Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (26)
  • Open Access

    ARTICLE

    Machine Learning-Based Decision-Making Mechanism for Risk Assessment of Cardiovascular Disease

    Cheng Wang1, Haoran Zhu2,*, Congjun Rao2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 691-718, 2024, DOI:10.32604/cmes.2023.029258

    Abstract Cardiovascular disease (CVD) has gradually become one of the main causes of harm to the life and health of residents. Exploring the influencing factors and risk assessment methods of CVD has become a general trend. In this paper, a machine learning-based decision-making mechanism for risk assessment of CVD is designed. In this mechanism, the logistics regression analysis method and factor analysis model are used to select age, obesity degree, blood pressure, blood fat, blood sugar, smoking status, drinking status, and exercise status as the main pathogenic factors of CVD, and an index system of risk assessment for CVD is established.… More >

  • Open Access

    ARTICLE

    MSEs Credit Risk Assessment Model Based on Federated Learning and Feature Selection

    Zhanyang Xu1, Jianchun Cheng1,*, Luofei Cheng1, Xiaolong Xu1,2, Muhammad Bilal3

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5573-5595, 2023, DOI:10.32604/cmc.2023.037287

    Abstract Federated learning has been used extensively in business innovation scenarios in various industries. This research adopts the federated learning approach for the first time to address the issue of bank-enterprise information asymmetry in the credit assessment scenario. First, this research designs a credit risk assessment model based on federated learning and feature selection for micro and small enterprises (MSEs) using multi-dimensional enterprise data and multi-perspective enterprise information. The proposed model includes four main processes: namely encrypted entity alignment, hybrid feature selection, secure multi-party computation, and global model updating. Secondly, a two-step feature selection algorithm based on wrapper and filter is… More >

  • Open Access

    ARTICLE

    Safety Risk Assessment of Overturning Construction of Towering Structure Based on Cloud Matter–Element Coupled Model

    Yingxue Sang1, Fengxia Han1,2,*, Qing Liu1,2, Liang Qiao3, Shouxi Wang3

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 1973-1998, 2023, DOI:10.32604/cmes.2023.026218

    Abstract Rapid urbanization has led to a surge in the number of towering structures, and overturning is widely used because it can better accommodate the construction of shaped structures such as variable sections. The complexity of the construction process makes the construction risk have certain randomness, so this paper proposes a cloud-based coupled matter-element model to address the ambiguity and randomness in the safety risk assessment of overturning construction of towering structures. In the pretended model, the digital eigenvalues of the cloud model are used to replace the eigenvalues in the matter–element basic element, and calculate the cloud correlation of the… More >

  • Open Access

    ARTICLE

    Cybersecurity Plan for a Healthcare Cloud-Based Solutions

    A. S. Yusuf1,*, A. Q. Ayinde2

    Journal of Cyber Security, Vol.4, No.3, pp. 185-188, 2022, DOI:10.32604/jcs.2022.035446

    Abstract Hospitals provide daily health services for thousands of patients. People, processes, and technologies drive the objectives and goals of the hospitals to ensure optimal and satisfactory health care services are rendered to their customers. Due to the sensitivity of the organization data and patient data, it is essential to ensure that the confidentiality, integrity, availability, and security of these data are considered. The leadership of the organization (managers and executives) must integrate a robust security plan when choosing the technologies that will be used to drive the organization’s processes. This paper will evaluate the existing technologies risk assessment, and the… More >

  • Open Access

    ARTICLE

    Analysis of the Lost Circulation Problem

    Xingquan Zhang1, Renjun Xie1, Kuan Liu2,*, Yating Li2, Yuqiang Xu2,*

    FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.6, pp. 1721-1733, 2023, DOI:10.32604/fdmp.2023.025578

    Abstract The well-known “lost circulation” problem refers to the uncontrolled flow of whole mud into a formation. In order to address the problem related to the paucity of available data, in the present study, a model is introduced for the lost-circulation risk sample profile of a drilled well. The model is built taking into account effective data (the Block L). Then, using a three-dimensional geological modeling software, relying on the variation function and sequential Gaussian simulation method, a three-dimensional block lost-circulation risk model is introduced able to provide relevant information for regional analyses. More >

  • Open Access

    ARTICLE

    Analysis of the Applicability of a Risk Quantitative Evaluation Method to High Temperature-Pressure Drilling Engineering

    Renjun Xie1, Xingquan Zhang1, Baolun He2,*, Ningyu Zheng2, Yuqiang Xu2,*

    FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.6, pp. 1385-1395, 2023, DOI:10.32604/fdmp.2023.025454

