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Meera Radhakrishnan^1,*, Anandan Panneerselvam², Nandhagopal Nachimuthu³
Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1185-1199, 2020, DOI:10.32604/iasc.2020.012069
Abstract Image segmentation is a crucial stage in the investigation of medical images and is predominantly implemented in various medical applications. In the case of investigating MRI brain images, the image segmentation is mainly employed to measure and visualize the anatomic structure of the brain that underwent modifications to delineate the regions. At present, distinct segmentation approaches with various degrees of accurateness and complexities are available. But, it needs tuning of various parameters to obtain optimal results. The tuning of parameters can be considered as an optimization issue using a similarity function in solution space. This paper presents a new Parametric… More >

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Ahmed Y. Hamed¹, Monagi H. Alkinani², M. R. Hassan^3,*
Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1201-1216, 2020, DOI:10.32604/iasc.2020.012663
Abstract Many applications require to send information from a source node to multiple destinations nodes. To support these applications, the paper presents a multi-objective based genetic algorithm, which is used in the construction of the multicast tree for data transmission in a computer network. The proposed algorithm simultaneously optimizes total weights (cost, delay, and hop) of the multicast tree. Experimental results prove that the proposed algorithm outperforms a recently published Multi-objective Multicast Algorithm specially designed for solving the multicast routing problem. Also, the proposed approach has been applied to ten-node and twenty-node network to illustrate its efficiency. In addition, the execution… More >

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Norjihan Binti Abdul Ghani¹, Muneer Ahmad¹, Zahra Mahmoud¹, Raja Majid Mehmood^2,*
Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1217-1231, 2020, DOI:10.32604/iasc.2020.011731
（This article belongs to the Special Issue: Soft Computing in Intrusion Detection)
Abstract Achievement of sustainable privacy preservation is mostly very challenging in a resource shared computer environment. This challenge demands a dedicated focus on the exponential growth of big data. Despite the existence of specific privacy preservation policies at the organizational level, still sustainable protection of a user’s data at various levels, i.e., data collection, utilization, reuse, and disclosure, etc. have not been implemented to its spirit. For every personal data being collected and used, organizations must ensure that they are complying with their defined obligations. We are proposing a new clustered-purpose based access control for users’ sustainable data privacy protection in… More >

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Rubina Aslam^1,*, Manzoor Illahi Tamimy¹, Waqar Aslam²
Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1233-1249, 2020, DOI:10.32604/iasc.2020.013086
（This article belongs to the Special Issue: Soft Computing in Intrusion Detection)
Abstract Machine Learning (ML) has revolutionized intelligent systems that range from self-driving automobiles, search engines, business/market analysis, fraud detection, network intrusion investigation, and medical diagnosis. Classification lies at the core of Machine Learning and Multi-label Classification (MLC) is the closest to real-life problems related to heuristics. It is a type of classification problem where multiple labels or classes can be assigned to more than one instance simultaneously. The level of complexity in MLC is increased by factors such as data imbalance, high dimensionality, label correlations, and noise. Conventional MLC techniques such as ensembles-based approaches, Multi-label Stacking, Random k-label sets, and Hierarchy… More >

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Ujala Yasmeen¹, Jamal Hussain Shah¹, Muhammad Attique Khan², Ghulam Jillani Ansari¹, Saeed ur Rehman¹, Muhammad Sharif¹, Seifedine Kadry³, Yunyoung Nam^4,*
Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1251-1266, 2020, DOI:10.32604/iasc.2020.012775
（This article belongs to the Special Issue: Recent Trends in Artificial Intelligence for Automated Complex Industrial Systems)
Abstract Detection of textual data from scene text images is a very thought-provoking issue in the field of computer graphics and visualization. This challenge is even more complicated when edge intelligent devices are involved in the process. The low-quality image having challenges such as blur, low resolution, and contrast make it more difficult for text detection and classification. Therefore, such exigent aspect is considered in the study. The technology proposed is comprised of three main contributions. (a) After synthetic blurring, the blurred image is preprocessed, and then the deblurring process is applied to recover the image. (b) Subsequently, the standard maximal… More >

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Fahd N. Al-Wesabi^1,*, Huda G. Iskandar², Mohammad Alamgeer³, Mokhtar M. Ghilan²
Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1267-1283, 2020, DOI:10.32604/iasc.2020.013782
Abstract In this paper, a robust approach INLPETWA (an Intelligent Natural Language Processing and English Text Watermarking Approach) is proposed to tampering detection of English text by integrating zero text watermarking and hidden Markov model as a soft computing and natural language processing techniques. In the INLPETWA approach, embedding and detecting the watermark key logically conducted without altering the plain text. Second-gram and word mechanism of hidden Markov model is used as a natural text analysis technique to extracts English text features and use them as a watermark key and embed them logically and validates them during detection process to detect… More >

