Journal of Intelligent & Fuzzy Systems - Volume 36, issue 6

Authors: Lopes, Márcio Nirlando Gomes | da Rocha, Brígida Ramati Pereira | Vieira, Alen Costa | de Sá, José Alberto Silva | Rolim, Pedro Alberto Moura | da Silva, Arilson Galdino

Article Type: Research Article

Abstract: This study both presents a novel methodology and compares the performance of computational intelligence techniques for the predictive modeling of the monthly potential for hydropower generation. Two different approaches are employed to forecasting energy generation: polynomial neural network and conventional artificial neural network (ANN). The first one technique is a deep learning type named group method of data handling (GMDH). And the second one is the multilayer perceptron (MLP) feed forward with back-propagation algorithm. The ANN dealt with two different optimization algorithms for training the model: Levenberg-Marquardt and Bayesian regulation. Rainfall data are used as inputs to feed the models. …The performance of each model is scrutinized based on three statistical performance criteria. The results found that the computational intelligence techniques can model the dynamic, seasonal and non-linear behavior of the studied issue. The predictions from the GMDH method resulted in slightly better accuracy than the values obtained by the conventional ANN. The analyzes also showed that the values that determine the steady energy of the hydropower plant are well captured by the models. This feature makes the model an important tool for energy planning and decision making. Show more

Keywords: GMDH, artificial neural network, hydropower generation, Amazon region

DOI: 10.3233/JIFS-181604

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 6, pp. 5757-5772, 2019

Authors: Jaya, C. K. | Sunitha, R. | Mathew, Abraham T.

Article Type: Research Article

Abstract: Even in the presence of renewable sources in the customer’s own premises, the utility’s supply needs to be maintained reliably to ensure the availability of electricity. Security prediction of the high voltage transmission system (HVTS) is significant in the modern scenario since the power failures results in huge economic loss or sometimes human life and comfort. The responsibility of the system operator is to supply electricity to its customers with a reasonably high degree of reliability and good power quality. This calls for assessing the security levels and initiating early steps to mitigate the effect of failures. Such prediction of …the system security levels ensures availability of service to customers and helps in the operations planning. Paper proposes a pattern recognition approach using the k -nearest neighbor (k-NN ) classifier for the security level predictions for HVTS from the failure rates assessed from the historical data on operation. This method is employing the newly developed Gaussian fuzzy index formulated by the authors using the failure rates in the HVTS. Both simulation and the validation using field data have been done and the results are given in the paper. The overall accuracy obtained is near 89.88%. Show more

Keywords: Power System Security, High voltage transmission system, Gaussian fuzzy index, Security Prediction

DOI: 10.3233/JIFS-181617

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 6, pp. 5773-5782, 2019

Authors: Krishankumar, R. | Saranya, R. | Nethra, R.P. | Ravichandran, K.S. | Kar, Samarjit

Article Type: Research Article

Abstract: This paper focuses on proposing a new decision framework under probabilistic linguistic term set (PLTS) for rational decision making. The PLTS concept is a generalization of hesitant fuzzy linguistic term set (HFLTS) which overcomes the limitation of HFLTS by associating occurring probability to each linguistic term. Initially, a new aggregation operator is presented for fusing decision makers’ (DMs) preferences. Following this, a new extension is put forward for statistical variance (SV) method under PLTS for criteria weight calculation and a new extension is presented for WASPAS (weighted arithmetic sum product assessment) method under PLTS context for ranking objects. The applicability …of the proposed decision framework is demonstrated by using a numerical example and the strength and weakness of the proposal are investigated by comparison with other state-of-the-art methods. Show more

Keywords: Decision making, probabilistic linguistic term set, statistical variance and WASPAS method

DOI: 10.3233/JIFS-181633

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 6, pp. 5783-5795, 2019

Authors: Mao, Hua | Zheng, Zhen

Article Type: Research Article

Abstract: In order to find a more efficient method for constructing a fuzzy concept lattice, This paper applies graph theory to a fuzzy formal context. A fuzzy formal context is represented by a weighted graph. A method for generating all crisp-fuzzy concepts by a weighted graph is proposed and the corresponding algorithm–Algorithm 1 is given in a fuzzy formal context. Through algorithm analysis, it is illustrated that Algorithm 1 is better than some of existed algorithms to obtain crisp-fuzzy concepts in a fuzzy formal context.

Keywords: Fuzzy formal concept, crisp-fuzzy concept, graph theory

DOI: 10.3233/JIFS-181642

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 6, pp. 5797-5805, 2019

Authors: Han, Meng | Ding, Jian

Article Type: Research Article

Abstract:  Time decay model (TDM) is frequently used for mining frequent patterns on data streams, because the information embedded in the data from the new transactions is particularly valuable. However, some existing methods on designing decay factor of TDM are random, so their results are unsteady. Some other methods focus on only 100% recall or 100% precision of algorithm, but the corresponding high precision or high recall is ignored. In order to balance high recall and high precision of algorithm, meanwhile, ensure the stability of the result, a novel average decay factor is designed. In addition, to further increase the weights …of the latest transactions and reduce the weights of historical transactions, another novel Gaussian decay factor is proposed. Hence, based on an analysis of existing decay factors, this paper aims to design two novel decay factors and two novel TDMs. Algorithms based on these two TDMs are designed to discover frequent patterns over data streams. The methods of mining frequent patterns on high density or low density data streams are evaluated via experiments. This paper’s research findings show that the application of average time decay factor can balance the high recall and high precision of algorithm. And Gaussian decay factor can produce better performance than existing algorithms. Show more

Keywords: Data streams mining, frequent pattern mining, decay factor, time decay model, Gaussian function

DOI: 10.3233/JIFS-181654

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 6, pp. 5807-5820, 2019

Authors: An, Truong Vinh | Vu, Ho | Van Hoa, Ngo

Article Type: Research Article

Abstract: In this paper, the existence and uniqueness results of solution for an initial value problem of fractional differential equations with the Caputo-Hadamard concept of fractional derivative in the case of the order α  ∈ (1, 2) have been investigated. Banach’s contraction mapping principle and Schauder fixed point theorem are used to obtain the existence and uniqueness results. In addition, the dependence of the solution on the initial conditions and the right-hand side of the given problem is analyzed.

