Symmetry

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4 pages, 206 KiB

Open AccessEditorial

Symmetry or Asymmetry? Complex Problems and Solutions by Computational Intelligence and Soft Computing

by László T. Kóczy

Symmetry 2022, 14(9), 1839; https://doi.org/10.3390/sym14091839 - 5 Sep 2022

Cited by 2 | Viewed by 1205

Abstract

What is the role of symmetry in the seemingly far away topics of solving complex applied problems by approaches offered by Soft Computing and Computational Intelligence [...] Full article

(This article belongs to the Special Issue Computational Intelligence and Soft Computing: Recent Applications)

Research

Jump to: Editorial

20 pages, 445 KiB

Open AccessFeature PaperArticle

Utilizing Language Models to Expand Vision-Based Commonsense Knowledge Graphs

by Navid Rezaei and Marek Z. Reformat

Symmetry 2022, 14(8), 1715; https://doi.org/10.3390/sym14081715 - 17 Aug 2022

Cited by 1 | Viewed by 2847

Abstract

The introduction and ever-growing size of the transformer deep-learning architecture have had a tremendous impact not only in the field of natural language processing but also in other fields. The transformer-based language models have contributed to a renewed interest in commonsense knowledge due [...] Read more.

The introduction and ever-growing size of the transformer deep-learning architecture have had a tremendous impact not only in the field of natural language processing but also in other fields. The transformer-based language models have contributed to a renewed interest in commonsense knowledge due to the abilities of deep learning models. Recent literature has focused on analyzing commonsense embedded within the pre-trained parameters of these models and embedding missing commonsense using knowledge graphs and fine-tuning. We base our current work on the empirically proven language understanding of very large transformer-based language models to expand a limited commonsense knowledge graph, initially generated only on visual data. The few-shot-prompted pre-trained language models can learn the context of an initial knowledge graph with less bias than language models fine-tuned on a large initial corpus. It is also shown that these models can offer new concepts that are added to the vision-based knowledge graph. This two-step approach of vision mining and language model prompts results in the auto-generation of a commonsense knowledge graph well equipped with physical commonsense, which is human commonsense gained by interacting with the physical world. To prompt the language models, we adapted the chain-of-thought method of prompting. To the best of our knowledge, it is a novel contribution to the domain of the generation of commonsense knowledge, which can result in a five-fold cost reduction compared to the state-of-the-art. Another contribution is assigning fuzzy linguistic terms to the generated triples. The process is end to end in the context of knowledge graphs. It means the triples are verbalized to natural language, and after being processed, the results are converted back to triples and added to the commonsense knowledge graph. Full article

(This article belongs to the Special Issue Computational Intelligence and Soft Computing: Recent Applications)

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13 pages, 2418 KiB

Open AccessArticle

A New Approach to COVID-19 Detection: An ANN Proposal Optimized through Tree-Seed Algorithm

by Muhammet Fatih Aslan, Kadir Sabanci and Ewa Ropelewska

Symmetry 2022, 14(7), 1310; https://doi.org/10.3390/sym14071310 - 24 Jun 2022

Cited by 12 | Viewed by 1785

Abstract

Coronavirus disease (COVID-19), which affects the whole world, continues to spread. This disease has infected and killed millions of people worldwide. To limit the rate of spread of the disease, early detection should be provided and then the infected person should be quarantined. [...] Read more.

Coronavirus disease (COVID-19), which affects the whole world, continues to spread. This disease has infected and killed millions of people worldwide. To limit the rate of spread of the disease, early detection should be provided and then the infected person should be quarantined. This paper proposes a Deep Learning-based application for early and accurate diagnosis of COVID-19. Compared to other studies, this application’s biggest difference and contribution are that it uses Tree Seed Algorithm (TSA)-optimized Artificial Neural Networks (ANN) to classify deep architectural features. Previous studies generally use fully connected layers for end-to-end learning classification. However, this study proves that even relatively simple AlexNet features can be classified more accurately with the TSA-ANN structure. The proposed hybrid model provides diagnosis with 98.54% accuracy for COVID-19 disease, which shows asymmetric distribution on Computed Tomography (CT) images. As a result, it is shown that using the proposed classification strategy, the features of end-to-end architectures can be classified more accurately. Full article

(This article belongs to the Special Issue Computational Intelligence and Soft Computing: Recent Applications)

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21 pages, 708 KiB

Open AccessArticle

Genetic-Algorithm-Inspired Difficulty Adjustment for Proof-of-Work Blockchains

by Zi Hau Chin, Timothy Tzen Vun Yap and Ian Kim Teck Tan

Symmetry 2022, 14(3), 609; https://doi.org/10.3390/sym14030609 - 18 Mar 2022

Cited by 3 | Viewed by 3830

Abstract

In blockchains, the principle of proof-of-work (PoW) is used to compute a complex mathematical problem. The computation complexity is governed by the difficulty, adjusted periodically to control the rate at which new blocks are created. The network hash rate determines this, a phenomenon [...] Read more.

