Complexity
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Acceptance rate43%
Submission to final decision63 days
Acceptance to publication35 days
CiteScore3.300
Journal Citation Indicator0.690
Impact Factor2.833

Article of the Year 2020

An Introduction to Complex Systems Science and Its Applications

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 Journal profile

Complexity publishes original research and review articles across a broad range of disciplines with the purpose of reporting important advances in the scientific study of complex systems.

 Editor spotlight

Chief Editor, Prof Sayama, is currently researching complex dynamical networks, human and social dynamics, artificial life, and interactive systems while working at Binghamton University, State University of New York.

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We currently have a number of Special Issues open for submission. Special Issues highlight emerging areas of research within a field, or provide a venue for a deeper investigation into an existing research area.

Latest Articles

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Review Article

On the Matrix Versions of Incomplete Extended Gamma and Beta Functions and Their Applications for the Incomplete Bessel Matrix Functions

In this paper, we first introduce the incomplete extended Gamma and Beta functions with matrix parameters; then, we establish some different properties for these new extensions. Furthermore, we give a specific application for the incomplete Bessel matrix function by using incomplete extended Gamma and Beta functions; at last, we construct the relation between the incomplete confluent hypergeometric matrix functions and incomplete Bessel matrix function.

Research Article

A Prediction Method of Electromagnetic Environment Effects for UAV LiDAR Detection System

With the rapid development of science and technology, UAVs (Unmanned Aerial Vehicles) have become a new type of weapon in the informatization battlefield by their advantages of low loss and zero casualty rate. In recent years, UAV navigation electromagnetic decoy and electromagnetic interference crashes have activated widespread international attention. The UAV LiDAR detection system is susceptible to electromagnetic interference in a complex electromagnetic environment, which results in inaccurate detection and causes the mission to fail. Therefore, it is very necessary to predict the effects of the electromagnetic environment. Traditional electromagnetic environment effect prediction methods mostly use a single model of mathematical model and machine learning, but the traditional prediction method has poor processing nonlinear ability and weak generalization ability. Therefore, this paper uses the Stacking fusion model algorithm in machine learning to study the electromagnetic environment effect prediction. This paper proposes a Stacking fusion model based on machine learning to predict electromagnetic environment effects. The method consists of Extreme Gradient Boosting algorithm (XGB), Gradient Boosting Decision Tree algorithm (GBDT), K Nearest Neighbor algorithm (KNN), and Decision Tree algorithm (DT). Experimental results show that, comprising with the other seven machine learning algorithms, the Stacking fusion model has a better classification prediction accuracy of 0.9762, a lower Hamming code distance of 0.0336, and a higher Kappa coefficient of 0.955. The fusion model proposed in this paper has a better predictive effect on electromagnetic environment effects and is of great significance for improving the accuracy and safety of UAV LiDAR detection systems under the complex electromagnetic environment on the battlefield.

Research Article

A Study on the Topic-Sentiment Evolution and Diffusion in Time Series of Public Opinion Derived from Emergencies

With the development of the Internet, social media has become an important platform for people to deal with emergencies and share information. When a public health emergency occurs, the public can understand the topics of the event and perceive the sentiments of others through social media, thus building a cooperative communication network. In this study, we took the public health emergency as the main research object and the natural disaster, accident, and social security event as the secondary research object and further revealed the law of the formation and evolution of public opinion through the analysis on temporal networks of topics and sentiments in social media platforms. Firstly, we identified the derived topics by constructing the topic model and used the sentiment classification model to divide the text sentiments of the derived topics into two types: positive sentiment and negative sentiment. Then, the ARIMA time series model was used to fit and predict the evolution and diffusion rules of topics and sentiments derived from public opinions on temporal networks. It was found that the evolution law of derived public opinions had similarities and differences in various types of emergencies and was closely related to government measures and media reports. The related research provides a foundation for the management of network public opinion and the realization of better emergency effects.

