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Qing-Hua He^a, Bin Yu^b, Xin Hong^a, Bo Lv^a, Tao Liu^b, Jian Ran^b, Yu-Tian Bi^a
Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 223-230, 2018, DOI:10.1080/10798587.2016.1261957
Abstract Lung abnormalities and respiratory diseases increase with the development of urban life. Lung sound analysis provides vital information of the present condition of the pulmonary. But lung sounds are easily interfered by noises in the transmission and record process, then it cannot be used for diagnosis of diseases. So the noised sound should be processed to reduce noises and to enhance the quality of signals received. On the basis of analyzing wavelet packet transform theory and the characteristics of traditional wavelet threshold de-noising method, we proposed a modified threshold selection method based on Particle Swarm Optimization (PSO) and support vector… More >

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Mohamed Ben Gharsallah^a, Issam Ben Mhammed^b, Ezzedine Ben Braiek^a
Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 231-240, 2018, DOI:10.1080/10798587.2016.1262457
Abstract In radiography imaging, contrast, sharpness and noise there are three fundamental factors that determine the image quality. Removing noise while preserving and sharpening image contours is a complicated task particularly for images with low contrast like radiography. This paper proposes a new anisotropic diffusion method for radiography image enhancement. The proposed method is based on the integration of geometric parameters derived from the local pixel intensity distribution in a nonlinear diffusion formulation that can concurrently perform the smoothing and the sharpening operations. The main novelty of the proposed anisotropic diffusion model is the ability to combine in one process noise… More >

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752

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S. Bartkevicius^a, O. Fiodorova^b, A. Knys^c, A. Derviniene^d, G. Dervinis^c, V. Raudonis^c, A. Lipnickas^c, V. Baranauskas^c, K. Sarkauskas^c, L. Balasevicius^c
Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 241-248, 2018, DOI:10.1080/10798587.2016.1264695
Abstract The paper deals with supervised robot navigation in known environments. The navigation task is divided into two parts, where one part of the navigation is done by the supervisor system i.e. the system sets the vector marks on the salient edges of the virtual environment map and guides the robot to reach these marks. Mobile robots have to perform a specific task according to the given paths and solve the local obstacles avoidance individually. The salient point’s detection, vector mark estimation and optimal path calculation are done on the supervisor computer using colored Petri nets. The proposed approach was extended… More >

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730

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Li-Hong Juang^a, Ming-Ni Wu^b, Shin-An Lin^b
Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 249-256, 2018, DOI:10.1080/10798587.2016.1272777
Abstract Detecting human gender from complex background, illumination variations and objects under computer vision system is very difficult but important for an adaptive information service. In this paper, a preliminary design and some experimental results of gender recognition will be presented from the walking movement that utilizes the gait-energy image (GEI) with denoised energy image (DEI) pre-processing as a machine learning support vector machine (SVM) classifier to train and extract its characteristics. The results show that the proposed method can adopt some characteristic values and the accuracy can reach up to 100% gender recognition rate under combining the horizontal added vertical… More >

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784

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Li-Hong Juang^a, Ming-Ni Wu^b
Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 257-266, 2018, DOI:10.1080/10798587.2016.1272778
Abstract In this paper we explore these math approaches for medical image applications. The application of the proposed method for detection tumor will be able to distinguish exactly tumor size and region. In this research, some major design and experimental results of tumor objects detection method for medical brain images is developed to utilize an automatic multi-thresholding method to handle this problem by combining the histogram analysis and the Otsu clustering. The histogram evaluations can decide the superior number of clusters firstly. The Otsu classification algorithm solves the given medical image by continuously separating the input gray-level image by multi-thresholding until… More >

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902

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Mohammed Algabri^a,c, Mohamed Abdelkader Bencherif^c, Mansour Alsulaiman^b,c, Ghulam Muhammad^b, Mohamed Amine Mekhtiche^c
Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 267-274, 2018, DOI:10.1080/10798587.2017.1278961
Abstract A method that uses fuzzy logic to classify two simple speech features for the automatic classification of voiced and unvoiced phonemes is proposed. In addition, two variants, in which soft computing techniques are used to enhance the performance of fuzzy logic by tuning the parameters of the membership functions, are also presented. The three methods, manually constructed fuzzy logic (VUFL), fuzzy logic optimized with genetic algorithm (VUFL-GA), and fuzzy logic with optimized particle swarm optimization (VUFL-PSO), are implemented and then evaluated using the TIMIT speech corpus. Performance is evaluated using the TIMIT database in both clean and noisy environments. Four… More >

