Stroke Risk Assessment Decision-Making Using a Machine Learning Model:Logistic-AdaBoost

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)algorithm is applied to transform the unbalanced stroke dataset into a balanced dataset.Finally,the stroke risk assessment decision-makingmodel Logistic-AB is constructed,and the overall prediction performance of this new model is evaluated by comparing it with ten other similar models.The comparison results show that the new model proposed in this paper performs better than the two single algorithms(logistic regression and AdaBoost)on the four indicators of recall,precision,F1 score,and accuracy,and the overall performance of the proposed model is better than that of common machine learning algorithms.The Logistic-AB model presented in this paper can more accurately predict patients’stroke risk.

Ethical Decision-Making Framework Based on Incremental ILP Considering Conflicts

Humans are experiencing the inclusion of artificial agents in their lives,such as unmanned vehicles,service robots,voice assistants,and intelligent medical care.If the artificial agents cannot align with social values or make ethical decisions,they may not meet the expectations of humans.Traditionally,an ethical decision-making framework is constructed by rule-based or statistical approaches.In this paper,we propose an ethical decision-making framework based on incremental ILP(Inductive Logic Programming),which can overcome the brittleness of rule-based approaches and little interpretability of statistical approaches.As the current incremental ILP makes it difficult to solve conflicts,we propose a novel ethical decision-making framework considering conflicts in this paper,which adopts our proposed incremental ILP system.The framework consists of two processes:the learning process and the deduction process.The first process records bottom clauses with their score functions and learns rules guided by the entailment and the score function.The second process obtains an ethical decision based on the rules.In an ethical scenario about chatbots for teenagers’mental health,we verify that our framework can learn ethical rules and make ethical decisions.Besides,we extract incremental ILP from the framework and compare it with the state-of-the-art ILP systems based on ASP(Answer Set Programming)focusing on conflict resolution.The results of comparisons show that our proposed system can generate better-quality rules than most other systems.

Hesitant Fuzzy-Sets Based Decision-Making Model for Security Risk Assessment

Security is an important component in the process of developing healthcare web applications.We need to ensure security maintenance;therefore the analysis of healthcare web application’s security risk is of utmost importance.Properties must be considered to minimise the security risk.Additionally,security risk management activities are revised,prepared,implemented,tracked,and regularly set up efficiently to design the security of healthcare web applications.Managing the security risk of a healthcare web application must be considered as the key component.Security is,in specific,seen as an add-on during the development process of healthcare web applications,but not as the key problem.Researchers must ensure that security is taken into account right from the earlier developmental stages of the healthcare web application.In this row,the authors of this study have used the hesitant fuzzy-based AHP-TOPSIS technique to estimate the risks of various healthcare web applications for improving security-durability.This approach would help to design and incorporate security features in healthcare web applications that would be able to battle threats on their own,and not depend solely on the external security of healthcare web applications.Furthermore,in terms of healthcare web application’s security-durability,the security risk variable is measured,and vice versa.Hence,the findings of our study will also be useful in improving the durability of several web applications in healthcare.

IoT Smart Devices Risk Assessment Model Using Fuzzy Logic and PSO

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 complete IoT botnet risk assessment system.The methodology helps security teams allocate resources by categorizing threats as high,medium,or low severity.This study shows how CICIoT2023 can assess cyber risks.Our research has implications beyond detection,as it provides a proactive approach to risk management and promotes the development of more secure IoT environments.

The Credit Risk Assessment Model of Internet Supply Chain Finance: Multi-Criteria Decision-Making Model with the Principle of Variable Weight

The characteristics of the financing model are firstly analyzed when the e-commerce enterprises participate in the supply chain finance. Internet supply chain finance models are divided into three categories with the standard of whether the electronic commerce enterprises provide funds for small and medium enterprises instead of banks. And then we further study the financing process and the functions of the e-commerce platform with specific examples. Finally, combined with the characteristics of the supply chain finance model, we set up a small and medium enterprises credit evaluation model based on the principle of variable weight with its dynamic data. At the same time, a multi-time points and multi-indicators decision-making method based on the principle of variable weight is proposed and a specific example is presented. In this paper, the multi-criteria decision-making model with the principle of variable weight has been used two times. At last, a typical case has been analyzed based on this model with a higher accuracy rate of credit risk assessment.

