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

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 31771

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Prof. Dr. Stelios Papadakis

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Department of Management Sciences & Technology, School of Management Sciences & Economics, Hellenic Mediterranean University, Agios Nikolaos, 72100 Crete, Greece
Interests: computational intelligence; machine learning; parallel and distributed algorithms

Special Issue Information

Dear Colleagues,

Computational intelligence is a discipline essentially consisting of three paradigms: a) Evolutionary computation (including nature inspired optimization algorithms) b) Fuzzy systems and c) Neural Networks. Recently, a breakthrough in CI is taking place owing to the introduction of deep learning, including deep neural networks, deep recurrent neural networks, Fuzzy deep neural networks, generative adversarial neural networks, nature inspired optimization algorithms etc. A wide range of applications became achievable, especially on handling and modeling non-numerical types of data. Contemporarily, the horizon of potential applications has considerably extended.

This special issue accepts original research papers that expands the aforementioned (or related) groundbreaking aspects of Computational intelligence by novel methods and theories, as well as innovative applications of CI for fronting real world problems, successfully. Applications in Management and Economic Sciences are especially welcomed.

Prof. Stelios Papadakis
Guest Editor

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Keywords

deep learning
convolutional neural networks
deep belief networks
fuzzy deep neural networks
deep recurrent networks
long short–term memory networks
generative adversarial neural networks
auto encoders
restricted boltzmann machines
nature inspired computing
evolutionary computation

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Published Papers (11 papers)

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Research

19 pages, 4012 KiB

Open AccessArticle

Deep Learning for Forecasting Electricity Demand in Taiwan

by Cheng-Hong Yang, Bo-Hong Chen, Chih-Hsien Wu, Kuo-Chang Chen and Li-Yeh Chuang

Mathematics 2022, 10(14), 2547; https://doi.org/10.3390/math10142547 - 21 Jul 2022

Cited by 7 | Viewed by 2540

Abstract

According to the World Energy Investment 2018 report, the global annual investment in renewable energy exceeded USD 200 billion for eight consecutive years until 2017. In this paper, a deep-learning-based time-series prediction method, namely a gated recurrent unit (GRU)-based prediction method, is proposed to predict energy generation in Taiwan. Data on thermal power (coal, oil, and gas power), renewable energy (conventional hydropower, solar power, and wind power), pumped hydropower, and nuclear power generation for 1991 to 2020 were obtained from the Bureau of Energy, Ministry of Economic Affairs, Taiwan, and the Taiwan Power Company. The proposed GRU-based method was compared with six common forecasting methods: autoregressive integrated moving average, exponential smoothing (ETS), Holt–Winters ETS, support vector regression (SVR), whale-optimization-algorithm-based SVR, and long short-term memory. Among the methods compared, the proposed method had the lowest mean absolute percentage error and root mean square error and thus the highest accuracy. Government agencies and power companies in Taiwan can use the predictions of accurate energy forecasting models as references to formulate energy policies and design plans for the development of alternative energy sources. Full article

(This article belongs to the Special Issue Advanced Aspects of Computational Intelligence with Its Applications)

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19 pages, 3472 KiB

Open AccessArticle

An Efficient Method for Breast Mass Classification Using Pre-Trained Deep Convolutional Networks

by Ebtihal Al-Mansour, Muhammad Hussain, Hatim A. Aboalsamh and Fazal-e-Amin

Mathematics 2022, 10(14), 2539; https://doi.org/10.3390/math10142539 - 21 Jul 2022

Cited by 5 | Viewed by 1892

Abstract

Masses are the early indicators of breast cancer, and distinguishing between benign and malignant masses is a challenging problem. Many machine learning- and deep learning-based methods have been proposed to distinguish benign masses from malignant ones on mammograms. However, their performance is not satisfactory. Though deep learning has been shown to be effective in a variety of applications, it is challenging to apply it for mass classification since it requires a large dataset for training and the number of available annotated mammograms is limited. A common approach to overcome this issue is to employ a pre-trained model and fine-tune it on mammograms. Though this works well, it still involves fine-tuning a huge number of learnable parameters with a small number of annotated mammograms. To tackle the small set problem in the training or fine-tuning of CNN models, we introduce a new method, which uses a pre-trained CNN without any modifications as an end-to-end model for mass classification, without fine-tuning the learnable parameters. The training phase only identifies the neurons in the classification layer, which yield higher activation for each class, and later on uses the activation of these neurons to classify an unknown mass ROI. We evaluated the proposed approach using different CNN models on the public domain benchmark datasets, such as DDSM and INbreast. The results show that it outperforms the state-of-the-art deep learning-based methods. Full article

(This article belongs to the Special Issue Advanced Aspects of Computational Intelligence with Its Applications)

