Ahmed S. Almasoud¹, Taiseer Abdalla Elfadil Eisa², Fahd N. Al-Wesabi^3,4, Abubakar Elsafi⁵, Mesfer Al Duhayyim⁶, Ishfaq Yaseen⁷, Manar Ahmed Hamza^7,*, Abdelwahed Motwakel⁷

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 871-886, 2022, DOI:10.32604/cmc.2022.024596 - 24 February 2022

Abstract Early detection of Parkinson's Disease (PD) using the PD patients’ voice changes would avoid the intervention before the identification of physical symptoms. Various machine learning algorithms were developed to detect PD detection. Nevertheless, these ML methods are lack in generalization and reduced classification performance due to subject overlap. To overcome these issues, this proposed work apply graph long short term memory (GLSTM) model to classify the dynamic features of the PD patient speech signal. The proposed classification model has been further improved by implementing the recurrent neural network (RNN) in batch normalization layer of GLSTM… More >

Jia Chen¹, Zhiqiang He¹, Dayong Zhu¹, Bei Hui^1,*, Rita Yi Man Li², Xiao-Guang Yue^3,4,5

CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 73-95, 2022, DOI:10.32604/cmes.2022.018565 - 24 January 2022

Abstract Medical image segmentation plays an important role in clinical diagnosis, quantitative analysis, and treatment process. Since 2015, U-Net-based approaches have been widely used for medical image segmentation. The purpose of the U-Net expansive path is to map low-resolution encoder feature maps to full input resolution feature maps. However, the consecutive deconvolution and convolutional operations in the expansive path lead to the loss of some high-level information. More high-level information can make the segmentation more accurate. In this paper, we propose MU-Net, a novel, multi-path upsampling convolution network to retain more high-level information. The MU-Net mainly More >

Kyu-haeng Lee^*

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5561-5580, 2022, DOI:10.32604/cmc.2022.024562 - 14 January 2022

Abstract Recently, as deep learning technologies have received much attention for their great potential in extracting the principal components of data, there have been many efforts to apply them to the Channel State Information (CSI) feedback overhead problem, which can significantly limit Multi-Input Multi-Output (MIMO) beamforming gains. Unfortunately, since most compression models can quickly become outdated due to channel variation, timely model updates are essential for reflecting the current channel conditions, resulting in frequent additional transmissions for model sharing between transceivers. In particular, the heavy network models employed by most previous studies to achieve high compression… More >

S. Sreenivasa Chakravarthi^1,*, R. Jagadeesh Kannan², V. Anantha Natarajan³, Xiao-Zhi Gao⁴

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5117-5133, 2022, DOI:10.32604/cmc.2022.022351 - 14 January 2022

Abstract In the global scenario one of the important goals for sustainable development in industrial field is innovate new technology, and invest in building infrastructure. All the developed and developing countries focus on building resilient infrastructure and promote sustainable developments by fostering innovation. At this juncture the cloud computing has become an important information and communication technologies model influencing sustainable development of the industries in the developing countries. As part of the innovations happening in the industrial sector, a new concept termed as ‘smart manufacturing’ has emerged, which employs the benefits of emerging technologies like internet… More >

Adel Binbusayyis^*

Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 501-513, 2022, DOI:10.32604/iasc.2022.022239 - 05 January 2022

Abstract The increasing user of Internet has witnessed a continued exploration in applications and services that can bring more convenience in people's life than ever before. At the same time, with the exploration of online services, the people face unprecedented difficulty in selecting the most relevant service on the fly. In this context, the need for recommendation system is of paramount importance especially in helping the users to improve their experience with best value-added service. But, most of the traditional techniques including collaborative filtering (CF) which is one of the most successful recommendation technique suffer from… More >

Gulshan Kumar^1,*, Hamed Alqahtani²

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.3, pp. 1271-1307, 2022, DOI:10.32604/cmes.2022.018418 - 30 December 2021

Abstract Cancer is one of the most critical diseases that has caused several deaths in today’s world. In most cases, doctors and practitioners are only able to diagnose cancer in its later stages. In the later stages, planning cancer treatment and increasing the patient’s survival rate becomes a very challenging task. Therefore, it becomes the need of the hour to detect cancer in the early stages for appropriate treatment and surgery planning. Analysis and interpretation of medical images such as MRI and CT scans help doctors and practitioners diagnose many diseases, including cancer disease. However, manual… More >

Shakra Mehak¹, M. Usman Ashraf², Rabia Zafar³, Ahmed M. Alghamdi⁴, Ahmed S. Alfakeeh⁵, Fawaz Alassery⁶, Habib Hamam⁷, Muhammad Shafiq^8,*

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3407-3423, 2022, DOI:10.32604/cmc.2022.022705 - 07 December 2021

Abstract Breast cancer (BC) is the most widely recognized cancer in women worldwide. By 2018, 627,000 women had died of breast cancer (World Health Organization Report 2018). To diagnose BC, the evaluation of tumours is achieved by analysis of histological specimens. At present, the Nottingham Bloom Richardson framework is the least expensive approach used to grade BC aggressiveness. Pathologists contemplate three elements, 1. mitotic count, 2. gland formation, and 3. nuclear atypia, which is a laborious process that witness's variations in expert's opinions. Recently, some algorithms have been proposed for the detection of mitotic cells, but… More >

Muhammad Usman Younus^1,*, Rabia Shafi², Ammar Rafiq³, Muhammad Rizwan Anjum⁴, Sharjeel Afridi⁵, Abdul Aleem Jamali⁶, Zulfiqar Ali Arain⁷

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2617-2631, 2022, DOI:10.32604/cmc.2022.022236 - 07 December 2021

Abstract The development of multimedia content has resulted in a massive increase in network traffic for video streaming. It demands such types of solutions that can be addressed to obtain the user's Quality-of-Experience (QoE). 360-degree videos have already taken up the user's behavior by storm. However, the users only focus on the part of 360-degree videos, known as a viewport. Despite the immense hype, 360-degree videos convey a loathsome side effect about viewport prediction, making viewers feel uncomfortable because user viewport needs to be pre-fetched in advance. Ideally, we can minimize the bandwidth consumption if we know… 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 >

Samah Ibrahim Alshathri^1,*, Desiree Juby Vincent², V. S. Hari²

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1371-1386, 2022, DOI:10.32604/cmc.2022.022458 - 03 November 2021

Abstract Invoice document digitization is crucial for efficient management in industries. The scanned invoice image is often noisy due to various reasons. This affects the OCR (optical character recognition) detection accuracy. In this paper, letter data obtained from images of invoices are denoised using a modified autoencoder based deep learning method. A stacked denoising autoencoder (SDAE) is implemented with two hidden layers each in encoder network and decoder network. In order to capture the most salient features of training samples, a undercomplete autoencoder is designed with non-linear encoder and decoder function. This autoencoder is regularized for… More >