Talha Saeed, Chu Kiong Loo^*, Muhammad Shahreeza Safiruz Kassim

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4357-4372, 2022, DOI:10.32604/cmc.2022.019076 - 11 October 2021

Abstract

Abnormalities of the gastrointestinal tract are widespread worldwide today. Generally, an effective way to diagnose these life-threatening diseases is based on endoscopy, which comprises a vast number of images. However, the main challenge in this area is that the process is time-consuming and fatiguing for a gastroenterologist to examine every image in the set. Thus, this led to the rise of studies on designing AI-based systems to assist physicians in the diagnosis. In several medical imaging tasks, deep learning methods, especially convolutional neural networks (CNNs), have contributed to the state-of-the-art outcomes, where the complicated nonlinear relation

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Zhipin Gu¹, Liangzhong He², Peiyan Li¹, Peng Sun³, Jiangyong Shi¹, Yuexiang Yang^1,*

Computer Systems Science and Engineering, Vol.39, No.3, pp. 307-320, 2021, DOI:10.32604/csse.2021.017969 - 12 August 2021

Abstract Federated learning is an ideal solution to the limitation of not preserving the users’ privacy information in edge computing. In federated learning, the cloud aggregates local model updates from the devices to generate a global model. To protect devices’ privacy, the cloud is designed to have no visibility into how these updates are generated, making detecting and defending malicious model updates a challenging task. Unlike existing works that struggle to tolerate adversarial attacks, the paper manages to exclude malicious updates from the global model’s aggregation. This paper focuses on Byzantine attack and backdoor attack in… More >

Fawaz Jaber Alsolami¹, Abdullah Saad Al-Malaise ALGhamdi², Asif Irshad Khan^1,*, Yoosef B. Abushark¹, Abdulmohsen Almalawi¹, Farrukh Saleem², Alka Agrawal³, Rajeev Kumar^3,4, Raees Ahmad Khan³

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 2895-2912, 2021, DOI:10.32604/cmc.2021.017469 - 06 May 2021

Abstract Ever since its outbreak in the Wuhan city of China, COVID-19 pandemic has engulfed more than 211 countries in the world, leaving a trail of unprecedented fatalities. Even more debilitating than the infection itself, were the restrictions like lockdowns and quarantine measures taken to contain the spread of Coronavirus. Such enforced alienation affected both the mental and social condition of people significantly. Social interactions and congregations are not only integral part of work life but also form the basis of human evolvement. However, COVID-19 brought all such communication to a grinding halt. Digital interactions have… More >

Weipeng Cao¹, Zhongwu Xie¹, Xiaofei Zhou², Zhiwu Xu¹, Cong Zhou¹, Georgios Theodoropoulos³, Qiang Wang^3,*

Journal on Artificial Intelligence, Vol.2, No.4, pp. 177-187, 2020, DOI:10.32604/jai.2020.014829 - 31 December 2020

Abstract Software verification is a key technique to ensure the correctness of software. Although numerous verification algorithms and tools have been developed in the past decades, it is still a great challenge for engineers to accurately and quickly choose the appropriate verification techniques for the software at hand. In this work, we propose a general learning framework for the intelligent selection of software verification algorithms, and instantiate the framework with two state-of-the-art learning algorithms: Broad learning (BL) and deep learning (DL). The experimental evaluation shows that the training efficiency of the BL-based model is much higher More >