Journals / IASC / Vol.29, No.2


    CNN-Based Voice Emotion Classification Model for Risk Detection

    Hyun Yoo1, Ji-Won Baek2, Kyungyong Chung3,*
    Intelligent Automation & Soft Computing, Vol.29, No.2, pp. 319-334, 2021, DOI:10.32604/iasc.2021.018115
    Abstract With the convergence and development of the Internet of things (IoT) and artificial intelligence, closed-circuit television, wearable devices, and artificial neural networks have been combined and applied to crime prevention and follow-up measures against crimes. However, these IoT devices have various limitations based on the physical environment and face the fundamental problem of privacy violations. In this study, voice data are collected and emotions are classified based on an acoustic sensor that is free of privacy violations and is not sensitive to changes in external environments, to overcome these limitations. For the classification of emotions in the voice, the data… More >


    A Pregnancy Prediction System based on Uterine Peristalsis from Ultrasonic Images

    Kentaro Mori1,*, Kotaro Kitaya2, Tomomoto Ishikawa2, Yutaka Hata3
    Intelligent Automation & Soft Computing, Vol.29, No.2, pp. 335-352, 2021, DOI:10.32604/iasc.2021.01010
    Abstract In infertility treatment, it is required to improve a success rate of the treatment. A purpose of this study is to develop a prediction system for pregnancy outcomes using ultrasonic images. In infertility treatment, it is typical to evaluate the endometrial shape by using ultrasonic images. The convolutional neural network (CNN) system developed in the current study predicted pregnancy outcome by velocity information. The velocity information has a movement feature of uterine. It is known that a uterine movement is deep related to infertility. Experiments compared the velocity-based and shape-based systems. The shape-based systems predict the optimal uterine features for… More >


    Parameter Estimation of Alpha Power Inverted Topp-Leone Distribution with Applications

    Gamal M. Ibrahim1, Amal S. Hassan2, Ehab M. Almetwally3,*, Hisham M. Almongy4
    Intelligent Automation & Soft Computing, Vol.29, No.2, pp. 353-371, 2021, DOI:10.32604/iasc.2021.017586
    Abstract We introduce a new two-parameter lifetime model, referred to alpha power transformed inverted Topp-Leone, derived by combining the alpha power transformation-G family with the inverted Topp-Leone distribution. Structural properties of the proposed distribution are implemented like; quantile function, residual and reversed residual life, Rényi entropy measure, moments and incomplete moments. The maximum likelihood, weighted least squares, maximum product of spacing, and Bayesian methods of estimation are considered. A simulation study is worked out to evaluate the restricted sample properties of the proposed distribution. Numerical results showed that the Bayesian estimates give more accurate results than the corresponding other estimates in… More >


    Investigating the Role of Trust Dimension as a Mediator on CC-SaaS Adoption

    Hiba Jasim Hadi*, Mohd Adan Omar, Wan Rozaini Sheik Osman
    Intelligent Automation & Soft Computing, Vol.29, No.2, pp. 373-386, 2021, DOI:10.32604/iasc.2021.018207
    (This article belongs to this Special Issue: Computational Intelligence for Internet of Medical Things and Big Data Analytics)
    Abstract The public sector of Iraq has been struggling from poor management of resources and numerous difficulties that affect its governmental organization’s development, such as financial issues resulting from corruption, insecurity, and the lack of IT resources and infrastructure. Thus, cloud computing Software as a Service (CC-SaaS) can be a useful solution to help governmental organizations increase their service efficiency through the adoption of low-cost technology and provision of better services. The adoption of CC-SaaS remains limited in Iraqi public organizations due to numerous challenges, including privacy and protection, legal policy, and trust. Trust was found to be an effective facilitator… More >


    RMCA-LSA: A Method of Monkey Brain Extraction

    Hongxia Deng1, Chunxiang Hu1, Zihao Zhou2, Jinxiu Guo1, Zhenxuan Zhang3, Haifang Li1,*
    Intelligent Automation & Soft Computing, Vol.29, No.2, pp. 387-402, 2021, DOI:10.32604/iasc.2021.016989
    Abstract The traditional level set algorithm selects the position of the initial contour randomly and lacks the processing of edge information. Therefore, it cannot accurately extract the edge of the brain tissue. In order to solve this problem, this paper proposes a level set algorithm that fuses partition and Canny function. Firstly, the idea of partition is fused, and the initial contour position is selected by combining the morphological information of each region, so that the initial contour contains more brain tissue regions, and the efficiency of brain tissue extraction is improved. Secondly, the canny operator is fused in the energy… More >


