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Social Network Analysis in Virtual Group Coaching
Published in William J. Rothwell, Cho Hyun Park, Virtual Coaching to Improve Group Relationships, 2020
Closeness, by definition, is the sum of “shortest distance” between the node or person and others in the network (Prell, 2012). It shows how quickly or how easily the node or person could connect to others. The quicker connections often mean easier access to the resources or information in the network. For example, they transferred my call three times to connect to someone in the HR department for my payroll questions while my supervisor only needs to make one call since he has the direct number to the payroll department. Here, my supervisor has higher closeness than I do in the scenario. We also find it is also that the person with high closeness usually is the one who has more power and influences in the organization (Prell, 2012).
Affective Natural Interaction Using EEG: Technologies, Applications and Future Directions
Published in Spyrou Evaggelos, Iakovidis Dimitris, Mylonas Phivos, Semantic Multimedia Analysis and Processing, 2017
Charline Hondrou, George Caridakis, Kostas Karpouzis, Stefanos Kollias
A very simple classifier can be based on a nearest-neighbor approach. In this method, one simply finds in the N-dimensional feature space the closest object from the training set to an object being classified [30]. Since the neighbor is nearby, it is likely to be similar to the object being classified, and so is likely to be in the same class as that object. Closeness is usually defined in terms of Euclidean distance [743].
General introduction
Published in Adedeji B. Badiru, Handbook of Industrial and Systems Engineering, 2013
During the first phase, an appropriate measure is selected for measuring interobject similarity. The proximity or closeness between each pair of objects is used as a measure of similarity. Since distance is complement of similarity, it is used as a measure of similarity.
Deep Learning-Based Model Using DensNet201 for Mobile User Interface Evaluation
Published in International Journal of Human–Computer Interaction, 2023
The KNN classifier is based on feature similarity function. The algorithm takes three inputs: A(m,n) which represents the matrix of MUI features, the number of the neighbors K and D(m,1) which denotes the class of each row of A that can be 1 if it is good or 0 if it is bad (lines 2–4). Our process starts by the normalization step to prevent features with initially large values from outweighing features with initially smaller values. This normalization is based on Z-SCORE formula: Here, is the mean value of the feature and is the standard deviation of the feature. After the normalization of the features like mentioned in line 5, the algorithm use five-cross validation which is a statistical method used to estimate the skill of machine learning models (Zhang, 1993, line 6). In each cross validation the KNN flows the three steps: train, test and predict (lines 8–12). The training is based on the fitcknn function to generate a model (line 10). When given an unknown instance, a k-nearest-neighbor classifier explores the search space for the k training instances that are nearest to the unknown instance. These k training instances are the k “closest neighbors” of the unknown instance. “Closeness” is determined based on the Euclidean distance. The Euclidean distance between two features F1 =(f11, f12,…, f) and F2 =(f21, f22,…, f) is determined by the following formula: Where F1 and F2 are the features vectors of MUIs.
Machine learning approach in mortality rate prediction for hemodialysis patients
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2022
Nevena Radović, Vladimir Prelević, Milena Erceg, Tanja Antunović
Finally, analysis was conducted with the K-nearest neighbor (KNN) algorithm as well (Han and Kamber 2007). KNN algorithm is based on assumption that similar objects exist in close proximity. A new object is assigned to the class most common among its k nearest neighbors. Closeness is defined in terms of a distance metric, such as Euclidean, Manhattan, and Minkowski distance. More details about the KNN algorithm can be found in Han and Kamber (2007).