Department of Analytical Chemistry, University of Granada, Granada, SpainSpringer Berlin HeidelbergAnalytical and Bioanalytical ChemistryRuiz-Samblas C, Cadenas JM, Pelta DA, et al. Application of data mining methods for classification and prediction of olive oil blends with other vegetable oils[J]. ...
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU. - catboost/catboost
MHS-VM: Multi-Head Scanning in Parallel Subspaces for Vision Mamba [paper] (2024.06.10) DualMamba: A Lightweight Spectral-Spatial Mamba-Convolution Network for Hyperspectral Image Classification [paper] (2024.06.11) Autoregressive Pretraining with Mamba in Vision [paper] [code] (2024.06.11) PixMa...
[Objective] To explore the association between the anxiety and other health examination indicators gotten by occupational health surveillance,the discriminant analysis classification was done with the random forest method. [Methods]The generalized anxiety disorder of workers who received occupational health su...
In the training data, individual AUC values were very high in all groups. The confusion matrix of the multiclassification problem in Fig. 1 resulted in a global accuracy of .89, a kappa coefficient of .84 which can both be regarded as very good agreement20 Precision (positive predictive valu...
This analysis has allowed us to determine the existence of a bubble pattern in Bitcoin based on the comparative analysis of a great number of financial bubbles. On the other hand, the application of different methods to the comparative analysis between selected historical financial bubbles and ...
Inclusion of analogous criteria may help to clarify classification at the genus level. For example, all members of the Enterovirus genus exhibit at least 35% amino acid identity in VP1 (Oberste et al., 1999b) and 43% identity in complete P1 capsid protein sequence (MSO, unpublished data)....
This type of methods usually extract session-level or packet-level statistical features about traffics [36], and then use machine learning algorithms for classification, e.g., Random Forest, K-Nearest Neighbors, clustering, etc. [37,38]. APPScanner [25] extracts statistical features from packet ...
Important note (2 June 2023): we do not endorse or recommend the use of any of the methods listed here. In fact, we find most if not all of them quite ridiculous, and in many cases a useless wste of space. If you send us a message asking to have your own recently-published paper...
In Natural Language Processing (NLP), most of the text and documents contain many words that are redundant for text classification, such as stopwords, miss-spellings, slangs, and etc. In this section, we briefly explain some techniques and methods for text cleaning and pre-processing text docum...