Yang Dai^*, Zhiyuan Luo

Journal of New Media, Vol.3, No.4, pp. 129-136, 2021, DOI:10.32604/jnm.2021.023981 - 05 November 2021

Abstract Person re-identification (re-ID) aims to match images of the same pedestrian across different cameras. It plays an important role in the field of security and surveillance. Although it has been studied for many years, it is still considered as an unsolved problem. Since the rise of deep learning, the accuracy of supervised person re-ID on public datasets has reached the highest level. However, these methods are difficult to apply to real-life scenarios because a large number of labeled training data is required in this situation. Pedestrian identity labeling, especially cross-camera pedestrian identity labeling, is heavy More >

Liang Mu, Hong Zhao^*, Yan Li, Xiaotong Liu, Junzheng Qiu, Chuanlong Sun

CMES-Computer Modeling in Engineering & Sciences, Vol.129, No.2, pp. 465-483, 2021, DOI:10.32604/cmes.2021.017276 - 08 October 2021

Abstract Traffic flow statistics have become a particularly important part of intelligent transportation. To solve the problems of low real-time robustness and accuracy in traffic flow statistics. In the DeepSort tracking algorithm, the Kalman filter (KF), which is only suitable for linear problems, is replaced by the extended Kalman filter (EKF), which can effectively solve nonlinear problems and integrate the Histogram of Oriented Gradient (HOG) of the target. The multi-target tracking framework was constructed with YOLO V5 target detection algorithm. An efficient and long-running Traffic Flow Statistical framework (TFSF) is established based on the tracking framework.… More >

Wenqian Li¹, Xing Deng^1,2,*, Haijian Shao¹, Xia Wang³

CMES-Computer Modeling in Engineering & Sciences, Vol.129, No.1, pp. 65-98, 2021, DOI:10.32604/cmes.2021.016981 - 24 August 2021

Abstract The COVID-19 has resulted in catastrophic situation and the deaths of millions of people all over the world. In this paper, the predictions of epidemiological propagation models, such as SIR and SEIR, are introduced to analyze the earlier COVID-19 propagation. The deep learning methods combined with transfer learning are familiar with classification-detection approaches based on chest X-ray and CT images are presented in detail. Besides, deep learning approaches have also been applied to lung ultrasound (LUS), which has been shown to be more sensitive than chest X-ray and CT images in detecting COVID-19. In the… More > Graphic Abstract

Yonghun Jang, Chang-Hyeon Park, Dong-Gun Lee, Yeong-Seok Seo^*

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3563-3579, 2021, DOI:10.32604/cmc.2021.018901 - 24 August 2021

Abstract Following the development of communication techniques and smart devices, the era of Artificial Intelligence (AI) and big data has arrived. The increased connectivity, referred to as hyper-connectivity, has led to the development of smart cities. People in these smart cities can access numerous online contents and are always connected. These developments, however, also lead to a lack of standardization and consistency in the propagation of information throughout communities due to the consumption of information through social media channels. Information cannot often be verified, which can confuse the users. The increasing influence of social media has… More >

Sujeet More¹, Jimmy Singla¹, Oh-Young Song^2,*, Usman Tariq³, Sharaf Malebary⁴

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3127-3143, 2021, DOI:10.32604/cmc.2021.018230 - 24 August 2021

Abstract Medical Resonance Imaging (MRI) is a noninvasive, nonradioactive, and meticulous diagnostic modality capability in the field of medical imaging. However, the efficiency of MR image reconstruction is affected by its bulky image sets and slow process implementation. Therefore, to obtain a high-quality reconstructed image we presented a sparse aware noise removal technique that uses convolution neural network (SANR_CNN) for eliminating noise and improving the MR image reconstruction quality. The proposed noise removal or denoising technique adopts a fast CNN architecture that aids in training larger datasets with improved quality, and SARN algorithm is used for More >

Yu-Dong Zhang¹, Muhammad Attique Khan², Ziquan Zhu³, Shui-Hua Wang^4,*

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3145-3162, 2021, DOI:10.32604/cmc.2021.018040 - 24 August 2021