In blockchains, the principle of proof-of-work (PoW) is used to compute a complex mathematical problem. The computation complexity is governed by the difficulty, adjusted periodically to control the rate at which new blocks are created. The network hash rate determines this, a phenomenon of symmetry, as the difficulty also increases when the hash rate increases. If the hash rate grows or declines exponentially, the block creation interval cannot be maintained. A genetic algorithm (GA) is proposed as an additional mechanism to the existing difficulty adjustment algorithm for optimizing the blockchain parameters. The study was conducted with four scenarios in mind, including a default scenario that simulates a regular blockchain. All the scenarios with the GA were able to achieve a lower standard deviation of the average block time and difficulty compared to the default blockchain network without GA. The scenario of a fixed difficulty adjustment interval with GA was able to reduce the standard deviation of the average block time by 80.1%, from 497.1 to 98.9, and achieved a moderate median block propagation time of 6.81 s and a stale block rate of 6.67%. Full article

(This article belongs to the Special Issue Computational Intelligence and Soft Computing: Recent Applications)

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17 pages, 2218 KiB

Open AccessArticle

Functional Evaluation Using Fuzzy FMEA for a Non-Invasive Measurer for Methane and Carbone Dioxide

by Lidilia Cruz-Rivero, María Leonor Méndez-Hernández, Carlos Eusebio Mar-Orozco, Alberto A. Aguilar-Lasserre, Alfonso Barbosa-Moreno and Josué Sánchez-Escobar

Symmetry 2022, 14(2), 421; https://doi.org/10.3390/sym14020421 - 20 Feb 2022

Cited by 10 | Viewed by 2088

Abstract

This paper combines the use of two tools: Failure Mode and Effect Analysis (FMEA) and Fuzzy Logic (FL), to evaluate the functionality of a quantifier prototype of Methane gas (CH₄) and Carbon Dioxide (CO₂), developed specifically to measure the [...] Read more.

This paper combines the use of two tools: Failure Mode and Effect Analysis (FMEA) and Fuzzy Logic (FL), to evaluate the functionality of a quantifier prototype of Methane gas (CH₄) and Carbon Dioxide (CO₂), developed specifically to measure the emissions generated by cattle. Unlike previously reported models for the same purpose, this device reduces damage to the integrity of the animal and does not interfere with the activities of livestock in their development medium. FMEA and FL are used to validate the device’s functionality, which involves identifying possible failure modes that represent a more significant impact on the operation and prevent the prototype from fulfilling the function for which it was created. As a result, this document presents the development of an intelligent fuzzy system type Mamdani, supported in the Fuzzy Inference System Toolbox of MatLabR2018b^®, for generating a risk priority index. A Fuzzy FMEA model was obtained to validate the prototype for measuring Methane and Carbon Dioxide emissions, which allows considering this prototype as a reliable alternative for the reliable measurement of these gases. This study was necessary as a complementary part in the validation of the design of the prototype quantifier of CH₄ and CO₂ emissions. The methods used (classic FMEA and Fuzzy FMEA) to evaluate the RPN show asymmetric graphs due to data disparity. Values in the classical method are mostly lower than the Mamdani model results due to the description of the criteria with which it is evaluated. Full article

(This article belongs to the Special Issue Computational Intelligence and Soft Computing: Recent Applications)

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24 pages, 7292 KiB

Open AccessArticle

A Novel Hybrid Method for KPI Anomaly Detection Based on VAE and SVDD

by Yun Zhao, Xiuguo Zhang, Zijing Shang and Zhiying Cao

Symmetry 2021, 13(11), 2104; https://doi.org/10.3390/sym13112104 - 5 Nov 2021

Cited by 5 | Viewed by 2803

Abstract

Key performance indicator (KPI) anomaly detection is the underlying core technology in Artificial Intelligence for IT operations (AIOps). It has an important impact on subsequent anomaly location and root cause analysis. Variational auto-encoder (VAE) is a symmetry network structure composed of encoder and [...] Read more.

Key performance indicator (KPI) anomaly detection is the underlying core technology in Artificial Intelligence for IT operations (AIOps). It has an important impact on subsequent anomaly location and root cause analysis. Variational auto-encoder (VAE) is a symmetry network structure composed of encoder and decoder, which has attracted extensive attention because of its ability to capture complex KPI data features and better detection results. However, VAE is not well applied to the modeling of KPI time series data and it is often necessary to set the threshold to obtain more accurate results. In response to these problems, this paper proposes a novel hybrid method for KPI anomaly detection based on VAE and support vector data description (SVDD). This method consists of two modules: a VAE reconstructor and SVDD anomaly detector. In the VAE reconstruction module, firstly, bi-directional long short-term memory (BiLSTM) is used to replace the traditional feedforward neural network in VAE to capture the time correlation of sequences; then, batch normalization is used at the output of the encoder to prevent the disappearance of KL (Kullback–Leibler) divergence, which prevents ignoring latent variables to reconstruct data directly. Finally, exponentially weighted moving average (EWMA) is used to smooth the reconstruction error, which reduces false positives and false negatives during the detection process. In the SVDD anomaly detection module, smoothed reconstruction errors are introduced into the SVDD for training to determine the threshold of adaptively anomaly detection. Experimental results on the public dataset show that this method has a better detection effect than baseline methods. Full article

