Open Access
ARTICLE
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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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