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19 pages, 4592 KiB

Open AccessArticle

Improved Deep Neural Network (IDNN) with SMO Algorithm for Enhancement of Third Zone Distance Relay under Power Swing Condition

by Cholleti Sriram, Jarupula Somlal, B. Srikanth Goud, Mohit Bajaj, Mohamed F. Elnaggar and Salah Kamel

Mathematics 2022, 10(11), 1944; https://doi.org/10.3390/math10111944 - 6 Jun 2022

Cited by 4 | Viewed by 2025

Abstract

A zone 3 distance relay is utilized to provide remote backup protection in the event that the primary protection fails. However, under stressful situations such as severe loads, voltage, and transient instability, the danger of malfunction in distance relay is relatively high since it collapses the system’s stability and reliability. During maloperation, the relay does not function properly to operate the transmission line. To overcome this problem, an advanced power swing blocking scheme has been developed. An improved DNN-based power swing blocking system is proposed to avoid the maloperation of the distance relay and improve the system’s reliability. The current and voltage signal of the system is sensed, and the sensed data is fed into the Improved Discrete Wavelet Transform (IMDWT). The IMDWT generates the coefficient value of the sensed data and further computes the standard deviation (SD) from the coefficient, which is used to detect the condition of a system, such as normal or stressed. The SD value is given to the most valuable algorithm for the improved Deep Neural Network (IDNN). In the proposed work, the improved DNN operates in two modes, the first mode is RDL-1 (normal condition), and the second mode is RDL-2 (power swing condition). The performance of the IDNN is enhanced by using the threshold-based blocking approach. Based on the threshold value, the proposed method detects an appropriate condition of the system. The proposed method is implemented in the Western System Coordinating Council (WSCC) IEEE 9 bus system, and the results are validated in MATLAB/Simulink software. The overall accuracy of the proposed method is 97%. The proposed method provides rapid operation and detects the power swing condition to trip the distance relay. Full article

(This article belongs to the Special Issue Advanced Aspects of Computational Intelligence with Its Applications)

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18 pages, 527 KiB

Open AccessArticle

Optimizing the Neural Structure and Hyperparameters of Liquid State Machines Based on Evolutionary Membrane Algorithm

by Chuang Liu, Haojie Wang, Ning Liu and Zhonghu Yuan

Mathematics 2022, 10(11), 1844; https://doi.org/10.3390/math10111844 - 27 May 2022

Cited by 8 | Viewed by 3173

Abstract

As one of the important artificial intelligence fields, brain-like computing attempts to give machines a higher intelligence level by studying and simulating the cognitive principles of the human brain. A spiking neural network (SNN) is one of the research directions of brain-like computing, characterized by better biogenesis and stronger computing power than the traditional neural network. A liquid state machine (LSM) is a neural computing model with a recurrent network structure based on SNN. In this paper, a learning algorithm based on an evolutionary membrane algorithm is proposed to optimize the neural structure and hyperparameters of an LSM. First, the object of the proposed algorithm is designed according to the neural structure and hyperparameters of the LSM. Second, the reaction rules of the proposed algorithm are employed to discover the best neural structure and hyperparameters of the LSM. Third, the membrane structure is that the skin membrane contains several elementary membranes to speed up the search of the proposed algorithm. In the simulation experiment, effectiveness verification is carried out on the MNIST and KTH datasets. In terms of the MNIST datasets, the best test results of the proposed algorithm with 500, 1000 and 2000 spiking neurons are 86.8%, 90.6% and 90.8%, respectively. The best test results of the proposed algorithm on KTH with 500, 1000 and 2000 spiking neurons are 82.9%, 85.3% and 86.3%, respectively. The simulation results show that the proposed algorithm has a more competitive advantage than other experimental algorithms. Full article

(This article belongs to the Special Issue Advanced Aspects of Computational Intelligence with Its Applications)

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20 pages, 5914 KiB

Open AccessArticle

Improving YOLOv4-Tiny’s Construction Machinery and Material Identification Method by Incorporating Attention Mechanism

by Jiale Yao, Dengsheng Cai, Xiangsuo Fan and Bing Li

Mathematics 2022, 10(9), 1453; https://doi.org/10.3390/math10091453 - 26 Apr 2022

