Journal of Intelligent & Fuzzy Systems - Volume 38, issue 3

Authors: Pandey, Prateek | Litoriya, Ratnesh

Article Type: Research Article

Abstract: Irrespective of the nature of the software, the choice of a software development process plays a crucial role. A good choice may help expand the business to heights while a bad choice may wreak havoc for the stakeholders. Therefore, selecting the right software development process is an indispensable element during project planning. Undoubtedly, web-based applications differ from conventional software and are becoming so popular in society due to their distinguishing features and widespread coverage. Web application software is generally found fit candidates to be developed according to agile software processes. This article attempts to provide a model for advising an …agile software process during the planning phase of a web application development project. The proposed model takes into account the underlying web project characteristics and uses the Fuzzy Analytic Hierarchy Process (FAHP) along with TOPSIS techniques for suitable agile candidate selection. The model is also validated on a primary dataset obtained by developing twenty web application projects through four student teams. The results show that the proposed model is successful in suggesting a correct agile process with a probability of the order of 0.8. Show more

Keywords: Agile process, fuzzy AHP, TOPSIS, web application, identification model

DOI: 10.3233/JIFS-190508

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 3, pp. 3359-3370, 2020

Authors: Fan, Jiashuang | Yu, Suihuai | Yu, Mingjiu | Chu, Jianjie | Tian, Baozhen | Li, Wenhua | Wang, Hui | Hu, Yukun | Chen, Chen

Article Type: Research Article

Abstract: In order to achieve optimal selection of design schemes in cloud environment, this paper proposed a novel hybrid Multi-Criteria Decision-Making (MCDM) model integrated Fuzzy Analytic Network Process (fuzzy-ANP) and Fuzzy Quality Function Development (fuzzy-QFD). There are three steps in the novel approach. In the first step, the evaluation target system of design scheme is identified considering four dimensions: economy, society, environment, and culture. The proper indicators are identified by the integration of multi-intelligent techniques. In the second step, decision-makers are asked to compare the decision indicators, and the weight for each indicator is determined using the intuitionistic fuzzy number and …fuzzy-ANP. In the third step, the decision-making system of design scheme is developed to compare and rank alternatives. The decision-makers are invited over to compare different options and rank them with the aid of fuzzy-QFD in the cloud environment. A case study is provided to validate the proposed approach. Twenty-five sensitivity analysis experiments are conducted to figure out the influence of evaluation indicators on decision making process. The novel approach makes use of the strength of fuzzy set theory in handing vagueness and uncertainty, the fuzzy-ANP in non-independent hierarchy evaluation on the indicator system, the advantage of fuzzy-QFD in multiple-objective decision analysis. Based on the comparative study and assessment, the results show that the proposed approach is more efficient and provided users with multidimensional evaluation. Show more

Keywords: Fuzzy analytic network process, fuzzy quality function development, multi-criteria decision-making, optimal selection, cloud environment

DOI: 10.3233/JIFS-190630

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 3, pp. 3371-3388, 2020

Authors: Tao, Liang | Siqi, Qian | Zhaochao, Meng | Gao Feng, Xie

Article Type: Research Article

Abstract: With the construction of large-scale wind turbines, how to reduce the operation and maintenance costs has become an urgent problem to be solved. In this paper, by extracting the actual operation data of the wind turbine in Supervisory Control and Data Acquisition (SCADA) system, the Bidirectional Recurrent Neural Networks (BRNN) is used to establish the wind turbine operation prediction model. By eliminating abnormal data points caused by accidental factors through box diagram, the fault risk threshold of wind turbine components is optimized. Then, based on the residual between the actual value and the measured value of the large sliding window, …the early fault warning is realized according to Wright criterion. Finally, the model proposed in this paper is applied to the actual wind turbine, which proves the reliability and accuracy of the method. Show more

Keywords: BRNN, box-plot, large sliding window, Letts’criterion, early fault warning