(This article belongs to the Special Issue Computational Intelligence and Soft Computing: Recent Applications)

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21 pages, 1106 KiB

Open AccessArticle

The Role of Metaheuristics as Solutions Generators

by Hanane El Raoui, Marcelino Cabrera-Cuevas and David A. Pelta

Symmetry 2021, 13(11), 2034; https://doi.org/10.3390/sym13112034 - 28 Oct 2021

Cited by 7 | Viewed by 1591

Abstract

Optimization problems are ubiquitous nowadays. Many times, their corresponding computational models necessarily leave out of consideration several characteristics and features of the real world, so trying to obtain the optimum solution can not be enough for a problem solving point of view. The [...] Read more.

Optimization problems are ubiquitous nowadays. Many times, their corresponding computational models necessarily leave out of consideration several characteristics and features of the real world, so trying to obtain the optimum solution can not be enough for a problem solving point of view. The aim of this paper is to illustrate the role of metaheuristics as solutions’ generators in a basic problem solving framework. Metaheuristics become relevant in two modes: firstly because every run (in the case of population based techniques) allows to obtain a set of potentially good solutions, and secondly, if a reference solution is available, one can set up a new optimization problem that allows to obtain solutions with similar quality in the objectives space but maximally different structure in the design space. Once a set of solutions is obtained, an example of an a posteriori analysis to rank them according with decision maker’s preferences is shown. All the problem solving framework steps, emphasizing the role of metaheuristics are illustrated with a dynamic version of the tourist trip design problem (for the first mode), and with a perishable food distribution problem (for the second one). These examples clearly show the benefits of the problem solving framework proposed. The potential role of the symmetry concept is also explored. Full article

(This article belongs to the Special Issue Computational Intelligence and Soft Computing: Recent Applications)

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20 pages, 2147 KiB

Open AccessArticle

The Use of Trapezoidal Oriented Fuzzy Numbers in Portfolio Analysis

by Anna Łyczkowska-Hanćkowiak

Symmetry 2021, 13(9), 1722; https://doi.org/10.3390/sym13091722 - 17 Sep 2021

Cited by 2 | Viewed by 1715

Abstract

Oriented fuzzy numbers are a convenient tool to manage an investment portfolio as they enable the inclusion of uncertain and imprecise information about the financial market in a portfolio analysis. This kind of portfolio analysis is based on the discount factor. Thanks to [...] Read more.

Oriented fuzzy numbers are a convenient tool to manage an investment portfolio as they enable the inclusion of uncertain and imprecise information about the financial market in a portfolio analysis. This kind of portfolio analysis is based on the discount factor. Thanks to this fact, this analysis is simpler than a portfolio analysis based on the return rate. The present value is imprecise due to the fact that it is modelled with the use of oriented fuzzy numbers. In such a case, the expected discount factor is also an oriented fuzzy number. The main objective of this paper is to conduct a portfolio analysis consisting of the instruments with the present value estimated as a trapezoidal oriented fuzzy number. We consider the portfolio elements as being positively and negatively oriented. We test their discount factor. Due to the fact that adding oriented fuzzy numbers is not associative, a weighted sum of positively oriented discount factors and a weighted sum of negatively oriented factors is calculated and consequently a portfolio discount factor is obtained as a weighted addition of both sums. Also, the imprecision risk of the obtained investment portfolio is estimated using measures of energy and entropy. All theoretical considerations are illustrated by an empirical case study. Full article

(This article belongs to the Special Issue Computational Intelligence and Soft Computing: Recent Applications)

16 pages, 9086 KiB

Open AccessArticle

StyleGANs and Transfer Learning for Generating Synthetic Images in Industrial Applications

by Harold Achicanoy, Deisy Chaves and Maria Trujillo

Symmetry 2021, 13(8), 1497; https://doi.org/10.3390/sym13081497 - 16 Aug 2021

Cited by 12 | Viewed by 5985

Abstract

Deep learning applications on computer vision involve the use of large-volume and representative data to obtain state-of-the-art results due to the massive number of parameters to optimise in deep models. However, data are limited with asymmetric distributions in industrial applications due to rare [...] Read more.

