Zahid Ullah^1,*, Mona Jamjoom²

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1457-1473, 2023, DOI:10.32604/cmc.2023.032752 - 22 September 2022

Abstract Alzheimer’s disease (AD) is a chronic and common form of dementia that mainly affects elderly individuals. The disease is dangerous because it causes damage to brain cells and tissues before the symptoms appear, and there is no medicinal or surgical treatment available yet for AD. AD causes loss of memory and functionality control in multiple degrees according to AD’s progression level. However, early diagnosis of AD can hinder its progression. Brain imaging tools such as magnetic resonance imaging (MRI), computed tomography (CT) scans, positron emission tomography (PET), etc. can help in medical diagnosis of AD.… More >

Rashid Jahangir^1,*, Muhammad Asif Nauman², Roobaea Alroobaea³, Jasem Almotiri³, Muhammad Mohsin Malik¹, Sabah M. Alzahrani³

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1069-1091, 2023, DOI:10.32604/cmc.2023.032719 - 22 September 2022

Abstract Environmental sound classification (ESC) involves the process of distinguishing an audio stream associated with numerous environmental sounds. Some common aspects such as the framework difference, overlapping of different sound events, and the presence of various sound sources during recording make the ESC task much more complicated and complex. This research is to propose a deep learning model to improve the recognition rate of environmental sounds and reduce the model training time under limited computation resources. In this research, the performance of transformer and convolutional neural networks (CNN) are investigated. Seven audio features, chromagram, Mel-spectrogram, tonnetz,… More >

Saleh Albahli^1,*, Ghulam Nabi Ahmad Hassan Yar^2,3

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2743-2757, 2023, DOI:10.32604/csse.2023.024463 - 01 August 2022

Abstract Corona Virus (COVID-19) is a novel virus that crossed an animal-human barrier and emerged in Wuhan, China. Until now it has affected more than 119 million people. Detection of COVID-19 is a critical task and due to a large number of patients, a shortage of doctors has occurred for its detection. In this paper, a model has been suggested that not only detects the COVID-19 using X-ray and CT-Scan images but also shows the affected areas. Three classes have been defined; COVID-19, normal, and Pneumonia for X-ray images. For CT-Scan images, 2 classes have been… More >

A. Deepak Kumar^1,2,*, T. Sasipraba¹

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2273-2288, 2023, DOI:10.32604/iasc.2023.028996 - 19 July 2022

Abstract Diabetic Retinopathy is a disease, which happens due to abnormal growth of blood vessels that causes spots on the vision and vision loss. Various techniques are applied to identify the disease in the early stage with different methods and parameters. Machine Learning (ML) techniques are used for analyzing the images and finding out the location of the disease. The restriction of the ML is a dataset size, which is used for model evaluation. This problem has been overcome by using an augmentation method by generating larger datasets with multidimensional features. Existing models are using only More >

R. Priyadharsini^*, A. Kunthavai

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 261-279, 2023, DOI:10.32604/iasc.2023.025884 - 06 June 2022

Abstract Radio waves and strong magnetic fields are used by Magnetic Resonance Imaging (MRI) scanners to detect tumours, wounds and visualize detailed images of the human body. Wi-Fi and other medical devices placed in the MRI procedure room produces RF noise in MRI Images. The RF noise is the result of electromagnetic emissions produced by Wi-Fi and other medical devices that interfere with the operation of the MRI scanner. Existing techniques for RF noise mitigation involve RF shielding techniques which induce eddy currents that affect the MRI image quality. RF shielding techniques are complex and lead… More >

Nitesh Kumar, Shiva Kumar^*

Frontiers in Heat and Mass Transfer, Vol.19, pp. 10-11, 2022, DOI:10.5098/hmt.19.10

Abstract In the present study, the use of wavy fin turbulators on the annulus body of a double pipe air heater has been numerically investigated. The inner pipe consists of hot water whereas the annular section consists of cold air whose Reynolds Number (Re) ranged from 3000-15,000. Rectangular crosssectioned wavy fin turbulators with various curvature ratios of 2, 3, 5, and 7.5 is numerically simulated to investigate the influence of curvature effects on turbulence. Results have been compared with the bare pipe and with rectangular straight fins. It is seen that wavy fin turbulators perform better More >

Syed Irtaza Haider¹, Khursheed Aurangzeb^2,*, Musaed Alhussein²

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1501-1526, 2022, DOI:10.32604/cmc.2022.025479 - 18 May 2022

Abstract The accurate segmentation of retinal vessels is a challenging task due to the presence of various pathologies as well as the low-contrast of thin vessels and non-uniform illumination. In recent years, encoder-decoder networks have achieved outstanding performance in retinal vessel segmentation at the cost of high computational complexity. To address the aforementioned challenges and to reduce the computational complexity, we propose a lightweight convolutional neural network (CNN)-based encoder-decoder deep learning model for accurate retinal vessels segmentation. The proposed deep learning model consists of encoder-decoder architecture along with bottleneck layers that consist of depth-wise squeezing, followed… More >

Pranamita Nanda^1,*, N. Duraipandian²

Computer Systems Science and Engineering, Vol.43, No.3, pp. 1099-1110, 2022, DOI:10.32604/csse.2022.024695 - 09 May 2022

Abstract In severe cases, diabetic retinopathy can lead to blindness. For decades, automatic classification of diabetic retinopathy images has been a challenge. Medical image processing has benefited from advances in deep learning systems. To enhance the accuracy of image classification driven by Convolutional Neural Network (CNN), balanced dataset is generated by data augmentation method followed by an optimized algorithm. Deep neural networks (DNN) are frequently optimized using gradient (GD) based techniques. Vanishing gradient is the main drawback of GD algorithms. In this paper, we suggest an innovative algorithm, to solve the above problem, Hypergradient Descent learning… More >

Saraswathi Sivamani¹, Sun Il Chon¹, Do Yeon Choi¹, Dong Hoon Lee², Ji Hwan Park^1,*

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4433-4452, 2022, DOI:10.32604/cmc.2022.026759 - 21 April 2022

Abstract Existing segmentation and augmentation techniques on convolutional neural network (CNN) has produced remarkable progress in object detection. However, the nominal accuracy and performance might be downturned with the photometric variation of images that are directly ignored in the training process, along with the context of the individual CNN algorithm. In this paper, we investigate the effect of a photometric variation like brightness and sharpness on different CNN. We observe that random augmentation of images weakens the performance unless the augmentation combines the weak limits of photometric variation. Our approach has been justified by the experimental… More >

Wael Alosaimi^1,*, M. Irfan Uddin²

CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.3, pp. 1387-1401, 2022, DOI:10.32604/cmes.2022.018433 - 19 April 2022

Abstract Deep Learning (DL) techniques as a subfield of data science are getting overwhelming attention mainly because of their ability to understand the underlying pattern of data in making classifications. These techniques require a considerable amount of data to efficiently train the DL models. Generally, when the data size is larger, the DL models perform better. However, it is not possible to have a considerable amount of data in different domains such as healthcare. In healthcare, it is impossible to have a substantial amount of data to solve medical problems using Artificial Intelligence, mainly due to… More >