The Credit Risk Assessment Model of Internet Supply Chain Finance: Multi-Criteria Decision-Making Model with the Principle of Variable Weight

The characteristics of the financing model are firstly analyzed when the e-commerce enterprises participate in the supply chain finance. Internet supply chain finance models are divided into three categories with the standard of whether the Electronic commerce enterprises provide funds for small and medium enterprises instead of banks. And then we further study the financing process and the functions of the e-commerce platform with specific examples. Finally, combined with the characteristics of the supply chain finance model, we set up a small and medium enterprises credit evaluation model based on the principle of variable weight with its dynamic data. At the same time, a multi time points and multi indicators decision-making method based on the principle of variable weight is proposed and a specific example is presented. In this paper, the Multi-criteria decision-making model with the principle of variable weight has been used two times. At last, a typical case has been analyzed based on this model with a higher accuracy rate of credit risk assessment.

Method for Risky Multiobjective Group Decision-Making and Its Application

The multiobjective group decision-making problem under risk is common in reality. This paper focuses on the study about risky multiobjective group decision-making problem where the index value is not certain. We give indexes classifying method and index normalizing formula of this type problem. By building objective function that minimizes general weighted distance from every alternative to the relatively best and worst alternative, the optimal membership degree of every decision-maker to every alternative can be obtained, and by building another objective function that minimizes general weighted distance from the optimal membership degree of every decision-maker to every alternative to the group optimal alternative and the group inferior alternative, the optimal membership degree of every decision-maker to every alternative can be obtained, which are both based on probability theory and fuzzy theory. Aftermost a model is established which collects group preferences. This method provides a new idea and approach for solving multiobjective decision-making problem among uncertain system, which is applicable for practical problem. Finally a case study shows a satisfactory result.

Optimal Decision-Making of Trans-Provincial Electricity Market Subjects with Risks under Renewable Portfolio Standards

The randomness and uncertainty of renewable energy generation are expected to significantly change the optimal decision-making of trans-provincial electricity market subjects.Therefore,it is beneficial to optimize the interests of each of these subjects,considering the unpredictable risks of renewable energy under the renewable portfolio standards(RPS)and researching their effects on the optimal decision-making of transprovincial electricity market multi-subjects.First,we develop a trans-provincial trading market mechanism for renewable energy and clarify the electricity supply and demand relation and the green certificates supply and demand relation of trans-provincial electricitymarketmulti-subjects.Then,under the RPS,we construct a multi-subject game model of the power supply chain that recognizes the risks,and adopt the reverse induction method to discuss the optimum risk-taking judgment of each subject in the trans-provincial electricity market.Finally,we useMATLAB to verify the viability and efficacy of the proposed gamemodel,and obtain a certain reference value for the optimal decision-making of trans-provincial electricity market subjects.In summary,we consider the uncertainty risks of renewable energy under RPS,study the effects of the green certificate price and risk aversion coefficient in the RPS mechanism on the optimal decisionmaking of trans-provincial electricity market subjects,and obtain the changing trends of two different power products and those of different electricity market subjects under the influence of the green certificate price and risk aversion coefficient,which have a certain reference value for studying the factors affecting the optimal decision-making of trans-provincial electricity market subjects.

Probabilistic Lane-Change Decision-Making and Planning for Autonomous Heavy Vehicles

To improve the safety and driving stability of the autonomous heavy truck, it is necessary to consider the differences of driving behavior and drivable trajectories between the heavy trucks and passenger cars. This study proposes a probabilistic decision-making and trajectory planning framework for the autonomous heavy trucks. Firstly, the driving decision process is divided into intention generation and feasibility evaluations, which are realized using the utility theory and risk assessment, respectively. Subsequently the driving decision is made and sent to the trajectory planning module. In order to reflect the greater risks of the truck to other surrounding vehicles, the aggressiveness index(AI) is proposed and quantified to infer the asymmetrical risk level of lane-change maneuver. In the planning stage, the lateral and roll dynamics stability domains are developed as the constraints to exclude the candidate trajectories that would cause vehicle instability. Finally, the simulation results are compared between the proposed model and the artificial potential filed model in the scenarios extracted from the naturalistic driving data. It is shown that the proposed framework can provide the human-like lane-change decisions and truck-friendly trajectories, and performs well in dynamic driving environments.

