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

Authors: Kostopoulos, G. | Livieris, I.E. | Kotsiantis, S. | Tampakas, V.

Article Type: Research Article

Abstract: Semi-supervised learning is an emerging subfield of machine learning, with a view to building efficient classifiers exploiting a limited pool of labeled data together with a large pool of unlabeled ones. Most of the studies regarding semi-supervised learning deal with classification problems, whose goal is to learn a function that maps an unlabeled instance into a finite number of classes. In this paper, a new semi-supervised classification algorithm, which is based on a voting methodology, is proposed. The term attributed to this ensemble method is called CST-Voting. Ensemble methods have been effectively applied in various scientific fields and often perform …better than the individual classifiers from which they are originated. The efficiency of the proposed algorithm is compared to three familiar semi-supervised learning methods on a plethora of benchmark datasets using three representative supervised classifiers as base learners. Experimental results demonstrate the predominance of the proposed method, outperforming classical semi-supervised classification algorithms as illustrated from the accuracy measurements and confirmed by the Friedman Aligned Ranks nonparametric test. Show more

Keywords: Semi-supervised learning, classification, voting, ensemble methods, accuracy

DOI: 10.3233/JIFS-169571

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 1, pp. 99-109, 2018

Authors: Wee, Yit Yin | Cheah, Wooi Ping | Ooi, Shih Yin | Tan, Shing Chiang | Wee, Kuokkwee

Article Type: Research Article

Abstract: Bayesian belief networks (BBN) and fuzzy cognitive maps (FCM) are two major causal knowledge frameworks that are frequently used in various domains for cause and effect analysis. However, most researchers use these as separate approaches to analyse the cause(s) and effect(s) of an event. In practice, both methods have their own strengths and weaknesses in both causal modelling and causal analysis. In this paper, a combination of BBN and FCM is used in order to model and analyse network intrusions. First, the BBN is learnt from network intrusion data; following this, an FCM is generated from the BBN, using a …migration method. A data-mining approach is suitable for use in the construction of a BBN for network intrusion since this is a data-rich domain, while an FCM is appropriate for the intuitive representation of complex domains. The proposed method of network intrusion analysis using both BBN and FCM consists of several stages, in order to leverage the capabilities of each approach in building the causal model and performing causal analysis. Both the intuitive representation of the causal model in FCM and the wide variety of reasoning methods supported by BBN are exploited in this research to facilitate network intrusion analysis. Show more

Keywords: Root cause analysis, fuzzy cognitive map, Bayesian belief network, causal reasoning, intrusion analysis

DOI: 10.3233/JIFS-169572

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 1, pp. 111-122, 2018

Authors: Zhu, Lei | Wang, Lei | Zheng, Xiang | Xu, Yuzhang

Article Type: Research Article

Abstract: Former complex network models focused on preferential attachment to get scale-free feature, lacking random connection mechanism that inevitably exists in real-life network. In this paper, we deeply study one new complex network model, named the Preferential-Random network model, by introducing random attachment to the evolving procedure of the BA scale-free model. The analysis and experiment results show that this new model is better in keeping with real networks, which reveals closer ties between neighbors as well as obvious small world feature. Significantly, the new model explains the 2–3 range of power-law exponent in real-life scale-free networks, and it has stronger …robustness against intentional attacks compared with the BA model. Focusing on the influence of random attachment on scale-free networks, our research may provide an effective guidance for modeling and constructing more reliable and realistic networks. Show more

Keywords: Random attachment, scale-free, network model, robustness

DOI: 10.3233/JIFS-169573

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 1, pp. 123-132, 2018

Authors: Yao, Changhua | Chen, Xueqiang | Wang, Lei | Tong, Wei | Wu, Xinrong | Zhang, Yuli | Yao, Kailing

