Decision Tree Based Customer Analysis Method for Energy Planning in Smart Cities

Yaman, Orhan; Yetiş, Hasan; Karaköse, Mehmet

doi:10.1109/icdabi51230.2020.9325644

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…For this reason, a Decision Tree is preferred rather than classifications such as KNN or SVM. The Decision Tree algorithm is fast compared to other classifiers [23]. For this reason, the Fine DT algorithm used has been compared with the Medium DT, Coarse DT, Ensemble Boosted Trees (EBT), and Linear Discriminant (LD) algorithms.…”

Section: Methodsmentioning

confidence: 99%

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TEKİN

Yaman

Tuncer

2022

International Journal of Innovative Engineering Applications

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Original scientific paperDevelopments in computer and network technologies have also positively affected internet technology. With the development of the Internet, the concept of IoT (Internet of Things) has been invented. Nowadays, IoT devices provide convenience in many areas, and the positive effects of IoT-based systems increase people's quality of life. People want to remotely monitor and manage smart cities, smart homes, and other platforms. However, IoT systems have many vulnerabilities and thus have become the target of attackers. Detecting such attacks and preventing security vulnerabilities will further increase the rate of use of IoT technology. In this work, an intelligent intrusion detection system (IDS) for IoT devices has been suggested. The presented intelligent IDS for IoT devices have been developed on a big attack dataset and this dataset contains 3,668,443 observations. In prior works which used this dataset, researchers worked on a binary classification problem (attacked and normal). However, this research aims to classify the attack types, hence, nine categories have been used. To propose a prompt responded IDS model, a fast classifier which is a decision tree (DT) has been employed. Our proposal attained 97.43% classification accuracy on this dataset using 10-fold cross-validation. This accuracy rate frankly demonstrates the classification ability of our proposed IDS model for IoT devices.

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Section: Methodsmentioning

confidence: 99%

“…International Journal of Innovative Engineering Applications 6, 1(2022),[17][18][19][20][21][22][23] …”

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confidence: 99%

Nesnelerin İnternetinde Karar Ağacı Tabanlı Saldırı Tespit Yöntemi

TEKİN

Yaman

Tuncer

2022

International Journal of Innovative Engineering Applications

Self Cite

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“…The application of ML techniques in business could bring a catalytic change in business [7]. A considerable number of studies investigate the application of ML techniques in BA [8][9][10][11][12][13][14][15][16][17]. In their study, Singh et al [7] stated that Decision tree-based algorithms and support vector machine algorithms are the most utilized supervised learning algorithm in Customer Relationship Management.…”

Section: Literature Reviewmentioning

confidence: 99%

A Boosted Tree-Based Predictive Model for Business Analytics

Al-Omari¹,

Qutaishat²,

Rawashdeh³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Business Analytics is one of the vital processes that must be incorporated into any business. It supports decision-makers in analyzing and predicting future trends based on facts (Data-driven decisions), especially when dealing with a massive amount of business data. Decision Trees are essential for business analytics to predict business opportunities and future trends that can retain corporations' competitive advantage and survival and improve their business value. This research proposes a tree-based predictive model for business analytics. The model is developed based on ranking business features and gradient-boosted trees. For validation purposes, the model is tested on a real-world dataset of Universal Bank to predict personal loan acceptance. It is validated based on Accuracy, Precision, Recall, and F-score. The experiment findings show that the proposed model can predict personal loan acceptance efficiently and effectively with better accuracy than the traditional tree-based models. The model can also deal with a massive amount of business data and support corporations' decision-making process.

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“…To this end, Tso et al [49] documented a comparison between DTs and machine learning methods for the prediction of electricity consumption, while Yu et al [50] developed a building energy demand predictive model based on the DT method, which was able to classify and predict categorical variables. Finally, Yaman et al [51], proposed a method to estimate energy consumption and plan maintenance works on energy lines according to energy consumption, analysing parameters such as temperature, pressure, and wind, using DT methods.…”

Section: Decision Trees In Energy Applicationsmentioning

confidence: 99%

A Decision Support Tool for Social Engagement, Alternative Financing and Risk Mitigation of Geothermal Energy Projects

Ιωάννου

Falcone

Baisch³

et al. 2023

Energies

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This paper presents a decision support tool for promoters/investors of geothermal energy projects, based on a decision tree (DT) structure. The DT aims to assist stakeholders to select public engagement strategies, alternative financing solutions and risk mitigation measures (or options) for geothermal energy projects. Public engagement is necessary for the successful development and operation of geothermal projects. Available studies (including toolkits and protocols) commonly list a set of practices for social engagement without providing information on the factors which render certain options more suitable than others. The presented tool offers a transparent framework to how relevant decisions could be managed by providing a sequence of questions that focus on social, environmental, resource risk, and financial influencing factors and to realise community engagement into geothermal projects. This work is part of the Horizon 2020 CROWDTHERMAL project, which aims at empowering the public to directly participate in the development of geothermal projects through social engagement tools and alternative financing schemes, like crowdfunding.

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Decision Tree Based Customer Analysis Method for Energy Planning in Smart Cities

Cited by 6 publications

References 12 publications

Nesnelerin İnternetinde Karar Ağacı Tabanlı Saldırı Tespit Yöntemi

Nesnelerin İnternetinde Karar Ağacı Tabanlı Saldırı Tespit Yöntemi

A Boosted Tree-Based Predictive Model for Business Analytics

A Decision Support Tool for Social Engagement, Alternative Financing and Risk Mitigation of Geothermal Energy Projects

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