Deep learning applications on computer vision involve the use of large-volume and representative data to obtain state-of-the-art results due to the massive number of parameters to optimise in deep models. However, data are limited with asymmetric distributions in industrial applications due to rare cases, legal restrictions, and high image-acquisition costs. Data augmentation based on deep learning generative adversarial networks, such as StyleGAN, has arisen as a way to create training data with symmetric distributions that may improve the generalisation capability of built models. StyleGAN generates highly realistic images in a variety of domains as a data augmentation strategy but requires a large amount of data to build image generators. Thus, transfer learning in conjunction with generative models are used to build models with small datasets. However, there are no reports on the impact of pre-trained generative models, using transfer learning. In this paper, we evaluate a StyleGAN generative model with transfer learning on different application domains—training with paintings, portraits, Pokémon, bedrooms, and cats—to generate target images with different levels of content variability: bean seeds (low variability), faces of subjects between 5 and 19 years old (medium variability), and charcoal (high variability). We used the first version of StyleGAN due to the large number of publicly available pre-trained models. The Fréchet Inception Distance was used for evaluating the quality of synthetic images. We found that StyleGAN with transfer learning produced good quality images, being an alternative for generating realistic synthetic images in the evaluated domains. Full article

(This article belongs to the Special Issue Computational Intelligence and Soft Computing: Recent Applications)

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26 pages, 5338 KiB

Open AccessArticle

An Improved Bees Algorithm for Training Deep Recurrent Networks for Sentiment Classification

by Sultan Zeybek, Duc Truong Pham, Ebubekir Koç and Aydın Seçer

Symmetry 2021, 13(8), 1347; https://doi.org/10.3390/sym13081347 - 26 Jul 2021

Cited by 12 | Viewed by 3136

Abstract

Recurrent neural networks (RNNs) are powerful tools for learning information from temporal sequences. Designing an optimum deep RNN is difficult due to configuration and training issues, such as vanishing and exploding gradients. In this paper, a novel metaheuristic optimisation approach is proposed for [...] Read more.

Recurrent neural networks (RNNs) are powerful tools for learning information from temporal sequences. Designing an optimum deep RNN is difficult due to configuration and training issues, such as vanishing and exploding gradients. In this paper, a novel metaheuristic optimisation approach is proposed for training deep RNNs for the sentiment classification task. The approach employs an enhanced Ternary Bees Algorithm (BA-3+), which operates for large dataset classification problems by considering only three individual solutions in each iteration. BA-3+ combines the collaborative search of three bees to find the optimal set of trainable parameters of the proposed deep recurrent learning architecture. Local learning with exploitative search utilises the greedy selection strategy. Stochastic gradient descent (SGD) learning with singular value decomposition (SVD) aims to handle vanishing and exploding gradients of the decision parameters with the stabilisation strategy of SVD. Global learning with explorative search achieves faster convergence without getting trapped at local optima to find the optimal set of trainable parameters of the proposed deep recurrent learning architecture. BA-3+ has been tested on the sentiment classification task to classify symmetric and asymmetric distribution of the datasets from different domains, including Twitter, product reviews, and movie reviews. Comparative results have been obtained for advanced deep language models and Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms. BA-3+ converged to the global minimum faster than the DE and PSO algorithms, and it outperformed the SGD, DE, and PSO algorithms for the Turkish and English datasets. The accuracy value and F1 measure have improved at least with a 30–40% improvement than the standard SGD algorithm for all classification datasets. Accuracy rates in the RNN model trained with BA-3+ ranged from 80% to 90%, while the RNN trained with SGD was able to achieve between 50% and 60% for most datasets. The performance of the RNN model with BA-3+ has as good as for Tree-LSTMs and Recursive Neural Tensor Networks (RNTNs) language models, which achieved accuracy results of up to 90% for some datasets. The improved accuracy and convergence results show that BA-3+ is an efficient, stable algorithm for the complex classification task, and it can handle the vanishing and exploding gradients problem of deep RNNs. Full article

(This article belongs to the Special Issue Computational Intelligence and Soft Computing: Recent Applications)

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21 pages, 1615 KiB

Open AccessArticle

A New Feature Selection Method Based on a Self-Variant Genetic Algorithm Applied to Android Malware Detection

by Le Wang, Yuelin Gao, Shanshan Gao and Xin Yong

Symmetry 2021, 13(7), 1290; https://doi.org/10.3390/sym13071290 - 18 Jul 2021

Cited by 18 | Viewed by 3351

Abstract

In solving classification problems in the field of machine learning and pattern recognition, the pre-processing of data is particularly important. The processing of high-dimensional feature datasets increases the time and space complexity of computer processing and reduces the accuracy of classification models. Hence, [...] Read more.

In solving classification problems in the field of machine learning and pattern recognition, the pre-processing of data is particularly important. The processing of high-dimensional feature datasets increases the time and space complexity of computer processing and reduces the accuracy of classification models. Hence, the proposal of a good feature selection method is essential. This paper presents a new algorithm for solving feature selection, retaining the selection and mutation operators from traditional genetic algorithms. On the one hand, the global search capability of the algorithm is ensured by changing the population size, on the other hand, finding the optimal mutation probability for solving the feature selection problem based on different population sizes. During the iteration of the algorithm, the population size does not change, no matter how many transformations are made, and is the same as the initialized population size; this spatial invariance is physically defined as symmetry. The proposed method is compared with other algorithms and validated on different datasets. The experimental results show good performance of the algorithm, in addition to which we apply the algorithm to a practical Android software classification problem and the results also show the superiority of the algorithm. Full article

(This article belongs to the Special Issue Computational Intelligence and Soft Computing: Recent Applications)

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13 pages, 544 KiB

Open AccessArticle

Flattening Layer Pruning in Convolutional Neural Networks

by Ernest Jeczmionek and Piotr A. Kowalski

Symmetry 2021, 13(7), 1147; https://doi.org/10.3390/sym13071147 - 27 Jun 2021

Cited by 21 | Viewed by 3849

Abstract

The rapid growth of performance in the field of neural networks has also increased their sizes. Pruning methods are getting more and more attention in order to overcome the problem of non-impactful parameters and overgrowth of neurons. In this article, the application of [...] Read more.

