ESEEG: An Efficient Epileptic Seizure Detection using EEG signals based on Machine Learning Algorithms

El-Aal

abdellatif³

2023

This paper aims to determine the most accurate algorithm for predicting the Egyptian gross domestic product (GDP) and not only and also to determine the relative weight of the effect of the components of the output on it to assist decision-makers in making good economic policies. It turned out that the gradient algorithm is the most accurate and highly efficient algorithm for predicting the Egyptian GDP. It also became clear that government investment is the biggest influence on the Egyptian GDP at 21%, followed by the consumer spending of the family sector at 19%, followed by investment spending by the private sector and imports by the same 15%, then exports and government spending by 14% and 13%, respectively. Thus, to stimulate and maximize the size of the Egyptian GDP, the decision-maker must focus on stimulating government investment spending and consumer spending for the household sector and investment spending for the private sector.

Section: Related Workmentioning

confidence: 99%

Nowcasting Egypt GDP Using machine learning Algorithms

El-Aal

abdellatif³

2023

“…They use different approaches, and the SVM has achieved the best results with an accuracy of around 91%. Also, other related work in [19][20][21][22][23][24][25][26][27] has been proposed in recent years to address machine learning and its application in different fields.…”

Section: Related Workmentioning

confidence: 99%

FER_ML: Facial Emotion Recognition using Machine Learning

Mostafa

Ghareeb

et al. 2023

Recently, facial recognition has been one of the most crucial technologies people need. Facial recognition has attracted a lot of the crowd; for example, it has been used in security on most modern devices. Using machine and deep learning, overall performance will be improved, and the identification accuracy will be more precise. We aim to discover how well these algorithms perform in classifying human facial expressions and whether or not we can depend on them. The steps are as follows. First, we embed the images from the dataset, then split the dataset into 70% training data and 30% testing data; after that, we apply five different algorithms: Support Vector Machine, K-nearest Neighbor, Logistic Regression, Naive Bayes, and Random Forest. Support Vector Machine achieved an accuracy rate of 36%, K-nearest Neighbor achieved an accuracy rate of 52.3%, Logistic regression achieved an accuracy rate of 64.2%, and Naive Bayes achieved an accuracy rate of 38.1%. Random Forest achieved an accuracy rate of 51.7%. The dataset used was a cleaned version of the FER13 dataset, which contains 16,780 images divided into five classes (angry, happy, neutral, disgust, and fear). The results show that Logistic Regression proved to be the most accurate classifier among the presented ones, with an F1-Score of 63.8% and an accuracy of 64.2%.

“…The paper couldn't showcase the performance of MLP, SVOMO, and SMOVA models with HOG Features. Also, other related work in [16][17][18][19][20][21][22][23][24][25] has been proposed in recent years to address machine learning and its application in different fields. [3] is a common dataset of handwritten digits that carries 60,000 handwritten digits for training and 10,000 handwritten digits for a machine learning model to be tested and verified that they work.…”

Section: Related Workmentioning

confidence: 99%

MLHandwrittenRecognition: Handwritten Digit Recognition using Machine Learning Algorithms

Essam

Samy

et al. 2023

Handwritten digit recognition has remained a topic of interest to computer vision scientists. Its origination precedes the emergence of the machine as it is a crucial component of the digital transformation of the majority of institutions in numerous fields. With the uprising of machine models, choosing a satisfactory and fit algorithm for this multi-class (0-9) classification problem became challenging. This paper aims to compare seven machine learning algorithms in terms of their performance metrics in recognizing handwritten digits employing two datasets. The -Nearest Neighbors (kNN), Support Vector Machine (SVM), Logistic Regression, Neural Network, Random Forest (RF), Naive Bayes, and Decision Tree models are accordingly evaluated concerning the Area Under the Curve (AUC), accuracy (ACC), F1-score (F1), precision (PREC), and recall (REC). The widely used Modified National Institute of Standards and Technology database (MNIST) dataset and the Handwritten Digit Classification dataset (HDC) have been the providers of the images on which this research is conducted. The results confirm that the Neural Networks model is a great classifier for this problem; however, it presents similar results to other machine learning classifiers in several cases. Therefore, this paper does not provide an absolute choice of a classifier for the handwritten digit recognition problem but rather explains the reason behind the performance of each model.