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Standard classifiers struggle with high-dimensional datasets due to increased computational complexity, difficulty in visualization and interpretation, and challenges in handling redundant or irrelevant features. This paper proposes a novel feature selection method based on the Mahalanobis distance for Parkinson's disease (PD) classification. The proposed feature selection identifies relevant features by measuring their distance from the dataset's mean vector, considering the covariance structure. Features with larger Mahalanobis distances are deemed more relevant as they exhibit greater discriminative power relative to the dataset's distribution, aiding in effective feature subset selection. Significant improvements in classification performance were observed across all models. On the "Parkinson Disease Classification Dataset", the feature set was reduced from 22 to 11 features, resulting in accuracy improvements ranging from 10.17 % to 20.34 %, with the K-Nearest Neighbors (KNN) classifier achieving the highest accuracy of 98.31 %. Similarly, on the "Parkinson Dataset with Replicated Acoustic Features", the feature set was reduced from 45 to 18 features, achieving accuracy improvements ranging from 1.38 % to 13.88 %, with the Random Forest (RF) classifier achieving the best accuracy of 95.83 %. By identifying convergence features and eliminating divergence features, the proposed method effectively reduces dimensionality while maintaining or improving classifier performance. Additionally, the proposed feature selection method significantly reduces execution time, making it highly suitable for real-time applications in medical diagnostics, where timely and accurate disease identification is critical for improving patient outcomes.

This study introduces a novel feature selection method based on Hellinger distance and particle swarm optimization (PSO) for reducing the dimensionality of features in electroencephalogram (EEG) signals and improving epileptic seizure detection accuracy. In the first phase, the Hellinger distance is used as a filter to remove redundant and irrelevant features by calculating the similarity between blocks within the feature, thus reducing the search space for the subsequent second phase. In the second phase, PSO searches the reduced feature space to select the best subset. Recognizing that both classification accuracy and dimensionality play crucial roles in the performance of feature subsets, PSO searches various sets of features (ranging from 410 to 2867 in EEG signals) derived from the first stage using Hellinger distance, rather than searching through the full set of 4047 features, to select the optimal subset. The proposed Hellinger-PSO approach demonstrates significant improvements in classification accuracy across multiple models. Specifically, Logistic Regression (LR) improved from 91% to 95% (4% improvement), Decision Tree (DT) from 95% to 97% (2% improvement), Naive Bayes (NB) from 94% to 99% (5% improvement), and Random Forest (RF) from 96% to 98% (2% improvement) on the Bonn dataset. Additionally, the method reduces dimensionality while maintaining high classification performance. The results validate the efficacy of the Hellinger-PSO technique, which enhances both the accuracy and efficiency of epileptic seizure detection. This approach has the potential to improve diagnostic accuracy in medical settings, aiding in better patient care and more effective clinical decision-making.

The task of recognizing and categorizing vehicles in videos and images as objects poses a considerable challenge in terms of appearance-based representation. However, it holds great importance in the practical implementation of Intelligent Transportation Systems (ITS), particularly in real-time applications. The fast advancement of deep learning has resulted in an increasing need within the computer vision field for the development of efficient, robust, and outstanding services across diverse domains. This paper provides an extensive analysis of various methodologies for vehicle detection and classification, along with their utilization in real-time targets, estimating the density of traffic, and related domains through the implementation of Deep Learning techniques. The major findings of our survey highlight crucial insights obtained from an extensive analysis of existing literature, shedding light on the current state-of-the-art techniques. Through a comprehensive review of deep learning methodologies, performance metrics, benchmark datasets, and a comprehensive exploration of the challenges encountered, our survey offers valuable contributions to the field. By synthesizing and presenting the collective knowledge in this domain, our paper serves as a key resource for researchers and practitioners alike, providing a holistic understanding of the advancements and challenges in vehicle identification and categorization within deep learning architectures.

