Due to its high delay resolution, the ultra-wideband (UWB) technique has been widely adopted for fine-grained indoor localization.
To address this issue, we propose methods to artificially create some of this metadata for synthetic tables.
The models were created using various machine learning algorithms such as SVM, KNN, Decision Trees, Random Forests, Naive Bayes, Logistic Regression, and ensemble voting classifiers.
Using this framework, we derive a metric called the radar detection coverage probability as a function of radar parameters such as transmitted power, system noise temperature and radar bandwidth; and clutter parameters such as clutter density and mean clutter cross-section.
For a compound geometry with local field enhancement by a factor of around 1000, a hybrid model is used where the vacuum field calculated using COMSOL is imported into the Particle-In-Cell code PASUPAT where the emission module is implemented.
Applied Physics Mesoscale and Nanoscale Physics Accelerator Physics Computational Physics Plasma Physics
This paper discusses the design of the system used for providing a solution for the problem given at SemEval-2020 Task 9 where sentiment analysis of code-mixed language Hindi and English needed to be performed.
The network is then fine-tuned on a combination of real and these newly constructed artificial labeled instances.
We propose a model for tagging unstructured texts with an arbitrary number of terms drawn from a tree-structured vocabulary (i. e., an ontology).
There is an abundance of temporal and non-temporal data in banking (and other industries), but such temporal activity data can not be used directly with classical machine learning models.
In a traditional setting, classifiers are trained to approximate a target function $f:X \rightarrow Y$ where at least a sample for each $y \in Y$ is presented to the training algorithm.