Zhihui Song, Jinchen Xu, Kewei Li, Zheng Shan^*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4573-4601, 2024, DOI:10.32604/cmc.2024.049310

Abstract Prior studies have demonstrated that deep learning-based approaches can enhance the performance of source code vulnerability detection by training neural networks to learn vulnerability patterns in code representations. However, due to limitations in code representation and neural network design, the validity and practicality of the model still need to be improved. Additionally, due to differences in programming languages, most methods lack cross-language detection generality. To address these issues, in this paper, we analyze the shortcomings of previous code representations and neural networks. We propose a novel hierarchical code representation that combines Concrete Syntax Trees (CST)… More >

Xin Liu¹, Zheng Liu^2,*, Zhilin Huang¹, Mingyu Ling¹, Kangchao Lin¹, Pengqing Chen¹, Xiaomin Huang¹, Yujia Zhai¹

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 685-707, 2023, DOI:10.32604/cmes.2023.025058

Abstract Ergonomic reliability plays a significant role in the safe operation of devices. With the spread of infectious diseases around the world, in work environments with high loads and high infection rates, medical staff work in a state of high self-protection. The use of visual display terminal (VDT) for medical equipment has undergone fundamental changes, and the traditional medical equipment human-machine interface design needs to be improved. After the completion of design and development, a VDT design enters the experimental testing stage, which has significant limitations for simulating the work of medical staff in the high-load… More >

Nagwa Elaraby^*, Sherif Barakat, Amira Rezk

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1837-1854, 2023, DOI:10.32604/cmc.2023.032288

Abstract Deep metric learning is one of the recommended methods for the challenge of supporting few/zero-shot learning by deep networks. It depends on building a Siamese architecture of two homogeneous Convolutional Neural Networks (CNNs) for learning a distance function that can map input data from the input space to the feature space. Instead of determining the class of each sample, the Siamese architecture deals with the existence of a few training samples by deciding if the samples share the same class identity or not. The traditional structure for the Siamese architecture was built by forming two… More >

Avaneesh Singh^*, Manish Kumar Bajpai

CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.3, pp. 991-1031, 2020, DOI:10.32604/cmes.2020.012503

Abstract We have proposed a new mathematical method, the SEIHCRD model, which has an excellent potential to predict the incidence of COVID-19 diseases. Our proposed SEIHCRD model is an extension of the SEIR model. Three-compartments have added death, hospitalized, and critical, which improves the basic understanding of disease spread and results. We have studied COVID-19 cases of six countries, where the impact of this disease in the highest are Brazil, India, Italy, Spain, the United Kingdom, and the United States. After estimating model parameters based on available clinical data, the model will propagate and forecast dynamic… More >