Ensemble learning-based classification on local patches from magnetic resonance images to detect iron depositions in the brain

Wilson, Beshiba; Dhas, Julia Punitha Malar; Sreedharan, Ruma Madhu; Krish, Ram P.

doi:10.1504/ijbic.2021.116608

Cited by 5 publications

(4 citation statements)

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“…The main tree-based algorithms are Decision Tree (DT), RF, and XGBoost. The learning process of these algorithms is supervised, and their operation is based on the branching through nodes, where each node represents a decision criterion based on a variable [24], and each branch denotes a value of the node [25]. The leaves represent the model's outputs, that is, the prediction.…”

Section: Models Trainingmentioning

confidence: 99%

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HyMOTree: Automatic Hyperparameters Tuning for Non-Technical Loss Detection Based on Multi-Objective and Tree-Based Algorithms

Coelho

Feitosa

Alcântara³

et al. 2023

Energies

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The most common methods to detect non-technical losses involve Deep Learning-based classifiers and samples of consumption remotely collected several times a day through Smart Meters (SMs) and Advanced Metering Infrastructure (AMI). This approach requires a huge amount of data, and training is computationally expensive. However, most energy meters in emerging countries such as Brazil are technologically limited. These devices can measure only the accumulated energy consumption monthly. This work focuses on detecting energy theft in scenarios without AMI and SM. We propose a strategy called HyMOTree intended for the hyperparameter tuning of tree-based algorithms using different multiobjective optimization strategies. Our main contributions are associating different multiobjective optimization strategies to improve the classifier performance and analyzing the model’s performance given different probability cutoff operations. HyMOTree combines NSGA-II and GDE-3 with Decision Tree, Random Forest, and XGboost. A dataset provided by a Brazilian power distribution company CPFL ENERGIA™ was used, and the SMOTE technique was applied to balance the data. The results show that HyMOTree performed better than the random search method, and then, the combination between Random Forest and NSGA-II achieved 0.95 and 0.93 for Precision and F1-Score, respectively. Field studies showed that inspections guided by HyMOTree achieved an accuracy of 76%.

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Section: Models Trainingmentioning

confidence: 99%

“…This helps reduce the tendency to overfitting while increasing the accuracy of the model [28]. The final prediction of the model is determined from the output of all the Decision Trees [25], considering the simple average or majority vote. The RF algorithm also performs well with large datasets or many features.…”

Section: Models Trainingmentioning

confidence: 99%

HyMOTree: Automatic Hyperparameters Tuning for Non-Technical Loss Detection Based on Multi-Objective and Tree-Based Algorithms

Coelho

Feitosa

Alcântara³

et al. 2023

Energies

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“…Bagging is an important part of the Random Forest algorithm, as it is responsible for creating the individual decision trees that make up the forest. The bagging procedure allows for the trees to be independent of each other, and therefore, the forest is created with a wide variety of trees, thus increasing the accuracy of the model [35]. Bagging helps to reduce the variance of the model and increases the accuracy of the predictions.…”

Section: Random Forestmentioning

confidence: 99%

Study of Potential Impact of Wind Energy on Electricity Price Using Regression Techniques

Kumar,

Tripathi,

Gupta

et al. 2023

Sustainability

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This paper seeks to investigate the impact analysis of wind energy on electricity prices in an integrated renewable energy market, using regression models. This is especially important as wind energy is hard to predict and its integration into electricity markets is still in an early stage. Price forecasting has been performed with consideration of wind energy generation to optimize energy portfolio investment and create an efficient energy-trading landscape. It provides an insight into future market trends which allow traders to price their products competitively and manage their risks within the volatile market. Through the analysis of an available dataset from the Austrian electricity market, it was found that the Decision Tree (DT) regression model performed better than the Linear Regression (LR), Random Forest (RF), and Least Absolute Shrinkage Selector Operator (LASSO) models. The accuracy of the model was evaluated using the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The MAE values considering wind energy generation and without wind energy generation for the Decision Tree model were found to be lowest (2.08 and 2.20, respectively) among all proposed models for the available dataset. The increasing deployment of wind energy in the European grid has led to a drop in prices and helped in achieving energy security and sustainability.

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“…The progress of artificial intelligence (AI) has promoted the process of intelligence in the medical industry [1,2]. However, the fragmentation and privacy of data have become the key factors that restricting the advance of intelligent medicine [3].…”

Section: Introductionmentioning

confidence: 99%

A many-objective evolutionary algorithm based on integrated strategy for skin cancer detection

2022

KSII TIIS

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Nowadays, artificial intelligence promotes the rapid development of skin cancer detection technology, and the federated skin cancer detection model (FSDM) and dual generative adversarial network model (DGANM) solves the fragmentation and privacy of data to a certain extent. To overcome the problem that the many-objective evolutionary algorithm (MaOEA) cannot guarantee the convergence and diversity of the population when solving the above models, a many-objective evolutionary algorithm based on integrated strategy (MaOEA-IS) is proposed. First, the idea of federated learning is introduced into population mutation, the new parents are generated through sub-populations employs different mating selection operators. Then, the distance between each solution to the ideal point (SID) and the Achievement Scalarizing Function (ASF) value of each solution are considered comprehensively for environment selection, meanwhile, the elimination mechanism is used to carry out the select offspring operation. Eventually, the FSDM and DGANM are solved through MaOEA-IS. The experimental results show that the MaOEA-IS has better convergence and diversity, and it has superior performance in solving the FSDM and DGANM. The proposed MaOEA-IS provides more reasonable solutions scheme for many scholars of skin cancer detection and promotes the progress of intelligent medicine.

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Ensemble learning-based classification on local patches from magnetic resonance images to detect iron depositions in the brain

Cited by 5 publications

References 0 publications

HyMOTree: Automatic Hyperparameters Tuning for Non-Technical Loss Detection Based on Multi-Objective and Tree-Based Algorithms

HyMOTree: Automatic Hyperparameters Tuning for Non-Technical Loss Detection Based on Multi-Objective and Tree-Based Algorithms

Study of Potential Impact of Wind Energy on Electricity Price Using Regression Techniques

A many-objective evolutionary algorithm based on integrated strategy for skin cancer detection

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