After deriving parameters for each of the GLIF models, we assessed how well these parameters could classify neurons into putative types corresponding to transgenic lines. We used two different clustering algori
Hierarchical clustering methods are relevant to developing classifiers of motor activities from data recorded using wearable systems. They allow users to assess feasibility of a classification problem and choose architectures that maximize accuracy. By relying on this approach, the clinical importance of di...
Cell classification methods typically involve manual gating or clustering of the expression matrix using algorithms that were developed for isolated cells, such as cytometry or single cell RNA sequencing (scRNAseq)8,13,19,24,25,26,27,28,29,30. However, deriving cell classifications from multiplexed...
K-means clusteringDesign principlesThe digital games industry has grown exponentially due to the diversification of games and the increasing multiplicity of the user target base. The market explosion and the great variety make digital game cataloguing and classification challenging issues whose effectiveness...
[2]. Multi-omic assays like CITE-Seq introduce new dimensionality to the data, but often require nuanced analyses to extract meaningful results. For example, a common single cell analysis consists of unsupervised clustering of cells followed by the classification of cell populations. However, there...
Various molecular typing methods that can differenti- ate across the 8 major TB lineages, have been used to gain clues as to whether a particular infection contains more than 1 M. tuberculosis strain. Restriction Fragment Length Polymorphism (RFLP) analysis relies on the posi- tioning and copy...
RGB color, histogram, and centroid are achieved using K-means clustering. 725 images were utilized for training, whereas 342 were used for testing [22]. In addition, Palakodati et al. introduced a methodology based on three different types of fruits to avoid the spreading of rottenness. (CNN...
The reason why one should apply different clustering algorithms and consider the best performance is to guarantee that the final evaluation is not penalized by the bad performance of a particular approach. Clustering approaches able to work both for spherical clusters (such as k-means; and Affinity...
Deep learning methods are commonly applied for foot type classification to bring potential transformations to the footwear industry. With the use of AI, personalized insole and footwear can be designed and created with high accuracy and efficiency by analyzing large datasets and integrating various ...
Specifically, by effectively extracting and utilizing this edge information, the model can better distinguish between various types of ground objects, thereby reducing confusion and the likelihood of misclassification. In future research, we can explore integrating an image spectral clustering module into ...