The rapid growth of performance in the field of neural networks has also increased their sizes. Pruning methods are getting more and more attention in order to overcome the problem of non-impactful parameters and overgrowth of neurons. In this article, the application of Global Sensitivity Analysis (GSA) methods demonstrates the impact of input variables on the model’s output variables. GSA gives the ability to mark out the least meaningful arguments and build reduction algorithms on these. Using several popular datasets, the study shows how different levels of pruning correlate to network accuracy and how levels of reduction negligibly impact accuracy. In doing so, pre- and post-reduction sizes of neural networks are compared. This paper shows how Sobol and FAST methods with common norms can largely decrease the size of a network, while keeping accuracy relatively high. On the basis of the obtained results, it is possible to create a thesis about the asymmetry between the elements removed from the network topology and the quality of the neural network. Full article

(This article belongs to the Special Issue Computational Intelligence and Soft Computing: Recent Applications)

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12 pages, 7365 KiB

Open AccessArticle

A Hybrid Discrete Bacterial Memetic Algorithm with Simulated Annealing for Optimization of the Flow Shop Scheduling Problem

by Anita Agárdi, Károly Nehéz, Olivér Hornyák and László T. Kóczy

Symmetry 2021, 13(7), 1131; https://doi.org/10.3390/sym13071131 - 24 Jun 2021

Cited by 9 | Viewed by 2214

Abstract

This paper deals with the flow shop scheduling problem. To find the optimal solution is an NP-hard problem. The paper reviews some algorithms from the literature and applies a benchmark dataset to evaluate their efficiency. In this research work, the discrete bacterial memetic [...] Read more.

This paper deals with the flow shop scheduling problem. To find the optimal solution is an NP-hard problem. The paper reviews some algorithms from the literature and applies a benchmark dataset to evaluate their efficiency. In this research work, the discrete bacterial memetic evolutionary algorithm (DBMEA) as a global searcher was investigated. The proposed algorithm improves the local search by applying the simulated annealing algorithm (SA). This paper presents the experimental results of solving the no-idle flow shop scheduling problem. To compare the proposed algorithm with other researchers’ work, a benchmark problem set was used. The calculated makespan times were compared against the best-known solutions in the literature. The proposed hybrid algorithm has provided better results than methods using genetic algorithm variants, thus it is a major improvement for the memetic algorithm family solving production scheduling problems. Full article

(This article belongs to the Special Issue Computational Intelligence and Soft Computing: Recent Applications)

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24 pages, 3567 KiB

Open AccessArticle

Dynamics of Fuzzy-Rough Cognitive Networks

by István Á. Harmati

Symmetry 2021, 13(5), 881; https://doi.org/10.3390/sym13050881 - 15 May 2021

Cited by 1 | Viewed by 1795

Abstract

Fuzzy-rough cognitive networks (FRCNs) are interpretable recurrent neural networks, primarily designed for solving classification problems. Their structure is simple and transparent, while the performance is comparable to the well-known black-box classifiers. Although there are many applications on fuzzy cognitive maps and recently for [...] Read more.

Fuzzy-rough cognitive networks (FRCNs) are interpretable recurrent neural networks, primarily designed for solving classification problems. Their structure is simple and transparent, while the performance is comparable to the well-known black-box classifiers. Although there are many applications on fuzzy cognitive maps and recently for FRCNS, only a very limited number of studies discuss the theoretical issues of these models. In this paper, we examine the behaviour of FRCNs viewing them as discrete dynamical systems. It will be shown that their mathematical properties highly depend on the size of the network, i.e., there are structural differences between the long-term behaviour of FRCN models of different size, which may influence the performance of these modelling tools. Full article

(This article belongs to the Special Issue Computational Intelligence and Soft Computing: Recent Applications)

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14 pages, 446 KiB

Open AccessArticle

Extractive Summarization Based on Dynamic Memory Network

by Ping Li and Jiong Yu

Symmetry 2021, 13(4), 600; https://doi.org/10.3390/sym13040600 - 3 Apr 2021

Cited by 1 | Viewed by 2227

Abstract

We present an extractive summarization model based on the Bert and dynamic memory network. The model based on Bert uses the transformer to extract text features and uses the pre-trained model to construct the sentence embeddings. The model based on Bert labels the [...] Read more.

