Mohd Asyraf Zulkifley^1,*, Ali Fayyaz Munir², Mohd Edil Abd Sukor³, Muhammad Hakimi Mohd Shafiai⁴

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4395-4418, 2023, DOI:10.32604/cmc.2023.036094

Abstract A well-managed financial market of stocks, commodities, derivatives, and bonds is crucial to a country’s economic growth. It provides confidence to investors, which encourages the inflow of cash to ensure good market liquidity. However, there will always be a group of traders that aims to manipulate market pricing to negatively influence stock values in their favor. These illegal trading activities are surely prohibited according to the rules and regulations of every country’s stock market. It is the role of regulators to detect and prevent any manipulation cases in order to provide a trading platform that is fair and efficient. However,… More >

Teng Zong^1,2,*, Fengsi Wang³, Xin Wei², Yibo Liu¹

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3447-3468, 2023, DOI:10.32604/iasc.2023.035456

Abstract With the expanding enrollments in higher education, the quality of college education and the learning gains of students have attracted much attention. It is important to study the influencing factors and mechanisms of individual students’ acquisition of learning gains to improve the quality of talent cultivation in colleges. However, in the context of information security, the original data of learning situation surveys in various universities involve the security of educational evaluation data and daily privacy of teachers and students. To protect the original data, data feature mining and correlation analyses were performed at the model level. This study selected 12,181… More >

Quan Yan¹, Yunfan Ye¹, Jing Xia¹, Zhiping Cai^1,*, Zhilin Wang², Qiang Ni³

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2545-2558, 2023, DOI:10.32604/iasc.2023.029857

Abstract Computed tomography has made significant advances since its introduction in the early 1970s, where researchers have mainly focused on the quality of image reconstruction in the early stage. However, radiation exposure poses a health risk, prompting the demand of the lowest possible dose when carrying out CT examinations. To acquire high-quality reconstruction images with low dose radiation, CT reconstruction techniques have evolved from conventional reconstruction such as analytical and iterative reconstruction, to reconstruction methods based on artificial intelligence (AI). All these efforts are devoted to constructing high-quality images using only low doses with fast reconstruction speed. In particular, conventional reconstruction… More >

Ziquan Zhu, Shui-Hua Wang, Yu-Dong Zhang^*

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2127-2172, 2023, DOI:10.32604/cmes.2023.025484

Abstract Problems: For people all over the world, cancer is one of the most feared diseases. Cancer is one of the major obstacles to improving life expectancy in countries around the world and one of the biggest causes of death before the age of 70 in 112 countries. Among all kinds of cancers, breast cancer is the most common cancer for women. The data showed that female breast cancer had become one of the most common cancers. Aims: A large number of clinical trials have proved that if breast cancer is diagnosed at an early stage, it could give patients more… More > Graphic Abstract

Meilin Huo^1,*, Zhen Yang²

International Journal of Mental Health Promotion, Vol.25, No.4, pp. 551-562, 2023, DOI:10.32604/ijmhp.2023.023406

Abstract Background: Depression and anxiety are highly prevalent among adolescents and have multiple negative effects on their physical and mental health. While exercise can reduce the symptoms of depression and anxiety, the relationship between mental disorders among American university students has been rarely reported. Accordingly, this study aimed to explore the association between exercise, depression and anxiety among American university students in the 2018–2019 academic year. Methods: In this cross-sectional study, the association between exercise, depression and anxiety was investigated in a large representative sample of American university students. In the 2018–2019 academic year, university students aged 18+ years old from… More >

Ali F. Khalifa¹, Eman Badr^1,2,*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1995-2024, 2023, DOI:10.32604/cmc.2023.035888

Abstract Image segmentation is crucial for various research areas. Many computer vision applications depend on segmenting images to understand the scene, such as autonomous driving, surveillance systems, robotics, and medical imaging. With the recent advances in deep learning (DL) and its confounding results in image segmentation, more attention has been drawn to its use in medical image segmentation. This article introduces a survey of the state-of-the-art deep convolution neural network (CNN) models and mechanisms utilized in image segmentation. First, segmentation models are categorized based on their model architecture and primary working principle. Then, CNN categories are described, and various models are… More >

Ahmad Moawad^*, Ahmed Ismail Ebada, Aya M. Al-Zoghby

Journal of Cyber Security, Vol.4, No.3, pp. 169-184, 2022, DOI:10.32604/jcs.2022.033537

Abstract In computer security, the number of malware threats is increasing and causing damage to systems for individuals or organizations, necessitating a new detection technique capable of detecting a new variant of malware more efficiently than traditional anti-malware methods. Traditional anti-malware software cannot detect new malware variants, and conventional techniques such as static analysis, dynamic analysis, and hybrid analysis are time-consuming and rely on domain experts. Visualization-based malware detection has recently gained popularity due to its accuracy, independence from domain experts, and faster detection time. Visualization-based malware detection uses the image representation of the malware binary and applies image processing techniques… More >

Sizhe Tang¹, Mengmeng Cui^1,*, Lianyong Qi², Xiaolong Xu¹

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 5-42, 2023, DOI:10.32604/cmes.2023.023684

Abstract With the rapid development of deep learning, the size of data sets and deep neural networks (DNNs) models are also booming. As a result, the intolerable long time for models’ training or inference with conventional strategies can not meet the satisfaction of modern tasks gradually. Moreover, devices stay idle in the scenario of edge computing (EC), which presents a waste of resources since they can share the pressure of the busy devices but they do not. To address the problem, the strategy leveraging distributed processing has been applied to load computation tasks from a single processor to a group of… More >

Xing Deng^1,2, Feipeng Da^1,*, Haijian Shao², Xia Wang³

Energy Engineering, Vol.120, No.2, pp. 385-408, 2023, DOI:10.32604/ee.2023.023480

Abstract Photovoltaic power generating is one of the primary methods of utilizing solar energy resources, with large-scale photovoltaic grid-connected power generation being the most efficient way to fully utilize solar energy. In order to provide reference strategies for pertinent researchers as well as potential implementation, this paper tries to provide a survey investigation and technical analysis of machine learning-related approaches, statistical approaches and optimization techniques for solar power generation and forecasting. Deep learning-related methods, in particular, can theoretically handle arbitrary nonlinear transformations through proper model structural design, such as hidden layer topology optimization and objective function analysis to save information that… More > Graphic Abstract

Qiqi Zhang¹, Shaojin Geng², Xingjuan Cai^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 1863-1900, 2023, DOI:10.32604/cmes.2023.022287

Abstract Cloud computing technology is favored by users because of its strong computing power and convenient services. At the same time, scheduling performance has an extremely efficient impact on promoting carbon neutrality. Currently, scheduling research in the multi-cloud environment aims to address the challenges brought by business demands to cloud data centers during peak hours. Therefore, the scheduling problem has promising application prospects under the multi-cloud environment. This paper points out that the currently studied scheduling problems in the multi-cloud environment mainly include independent task scheduling and workflow task scheduling based on the dependencies between tasks. This paper reviews the concepts,… More > Graphic Abstract