Open Access

ARTICLE

Eslam Mohammed Abdelkader^1,2,*, 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 More >

View

3932

Download

3005

Like

2

Cited by

4

Open Access

ARTICLE

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 More >

View

2485

Download

1687

Like

2

Cited by

7

Open Access

ARTICLE

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 More >

View

2174

Download

1427

Like

2

Cited by

5

Open Access

ARTICLE

Abdul Qayyum^1,*, 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 More >

View

2600

Download

1403

Like

2

Cited by

5

Open Access

ARTICLE

Yi-Chih Hsieh¹, Peng-Sheng You^2,*
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 More >

View

2049

Download

1296

Like

2

Cited by

2

Open Access

ARTICLE

Juan Du^1,*, 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 More >

View

2691

Download

1736

Like

2

Cited by

4

Open Access

ARTICLE

Aysh Alhroob^1,*, 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… More >

View

3275

Download

1896

Like

2

Cited by

5

Open Access

EDITORIAL

B. Nagaraj^1,*, 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 >

View

2152

Download

1414

Like

2

Cited by

1

Open Access

ARTICLE

Hong Zhu^1,2,*
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… More >

View

2249

Download

1421

Like

2

Cited by

1

Open Access

ARTICLE

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… More >

View

1971

Download

1356

Like

2

Cited by

1

Open Access

ARTICLE

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… More >

View

2398

Download

1474

Like

2

Cited by

5

Open Access

ARTICLE

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… More >

View

2295

Download

1545

Like

2

Cited by

1

Open Access

ARTICLE

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 More >

View

2351

Download

1478

Like

2

Cited by

1

Open Access

ARTICLE

Rui Zhang¹, Xiaoxiong Zhao^2,*
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 More >

View

2109

Download

1548

Like

2

Cited by

1

Open Access

ARTICLE

B. Indira^1,*, 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… More >

View

2985

Download

1450

Like

2

Cited by

3

Open Access

ARTICLE

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… More >

View

2310

Download

1496

Like

2

Cited by

4

Open Access

ARTICLE

U. Venkateshkumar^1,*, 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 More >

View

2106

Download

1271

Like

2

Cited by

1

Open Access

ARTICLE

Lei Feng^1,2, Haibin Li^1,*, 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 More >

View

2168

Download

1523

Open Access

ARTICLE

Hui Li¹, Wei Zeng^1,*, 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. More >

View

2405

Download

1313

Open Access

REVIEW

Jiajie Mai¹, Xuemiao Xu^2,*, 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 More >

View

2434

Download

1493