A Survey On The Applications Of K-Nearest Neighbour Algorithm And Its VariantsBigdataKNN ClassificationData CenterMedical ApplicationNN CLASSIFICATIONDISTANCEBig data Analytics are a developing field that analyses huge amount of structured, semi-structured and unstructured data that has a possibility to be...
Ischemic, one of the fatal diseases characterized by insufficient blood supply to tissues poses a significant global health burden, necessitating the devel
Generally said, machine learning describes a computer program or algorithm that learns and therefore improves automatically from experience [30, 31]. Two of the most important areas in which it makes sense to use programs that learn from their experience and improve themselves are complex problems ...
proposed using the K-nearest neighbor (KNN) algorithm to classify iEEG data into “pre-“ or “interictal” and reported 87.5–100% prediction accuracy30. This application of KNN was to develop a warning system for upcoming seizures and would need to be paired with an electrical stimulation ...
The extracted information has helped in the estimation of the movement intention and has been used to train an adaptive movement prediction algorithm. Monkeys have been used to move a brain-controlled robot arm in virtual reality. Researchers have also succeeded in assisting them to eat with a ...
If most of the instances belong to a category, the new data set belongs to this category. At present, there are many packages developed for the kNN algorithm in the Python language. Among these, scikit-learn and Pypl are the most commonly used packages. It should be noted that scikit-...
Finally, a classification model is constructed based on Random Forest and improved KNN algorithm to classify the entities. By constructing different clas- sification models for experimental comparison, the accuracy and stability advantages of the proposed method for classifi- cation in service community...
(2005) proposed an algorithm to extract the IMFs of a signal. After the decomposition process, the original signal is characterized as the combination of the extracted IMFs and the residues ri+1. Mathematically, it can be represented using the following equation (13): (13) Figure 9 ...
The combination of algorithms to create an ensemble can be homogeneous, by applying the same algorithm for all combined estimators (e.g., decision trees), or heterogeneous, by combining a variety of algorithms (e.g., support vector machines with logistic regression). Notably, the combination of...
A novel grey prediction evolution algorithm for multimodal multiobjective optimization Ting Zhou, Zhongbo Hu, Quan Zhou, Shixiong Yuan Article 104173 select article Modified Kalman particle swarm optimization: Application for trim problem of very flexible aircraft ...