Multiview decision tree-based segmentation of tumors in MR brain medical images

Fred, A. Lenin; Kumar, S. N.; Gulyás, Balázs; Haridhas, Ajay Kumar; Dayana, Nisha

doi:10.1016/b978-0-12-822271-3.00012-8

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…Decision tree (DT) is a decisionmaking method easy to use, implement, and provides good results. It was widely used in several fields such as medicine [14], traffic control [15], and tourism [16]. In addition, it was used also as a decision-making tool for MG energy management.…”

Section: Introductionmentioning

confidence: 99%

A novel decision-making approach based on a decision tree for micro-grid energy management

Mansoury

Bourakadi

Yahyaouy

et al. 2023

IJEECS

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Environmental challenges such as climate change have accelerated humanity's need for renewable alternative energy sources. For this reason, we propose in this paper a decision-making strategy that allows controlling the flows of energy into a micro-grid (MG) compound of solar energy, batteries, and diesel generator (DG), and connected to the distributed network (DN). Therefore, the power supply to the loads is obtained either from the energy produced by solar sources, from the batteries, from the DN, or from the DG when renewable energy (RE) and batteries are depleted. To make the final decision, we consider four parameters at the same time: the energy produced by solar energy, the requested load, the state of charge of batteries (SoC), and the purchase or sale price. Decision tree (DT) is used to build the energy management strategy to ensure the availability of power on demand by making logical decisions about charging batteries, discharging batteries, buying necessary energy from DN, selling excess energy to DN, and recovering necessary energy from DG. The suggested DT approach is applied to a real MG to minimize the cost-benefit balance, and the comparison analysis demonstrates good results when compared to related works.

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Section: Introductionmentioning

confidence: 99%

A novel decision-making approach based on a decision tree for micro-grid energy management

Mansoury

Bourakadi

Yahyaouy

et al. 2023

IJEECS

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“…Machine learning is widely used in numerous fields of medical diagnostics and prognostics [ 39 , 40 , 41 , 42 ]. One possible method of estimating parameters is finding them by using the method of linear regression.…”

Section: Introductionmentioning

confidence: 99%

Beyond Henssge’s Formula: Using Regression Trees and a Support Vector Machine for Time of Death Estimation in Forensic Medicine

et al. 2023

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Henssge’s nomogram is a commonly used method to estimate the time of death. However, uncertainties arising from the graphical solution of the original mathematical formula affect the accuracy of the resulting time interval. Using existing machine learning techniques/tools such as support vector machines (SVMs) and decision trees, we present a more accurate and adaptive method for estimating the time of death compared to Henssge’s nomogram. Using the Python programming language, we built a synthetic data-driven model in which the majority of the selected tools can estimate the time of death with low error rates even despite having only 3000 training cases. An SVM with a radial basis function (RBF) kernel and AdaBoost+SVR provided the best results in estimating the time of death with the lowest error with an estimated time of death accuracy of approximately ±20 min or ±9.6 min, respectively, depending on the SVM parameters. The error in the predicted time (tp[h]) was tp±0.7 h with a 94.45% confidence interval. Because training requires only a small quantity of data, our model can be easily customized to specific populations with varied anthropometric parameters or living in different climatic zones. The errors produced by the proposed method are a magnitude smaller than any previous result.

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A Review of Brain Tumor Segmentation Using MRIs from 2019 to 2023 (Statistical Information, Key Achievements, and Limitations)

Zakeri,

Karasfi,

Jalalian

2024

J. Med. Biol. Eng.

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Multiview decision tree-based segmentation of tumors in MR brain medical images

Cited by 3 publications

References 7 publications

A novel decision-making approach based on a decision tree for micro-grid energy management

A novel decision-making approach based on a decision tree for micro-grid energy management

Beyond Henssge’s Formula: Using Regression Trees and a Support Vector Machine for Time of Death Estimation in Forensic Medicine

A Review of Brain Tumor Segmentation Using MRIs from 2019 to 2023 (Statistical Information, Key Achievements, and Limitations)

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