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Intelligent Automation & Soft Computing

About the Journal

Intelligent Automation & Soft Computing: An International Journal seeks to provide a common forum for the dissemination of accurate results about the world of artificial intelligence, intelligent automation, control, computer science, modeling and systems engineering. It is intended that the articles published in the journal will encompass both the short and the long term effects of soft computing and other related fields such as robotics, control, computer, cyber security, vision, speech recognition, pattern recognition, data mining, big data, data analytics, machine intelligence and deep learning. It further hopes it will address the existing and emerging relationships between automation, systems engineering, system of computer engineering and soft computing.

Indexing and Abstracting

SCIE: 2019 Impact Factor 1.276; Scopus CiteScore (Impact per Publication 2019): 2.0; SNIP (Source Normalized Impact per Paper 2019): 0.85; Essential Science Indicators(ESI), etc.

Previously published by TSI Press (http://www.wacong.org/autosoft/auto/), Intelligent Automation & Soft Computing starts to be published by Tech Science Press from the third issue of 2020 and supports Open Access Policy.

  • A Multi-objective Invasive Weed Optimization Method for Segmentation of Distress Images
  • 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
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  • Systematic Procedure for Optimal Controller Implementation Using Metaheuristic Algorithms
  • 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
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  • Reducing Operational Time Complexity of k-NN Algorithms Using Clustering in Wrist-Activity Recognition
  • 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
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  • Object Detection and Fuzzy-Based Classification Using UAV Data
  • 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
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  • A Generalized Museum Visitor Routing Problem Considering the Preferences and Congestion for Multiple Groups: An Example in Taiwan
  • 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
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  • Dynamic Production Scheduling for Prefabricated Components Considering the Demand Fluctuation
  • 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
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  • The Genetic Algorithm and Binary Search Technique in the Program Path Coverage for Improving Software Testing Using Big Data
  • 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
  •   Views:9       Downloads:6        Download PDF
  • Research on Maximum Return Evaluation of Human Resource Allocation Based on Multi-Objective Optimization
  • 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
  •   Views:9       Downloads:6        Download PDF
  • The Data Classification Query Optimization Method for English Online Examination System Based on Grid Image Analysis
  • 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
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  • Design of the Sports Training Decision Support System Based on the Improved Association Rule, the Apriori Algorithm
  • 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
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  • The Construction and Path Analysis of the School-Enterprise Cooperative Innovation Model under the Background of the Open Independent Innovation
  • 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
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  • An Improved TCP Vegas Model for the Space Networks of the Bandwidth Asymmetry
  • 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
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  • The Application of Folk Art with Virtual Reality Technology in Visual Communication
  • 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
  •   Views:11       Downloads:7        Download PDF
  • A Perspective of the Machine Learning Approach for the Packet Classification in the Software Defined Network
  • 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
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  • Construction and Application of the Multi-Intermediate Multi-media English Oral Teaching Mode
  • 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
  •   Views:11       Downloads:8        Download PDF
  • Detection of the Spectrum Hole from N-number of Primary Users Using the Gencluster Algorithm
  • 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
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  • The Application of Sparse Reconstruction Algorithm for Improving Background Dictionary in Visual Saliency Detection
  • 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
  •   Views:10       Downloads:7        Download PDF
  • The Instance-Aware Automatic Image Colorization Based on Deep Convolutional Neural Network
  • 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
  •   Views:10       Downloads:7        Download PDF
  • PGCA-Net: Progressively Aggregating Hierarchical Features with the Pyramid Guided Channel Attention for Saliency Detection
  • 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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