DOI: 10.3233/JIFS-190642

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 3, pp. 3389-3401, 2020

Authors: Jiang, Bao | Zou, Zezhou | Lio, Waichon | Li, Jian

Article Type: Research Article

Abstract:  Identifying the specific scale status by evaluating scale efficiency of each DMU (decision making unit) is critical for policy makers to make a correct decision on inefficiency improvement. Although some traditional DEA (Data Envelopment Analysis) models can evaluate different scale efficiencies and identify the specific status of each DMU, they are unable to deal with the evaluation indicators with imprecise observation. Therefore, we propose two different uncertain DEA models with imprecise inputs and outputs to recognize two scale status of increasing returns to scale (IRS) and decreasing returns to scale (DRS), which can better help policy makers make a correct …decision on inefficiency improvement without accurate data. Moreover, we analyse the stability of these uncertain DEA models to make effective suggestions for decision makers to further improve the inefficient DMUo or to adjust the inputs and outputs on the premise of remaining the new DMUo efficient. Show more

Keywords: Data envelopment analysis, uncertainty theory, specific scale efficiency identification, stability analysis

DOI: 10.3233/JIFS-190662

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 3, pp. 3403-3417, 2020

Authors: Choudhury, Hussain Ahmed | Sinha, Nidul | Saikia, Monjul

Article Type: Research Article

Abstract: Video compression is applied for reducing the requirement of hardware, bandwidth, hard drives and power consumption for storing and processing an excessive amount of data generated by videos. The computationally intensive and most time-consuming segment of video compression is known as motion estimation (ME). ME process can be regarded as an optimization problem where search is carried out in a predefined search area of the target frame to locate the identical macroblock (MB) corresponding to each MB in anchor frame by minimizing the objective function cum search criterion as minimum value of search criterion identifies the location of the best …matching MB. Since the efficiency of ME decides the efficiency of Video compression, a rich number of fast block matching algorithms (BMAs) were reported to maintain the tradeoff between the computational complexity and visual experience of video during the ME process. Investigation reveals that most of the pattern-based BMAs are prone to the local optimum and stuck in sub-optimal results. Due to the emergence of various nature-inspired algorithms (NIA) like particle swarm optimization (PSO), genetic algorithm (GA), evolutionary algorithm, etc. and their application in optimizing all types of day to day problems has opened a new era in the field of ME. Our investigation focuses on the application of all types of NIA reported to date for optimizing the ME process in terms of speed, accuracy, and quality. This investigation will analyze all the NIAs and their methodologies through an extensive study of their accompanying publications and will enable us to do a detailed comparison to highlight the competitive advantage of soft computing techniques over existing pattern-based algorithms. Show more

Keywords: Motion estimation, nature inspired algorithms, genetic algorithm, particle swarm optimization, cuckoo search

DOI: 10.3233/JIFS-190308

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 3, pp. 3419-3443, 2020

Authors: Yang, Yunlei | Hou, Muzhou | Luo, Jianshu | Tian, Zhongchu

Article Type: Research Article

Abstract: In this paper, block neural network (BNN) method is proposed to solve several kinds of differential equations. BNN is used to construct approximating functions and its derivatives, the improved extreme learning machine (IELM) algorithm are designed to train network weights. To evaluate the performance of the proposed method, numerical examples are performed by the presented method. Comparison of the numerical results with exact solutions validate the feasibility of the proposed method in accuracy. Results compared with other recent research works also validate the superiority of the proposed approach. Numerical results show that the proposed BNN with IELM algorithm perform well …in accuracy and requires less hidden neurons. Show more

