Ashraf S. Mashaleh^1,2,*, Noor Farizah Binti Ibrahim¹, Mohammad Alauthman³, Mohammad Almseidin⁴, Amjad Gawanmeh⁵

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2245-2267, 2024, DOI:10.32604/cmc.2023.047323

Abstract Increasing Internet of Things (IoT) device connectivity makes botnet attacks more dangerous, carrying catastrophic hazards. As IoT botnets evolve, their dynamic and multifaceted nature hampers conventional detection methods. This paper proposes a risk assessment framework based on fuzzy logic and Particle Swarm Optimization (PSO) to address the risks associated with IoT botnets. Fuzzy logic addresses IoT threat uncertainties and ambiguities methodically. Fuzzy component settings are optimized using PSO to improve accuracy. The methodology allows for more complex thinking by transitioning from binary to continuous assessment. Instead of expert inputs, PSO data-driven tunes rules and membership functions. This study presents a… More >

Congjun Rao¹, Mengxi Li¹, Tingting Huang^2,*, Feiyu Li¹

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 699-724, 2024, DOI:10.32604/cmes.2023.044898

Abstract Stroke is a chronic cerebrovascular disease that carries a high risk. Stroke risk assessment is of great significance in preventing, reversing and reducing the spread and the health hazards caused by stroke. Aiming to objectively predict and identify strokes, this paper proposes a new stroke risk assessment decision-making model named Logistic-AdaBoost (Logistic-AB) based on machine learning. First, the categorical boosting (CatBoost) method is used to perform feature selection for all features of stroke, and 8 main features are selected to form a new index evaluation system to predict the risk of stroke. Second, the borderline synthetic minority oversampling technique (SMOTE)… More >

Hong Sun¹, Fangquan Yang², Peiwen Zhang^3,*, Yang Jiao⁴, Yunxiang Zhao⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2549-2569, 2024, DOI:10.32604/cmes.2023.030131

Abstract With the development of the integration of aviation safety and artificial intelligence, research on the combination of risk assessment and artificial intelligence is particularly important in the field of risk management, but searching for an efficient and accurate risk assessment algorithm has become a challenge for the civil aviation industry. Therefore, an improved risk assessment algorithm (PS-AE-LSTM) based on long short-term memory network (LSTM) with autoencoder (AE) is proposed for the various supervised deep learning algorithms in flight safety that cannot adequately address the problem of the quality on risk level labels. Firstly, based on the normal distribution characteristics of… More >

Cheng Wang¹, Haoran Zhu^2,*, Congjun Rao^2,*

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 >

Zhanyang Xu¹, Jianchun Cheng^1,*, Luofei Cheng¹, Xiaolong Xu^1,2, 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 >

Yingxue Sang¹, Fengxia Han^1,2,*, Qing Liu^1,2, 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 >

A. S. Yusuf^1,*, 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 >

Xingquan Zhang¹, Renjun Xie¹, Kuan Liu^2,*, Yating Li², Yuqiang Xu^2,*

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 >

Renjun Xie¹, Xingquan Zhang¹, Baolun He^2,*, Ningyu Zheng², Yuqiang Xu^2,*

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

Shaista Habib¹, Muhammad Akram^2,*, 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