Journal of Intelligent & Fuzzy Systems - Volume 29, issue 6

Authors: Dong, Xiangjun | Liu, Chuanlu | Xu, Tiantian | Wang, Dakui

Article Type: Research Article

Abstract: Negative sequential patterns (NSP) refer to sequences with non-occurring and occurring items, and can play an irreplaceable role in understanding and addressing many business applications. However, some problems occur after mining NSP, the most urgent one of which is how to select the actionable positive or negative sequential patterns. This is due to the following factors: 1) positive sequential patterns (PSP) mined before considering NSP may mislead decisions; and 2) it is much more difficult to select actionable patterns after mining NSP, as the number of NSPs is much greater than PSPs. In this paper, an improved method of pruning …uninteresting itemsets to fit for a selecting actionable sequential pattern (ASP) is proposed. Then, a novel and efficient method, called SAP, is proposed to select the actionable positive and negative sequential patterns. Experimental results indicate that SAP is very efficient in the selection of ASP. To the best of our knowledge, SAP is the best method for the selection of actionable positive and negative sequential patterns. Show more

Keywords: Actionable, negative sequential patterns, positive sequential patterns

DOI: 10.3233/IFS-151980

Citation: Journal of Intelligent & Fuzzy Systems, vol. 29, no. 6, pp. 2759-2767, 2015

Authors: Guo, Junfei | Liu, Juan | Han, Qi | Chen, Xianlong | Zhao, Yi

Article Type: Research Article

Abstract: Massive amounts of data for data mining consist of natural language data. A challenge in natural language is to translate the data into a particular language. Machine translation can do the translation automatically. However, the models trained on data from a domain tend to perform poorly for different domains. One way to resolve this issue is to train domain adaptation translation and language models. In this work, we use visualizations to analyze the similarities of domains and explore domain detection methods by using text clustering and domain language models to discover the domain of the test data. Furthermore, we present …domain adaptation language models based on tunable discounting mechanism and domain interpolation. A cross-domain evaluation of the language models is performed based on perplexity, in which considerable improvements are obtained. The performance of the domain adaptation models are also evaluated in Chinese-to-English machine translation tasks. The experimental BLEU scores indicate that the domain adaptation system significantly outperforms the baseline especially in domain adaptation scenarios. Show more

Keywords: Text clustering, domain detection, domain adaptation, language models, machine translation

DOI: 10.3233/IFS-151981

Citation: Journal of Intelligent & Fuzzy Systems, vol. 29, no. 6, pp. 2769-2777, 2015

Authors: Zhang, Jindong | Jia, Xiaoyan | Li, Jinfeng

Article Type: Research Article

Abstract: In order to detect lane rapidly and accurately, the integration of scanning and image processing algorithms (SIP) based on the fuzzy method is proposed. Further, combination of the proposed algorithm with an adaptive threshold value for image binarization, the least-square method and Bessel curve algorithm are proposed for detection and to fit the lane. The proposed SIP algorithm was evaluated by various tests. The experimental results indicate that the average time consumed for the detection of lane in each frame is 16.7639 ms, and the accuracy of lane detection is 95% . The proposed algorithm demonstrates good robustness, and can be …used as the core algorithm for further application in lane departure warning systems. Show more

Keywords: Lane detection, fuzzy, computer vision, lane recognition

DOI: 10.3233/IFS-151982

Citation: Journal of Intelligent & Fuzzy Systems, vol. 29, no. 6, pp. 2779-2786, 2015

Authors: Li, Da-Wei | Yang, Feng-Bao | Wang, Xiao-Xia

Article Type: Research Article

Abstract: Crop area statistics and yield prediction will affect adjustment of agricultural policy, to a certain extent. With the development of computer automatic classification techniques, the performance of classifiers are influenced by feature preprocessing and sample selection. Remote sensing classification according to spectral information is affected by false negatives and miscalculation in the complex spectrum area. Corn planting areas and other land-cover objects contain different surface structures and smoothness; other vegetation and villages have coarse textures. This paper introduces texture information based on a Gabor filter group to enrich land-cover information and establish a spectrum-texture feature set. With more samples, the …algorithm efficiency is greatly affected. This paper proposes an improved fuzzy ARTMAP (FAM) with an adaptive boost strategy, namely Adaboost_FAM. Weak classifiers are trained to construct strong classifiers so as to improve operation efficiency. Meanwhile, classification accuracy will not be greatly improved. Experimental results indicate that the proposed method improves extraction accuracy when compared to classical algorithms, and improves efficiency when compared to algorithms which contain a great number of samples. Show more

Keywords: Remote sensing, corn region extraction, fuzzy ARTMAP, adaptive boost

DOI: 10.3233/IFS-151983

Citation: Journal of Intelligent & Fuzzy Systems, vol. 29, no. 6, pp. 2787-2794, 2015

Authors: Zhang, Dailin | Zhang, Dengming | Xie, Jingming | Chen, Youping

Article Type: Research Article

Abstract: While the heights and numbers of tires in the assembly line are changing, the traditional methods are difficult to identify the types of the tires, so an indirect tire identification method is proposed to identify the types of the tires, which uses the widths of the tires, the diameters and the shapes of the hubs to classify the tires. First, the tires are identified according to the widths and diameters by using the fuzzy C-means clustering algorithm. Second, the shapes of the hubs are used to distinguish the tires with similar dimensions. Finally, a two-layered fuzzy scheme is designed to …identify the tires. Experimental results show that the two-layered fuzzy scheme is more effective than the fuzzy C-means clustering algorithm. And the proposed indirect tire identification method can achieve an accuracy of above 99.9% in the assembly line of tires. Show more

Keywords: Fuzzy C-means clustering algorithm, two-layered fuzzy scheme, tire identification, machine vision

DOI: 10.3233/IFS-151984

Citation: Journal of Intelligent & Fuzzy Systems, vol. 29, no. 6, pp. 2795-2800, 2015

Authors: Huang, Liping | Yang, Yongjian | Cui, Chunsheng

Article Type: Research Article

Abstract: The cross-layered concept of wireless network breaks the traditional network layered concept to realize interaction between layers. In addition, the broadcasting nature of a wireless environment makes cooperative communications possible. In this paper, by utilizing the cross-layer optimization scheme, we introduce the fuzzy time series predication model that has linear computational complexity to forecast network performance. When we establish a fuzzy time series model, the sent queue length of the link layer is taken into consideration for the network layer’s throughput prediction. In addition, we propose a fuzzy time series-based congestion control framework that is combined with a cooperative communication …mechanism, and we apply the fuzzy time series prediction results in the preprocessing of network congestion. Through simulation, we demonstrate that the proposed cross-layer cooperative strategies achieve significant network throughput improvement and average packet delivery ratio. Show more

Keywords: Fuzzy time series, throughput prediction, cross-layer design, node cooperation

DOI: 10.3233/IFS-151985

Citation: Journal of Intelligent & Fuzzy Systems, vol. 29, no. 6, pp. 2801-2807, 2015

Article Type: Other

Citation: Journal of Intelligent & Fuzzy Systems, vol. 29, no. 6, pp. 2809-2822, 2015