We present an extractive summarization model based on the Bert and dynamic memory network. The model based on Bert uses the transformer to extract text features and uses the pre-trained model to construct the sentence embeddings. The model based on Bert labels the sentences automatically without using any hand-crafted features and the datasets are symmetry labeled. We also present a dynamic memory network method for extractive summarization. Experiments are conducted on several summarization benchmark datasets. Our model shows comparable performance compared with other extractive summarization methods. Full article

(This article belongs to the Special Issue Computational Intelligence and Soft Computing: Recent Applications)

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17 pages, 1641 KiB

Open AccessArticle

An Enhanced Spectral Clustering Algorithm with S-Distance

by Krishna Kumar Sharma, Ayan Seal, Enrique Herrera-Viedma and Ondrej Krejcar

Symmetry 2021, 13(4), 596; https://doi.org/10.3390/sym13040596 - 2 Apr 2021

Cited by 20 | Viewed by 2729

Abstract

Calculating and monitoring customer churn metrics is important for companies to retain customers and earn more profit in business. In this study, a churn prediction framework is developed by modified spectral clustering (SC). However, the similarity measure plays an imperative role in clustering [...] Read more.

Calculating and monitoring customer churn metrics is important for companies to retain customers and earn more profit in business. In this study, a churn prediction framework is developed by modified spectral clustering (SC). However, the similarity measure plays an imperative role in clustering for predicting churn with better accuracy by analyzing industrial data. The linear Euclidean distance in the traditional SC is replaced by the non-linear S-distance (

S d

). The

S d

is deduced from the concept of S-divergence (SD). Several characteristics of

S d

are discussed in this work. Assays are conducted to endorse the proposed clustering algorithm on four synthetics, eight UCI, two industrial databases and one telecommunications database related to customer churn. Three existing clustering algorithms—k-means, density-based spatial clustering of applications with noise and conventional SC—are also implemented on the above-mentioned 15 databases. The empirical outcomes show that the proposed clustering algorithm beats three existing clustering algorithms in terms of its Jaccard index, f-score, recall, precision and accuracy. Finally, we also test the significance of the clustering results by the Wilcoxon’s signed-rank test, Wilcoxon’s rank-sum test, and sign tests. The relative study shows that the outcomes of the proposed algorithm are interesting, especially in the case of clusters of arbitrary shape. Full article

(This article belongs to the Special Issue Computational Intelligence and Soft Computing: Recent Applications)

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23 pages, 1034 KiB

Open AccessArticle

On Present Value Evaluation under the Impact of Behavioural Factors Using Oriented Fuzzy Numbers

by Krzysztof Piasecki and Anna Łyczkowska-Hanćkowiak

Symmetry 2021, 13(3), 468; https://doi.org/10.3390/sym13030468 - 12 Mar 2021

Cited by 2 | Viewed by 1944

Abstract

In general, the present value (PV) concept is ambiguous. Therefore, behavioural factors may influence on the PV evaluation. The main aim of our paper is to propose some method of soft computing PV evaluated under the impact of behavioural factors. The starting point [...] Read more.

In general, the present value (PV) concept is ambiguous. Therefore, behavioural factors may influence on the PV evaluation. The main aim of our paper is to propose some method of soft computing PV evaluated under the impact of behavioural factors. The starting point for our discussion is the notion of the Behavioural PV (BPV) defined as an imprecisely real-valued function of distinguished variables which can be evaluated using objective financial knowledge or subjective behavioural premises. In our paper, a BPV is supplemented with a forecast of the asset price closest to changes. Such BPV is called the oriented BPV (O-BPV). We propose to evaluate an O-BPV by oriented fuzzy numbers which are more useful for portfolio analysis than fuzzy numbers. This fact determines the significance of the research described in this article. O-BPV may be applied as input signal for systems supporting invest-making. We consider here six cases of O-BPV: overvalued asset with the prediction of a rise in its price, overvalued asset with the prediction of a fall in its price, undervalued asset with the prediction of a rise in its price, undervalued asset with the prediction of a fall in its price, fully valued asset with the prediction of a rise in its rice and fully valued asset with the prediction of a fall in its rice. All our considerations are illustrated by numerical examples. Presented examples show the way in which we transform superposition of objective market knowledge and subjective investment opinion into simple return rate. Full article

(This article belongs to the Special Issue Computational Intelligence and Soft Computing: Recent Applications)

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20 pages, 806 KiB

Open AccessArticle

A Novel Neural Network Training Algorithm for the Identification of Nonlinear Static Systems: Artificial Bee Colony Algorithm Based on Effective Scout Bee Stage

by Ebubekir Kaya and Ceren Baştemur Kaya

Symmetry 2021, 13(3), 419; https://doi.org/10.3390/sym13030419 - 5 Mar 2021

Cited by 14 | Viewed by 2411

Abstract

In this study, a neural network-based approach is proposed for the identification of nonlinear static systems. A variant called ABCES (ABC Based on Effective Scout Bee Stage) is introduced for neural network training. Two important changes are carried out with ABCES. The first [...] Read more.

