Home / Journals / IASC / Vol.26, No.6, 2020
  • Open Access

    ARTICLE

    Canny Edge Detection Model in MRI Image Segmentation Using Optimized Parameter Tuning Method

    Meera Radhakrishnan1,*, 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 >

  • Open Access

    ARTICLE

    A Genetic Algorithm Optimization for Multi-Objective Multicast Routing

    Ahmed Y. Hamed1, Monagi H. Alkinani2, M. R. Hassan3,*
    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 >

  • Open Access

    ARTICLE

    A Pursuit of Sustainable Privacy Protection in Big Data Environment by an Optimized Clustered-Purpose Based Algorithm

    Norjihan Binti Abdul Ghani1, Muneer Ahmad1, Zahra Mahmoud1, Raja Majid Mehmood2,*
    Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1217-1231, 2020, DOI:10.32604/iasc.2020.011731
    (This article belongs to this 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 >

  • Open Access

    ARTICLE

    Soft Computing Based Evolutionary Multi-Label Classification

    Rubina Aslam1,*, 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 this 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 >

  • Open Access

    ARTICLE

    Text Detection and Classification from Low Quality Natural Images

    Ujala Yasmeen1, Jamal Hussain Shah1, Muhammad Attique Khan2, Ghulam Jillani Ansari1, Saeed ur Rehman1, Muhammad Sharif1, Seifedine Kadry3, Yunyoung Nam4,*
    Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1251-1266, 2020, DOI:10.32604/iasc.2020.012775
    (This article belongs to this 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 >

  • Open Access

    ARTICLE

    Proposing a High-Robust Approach for Detecting the Tampering Attacks on English Text Transmitted via Internet

    Fahd N. Al-Wesabi1,*, 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 >

  • Open Access

    ARTICLE

    A Successful Framework for the ABET Accreditation of an Information System Program

    Waleed Rashideh1,*, 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 >

  • Open Access

    ARTICLE

    Brent Oil Price Prediction Using Bi-LSTM Network

    Anh H. Vo1, Trang Nguyen2, Tuong Le1,3,*
    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 >

  • Open Access

    ARTICLE

    Framework for Cybersecurity Centers to Mass Scan Networks

    Waiel M. Eid1,2, Samer Atawneh1, Mousa Al-Akhras1,3,*
    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 >

  • Open Access

    ARTICLE

    Experimental Evaluation of Clickbait Detection Using Machine Learning Models

    Iftikhar Ahmad1,*, 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 >

  • Open Access

    ARTICLE

    Improving Availability in Component-Based Distributed Systems

    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 >

  • Open Access

    ARTICLE

    Feature Point Detection for Repacked Android Apps

    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 >

  • Open Access

    ARTICLE

    Spam Detection in Reviews Using LSTM-Based Multi-Entity Temporal Features

    Lingyun Xiang1,2,3, Guoqing Guo2, Qian Li4, Chengzhang Zhu5,*, 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 >

  • Open Access

    ARTICLE

    Human Face Sketch to RGB Image with Edge Optimization and Generative Adversarial Networks

    Feng Zhang1, Huihuang Zhao1,2,*, Wang Ying1,2, Qingyun Liu1,2, 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 >

  • Open Access

    ARTICLE

    City-Level Homogeneous Blocks Identification for IP Geolocation

    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 >

  • Open Access

    ARTICLE

    Battlefield Situation Information Recommendation Based on Recall-Ranking

    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 >

  • Open Access

    ARTICLE

    Application of Low Cost Integrated Navigation System in Precision Agriculture

    Qi Wang1,2,3,*, Changsong Yang2,3,5, Yuxiang Wang1,2,3, 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 >

  • Open Access

    ARTICLE

    Deep 3D-Multiscale DenseNet for Hyperspectral Image Classification Based on Spatial-Spectral Information

    Haifeng Song1, Weiwei Yang1,*, 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 >

  • Open Access

    ARTICLE

    SRI-XDFM: A Service Reliability Inference Method Based on Deep Neural Network

    Yang Yang1,*, 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 >

  • Open Access

    ARTICLE

    Hybrid Imperialist Competitive Evolutionary Algorithm for Solving Biobjective Portfolio Problem

    Chun’an Liu1,*, 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 >

  • Open Access

    ARTICLE

    Multi-Focus Image Region Fusion and Registration Algorithm with Multi-Scale Wavelet

    Hai Liu1,*, Xiangchao Zhou2,3
    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 >

  • Open Access

    ARTICLE

    Sliding-Mode Control of Unmanned Underwater Vehicle Using Bio-Inspired Neurodynamics for Discrete Trajectories

    Zhigang Deng1,*, 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 >

  • Open Access

    ARTICLE

    HGG-CNN: The Generation of the Optimal Robotic Grasp Pose Based on Vision

    Shiyin Qiu1,*, 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 >

  • Open Access

    ARTICLE

    A Study of Unmanned Path Planning Based on a Double-Twin RBM-BP Deep Neural Network

    Xuan Chen1,*, 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 >

  • Open Access

    ARTICLE

    Multiple Faces Tracking Using Feature Fusion and Neural Network in Video

    Boxia Hu1,2,*, Huihuang Zhao1, Yufei Yang1,3, 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 >

  • Open Access

    ARTICLE

    Identifying Event-Specific Opinion Leaders by Local Weighted LeaderRank

    Wanxia Yang1,*, 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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