    Software Defect Prediction Using Supervised Machine Learning Techniques: A Systematic Literature Review

    Faseeha Matloob1, Shabib Aftab1,2, Munir Ahmad2, Muhammad Adnan Khan3,*, Areej Fatima4, Muhammad Iqbal2, Wesam Mohsen Alruwaili5, Nouh Sabri Elmitwally5,6
    Intelligent Automation & Soft Computing, Vol.29, No.2, pp. 403-421, 2021, DOI:10.32604/iasc.2021.017562
    Abstract Software defect prediction (SDP) is the process of detecting defect-prone software modules before the testing stage. The testing stage in the software development life cycle is expensive and consumes the most resources of all the stages. SDP can minimize the cost of the testing stage, which can ultimately lead to the development of higher-quality software at a lower cost. With this approach, only those modules classified as defective are tested. Over the past two decades, many researchers have proposed methods and frameworks to improve the performance of the SDP process. The main research topics are association, estimation, clustering, classification, and… More >


    A Deep Learning Approach for the Mobile-Robot Motion Control System

    Rihem Farkh1,4,*, Khaled Al jaloud1, Saad Alhuwaimel2, Mohammad Tabrez Quasim3, Moufida Ksouri4
    Intelligent Automation & Soft Computing, Vol.29, No.2, pp. 423-435, 2021, DOI:10.32604/iasc.2021.016219
    (This article belongs to this Special Issue: Machine Learning and Deep Learning for Transportation)
    Abstract A line follower robot is an autonomous intelligent system that can detect and follow a line drawn on floor. Line follower robots need to adapt accurately, quickly, efficiently, and inexpensively to changing operating conditions. This study proposes a deep learning controller for line follower mobile robots using complex decision-making strategies. An Arduino embedded platform is used to implement the controller. A multilayered feedforward network with a backpropagation training algorithm is employed. The network is trained offline using Keras and implemented on a ATmega32 microcontroller. The experimental results show that it has a good control effect and can extend its application. More >


    Improved Short-video User Impact Assessment Method Based on PageRank Algorithm

    Lei Hong1,*, Jie Yin1, Ling-Ling Xia1, Chao-Fan Gong1, Qi Huang2
    Intelligent Automation & Soft Computing, Vol.29, No.2, pp. 437-449, 2021, DOI:10.32604/iasc.2021.016259
    Abstract The short-video platform is a social network where users’ content accelerates the speed of information dissemination. Hence, it is necessary to identify important users to effectively obtain information. Four algorithms (Followers Rank, Average Forwarding, K Coverage, and Expert Survey and Evaluation) have been proposed to calculate users’ influence and determine their importance. These methods simply take the number of a user’s fans or posts as the standard of influence evaluation, ignoring factors such as the paid posters, which makes such evaluations inaccurate. To solve these problems, we propose the short-video user influence rank (SVUIR) algorithm, which combines direct and indirect… More >


    PSO Based Torque Ripple Minimization Of Switched Reluctance Motor Using FPGA Controller

    A. Manjula1,*, L. Kalaivani2, M. Gengaraj2
    Intelligent Automation & Soft Computing, Vol.29, No.2, pp. 451-465, 2021, DOI:10.32604/iasc.2021.016088
    Abstract The fast-growing field of mechanical robotization necessitates a well-designed and controlled version of electric drives. The concept of control concerning mechanical characteristics also requires a methodology in which the system needs to be modeled precisely and deals with uncertainty. The proposed method provides the enhanced performance of Switched Reluctance Motor (SRM) by controlling its speed and minimized torque ripple. Proportional-Integral-Derivative (PID) controllers have drawn more attention in industry automation due to their ease and robustness. The performances are further improved by using fractional order (Non-integer) controllers. The Modified Particle Swarm Optimization (MPSO) based optimization approach is employed to acquire the… More >


    SVSF-Based Robust UGV/UAV Control/Tracking Architecture in Disturbed Environment

    Abdelatif Oussar1,*, Abdelmoumen Ferrag1, Mohamed Guiatni1, Mustapha Hamerlain2
    Intelligent Automation & Soft Computing, Vol.29, No.2, pp. 467-495, 2021, DOI:10.32604/iasc.2021.01000
    Abstract This paper presents the design of a robust architecture for the tracking of an unmanned ground vehicle (UGV) by an unmanned aerial vehicle (UAV). To enhance the robustness of the ground vehicle in the face of external disturbances and handle the non-linearities due to inputs saturation, an integral sliding mode controller was designed for the task of trajectory tracking. Stabilization of the aerial vehicle is achieved using an integral-backstepping solution. Estimation of the relative position between the two agents was solved using two approaches: the first solution (optimal) is based on a Kalman filter (KF) the second solution (robust) uses… More >