Article Type: Research Article

Abstract: This paper investigates the energy optimization for high dynamic heterogeneous UAV swarm network. Due to the high dynamism of the UAV swarm’s topology, the approaches with high complexity, long computing time, or low converging speed would be inappropriate to efficiently form stable communication network. We propose an UAV relay selection coalition game model and the distributed fast UAV relay coalition formation algorithm to optimize the energy efficiency of the UAV swarm. First, we formulate the UAV relay selection problem as a coalition game model. Then we design a distributed fast UAV relay coalition formation algorithm which could converge to the …stable state rapidly and optimize the swarm’s energy efficiency. The proposed approach could help UAV swarm form high energy efficient network in the high dynamism situation. Show more

Keywords: Distributed learning, coalition game, intelligent decision, heterogeneous, high dynamism

DOI: 10.3233/JIFS-169574

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 1, pp. 133-140, 2018

Authors: Ram, Rashmirekha | Mohanty, Mihir Narayan

Article Type: Research Article

Abstract: In this digitized world, the demand of users emphasizes the quality and accuracy. Practically, all variants of signals are analog in nature along with contaminated with noise. In this paper, speech signal is considered. Basically speech signal varies from person to person and time to time. It requires enhancement of the signal for different applications like engineering, medicine and social purposes. Reduction of noise as well as redundant data from the signal can be produced with enhanced versions. As the speech is of nonstationary in nature, in the initial phase, it is processed and normalized. To analyze the speech signal, …spectral domain is most suitable and has been utilized. For this purpose, Discrete Cosine Transform (DCT-II) is used. As it has the advantage over other transforms and the calculation is simpler, DCT-II coefficients are further used for Deep Neural Network (DNN) model to reduce the noise and enhance the signal. So that the signal of any environment and of any amount can be enhanced using this model. 100 sentences have been collected form both males and females of 5 each. The sentences have been uttered by the corresponding males and females, 10 sentences each. Though DCT-II and DNN have been applied by many researchers for signal features and image classification, the same have been utilized here for speech enhancement, which is the novelty of this work. The results found better than the other methods applied earlier and it can be best utilized for any real time application. In the result section, the visual inspection is exhibited along with the comparison values. The measuring parameters show its efficacy. Show more

Keywords: Discrete cosine transform, deep neural network, speech enhancement, perceptual evaluation of speech quality, segmental signal-to-noise

DOI: 10.3233/JIFS-169575

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 1, pp. 141-148, 2018

Authors: Kim, Kwang Baek | Song, Yu-Seon | Park, Hyun Jun | Song, Doo Heon | Choi, Byung Kwan

Article Type: Research Article

Abstract: Rotator cuff tear is a common cause of shoulder pain and disability. It is especially critical for elderly people since such injuries are strongly related with age and reduce the quality of life due to the shoulder pain and weakness with shoulder flexion and abduction. Ultrasound of the shoulder is widely used in examining the state of rotator cuff but is often criticized as operator-dependent because of the complexity of the shoulder anatomy or anisotropy and lack of intensity contrast from ultrasonographic images. Automatic segmentation of the related tendon tear by computer assisted software will be the answer for that …problem. In this paper, we propose a fully automatic extractor of partial/full thickness tear of rotator cuff tendon with Fuzzy C-Means based quantization for pixel classification and fuzzy stretching for image contrast enhancement. In experiment, our method exhibits sufficient agreement with human expert. For 12 partial thickness tear cases, the sensitivity was 96.5% and specificity was 91.1% whereas the sensitivity was 92.6% and the specificity was 96.4% for 44 full thickness tear cases. Show more

Keywords: Rotator cuff, fuzzy c-means, fuzzy stretching, shoulder pain, tendon tear

DOI: 10.3233/JIFS-169576

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 1, pp. 149-158, 2018

Authors: Yan, Chun | Sun, Haitang | Liu, Wei | Chen, Jin

Article Type: Research Article

Abstract: The analysis of lifetime value of property insurance company customers can not only help the company to allocate customer relationship management resources reasonably, save the management cost, but also help the company to identify risk timely and effectively, so that the risk control and management can be implemented. In this paper, based on RFM model, adding claim index of evaluating clients’ risk is to evaluate the lifetime value of property insurance customers quantitatively. At the same time, in view of massive uncertainties in practical decision-making, with hesitant fuzzy theory, the attributes will be weighted by hesitant fuzzy entropy. Secondly, the …similarity measure theory based on hesitant fuzzy set is used to do cluster analysis and four customer homogeneous groups are obtained. Finally, calculate the lifetime value score of these four groups based on a quantitative method and analyze their characteristics from the quantitative perspective. Show more

