Ashraf Ahmed¹, Yousef AbuHour^2,*, Ammar El-Hassan¹

Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 979-990, 2022, DOI:10.32604/iasc.2022.020726

Abstract The Corona (COVID-19) epidemic has triggered interest in many fields of technology, medicine, science, and politics. Most of the mathematical research in this area focused on analyzing the dynamics of the spread of the virus. In this article, after a review of some current methodologies, a non-linear system of differential equations is developed to model the spread of COVID-19. In order to consider a wide spectrum of scenarios, we propose a susceptible-exposed-infected-quarantined-recovered (SEIQRS)-model which was analyzed to determine threshold conditions for its stability, and the number of infected cases that is an infected person will transmit on a virus to,… More >

Raed Alsaqour¹, Ammar Hadi², Maha Abdelhaq^3,*

Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 675-692, 2022, DOI:10.32604/iasc.2022.020482

Abstract The need to ensure the quality of video streaming transmitted over wireless networks is growing every day. Video streaming is typically used for applications that are sensitive to poor quality of service (QoS) due to insufficient bandwidth, packet loss, or delay. These challenges hurt video streaming quality since they affect throughput and packet delivery of the transmitted video. To achieve better video streaming quality, throughput must be high, with minimal packet delay and loss ratios. A current study, however, found that the adoption of the adaptive multiple TCP connections (AM-TCP), as a transport layer protocol, improves the quality of video… More >

Ben Attia Selma^*, Ouerfelli Houssem Eddine, Salhi Salah

Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 795-810, 2022, DOI:10.32604/iasc.2022.020435

Abstract This study addressed sufficient conditions for the robust monotonic convergence of repetitive discrete-time linear parameter varying systems, with the parameter variation rate bound. The learning law under consideration is an anticipatory iterative learning control. Of particular interest in this study is that the iterations can eliminate the influence of disturbances. Based on a simple quadratic performance function, a sufficient condition for the proposed learning algorithm is presented in terms of linear matrix inequality (LMI) by imposing a polytopic structure on the Lyapunov matrix. The set of LMIs to be determined considers the bounds on the rate of variation of the… More >

Donglei Lu¹, Yundong Sun², Qinrui Dai², Xiaofang Li^3,*, Dongjie Zhu⁴, Haiwen Du², Yansong Wang⁴, Rongning Qu³, Ning Cao¹, Gregory M. P. O’Hare⁵

Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 1141-1151, 2022, DOI:10.32604/iasc.2022.020218

Abstract The heterogeneity of knowledge graphs brings great challenges to entity alignment. In particular, the attributes of network entities in the real world are complex and changeable. The key to solving this problem is to expand the neighborhoods in different ranges and extract the neighborhood information efficiently. Based on this idea, we propose Multi-neighborhood Sampling Matching Network (MSM), a new KG alignment network, aiming at the structural heterogeneity challenge. MSM constructs a multi-neighborhood network representation learning method to learn the KG structure embedding. It then adopts a unique sampling and cosine cross-matching method to solve different sizes of neighborhoods and distinct… More >

R. Rajakumari^1,*, L. Kalaivani²

Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 1089-1107, 2022, DOI:10.32604/iasc.2022.020178

Abstract Breast cancer is a commonly diagnosed disease in women. Early detection, a personalized treatment approach, and better understanding are necessary for cancer patients to survive. In this work, a deep learning network and traditional convolution network were both employed with the Digital Database for Screening Mammography (DDSM) dataset. Breast cancer images were subjected to background removal followed by Wiener filtering and a contrast limited histogram equalization (CLAHE) filter for image restoration. Wavelet packet decomposition (WPD) using the Daubechies wavelet level 3 (db3) was employed to improve the smoothness of the images. For breast cancer recognition, these preprocessed images were first… More >

Wei-Lung Mao^1,*, Yu-Ying Chiu¹, Chao-Ting Chu², Bing-Hong Lin¹, Jian-Jie Hung³

Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 923-936, 2022, DOI:10.32604/iasc.2022.019555