Mapping the risk of winter storm damage using GIS-based fuzzy logic

Abiotic and biotic factors that cause damage to forest trees also threaten the sustainability of forest resources.Although winter storms can be one of the most damaging forces,very few studies have focused on winter storm damage in Turkey.To prevent or minimize storm damage,we must evaluate the factors that influence the degree of damage and develop storm risk maps for the forested areas.Here,a GIS-based mathematical model(fuzzy logic)was used to develop such a risk map by considering risk factors such as tree species,tree age,crown density,site quality,topographical features(elevation,slope,aspect),climatic variables(wind,precipitation),and soil depth.The Alabarda Forest Enterprise Chief within the borders of Tavs?anl?Forest Enterprise Directorate was selected as the study area due to high occurrence of storm damage in the region during winter 2015–2016.For model verification,the risk map of storm damage was compared to the actual areal distribution of storm damage reported by the Enterprise Directorate.The model based on fuzzy logic indicated that slightly more than half of the study area(52.49%)was under very low storm damage risk,28.12%was under low risk and 19.19%was classified as high risk.A very small portion of the total study area was classified as very high risk.These results indicated a close relation with the storm damage map generated using Tavsanl?FED records.The results revealed that the most effective risk predictors for overall storm damage risk were wind direction and speed,followed by slope and site quality factors.

A Fuzzy Logic-Based Method for Risk Assessment of Bridges during Construction

There are many potential sources of risks which may cause bridge failures and result in numerous economic and human losses during the construction of bridges. Therefore,risk assessment for bridges during construction should be taken rigorously to avert bridge failures and casualties. This article presents a fuzzy logic-based method which integrates the fuzzy analytical hierarchy process( FAHP) method based on a 3-point scale,fuzzy logic,and fuzzy set theory into a single synthetic method. In this method,the FAHP method based on a 3-point scale was used to identify and rank diverse risk factors,and fuzzy logic and fuzzy set theory were used to process inaccurate datasets including non-statistical information. After the concept and procedure of the FAHP method based on a 3-point scale were demonstrated,the proposed fuzzy logic-based method was used to perform risk assessment on the Aizhai Suspension Bridge with a main span length of1 176 m in China. The results show that the proposed method can more effectively carry out risk assessment of bridges during construction.

GIS Application in Urban Flood Risk Analysis: Midar as a Case Study

The significance of this study lies in its exploration of the advanced applications of Geographic Information Systems (GIS) in assessing urban flood risks, with a specific focus on Midar, Morocco. This research is pivotal as it showcases that GIS technology is not just a tool for mapping, but a critical component in urban planning and emergency management strategies. By meticulously identifying and mapping flood-prone areas in Midar, the study provides invaluable insights into the potential vulnerabilities of urban landscapes to flooding. Moreover, this research demonstrates the practical utility of GIS in mitigating material losses, a significant concern in flood-prone urban areas. The proactive approach proposed in this study, centered around the use of GIS, aims to safeguard Midar’s population and infrastructure from the devastating impacts of floods. This approach serves as a model for other urban areas facing similar challenges, highlighting the indispensable role of GIS in disaster preparedness and response. Overall, the study underscores the transformative potential of GIS in enhancing urban resilience, making it a crucial tool in the fight against natural disasters like floods.

Fuzzy rule based seismic risk assessment of one-story precast industrial buildings

Effi cient tools capable of using uncertain data to produce fast and approximate results are more practical in rapid decision-making applications when compared to conventional methods. From this point of view, this study introduces a risk assessment model for one-story precast industrial buildings by fuzzy logic which builds a bridge between uncertainty and precision. The input, output and relations of the fuzzy based risk assessment model (FBRAM) were determined by reference buildings. The Monte Carlo simulation method was used to handle uncertainties associated with the structural characteristics of the reference buildings. Section dimension, longitudinal reinforcement ratio, column height related to building elevation, confi nement ratio and seismic hazard are regarded as input and the plastic demand ratio is considered as the output parameter by the mathematical formulation of strength and deformation capacity of the buildings. The supervised learning method was used to determine the membership function of fuzzy sets. Fuzzy rules of FBRAM were constructed from Monte Carlo simulation by mapping of inputs and output. FBRAM was evaluated by a group of simulated buildings and two existing precast industrial buildings. Comparisons have shown signifi cant agreement with analytical model results in both cases. Consequently, it is anticipated that the proposed model can be used for the seismic risk mitigation of precast buildings.

