بحوث سكوبس — مصطفى حسين محمد السراراتي

In this paper, a novel classifier based on Robert’s similarity measure is introduced for emotion detection using electroencephalogram (EEG) signals. Traditional machine learning classifiers machine learning such as k-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), and Random Forest (RF), often struggle to accurately capture both linear and nonlinear patterns in EEG signals and face limitations in handling high-dimensional datasets. The proposed classifier addresses these challenges by segmenting EEG signals into block sizes categorized as small (1 to 10 samples), medium (20 to 100 samples), and large (200 to 1,000 samples), demonstrating particularly strong performance with medium and large block sizes to capture essential features. Integration of Particle Swarm Optimization (PSO) for feature selection, with Robert’s similarity as the fitness function, effectively refines the feature set, boosting classification accuracy and computational efficiency. Evaluation on an EEG brainwave dataset demonstrated that the method achieved an accuracy of 98.75% with feature selection, compared to 94.04% without it in emotional state detection. The results demonstrate that the proposed classifier is a valuable tool for diverse fields, including healthcare by detecting patient stress, education by assessing student engagement, customer service by monitoring satisfaction, and smart environments by enabling adaptive responses. Furthermore, the classifier has potential for broader industrial applications, such as improving workplace productivity by monitoring employee stress and enhancing safety in autonomous vehicle systems, making it a versatile solution for emotionally-aware systems across multiple domains. © 2013 IEEE.

This paper presents a novel classifier based on the Clark distance for early detection of Parkinson's disease (PD) using voice data. The nonlinear nature of human voice signals and their inherent fluctuations pose significant challenges for traditional machine learning classifiers such as Random Forest (RF), Support Vector Machines (SVM), Decision Trees (DT), K-Nearest Neighbors (KNN), and Logistic Regression (LR), which struggle to capture meaningful relationships within the voice data for accurate classification. The proposed classifier addresses these limitations by segmenting the data features of each voice sample into smaller blocks (ranging in size from 2 to 10 features per block), to better capture relationships and minimize fluctuations that negatively impact classification accuracy. To further enhance the performance of the classifier, Grey Wolf Optimization (GWO) was integrated to select high-harmony (convergent) features while removing irrelevant and redundant (divergent) features. The proposed approach achieved an accuracy of 94.6% without GWO and an impressive 98.305% accuracy with GWO, using only 12 selected features. Additionally, the classifier demonstrates efficient execution time, making it well-suited for real-time applications in medical organizations. The combination of the Clark distance classifier with GWO not only improves classification accuracy but also ensures computational efficiency, enabling its deployment in diverse settings such as hospitals and home monitoring systems for early detection and continuous monitoring of PD patients. This study highlights the potential of the proposed approach as a reliable, cost-effective, and scalable solution for voice-based PD detection. © 2025 The Authors