Journal of Intelligent & Fuzzy Systems - Volume 39, issue 5

Authors: Akbari-Bengar, Davood | Ebrahimnejad, Ali | Motameni, Homayun | Golsorkhtabaramiri, Mehdi

Article Type: Research Article

Abstract: Internet is one of the most influential new communication technologies has influenced all aspects of human life. Extensive use of the Internet and the rapid growth of network services have increased network traffic and ultimately a slowdown in internet speeds around the world. Such traffic causes reduced network bandwidth, server response latency, and increased access time to web documents. Cache memory is used to improve CPU performance and reduce response time. Due to the cost and limited size of cache compared to other devices that store information, an alternative policy is used to select and extract a page to make …space for new pages when the cache is filled. Many algorithms have been introduced which performance depends on a high-speed web cache, but it is not well optimized. The general feature of most of them is that they are developed from the famous LRU and LFU designs and take advantage of both designs. In this research, a page replacement algorithm called FCPRA (Fuzzy Clustering based Page Replacement Algorithm) is presented, which is based on four features. When the cache space can’t respond to a request for a new page, it selects a page of the lowest priority cluster and the largest login order; then, removes it from the cache memory. The results show that FCPRA has a better hit rate with different data sets and can improve the cache memory performance compared to other algorithms. Show more

Keywords: Web cache, performance, response time, page replacement algorithm, hit rate

DOI: 10.3233/JIFS-201360

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7899-7908, 2020

Authors: Wang, Chuantao | Yang, Xuexin | Ding, Linkai

Article Type: Research Article

Abstract: The purpose of sentiment classification is to solve the problem of automatic judgment of sentiment tendency. In the sentiment classification task of text data (such as online reviews), the traditional deep learning model focuses on algorithm optimization, but ignores the characteristics of the imbalanced distribution of the number of samples in each classification, which will cause the classification performance of the model to decrease in practical applications. In this paper, the experiment is divided into two stages. In the first stage, samples of minority class in the sample distribution are used to train a sequence generative adversarial nets, so that …the sequence generative adversarial nets can learn the features of the samples of minority class in depth. In the second stage, the trained generator of sequence generative adversarial nets is used to generate false samples of minority class and mix them with the original samples to balance the sample distribution. After that, the mixed samples are input into the sentiment classification deep model to complete the model training. Experimental results show that the model has excellent classification performance in comparing a variety of deep learning models based on classic imbalanced learning methods in the sentiment classification task of hotel reviews. Show more

Keywords: Sentiment classification, imbalanced classification, deep learning, generative adversarial network

DOI: 10.3233/JIFS-201370

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7909-7919, 2020

Authors: Fan, Liu | Yager, Ronald R. | Mesiar, Radko | Jin, LeSheng

Article Type: Research Article

Abstract: The evaluation for online shopping platform is the basis for further decision and policy taking. The collected individual opinion and evaluation information are often represented by some linguistic/preference vectors. Further aggregating those vector needs to simultaneously consider two contradictory factors: the original weights assigned and the inconsistencies involved which requires some new weights assigned. Around those weights allocation factors, to mitigate the negative effect of inconsistency in the collected information, we propose an integrated evaluation model. The model uses the scatter degree as a main indicator, and extends some weights allocation methods such as regular increasing monotone (RIM) quantifier based …weights allocation in a new environment, and applies the three sets expression based paradigm and formulation. The proposed model is able to simultaneously give emphasis on those input data with high consistency and to consider the preferences of decision makers. Some detailed evaluation processes and numerical examples are also provided for practitioners to refer to. Show more

Keywords: Aggregation operators, evaluation for online shopping platform, information fusion, multi-criteria decision making, weights adjustment and allocation

