Epileptic Seizures Detection from EEG Recordings Based on a Hybrid system of Gaussian Mixture Model and Random Forest Classifier

Ohannesian, Garineh Sarkies; Harfash, Esraa Jasim

doi:10.31449/inf.v46i6.4203

Cited by 5 publications

(3 citation statements)

References 29 publications

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“…It was first introduced in 2001 by University of California, Berkeley professor Leo Breiman. Random Decision Forests is another name for this method [29]. The model is constructed by splitting the input into several samples based on how many trees there are, then constructing an easy forecasting model inside each section, and finally merging the results of these models using a bagging method to arrive at the final forecast [30].…”

Section: Random Forest Regressor (Rfr)mentioning

confidence: 99%

Oil and Gas Production Forecasting Using Decision Trees, Random Forst, and XGBoost

A. Al shabaan,

N. Nemer

2024

J. Al-Qadisiyah Comp. Sci. Math.

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Oil and gas production forecasting has always been a hot topic in the petroleum industry. Production forecasting in this sector aims to estimate future production rates, facilitating operational planning, production optimization, and resource allocation for companies. Scientists have traditionally attempted to forecast oil and gas production using methods such as Numerical Reservoir Simulation (NRS) and Decline Curve Analysis (DCA). However, these methods present challenges including time-consuming processes lasting hours or even days, uncertain accuracy, reliance on accurate static models, and uncertainty in dynamic model parameters. In this research, aim to address these limitations by leveraging machine learning models for production forecasting. These models enable faster and more precise decision-making by accurately predicting future outcomes based on historical data. Our study employs three models: Decision Trees (DTR), Random Forest (RFR), and XGBoost. In this reserch utilize the Python programming language and a dataset from wells in New York State, USA. Experimental results demonstrate that the RFR model achieves the highest accuracy (99%) for oil and gas production compared to the XGBoost and DTR models.

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Section: Random Forest Regressor (Rfr)mentioning

confidence: 99%

Oil and Gas Production Forecasting Using Decision Trees, Random Forst, and XGBoost

A. Al shabaan,

N. Nemer

2024

J. Al-Qadisiyah Comp. Sci. Math.

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“…Predictions and data clustering may then be done using these patterns [14]. Classification models are used to classify input data, with output values or the goal (Y) being categorical, an example of classification is used to determine whether or not a patient is sick [15].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Webpage Classification Using Ensemble Machine Learning

Altaee

2023

IJICI

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These days, it is not easy to get the correct information after typing a keyword into a search engine because so many results are returned. Classification of Web pages is a technique that helps us locate the wanted information quickly and effectively. In addition, website categorization is crucial for businesses that provide marketing and analytical solutions because it enables them to create a well-balanced mix of search engine and directory listings. This will give marketers a better idea of where their local company listings appear online, allowing them to have more judgment about initiative and strategy. Therefore, the research aimed to construct a classification system based on a dataset of English web pages. This information has been acquired from the Kaggle website and consisted of 1408 distinct rows organized into 16 categories. The research has employed mixed strategies to determine which strategy for Web page categorization would yield the best results. The first strategy puts into practice a collection of machine-learning algorithms. It assesses how well they accomplish the given classification task. Ensemble stacking is the second strategy, and it is employed to enhance the classification of websites. Comparing the results of the two strategies reveals that Ensemble stacking, the second strategy, was the more influential architecture for classifying web pages this approach had 0.95 F1-score, 0.95 accuracy, 0.95 precision, and 0.95 recall achieved by this method. The first approach, which made use of machine learning techniques, on the other hand, received an F1-score of 0.93, 0.94 for precision, 0.93 for recall, and 0.93 for accuracy.

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“…Random Forest-It is one of the supervised learning algorithm, 9,24 and the primary idea behind this approach is that using a variety of learning models would boost the final result. The class with the most votes becomes the model's prediction when each individual tree distributes a class prediction in the RF.…”

Section: Related Workmentioning

confidence: 99%

A novel finite spectral entropy: Gated term memory unit recursive network integrated with Ladybug Beetle Optimization algorithm for epileptic seizure detection

Golla,

Maloji

2023

Numer Methods Biomed Eng

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Professional medical experts use a visual electroencephalography (EEG) signal for epileptic seizure detection, although this method is time‐consuming and highly subject to bias. The majority of previous epileptic detection techniques have poor efficiency, detection performance and also which are unsuited to handle large datasets. In order to solve the aforementioned issues and to assist medical professionals with an advanced technology, a computerized epileptic seizure detection system is essential. Therefore, the proposed work intends to design an automated detection tool for predicting an epileptic seizure from EEG signals. For this purpose, a novel non‐linear feature analysis and deep learning algorithms are deployed in this work. Initially, the signal decomposition, filtering and artifacts removal operations are carried out with the use of finite Haar wavelet transformation technique. After that, the finite spectral entropy (FSE) based feature extraction model has been used to extract the time, frequency, and time‐frequency features from the normalized signal. Consequently, the novel gated term memory unit recursive network (GTRN) model is employed to predict the given EEG signal as whether healthy or seizure affected including the class with high accuracy. During this process, the recently developed Ladybug Beetle Optimization (LBO) algorithm is used to compute the logistic sigmoid function based on the solution. The purpose of using this algorithm is to simplify the process of classification with increased seizure prediction accuracy and performance. Moreover, the standard and popular benchmark EEG datasets are used to validate and test the results of the proposed FSE‐GTRN‐LBO mechanism. By leveraging the finite Haar wavelet transformation and FSE‐based feature extraction, we can efficiently process EEG signals. The utilization of the GTRN model enables accurate classification of healthy and seizure‐affected EEG data. To optimize the classification process further, we integrate the LBO algorithm, streamlining the computation of the logistic sigmoid function. Through comprehensive validation on standard EEG datasets, our proposed FSE‐GTRN‐LBO mechanism achieves outstanding seizure prediction accuracy and performance, surpassing existing state‐of‐the‐art techniques.

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Epileptic Seizures Detection from EEG Recordings Based on a Hybrid system of Gaussian Mixture Model and Random Forest Classifier

Cited by 5 publications

References 29 publications

Oil and Gas Production Forecasting Using Decision Trees, Random Forst, and XGBoost

Oil and Gas Production Forecasting Using Decision Trees, Random Forst, and XGBoost

Webpage Classification Using Ensemble Machine Learning

A novel finite spectral entropy: Gated term memory unit recursive network integrated with Ladybug Beetle Optimization algorithm for epileptic seizure detection

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