Mrim M. Alnfiai^*

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3763-3780, 2023, DOI:10.32604/cmc.2023.033448

Abstract Developments in data storage and sensor technologies have allowed the cumulation of a large volume of data from industrial systems. Both structural and non-structural data of industrial systems are collected, which covers data formats of time-series, text, images, sound, etc. Several researchers discussed above were mostly qualitative, and ceratin techniques need expert guidance to conclude on the condition of gearboxes. But, in this study, an improved symbiotic organism search with deep learning enabled fault diagnosis (ISOSDL-FD) model for gearbox fault detection in industrial systems. The proposed ISOSDL-FD technique majorly concentrates on the identification and classification of faults in the gearbox… More >

Zeynep Ekicioglu Kuzeci^1,*, Surkay D. Akbarov^2,3

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4337-4362, 2023, DOI:10.32604/cmc.2023.033446

Abstract The present work investigates the mechanically forced vibration of the hydro-elasto-piezoelectric system consisting of a two-layer plate “elastic+PZT”, a compressible viscous fluid, and a rigid wall. It is assumed that the PZT (piezoelectric) layer of the plate is in contact with the fluid and time-harmonic linear forces act on the free surface of the elastic-metallic layer. This study is valuable because it considers for the first time the mechanical vibration of the metal+piezoelectric bilayer plate in contact with a fluid. It is also the first time that the influence of the volumetric concentration of the constituents on the vibration of… More >

Jeonghoon Choi¹, Dongjun Suh^1,*, Marc-Oliver Otto²

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2945-2966, 2023, DOI:10.32604/cmc.2023.033417

Abstract Recently, machine learning-based technologies have been developed to automate the classification of wafer map defect patterns during semiconductor manufacturing. The existing approaches used in the wafer map pattern classification include directly learning the image through a convolution neural network and applying the ensemble method after extracting image features. This study aims to classify wafer map defects more effectively and derive robust algorithms even for datasets with insufficient defect patterns. First, the number of defects during the actual process may be limited. Therefore, insufficient data are generated using convolutional auto-encoder (CAE), and the expanded data are verified using the evaluation technique… More >

Akram Ali Alhadad^1,2,*, Omar Tarawneh³, Reham R. Mostafa¹, Hazem M. El-Bakry¹

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3333-3350, 2023, DOI:10.32604/cmc.2023.033413

Abstract COVID-19 is the common name of the disease caused by the novel coronavirus (2019-nCoV) that appeared in Wuhan, China in 2019. Discovering the infected people is the most important factor in the fight against the disease. The gold-standard test to diagnose COVID-19 is polymerase chain reaction (PCR), but it takes 5–6 h and, in the early stages of infection, may produce false-negative results. Examining Computed Tomography (CT) images to diagnose patients infected with COVID-19 has become an urgent necessity. In this study, we propose a residual attention deep support vector data description SVDD (RADSVDD) approach to diagnose COVID-19. It is… More >

Mehrdad Ahmadi Kamarposhti^1,*, Phatiphat Thounthong², Ilhami Colak³, Kei Eguchi⁴

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4149-4164, 2023, DOI:10.32604/cmc.2023.033405

Abstract The islanded mode is one of the connection modes of the grid distributed generation resources. In this study, a distributed generation resource is connected to linear and nonlinear loads via a three-phase inverter where a control method needing no current sensors or compensator elements is applied to the distribute generation system in the islanded mode. This control method has two main loops in each phase. The first loop controls the voltage control loops that adjust the three-phase point of common coupling, the amplitude of the non-sinusoidal reference waveform and the near-state pulse width modulation (NSPWM) method. The next loop compensatesthe… More >

Shuai Zhou¹, Dazhi Wang^1,*, Mingtian Du², Ye Li¹, Shuo Cao³

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3391-3404, 2023, DOI:10.32604/cmc.2023.033397

