Journal of Intelligent & Fuzzy Systems - Volume 35, issue 1

Authors: He, H.

Article Type: Research Article

Abstract: Under smart city environment, the internet public opinion management is more difficult, therefore the combined prediction model based on grey system theory and fuzzy neural network is constructed. Firstly, the internet public opinion characteristics under smart city is discussed. Secondly, the mathematical model of the grey system theory is studied. the basic structure and mathematical model of fuzzy neural network are analyzed, and then the training algorithm is designed. Finally, simulation analysis of internet public opinion is carried out, simulation results show that the new method can improve prediction correctness of internet public opinion effectively, and the internet public opinion …controlling level can be improved. Show more

Keywords: Prediction, internet public opinion, grey system theory, fuzzy neural network

DOI: 10.3233/JIFS-169591

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 1, pp. 325-332, 2018

Authors: Gao, Yang Jun | Zhang, Feng Ming | Guo, Qing | Li, Chao

Article Type: Research Article

Abstract: Flower pollination algorithm is a new type of heuristic algorithm, which uses Lévy random walk as the key element for high efficiency of global searching. In order to explore the search performance of pollination algorithms under different random walk models, three random walk models are taken into account, including levy random walk model used by original flower pollination algorithm and two new random walk models based on McCulloch algorithm introduced in this paper. The analysis of searching performance and adaptive of Flower Pollination Algorithm with three random walks from two aspects of the model structure and the numerical simulation is …given. The result shows that Cauchy random has a great competitive advantage for the low dimensional searching problem, and the Gauss random is more suitable for dealing with the multi-dimension unimodal case, while the Lévy random is able to provide better performance of solving the multi-dimension multimodal case. Simulation results and analysis will have a significant impact on the design of the randomness mechanism of the meta-heuristics algorithm and the improvement of Classical algorithms. Show more

Keywords: Flower pollination algorithm, meta-heuristic algorithm, random walk, multi-dimension multimodal

DOI: 10.3233/JIFS-169592

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 1, pp. 333-341, 2018

Authors: Yu, Xiaodong | Dong, Hongbin

Article Type: Research Article

Abstract: In order to improve the accuracy of support vector machine (SVM) classification of remote sensing image, SVM parameter selection is an important part. In this paper, we analyze the influence of SVM parameters on classification performance. Aiming at the characteristics of particle swarm optimization (PSO) and genetic algorithm (GA) in optimization, a method of optimizing SVM parameters based on dynamic co-evolutionary algorithm (PSO-GA) is proposed. This method can dynamically adjust the selection probability of PSO and GA strategy, realize the complementarity of evolution between PSO and GA, improve the convergence speed and realize the optimization of depth and breadth. The …experimental results show that the method improves the parameter selection efficiency of SVM, and the obtained parameters are optimal for the classification of the test samples. Show more

Keywords: Remote sensing image classification, dynamic co-evolutionary, SVM, PSO, GA

DOI: 10.3233/JIFS-169593

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 1, pp. 343-351, 2018

Authors: Vázquez, Eder | Arnulfo García-Hernández, René | Ledeneva, Yulia

Article Type: Research Article

Abstract: Preprocessing, term selection, term weighting, sentence weighting, and sentence selection are the main issues in generating extractive summaries of text sentences. Although many outstanding related works only are focused in the last step, they show sophisticated features in each one. In order to determine the relevance of the sentences (sentence selection step) many sentence features have been proposed in this task (in fact, these features are related to all the steps). Recently, some good related works have coincided in the same features but they present different ways for weighting these features. In this paper, a method to optimize the …combination of previous relevant features in each step based on a genetic algorithm is presented. The proposed method not only outperforms previous related works in two standard document collections, but also shows the relevance of these features to this problem. Show more

Keywords: Extractive text summarization, genetic algorithms, sentence feature selection, fitness function

