Optimization of integrated fuzzy decision tree and regression models for selection of oil spill response method in the Arctic

Mohammadiun, Saeed; Hu, Guangji; Gharahbagh, Abdorreza Alavi; Mirshahi, Reza; Li, Jianbing; Hewage, Kasun; Sadiq, Rehan

doi:10.1016/j.knosys.2020.106676

Cited by 26 publications

(15 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are studies with the joint application of the Promethee multicriteria method and Machine Learning prediction in financial decision making (Mousavi & Lin, 2020); and a new hybrid fuzzy prediction method is introduced by combining the Fuzzy Analytic Hierarchy Process (FAHP) and machine learning model (Ozdemir et al, 2021). For applications to energy or classification problems: fuzzy interval time series energy and financial forecasting model (Liu et al, 2020); flood hazards susceptibility mapping using statistical, fuzzy logic and MCDM methods (Akay, 2021); assessment of a failure prediction model in the energy sector with multicriteria discrimination approach, Promethee based classification (Angilella & Pappalardo, 2021); hybrid neurofuzzy investigation of short-term variability of wind resource (Adedeji et al, 2021); TOPSIS-ELM framework for stock index price movement prediction (Samal & Dash, 2021); cost-sensitive business failure prediction when misclassification costs are uncertain (Bock et al, 2020); multi objective optimization of crude oil supply portfolio based on interval prediction data (Sun et al, 2022); optimization of integrated fuzzy decision tree and regression models for selection of oil spill response (Mohammadiun et al, 2021); use of PairCode algorithm for ordinal classification based on pairwise comparison (Yang et al, 2020); and client profile prediction using convolutional neural networks (Nedjah et al, 2022). In addition, there are studies that show the application of the PROMETHEE-SAPEVO-M1 multi-criteria method to the analysis of OECD countries (Pereira et al, 2022) and multicriteria analysis applied to aircraft selection, case in Brazilian Navy (Maêda et al, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

Model proposition for predicting sustainability classes using multicriteria decision support and artificial intelligence

Couto

Rangel

2022

Gest. Prod.

View full text Add to dashboard Cite

The current study proposes a novel prediction model of sustainability classes for electricity distribution companies in Brazil, based on sustainability indicators, aiming at a more effective risk management for a certain company among their competitors. Because such indicators are based on quantitative and qualitative measures and are very likely to incur imprecisions in their measures, the model to be proposed is based on a Multicriteria Decision Support, Rough Sets Theory, which allows the mathematical treatment of those imprecisions, and Artificial Intelligence, in this case, Machine Learning by rules inference. Consequently, decision tables are generated with condition attributes, sustainability indicators, and decision attributes, sustainability classes: high, medium or low. As a result, it is possible to predict sustainability classes based in temporal series of indicators and rules inference from decision tables, using RoughSets package in R and the jMAF software, demonstrating the use of five rule generation algorithms and their respective accuracies.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Model proposition for predicting sustainability classes using multicriteria decision support and artificial intelligence

Couto

Rangel

2022

Gest. Prod.

View full text Add to dashboard Cite

show abstract

“…C5.0 not only has low time and memory consumptions compared to C4.5 and ID3 but also is efficient as it splits the nodes in terms of the field that has the maximum information gain. Recently, publications related to decision tree-based machine learning methods exist in different variants such as random forest [36,37], gradient boosting decision tree [38], regression decision tree [39,40], and so on.…”

Section: Introductionmentioning

confidence: 99%

Application of Decision Tree‐Based Classification Algorithm on Content Marketing

Yang

2022

Journal of Mathematics

View full text Add to dashboard Cite

Traditional content marketing methods resort grossly to market requirements but barely obtain relatively accurate marketing prediction under loads of requirements. Machine learning-based approaches nowadays are widely used in multiple fields as they involve a training process to deal with big data problems. In this paper, decision tree-based methods are introduced to the field of content marketing, and decision tree-based methods intrinsically follow the process of human decision making. Specifically, this paper considers a well-known method, called C4.5, which can deal well with continuous values. Based on four validation metrics, experimental results obtained from several machine learning-based methods indicate that the C4.5-based decision tree method has the ability to handle the content marketing dataset. The results show that the decision tree-based method can provide reasonable and accurate suggestions for content marketing.

show abstract

“…ese achievements can be categorized into qualitative analysis methods, quantitative analysis methods, and analysis methods integrating qualitative and quantitative analysis [9,10] based on different calculation principles. Some of the more widely used risk assessment methods are as follows: decision tree analysis [11], fault tree analysis [12], analytic hierarchy process [13], fuzzy mathematical analysis [14], matrix analysis [15], probabilistic risk assessment [16], dynamic risk probabilistic assessment [17], and cause-consequence assessment [18].…”

Section: Introductionmentioning

confidence: 99%

Information Security Risk Management Model for Big Data

Yang

2022

Advances in Multimedia

View full text Add to dashboard Cite

In the current society of rapid expansion of information, big data have achieved vigorous development in all walks of life, considerably promoting data transmission and information sharing. Meanwhile, individuals are becoming increasingly reliant on big data and the Internet, but at the same time, the threat of information security posed by big data is becoming increasingly visible. As a result, how to protect the information security of big data has piqued the interest of both government and businesses. The essence of information security management is risk management, which is closely related to each other. Therefore, this study focuses on the following two aspects of research work. On the one hand, most existing risk management models merely describe risk management in the abstract from a macro-level, and they lack research on risk assessment, making them ineffective. This research builds a novel information security risk management model on the basis of existing risk management models based on the concept of multidimensional risk management. To achieve multidimensional dynamic management of big data risks and to keep them within an acceptable range as much as possible, the model is divided into five levels and two dimensions. On the other hand, this research also optimizes and improves the fuzzy mathematical analysis method and proposes a fuzzy comprehensive assessment method as the core algorithm for the risk assessment layer in the model. As a post-event risk assessment method, the advantage of this method is that it can comprehensively consider factors affecting risk and can quantify some assessment factors in the real network to achieve an effective combination of qualitative and quantitative, thereby providing a basis for decision-making in risk analysis and risk control. Finally, the effectiveness of the risk model in the real application is verified by example analysis, and it is intended that the study work would provide assistance and assurance for big data information security management.

show abstract

Optimization of integrated fuzzy decision tree and regression models for selection of oil spill response method in the Arctic

Cited by 26 publications

References 37 publications

Model proposition for predicting sustainability classes using multicriteria decision support and artificial intelligence

Model proposition for predicting sustainability classes using multicriteria decision support and artificial intelligence

Application of Decision Tree‐Based Classification Algorithm on Content Marketing

Information Security Risk Management Model for Big Data

Contact Info

Product

Resources

About