Keywords: Property insurance customers, customer lifetime value analysis, customer classification, hesitant fuzzy set

DOI: 10.3233/JIFS-169577

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 1, pp. 159-169, 2018

Authors: Li, Xiang | Wang, Zhijian

Article Type: Research Article

Abstract: Conventional recommender systems of cold chain logistics distribution mainly focus on the recommendations of the source of cargos, refrigerator trucks and refrigerators in the supply and demand link of cold chain, but ignore contextual information such as time, position and user devices. In this paper, we analyze the contextual information on cold chain logistics distribution and propose a multidimensional context-aware recommendation algorithm(MCARA). MCARA firstly carries out fuzzy clustering on contextual information in historical data set and obtains the contextual clusters. In addition, MCARA compares current user context with historical contexts to get current contextual cluster, and selects out the data …with same contextual clusters from historical data set. Finally, MCARA uses the user-based collaborative filtering algorithm to perform personalized recommendations. The simulation results show that MCARA can improve the forecast accuracy of cold chain logistics distribution, with about 10% improvement over other eight approaches. Show more

Keywords: Cold chain logistics, intelligent distribution, context-aware, recommender systems, vehicle routing problem

DOI: 10.3233/JIFS-169578

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 1, pp. 171-185, 2018

Authors: Song, Shujie | Guo, Kangquan

Article Type: Research Article

Abstract: This study focused on the high-precision and efficient steering control of a flexible chassis with a fuzzy-PI composite technique. The flexible chassis is a four-wheel independent steering and four-wheel independent drive electric chassis based on an off-centered steering axis module. The off-centered steering axis module is steered by jointly controlling the speed of the wheel and the locking torque of the electromagnetic steering lock. The fuzzy controller is applied to control the electromagnetic steering lock to reduce steering resistance and accelerate the steering process when the steering angle error is big, and the PI controller is designed to stabilize the …steering process and to ensure the accuracy when the steering angle error is small. Simulation results showed that the proposed composite control system is very robust to steering angle without sacrificing high precision and efficiency. Show more

Keywords: Flexible chassis, off-centered steering axis, fuzzy control, PI control, steering system

DOI: 10.3233/JIFS-169579

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 1, pp. 187-195, 2018

Authors: Jude Hemanth, D. | Anitha, J. | Popescu, Daniela Elena | Son, Le Hoang

Article Type: Research Article

Abstract: Image steganography plays a vital role in hiding significant secret information within any input image. Numerous steganography techniques have been carried out to hide the secret information in images. In the current scenario, frequency domain techniques are widely preferred which are mostly transform based approaches. Conventionally, the secret data is hidden randomly in the coefficients of the transformed image. However, such random data hiding techniques lead to inferior performance of the overall system. Thus, using an optimization algorithm is obligatory to find out the optimal coefficients. Genetic Algorithm (GA) is normally used for selecting the optimal transform coefficients to enhance …the system performance. However, conventional GA based approaches are highly random in nature which again leads to inaccurate results. In this work, a modified GA approach was proposed to determine the optimal coefficients in order to improve the embedding capacity and stego image quality. The achieved average peak signal to noise ratio (PSNR) was 50.29 dB with embedding capacity of 139361 bits. These experimental results validate the practical feasibility of the proposed methodology for image steganography. Show more

Keywords: Fresnelet transform, frequency domain steganography, genetic algorithm, modified genetic algorithm, embedding capacity and peak signal to noise ratio

DOI: 10.3233/JIFS-169580

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 1, pp. 197-209, 2018