DOI: 10.3233/JIFS-201376

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7921-7930, 2020

Authors: Tripathi, Gaurav | Singh, Kuldeep | Vishwakarma, Dinesh Kumar

Article Type: Research Article

Abstract: Violence detection is a challenging task in the computer vision domain. Violence detection framework depends upon the detection of crowd behaviour changes. Violence erupts due to disagreement of an idea, injustice or severe disagreement. The aim of any country is to maintain law and order and peace in the area. Violence detection thus becomes an important task for authorities to maintain peace. Traditional methods have existed for violence detection which are heavily dependent upon hand crafted features. The world is now transitioning in to Artificial Intelligence based techniques. Automatic feature extraction and its classification from images and videos is the …new norm in surveillance domain. Deep learning platform has provided us the platter on which non-linear features can be extracted, self-learnt and classified as per the appropriate tool. One such tool is the Convolutional Neural Networks, also known as ConvNets, which has the ability to automatically extract features and classify them in to their respective domain. Till date there is no survey of deciphering violence behaviour techniques using ConvNets. We hope that this survey becomes an exclusive baseline for future violence detection and analysis in the deep learning domain. Show more

Keywords: Violence detection, crowd behaviour, ConvNets, convolutional neural networks, deep learning, survey

DOI: 10.3233/JIFS-201400

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7931-7952, 2020

Authors: Sun, Yuan | Lin, Chih-Min

Article Type: Research Article

Abstract: This study presents a fuzzy brain emotional learning classifier (FBELC), combined with a modified particle swarm optimization (PSO) algorithm, that allows a network to automatically determine the optimum values for a reward signal and a classification threshold. The designed FBELC model imitates the brain decision process including the emotion information. To verify the predictive performance, a novel fitness function based on the accuracy of the training and cross-validation datasets is used for a PSO algorithm. This PSO-FBELC model is used to diagnose breast tumors and heart diseases. A comparison of simulations using the proposed PSO-FBELC with other processes shows that …the proposed model performs better in terms of recognition accuracy. Show more

Keywords: Fuzzy brain emotional learning classifier (FBELC), particle swarm optimization (PSO), disease diagnosis

DOI: 10.3233/JIFS-201418

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7953-7960, 2020

Authors: Hu, Junhua | Liu, Jie | Liang, Pei | Li, Bo

Article Type: Research Article

Abstract: Malaria is one of the three major diseases with the highest mortality worldwide and can turn fatal if not taken seriously. The key to surviving this disease is its early diagnosis. However, manual diagnosis is time consuming and tedious due to the large amount of image data. Generally, computer-aided diagnosis can effectively improve doctors’ perception and accuracy. This paper presents a medical diagnosis method powered by convolutional neural network (CNN) to extract features from images and improve early detection of malaria. The image sharpening and histogram equalization method are used aiming at enlarging the difference between parasitized regions and other …area. Dropout technology is employed in every convolutional layer to reduce overfitting in the network, which is proved to be effective. The proposed CNN model achieves a significant performance with the best classification accuracy of 99.98%. Moreover, this paper compares the proposed model with the pretrained CNNs and other traditional algorithms. The results indicate the proposed model can achieve state-of-the-art performance from multiple metrics. In general, the novelty of this work is the reduction of the CNN structure to only five layers, thereby greatly reducing the running time and the number of parameters, which is demonstrated in the experiments. Furthermore, the proposed model can assist clinicians to accurately diagnose the malaria disease. Show more

Keywords: Medical diagnosis, computer-aided diagnosis, deep learning, convolutional neural network, malaria

DOI: 10.3233/JIFS-201427

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7961-7976, 2020

Authors: Wu, Yixiang

Article Type: Research Article

Abstract: The product form evolutionary design based on multi-objective optimization can satisfy the complex emotional needs of consumers for product form, but most relevant literatures mainly focus on single-objective optimization or convert multiple-objective optimization into the single objective by weighting method. In order to explore the optimal product form design, we propose a hybrid product form design method based on back propagation neural networks (BP-NN) and non-dominated sorting genetic algorithm-II (NSGA-II) algorithms from the perspective of multi-objective optimization. First, the product form is deconstructed and encoded by morphological analysis method, and then the semantic difference method is used to enable consumers …to evaluate product samples under a series of perceptual image vocabularies. Then, the nonlinear complex functional relation between the consumers’ perceptual image and the morphological elements is fitted with the BP-NN. Finally, the trained BP-NN is embedded into the NSGA-II multi-objective evolutionary algorithm to derive the Pareto optimal solution. Based on the hybrid BP-NN and NSGA-II algorithms, a multi-objective optimization based product form evolutionary design system is developed with the electric motorcycle as a case. The system is proved to be feasible and effective, providing theoretical reference and method guidance for the multi-image product form design. Show more