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731

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Yonghua Xiong^a,b,d, Jinhua She^a,b,c, Keyuan Jiang^d
Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 275-284, 2018, DOI:10.1080/10798587.2017.1280262
Abstract Wireless network is crucial for the Mobile Transparent Computing (MTC), in which a mobile device without any Operating System (OS) support needs to load the demanded OSes and applications through accessing the wireless network connection. In this paper, a lightweight approach based on the Boot Management System (BMS) was proposed to ensure the wireless network connection before booting OS. In BMS, the Virtual File System (VFS) technology was used to drive the wireless network card and establish a stable network connection. A prototype of the BMS was tested on ARM11 hardware platform and the results demonstrate the validity of the… More >

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759

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Sayed Chhattan Shah
Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 285-298, 2018, DOI:10.1080/10798587.2017.1280995
Abstract Grid and cloud computing systems have been extensively used to solve large and complex problems in science and engineering fields. These systems include powerful computing resources that are connected through high-speed networks. Due to the recent advances in mobile computing and networking technologies, it has become feasible to integrate various mobile devices, such as robots, aerial vehicles, sensors, and smart phones, with grid and cloud computing systems. This integration enables the design and development of the next generation of applications by sharing of resources in mobile environments and introduces several challenges due to a dynamic and unpredictable network. This paper… More >

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738

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Moeiz Miraoui^a, Sherif El-Etriby^b, Chakib Tadj^c, Abdulbasit Zaid Abid^a
Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 299-308, 2018, DOI:10.1080/10798587.2017.1281565
Abstract Smart spaces have attracted considerable amount of interest over the past few years. The introduction of sensor networks, powerful electronics and communication infrastructures have helped a lot in the realization of smart homes. The main objective of smart homes is the automation of tasks that might be complex or tedious for inhabitants by distracting them from concentrating on setting and configuring home appliances. Such automation could improve comfort, energy savings, security, and tremendous benefits for elderly persons living alone or persons with disabilities. Context awareness is a key enabling feature for development of smart homes. It allows the automation task… More >

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783

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Samia Allaoua Chelloug^a, Mohamed A. El-Zawawy^b,c
Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 309-318, 2018, DOI:10.1080/10798587.2017.1290328
Abstract The Internet of things (IoT) applications span many potential fields. Furthermore, smart homes, smart cities, smart vehicular networks, and healthcare are very attractive and intelligent applications. In most of these applications, the system consists of smart objects that are equipped by sensors and Radio Frequency Identification (RFID) and may rely on other technological computing and paradigm solutions such as M2 M (machine to machine) computing, Wifi, Wimax, LTE, cloud computing, etc. Thus, the IoT vision foresees that we can shift from traditional sensor networks to pervasive systems, which deliver intelligent automation by running services on objects. Actually, a significant attention has… More >

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977

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E. Grigoroudis, F. Kanellos, V. S. Kouikoglou, Y. A. Phillis
Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 319-330, 2018, DOI:10.1080/10798587.2017.1292716
Abstract Climate change due to anthropogenic CO2 and other greenhouse gas emissions has had and will continue to have widespread negative impacts on human society and natural ecosystems. Drastic and concerted actions should be undertaken immediately if such impacts are to be prevented. The Paris Agreement on climate change aims to limit global mean temperature below 2 °C compared to the pre-industrial level. Using simulation and optimization tools and the most recent data, this paper investigates optimal emissions policies satisfying certain temperature constraints. The results show that only if we consider negative emissions coupled with drastic emissions reductions, temperature could be stabilized… More >

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Dongping Tian^a,b
Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 331-342, 2018, DOI:10.1080/10798587.2017.1293881
Abstract Particle swarm optimization (PSO) is a population based swarm intelligence algorithm that has been deeply studied and widely applied to a variety of problems. However, it is easily trapped into the local optima and premature convergence appears when solving complex multimodal problems. To address these issues, we present a new particle swarm optimization by introducing chaotic maps (Tent and Logistic) and Gaussian mutation mechanism as well as a local re-initialization strategy into the standard PSO algorithm. On one hand, the chaotic map is utilized to generate uniformly distributed particles to improve the quality of the initial population. On the other… More >