Cited by 7 | Viewed by 3121

Abstract

To facilitate the development of intelligent unmanned loaders and improve the recognition accuracy of loaders in complex scenes, we propose a construction machinery and material target detection algorithm incorporating an attention mechanism (AM) to improve YOLOv4-Tiny. First, to ensure the robustness of the proposed algorithm, we adopt style migration and sliding window segmentation to increase the underlying dataset’s diversity. Second, to address the problem that YOLOv4-Tiny’s (the base network) framework only adopts a layer-by-layer connection form, which demonstrates an insufficient feature extraction ability, we adopt a multilayer cascaded residual module to deeply connect low- and high-level information. Finally, to filter redundant feature information and make the proposed algorithm focus more on important feature information, a channel AM is added to the base network to perform a secondary screening of feature information in the region of interest, which effectively improves the detection accuracy. In addition, to achieve small-scale object detection, a multiscale feature pyramid network structure is employed in the prediction module of the proposed algorithm to output two prediction networks with different scale sizes. The experimental results show that, compared with the traditional network structure, the proposed algorithm fully incorporates the advantages of residual networks and AM, which effectively improves its feature extraction ability and recognition accuracy of targets at different scales. The final proposed algorithm exhibits the features of high recognition accuracy and fast recognition speed, with mean average precision and detection speed reaching 96.82% and 134.4 fps, respectively. Full article

(This article belongs to the Special Issue Advanced Aspects of Computational Intelligence with Its Applications)

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20 pages, 1181 KiB

Open AccessArticle

A Modern Industrial Policy for the Czech Republic: Optimizing the Structure of Production

by Maria Markaki, Stelios Papadakis and Anna Putnová

Mathematics 2021, 9(23), 3095; https://doi.org/10.3390/math9233095 - 30 Nov 2021

Cited by 3 | Viewed by 3284

Abstract

The decreased demand for new vehicles will put pressure on the economy of the Czech Republic, a country deeply integrated into global value chains, as part of global vehicle production. The aim of this research was to define an appropriate industrial policy for the Czech Republic that will ensure that the country maintains its competitive position in the global market. A constrained optimization model was built, based on input–output analysis, to determine the optimal value-added structure and the intersectoral structure of the Czech economy for the country to retain its exporting character. The optimization problem was solved by using a particle swarm optimization algorithm. The results suggest that the optimal industrial policy plan for the country is the structural transformation of production, mainly targeting the development of technologically advanced sectors of manufacturing (such as: chemicals and chemical products; basic pharmaceutical products; computer, electronic, and optical products; electrical equipment; and machinery and equipment). The suggested restructuring process increased the domestic value-added in gross exports as a share of total exports by 6.77%, creating optimal production capabilities for the economy. The Czech Republic appears to have the potential for the implementation of an industrial policy, avoiding the increasingly vulnerable motor-vehicle sector. Full article

(This article belongs to the Special Issue Advanced Aspects of Computational Intelligence with Its Applications)

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18 pages, 12579 KiB

Open AccessArticle

Algorithm for Preventing the Spread of COVID-19 in Airports and Air Routes by Applying Fuzzy Logic and a Markov Chain

by Cesar Guevara and Diego Bonilla

Mathematics 2021, 9(23), 3040; https://doi.org/10.3390/math9233040 - 26 Nov 2021

Cited by 4 | Viewed by 2733

Abstract

Since the start of COVID-19 and its growth into an uncontrollable pandemic, the spread of diseases through airports has become a serious health problem around the world. This study presents an algorithm to determine the risk of spread in airports and air routes. Graphs are applied to model the air transport network and Dijkstra’s algorithm is used for generating routes. Fuzzy logic is applied to evaluate multiple demographics, health, and transport variables and identify the level of spread in each airport. The algorithm applies a Markov chain to determine the probability of the arrival of an infected passenger with the COVID-19 virus to an airport in any country in the world. The results show the optimal performance of the proposed algorithm. In addition, some data are presented that allow for the application of actions in health and mobility policies to prevent the spread of infectious diseases. Full article

(This article belongs to the Special Issue Advanced Aspects of Computational Intelligence with Its Applications)

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23 pages, 671 KiB

Open AccessArticle

Topic-Based Document-Level Sentiment Analysis Using Contextual Cues

by Ciprian-Octavian Truică, Elena-Simona Apostol, Maria-Luiza Șerban and Adrian Paschke

Mathematics 2021, 9(21), 2722; https://doi.org/10.3390/math9212722 - 27 Oct 2021

Cited by 22 | Viewed by 4317

Abstract

Document-level Sentiment Analysis is a complex task that implies the analysis of large textual content that can incorporate multiple contradictory polarities at the phrase and word levels. Most of the current approaches either represent textual data using pre-trained word embeddings without considering the local context that can be extracted from the dataset, or they detect the overall topic polarity without considering both the local and global context. In this paper, we propose a novel document-topic embedding model, DocTopic2Vec, for document-level polarity detection in large texts by employing general and specific contextual cues obtained through the use of document embeddings (Doc2Vec) and Topic Modeling. In our approach, (1) we use a large dataset with game reviews to create different word embeddings by applying Word2Vec, FastText, and GloVe, (2) we create Doc2Vecs enriched with the local context given by the word embeddings for each review, (3) we construct topic embeddings Topic2Vec using three Topic Modeling algorithms, i.e., LDA, NMF, and LSI, to enhance the global context of the Sentiment Analysis task, (4) for each document and its dominant topic, we build the new DocTopic2Vec by concatenating the Doc2Vec with the Topic2Vec created with the same word embedding. We also design six new Convolutional-based (Bidirectional) Recurrent Deep Neural Network Architectures that show promising results for this task. The proposed DocTopic2Vecs are used to benchmark multiple Machine and Deep Learning models, i.e., a Logistic Regression model, used as a baseline, and 18 Deep Neural Networks Architectures. The experimental results show that the new embedding and the new Deep Neural Network Architectures achieve better results than the baseline, i.e., Logistic Regression and Doc2Vec. Full article