    Handwritten Character Recognition Based on Improved Convolutional Neural Network

    Yu Xue1,2,*, Yiling Tong1, Ziming Yuan1, Shoubao Su2, Adam Slowik3, Sam Toglaw4
    Intelligent Automation & Soft Computing, Vol.29, No.2, pp. 497-509, 2021, DOI:10.32604/iasc.2021.016884
    Abstract Because of the characteristics of high redundancy, high parallelism and nonlinearity in the handwritten character recognition model, the convolutional neural networks (CNNs) are becoming the first choice to solve these complex problems. The complexity, the types of characters, the character similarity of the handwritten character dataset, and the choice of optimizers all have a great impact on the network model, resulting in low accuracy, high loss, and other problems. In view of the existence of these problems, an improved LeNet-5 model is proposed. Through increasing its convolutional layers and fully connected layers, higher quality features can be extracted. Secondly, a… More >


    Semantic Modeling of Events Using Linked Open Data

    Sehrish Jamil1, Salma Noor1,*, Iftikhar Ahmed2, Neelam Gohar1, Fouzia1
    Intelligent Automation & Soft Computing, Vol.29, No.2, pp. 511-524, 2021, DOI:10.32604/iasc.2021.017770
    Abstract Significant happenings in terms of spatio-temporal factors are called events. In the digital age, these events and their associated features are scattered in various databases on the Internet. The event data are in heterogeneous formats, which are often not machine-readable. This leads to a lack of unification of event-related knowledge across different domains and results in a research gap in terms of event modeling and representation. Specialized event models are needed to overcome this gap and integrate relevant information of different similar events occurring worldwide. Our research explores the problem of heterogeneity in specialized event modeling and takes modeling for… More >


    Optimization for Variable Height Wind Farm Layout Model

    Bin Xu1,2,3,*, Jianming Zhu1, Junzhe Wen1, Shanshan Lin1, Yunkai Zhao1, Jin Qi1,2, Yu Xue4, Sichong Qin5
    Intelligent Automation & Soft Computing, Vol.29, No.2, pp. 525-537, 2021, DOI:10.32604/iasc.2021.018338
    Abstract The optimization of wind farm layouts is very important for the effective utilization of wind resources. A fixed wind turbine hub height in the layout of wind farms leads to a low wind energy utilization and a higher LCOE (levelized cost of electricity). WOMH (Wind Farm Layout Optimization Model Considering Multiple Hub Heights) is proposed in this paper to tackle the above problem. This model is different from the traditional fixed hub height model, as it uses a variable height wind turbine. In WOMH, the Jensen wake and Weibull distribution are used to describe the wake effect on the wind… More >


    Design and Experimentation of Causal Relationship Discovery among Features of Healthcare Datasets

    Y. Sreeraman*, S. Lakshmana Pandian
    Intelligent Automation & Soft Computing, Vol.29, No.2, pp. 539-557, 2021, DOI:10.32604/iasc.2021.017256
    Abstract Causal relationships in a data play vital role in decision making. Identification of causal association in data is one of the important areas of research in data analytics. Simple correlations between data variables reveal the degree of linear relationship. Partial correlation explains the association between two variables within the control of other related variables. Partial association test explains the causality in data. In this paper a couple of causal relationship discovery strategies are proposed using the design of partial association tree that makes use of partial association test among variables. These decision trees are different from normal decision trees in… More >


    Paralleling Collision Detection on Five-Axis Machining

    Cheng-Yan Siao1, Jhe-Wei Lin1, Ting-Hsuan Chien2,*, Rong-Guey Chang1
    Intelligent Automation & Soft Computing, Vol.29, No.2, pp. 559-569, 2021, DOI:10.32604/iasc.2021.018252
    (This article belongs to this Special Issue: Machine Learning and Deep Learning for Transportation)
    Abstract With the rapid growth of the Fourth Industrial Revolution (or Industry 4.0), five-axis machining has played an important role nowadays. Due to the expensive cost of five-axis machining, how to solve the collision detection for five-axis machining in real-time is very critical. In this paper, we present a parallel method to detect collision for five-axis machining. Moreover, we apply the bounding volume hierarchy technique with two-level bounding volume represent the surface or solid of the object to reduce triangle meshes inside each axis of the five-axis machine tool, and then matching the operating range limit of the five-axis machine tool… More >


