Decision Tree-based Adaptive Approximate Accelerators for Enhanced Quality

Masadeh, Mahmoud; Aoun, Alain; Hasan, Osman; Tahar, Sofiène

doi:10.1109/syscon47679.2020.9275856

Cited by 2 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sun et al [67] used thirty subjects using an ECG signal to record the cardiac signal. Additionally, they classified the signal using a Decision Tree (DT) classifier, which achieved 80% accuracy, where DT is easy to understand efficiently [68]. Previously, many smart devices, including accelerometer, Galvanic Skin Response (GSR), smart watch, and electrodes for the recording of the physiological signal such as ECG, EEG, and EMG, are used to detect stress [69,70] .…”

Section: Comparison Between Previously Selected and Proposed Methodsmentioning

confidence: 99%

Frontal lobe real-time EEG analysis using machine learning techniques for mental stress detection

AlShorman¹,

Masadeh²,

Heyat³

et al. 2022

J. Integr. Neurosci.

Self Cite

View full text Add to dashboard Cite

Stress has become a dangerous health problem in our life, especially in student education journey. Accordingly, previous methods have been conducted to detect mental stress based on biological and biochemical e fects. Moreover, hormones, physiological e fects, and skin temperature have been extensively used for stress detection. However, based on the recent literature, biological, biochemical, and physiological-based methods have shown inconsistent findings, which are initiated due to hormones' instability. Therefore, it is crucial to study stress using di ferent mechanisms such as Electroencephalogram (EEG) signals. In this research study, the frontal lobes EEG spectrum analysis is applied to detect mental stress. Initially, we apply a Fast Fourier Transform (FFT) as a feature extraction stage to measure all bands' power density for the frontal lobe. A ter that, we used two type of classifications such as subject wise and mix (mental stress vs. control) using Support Vector Machine (SVM) and Naive Bayes (NB) machine learning classifiers. Our obtained results of the average subject wise classification showed that the proposed technique has better accuracy (98.21%). Moreover, this technique has low complexity, high accuracy, simple and easy to use, no over fitting, and it could be used as a real-time and continuous monitoring technique for medical applications.

show abstract

Section: Comparison Between Previously Selected and Proposed Methodsmentioning

confidence: 99%

Frontal lobe real-time EEG analysis using machine learning techniques for mental stress detection

AlShorman¹,

Masadeh²,

Heyat³

et al. 2022

J. Integr. Neurosci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The naive Bayes classifier is based on Bayes Theorem, which operates on conditional probability. The conditional probability is the likelihood that something will happen if something else has already happened [15]. Eq.…”

Section: ) Multinomial Naive Bayes (Mnb)mentioning

confidence: 99%

Investigating the Impact of Preprocessing Techniques and Representation Models on Arabic Text Classification using Machine Learning

Masadeh,

et al. 2024

IJACSA

View full text Add to dashboard Cite

Arabic Text Classification (ATC) is a crucial step for various Natural Language Processing (NLP) applications. It emerged as a response to the exponential growth of online content like social posts and review comments. In this study, preprocessing techniques and representation models are used to evaluate the effectiveness of ATC using Machine Learning (ML). Generally, the ATC operation depends on various factors, such as stemming in preprocessing, feature extraction and selection, and the nature of the dataset. To enhance the overall classification performance, preprocessing methodologies are primarily employed to transform each Arabic term into its root form and reduce the dimensionality of representation. In the representation of Arabic text, feature extraction and selection processes are imperative, as they significantly enhance the performance of ATC. This study implements the chosen classifiers using various feature selection algorithms. The comprehensive assessment of classification outcomes is conducted by comparing various classifiers, including Multinomial Naive Bayes (MNB), Bernoulli Naive Bayes (BNB), Stochastic Gradient Descent (SGD), Support Vector Classifier (SVC), Logistic Regression (LR), and linear Support Vector Classifier (LSVC). These ML classifiers are assessed utilizing short and long Arabic text benchmark datasets called BBC Arabic corpus and the COVID-19 dataset. The assessment findings indicate that the efficacy of classification is significantly influenced by the preprocessing methods, representation model, classification algorithm, and the datasets' characteristics. In most cases, the SGDC and LSVC have consistently surpassed other classifiers for the datasets under consideration when significant features are chosen.

show abstract

Decision Tree-based Adaptive Approximate Accelerators for Enhanced Quality

Cited by 2 publications

References 18 publications

Frontal lobe real-time EEG analysis using machine learning techniques for mental stress detection

Frontal lobe real-time EEG analysis using machine learning techniques for mental stress detection

Investigating the Impact of Preprocessing Techniques and Representation Models on Arabic Text Classification using Machine Learning

Contact Info

Product

Resources

About