(This article belongs to the Special Issue Advanced Aspects of Computational Intelligence with Its Applications)

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20 pages, 4006 KiB

Open AccessArticle

An Optimal Weighted Combined Model Coupled with Feature Reconstruction and Deep Learning for Multivariate Stock Index Forecasting

by Jujie Wang, Yinan Liao, Zhenzhen Zhuang and Dongming Gao

Mathematics 2021, 9(21), 2640; https://doi.org/10.3390/math9212640 - 20 Oct 2021

Cited by 3 | Viewed by 1724

Abstract

Stock index prediction plays an important role in the creation of better investment strategies. However, prediction can be difficult due to the random fluctuation of financial time series. In pursuit of improved stock index prediction, a hybrid prediction model is proposed in this paper, which contains two-step data pretreatment, double prediction models, and smart optimization. In the data pretreatment stage, in order to carry more information about the prediction target, multidimensional explanatory variables are selected by the Granger causality test, and to eliminate data redundancy, feature extraction is inserted with the help of principal component analysis; both of these can provide a higher-quality dataset. Bi-directional long short-term memory and bi-directional gated recurrent unit network, as the concurrent prediction models, can improve not only the precision, but also the robustness of results. In the last stage, the proposed model integrates the weight optimization of the cuckoo search of the two prediction results to take advantage of both. For the model performance test, four main global stock indices are used. The experimental results show that our model performs better than other benchmark models, which indicates the potential of the proposed model for wide application. Full article

(This article belongs to the Special Issue Advanced Aspects of Computational Intelligence with Its Applications)

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13 pages, 332 KiB

Open AccessArticle

Can Recurrent Neural Networks Predict Inflation in Euro Zone as Good as Professional Forecasters?

by Tea Šestanović and Josip Arnerić

Mathematics 2021, 9(19), 2486; https://doi.org/10.3390/math9192486 - 4 Oct 2021

Cited by 6 | Viewed by 2503

Abstract

This paper investigates whether a specific type of a recurrent neural network, in particular Jordan neural network (JNN), captures the expected inflation better than commonly used feedforward neural networks and traditional parametric time-series models. It also considers competing survey-based and model-based expected inflation towards ex-post actual inflation to find whose predictions are more accurate; predictions from survey respondents or forecasting modelers. Further, it proposes neural network modelling strategy when dealing with nonstationary time-series which exhibit long-memory property and nonlinear dependence with respect to lagged inputs and exogenous inputs as well. Following this strategy, overfitting problem was reduced until no improvement in forecasting accuracy of expected inflation is achieved. The main finding is that JNN predicts inflation in euro zone quite accurately within forecasting horizon of 2 years. Regarding rational expectation principle we have found a set of demand-pull and cost-push inflation characteristics as exogenous inputs which helps in reducing overfitting problem of recurrent neural network even more. The sample includes euro zone aggregated monthly observations from January 2000 to December 2019. The results also confirm that inflation expectations obtained from JNN are consistent with Survey of professional forecasters (SPF), and thus, monetary policy makers can use JNN as a complementary tool in shortcomings of other inflation expectations measures. Full article

(This article belongs to the Special Issue Advanced Aspects of Computational Intelligence with Its Applications)

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15 pages, 981 KiB

Open AccessArticle

Automated Support for Battle Operational–Strategic Decision-Making

by Gerardo Minguela-Castro, Ruben Heradio and Carlos Cerrada

Mathematics 2021, 9(13), 1534; https://doi.org/10.3390/math9131534 - 30 Jun 2021

Cited by 2 | Viewed by 3130

Abstract

Battle casualties are the subject of study in military operations research, which applies mathematical models to quantify the probability of victory vs. loss. In particular, different approaches have been proposed to model the course of battles. However, none of them provide adequate decision-making support for high-level command. To overcome this situation, this paper presents an innovative high-level decision-making model, which uses an adaptive and predictive control architecture. The paper reports empirical evidence supporting our model by considering one of the greatest battles of World War II: the Battle of Crete. Full article

(This article belongs to the Special Issue Advanced Aspects of Computational Intelligence with Its Applications)

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