Quantified Risk Assessment for Plants Producing and Storing Explosives

This paper presents a methodology for risk assessment of plants producing and storing explosives. The major procedural steps for quantified risk assessment （QRA） in explosive plants are the following： hazard identification, accident sequence modeling, data acquisition, accident sequence quantification, consequence assessment and integration of results. This methodology is demonstrated and applied in an explosive plant consisting of four separate units, which produce detonating cord, nitroglycol, dynamites and ammonium nitrate fuel oil （ANFO）. A GIS platform is used for depicting individual risk from explosions in this plant. Total individual risk is equal to 1.0 × 10^-4/y in a distance of 340m from the center of the plant, and 1.0 × 10^-6/y in a distance of 390m from the center of the plant.

A RISK ASSESSMENT METHOD OF THE WIRELESS NETWORK SECURITY

The core of network security is the risk assessment. In this letter,a risk assessment method is introduced to estimate the wireless network security. The method,which combines Analytic Hier-archy Process (AHP) method and fuzzy logical method,is applied to the risk assessment. Fuzzy logical method is applied to judge the important degree of each factor in the aspects of the probability,the influence and the uncontrollability,not to directly judge the important degree itself. The risk as-sessment is carved up 3 layers applying AHP method,the sort weight of the third layer is calculated by fuzzy logical method. Finally,the important degree is calculated by AHP method. By comparing the important degree of each factor,the risk which can be controlled by taking measures is known. The study of the case shows that the method can be easily used to the risk assessment of the wireless network security and its results conform to the actual situation.

Managing Security-Risks for Improving Security-Durability of Institutional Web-Applications: Design Perspective

The advanced technological need,exacerbated by the flexible time constraints,leads to several more design level unexplored vulnerabilities.Security is an extremely vital component in software development;we must take charge of security and therefore analysis of software security risk assumes utmost significance.In order to handle the cyber-security risk of the web application and protect individuals,information and properties effectively,one must consider what needs to be secured,what are the perceived threats and the protection of assets.Security preparation plans,implements,tracks,updates and consistently develops safety risk management activities.Risk management must be interpreted as the major component for tackling security efficiently.In particular,during application development,security is considered as an add-on but not the main issue.It is important for the researchers to stress on the consideration of protection right from the earlier developmental stages of the software.This approach will help in designing software which can itself combat threats and does not depend on external security programs.Therefore,it is essential to evaluate the impact of security risks during software design.In this paper the researchers have used the hybrid Fuzzy AHPTOPSIS method to evaluate the risks for improving security durability of different Institutional Web Applications.In addition,the e-component of security risk is measured on software durability,and vice versa.The paper’s findings will prove to be valuable for enhancing the security durability of different web applications.

Fire monitoring in coal mines using wireless underground sensor network and interval type-2 fuzzy logic controller

From the view of underground coal mining safety system, it is extremely important to continuous monitoring of coal mines for the prompt detection of fires or related problems inspite of its uncertainty and imprecise characteristics. Therefore, evaluation and inferring the data perfectly to prevent fire related accidental risk in underground coal mining (UMC) system are very necessary. In the present article, we have proposed a novel type-2 fuzzy logic system (T2FLS) for the prediction of fire intensity and its risk assessment for risk reduction in an underground coal mine. Recently, for the observation of underground coal mines, wireless underground sensor network (WUSN) are being concerned frequently. To implement this technique IT2FLS, main functional components are sensor nodes which are installed in coal mines to accumulate different imprecise environmental data like, temperature, relative humidity, different gas concentrations etc. and these are sent to a base station which is connected to the ground observation system through network. In the present context, a WUSN based fire monitoring system is developed using fuzzy logic approach to enhance the consistency in decision making system to improve the risk chances of fire during coal mining. We have taken Mamdani IT2FLS as fuzzy model on coal mine monitoring data to consider real-time decision making (DM). It is predicted from the simulated results that the recommended system is highly acceptable and amenable in the case of fire hazard safety with compared to the wired and off-line monitoring system for UMC. Legitimacy of the suggested model is prepared using statistical analysis and multiple linear regression analysis.