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Waleed Rashideh^1,*, Omar Abdullah Alshathry¹, Samer Atawneh², Hussein Al Bazar³, Mohammed Said AbualRub⁴
Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1285-1307, 2020, DOI:10.32604/iasc.2020.012995
Abstract Scientific programs in higher educational institutes are measured by their performances in showing evidences for expected quality assurance levels and for obtaining the academic accreditation. However, obtaining an accreditation for a scientific program is a lengthy process, as it requires extensive efforts from all members belonging to the program. The Accreditation Board for Engineering and Technology (ABET) accreditation is a common form of quality assurance that is based on different areas such as computing, engineering and sciences. In order to determine the ABET accreditation, several processes and methods are assessed to guarantee the required accreditation. Similar to many other programs,… More >

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Anh H. Vo¹, Trang Nguyen², Tuong Le^1,3,*
Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1307-1317, 2020, DOI:10.32604/iasc.2020.013189
Abstract Brent oil price fluctuates continuously causing instability in the economy. Therefore, it is essential to accurately predict the trend of oil prices, as it helps to improve profits for investors and benefits the community at large. Oil prices usually fluctuate over time as a time series and as such several sequence-based models can be used to predict them. Hence, this study proposes an efficient model named BOP-BL based on Bidirectional Long Short-Term Memory (Bi-LSTM) for oil price prediction. The proposed framework consists of two modules as follows: The first module has three Bi-LSTM layers which help learning useful information features… More >

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Waiel M. Eid^1,2, Samer Atawneh¹, Mousa Al-Akhras^1,3,*
Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1319-1334, 2020, DOI:10.32604/iasc.2020.013678
Abstract The huge number of devices available in cyberspace and the increasing number of security vulnerabilities discovered daily have added many difficulties in keeping track of security vulnerabilities, especially when not using special security tools and software. Mass scanning of the Internet has opened a broad range of possibilities for security tools that help cybersecurity centers detect weaknesses and vulnerabilities in cyberspace. However, one critical issue faced by national cybersecurity centers is the collection of information about IP addresses and subnet ranges. To develop a data collection mechanism for such information and maintain this information with continuous updates, a scanning system… More >

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Iftikhar Ahmad^1,*, Mohammed A. Alqarni², Abdulwahab Ali Almazroi³, Abdullah Tariq¹
Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1335-1344, 2020, DOI:10.32604/iasc.2020.013861
Abstract The exponential growth of social media has been instrumental in directing the news outlets to rely on the stated platform for the dissemination of news stories. While social media has helped in the fast propagation of breaking news, it also has allowed many bad actors to exploit this medium for political and monetary purposes. With such an intention, tempting headlines, which are not aligned with the content, are being used to lure users to visit the websites that often post dodgy and unreliable information. This phenomenon is commonly known as clickbait. A number of machine learning techniques have been developed… More >

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Fahd N. Al-Wesabi^*
Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1345-1357, 2020, DOI:10.32604/iasc.2020.013835
Abstract Assuring high availability is an important factor to develop component-based systems, particularly when different workloads and configurations are common. Several methods have been proposed in the literature to redeploy and replicate software components to find the best deployment architecture that guarantees high availability of component-based systems. In this paper, an extended method has been proposed to improve the availability of component-based systems by adding new CPU factors. The proposed method has been implemented by a self-developed program and using a java programming language with Eclipse KEPLER. Several simulations and experiment scenarios have been performed to evaluate the availability with related… More >

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M. A. Rahim Khan^*, Manoj Kumar Jain
Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1359-1373, 2020, DOI:10.32604/iasc.2020.013849
Abstract Repacked mobile applications and obfuscation attacks constitute a significant threat to the Android technological ecosystem. A novel method using the Constant Key Point Selection and Limited Binary Pattern Feature (CKPS: LBP) extraction-based Hashing has been proposed to identify repacked Android applications in previous works. Although the approach was efficient in detecting the repacked Android apps, it was not suitable for detecting obfuscation attacks. Additionally, the time complexity needed improvement. This paper presents an optimization technique using Scalable Bivariant Feature Transformation extract optimum feature-points extraction, and the Harris method applied for optimized image hashing. The experiments produced better results than the… More >

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Lingyun Xiang^1,2,3, Guoqing Guo², Qian Li⁴, Chengzhang Zhu^5,*, Jiuren Chen⁶, Haoliang Ma²
Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1375-1390, 2020, DOI:10.32604/iasc.2020.013382
Abstract Current works on spam detection in product reviews tend to ignore the temporal relevance among reviews in the user or product entity, resulting in poor detection performance. To address this issue, the present paper proposes a spam detection method that jointly learns comprehensive temporal features from both behavioral and text features in user and product entities. We first extract the behavioral features of a single review, then employ a convolutional neural network (CNN) to learn the text features of this review. We next combine the behavioral features with the text features of each review and train a Long-Short-Term Memory (LSTM)… More >

