Forecasting and classification of new cases of COVID 19 before vaccination using decision trees and Gaussian mixture model

Hamdi, Monia; Hilali-Jaghdam, Inès; Elnaim, Bushra M.E.; Elhag, Azhari A.

doi:10.1016/j.aej.2022.07.011

Cited by 15 publications

(8 citation statements)

References 18 publications

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“…Os resultados obtidos com o modelo proposto são comparados com os obtidos por modelos clássicos e relevantes apresentados na literatura: i) modelos estatísticos (ARIMA [30], LINREG [30], REGTREE [43] e SVR [44]), ii) modelos de rede neural (MLP [35], RBF [36], CNN [37]), e iii) modelos dinâmicos (NARX [31] e LSTM [32]).…”

Section: Simulac ¸õEs E Resultados Experimentaisunclassified

Um Modelo Profundo Crescente para Previsão de óbitos por COVID-19 no Brasil

Araújo,

Brito,

Mghazli

et al. 2024

Anais Do XVI Congresso Brasileiro De Inteligência Computacional

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A doença do coronavírus 2019 (COVID-19) foi declarada pela Organização Mundial da Saúde (OMS) como uma pandemia sem precedentes, tendo sobrecarregado os sistemas de saúde em todo o mundo devido a alta demanda de internações em unidades de terapia intensiva. Nesse cenário, entender a dinâmica da pandemia e essencial para orientar a estratégia para lidar simultaneamente com o aumento da demanda por apoio a saúde e os efeitos diretos e colaterais na economia. Neste trabalho é apresentado um estudo empírico sobre a serie temporal de óbitos por COVID-19 no Brasil e, baseado neste estudo, propomos um modelo de dilatação-erosão profunda, projetado por um processo de aprendizado baseado em gradiente descendente, capaz de prever esse tipo particular de serie temporal. Os resultados obtidos mostram que o modelo proposto supera modelos clássicos e recentes de aprendizado de máquina apresentados na literatura para estimar a dinâmica da pandemia de COVID-19 no Brasil.

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Section: Simulac ¸õEs E Resultados Experimentaisunclassified

Um Modelo Profundo Crescente para Previsão de óbitos por COVID-19 no Brasil

Araújo,

Brito,

Mghazli

et al. 2024

Anais Do XVI Congresso Brasileiro De Inteligência Computacional

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“…ID3, short for Iterative Dichotomize 3, is a type of decision tree that works on discrete classes and cannot be used on missing data. [17]. ID3 is the basic algorithm that has been developed on the C4.5 and CART methods.…”

Section: Data Modelling (Id3 C45 Cart)mentioning

confidence: 99%

“…Meanwhile, if the target is categorical, the resulting tree is a classification tree [21]. The advantage of CART compared to ID3 and C4.5 is the construction of a tree with only two children per node [17]. Establishing a CART tree is done through the process of collecting datasets at the root node of the tree with each node divided into two child nodes with a separation variable which is one of the predictor variables [22].…”

Section: Data Modelling (Id3 C45 Cart)mentioning

confidence: 99%

Decision Tree based Data Modelling for First Detection of Thalassemia Major

Setiawan,

Permata,

Yuda

2024

SISFOKOM

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Thalassemia is an inherited blood disease which lacks hemoglobin, the protein that is carrying oxygen to the body. The severe one is called Thalassemia Major which needs special care about blood transfusion. The use of rule-based method to create an inference as the first diagnosis of Thalassemia Major is not effective as rules have to be achieved from long interview with the medical personnel. This research aims to create a model based on decision tree for first detection of Thalassemia Major. The dataset is obtained by interview of Thalassemia symptoms and primary data of medical records from a hospital. Classical decision tree models used are ID3, C4.5 and CART. The models are evaluated by Train-Test Split consists of 70% training and 30% testing data and k-Fold Validation for checking model’s overfitting or underfitting. The output of this research is a final tree model from the best performance of decision tree models. The final result shows that C4.5 has the best performance with accuracy 100% and not overfitting or underfitting. Also, C4.5 performs feature selections to its tree modeling to simplify the inference. In brief, decision tree based modeling is effective to be used as first detection of Thalassemia Major by interview symptoms with generating automatic rules from its tree model.

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“…Decision Tree (D-Tree) is a supervised machine learning algorithm that can perform modeling for both classification, regression, data mining, and statistical problems [20]. When the D-Tree algorithm performs classification, it creates decision nodes based on features and goals, and from these nodes, it also creates leaf nodes and carries out the classification process.…”

Section: ) Decision Treementioning

confidence: 99%

A Novel Deep Learning-Based Hybrid Method for the Determination of Productivity of Agricultural Products: Apple Case Study

Bal

Kayaalp

2023

IEEE Access

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The production of agricultural products and the high yield in these products are of critical importance for the continuation of human life. In recent years, machine learning and deep learning technologies have been widely used in determining agricultural productivity. The purpose of this study was to estimate the yield of apple fruit by using a novel deep learning-based hybrid method. First, by using images belonging to the golden and royal gala apple varieties, a classification was made with the help of a convolutional neural network (CNN) that was designed for the study. Then, using classical machine learning algorithms and bagging and boosting algorithms, a hybrid application was performed by classifying the images whose feature extractions were done with the designed CNN. The results of the study, presented on 4 separate datasets (Datasets A, B, C, and D), were evaluated based on accuracy, precision, recall, F-measure, and Cohen kappa scores. Considering the accuracy results for Datasets B, C, and D, it was determined that the hybrid model that gave the best result was the CNN-SVM model. For Dataset A, the CNN-SVM and CNN-Gradient Boosting hybrid models gave the best and same accuracy. Dataset C was determined as the most appropriate dataset in terms of the more balanced distribution of train, test, and validation size in the datasets, the results of the proposed hybrid CNN model, and the evaluation of the results of the model. For Dataset C, it was found that the accuracy of the hybrid model was 99.70%. Precision, recall, f-measure, and Cohen kappa scores were 99%. The results of the study revealed that the hybrid models showed effective results in determining the productivity of apple fruit through images belonging to the golden and royal gala varieties.INDEX TERMS Apple yield prediction, deep learning, ensemble methods, machine learning, smart farm.

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Forecasting and classification of new cases of COVID 19 before vaccination using decision trees and Gaussian mixture model

Cited by 15 publications

References 18 publications

Um Modelo Profundo Crescente para Previsão de óbitos por COVID-19 no Brasil

Um Modelo Profundo Crescente para Previsão de óbitos por COVID-19 no Brasil

Decision Tree based Data Modelling for First Detection of Thalassemia Major

A Novel Deep Learning-Based Hybrid Method for the Determination of Productivity of Agricultural Products: Apple Case Study

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