    Smartphone Security Using Swipe Behavior-based Authentication

    Adnan Bin Amanat Ali1, Vasaki Ponnusamy1, Anbuselvan Sangodiah1, Roobaea Alroobaea2, N. Z. Jhanjhi3,*, Uttam Ghosh4, Mehedi Masud2
    Intelligent Automation & Soft Computing, Vol.29, No.2, pp. 571-585, 2021, DOI:10.32604/iasc.2021.015913
    Abstract Most smartphone users prefer easy and convenient authentication without remembering complicated passwords or drawing intricate patterns. Preferably, after one-time authentication, there is no verification of the user’s authenticity. Therefore, security and privacy against unauthorized users is a crucial research area. Behavioral authentication is an emerging security technique that is gaining attention for its uniqueness and transparency. In this paper, a behavior-based authentication system is built using swipe movements to continuously authenticate the user after one-time traditional authentication. The key feature is the selection of an optimal feature set for the swipe movement. Five machine learning classifiers are used, of which… More >


    Impact of Skim Reading Based on Different Screen Sizes

    Sara Mehmood1, Naeem Ahmed Mahoto2, Asadullah Shaikh3, Hani Alshahrani3, Mana Saleh Al Reshan3,*
    Intelligent Automation & Soft Computing, Vol.29, No.2, pp. 587-604, 2021, DOI:10.32604/iasc.2021.017843

    Digital technologies have identified themselves in several application domains. This has resulted in massive data availability over the internet. These web contents are generally too long to read. The reader, therefore, skims over the matter because of the limited time available while focusing on understanding the concept of the subject. A hypothesis suggests that full-screen skimming provides a better understanding of ideas as compared to mobile screen skimming. The small size of a mobile device screen is facilitated by a scrolling feature to cover the entire text. In contrast, a full screen provides a larger chunk of text on the… More >


    Attitude Towards Adopting Cloud Computing in the Saudi Banking Sector

    Mohammed Hamdi1, Fekry Olayah2, Amin A. Al-Awady3, Ahlam F. Shamsan4, Mokhtar M. Ghilan5,*
    Intelligent Automation & Soft Computing, Vol.29, No.2, pp. 605-617, 2021, DOI:10.32604/iasc.2021.018170
    (This article belongs to this Special Issue: Digital Technologies in Business, Education and Social Transformation)
    Abstract Cloud computing plays a significant role in business organisations by offering many benefits and opportunities. However, the adoption of cloud computing involves some trepidation. The adoption of cloud computing in developing countries is still in the early phase. The bank sector in Saudi Arabia aims to benefit from opportunities offered by the cloud computing technology; however, some banks continue to hesitate in the implementation of this technology. Therefore, this study aims to investigate factors that influence the attitude of the Saudi Arabian bank sector towards adopting cloud computing. A model that incorporates factors derived from the literature is developed in… More >


    Optimal Parameter Estimation of Proton Exchange Membrane Fuel Cells

    A. M. Abdullah1, Hegazy Rezk2,3,*, A. Hadad1, Mohamed K. Hassan1,4, A. F. Mohamed1,5
    Intelligent Automation & Soft Computing, Vol.29, No.2, pp. 619-631, 2021, DOI:10.32604/iasc.2021.018289
    Abstract The problem of parameter estimation of the proton exchange membrane fuel cell (PEMFC) model plays a significant role in the simulation and optimization of a PEMFC system. In the current research, a moth flame optimization algorithm (MFOA) is used to identify the best parameters of PEMFC. Two different PEMFCs, Nedstack PS6, 6 kW, and SR-12 PEM 500 W are used to demonstrate the accuracy of the MFOA. Throughout the optimization process, seven unidentified parameters (1, 2, 3, 4, λ, ℛ, and B) of PEMFC are appointed to be decision variables. The fitness function, which needed to be minimum, is represented… More >


    A Hybrid Scheme for Secure Wireless Communications in IoT

    Muhammad Irshad Nazeer1,2,*, Ghulam Ali Mallah1, Raheel Ahmed Memon2
    Intelligent Automation & Soft Computing, Vol.29, No.2, pp. 633-648, 2021, DOI:10.32604/iasc.2021.017771
    (This article belongs to this Special Issue: Recent Advances in Intelligent Systems and Communication)
    Abstract Network Coding is a potential technology for the future wireless communications and Internet of Things (IoT) as it reduces the number of transmissions and offers energy efficiency. It is vulnerable to threat and attack that can harm intermediate nodes. Indeed, it exhibits an ability to incorporate security of transmitted data, yet a lot of work needs to be done to provide a safeguard from threats. The purpose of this study is to strengthen the existing Network Coding scheme with a set of generic requirements for Network Coding Protocols by adopting system models and a Genetic Algorithm based cryptosystem. A hybrid… More >

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