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Feng Zhang¹, Huihuang Zhao^1,2,*, Wang Ying^1,2, Qingyun Liu^1,2, Alex Noel Joseph Raj³, Bin Fu⁴
Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1391-1401, 2020, DOI:10.32604/iasc.2020.011750
Abstract Generating an RGB image from a sketch is a challenging and interesting topic. This paper proposes a method to transform a face sketch into a color image based on generation confrontation network and edge optimization. A neural network model based on Generative Adversarial Networks for transferring sketch to RGB image is designed. The face sketch and its RGB image is taken as the training data set. The human face sketch is transformed into an RGB image by the training method of generative adversarial networks confrontation. Aiming to generate a better result especially in edge, an improved loss function based on… More >

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Fuxiang Yuan, Fenlin Liu, Chong Liu, Xiangyang Luo^*
Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1403-1417, 2020, DOI:10.32604/iasc.2020.011902
Abstract IPs in homogeneous blocks are tightly connected and close to each other in topology and geography, which can help geolocate sensitive target IPs and maintain network security. Therefore, this manuscript proposes a city-level homogeneous blocks identification algorithm for IP geolocation. Firstly, IPs with consistent geographic location information in multiple databases and some landmarks in a specific area are obtained as targets; the /31 containing each target is used as a candidate block; vantage points are deployed to probe IPs in the candidate blocks to obtain delays and paths, and alias resolution is performed. Then, based on the analysis of paths… More >

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943

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Chunhua Zhou^*, Jianjing Shen, Yuncheng Wang, Xiaofeng Guo
Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1429-1440, 2020, DOI:10.32604/iasc.2020.011757
Abstract With the rapid development of information technology, battlefield situation data presents the characteristics of “4V” such as Volume, Variety, Value and Velocity. While enhancing situational awareness, it also brings many challenges to battlefield situation information recommendation (BSIR), such as big data volume, high timeliness, implicit feedback and no negative feedback. Focusing on the challenges faced by BSIR, we propose a two-stage BSIR model based on deep neural network (DNN). The model utilizes DNN to extract the nonlinear relationship between the data features effectively, mine the potential content features, and then improves the accuracy of recommendation. These two stages are the… More >

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1392

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Qi Wang^1,2,3,*, Changsong Yang^2,3,5, Yuxiang Wang^1,2,3, Shao-en Wu⁴
Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1433-1442, 2020, DOI:10.32604/iasc.2020.012759
Abstract To improve the positioning accuracy of farming vehicle in precision agriculture, an integrated positioning system is proposed based on Global Navigation Satellite System (GNSS)/Strapdown Inertial Navigation System (SINS)/Wireless Sensor Networks (WSN) with low cost and high reliability. The principles of commonly used localization technologies in vehicle positioning are compared and the Received Signal Strength Indication (RSSI) based measurement method is chosen as the integrated positioning system for information fusion considering the complexity of the algorithm, positioning accuracy and hardware requirements in the application scenario. The research of wireless signal propagation loss model of farmland environment was conducted. A set of… More >

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Haifeng Song¹, Weiwei Yang^1,*, Haiyan Yuan², Harold Bufford³
Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1441-1458, 2020, DOI:10.32604/iasc.2020.011988
Abstract There are two main problems that lead to unsatisfactory classification performance for hyperspectral remote sensing images (HSIs). One issue is that the HSI data used for training in deep learning is insufficient, therefore a deeper network is unfavorable for spatial-spectral feature extraction. The other problem is that as the depth of a deep neural network increases, the network becomes more prone to overfitting. To address these problems, a dual-channel 3D-Multiscale DenseNet (3DMSS) is proposed to boost the discriminative capability for HSI classification. The proposed model has several distinct advantages. First, the model consists of dual channels that can extract both… More >

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Yang Yang^1,*, Jianxin Wang¹, Zhipeng Gao¹, Yonghua Huo², Xuesong Qiu¹
Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1459-1475, 2020, DOI:10.32604/iasc.2020.011688
Abstract With the vigorous development of the Internet industry and the iterative updating of web service technologies, there are increasing web services with the same or similar functions in the ocean of platforms on the Internet. The issue of selecting the most reliable web service for users has received considerable critical attention. Aiming to solve this task, we propose a service reliability inference method based on deep neural network (SRI-XDFM) in this article. First, according to the pattern of the raw data in our scenario, we improve the performance of embedding by extracting self-correlated information with the help of character encoding… More >