Research Article

Study of Keyword Extraction Techniques for Electric Double-Layer Capacitor Domain Using Text Similarity Indexes: An Experimental Analysis

Keywords perform a significant role in selecting various topic-related documents quite easily. Topics or keywords assigned by humans or experts provide accurate information. However, this practice is quite expensive in terms of resources and time management. Hence, it is more satisfying to utilize automated keyword extraction techniques. Nevertheless, before beginning the automated process, it is necessary to check and confirm how similar expert-provided and algorithm-generated keywords are. This paper presents an experimental analysis of similarity scores of keywords generated by different supervised and unsupervised automated keyword extraction algorithms with expert-provided keywords from the electric double layer capacitor (EDLC) domain. The paper also analyses which texts provide better keywords such as positive sentences or all sentences of the document. From the unsupervised algorithms, YAKE, TopicRank, MultipartiteRank, and KPMiner are employed for keyword extraction. From the supervised algorithms, KEA and WINGNUS are employed for keyword extraction. To assess the similarity of the extracted keywords with expert-provided keywords, Jaccard, Cosine, and Cosine with word vector similarity indexes are employed in this study. The experiment shows that the MultipartiteRank keyword extraction technique measured with cosine with word vector similarity index produces the best result with 92% similarity with expert-provided keywords. This study can help the NLP researchers working with the EDLC domain or recommender systems to select more suitable keyword extraction and similarity index calculation techniques.

Research Article

Predicting the Direction Movement of Financial Time Series Using Artificial Neural Network and Support Vector Machine

Prediction of financial time series such as stock and stock indexes has remained the main focus of researchers because of its composite nature and instability in almost all of the developing and advanced countries. The main objective of this research work is to predict the direction movement of the daily stock prices index using the artificial neural network (ANN) and support vector machine (SVM). The datasets utilized in this study are the KSE-100 index of the Pakistan stock exchange, Korea composite stock price index (KOSPI), Nikkei 225 index of the Tokyo stock exchange, and Shenzhen stock exchange (SZSE) composite index for the last ten years that is from 2011 to 2020. To build the architect of a single layer ANN and SVM model with linear, radial basis function (RBF), and polynomial kernels, different technical indicators derived from the daily stock trading, such as closing, opening, daily high, and daily low prices and used as input layers. Since both the ANN and SVM models were used as classifiers; therefore, accuracy and F-score were used as performance metrics calculated from the confusion matrix. It can be concluded from the results that ANN performs better than SVM model in terms of accuracy and F-score to predict the direction movement of the KSE-100 index, KOSPI index, Nikkei 225 index, and SZSE composite index daily closing price movement.

Research Article

Synchronization of Chaotic Systems: A Generic Nonlinear Integrated Observer-Based Approach

The purpose of this research is to study the synchronization of two integrated nonlinear systems with time delay and disturbances. A nonlinear system is a system in which the difference in output is not relative to the difference in input. A new control methodology for synchronization of the two chaotic systems master and slave is recognized by means of the unique integrated chaotic synchronous observer and the integrated chaotic adaptive synchronous observer. The instantaneous approximation states of the master and slave systems are accomplished by means of methods for suggesting observers for every one of the master and slave systems and by the production of error signals between these approximated states. This approximated synchronization error signal and state approximation errors meet at the origin by means of methods involving a particular observer-based feedback control signal to ensure synchronization and state approximation. Using Lyapunov stability theory, adaptive and nonadaptive laws for control systems, and nonlinear properties, the intermingling conditions for state approximation errors and approximated synchronization errors are established as nonlinear matrix inequalities. A solution to the resulting inequality constraints using a two-step linear matrix inequality (LMI)-based approach is introduced, giving essential and adequate conditions to extract values from the controller gain and observer gain matrices. Simulation of the suggested synchronization procedure for FitzHugh–Nagumo neuronal systems is demonstrated to expand the viability of the suggested observer-based control techniques.

Complexity
Publishing Collaboration
More info
Wiley Hindawi logo
 Journal metrics
Acceptance rate43%
Submission to final decision63 days
Acceptance to publication35 days
CiteScore3.300
Journal Citation Indicator0.690
Impact Factor2.833
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Article of the Year Award: Outstanding research contributions of 2020, as selected by our Chief Editors. Read the winning articles.