Home / Journals / IASC / Vol.26, No.4, 2020
  • Open Access

    ARTICLE

    A Multi-objective Invasive Weed Optimization Method for Segmentation of Distress Images

    Eslam Mohammed Abdelkader1,2,*, Osama Moselhi3, Mohamed Marzouk4, Tarek Zayed5
    Intelligent Automation & Soft Computing, Vol.26, No.4, pp. 643-661, 2020, DOI:10.32604/iasc.2020.010100
    Abstract Image segmentation is one of the fundamental stages in computer vision applications. Several meta-heuristics have been applied to solve the segmentation problems by extending the Otsu and entropy functions. However, no single-objective function can optimally handle the diversity of information in images besides the multimodality issues of gray-level images. This paper presents a self-adaptive multi-objective optimization-based method for the detection of crack images in reinforced concrete bridges. The proposed method combines the flexibility of information theory functions in addition to the invasive weed optimization algorithm for bi-level thresholding. The capabilities of the proposed method are demonstrated through comparisons with singleobjective… More >

  • Open Access

    ARTICLE

    Systematic Procedure for Optimal Controller Implementation Using Metaheuristic Algorithms

    Viorel Minzu*, Adrian Serbencu
    Intelligent Automation & Soft Computing, Vol.26, No.4, pp. 663-677, 2020, DOI:10.32604/iasc.2020.010101
    Abstract The idea for this work starts from the situation in which a metaheuristic-based algorithm has already been developed in order to solve an optimal control problem. This algorithm yields an offline "optimal" solution. On the other hand, the Receding Horizon Control (RHC) structure can be implemented if a process model is available. This work underlines some of the practical aspects of joining the RHC to an existing metaheuristic-based algorithm in order to obtain a closed-loop control structure that can be further used in real-time control. The result is a systematic procedure that integrates a given metaheuristic-based algorithm into a RHC… More >

  • Open Access

    ARTICLE

    Reducing Operational Time Complexity of k-NN Algorithms Using Clustering in Wrist-Activity Recognition

    Sun-Taag Choe, We-Duke Cho*, Jai-Hoon Kim, and Ki-Hyung Kim
    Intelligent Automation & Soft Computing, Vol.26, No.4, pp. 679-691, 2020, DOI:10.32604/iasc.2020.010102
    Abstract Recent research on activity recognition in wearable devices has identified a key challenge: k-nearest neighbors (k-NN) algorithms have a high operational time complexity. Thus, these algorithms are difficult to utilize in embedded wearable devices. Herein, we propose a method for reducing this complexity. We apply a clustering algorithm for learning data and assign labels to each cluster according to the maximum likelihood. Experimental results show that the proposed method achieves effective operational levels for implementation in embedded devices; however, the accuracy is slightly lower than that of a traditional k-NN algorithm. Additionally, our method provides the advantage of controlling the… More >

  • Open Access

    ARTICLE

    Object Detection and Fuzzy-Based Classification Using UAV Data

    Abdul Qayyum1,*, Iftikhar Ahmad2, Mohsin Iftikhar3, Moona Mazher4
    Intelligent Automation & Soft Computing, Vol.26, No.4, pp. 693-702, 2020, DOI:10.32604/iasc.2020.010103
    Abstract UAV (Unmanned Aerial Vehicle) equipped with remote sensing devices can acquire spatial data with a relevant area of interest. In this paper, we have acquired UAV data for high voltage power poles, urban areas and vegetation/trees near power lines. For object classification, the proposed approach based on the fuzzy classifier is compared with the traditional minimum distance classifier and maximum likelihood classifier on our three defined segments of UAV images. The performance evaluation of all the classifiers was based on the statistics parameters which included the mean, standard deviation and PDF (probability density function) of each object present in the… More >

  • Open Access

    ARTICLE

    A Generalized Museum Visitor Routing Problem Considering the Preferences and Congestion for Multiple Groups: An Example in Taiwan