DOI: 10.3233/JIFS-169594

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 1, pp. 353-365, 2018

Authors: Quintana, David | Chávez, Francisco | Luque Baena, Rafael M. | Luna, Francisco

Article Type: Research Article

Abstract: Initial public offerings often show abnormal fist-day returns. These, usually referred to as underpricing, are difficult to predict. Among the main obstacles, we could mention challenges like the fact that not all relevant variables have been identified yet; the mix of weak and strong indicators or the prevalence of outliers. In this context, we suggest that adaptive neuro-fuzzy inference systems and fuzzy rule-based system with genetic optimization have a lot to bring to the table. We test the predictive performance of these on a sample of 866 US IPOs and we benchmark them against six fuzzy algorithms and a set …of eight classic machine learning alternatives. We conclude that both fuzzy systems, especially the former should be seriously considered in this domain. Show more

Keywords: Fuzzy rule-based system, ANFIS, initial public offering, underpricing

DOI: 10.3233/JIFS-169595

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 1, pp. 367-381, 2018

Authors: Petinrin, Olutomilayo Olayemi | Saeed, Faisal

Article Type: Research Article

Abstract: The current rise in the amount of data generated has necessitated the use of machine learning in the drug discovery process to increase productivity. It is therefore important to predict molecular compounds which are biologically active and capable of drug-target interaction. Various machine learning methods have been used in predicting bioactive molecular compounds in order to deal with the large volume of data being generated. This study investigates the Majority Voting ensemble method using different combinations of 5 commonly-used machine learning algorithms, including Support Vector Machine, Decision Tree, Naïve Bayes, k-Nearest Neighbor, and Random Forest on three chemical datasets DS1, …DS2, and DS3 which consist of structurally heterogeneous and homogeneous molecules and are commonly used in other studies. The results show that Majority Voting has a better performance, based on all the evaluation metrics used, compared to each of the machine learning algorithms as individual classifiers. It also shows the Majority Voting ensemble method as effective in the prediction of both heterogeneous and homogeneous bioactive molecular compounds, using statistical evaluation. Show more

Keywords: Bioactivity prediction, chemoinformatics, drug discovery, ensemble classification, majority voting

DOI: 10.3233/JIFS-169596

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 1, pp. 383-392, 2018

Authors: Xue, Hong | Lin, Yiliang | Yuan, Yi | Cai, Jinyu

Article Type: Research Article

Abstract: The early warning classification plays an important role in the emergency management of cluster supply chain. This paper proposed the high-dimensional datastream evolutionary clustering algorithm of early warning classification for cluster supply chain emergency based on cloud model. It solved the bottleneck problem of early warning classification of cluster supply chain emergency with the high-dimensional datastream and composite uncertainty characteristics. The cloud model generation algorithm of early warning summary is used to generate the early warning summary data based on the multiple data fusion method. The evolutionary datastream clustering algorithm of early warning classification is used to dynamically forecast the …harming degree of cluster supply chain emergency based on time decaying model and sliding window model. Compared to other similar algorithms, the algorithm proposed in this paper increased the classification accuracy by 92.6% while reduced operation time by 66.7%. The algorithm can provide more accurate decision supports for design and implementation of emergency preplan of cluster supply chain emergency. The feasibility of this algorithm has been demonstrated by multiple experiments conducted on the algorithm. Show more

Keywords: Cloud model generation algorithm of early warning summary, high-dimensional datastream, composite uncertainty characteristics, evolutionary datastream clustering algorithm of early warning classification, early warning classification of cluster supply chain emergency

