With respect to electromagnetic waves, the majority of the spectrum has already been explored, from the high-frequency end (as in X-rays and PET) to low-megahertz bands (where MRI operates). The portion of the spectrum that is least investigated and has been generating a lot of interest ...
After removing the unnecessary windows, the extracted non-fiducial features were used to train three classification algorithms (Nearest Neighbor, SVM and LDA). An experiment was conducted using Normal Sinus Rhythm, PTB Diagnostic and QT databases. This research shows that the recognition rate of both...
When processing multivariate time series, DBSCAN treats each time window as a point, with the anomaly score being the distance between the point and the nearest cluster [39]. One-class support vector machine (OC-SVM) [40] is another data clustering algorithm that employ kernel ticks. OC-SVM...
3.4. K-Nearest Neighbors (KNN) K-nearest neighbors (KNN) is a non-parametric and instance-based machine learning algorithm used for classification tasks. The algorithm operates by identifying the K most similar data points (neighbors) from the labeled dataset to a given query example [108]. Th...
Machine learning (ML) methods such as artificial neural networks (ANN) [6,7,8], support vector machines (SVM) [9,10], deep neural networks (Deep NN) [11], and k-nearest neighbours (KNN) [12] have also been applied to simulate WQ [13]. Artificial intelligence (AI) techniques are ...
3.4. K-Nearest Neighbors (KNN) K-nearest neighbors (KNN) is a non-parametric and instance-based machine learning algorithm used for classification tasks. The algorithm operates by identifying the K most similar data points (neighbors) from the labeled dataset to a given query example [108]. Th...
Nevertheless, the need to store and transmit data to a centralized server may compromise privacy and security. In contrast, Federated Learning (FL), a decentralized learning approach that protects privacy, trains models locally before sending only the parameters to the centralized server. Even though...
Instance-based learning, memory-based learning, refers to a group of ML techniques that can adapt to unseen data and are commonly used for real-time data processing. These methods use the concepts of the nearest neighbor optimization algorithm to classify or predict data. The memory-based algorit...
Interference and wall penetration losses are the main challenges to be handled in smart homes. More flexibility is needed, which translates into taking advantage of the unused spectrum. There is a need for technology which connects the smart homes toward developing a smart city infrastructure, and ...
As an alternative solution, visible light communication (VLC) has been proposed, an optical wireless communication technology that uses the visible light spectrum with wavelengths between 375 and 780 nm [3]. This technology uses light-emitting diodes (LEDs) that produce incoherent light to illuminate...