Abstract (Aim) COVID-19 is an ongoing infectious disease. It has caused more than 107.45 m confirmed cases and 2.35 m deaths till 11/Feb/2021. Traditional computer vision methods have achieved promising results on the automatic smart diagnosis. (Method) This study aims to propose a novel deep learning method that can obtain better performance. We use the pseudo-Zernike moment (PZM), derived from Zernike moment, as the extracted features. Two settings are introducing: (i) image plane over unit circle; and (ii) image plane inside the unit circle. Afterward, we use a deep-stacked sparse autoencoder (DSSAE) as the classifier. Besides, multiple-way… More >

Sadia Kiran¹, Muhammad Attique Khan¹, Muhammad Younus Javed¹, Majed Alhaisoni², Usman Tariq³, Yunyoung Nam^4,*, Robertas Damaševičius⁵, Muhammad Sharif⁶

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 4061-4075, 2021, DOI:10.32604/cmc.2021.017800 - 24 August 2021

Abstract Human Action Recognition (HAR) is an active research topic in machine learning for the last few decades. Visual surveillance, robotics, and pedestrian detection are the main applications for action recognition. Computer vision researchers have introduced many HAR techniques, but they still face challenges such as redundant features and the cost of computing. In this article, we proposed a new method for the use of deep learning for HAR. In the proposed method, video frames are initially pre-processed using a global contrast approach and later used to train a deep learning model using domain transfer learning.… More >

A. Sumathi^1,*, S. Meganathan¹, B. Vijila Ravisankar²

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3109-3126, 2021, DOI:10.32604/cmc.2021.017612 - 24 August 2021

Abstract Gestational Diabetes Mellitus (GDM) is one of the commonly occurring diseases among women during pregnancy. Oral Glucose Tolerance Test (OGTT) is followed universally in the diagnosis of GDM diagnosis at early pregnancy which is costly and ineffective. So, there is a need to design an effective and automated GDM diagnosis and classification model. The recent developments in the field of Deep Learning (DL) are useful in diagnosing different diseases. In this view, the current research article presents a new outlier detection with deep-stacked Autoencoder (OD-DSAE) model for GDM diagnosis and classification. The goal of the… More >

Muhammad Rizwan Latif¹, Muhamamd Attique Khan¹, Muhammad Younus Javed¹, Haris Masood², Usman Tariq³, Yunyoung Nam^4,*, Seifedine Kadry⁵

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 2917-2932, 2021, DOI:10.32604/cmc.2021.017364 - 24 August 2021

Abstract Globally, Pakistan ranks 4 in cotton production, 6 as an importer of raw cotton, and 3 in cotton consumption. Nearly 10% of GDP and 55% of the country's foreign exchange earnings depend on cotton products. Approximately 1.5 million people in Pakistan are engaged in the cotton value chain. However, several diseases such as Mildew, Leaf Spot, and Soreshine affect cotton production. Manual diagnosis is not a good solution due to several factors such as high cost and unavailability of an expert. Therefore, it is essential to develop an automated technique that can accurately detect and recognize these… More >

Harshit Kaushik¹, Dilbag Singh², Shailendra Tiwari³, Manjit Kaur², Chang-Won Jeong⁴, Yunyoung Nam^5,*, Muhammad Attique Khan⁶

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3459-3475, 2021, DOI:10.32604/cmc.2021.017337 - 24 August 2021

Abstract In March 2020, the World Health Organization declared the coronavirus disease (COVID-19) outbreak as a pandemic due to its uncontrolled global spread. Reverse transcription polymerase chain reaction is a laboratory test that is widely used for the diagnosis of this deadly disease. However, the limited availability of testing kits and qualified staff and the drastically increasing number of cases have hampered massive testing. To handle COVID-19 testing problems, we apply the Internet of Things and artificial intelligence to achieve self-adaptive, secure, and fast resource allocation, real-time tracking, remote screening, and patient monitoring. In addition, we… More >