Sivaramakrishnan Rajendar, Vishnu Kumar Kaliappan^*

Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 859-875, 2022, DOI:10.32604/iasc.2022.020936 - 17 November 2021

Abstract Automated Vehicles (AVs) reform the automotive industry by enabling real-time and efficient data exchange between the vehicles. While connectivity and automation of the vehicles deliver a slew of benefits, they may also introduce new safety, security, and privacy risks. Further, AVs rely entirely on the sensor data and the data from other vehicles too. On the other hand, the sensor data is susceptible to anomalies caused by cyber-attacks, errors, and faults, resulting in accidents and fatalities. Hence, it is essential to create techniques for detecting anomalies and identifying their sources before the wide adoption of More >

Yun-Kyu Lee¹, Dong-Sung Pae², Dae-Ki Hong³, Myo-Taeg Lim¹, Tae-Koo Kang^4,*

Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 657-673, 2022, DOI:10.32604/iasc.2022.020849 - 17 November 2021

Abstract With the advancement of human-computer interaction and artificial intelligence, emotion recognition has received significant research attention. The most commonly used technique for emotion recognition is EEG, which is directly associated with the central nervous system and contains strong emotional features. However, there are some disadvantages to using EEG signals. They require high dimensionality, diverse and complex processing procedures which make real-time computation difficult. In addition, there are problems in data acquisition and interpretation due to body movement or reduced concentration of the experimenter. In this paper, we used photoplethysmography (PPG) and electromyography (EMG) to record… More >

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 - 17 November 2021

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… 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 - 17 November 2021

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, 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 - 17 November 2021

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 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 - 17 November 2021

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 More >

R. Rajakumari^1,*, L. Kalaivani²

Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 1089-1107, 2022, DOI:10.32604/iasc.2022.020178 - 17 November 2021

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,… 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 - 17 November 2021

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… 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 - 17 November 2021

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… 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 - 17 November 2021

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 More >