the adversarial framework for generative modelling, which enables the estimation of any f-divergence in the near term. Based on this capability, we introduce two heuristics which demonstrably improve the training of the born machine. The first is based on f-divergence switching during training. The second introduces locality to the divergence, a strategy which has proved important in similar applications in terms of mitigating barren plateaus. Finally, we discuss the long-term implications of quantum devices for computing f-divergences, including algorithms which provide quadratic speedups to their estimation. In particular, we generalise existing algorithms for estimating the Kullback–Leibler divergence and the total variation distance to obtain a fault-tolerant quantum algorithm for estimating another f-divergence, namely, the Pearson divergence. Full article
(This article belongs to the Special Issue Entropy in Soft Computing and Machine Learning Algorithms)
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Article
An Electric Fish-Based Arithmetic Optimization Algorithm for Feature Selection
Entropy 2021, 23(9), 1189; https://0-doi-org.brum.beds.ac.uk/10.3390/e23091189 - 09 Sep 2021
Viewed by 630
Abstract
With the widespread use of intelligent information systems, a massive amount of data with lots of irrelevant, noisy, and redundant features are collected; moreover, many features should be handled. Therefore, introducing an efficient feature selection (FS) approach becomes a challenging aim. In the [...] Read more.
With the widespread use of intelligent information systems, a massive amount of data with lots of irrelevant, noisy, and redundant features are collected; moreover, many features should be handled. Therefore, introducing an efficient feature selection (FS) approach becomes a challenging aim. In the recent decade, various artificial methods and swarm models inspired by biological and social systems have been proposed to solve different problems, including FS. Thus, in this paper, an innovative approach is proposed based on a hybrid integration between two intelligent algorithms, Electric fish optimization (EFO) and the arithmetic optimization algorithm (AOA), to boost the exploration stage of EFO to process the high dimensional FS problems with a remarkable convergence speed. The proposed EFOAOA is examined with eighteen datasets for different real-life applications. The EFOAOA results are compared with a set of recent state-of-the-art optimizers using a set of statistical metrics and the Friedman test. The comparisons show the positive impact of integrating the AOA operator in the EFO, as the proposed EFOAOA can identify the most important features with high accuracy and efficiency. Compared to the other FS methods whereas, it got the lowest features number and the highest accuracy in 50% and 67% of the datasets, respectively. Full article
(This article belongs to the Special Issue Entropy in Soft Computing and Machine Learning Algorithms)
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Article
Metaheuristics in the Optimization of Cryptographic Boolean Functions
Entropy 2020, 22(9), 1052; https://0-doi-org.brum.beds.ac.uk/10.3390/e22091052 - 21 Sep 2020
Cited by 2 | Viewed by 1405
Abstract
Generating Boolean Functions (BFs) with high nonlinearity is a complex task that is usually addresses through algebraic constructions. Metaheuristics have also been applied extensively to this task. However, metaheuristics have not been able to attain so good results as the algebraic techniques. This [...] Read more.
Generating Boolean Functions (BFs) with high nonlinearity is a complex task that is usually addresses through algebraic constructions. Metaheuristics have also been applied extensively to this task. However, metaheuristics have not been able to attain so good results as the algebraic techniques. This paper proposes a novel diversity-aware metaheuristic that is able to excel. This proposal includes the design of a novel cost function that combines several information from the Walsh Hadamard Transform (WHT) and a replacement strategy that promotes a gradual change from exploration to exploitation as well as the formation of clusters of solutions with the aim of allowing intensification steps at each iteration. The combination of a high entropy in the population and a lower entropy inside clusters allows a proper balance between exploration and exploitation. This is the first memetic algorithm that is able to generate 10-variable BFs of similar quality than algebraic methods. Experimental results and comparisons provide evidence of the high performance of the proposed optimization mechanism for the generation of high quality BFs. Full article
(This article belongs to the Special Issue Entropy in Soft Computing and Machine Learning Algorithms)
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Article
Multi-Level Image Thresholding Based on Modified Spherical Search Optimizer and Fuzzy Entropy
Entropy 2020, 22(3), 328; https://0-doi-org.brum.beds.ac.uk/10.3390/e22030328 - 12 Mar 2020
Cited by 10 | Viewed by 1324
Abstract
Multi-level thresholding is one of the effective segmentation methods that have been applied in many applications. Traditional methods face challenges in determining the suitable threshold values; therefore, metaheuristic (MH) methods have been adopted to solve these challenges. In general, MH methods had been [...] Read more.
Multi-level thresholding is one of the effective segmentation methods that have been applied in many applications. Traditional methods face challenges in determining the suitable threshold values; therefore, metaheuristic (MH) methods have been adopted to solve these challenges. In general, MH methods had been proposed by simulating natural behaviors of swarm ecosystems, such as birds, animals, and others. The current study proposes an alternative multi-level thresholding method based on a new MH method, a modified spherical search optimizer (SSO). This was performed by using the operators of the sine cosine algorithm (SCA) to enhance the exploitation ability of the SSO. Moreover, Fuzzy entropy is applied as the main fitness function to evaluate the quality of each solution inside the population of the proposed SSOSCA since Fuzzy entropy has established its performance in literature. Several images from the well-known Berkeley dataset were used to test and evaluate the proposed method. The evaluation outcomes approved that SSOSCA showed better performance than several existing methods according to different image segmentation measures. Full article
(This article belongs to the Special Issue Entropy in Soft Computing and Machine Learning Algorithms)
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