DOI: 10.3233/JIFS-169597

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 1, pp. 393-403, 2018

Authors: Shen, Chuanhe | Feng, Liang | Li, Ying

Article Type: Research Article

Abstract: This paper is aimed to solve uncertainty in financial data mining by using fuzzy support vector machine (FSVM), and prior wavelet denoising and subsequent error adjustment based on generalized autoregressive conditional heteroskedasticity (GARCH) model are also supplemented respectively. The hybrid information capturing methodology proposed above can thus be supposed to address complex nonlinear dynamics behind the noise of financial data. To this end, a key approach to ascertain membership values for financial sample data is introduced in order to build the FSVM in terms of statistical characteristics of the financial data from the prior wavelet denoising stage. Moreover, the GARCH …model is also employed in the final step so that the test errors from the preliminary test based on FSVM are deeply analyzed to capture missed price volatility information which are often neglected by existing approaches involving the traditional SVM models. The methodology proposed is thus enabled to sufficiently tackle the styled facts, such as nonlinearity, instability, strong noise, skewed distribution, and so on, because two factors of influencing price volatility, that is, the market factor and the time series factor, are all accounted for, and its prediction outperformance appears in empirical analysis of S&P 500 index. Show more

Keywords: Nonlinear dynamics, information capturing, fuzzy support vector machine (FSVM), wavelet denoising, GARCH

DOI: 10.3233/JIFS-169598

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 1, pp. 405-414, 2018

Authors: Fang, Ruiming | Shang, Rongyan | Jiang, Shunhui | Peng, Changqing | Ye, Zhijun

Article Type: Research Article

Abstract: This paper deals with the identification of anomalies in wind turbine (WT) gearbox by temperature trend analysis approach. Support vector regression (SVR) is adopted to build two models for forecasting operating temperature of WT gearbox. One model is trained with historical supervisory control and data acquisitions (SCADA) data in the normal state, and the other is trained with abnormal state data. The prediction accuracy of two models is compared, and the sequences of relative error (SRE) index for two models are calculated. Then, two trend cloud model, namely normal cloud, and abnormal cloud, are built based on an improved inverse …normal cloud generator, meanwhile the SRE are used as inputs of the generator, and the parameters of different trend cloud models are obtained as outputs. The closeness degree of the current state related to the normal or abnormal cloud can be calculated using the current SCADA data, and the principle of maximum closeness degree is adopted to judge the anomaly. The proposed approach has been used to analyze a real gearbox failure occurred in a 1.5 MW WT. The results obtained confirm the feasibility and efficiency of the proposed approach. Show more

Keywords: Wind turbine gearbox, SCADA data, anomaly identification, support vector regression, normal cloud model

DOI: 10.3233/JIFS-169599

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 1, pp. 415-421, 2018

Authors: Ghaleb, Fuad A. | Zainal, Anazida | Rassam, Murad A. | Saeed, Faisal

Article Type: Research Article

Abstract:  In vehicular ad hoc networks, vehicles need to exchange their recent mobility information at a high rate to maintain network agility and to preserve the performance of applications. Unfortunately, a high broadcasting rate affects the performance of both network reliability and information accuracy. The aim of this paper is to reduce the broadcasting rate while preserving information accuracy. A Driving-Situation-Aware Adaptive Broadcasting Rate Scheme (DSA-ABR)is proposed based on effective mobility prediction algorithm operates in between message transmissions, to reduce the communication rate. The scheme contains two algorithms which are Self-Predictor and Neighboring-Predictor based on an adaptive version of the Extended …Kalman Filter. Firstly, the Self-Predictor algorithm estimates the current mobility state, with the help of the previous mobility state and knowledge about the driving situation and measurement uncertainties. Individual driving situation prediction models are obtained online through training on historical data. A vehicle decides whether to send or omit the beacon messagesbased on the accuracy of the Self-Predictor. Secondly, the Neighbouring-Predictor algorithm predicts the omitted or lost beacon messages with the help of knowledge shared by the sender vehicles. The results show the effectiveness and the efficiencyof the proposed scheme under unreliable communication conditions. Show more

Keywords: VANET, ITS, kalman filter, adaptive broadcasting, mobility prediction

DOI: 10.3233/JIFS-169600

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 1, pp. 423-438, 2018