In this study, a neural network-based approach is proposed for the identification of nonlinear static systems. A variant called ABCES (ABC Based on Effective Scout Bee Stage) is introduced for neural network training. Two important changes are carried out with ABCES. The first is an update of “limit” control parameters. In ABC algorithm, “limit” value is fixed. It is adaptively adjusted according to number of iterations in ABCES. In this way, the efficiency of the scout bee stage is increased. Secondly, a new solution-generating mechanism for the scout bee stage is proposed. In ABC algorithm, new solutions are created randomly. It is aimed at developing previous solutions in the scout bee stage of ABCES. The performance of ABCES is analyzed on two different problem groups. First, its performance is evaluated on 13 numerical benchmark test problems. The results are compared with ABC, GA, PSO and DE. Next, the neural network is trained by ABCES to identify nonlinear static systems. 6 nonlinear static test problems are used. The performance of ABCES in neural network training is compared with ABC, PSO and HS. The results show that ABCES is generally effective in the identification of nonlinear static systems based on neural networks. Full article

(This article belongs to the Special Issue Computational Intelligence and Soft Computing: Recent Applications)

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13 pages, 6519 KiB

Open AccessArticle

Assessing a Multi-Objective Genetic Algorithm with a Simulated Environment for Energy-Saving of Air Conditioning Systems with User Preferences

by Alejandro Humberto García Ruiz, Salvador Ibarra Martínez, José Antonio Castán Rocha, Jesús David Terán Villanueva, Julio Laria Menchaca, Mayra Guadalupe Treviño Berrones, Mirna Patricia Ponce Flores and Aurelio Alejandro Santiago Pineda

Symmetry 2021, 13(2), 344; https://doi.org/10.3390/sym13020344 - 20 Feb 2021

Cited by 1 | Viewed by 2369

Abstract

Electricity is one of the most important resources for the growth and sustainability of the population. This paper assesses the energy consumption and user satisfaction of a simulated air conditioning system controlled with two different optimization algorithms. The algorithms are a genetic algorithm [...] Read more.

Electricity is one of the most important resources for the growth and sustainability of the population. This paper assesses the energy consumption and user satisfaction of a simulated air conditioning system controlled with two different optimization algorithms. The algorithms are a genetic algorithm (GA), implemented from the state of the art, and a non-dominated sorting genetic algorithm II (NSGA II) proposed in this paper; these algorithms control an air conditioning system considering user preferences. It is worth noting that we made several modifications to the objective function’s definition to make it more robust. The energy-saving optimization is essential to reduce CO2 emissions and economic costs; on the other hand, it is desirable for the user to feel comfortable, yet it will entail a higher energy consumption. Thus, we integrate user preferences with energy-saving on a single weighted function and a Pareto bi-objective problem to increase user satisfaction and decrease electrical energy consumption. To assess the experimentation, we constructed a simulator by training a backpropagation neural network with real data from a laboratory’s air conditioning system. According to the results, we conclude that NSGA II provides better results than the state of the art (GA) regarding user preferences and energy-saving. Full article

(This article belongs to the Special Issue Computational Intelligence and Soft Computing: Recent Applications)

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21 pages, 3346 KiB

Open AccessArticle

A Coordinated Air Defense Learning System Based on Immunized Classifier Systems

by Sulemana Nantogma, Yang Xu and Weizhi Ran

Symmetry 2021, 13(2), 271; https://doi.org/10.3390/sym13020271 - 5 Feb 2021

Cited by 6 | Viewed by 3287

Abstract

Autonomous (unmanned) combat systems will become an integral part of modern defense systems. However, limited operational capabilities, the need for coordination, and dynamic battlefield environments with the requirement of timeless in decision-making are peculiar difficulties to be solved in order to realize intelligent [...] Read more.

Autonomous (unmanned) combat systems will become an integral part of modern defense systems. However, limited operational capabilities, the need for coordination, and dynamic battlefield environments with the requirement of timeless in decision-making are peculiar difficulties to be solved in order to realize intelligent systems control. In this paper, we explore the application of Learning Classifier System and Artificial Immune models for coordinated self-learning air defense systems. In particular, this paper presents a scheme that implements an autonomous cooperative threat evaluation and weapon assignment learning approach. Taking into account uncertainties in a successful interception, target characteristics, weapon type and characteristics, closed-loop coordinated behaviors, we adopt a hierarchical multi-agent approach to coordinate multiple combat platforms to achieve optimal performance. Based on the combined strengths of learning classifier system and artificial immune-based algorithms, the proposed scheme consists of two categories of agents; a strategy generation agent inspired by learning classifier system, and strategy coordination inspired by Artificial Immune System mechanisms. An experiment in a realistic environment shows that the adopted hybrid approach can be used to learn weapon-target assignment for multiple unmanned combat systems to successfully defend against coordinated attacks. The presented results show the potential for hybrid approaches for an intelligent system enabling adaptable and collaborative systems. Full article

(This article belongs to the Special Issue Computational Intelligence and Soft Computing: Recent Applications)

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35 pages, 827 KiB

Open AccessArticle

Fuzzy Interpolation with Extensional Fuzzy Numbers

by Michal Holčapek, Nicole Škorupová and Martin Štěpnička

Symmetry 2021, 13(2), 170; https://doi.org/10.3390/sym13020170 - 22 Jan 2021

Cited by 3 | Viewed by 2390

Abstract

The article develops further directions stemming from the arithmetic of extensional fuzzy numbers. It presents the existing knowledge of the relationship between the arithmetic and the proposed orderings of extensional fuzzy numbers—so-called

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-orderings—and investigates distinct properties of such orderings. The desirable investigation [...] Read more.

