Classification of the Toxicity of Some Organic Compounds to Tadpoles (Rana Temporaria ) Through Integrating Multiple Classifiers

2011 ◽  
Vol 30 (8) ◽  
pp. 672-675 ◽  
Author(s):  
Qiang Su ◽  
Wen-cong Lu ◽  
Bing Niu ◽  
Xu Liu ◽  
Tian-hong Gu
Keyword(s):  
Organic Compounds ◽  
Rana Temporaria ◽  
Multiple Classifiers

scholarly journals Proof of concept study: Testing human volatile organic compounds as tools for age classification of films

PLoS ONE ◽  
2018 ◽  
Vol 13 (10) ◽  
pp. e0203044 ◽  
Author(s):  
C. Stönner ◽  
A. Edtbauer ◽  
B. Derstroff ◽  
E. Bourtsoukidis ◽  
T. Klüpfel ◽  
...  
Keyword(s):  
Volatile Organic Compounds ◽  
Organic Compounds ◽  
Proof Of Concept ◽  
Concept Study ◽  
Volatile Organic

scholarly journals Classification of CT images in COVID-19 and Non-COVID-19 using CNN to extract features and multiple classifiers

2020 ◽  
Author(s):  
Edelson Carvalho ◽  
Edson Carvalho
Keyword(s):  
Ct Images ◽  
Multiple Classifiers

O COVID-19 é uma doença respiratória que já infectou mais de 12.3 milhões de pessoas em todo o mundo e é responsável por mais de 556.300 mortes. O diagnóstico precoce do COVID-19 é essencial para a cura e controle da doença. A tomografia computadorizada (TC) apresentou resultados eficientes na avaliação de pacientes com suspeita de infecção por COVID-19. A análise da TC requer o esforço de um especialista, o que pode levar a erros de diagnóstico. O uso de sistemas de diagnóstico auxiliado por computador pode minimizar os problemas gerados pela análise de TCs por especialistas. Este artigo apresenta uma metodologia para diagnosticar a COVID-19 usando CNN para extração de características e múltiplos classificadores em imagens de TC. A metodologia apresentou uma acurácia de 99,79%, recall de 99,79%, precisão de 99,80%, F-score de 0,997, AUC de 0,997 e índice kappa de 0,995. Os resultados obtidos mostram que a metodologia proposta pode ser utilizada como um sistema de auxílio ao diagnóstico.

scholarly journals Ensemble Deep Learning for Multilabel Binary Classification of User-Generated Content

Algorithms ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 83 ◽  
Author(s):  
Giannis Haralabopoulos ◽  
Ioannis Anagnostopoulos ◽  
Derek McAuley
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
Ensemble Learning ◽  
Affective Computing ◽  
Binary Classification ◽  
User Generated Content ◽  
Emotional Information ◽  
Multiple Classifiers ◽  
Different Levels

Sentiment analysis usually refers to the analysis of human-generated content via a polarity filter. Affective computing deals with the exact emotions conveyed through information. Emotional information most frequently cannot be accurately described by a single emotion class. Multilabel classifiers can categorize human-generated content in multiple emotional classes. Ensemble learning can improve the statistical, computational and representation aspects of such classifiers. We present a baseline stacked ensemble and propose a weighted ensemble. Our proposed weighted ensemble can use multiple classifiers to improve classification results without hyperparameter tuning or data overfitting. We evaluate our ensemble models with two datasets. The first dataset is from Semeval2018-Task 1 and contains almost 7000 Tweets, labeled with 11 sentiment classes. The second dataset is the Toxic Comment Dataset with more than 150,000 comments, labeled with six different levels of abuse or harassment. Our results suggest that ensemble learning improves classification results by 1.5 % to 5.4 % .

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