Digital health interventions refer to the use of digital technology and connected devices to improve health outcomes and healthcare delivery. This includes
Plant biology has the potential of providing partial solutions to several of the most daunting problems faced by our planet in the twenty-first century viz. increasing scarcity of food, the depletion of global oil reserves, and a shortage of freshwater. Plants are greatly suited for systems analy...
Support Vector Machines (SVM) and Long Short-Term Memory (LSTM) are employed for these tasks. SVMs are a classification algorithm that determines a separating "line" or "plane" in a graph to classify data points. LSTM, on the other hand, is a type of neural network with enhanced memory ...
When analysing currently available global and continental land cover products, it can be observed that they are mostly created by employing traditional machine learning methods such as random forest (RF) and support vector machines (SVM). Both methods have over the years been established as the mos...
support vector machines TENG triboelectric nanogenerator THT through hole technology VTB virtual test bed VR virtual reality XR extended reality 1. Introduction Human civilisation has undergone a shift in technological paradigm with the advent of Industry 4.0. In this new era, where industry, automation...
[95] utilises an approach similar to Nanopolish to detect BrdU incorporation, while EpiNano uses support vector machines (SVMs) to detect RNA m6A. Recent methods use neural network classifiers to detect 6mA and 5mC (mCaller [108], DeepSignal [110], DeepMod [111]). The accuracy of these...
The Internet of Things (IoT) has been rapidly evolving towards making a greater impact on everyday life to large industrial systems. Unfortunately, this has attracted the attention of cybercriminals who made IoT a target of malicious activities, opening
*DTW: Dynamic Time Wrap, GMM: Gaussian Mixture Model, KNN: k-nearest neighbors, SVM: Support Vector Machines. 5.1. Statistical methods Some researchers attempted to detect meaningful correlations among features that describe driving behavior. Paefgen et al. (2012) present a number of important ...
While traditional algorithms such as logistic regression and support vector machines (SVMs) laid the foundation, contemporary DD relies on sophisticated tools like recurrent neural networks, convolutional neural networks, autoencoders, and transformer models. These tools excel in uncovering intricate molecula...
Following a different idea, BundleMAP [27] uses support vector machines on the mean and covariance of the coordinates of the streamlines in a bundle to detect FPs. Clustering methods based on deep learning have the potential to be computationally more efficient than classical approaches. In [40...