    Abstract The optimization of methods for the quantitative evaluation of risks in drilling engineering is an effective means to ensure safety in situations where high temperature and high pressure blocks are considered. In such a context, this study analyzes the complexity of the drilled wells in such blocks. It is shown that phenomena such as well kick, loss, circulation, and sticking, are related to the imbalance of wellbore pressure. A method for risk quantitative evaluation is proposed accordingly. The method is used to evaluate the risk for 9 drilled wells. By comparing the predictions of the method with actual historical data… More > Graphic Abstract

    Analysis of the Applicability of a Risk Quantitative Evaluation Method to High Temperature-Pressure Drilling Engineering

  • Open Access

    ARTICLE

    Comparative Analysis of Pythagorean MCDM Methods for the Risk Assessment of Childhood Cancer

    Shaista Habib1, Muhammad Akram2,*, M. M. Ali Al-Shamiri3

    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 2585-2615, 2023, DOI:10.32604/cmes.2023.024551

    Abstract According to the World Health Organization (WHO), cancer is the leading cause of death for children in low and middle-income countries. Around 400,000 kids get diagnosed with this illness each year, and their survival rate depends on the country in which they live. In this article, we present a Pythagorean fuzzy model that may help doctors identify the most likely type of cancer in children at an early stage by taking into account the symptoms of different types of cancer. The Pythagorean fuzzy decision-making techniques that we utilize are Pythagorean Fuzzy TOPSIS, Pythagorean Fuzzy Entropy (PF-Entropy), and Pythagorean Fuzzy Power… More > Graphic Abstract

    Comparative Analysis of Pythagorean MCDM Methods for the Risk Assessment of Childhood Cancer

  • Open Access

    ARTICLE

    Health Risks Assessment of Heavy Metal Pollution in the Soil-Crop System from an E-Waste Dismantling Area

    Shengting Rao#, Jia Fang#, Keli Zhao*

    Phyton-International Journal of Experimental Botany, Vol.91, No.12, pp. 2669-2685, 2022, DOI:10.32604/phyton.2022.022416

    Abstract

    Soil is an essential resource for agricultural production. In order to investigate the pollution situation of heavy metals in the soil-crop system in the e-waste dismantling area, the crop and soil samples (226 pairs, including leaf vegetables, solanaceous vegetables, root vegetables, and fruits) around the e-waste dismantling area in southeastern Zhejiang Province were collected. The concentrations of Cd, Cu, Pb, and Cr were determined. The average concentrations of Cd, Cu, Pb, and Cr in soils were 0.94, 107.79, 80.28, and 78.14 mg kg-1, respectively, and their corresponding concentrations in crops were 0.024, 0.7, 0.041, and 0.06 mg kg-1, respectively. The… More >

  • Open Access

    ARTICLE

    Intelligent Feature Selection with Deep Learning Based Financial Risk Assessment Model

    Thavavel Vaiyapuri1, K. Priyadarshini2, A. Hemlathadhevi3, M. Dhamodaran4, Ashit Kumar Dutta5, Irina V. Pustokhina6,*, Denis A. Pustokhin7

    CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2429-2444, 2022, DOI:10.32604/cmc.2022.026204

    Abstract Due to global financial crisis, risk management has received significant attention to avoid loss and maximize profit in any business. Since the financial crisis prediction (FCP) process is mainly based on data driven decision making and intelligent models, artificial intelligence (AI) and machine learning (ML) models are widely utilized. This article introduces an intelligent feature selection with deep learning based financial risk assessment model (IFSDL-FRA). The proposed IFSDL-FRA technique aims to determine the financial crisis of a company or enterprise. In addition, the IFSDL-FRA technique involves the design of new water strider optimization algorithm based feature selection (WSOA-FS) manner to… More >

  • Open Access

    ARTICLE

    A Lightning Disaster Risk Assessment Model Based on SVM

    Jianqiao Sheng1, Mengzhu Xu2, Jin Han3,*, Xingyan Deng2

    Journal on Big Data, Vol.3, No.4, pp. 183-190, 2021, DOI:10.32604/jbd.2021.024892

    Abstract Lightning disaster risk assessment, as an intuitive method to reflect the risk of regional lightning disasters, has aroused the research interest of many researchers. Nowadays, there are many schemes for lightning disaster risk assessment, but there are also some shortcomings, such as the resolution of the assessment is not clear enough, the accuracy rate cannot be verified, and the weight distribution has a strong subjective trend. This paper is guided by lightning disaster data and combines lightning data, population data and GDP data. Through support vector machine (SVM), it explores a way to combine artificial intelligence algorithms with lightning disaster… More >

Displaying 1-10 on page 1 of 26. Per Page