Wen Zhang^1,2, Ting Wu^1,2, Xijian Chen^1,3, Hong Luo^1,2,*, Yifei Li^1,4,*

Congenital Heart Disease, Vol.19, No.4, pp. 399-406, 2024, DOI:10.32604/chd.2024.055178 - 31 October 2024

Abstract This article has no abstract. More >

Yanzhong Sun¹, Xiaoni Du^1,*, Shufen Niu², Xiaodong Yang²

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3287-3310, 2024, DOI:10.32604/cmes.2023.043589 - 11 March 2024

Abstract Traditional email systems can only achieve one-way communication, which means only the receiver is allowed to search for emails on the email server. In this paper, we propose a blockchain-based certificateless bidirectional authenticated searchable encryption model for a cloud email system named certificateless authenticated bidirectional searchable encryption (CL-BSE) by combining the storage function of cloud server with the communication function of email server. In the new model, not only can the data receiver search for the relevant content by generating its own trapdoor, but the data owner also can retrieve the content in the same… More >

Wei Sun¹, Zhanhe Du^2,*

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 1181-1195, 2023, DOI:10.32604/iasc.2023.037497 - 29 April 2023

Abstract Currently, it is difficult to extract the depth feature of the frontal emergency stops dangerous activity signal, which leads to a decline in the accuracy and efficiency of the frontal emergency stops the dangerous activity. Therefore, a recognition for frontal emergency stops dangerous activity algorithm based on Nano Internet of Things Sensor (NIoTS) and transfer learning is proposed. First, the NIoTS is installed in the athlete’s leg muscles to collect activity signals. Second, the noise component in the activity signal is removed using the de-noising method based on mathematical morphology. Finally, the depth feature of… More >

Sami Gazzah^1,*, Rida Bayi², Soulaimane Kaloun², Omar Bencharef²

Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 677-692, 2022, DOI:10.32604/iasc.2022.019360 - 22 September 2021

Abstract A new virus called SARS-CoV-2 appeared at the end of space 2019 in Wuhan, China. This virus immediately spread throughout the world due to its highly contagious nature. Moreover, SARS-CoV-2 has changed the way of our life and has caused a huge economic and public health disaster. Therefore, it is urgent to identify positive cases as soon as possible and treat them as isolated. Automatic detection of viruses using computer vision and machine learning will be a valuable contribution to detecting and limiting the spread of this epidemic. The delay introduction of X-ray technology as… More >

Bingtao Hu^*, Jinwei Wang

Journal of Information Hiding and Privacy Protection, Vol.2, No.2, pp. 95-105, 2020, DOI:10.32604/jihpp.2020.010464 - 11 November 2020

Abstract With the development of computer graphics, realistic computer graphics (CG) have become more and more common in our field of vision. This rendered image is invisible to the naked eye. How to effectively identify CG and natural images (NI) has been become a new issue in the field of digital forensics. In recent years, a series of deep learning network frameworks have shown great advantages in the field of images, which provides a good choice for us to solve this problem. This paper aims to track the latest developments and applications of deep learning in More >

Mohan Li¹, Yanbin Sun^{1, *}, Shen Su¹, Zhihong Tian¹, Yuhang Wang^{1, *}, Xianzhi Wang²

CMC-Computers, Materials & Continua, Vol.59, No.1, pp. 331-344, 2019, DOI:10.32604/cmc.2019.05379

Abstract Maliciously manufactured user profiles are often generated in batch for shilling attacks. These profiles may bring in a lot of quality problems but not worthy to be repaired. Since repairing data always be expensive, we need to scrutinize the data and pick out the data that really deserves to be repaired. In this paper, we focus on how to distinguish the unintentional data quality problems from the batch generated fake users for shilling attacks. A two-steps framework named DPIF is proposed for the distinguishment. Based on the framework, the metrics of homology and suspicious degree More >