Search results for " Classification"

Comparison of different assembly and annotation tools on analysis of simulated viral metagenomic communities in the gut

2013

Abstract Background The main limitations in the analysis of viral metagenomes are perhaps the high genetic variability and the lack of information in extant databases. To address these issues, several bioinformatic tools have been specifically designed or adapted for metagenomics by improving read assembly and creating more sensitive methods for homology detection. This study compares the performance of different available assemblers and taxonomic annotation software using simulated viral-metagenomic data. Results We simulated two 454 viral metagenomes using genomes from NCBI's RefSeq database based on the list of actual viruses found in previously published metagenomes. Three different ass…

Effectiveness of Technology-Based Distance Physical Rehabilitation Interventions for Improving Physical Functioning in Stroke: A Systematic Review an…

2019

OBJECTIVE: To study the effectiveness of technology-based distance physical rehabilitation interventions on physical functioning in stroke. DATA SOURCES: A systematic literature search was conducted in 6 databases from January 2000 to May 2018. STUDY SELECTION: Inclusion criteria applied the patient, intervention, comparison, outcome, study design framework as follows: (P) stroke; (I) technology-based distance physical rehabilitation interventions; (C) any comparison without the use of technology; (O) physical functioning; (S) randomized controlled trials (RCTs). The search identified in total 693 studies, and the screening of 162 full-text studies revealed 13 eligible studies. DATA EXTRACT…

Open data from the first and second observing runs of advanced LIGO and advanced Virgo

2021

Abbot, Rich, et al. (Virgo and MAGIC Collaboration)

Review of Non-English Corpora Annotated for Emotion Classification in Text

2020

In this paper we try to systematize the information about the available corpora for emotion classification in text for languages other than English with the goal to find what approaches could be used for low-resource languages with close to no existing works in the field. We analyze the corresponding volume, emotion classification schema, language of each corresponding corpus and methods employed for data preparation and annotation automation. We’ve systematized twenty-four papers representing the corpora and found that corpora were mostly for the most spoken world languages: Hindi, Chinese, Turkish, Arabic, Japanese etc. A typical corpus contained several thousand of manually-annotated ent…

Weights Space Exploration Using Genetic Algorithms for Meta-classifier in Text Document Classification

2012

Aspects Concerning SVM Method’s Scalability

2008

In the last years the quantity of text documents is increasing continually and automatic document classification is an important challenge. In the text document classification the training step is essential in obtaining a good classifier. The quality of learning depends on the dimension of the training data. When working with huge learning data sets, problems regarding the training time that increases exponentially are occurring. In this paper we are presenting a method that allows working with huge data sets into the training step without increasing exponentially the training time and without significantly decreasing the classification accuracy.

Development of an Automatic Pollen Classification System Using Shape, Texture and Aperture Features

2015

International audience; Automatic detection and classification of pollen species has value for use inside of palynologic allergen studies. Traditional labeling of different pollen species requires an expert biologist to classify particles by sight, and is therefore time-consuming and expensive. Here, an automatic process is developed which segments the particle contour and uses the extracted features for the classification process. We consider shape features, texture features and aperture features and analyze which are useful. The texture features analyzed include: Gabor Filters, Fast Fourier Transform, Local Binary Patterns, Histogram of Oriented Gradients, and Haralick features. We have s…

The laparoscopic approach to Acute Mesenteric Ischemia is today unclear and less debated (AMI). There are in fact no clinical evidences on this parti…

2016

The laparoscopic approach to Acute Mesenteric Ischemia is today unclear and less debated (AMI). There are in fact no clinical evidences on this particular focus and only few articles can be found in several databases (pubmed, cochrane library, etc.), and the problem concerns both diagnostic and therapeutic utilization of the procedure. These considerations were already taken into account in 2012 EAES guidelines where the Grade of Recommendation (GoR) of laparoscopy in AMI was low in both diagnostic and therapeutic aspects. According to the new Oxford Classification [1], the use of laparoscopy in patients with suspicious or diagnosed AMI presents a weak GoR.

Global Upscaling of the MODIS Land Cover with Google Earth Engine and Landsat Data

2021

Image classification has become one of the most common applications in remote sensing yielding to the creation of a variety of operational thematic maps at multiple spatio-temporal scales. The information contained in these maps summarizes key characteristics related with the physical environment and provides fundamental information of the Earth for vegetation monitoring or land use status over time. However, high spatial resolution land cover maps are usually only produced for specific small regions or in an image tile. We present a general methodology to obtain a high spatial resolution land cover maps using Landsat spectral information, the powerful Google Earth Engine platform, and oper…

Increasing the Inference and Learning Speed of Tsetlin Machines with Clause Indexing

2020

The Tsetlin Machine (TM) is a machine learning algorithm founded on the classical Tsetlin Automaton (TA) and game theory. It further leverages frequent pattern mining and resource allocation principles to extract common patterns in the data, rather than relying on minimizing output error, which is prone to overfitting. Unlike the intertwined nature of pattern representation in neural networks, a TM decomposes problems into self-contained patterns, represented as conjunctive clauses. The clause outputs, in turn, are combined into a classification decision through summation and thresholding, akin to a logistic regression function, however, with binary weights and a unit step output function. …