An Approach to Training Decision Trees with the Relearning of Nodes

Mitrofanov, S. A.; Semenkin, ‪Eugene

doi:10.1109/infotech52438.2021.9548520

Cited by 19 publications

(13 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We also used traditional machine learning methods to predict the drain transient current pulse. These methods included decision tree (DT), support vector regression (SVR), K-nearest neighbor (KNN), and ridge regression (RR) [ 35 , 36 , 37 , 38 ]. We compared the mean relative error predicted by traditional machine learning methods with the prediction result of a deep neural network, as shown in Figure 8 .…”

Section: Resultsmentioning

confidence: 99%

Prediction of Single-Event Effects in FDSOI Devices Based on Deep Learning

Zhao

Wang

et al. 2023

Micromachines

View full text Add to dashboard Cite

Single-event effects (SEE) are an important index of radiation resistance for fully depleted silicon on insulator (FDSOI) devices. The research into traditional FDSOI devices is based on simulation software, which is time consuming, requires a large amount of calculation, and has complex operations. In this paper, a prediction method for the SEE of FDSOI devices based on deep learning is proposed. The characterization parameters of SEE can be obtained quickly and accurately by inputting different particle incident conditions. The goodness of fit of the network curve for predicting drain transient current pulses can reach 0.996, and the accuracy of predicting the peak value of drain transient current and total collected charge can reach 94.00% and 96.95%, respectively. Compared with TCAD Sentaurus software, the simulation speed is increased by 5.10 × 102 and 1.38 × 103 times, respectively. This method can significantly reduce the computational cost, improve the simulation speed, and provide a new feasible method for the study of the single-event effect in FDSOI devices.

show abstract

Section: Resultsmentioning

confidence: 99%

Prediction of Single-Event Effects in FDSOI Devices Based on Deep Learning

Zhao

Wang

et al. 2023

Micromachines

View full text Add to dashboard Cite

show abstract

“…Various algorithms such as Decision Tree (DT) [26], Naive Bayes (NB) [27], Support Vector Machine (SVM) [28], K-Nearest Neighbor (KNN) [29], and Logistic Regression (LR) [30] are widely used in human activity recognition. Some of these algorithms were used beside Linear Discriminant Analysis (LDA) [31] and Ensemble Learning (EL) [32] to recognize activities of elderly people who are living alone as proposed in [7].…”

Section: ) Human Activity Recognitionmentioning

confidence: 99%

Future Activities Prediction Framework in Smart Homes Environment

2022

View full text Add to dashboard Cite

Smart homes have been recently important sources for providing Activity of Daily Living (ADL) data about their residents. ADL data can be a great asset while analyzing residents' behavior to provide residents with better and optimized services. A popular example is to analyze residents' behavior to predict their future activities and optimize smart homes performance accordingly. This paper proposes a forecasting framework that utilizes ADL data to predict residents' next activities in a smart home environment. Forecasting is performed via the conjunction of embedding algorithm to encode the data and Bidirectional Long Short-Term Memory (BiLSTM) deep neural networks to process the data. The proposed framework is evaluated over five real ADL datasets where the experiments show the outperformance of the proposed framework with accuracy scores ranging from 98.7% to 93.8%.

show abstract

“…The authors of [71] introduced an extensive measurement experiment of VLC (visiblelight communication) channel characterization for I2V (infrastructure-to-vehicle) communication. Traffic lights were used to carry out the experiment.…”

Section: Linear Regressionmentioning

confidence: 99%

How to Implement Automotive Fault Diagnosis Using Artificial Intelligence Scheme

Gong

Chen

et al. 2022

Micromachines

View full text Add to dashboard Cite

The necessity of vehicle fault detection and diagnosis (VFDD) is one of the main goals and demands of the Internet of Vehicles (IoV) in autonomous applications. This paper integrates various machine learning algorithms, which are applied to the failure prediction and warning of various types of vehicles, such as the vehicle transmission system, abnormal engine operation, and tire condition prediction. This paper first discusses the three main AI algorithms, such as supervised learning, unsupervised learning, and reinforcement learning, and compares the advantages and disadvantages of each algorithm in the application of system prediction. In the second part, we summarize which artificial intelligence algorithm architectures are suitable for each system failure condition. According to the fault status of different vehicles, it is necessary to carry out the evaluation of the digital filtering process. At the same time, it is necessary to preconstruct its model analysis and adjust the parameter attributes, types, and number of samples of various vehicle prediction models according to the analysis results, followed by optimization to obtain various vehicle models. Finally, through a cross-comparison and sorting, the artificial intelligence failure prediction models can be obtained, which can correspond to the failure status of a certain car model and a certain system, thereby realizing a most appropriate AI model for a specific application.

show abstract

An Approach to Training Decision Trees with the Relearning of Nodes

Cited by 19 publications

References 8 publications

Prediction of Single-Event Effects in FDSOI Devices Based on Deep Learning

Prediction of Single-Event Effects in FDSOI Devices Based on Deep Learning

Future Activities Prediction Framework in Smart Homes Environment

How to Implement Automotive Fault Diagnosis Using Artificial Intelligence Scheme

Contact Info

Product

Resources

About