Keywords: Morphological analysis method, kansei engineering, back propagation neural networks, multi-objective evolutionary algorithm

DOI: 10.3233/JIFS-201439

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7977-7991, 2020

Authors: Ferrari, Allan Christian Krainski | Coelho, Leandro dos Santos | Leandro, Gideon Villar | Osinski, Cristiano | da Silva, Carlos Alexandre Gouvea

Article Type: Research Article

Abstract: The Whale Optimization Algorithm (WOA) is a recent meta-heuristic that can be explored in global optimization problems. This paper proposes a new parameter adjustment mechanism that influences the probability of the food recognition process in the whale algorithm. The adjustment is performed using a fuzzy inference system that uses the current iteration number as input information. Our simulation results are compared with other meta-heuristics such as the conventional version of WOA, Particle Swarm Optimization (PSO) and Differential Evolution (DE). All algorithms are used to optimize ten test functions (Sphere, Schwefel 2.22, Quartic, Rosenbrock, Ackley, Rastrigin, Penalty 1, Schwefel 2.21, Six …hump camel back and Shekel 1) in order to obtain their respective optimal values for be used as criteria for analysis and comparison. The results of the simulations show that the proposed fuzzy inference system improves the convergence of WOA and also is competitive in relation to the other algorithms, i.e., classical WOA, PSO and DE. Show more

Keywords: Benchmark functions, fuzzy system, global optimization, meta-heuristics optimization, whale optimization algorithm

DOI: 10.3233/JIFS-201459

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7993-8000, 2020

Authors: Lin, Yidong | Li, Jinjin | Liao, Shujiao | Zhang, Jia | Liu, Jinghua

Article Type: Research Article

Abstract: Knowledge reduction is one of critical problems in data mining and information processing. It can simplify the structure of the lattice during the construction of fuzzy-crisp concept lattice. In terms of fuzzy-crisp concept, we develop an order-class matrix to represent extents and intents of concepts, respectively. In order to improve the computing efficiency, it is necessary to reduce the size of lattices as much as possible. Therefore the judgement theorem of meet-irreducible elements is proposed. To deal with attribute reductions, we develop a discernibility Boolean matrix in formal fuzzy contexts by preserving extents of meet-irreducible elements via order-class matrix. A …heuristic attribute-reduction algorithm is proposed. Then we extend the proposed model to consistent formal fuzzy decision contexts. Our methods present a new framework for knowledge reduction in formal fuzzy contexts. Show more

Keywords: Attribute reduction, discernibility matrix, fuzzy-crisp concept, meet-irreducible elements

DOI: 10.3233/JIFS-201485

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 8001-8013, 2020

Authors: Lei, Fei | Dong, Xueying | Ma, Xiaohe

Article Type: Research Article

Abstract: With the development of the urban industry in recent years, air pollution in areas such as factories and streets has become more and more serious. Air quality problems directly affect the normal lives of residents. Effectively predicting the future air condition in the area through relevant historical data has high application value for early warning of this area. Through the study of the previous monitoring data, it is found that the pollutant data of adjacent monitoring stations are correlated in more periods. Therefore, this paper proposes a hybrid model based on CNN and Bi-LSTM, using CNN to synthesize multiple adjacent …stations with strong correlations to extract spatial features between data, and using Bi-LSTM to extract features in the time dimension to finally achieve pollutant concentration prediction. Using the historical data of 40 monitoring stations in different locations of Fushun city to conduct research. By comparing with the traditional prediction model, the results prove that the model proposed in this paper has higher accuracy and stronger robustness. Show more

Keywords: CNN, Bi-LSTM, temporal and spatial features, correlation analysis, PM2.5 prediction

DOI: 10.3233/JIFS-201515

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 8015-8025, 2020