Keywords: Caputo-Hadamard-type fuzzy derivative, Fractional fuzzy differential equations, Fuzzy fractional calculus

DOI: 10.3233/JIFS-181657

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 6, pp. 5821-5834, 2019

Authors: Jothikumar, C. | Venkataraman, Revathi | Sai Raj, T. | Selva, Rohin

Article Type: Research Article

Abstract:  Wireless Sensor Network comprises several sensor nodes which communicate with each other for data forwarding. The node senses, computes, and transfers the data to the so-called base station. The applications of WSNs include nuclear power plants, environmental monitoring, disaster surveillance, agricultural control and security. The lifespan is an essential constraint in the design part of routing in a sensor network environment. The main purpose is to minimize the consumption of energy in data sharing (transmission and reception) and extend the network lifespan using the Cluster Optimized Routing Protocol (CORP). This work improves upon the existing model by selecting the near …optimal cluster head for data transmission till the data reaches the sink. The proposed system may decrease energy consumption in data forwarding and perhaps prolong the lifespan of the node. By this approach, we tend to increase the energy efficiency and enhance the network’s lifetime. The results of the analysis and simulation indicate the performance of the proposed system is far better than the currently used comparative protocols. Show more

Keywords: Energy efficiency, wireless sensor network(WSN), residual energy, cluster head, routing

DOI: 10.3233/JIFS-181658

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 6, pp. 5835-5844, 2019

Authors: Nagpal, Arpita | Singh, Vijendra

Article Type: Research Article

Abstract: High Dimensional cancer microarray is devilishly challenging while finding the best features for classification. In this paper a new algorithm is proposed based on iterative qualitative mutual information to choose the features that can provide optimal feature set with reliability, stability, and best classification results. It finds the qualitative (i.e. utility) score of each feature with the help of Random Forest algorithm and combines it with mutual information of each feature with its class variable. Adding a qualitative measure along with mutual information can improve the robustness and find redundant features in data. The proposed algorithm has been compared with …other representative methods through the ten microarray based cancer datasets in terms of number of features and classification accuracy of three well-known classifiers: Naïve Bayes, IB1 and C4.5. Experimental results show that the proposed approach is effective in producing an optimal feature subset and improves the accuracy of these datasets. Show more

Keywords: Feature selection, microarray, classification, wrapper, filter model, random forest, mutual information

DOI: 10.3233/JIFS-181665

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 6, pp. 5845-5856, 2019

Authors: Akram, Muhammad | Ilyas, Farwa | Borumand Saeid, Arsham

Article Type: Research Article

Abstract:  Graph operations produce new classes of graphs from initial ones which in turn may be useful for the modeling and recognition of computer network designs. A Pythagorean fuzzy set-based model offers more flexibility to cope with human evaluation information as compared to intuitionistic fuzzy model. The main objective of this research study is to expand the area of discussion on Pythagorean fuzzy graphs by establishing fruitful results and notions related to an operation on Pythagorean fuzzy graphs called strong product. Certain concepts, including connectedness, completeness, regularity and partially regularity of strong product Pythagorean fuzzy graphs are discussed. It is shown …that the strong product of two regular Pythagorean fuzzy graphs may not be regular. Moreover, some necessary and sufficient conditions for the strong product of two regular Pythagorean fuzzy graphs to be regular are established. Finally, an algorithm for Pythagorean fuzzy multi-criteria decision making is presented and problem concerning to evaluation of domestic airlines in Japan is solved to demonstrate the applicability of PFGs in realistic scenarios. Show more

Keywords: Strong Pythagorean fuzzy graph, connected Pythagorean fuzzy graph, complete Pythagorean fuzzy graph, regular Pythagorean fuzzy graph, multi-criteria decision making

DOI: 10.3233/JIFS-181697

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 6, pp. 5857-5874, 2019

Authors: Nunes, Waldir | Vellasco, Marley | Tanscheit, Ricardo

Article Type: Research Article

Abstract: This work presents a new model for the automatic synthesis of fuzzy classifiers, based on quantum-inspired evolutionary algorithms, which overcomes the difficulties inherent to the use of hybrid representations and the treatment of multiple objectives, both necessary for the synthesis of these types of systems. Without any a priori information about the classifier rules or any initial adjustment of individuals, the results obtained are comparable to those of other techniques that start from classifier populations previously adjusted to obtain good performance. According to the current trend, the aim was to build classifiers with good accuracy and simultaneous high interpretability of …their fuzzy rule base. Show more

Keywords: Fuzzy classifier, quantum-inspired evolutionary algorithms, multi-objective optimization

DOI: 10.3233/JIFS-181710

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 6, pp. 5875-5887, 2019