Risk assessment:method and case study for traffic projects

A comprehensive project evaluation and decision-making method considering multiple objectives,stakeholders,and attributes of proposed traffic treatments is inherently complicated.Although individual techniques in evaluating operations,safety,economic,and stakeholder objectives are available,a practical method that integrates all these risk factors and their uncertainties into a multiattribute decision-making tool is absent.A three-level project decision-making process was developed to model and assess multiple-attribute risk in a proposed traffic treatment from the perspective of multiple stakeholders.The direct benefits from reducing delay and safety risk(basic objectives of traffic treatments) are computed in Level 1 with established methods.Feasibility and performance analysis in Level 2 examine site-specific constraints and conduct detailed performance analysis using advanced analysis tools.In Level 3,this paper introduces an innovative and integrated multiple attributes evaluation process under fuzziness and uncertainty(MAFU) process for evaluation and decision-making.The MAFU is a comprehensive and systematic assessment and decision-making procedure that can assess the magnitudes of project performance and to integrate conflicting interests and tradeoffs among stakeholders.A case study illustrates theapplication of MAFU for the selection of a traffic alternative involving several evaluation attributes and stakeholders.Results show that the MAFU produced the smallest variance for each alternative.With traditional cost–benefit evaluation methods,the uncertainty associated with performance of a traffic project in terms of operation,safety,environmental impacts,etc.,is unrestricted and cumulative.Therefore,a reliable multi-attribute evaluation of complex traffic projects should not be made with conventional cost–benefit analysis alone but with a process like MAFU.

Fuzzy logic applied to value of information assessment in oil and gas projects

The concept of value of information(VOI)has been widely used in the oil industry when making decisions on the acquisition of new data sets for the development and operation of oil fields.The classical approach to VOI assumes that the outcome of the data acquisition process produces crisp values,which are uniquely mapped onto one of the deterministic reservoir models representing the subsurface variability.However,subsurface reservoir data are not always crisp;it can also be fuzzy and may correspond to various reservoir models to different degrees.The classical approach to VOI may not,therefore,lead to the best decision with regard to the need to acquire new data.Fuzzy logic,introduced in the 1960 s as an alternative to the classical logic,is able to manage the uncertainty associated with the fuzziness of the data.In this paper,both classical and fuzzy theoretical formulations for VOI are developed and contrasted using inherently vague data.A case study,which is consistent with the future development of an oil reservoir,is used to compare the application of both approaches to the estimation of VOI.The results of the VOI process show that when the fuzzy nature of the data is included in the assessment,the value of the data decreases.In this case study,the results of the assessment using crisp data and fuzzy data change the decision from"acquire"the additional data(in the former)to"do not acquire"the additional data(in the latter).In general,different decisions are reached,depending on whether the fuzzy nature of the data is considered during the evaluation.The implications of these results are significant in a domain such as the oil and gas industry(where investments are huge).This work strongly suggests the need to define the data as crisp or fuzzy for use in VOI,prior to implementing the assessment to select and define the right approach.

A Fuzzy Logic-Based Modeling Method of Deciding Maintenance Policies

To make equipment maintenance policies more suitable to real operations, a fuzzy logic-based modeling method of deciding maintenance policies was proposed in this paper. Five maintenance policies which are built with downtime and breakdown frequency as decision criteria are discussed. To decide efficiently maintenance policies, the decision criteria are divided into low, medium, and high levels. On the basis of the levels, fuzzy sets and membership functions of the decision criteria are established with a fuzzy logic theory. A fuzzy logic algorithm of solving overall degree of satisfaction is presented to select the most efficient maintenance policy for equipment. Finally, the modeling method is tested. Experiment results show that the proposed method can solve efficiently problems of the traditional decision-making bias, and it is more valid and practical than the method of traditional decision-making grid (DMG).