Keywords: Differential equations, block neural network, IELM algorithm

DOI: 10.3233/JIFS-190406

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 3, pp. 3445-3461, 2020

Authors: Lin, Zhongqi | Jia, Jingdun | Gao, Wanlin | Huang, Feng

Article Type: Research Article

Abstract: Small-target categorization of butterflies suffers from large-scale search space of candidate target locations, subtler discriminations, camouflaged appearances, and complex backgrounds. Precise localization and domain-specific discrimination extraction are crucial for this issue. In this work, a novel hierarchical coarse-to-fine convolutional neural network (C-t-FCNN) was proposed. It consists of CoarseNet and FineNet, which incorporate object-level and part-level representations into framework. Specifically, the coarse-grained features containing the orientation description are generated by CoarseNet, while the fine-grained discriminations with semantic distinctiveness are captured by FineNet. Next, the correspondences are established to mark the target regions, background regions, and mismatched regions depending on the quantification …of scale-invariant feature transform (SIFT) descriptors. Then, the features are subsampled via spatial pyramid pooling (SPP) for size uniformity and integration. Finally, the irrelevant background and mismatched regions are eliminated by the support vector machine (SVM) with a radial basis function (RBF) kernel, leaving only the target-specific patches for finer-scale extraction. Hence the numeracy can be economized from identifying irrelevant areas and can be rescheduled in feature extraction and final decision, which can suppress time complexity simultaneously. A total of 119,016 augmented butterfly images spanning 47 categories are utilized for model training, while 13,734 images are evaluated for effectiveness verification. The C-t-FCNN delivers impressive performance, i.e., it achieves a validation accuracy of 92.08% and a testing accuracy of 91.6%, which outperforms state-of-the-arts. Show more

Keywords: Convolutional neural network, coarse-to-fine perception, deep learning, small target categorization, butterfly images

DOI: 10.3233/JIFS-190747

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 3, pp. 3463-3487, 2020

Authors: Wen, Meilin | Chen, Yubing | Yang, Yi | Kang, Rui | Guo, Miaomiao

Article Type: Research Article

Abstract: Stocking strategy for spare parts has a pivotal influence on the productivity and efficiency of industrial plants. Considering the situation of lacking historical statistics causing by uncertainty, this paper proposes a method to optimize spares varieties based on uncertainty theory and uncertain data envelopment analysis (DEA) model. Firstly, a recursive hierarchy structure is established to construct an evaluation system to meet the requirement of avoiding attributes’ redundancy. Then, an uncertain spares optimization model (USOM), which is based on uncertain DEA model, is developed to optimize spares varieties. Furthermore, the uncertainty theory is utilized to convert the USOM into an equivalent …deterministic model for simplification. Finally, a numerical example is given to illustrate the performance of this model. The results show that the stocking strategy obtained from the proposed decision model can satisfy the purpose of saving resources and prompting continuous operation. Show more

Keywords: Spares varieties, uncertain systems, optimization, data envelopment analysis

DOI: 10.3233/JIFS-190838

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 3, pp. 3489-3499, 2020

Authors: Meng, Fan | Qi, Zhiquan | Chen, Zhensong | Wang, Bo | Shi, Yong

Article Type: Research Article

Abstract: Crack detection has drawn much attention in the last two decades, because of dramatic bloom in monitoring images and the urgent need of corresponding crack detection. However, recent methods have not taken advantage of structure information effectively, resulting in low accuracy when dealing with crack-like noises. In this paper, we propose a novel crack detection framework, which is able to identify cracks from noisy background. The main contributions of this paper are as follows: (1) giving a new edge-based crack detection framework to improve the detection performance; (2) proposing a novel mid-level feature, named Crack Token , which captures the …local structure information of cracks; (3) introducing a new evaluation strategy for crack detection task, which provides a comprehensive system for approach evaluation and comparison in this area. In addition, we provide a novel definition of pavement crack and verify our framework and evaluation strategy in this real world application. Extensive experiments demonstrate the state-of-the-art results of the proposed framework. Show more

Keywords: Crack detection, crack token, machine learning, edge detection, crack recognition

DOI: 10.3233/JIFS-190868

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 3, pp. 3501-3513, 2020

Authors: Zhao, Youen | Ji, Xiuhua | Liu, Zhaoguang

Article Type: Research Article

Abstract: Blind image quality assessment (BIQA) aims to evaluate the quality of an image without information regarding its reference image. In this paper, we proposed a novel BIQA method, which combines thirty six natural scene statistics (NSS) features, two color statistics features and four perceptual features to construct an image quality assessment model. Support Vector Regression (SVR) is adopted to build the relationship between these features and image quality scores, yielding a measure of image quality. Experimental results in LIVE, TID2013 databases and their cross validations show that the proposed method records a higher correlations with human subjective judgments of visual …quality and delivers highly competitive performance with state-of-the-art BIQA models. Show more

Keywords: Blind image quality assessment, natural scene statistics feature, perceptual feature, color statistics feature, support vector regression

DOI: 10.3233/JIFS-190998

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 3, pp. 3515-3526, 2020