    Yi-Chih Hsieh1, Peng-Sheng You2,*
    Intelligent Automation & Soft Computing, Vol.26, No.4, pp. 703-713, 2020, DOI:10.32604/iasc.2020.010104
    Abstract In this study we present a generalized museum visitor routing problem considering the preferences and congestion of multiple groups with each group having its own must-see and select-see exhibition rooms based on their preferences in exhibits. The problem aims to minimize the makespan of all groups. An effective encoding scheme is proposed to simultaneously determine the scheduling of exhibition rooms for all groups and an immune-based algorithm (IBA) is developed. Numerical results, compared with those of a genetic algorithm and particle swarm optimization, on a museum in Taiwan are reported and discussed to show the performance of the IBA. More >

  • Open Access

    ARTICLE

    Dynamic Production Scheduling for Prefabricated Components Considering the Demand Fluctuation

    Juan Du1,*, Peng Dong2, Vijayan Sugumaran3
    Intelligent Automation & Soft Computing, Vol.26, No.4, pp. 715-723, 2020, DOI:10.32604/iasc.2020.010105
    Abstract A dynamic optimized production scheduling which takes into account demand fluctuation and uncertainty is very critical for the efficient performance of Prefabricated Component Supply Chain. Previous studies consider only the conditions in the production factory and develop corresponding models, ignoring the dynamic demand fluctuation that often occurs at the construction site and its impact on the entire lifecycle of prefabricated construction project. This paper proposes a dynamic flow shop scheduling model for prefabricated components production, which incorporates demand fluctuation such as the advance of due date, insertion of urgent component and order cancellation. An actual prefabrication construction project has been… More >

  • Open Access

    ARTICLE

    The Genetic Algorithm and Binary Search Technique in the Program Path Coverage for Improving Software Testing Using Big Data

    Aysh Alhroob1,*, Wael Alzyadat2, Ayad Tareq Imam1, Ghaith M. Jaradat3
    Intelligent Automation & Soft Computing, Vol.26, No.4, pp. 725-733, 2020, DOI:10.32604/iasc.2020.010106
    Abstract Software program testing is the procedure of exercising a software component with a selected set of test cases as a way to discover defects and assess quality. Using software testing automation, especially the generating of testing data increases the effectiveness and efficiency of software testing as a whole. Instead of creating testing data from scratch, Big Data (BD) offers an important source of testing data. Although it is a good source, there is a need to select a proper set of testing data for the sake of selecting an optimal sub-domain input values from the BD. To refine the efficiency… More >

  • Open Access

    EDITORIAL

    Special Section on Emerging Challenges in Computational Intelligence for Signal Processing Applications

    B. Nagaraj1,*, Danilo Pelusi2, Joy I.-Z. Chen3
    Intelligent Automation & Soft Computing, Vol.26, No.4, pp. 737-739, 2020, DOI:10.32604/iasc.2020.010107
    Abstract This article has no abstract. More >

  • Open Access

    ARTICLE

    Research on Maximum Return Evaluation of Human Resource Allocation Based on Multi-Objective Optimization

    Hong Zhu1,2,*
    Intelligent Automation & Soft Computing, Vol.26, No.4, pp. 741-748, 2020, DOI:10.32604/iasc.2020.010108
    Abstract In this paper, a human resource allocation method based on the multi-objective hybrid genetic algorithm is proposed, which uses the multi-stage decision model to resolve the problem. A task decision is the result of an interaction under a set of conditions. There are some available decisions in each stage, and it is easy to calculate their immediate effects. In order to give a set of optimal solutions with limited submissions, a multi-objective hybrid genetic algorithm is proposed to solve the combinatorial optimization problems, i.e. using the multiobjective hybrid genetic algorithm to find feasible solutions at all stages and the bilateral… More >

  • Open Access

    ARTICLE

    The Data Classification Query Optimization Method for English Online Examination System Based on Grid Image Analysis

    Kun Liu*
    Intelligent Automation & Soft Computing, Vol.26, No.4, pp. 749-754, 2020, DOI:10.32604/iasc.2020.010109
    Abstract In the English network examination system, the big data distribution is highly coupled, the cost of data query is large, and the precision is not good. In order to improve the ability of the data classification and query in the English network examination system, a method of data classification and query in the English network examination system is proposed based on the grid region clustering and frequent itemset feature extraction of the association rules. Using the grid image analysis to improve the statistical analysis of the English performance analysis, the collection and storage structure analysis of the information resource data… More >

