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 >

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 >

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 >

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 >

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 >

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 >

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 >

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 >

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 >

Feiyu Li¹, Siqi Zuo¹, Yongzhou Chi¹, Chunying Du¹, Zhaopeng Shen¹, Xihong Han^2,3, Xiaohui Wang^2,*, Peng Wang^1,*

Journal of Renewable Materials, Vol.8, No.11, pp. 1519-1529, 2020, DOI:10.32604/jrm.2020.011453

Abstract Drought is one of the major abiotic stresses that affect plant growth and reduce agricultural productivity. Use of algal extract as a biostimulant is gaining increased attention from researchers. This study aimed to investigate the potential of Ulva prolifera extract (UE) as a biostimulant when enzymatically extracted under conditions of water deficit. UE treatments (0.02%, 0.06%, and 0.1%) significantly improved the shoot length, root length, and dry weight of roots after 120 h of drought stress relative to that in treatment with the negative control. An increase in catalase (CAT) and peroxidase (POD) activity was also More >