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Chun’an Liu^1,*, Qian Lei², Huamin Jia³
Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1477-1492, 2020, DOI:10.32604/iasc.2020.011853
Abstract Portfolio optimization is an effective way to diversify investment risk and optimize asset management. Many multiobjective optimization mathematical models and metaheuristic intelligent algorithms have been proposed to solve portfolio problem under an ideal condition. This paper presents a biobjective portfolio optimization model under the assumption of no short selling. In order to obtain sufficient number of portfolio optimal solutions uniformly distributed on the portfolio efficient Pareto front, a hybrid imperialist competitive evolutionary algorithm which combines a multi-colony levy crossover operator and a simple-colony moving operator with random perturbation is also given. The performance of the given algorithm is verified by… More >

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Hai Liu^1,*, Xiangchao Zhou^2,3
Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1493-1501, 2020, DOI:10.32604/iasc.2020.012159
Abstract Aiming at the problems of poor brightness control effect and low registration accuracy in traditional multi focus image registration, a wavelet multi-scale multi focus image region fusion registration method is proposed. The multi-scale Retinex algorithm is used to enhance the image, the wavelet decomposition similarity analysis is used for image interpolation, and the EMD method is used to decompose the multi focus image. Finally, the image reconstruction is completed and the multi focus image registration is realized. In order to verify the multi focus image fusion registration effect of different methods, a comparative experiment was designed. Experimental results show that… More >

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Zhigang Deng^1,*, Zhenzhong Chu², Zaman Mohammed Tousif³
Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1503-1515, 2020, DOI:10.32604/iasc.2020.010798
Abstract Trajectory tracking control can be considered as one of the main researches of unmanned underwater vehicles (UUV). The bio-inspired neurodynamics model was used to make the output continuous and smooth for the inflection points to deal with the speed jump of the conventional tracking controller for discrete trajectories. A horizon-plane trajectory tracking control law is designed using the bio-inspired neurodynamics model and sliding-mode method without chattering. Finally, the simulation of the mentioned two methods is compared with the results showing this as effective and feasible. More >

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Shiyin Qiu^1,*, David Lodder², Feifan Du²
Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1517-1529, 2020, DOI:10.32604/iasc.2020.012144
Abstract Robotic grasping is an important issue in the field of robot control. In order to solve the problem of optimal grasping pose of the robotic arm, based on the Generative Grasping Convolutional Neural Network (GG-CNN), a new convolutional neural network called Hybrid Generative Grasping Convolutional Neural Network (HGG-CNN) is proposed by combining three small network structures called Inception Block, Dense Block and SELayer. This new type of convolutional neural network structure can improve the accuracy rate of grasping pose based on the GG-CNN network, thereby improving the success rate of grasping. In addition, the HGG-CNN convolutional neural network structure can… More >

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Xuan Chen^1,*, Zhiping Wan¹, Jiatong Wang²
Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1531-1548, 2020, DOI:10.32604/iasc.2020.011723
Abstract Addressing the shortcomings of unmanned path planning, such as significant error and low precision, a path-planning algorithm based on the whale optimization algorithm (WOA)-optimized double-blinking restricted Boltzmann machine-back propagation (RBM-BP) deep neural network model is proposed. The model consists mainly of two twin RBMs and one BP neural network. One twin RBM is used for feature extraction of the unmanned path location, and the other RBM is used for the path similarity calculation. The model uses the WOA algorithm to optimize parameters, which reduces the number of training sessions, shortens the training time, and reduces the training errors of the… More >

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Boxia Hu^1,2,*, Huihuang Zhao¹, Yufei Yang^1,3, Bo Zhou⁴, Alex Noel Joseph Raj⁵
Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1549-1560, 2020, DOI:10.32604/iasc.2020.011721
Abstract Face tracking is one of the most challenging research topics in computer vision. This paper proposes a framework to track multiple faces in video sequences automatically and presents an improved method based on feature fusion and neural network for multiple faces tracking in a video. The proposed method mainly includes three steps. At first, it is face detection, where an existing method is used to detect the faces in the first frame. Second, faces tracking with feature fusion. Given a video that has multiple faces, at first, all faces in the first frame are detected correctly by using an existing… More >

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Wanxia Yang^1,*, Sadaqatur Rehman², Wenhui Que³
Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1561-1574, 2020, DOI:10.32604/iasc.2020.012480
Abstract Identifying event-specific opinion leaders is essential for understanding event developments and influencing public opinion. News articles are informative and formal in expression, and include valuable information on specific events. In this paper, we propose an improved variant of LeaderRank, called local weighted LeaderRank, to measure the event-specific influence of person nodes in a weighted and undirected person cooccurrence network constructed using news articles related to a specific event. Our proposed method measures the influence of person nodes by considering both the cooccurrence strength between persons, and additional local link weight information for each local person node. To evaluate the performance… More >

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