E. David Crawford¹, David Albala², Marc B. Garnick³, Andrew W. Hahn⁴, Paul Maroni⁵, Rana R. McKay⁶, Martin Miner⁷, Peter Orio III⁸, Kshitij Pandit¹, Scott Sellinger⁹, Evan Y. Yu¹⁰, Robert H. Eckel¹¹

Canadian Journal of Urology, Vol.31, No.2, pp. 11820-11825, 2024

Abstract Introduction: Risk of cardiovascular disease is higher among men with prostate cancer than men without, and prostate cancer treatments (especially those that are hormonally based) are associated with increased cardiovascular risk.
Materials and methods: An 11-member panel of urologic, medical, and radiation oncologists (along with a men’s health specialist and an endocrinologist/ preventive cardiologist) met to discuss current practices and challenges in the management of cardiovascular risk in prostate cancer patients who are taking androgen deprivation therapies (ADT) including LHRH analogues, alone and in combination with androgen-targeted therapies (ATTs).
Results: The panel developed an assessment algorithm to categorize… More >

Mohammed Amin Almaiah^1,*, Leen Mohammad Saqr¹, Leen Ahmad Al-Rawwash¹, Layan Ahmed Altellawi¹, Romel Al-Ali^2,*, Omar Almomani³

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3189-3220, 2024, DOI:10.32604/cmc.2024.057673 - 18 November 2024

Abstract Database systems have consistently been prime targets for cyber-attacks and threats due to the critical nature of the data they store. Despite the increasing reliance on database management systems, this field continues to face numerous cyber-attacks. Database management systems serve as the foundation of any information system or application. Any cyber-attack can result in significant damage to the database system and loss of sensitive data. Consequently, cyber risk classifications and assessments play a crucial role in risk management and establish an essential framework for identifying and responding to cyber threats. Risk assessment aids in understanding… More >

Grace Gau¹, Minerva Singh^2,3,*

Revue Internationale de Géomatique, Vol.33, pp. 427-443, 2024, DOI:10.32604/rig.2024.055752 - 11 October 2024

Abstract This study examines how socio-economic characteristics predict flood risk in London, England, using machine learning algorithms. The socio-economic variables considered included race, employment, crime and poverty measures. A stacked generalization (SG) model combines random forest (RF), support vector machine (SVM), and XGBoost. Binary classification issues employ RF as the basis model and SVM as the meta-model. In multiclass classification problems, RF and SVM are base models while XGBoost is meta-model. The study utilizes flood risk labels for London areas and census data to train these models. This study found that SVM performs well in binary… More >

Li-Anne Gacul¹, Dexter Ferrancullo¹, Romel Gallano¹, KC Jane Fadriquela¹, Kyla Jane Mendez¹, John Rommel Morada¹, John Kevin Morgado¹, Jerome Gacu^1,2,*

Revue Internationale de Géomatique, Vol.33, pp. 295-320, 2024, DOI:10.32604/rig.2024.055085 - 03 September 2024

Abstract Risk assessment is vital for humanities, especially in assessing natural and manmade hazards. Romblon, an archipelagic province in the Philippines, faces frequent typhoons and heavy rainfall, resulting in floods, with the Municipality of Santa Fe being particularly vulnerable to its severe damage. Thus, this research study intends to evaluate the flood risk of Santa Fe spatially using the fuzzy analytical hierarchy process (FAHP), taking into account data sourced from various government agencies and online databases. GIS was utilized to map flood-prone areas in the municipality. Hazard assessment factors included average annual rainfall, elevation, slope, soil… More > Graphic Abstract

Maan Habib^1,*, Ahed Habib², Mohammad Abboud³

Revue Internationale de Géomatique, Vol.33, pp. 247-271, 2024, DOI:10.32604/rig.2024.053857 - 07 August 2024

Abstract Digital elevation models (DEMs) are essential tools in environmental science, particularly for hazard assessments and landscape analyses. However, their application across multiple environmental hazards simultaneously remains in need for a multi-aspect critical assessment to promote their effectiveness in comprehensive risk management. This paper aims to review and critically assess the application of DEMs in mapping and managing specific environmental hazards, namely floods, landslides, and coastal erosion. In this regard, it seeks to promote their utility of hazard maps as key tools in disaster risk reduction and environmental planning by employing high-resolution DEMs integrated with advanced More >

Kenian Wang^1,2,*, Yuan Hong^1,2, Chunxiao Li²

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1123-1142, 2024, DOI:10.32604/cmc.2024.052088 - 18 July 2024

Abstract With the exponential increase in information security risks, ensuring the safety of aircraft heavily relies on the accurate performance of risk assessment. However, experts possess a limited understanding of fundamental security elements, such as assets, threats, and vulnerabilities, due to the confidentiality of airborne networks, resulting in cognitive uncertainty. Therefore, the Pythagorean fuzzy Analytic Hierarchy Process (AHP) Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) is proposed to address the expert cognitive uncertainty during information security risk assessment for airborne networks. First, Pythagorean fuzzy AHP is employed to construct an index system… More >

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 - 27 February 2024

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 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 - 30 December 2023

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… 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 - 15 December 2023

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… 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 - 22 September 2023

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 More >