Y. A. Amer¹, Taher A. Bahnasy^2,*

Sound & Vibration, Vol.55, No.3, pp. 191-201, 2021, DOI:10.32604/sv.2021.014358 - 15 July 2021

Abstract An externally excited Duffing oscillator under feedback control is discussed and analyzed under the worst resonance case. Multiple time scales method is applied for this system to find analytic solution with the existence and nonexistence of the time delay on control loop. An appropriate stability analysis is also performed and appropriate choices for the feedback gains and the time delay are found in order to reduce the amplitude peak. Different response curves are involved to show and compare controller effects. In addition, analytic solutions are compared with numerical approximation solutions using Rung-Kutta method of fourth More >

Laura Schweikert¹, Dominik Gabbert¹, Sylvia Krupickova², Inga Voges^1,*

Congenital Heart Disease, Vol.16, No.6, pp. 551-560, 2021, DOI:10.32604/CHD.2021.016635 - 08 July 2021

Abstract Background: An isolated bicuspid aortic valve (BAV) is associated with structural and functional abnormalities of the aorta and the left ventricle (LV). Although ~50% of patients with aortic coarctation (CoA) have a BAV, less is known about its impact on LV function and aortic geometry and function in CoA patients. In this cardiovascular magnetic resonance imaging (CMR) study, we analysed markers of LV and aortic function as well as aortic geometry in a large cohort of CoA patients with a BAV and compared them with CoA patients with a tricuspid aortic valve (TAV). Methods: We included… More >

Hongxia Deng¹, Chunxiang Hu¹, Zihao Zhou², Jinxiu Guo¹, Zhenxuan Zhang³, Haifang Li^1,*

Intelligent Automation & Soft Computing, Vol.29, No.2, pp. 387-402, 2021, DOI:10.32604/iasc.2021.016989 - 16 June 2021

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 More >

Yong Wang, Yongyan Wang^*, Nan Qin, Sa Huang, Le Chang, Shunzheng Hou

Computer Systems Science and Engineering, Vol.39, No.1, pp. 121-131, 2021, DOI:10.32604/csse.2021.014464 - 10 June 2021

Abstract The work presented in this paper was conducted to quantify the relationship between the pore characteristics and mechanical properties of white sandstone. The study include tests carried out under the coupling effects of chemical corrosion, temperature, nuclear magnetic resonance, and mechanical tests. Computer fractal theory was employed to describe and quantify the characteristics of the growth of pores in white sandstone under the same coupling effect. A custom developed program code, in the MATLAB software platform, was used for calculating the growths of the pores in white sandstone when subjected to coupling effects. The correlation… More >

Mohamed Abdel-Basset¹, Reda Mohamed¹, Mohamed Abouhawwash^2,3, Ripon K. Chakrabortty⁴, Michael J. Ryan⁴, Yunyoung Nam^5,*

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 2961-2977, 2021, DOI:10.32604/cmc.2021.016956 - 06 May 2021

Abstract Image segmentation is vital when analyzing medical images, especially magnetic resonance (MR) images of the brain. Recently, several image segmentation techniques based on multilevel thresholding have been proposed for medical image segmentation; however, the algorithms become trapped in local minima and have low convergence speeds, particularly as the number of threshold levels increases. Consequently, in this paper, we develop a new multilevel thresholding image segmentation technique based on the jellyfish search algorithm (JSA) (an optimizer). We modify the JSA to prevent descents into local minima, and we accelerate convergence toward optimal solutions. The improvement is… More >

J. Jayanthi^1,*, M. Kavitha², T. Jayasankar³, A. Sagai Francis Britto⁴, N. B. Prakash⁵, Mohamed Yacin Sikkandar⁶, C. Bharathiraja⁷

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 4191-4203, 2021, DOI:10.32604/cmc.2021.012252 - 06 May 2021

Abstract Image recognition is considered to be the pre-eminent paradigm for the automatic detection of tumor diseases in this era. Among various cancers identified so far, glioma, a type of brain tumor, is one of the deadliest cancers, and it remains challenging to the medicinal world. The only consoling factor is that the survival rate of the patient is increased by remarkable percentage with the early diagnosis of the disease. Early diagnosis is attempted to be accomplished with the changes observed in the images of suspected parts of the brain captured in specific interval of time.… More >

Tapan Kumar Das¹, Pradeep Kumar Roy², Mohy Uddin³, Kathiravan Srinivasan¹, Chuan-Yu Chang^4,*, Shabbir Syed-Abdul⁵

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2413-2429, 2021, DOI:10.32604/cmc.2021.016698 - 13 April 2021

Abstract Machine learning based image analysis for predicting and diagnosing certain diseases has been entirely trustworthy and even as efficient as a domain expert’s inspection. However, the style of non-transparency functioning by a trained machine learning system poses a more significant impediment for seamless knowledge trajectory, clinical mapping, and delusion tracing. In this proposed study, a deep learning based framework that employs deep convolution neural network (Deep-CNN), by utilizing both clinical presentations and conventional magnetic resonance imaging (MRI) investigations, for diagnosing tumors is explored. This research aims to develop a model that can be used for… More >

Ying Fu^1,2,*, Minxue Gong¹, Guang Yang¹, Hong Wei³, Jiliu Zhou^1,2

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1359-1374, 2021, DOI:10.32604/cmc.2021.016536 - 22 March 2021

Abstract Generative adversarial networks (GANs) have considerable potential to alleviate challenges linked to data scarcity. Recent research has demonstrated the good performance of this method for data augmentation because GANs synthesize semantically meaningful data from standard signal distribution. The goal of this study was to solve the overfitting problem that is caused by the training process of convolution networks with a small dataset. In this context, we propose a data augmentation method based on an evolutionary generative adversarial network for cardiac magnetic resonance images to extend the training data. In our structure of the evolutionary GAN,… More >

Gurparsad Singh Suri¹, Gurleen Kaur¹, Sara Moein^2,*

Intelligent Automation & Soft Computing, Vol.27, No.3, pp. 723-735, 2021, DOI:10.32604/iasc.2021.015049 - 01 March 2021

Abstract Schizophrenia (SZ) is a mental heterogeneous psychiatric disorder with unknown cause. Neuroscientists postulate that it is related to brain networks. Recently, scientists applied machine learning (ML) and artificial intelligence for the detection, monitoring, and prognosis of a range of diseases, including SZ, because these techniques show a high performance in discovering an association between disease symptoms and disease. Regions of the brain have significant connections to the symptoms of SZ. ML has the power to detect these associations. ML interests researchers because of its ability to reduce the number of input features when the data More >

Ishu Anand¹, Himani Negi¹, Deepika Kumar¹, Mamta Mittal², Tai-hoon Kim^3,*, Sudipta Roy⁴

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3107-3127, 2021, DOI:10.32604/cmc.2021.014229 - 01 March 2021

Abstract Breast cancer positions as the most well-known threat and the main source of malignant growth-related morbidity and mortality throughout the world. It is apical of all new cancer incidences analyzed among females. Two features substantially influence the classification accuracy of malignancy and benignity in automated cancer diagnostics. These are the precision of tumor segmentation and appropriateness of extracted attributes required for the diagnosis. In this research, the authors have proposed a ResU-Net (Residual U-Network) model for breast tumor segmentation. The proposed methodology renders augmented, and precise identification of tumor regions and produces accurate breast tumor… More >