Machine Learning Classification
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2022 ◽  
Vol 176 ◽  
pp. 121466
Author(s):  
Rubén Herrera ◽  
Francisco Climent ◽  
Pedro Carmona ◽  
Alexandre Momparler

SinkrOn ◽  
2022 ◽  
Vol 7 (1) ◽  
pp. 59-65
Author(s):  
Artika Arista

Many people today are unsure whether they have COVID-19. The frequent fever, dry cough, and sore throat are all signs and symptoms of COVID-19. If a person has signs or symptoms of coronavirus disease 2019 (COVID-19), he/she should see the doctor or go to a clinic as soon as possible. As a result, it's vital to learn and comprehend the fundamental differences. COVID-19 can cause a wide range of symptoms. The experiments were carried out using two Machine Learning Classification Algorithms, namely Decision Tree (DT) and Logistic Regression (LR). Both algorithms were written and analyzed using the Python program in Jupyter Notebook 6.4.5. From the results obtained in the experiments of covid symptoms dataset, on average, the DT model has obtained the best cross-validation average and the testing performance average compared to the LR machine learning models. For cross-validation results, the DT model has achieved an accuracy of 98.0%. For performance testing, the DT model has achieved an accuracy of 98.0%. The LR has obtained the second-best result on the average of cross-validation performance and the testing results. For cross-validation results, the LR model has achieved an accuracy of 96.0%. For performance testing, the LR model has achieved an accuracy of 97.0%. Consequently, the DT for the COVID-19 symptoms dataset is outperforming the LR for cross-validation and testing results.


Webology ◽  
2021 ◽  
Vol 18 (2) ◽  
pp. 740-755
Author(s):  
V. Vijay Priya ◽  
M. Uma

Drowsiness is the main cause of road accidents and it leads to severe physical injury, death, and significant economic losses. To monitor driver drowsiness various methods like Behaviour measures, Vehicle measures, Physiological measures and Hybrid measures have been used in previous research. This paper mainly focuses on physiological methods to predict the driver’s drowsiness. Several physiological methods are used to predict drowsiness. Among those methods, Electroencephalography is one of the non-invasive physiological methods to measure the brain activity of the subject. EEG brain signal extracted from the human scalp is analysed with various features and used for various health application like predicting drowsiness, fatigue etc. The main objective of the proposed system is to early predict the driver drowsiness with high accuracy so that we have divided our work into two steps. The first step is to collect the publicly available dataset of EEG based Eye state as (Eye open and Eye closed) where the signal acquisition process was done from Emotiv EEG Neuroheadset (14 electrodes) and analysed various feature engineering techniques and statistical techniques. The second step was applied with the machine learning classification model as K-NN and performance-based predicting models are used. In the Existing System, they used various machine learning classification models like K-NN and SVM for EEG Eye state classification and produced results around 80% -97%. Compared to the Existing system our proposed method produced better classification models for predicting driver drowsiness using different Feature engineering process and classification models as K-NN produced 98% of accuracy.


2021 ◽  
Vol 16 (59) ◽  
pp. 172-187
Author(s):  
Tran-Hieu Nguyen ◽  
Anh-Tuan Vu

Transmission towers are tall structures used to support overhead power lines. They play an important role in the electrical grids. There are several types of transmission towers in which lattice towers are the most common type. Designing steel lattice transmission towers is a challenging task for structural engineers due to a large number of members. Therefore, discovering effective ways to design lattice towers has attracted the interest of researchers. This paper presents a method that integrates Differential Evolution (DE), a powerful optimization algorithm, and a machine learning classification model to minimize the weight of steel lattice towers. A classification model based on the Adaptive Boosting algorithm is developed in order to eliminate unpromising candidates during the optimization process. A feature handling technique is also introduced to improve the model quality. An illustrated example of a 160-bar tower is conducted to demonstrate the efficiency of the proposed method. The results show that the application of the Adaptive Boosting model saves about 38% of the structural analyses. As a result, the proposed method is 1.5 times faster than the original DE algorithm. In comparison with other algorithms, the proposed method obtains the same optimal weight with the least number of structural analyses.


EBioMedicine ◽  
2022 ◽  
Vol 75 ◽  
pp. 103757
Author(s):  
Salvatore Gitto ◽  
Renato Cuocolo ◽  
Kirsten van Langevelde ◽  
Michiel A.J. van de Sande ◽  
Antonina Parafioriti ◽  
...  

2022 ◽  
Vol 188 ◽  
pp. 111449
Author(s):  
Yilin Chen ◽  
Chuanshi Liu ◽  
Yiming Du ◽  
Jing Zhang ◽  
Jiayuan Yu ◽  
...  

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