Fracture Type Identification Using Extra Tree Classifier

Upadhyay, Rocky; Tanwar, Prakash Singh; Degadwala, Sheshang

doi:10.1109/i-smac52330.2021.9640957

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…If the ML Models are trained with higher weightage attributes while ignoring the lower ones, then the result obtained will be phenomenal. Two of the most famous Feature Selection Techniques namely, Extra Trees Classifier [17], [18] and Mutual Information [19], [20] are used for the implementation of the ML Models. A brief description is given below.…”

Section: Feature Selectionmentioning

confidence: 99%

Analysis and Prediction of Parkinson's Disease using Machine Learning Algorithms

Sen¹,

GHOSH²

2022

Preprint

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Section: Feature Selectionmentioning

confidence: 99%

Analysis and Prediction of Parkinson's Disease using Machine Learning Algorithms

Sen¹,

GHOSH²

2022

Preprint

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“…It consists of two subsystems; the first subsystem is responsible for classifying water quality based on nine AI models that have been applied, tested, and compared to classify various samples of drinking water as safe to drink or unsafe to drink. The applied nine AI www.ijacsa.thesai.org models are: Extreme Gradient Boosting (XGBoost) [10], Light Gradient Boosting Machine (Light GBM) [11], Decision Tree (DT) [12], Extra Tree (ET) [13], Multi-layer Perceptron (MLP) [14], Gradient Boosting (GB) [15], Support Vector Machine (SVM) [16], Artificial Neural Network (ANN) classification [17], and Random Forest (RF) Classifier [18]. The second subsystem is responsible for predicting water quality index (WQI) based on six regression models, LGBM regression, XGB regression, ExtraTrees regression, DT Regression, RF regression, and linear regression.…”

Section: Introductionmentioning

confidence: 99%

Recognizing Safe Drinking Water and Predicting Water Quality Index using Machine Learning Framework

Torky¹,

Bakhiet²,

Bakrey³

et al. 2023

IJACSA

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Water quality monitoring, analysis, and prediction have emerged as important challenges in several uses of water in our life. Recent water quality problems have raised the need for artificial intelligence (AI) models for analyzing water quality, classifying water samples, and predicting water quality index (WQI). In this paper, a machine-learning framework has been proposed for classify drinking water samples (safe/unsafe) and predicting water quality index. The classification tier of the proposed framework consists of nine machine-learning models, which have been applied, tested, validated, and compared for classifying drinking water samples into two classes (safe/unsafe) based on a benchmark dataset. The regression tier consists of six regression models that have been applied to the same dataset for predicting WQI. The experimental results clarified good classification results for the nine models with average accuracy, of 94.7%. However, the obtained results showed the superiority of Random Forest (RF), and Light Gradient Boosting Machine (Light GBM) models in recognizing safe drinking water samples regarding training and testing accuracy compared to the other models in the proposed framework. Moreover, the regression analysis results proved the superiority of LGBM regression, and Extra Trees Regression models in predicting WQI according to training, testing accuracy, 0.99%, and 0.95%, respectively.Moreover, the mean absolute error (MAE) results proved that the same models achieved less error rate, 10% than other applied regression models. These findings have significant implications for the understanding of how novel deep learning models can be developed for predicting water quality, which is suitable for other environmental and industrial purposes.

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Artificial Intelligence Implementation in Internet of Things Embedded System for Real-Time Person Presence in Bed Detection and Sleep Behaviour Monitor

Hoang,

Matrella,

Ciampolini

2024

Electronics

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This paper works on detecting a person in bed for sleep routine and sleep pattern monitoring based on the Micro-Electro-Mechanical Systems (MEMS) accelerometer and Internet of Things (IoT) embedded system board. This work provides sleep information, patient assessment, and elderly care for patients who live alone via tele-distance to doctors or family members. About 216,000 pieces of acceleration data were collected, including three classes: no person in bed, a static laying position, and a moving state for Artificial Intelligence (AI) application. Six well-known Machine-Learning (ML) algorithms were evaluated with precision, recall, F1-score, and accuracy in the workstation before implementing in the STM32-microcontroller for real-time state classification. The four best algorithms were selected to be programmed into the IoT board and applied for real-time testing. The results demonstrate the high accuracy of the ML performance, more than 99%, and the Classification and Regression Tree algorithm is among the best models with a light code size of 1583 bytes. The smart bed information is sent to the IoT dashboard of Node-RED via a Message Queuing Telemetry broker (MQTT).

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Fracture Type Identification Using Extra Tree Classifier

Cited by 3 publications

References 6 publications

Analysis and Prediction of Parkinson's Disease using Machine Learning Algorithms

Analysis and Prediction of Parkinson's Disease using Machine Learning Algorithms

Recognizing Safe Drinking Water and Predicting Water Quality Index using Machine Learning Framework

Artificial Intelligence Implementation in Internet of Things Embedded System for Real-Time Person Presence in Bed Detection and Sleep Behaviour Monitor

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