  • Open Access

    ARTICLE

    Design of the Sports Training Decision Support System Based on the Improved Association Rule, the Apriori Algorithm

    Xinbao Wang*, Dawu Huang, Xuemin Zhao
    Intelligent Automation & Soft Computing, Vol.26, No.4, pp. 755-763, 2020, DOI:10.32604/iasc.2020.010110
    Abstract In order to improve the judgment decision ability of the sports training effect, a design method of the sports training decision support system based on the improved association rule, the Apriori algorithm is proposed, and a phase space model of the sports training decision support data association rule distribution is constructed. The association rule mining method is used to support the data mining model of sports training, and the decision judgment of the sports training effect is carried out in the mixed cloud computing environment. The fuzzy information fusion and the data structure feature reorganization method is adopted, and the… More >

  • Open Access

    ARTICLE

    The Construction and Path Analysis of the School-Enterprise Cooperative Innovation Model under the Background of the Open Independent Innovation

    Xiaoyan Wang*, Shui Jing
    Intelligent Automation & Soft Computing, Vol.26, No.4, pp. 765-771, 2020, DOI:10.32604/iasc.2020.010111
    Abstract The organic combination of the independent innovation and open innovation opens a new pattern of innovation. Under the background of the open independent innovation, the cooperative innovation model of the school and enterprise is established, and an optimal development path model of the cooperative innovation of the school and enterprise based on the fuzzy decision control algorithm is proposed. Based on the rough set theory, a path search model of the cooperative innovation between a school and enterprise is established under the background of the open independent innovation. Under the background of the open independent innovation, the fuzzy decision-making method… More >

  • Open Access

    ARTICLE

    An Improved TCP Vegas Model for the Space Networks of the Bandwidth Asymmetry

    Qixue Guan*, Yueqiu Jiang
    Intelligent Automation & Soft Computing, Vol.26, No.4, pp. 773-781, 2020, DOI:10.32604/iasc.2020.010112
    Abstract It is known that congestion in the reverse direction happens in advance of the congestion in the forward direction due to the significant bandwidth asymmetry in the two directions of the space networks, especially in the satellite networks, which enables the TCP Vegas to enter the phase of the congestion avoidance blindly and reduce the throughput of the forward direction. To solve this problem, a congestion control model, TCP Vegas-DDA, which maintains the frequency of the acknowledgments in the reverse direction is proposed. The model sets the interval time between acknowledgments dynamically based on the variation of the queuing delay… More >

  • Open Access

    ARTICLE

    The Application of Folk Art with Virtual Reality Technology in Visual Communication

    Rui Zhang1, Xiaoxiong Zhao2,*
    Intelligent Automation & Soft Computing, Vol.26, No.4, pp. 783-793, 2020, DOI:10.32604/iasc.2020.010113
    Abstract At the end of the 20th century, the emergence and development of virtual reality display methods based on virtual reality technology is one of the most remarkable achievements in the field of digital design. In the late 20th century, rapidly developing virtual reality technology was gradually combined with computer multimedia display technology, and emerging digital information display means thus quickly became widely used in the design field. In today's information multimedia display field, multimedia display design using virtual reality technology has become one of the most important means of information display. In fact, in many fields, virtual reality display has… More >

  • Open Access

    ARTICLE

    A Perspective of the Machine Learning Approach for the Packet Classification in the Software Defined Network

    B. Indira1,*, K. Valarmathi2
    Intelligent Automation & Soft Computing, Vol.26, No.4, pp. 795-805, 2020, DOI:10.32604/iasc.2020.010114
    Abstract Packet classification is a major bottleneck in Software Defined Network (SDN). Each packet has to be classified based on the action specified in each rule in the given flow table. To perform classification, the system requires much of the CPU clock time. Therefore, developing an efficient packet classification algorithm is critical for high speed inter networking. Existing works make use of exact matching, range matching and longest prefix matching for classification and these techniques sometime enlarges rule databases, thus resulting in huge memory consumption and inefficient searching performance. In order to select an efficient packet classification algorithm with less memory… More >

  • Open Access

    ARTICLE

    Construction and Application of the Multi-Intermediate Multi-media English Oral Teaching Mode