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ARTICLE

Achin Srivastav, Sunil Agrawal
Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 343-350, 2018, DOI:10.1080/10798587.2017.1293891
Abstract This paper focuses on the development of a multi-objective lot size–reorder point backorder inventory model for a slow moving item. The three objectives are the minimization of (1) the total annual relevant cost, (2) the expected number of stocked out units incurred annually and (3) the expected frequency of stockout occasions annually. Laplace distribution is used to model the variability of lead time demand. The multi-objective Cuckoo Search (MOCS) algorithm is proposed to solve the model. Pareto curves are generated between cost and service levels for decision-makers. A numerical problem is considered on a slow moving item to illustrate the… More >

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684

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Feng-Nong Chen^a,b#, Pu-Lan Chen^c#, Kai Fan^a, Fang Cheng^d
Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 351-358, 2018, DOI:10.1080/10798587.2017.1293927
Abstract In recent years, foodstuff quality has triggered tremendous interest and attention in our society as a series of food safety problems. The hyperspectral imaging techniques have been widely applied for foodstuff quality. In this study, we were undertaken to explore the possibility of unsound kernel detecting (Triticum durum Desf), which were defined as black germ kernels, moldy kernels and broken kernels, by selecting the best band in hyperspectral imaging system. The system possessed a wavelength in the range of 400 to 1,000  nm with neighboring bands 2.73  nm apart, acquiring images of bulk wheat samples from different wheat varieties. A… More >

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807

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Hong Choon Ong^a, Surafel Luleseged Tilahun^b, Wai Soon Lee^a, Jean Meadard T. Ngnotchouye^b
Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 359-366, 2018, DOI:10.1080/10798587.2017.1294811
Abstract Metaheuristic algorithms are found to be promising for difficult and high dimensional problems. Most of these algorithms are inspired by different natural phenomena. Currently, there are hundreds of these metaheuristic algorithms introduced and used. The introduction of new algorithm has been one of the issues researchers focused in the past fifteen years. However, there is a critic that some of the new algorithms are not in fact new in terms of their search behavior. Hence, a comparative study in between existing algorithms to highlight their differences and similarity needs to be studied. Apart from knowing the similarity and difference in… More >

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745

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Wenjie Yu, Xunbo Li, Hang Yang, Bo Huang
Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 367-376, 2018, DOI:10.1080/10798587.2017.1294873
Abstract This paper studies how to deploy relay nodes into traditional wireless sensor networks with constraint aiming to simultaneously optimize two important factors; average energy consumption and average network reliability. We consider tackling this multi-objective (MO) optimization problem with three metaheuristics, which employ greatly different evolutional strategies, and aim at an in-depth analysis of different performances of these metaheuristics to our problem. For this purpose, a statistical procedure is employed to analyse the results for confidence, in consideration of two MO quality metrics; hypervolume and coverage of two sets. After comprehensive analysis of the results, it is concluded that NSGA-II provides… More >

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776

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Qamar Abbas^a, Jamil Ahmad^b, Hajira Jabeen^a
Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 377-390, 2018, DOI:10.1080/10798587.2017.1295678
Abstract This paper presents a novel random controlled pool base differential evolution algorithm (RCPDE) where powerful mutation strategy and control parameter pools have been used. The mutation strategy pool contains mutations strategies having diverse parameter values, whereas the control parameter pool contains varying nature pairs of control parameter values. It has also been observed that with the addition of rarely used control parameter values in these pools are highly beneficial to enhance the performance of the DE algorithm. The proposed mutation strategy and control parameter pools improve the solution quality and the convergence speed of DE algorithm. The simulation results of… More >

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Liang Wang^a,b, Jiping Liu^a,b, Shenghua Xu^b, Jinjin Dong^c, Yi Yang^d
Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 391-398, 2018, DOI:10.1080/10798587.2017.1296660
Abstract Forest biomass is a significant indicator for substance accumulation and forest succession, and can provide valuable information for forest management and scientific planning. Accurate estimations of forest biomass at a fine resolution are important for a better understanding of the forest productivity and carbon cycling dynamics. In this study, considering the low efficiency and accuracy of the existing biomass estimation models for remote sensing data, Landsat 8 OLI imagery and field data cooperated with the radial basis function artificial neural network (RBF ANN) approach is used to estimate the forest Above Ground Biomass (AGB) in the Mount Tai area, Shandong… More >