Abstract Electromagnets are commonly used as support for machine components and parts in magnetic bearing systems (MBSs). Compared with conventional mechanical bearings, the magnetic bearings have less noise, friction, and vibration, but the magnetic force has a highly nonlinear relationship with the control current and the air gap. This research presents a dynamic sliding mode backstepping control (DSMBC) designed to track the height position of modeless vertical MBS. Because MBS is nonlinear with model uncertainty, the design of estimator should be able to solve the lumped uncertainty. The proposed DSMBC controller can not only stabilize the nonlinear system under mismatched uncertainties,… More >

Muhammad Umair¹, Fiaz Majeed¹, Muhammad Shoaib², Muhammad Qaiser Saleem³, Mohmmed S. Adrees³, Abdelrahman Elsharif Karrar⁴, Shahzada Khurram⁵, Muhammad Shafiq^6,*, Jin-Ghoo Choi⁶

Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 1179-1194, 2022, DOI:10.32604/iasc.2022.018952

Abstract Citations are references used by researchers to recognize the contributions of researchers in their articles. Citations can be used to discover hidden patterns in the research domain, and can also be used to perform various analyses in data mining. Citation analysis is a quantitative method to identify knowledge dissemination and influence papers in any research area. Citation analysis involves multiple techniques. One of the most commonly used techniques is Main Path Analysis (MPA). According to the specific use of MPA, it has evolved into various variants. Currently, MPA is carried out in different domains, but deep learning in the field… More >

Xiaorui Zhang^1,2,*, Xun Sun¹, Wei Sun², Tong Xu¹, Pengpai Wang¹, Sunil Kumar Jha³

Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 1041-1053, 2022, DOI:10.32604/iasc.2022.016543

Abstract This paper proposes a soft tissue grasping deformation model, where BP neural network optimized by the genetic algorithm is used to realize the real-time and accurate interaction of soft tissue grasping during virtual surgery. In the model, the soft tissue epidermis is divided into meshes, and the meshes generate displacements under the action of tension. The relationship between the tension and displacement of the mesh is determined by the proposed cylindrical spiral spring model. The optimized BP neural network is trained based on the sample data of the mesh point and vertical tension, so as to obtain the force and… More >

Dmitrii Kuzmenkov^1,*, Pavel Struchalin^1,2, Yulia Litvintsova¹, Maksim Delov¹, Vladimir Skrytnyy¹, Kirill Kutsenko¹

FDMP-Fluid Dynamics & Materials Processing, Vol.18, No.1, pp. 1-14, 2022, DOI:10.32604/fdmp.2022.018526

Abstract

Nanofluids have great potential for solar energy harvesting due to their suitable optical and thermophysical properties. One of the promising applications of nanofluids is utilization in solar collectors with the direct absorption of light (DASC). The design of a DASC requires detailed knowledge of the optical properties of nanofluids, which can be significantly affected by the particle size distribution. The paper presents the method to take into account the particle size distribution when calculating nanofluid extinction spectra. To validate the proposed model, the particle size distribution and spectral absorbance were measured for aqueous suspension with multi-walled graphite nanotubes; the minimum… More >

Xiao Wu^1,*, Zhaoting Wang¹, Mei Dong², Longfei Dong¹, Quan Ge¹

FDMP-Fluid Dynamics & Materials Processing, Vol.18, No.1, pp. 145-158, 2022, DOI:10.32604/fdmp.2022.018227

Abstract Liquefied natural gas (LNG) is an important energy source and occupies an important proportion in natural gas consumption, therefore, the selection of appropriate liquefaction processes and related optimization should be seen as subjects of great importance. Accordingly, in the present review, we provide a comparative and critical analysis of the current status of natural gas liquefaction technology through examination of the advantages and disadvantages associated with three natural gas liquefaction processes (namely, the cascade liquefaction cycle, the expander-based cycle and the mixed refrigerant cycle). It is shown that the energy consumption related to the cascade refrigeration cycle is the lowest.… More >