The article develops further directions stemming from the arithmetic of extensional fuzzy numbers. It presents the existing knowledge of the relationship between the arithmetic and the proposed orderings of extensional fuzzy numbers—so-called

S

-orderings—and investigates distinct properties of such orderings. The desirable investigation of the

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-orderings of extensional fuzzy numbers is directly used in the concept of

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-function—a natural extension of the notion of a function that, in its arguments as well as results, uses extensional fuzzy numbers. One of the immediate subsequent applications is fuzzy interpolation. The article provides readers with the basic fuzzy interpolation method, investigation of its properties and an illustrative experimental example on real data. The goal of the paper is, however, much deeper than presenting a single fuzzy interpolation method. It determines direction to a wide variety of fuzzy interpolation as well as other analytical methods stemming from the concept of

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-function and from the arithmetic of extensional fuzzy numbers in general. Full article

(This article belongs to the Special Issue Computational Intelligence and Soft Computing: Recent Applications)

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27 pages, 1844 KiB

Open AccessArticle

A Chaotic Hybrid Butterfly Optimization Algorithm with Particle Swarm Optimization for High-Dimensional Optimization Problems

by Mengjian Zhang, Daoyin Long, Tao Qin and Jing Yang

Symmetry 2020, 12(11), 1800; https://doi.org/10.3390/sym12111800 - 30 Oct 2020

Cited by 78 | Viewed by 5047

Abstract

In order to solve the problem that the butterfly optimization algorithm (BOA) is prone to low accuracy and slow convergence, the trend of study is to hybridize two or more algorithms to obtain a superior solution in the field of optimization problems. A [...] Read more.

In order to solve the problem that the butterfly optimization algorithm (BOA) is prone to low accuracy and slow convergence, the trend of study is to hybridize two or more algorithms to obtain a superior solution in the field of optimization problems. A novel hybrid algorithm is proposed, namely HPSOBOA, and three methods are introduced to improve the basic BOA. Therefore, the initialization of BOA using a cubic one-dimensional map is introduced, and a nonlinear parameter control strategy is also performed. In addition, the particle swarm optimization (PSO) algorithm is hybridized with BOA in order to improve the basic BOA for global optimization. There are two experiments (including 26 well-known benchmark functions) that were conducted to verify the effectiveness of the proposed algorithm. The comparison results of experiments show that the hybrid HPSOBOA converges quickly and has better stability in numerical optimization problems with a high dimension compared with the PSO, BOA, and other kinds of well-known swarm optimization algorithms. Full article

(This article belongs to the Special Issue Computational Intelligence and Soft Computing: Recent Applications)

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15 pages, 3580 KiB

Open AccessArticle

Segmentation of Lung Nodules Using Improved 3D-UNet Neural Network

by Zhitao Xiao, Bowen Liu, Lei Geng, Fang Zhang and Yanbei Liu

Symmetry 2020, 12(11), 1787; https://doi.org/10.3390/sym12111787 - 28 Oct 2020

Cited by 88 | Viewed by 8279

Abstract

Lung cancer has one of the highest morbidity and mortality rates in the world. Lung nodules are an early indicator of lung cancer. Therefore, accurate detection and image segmentation of lung nodules is of great significance to the early diagnosis of lung cancer. [...] Read more.

Lung cancer has one of the highest morbidity and mortality rates in the world. Lung nodules are an early indicator of lung cancer. Therefore, accurate detection and image segmentation of lung nodules is of great significance to the early diagnosis of lung cancer. This paper proposes a CT (Computed Tomography) image lung nodule segmentation method based on 3D-UNet and Res2Net, and establishes a new convolutional neural network called 3D-Res2UNet. 3D-Res2Net has a symmetrical hierarchical connection network with strong multi-scale feature extraction capabilities. It enables the network to express multi-scale features with a finer granularity, while increasing the receptive field of each layer of the network. This structure solves the deep level problem. The network is not prone to gradient disappearance and gradient explosion problems, which improves the accuracy of detection and segmentation. The U-shaped network ensures the size of the feature map while effectively repairing the lost features. The method in this paper was tested on the LUNA16 public dataset, where the dice coefficient index reached 95.30% and the recall rate reached 99.1%, indicating that this method has good performance in lung nodule image segmentation. Full article

(This article belongs to the Special Issue Computational Intelligence and Soft Computing: Recent Applications)

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