This paper introduces a new classifier based on the Hellinger distance to overcome the challenges encountered by standard classifiers in accurately diagnosing seizure epilepsy using electroencephalogram (EEG) signals. They mainly suffer from poor discriminative capacity and sensitivity towards datasets with class imbalance and inefficiency towards handling high-dimensional datasets. In an attempt to overcome such challenges, we include the Hellinger distance classifier with Particle Swarm Optimization (PSO) in our proposed work. We incorporate this dynamic approach in such a way that features of EEG signals are effectively selected, which increases classifier accuracy and reduces the dataset time and dimensionality. The experimental results show that our approach strongly increases the accuracy of our classifier on the Bonn dataset, up to 96.25%, and even the F1-score of 97.74%, recall of 95.59%, and precision of 100%. These results position our method as an effective tool for academic as well as medical applications. In addition, this approach gives a very precise solution to seizure epilepsy detection in EEG signals.

Background Traditional classifier for the classification of diseases, such as K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Random Forest (RF), Logistic Regression (LR), and Support Vector Machine (SVM), often struggle with high-dimensional medical datasets. Objective This study presents a novel classifier to overcome the limitations of traditional classifiers in Parkinson’s disease (PD) detection based on Gower distance. Methods We present the Gower distance metric to handle diverse feature sets in voice recordings, which acts as a dissimilarity measure for all feature types, making the model adept at identifying subtle patterns indicative of PD. Additionally, the Cuckoo Search algorithm is employed for feature selection, reducing dimensionality by focusing on key features, thereby lessening the computational load associated with high-dimensional datasets. Results The proposed classifier based on Gower distance resulted in an accuracy rate of 98.3% with feature selection and achieved an accuracy of 94.92% without the feature selection method. It outperforms traditional classifiers and recent studies in PD detection from voice recordings. Conclusions This accuracy shows the capability of the approach in the correct classification of instances and points out the potential of the approach as a reliable diagnostic tool for the medical practitioner. The findings state that the proposed approach holds promise for improving the diagnosis and monitoring of PD, both within medical institutions and at homes for the elderly.

Embedded systems with cameras and deep learning techniques have been shown to be flexible and good at finding different targets in the areas of intelligent monitoring and urban mobility. These use cases are present in diverse situations and regions. The collection of pertinent data from the deployment site is of utmost importance. This study introduces an innovative methodology for a comprehensive system that integrates vehicle category identification with license plate recognition using the YOLOv8 algorithm. The system comprises three main components: vehicle type detection and recognition, detection of the license plate, and detection of the license plate characters and numbers. The suggested approach intends to enhance the identification system’s applicability in the unique context of Iraqi vehicles, particularly on roadways and in cities and their environments. The dataset used in this study was obtained from various areas inside Iraq. The detection system employed in our research successfully identified three distinct vehicle classes as well as detected and recognized license plates in both Arabic and English. The mean average precision achieved for the aforementioned tasks was 97.5%, 98.94%, 98.6%, and 98.4%, respectively. Through the use of visual data, such as images and videos, our system successfully identified license plates with reduced dimensions. It is posited that our technology has the potential to be used in densely populated areas in order to cater to the substantial requirements for improved visual acuity in smart urban environments.

This study introduces a new classifier tailored to address the limitations inherent in conventional classifiers such as K-nearest neighbor (KNN), random forest (RF), decision tree (DT), and support vector machine (SVM) for arrhythmia detection. The proposed classifier leverages the Chi-square distance as a primary metric, providing a specialized and original approach for precise arrhythmia detection. To optimize feature selection and refine the classifier's performance, particle swarm optimization (PSO) is integrated with the Chi-square distance as a fitness function. This synergistic integration enhances the classifier’s capabilities, resulting in a substantial improvement in accuracy for arrhythmia detection. Experimental results demonstrate the efficacy of the proposed method, achieving a noteworthy accuracy rate of 98% with PSO, higher than 89% achieved without any previous optimization. The classifier outperforms machine learning (ML) and deep learning (DL) techniques, underscoring its reliability and superiority in the realm of arrhythmia classification. The promising results render it an effective method to support both academic and medical communities, offering an advanced and precise solution for arrhythmia detection in electrocardiogram (ECG) data.