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695

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Qian Wang^a,b,c, Jiadong Ren^a,b, Darryl N Davis^c, Yongqiang Cheng^c
Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 399-404, 2018, DOI:10.1080/10798587.2017.1340135
Abstract Frequent pattern mining usually requires much run time and memory usage. In some applications, only the patterns with top frequency rank are needed. Because of the limited pattern numbers, quality of the results is even more important than time and memory consumption. A Frequent Pattern algorithm for mining Top-rank-K patterns, FP_TopK, is proposed. It is based on a Node-list data structure extracted from FTPP-tree. Each node is with one or more triple sets, which contain supports, preorder and postorder transversal orders for candidate pattern generation and top-rank-k frequent pattern mining. FP_ TopK uses the minimal support threshold for pruning strategy… More >

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735

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Kazim Sari
Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 405-412, 2018, DOI:10.1080/10798587.2017.1352258
Abstract Nowadays, there has been a great interest for business enterprises to work together or collaborate in the supply chain. It is thus possible for them to gain a competitive advantage in the marketplace. However, determining the right collaboration strategy is not an easy task. Namely, there are several factors that need to be considered at the same time. In this regard, an expert system based on fuzzy rules is proposed to choose an appropriate collaboration strategy for a given supply chain. To this end, firstly, the factors that are significant for supply chain collaboration are extracted via an extensive review… More >

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763

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Amin Mohajer^a, Morteza Barari^a, Houman Zarrabi^b
Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 413-420, 2018, DOI:10.1080/10798587.2017.1312893
Abstract It is essential to satisfy class-specific QoS constraints to provide broadband services for new generation wireless networks. A self-optimization technique is introduced as the only viable solution for controlling and managing this type of huge data networks. This technique allows control of resources and key performance indicators without human intervention, based solely on the network intelligence. The present study proposes a big data based self optimization networking (BD-SON) model for wireless networks in which the KPI parameters affecting the QoS are assumed to be controlled through a multidimensional decision-making process. Also, Resource Management Center (RMC) was used to allocate the… More >

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804

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Imed Kacem, Ahmed Kadri, Pierre Laroche
Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 421-430, 2018, DOI:10.31209/2018.100000016
Abstract This paper addresses an inventory regulation problem in bicycle sharingsystems. The problem is to balance a network consisting of a set of stations by using a single vehicle, with the aim of minimizing the weighted sum of the waiting times during which some stations remain imbalanced. Motivated by the complexity of this problem, we propose a two-stage procedure based on decomposition. First, the network is divided into multiple zones by using two different clustering strategies. Then, the balancing problem is solved in each zone. Finally, the order in which the zones must be visited is defined. To solve these problems,… More >

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825

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Carlos Lopez-Franco¹, Javier Gomez-Avila², Nancy Arana-Daniel³, Alma Y. Alanis
Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 431-442, 2018, DOI:10.31209/2018.100000000
Abstract This paper presents a method for 3D pose estimation using visual information and a soft-computing algorithm. The algorithm uses quaternions to represent rotations, and Particle Swarm Optimization to estimate such quaternion. The rotation estimation problem is cast as a minimization problem, which finds the best quaternion for the given data using the PSO algorithm. With this technique, the algorithm always returns a valid quaternion, and therefore a valid rotation. During the estimation process, the algorithm is able to detect and reject outliers. The simulations and experimental results show the robustness of algorithm against noise and outliers. More >

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723

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Qais A. Khasawneh^1,3, Mohammad Abdel Kareem Jaradat^1,2, Mohammad Al-Shabi⁴, Hala Khalaf¹
Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 443-457, 2018, DOI:10.31209/2018.100000008
Abstract This work presents a new micro-positioning system that is implemented in an inchworm robot to move into desired locations. The system consists of four-bar mechanism; one link is fixed, and each one of the remaining links carries a piezoelectric actuator (PZT). PZTs are specifically chosen since they provide fast response and small displacements; up to ±30 µm for ±100 Volts. The system’s mathematical model is derived and is numerically simulated by MATLAB. Three fuzzy PI controllers, which are tuned automatically by genetic algorithm, are designed to control the system. Results indicate an error of less than 1% although disturbances present. More >

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