    Na Li*
    Intelligent Automation & Soft Computing, Vol.26, No.4, pp. 807-815, 2020, DOI:10.32604/iasc.2020.010115
    Abstract The study of the English language has always been a focus of education and teaching in China. The traditional English language teaching model no longer meets the needs of modern education, especially when spoken in English. Spoken English represents the actual effect of English teaching to a certain extent. Good oral English ability reflects one's English level. The traditional oral English teaching mode is only limited to the interactive training of the oral mechanization between the teacher and student, or the non-targeted dialogue training with foreign teachers, which ignores the factors such as environment, language sense and emotion. With the… More >

  • Open Access

    ARTICLE

    Detection of the Spectrum Hole from N-number of Primary Users Using the Gencluster Algorithm

    U. Venkateshkumar1,*, S. Ramakrishnan2
    Intelligent Automation & Soft Computing, Vol.26, No.4, pp. 817-830, 2020, DOI:10.32604/iasc.2020.010116
    Abstract A hybrid form of the genetic algorithm and the modified K-Means cluster algorithm forms as a Gencluster to detect a spectrum hole among n-number of primary users (PUs) is present in the cooperative spectrum sensing model. The fusion center (FC), applies the genetic algorithm to identify the best chromosome, which contains many PUs cluster centers and by applying the modified K-Means cluster algorithm identifies the cluster with the PU vacant spectrum showing high accuracy, and maximum probability of detection with minimum false alarm rates are achieved. The graphical representation of the performance metric of the system model shows 95% accuracy… More >

  • Open Access

    ARTICLE

    The Application of Sparse Reconstruction Algorithm for Improving Background Dictionary in Visual Saliency Detection

    Lei Feng1,2, Haibin Li1,*, Yakun Gao1, Yakun Zhang1
    Intelligent Automation & Soft Computing, Vol.26, No.4, pp. 831-839, 2020, DOI:10.32604/iasc.2020.010117
    Abstract In the paper, we apply the sparse reconstruction algorithm of improved background dictionary to saliency detection. Firstly, after super-pixel segmentation, two bottom features are extracted: the color information of LAB and the texture features of the image by Gabor filter. Secondly, the convex hull theory is used to remove object region in boundary region, and K-means clustering algorithm is used to continue to simplify the background dictionary. Finally, the saliency map is obtained by calculating the reconstruction error. Compared with the mainstream algorithms, the accuracy and efficiency of this algorithm are better than those of other algorithms. More >

  • Open Access

    ARTICLE

    The Instance-Aware Automatic Image Colorization Based on Deep Convolutional Neural Network

    Hui Li1, Wei Zeng1,*, Guorong Xiao2, Huabin Wang1
    Intelligent Automation & Soft Computing, Vol.26, No.4, pp. 841-846, 2020, DOI:10.32604/iasc.2020.010118
    Abstract Recent progress on image colorization is substantial and benefiting mostly from the great development of the deep convolutional neural networks. However, one type of object can be colored by different kinds of colors. Due to the uncertain relationship between the object and color, the deep neural network is unstable and difficult to converge during the training process. In order to solve this problem, this paper proposes an instance-aware automatic image colorization algorithm, which uses the semantic features of the object instance as prior knowledge to guide the deep neural network to do the colorization task. Meanwhile, we design a discrete… More >

  • Open Access

    REVIEW

    PGCA-Net: Progressively Aggregating Hierarchical Features with the Pyramid Guided Channel Attention for Saliency Detection

    Jiajie Mai1, Xuemiao Xu2,*, Guorong Xiao3, Zijun Deng2, Jiaxing Chen2
    Intelligent Automation & Soft Computing, Vol.26, No.4, pp. 847-855, 2020, DOI:10.32604/iasc.2020.010119
    Abstract The Salient object detection aims to segment out the most visually distinctive objects in an image, which is a challenging task in computer vision. In this paper, we present the PGCA-Net equipped with the pyramid guided channel attention fusion block (PGCAFB) for the saliency detection task. Given an input image, the hierarchical features are extracted using a deep convolutional neural network (DCNN), then starting from the highest-level semantic features, we stage-by-stage restore the spatial saliency details by aggregating the lowerlevel detailed features. Since for the weak discriminative ability of the shallow detailed features, directly introducing them to the semantic features… More >

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