Abstract The parameters of permanent magnet synchronous motor (PMSM) affect the performance of vector control servo system. Because of the complexity of nonlinear model of PMSM, it is very difficult to identify the parameters of PMSM. Aiming at the problems of large amount of data calculation, low identification accuracy and poor robustness in the process of multi parameter identification of permanent magnet synchronous motor, this paper proposes a weighted differential evolutionary particle swarm optimization algorithm based on double update strategy. By introducing adaptive judgment factor to control the proportion of weighted difference evolution (WDE) algorithm and particle swarm optimization (PSO) algorithm… More >

Ibrahim Mahmoud El-Henawy¹, Mostafa Elbaz², Zainab H. Ali^3,*, Noha Sakr⁴

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3489-3502, 2023, DOI:10.32604/cmc.2023.033356

Abstract Medical image segmentation is a crucial process for computer-aided diagnosis and surgery. Medical image segmentation refers to portioning the images into small, disjointed parts for simplifying the processes of analysis and examination. Rician and speckle noise are different types of noise in magnetic resonance imaging (MRI) that affect the accuracy of the segmentation process negatively. Therefore, image enhancement has a significant role in MRI segmentation. This paper proposes a novel framework that uses 3D MRI images from Kaggle and applies different diverse models to remove Rician and speckle noise using the best possible noise-free image. The proposed techniques consider the… More >

Zulqurnain Sabir¹, Sánchez-Chero Manuel², Muhammad Asif Zahoor Raja³, Gilder-Cieza–Altamirano⁴, María-Verónica Seminario-Morales², Fernández Vásquez José Arquímedes⁵, Purihuamán Leonardo Celso Nazario⁶, Thongchai Botmart^7,*, Wajaree Weera⁷

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3455-3470, 2023, DOI:10.32604/cmc.2023.033352

Abstract The purpose of these investigations is to find the numerical outcomes of the fractional kind of biological system based on Leptospirosis by exploiting the strength of artificial neural networks aided by scale conjugate gradient, called ANNs-SCG. The fractional derivatives have been applied to get more reliable performances of the system. The mathematical form of the biological Leptospirosis system is divided into five categories, and the numerical performances of each model class will be provided by using the ANNs-SCG. The exactness of the ANNs-SCG is performed using the comparison of the reference and obtained results. The reference solutions have been obtained… More >

Husam Yahya¹, Dumitru Baleanu^2,3,4, Rabha W. Ibrahim^5,*, Nadia M.G. Al-Saidi⁶

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2531-2539, 2023, DOI:10.32604/cmc.2023.033282

Abstract The current study provides a quantum calculus-based medical image enhancement technique that dynamically chooses the spatial distribution of image pixel intensity values. The technique focuses on boosting the edges and texture of an image while leaving the smooth areas alone. The brain Magnetic Resonance Imaging (MRI) scans are used to visualize the tumors that have spread throughout the brain in order to gain a better understanding of the stage of brain cancer. Accurately detecting brain cancer is a complex challenge that the medical system faces when diagnosing the disease. To solve this issue, this research offers a quantum calculus-based MRI… More >

Reem Alkanhel¹, El-Sayed M. El-kenawy², Abdelaziz A. Abdelhamid^3,4, Abdelhameed Ibrahim⁵, Manal Abdullah Alohali⁶, Mostafa Abotaleb⁷, Doaa Sami Khafaga^8,*

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2677-2693, 2023, DOI:10.32604/cmc.2023.033273

Abstract Applications of internet-of-things (IoT) are increasingly being used in many facets of our daily life, which results in an enormous volume of data. Cloud computing and fog computing, two of the most common technologies used in IoT applications, have led to major security concerns. Cyberattacks are on the rise as a result of the usage of these technologies since present security measures are insufficient. Several artificial intelligence (AI) based security solutions, such as intrusion detection systems (IDS), have been proposed in recent years. Intelligent technologies that require data preprocessing and machine learning algorithm-performance augmentation require the use of feature selection… More >