Rong Fu^*, Luze Xie

International Journal of Mental Health Promotion, Vol.26, No.1, pp. 21-30, 2024, DOI:10.32604/ijmhp.2023.041730

Abstract COVID-19, as one of the most serious sudden public health problems in this century, is a serious threat to people’s mental health. College students, as a vulnerable group, are more likely to develop mental health problems. When the body is unable to adapt to new changes in the environment, the main mental health problem that arises is adjustment disorder. The aim of this study was to assess the prevalence and influencing factors of adjustment disorder among college students during the COVID-19 outbreak in China. Cross-sectional data collected by web-based questionnaires were obtained through convenience sampling and snowball sampling between March… More >

XIN MENG^*, YAN LIU, SHUJUN XU, LIANRONG YANG, RUI YIN

BIOCELL, Vol.48, No.1, pp. 65-78, 2024, DOI:10.32604/biocell.2023.045986

Abstract Over the past decade, the swift advancement of metabolomics can be credited to significant progress in technologies such as mass spectrometry, nuclear magnetic resonance, and multivariate statistics. Currently, metabolomics garners widespread application across diverse fields including drug research and development, early disease detection, toxicology, food and nutrition science, biology, prescription, and chinmedomics, among others. Metabolomics serves as an effective characterization technique, offering insights into physiological process alterations in vivo. These changes may result from various exogenous factors like environmental conditions, stress, medications, as well as endogenous elements including genetic and protein-based influences. The potential scientific outcomes gleaned from these insights… More > Graphic Abstract

Ou Ye, Xinli Wei, Zhenhua Yu^*, Yan Fu, Ying Yang

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1071-1093, 2024, DOI:10.32604/cmc.2023.046418

Abstract In the video captioning methods based on an encoder-decoder, limited visual features are extracted by an encoder, and a natural sentence of the video content is generated using a decoder. However, this kind of method is dependent on a single video input source and few visual labels, and there is a problem with semantic alignment between video contents and generated natural sentences, which are not suitable for accurately comprehending and describing the video contents. To address this issue, this paper proposes a video captioning method by semantic topic-guided generation. First, a 3D convolutional neural network is utilized to extract the… More >

Sumaya S. Sulaiman^1,2,*, Ibraheem Nadher³, Sarab M. Hameed²

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1049-1069, 2024, DOI:10.32604/cmc.2023.046051

Abstract Fraud of credit cards is a major issue for financial organizations and individuals. As fraudulent actions become more complex, a demand for better fraud detection systems is rising. Deep learning approaches have shown promise in several fields, including detecting credit card fraud. However, the efficacy of these models is heavily dependent on the careful selection of appropriate hyperparameters. This paper introduces models that integrate deep learning models with hyperparameter tuning techniques to learn the patterns and relationships within credit card transaction data, thereby improving fraud detection. Three deep learning models: AutoEncoder (AE), Convolution Neural Network (CNN), and Long Short-Term Memory… More >

Chengsheng Yuan^1,2, Baojie Cui^1,2, Zhili Zhou³, Xinting Li^4,*, Qingming Jonathan Wu⁵

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 899-914, 2024, DOI:10.32604/cmc.2023.045854

Abstract In recent years, deep learning has been the mainstream technology for fingerprint liveness detection (FLD) tasks because of its remarkable performance. However, recent studies have shown that these deep fake fingerprint detection (DFFD) models are not resistant to attacks by adversarial examples, which are generated by the introduction of subtle perturbations in the fingerprint image, allowing the model to make fake judgments. Most of the existing adversarial example generation methods are based on gradient optimization, which is easy to fall into local optimal, resulting in poor transferability of adversarial attacks. In addition, the perturbation added to the blank area of… More >

Xin Fan^1,2,3, Jingen Mao^2,3,*, Liangjue Lian^2,3, Li Yu¹, Wei Zheng^2,3, Yun Ge^2,3

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 65-84, 2024, DOI:10.32604/cmc.2023.045522

Abstract The purpose of software defect prediction is to identify defect-prone code modules to assist software quality assurance teams with the appropriate allocation of resources and labor. In previous software defect prediction studies, transfer learning was effective in solving the problem of inconsistent project data distribution. However, target projects often lack sufficient data, which affects the performance of the transfer learning model. In addition, the presence of uncorrelated features between projects can decrease the prediction accuracy of the transfer learning model. To address these problems, this article propose a software defect prediction method based on stable learning (SDP-SL) that combines code… More >

Deyong Shang^1,2, Xi Zhang¹, Fengqi Liang¹, Chunde Zhai¹, Hang Yang¹, Yanqi Niu^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 2027-2041, 2024, DOI:10.32604/cmes.2023.045750

Abstract When the amphibious vehicle navigates in water, the angle of the anti-wave plate and the position of the center of gravity greatly influence the navigation characteristics. In the relevant research on reducing the navigation resistance of amphibious vehicles by adjusting the angle of the anti-wave plate, there is a lack of scientific selection of parameters and reasonable research of simulation results by using mathematical methods, and the influence of the center of gravity position on navigation characteristics is not considered at the same time. To study the influence of the combinations of the angle of the anti-wave plate and the… More >

Fengyong Li^1,*, Weicheng Shen¹, Zhongqin Bi¹, Xiangjing Su²

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 2095-2115, 2024, DOI:10.32604/cmes.2023.044431

Abstract False data injection attack (FDIA) is an attack that affects the stability of grid cyber-physical system (GCPS) by evading the detecting mechanism of bad data. Existing FDIA detection methods usually employ complex neural network models to detect FDIA attacks. However, they overlook the fact that FDIA attack samples at public-private network edges are extremely sparse, making it difficult for neural network models to obtain sufficient samples to construct a robust detection model. To address this problem, this paper designs an efficient sample generative adversarial model of FDIA attack in public-private network edge, which can effectively bypass the detection model to… More >

Artyom M. Grigoryan^1,*, Sos S. Agaian²

Journal of Quantum Computing, Vol.6, pp. 1-14, 2024, DOI:10.32604/jqc.2023.026981

Abstract In this work, we describe a method of calculation of the 1-D circular quantum convolution of signals represented by 3-qubit superpositions in the computational basis states. The examples of the ideal low pass and high pass filters are described and quantum schemes for the 3-qubit circular convolution are presented. In the proposed method, the 3-qubit Fourier transform is used and one addition qubit, to prepare the quantum superposition for the inverse quantum Fourier transform. It is considered that the discrete Fourier transform of one of the signals is known and calculated in advance and only the quantum Fourier transform of… More >

Sheng Qian¹, Qi Tong^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.27, No.3, pp. 1-1, 2023, DOI:10.32604/icces.2023.010116

Abstract Applications such as aircrafts and electronic devices require the noise and vibration reduction without much extra burden, such as extra damping systems. High damping metallic materials that exhibit the ability to dissipate mechanical energy are potential candidates in these application via directly being part of the functional components, such as the frame materials. The energy damping in polycrystalline metals depends on the activities of defects such as dislocation and grain boundary. However, operating defects has the opposite effect on strength and damping capacity. In the quest for high damping metals, maintaining the level of strength is desirable in practice. In… More >