scholarly journals THE APPLICATION OF DATA MINING BY CLASSIFICATION IN A DATABASE OF NOTIFIED COVID-19 CASES IN MANAUS-AM

2021 ◽  
Vol 9 (4) ◽  
pp. 318-330
Author(s):  
Fábio Gomes Cantuário ◽  
Luiz Eduardo Santos de Araújo ◽  
Rilmar Pereira Gomes ◽  
David Barbosa de Alencar
Keyword(s):  
Data Mining ◽  
Scientific Article ◽  
Ministry Of Health ◽  
Risk Of Death ◽  
Present Information ◽  
Clinical Conditions ◽  
The City ◽  
Knowledge Learning

This scientific article aims to present information on the cases of comorbidity that most aggravate the symptoms of SARS-CoV-2 (Covid 19) with data extracted from the database of the official website of the Ministry of Health, which defined a system to monitor the information detected in the diagnoses of each patient. Since the beginning of the pandemic, the city of Manaus has suffered great consequences in relation to the SARS-CoV-2 virus (Covid-19). predicting patients at higher risk of death. We describe the origin and spread of the virus and the use of the SGBD software MySql and MySql Workbench to improve data in the selection and pre-processing, with the resources of the weka tool for knowledge learning, ending with the objective achieved in the classification of comorbidities that further aggravate the clinical conditions.

Potential classification of Smart Village – Smart Economy with Deep Learning methods

2021 ◽  
Vol 2 (3) ◽  
pp. 147-162
Author(s):  
Runanto Runanto ◽  
Muhammad Fahmi Mislahudin ◽  
Fauzan Azmi Alfiansyah ◽  
Maudy Khairunnisa Maisun Taqiyyah ◽  
Eneng Tita Tosida
Keyword(s):  
Data Mining ◽  
Deep Learning ◽  
Development Program ◽  
City Development ◽  
Target Class ◽  
Learning Methods ◽  
Development Goals ◽  
The Village ◽  
The City

Development gap in the city and village is still happening on Indonesia. It happened because of the massive urbanization factors. Poverty in the Indonesian villages are relatively higher than on the urbans. In order to reach the maximal city development, Ministry of Village, Development of Disadvantaged Regions and Transmigration of Indonesia created a sustainable village development program namely Village’s Sustainable Development Goals (SDGs) and optimized the village potential data. This study aimed to design the smart village – smart economy classification system by using deep learning methods on village potential data on Indonesia at 2020. The method used in this study is data mining processes namely KDD (Knowledge Discovery and Data mining). The result in this study showed the best models were obtained which consisting of 2 hidden layers and each layer is 128, 128 layers which using target class from the process of calculating the score is able to reach 94.93% of the accuracy from the training process and 96% on the testing process and succeeded to classify the potentials of smart village – smart economy.

STUDY OF ORNAMENTS IN HUNTO SULTAN AMAY MOSQUE GORONTALO

ARTic ◽  
2019 ◽  
Vol 4 ◽  
pp. 167-176
Author(s):  
Risti Puspita Sari Hunowu
Keyword(s):  
Qualitative Method ◽  
The Other ◽  
Visual Form ◽  
Research Material ◽  
Other Hand ◽  
The World ◽  
Existing Form ◽  
The City ◽  
Dutch Colonial

This research is aimed at studying the Hunto Sultan Amay Mosque located in Gorontalo City. Hunto Sultan Amay Mosque is the oldest mosque in the city of Gorontalo The Hunto Sultan Amay Mosque was built as proof of Sultan Amay's love for a daughter and is a representation of Islam in Gorontalo. Researchers will investigate the visual form of the Hunto Sultan Amay Mosque which was originally like an ancient mosque in the archipelago. can be seen from the shape of the roof which initially used an overlapping roof and then converted into a dome as well as mosques in the world, we can be sure the Hunto Sultan Amay Mosque uses a dome roof after the arrival of Dutch Colonial. The researcher used a qualitative method by observing the existing form in detail from the building of the mosque with an aesthetic approach, reviewing objects and selecting the selected ornament giving a classification of the shapes, so that the section became a reference for the author as research material. Based on the analysis of this thesis, the form  of the Hunto Sultan Amay mosque as well as the mosques located in the archipelago and the existence of ornaments in the Hunto Sultan Amay Mosque as a decorative structure support the grandeur of a mosque. On the other hand, Hunto Mosque ornaments reveal a teaching. The form of a teaching is manifested in the form of motives and does not depict living beings in a realist or naturalist manner. the decorative forms of the Hunto Sultan Sultan Mosque in general tend to lead to a form of flora, geometric ornaments, and ornament of calligraphy dominated by the distinctive colors of Islam, namely gold, white, red, yellow and green.

scholarly journals Comparison of Night, Day and 24 h Motor Activity Data for the Classification of Depressive Episodes

Diagnostics ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 162 ◽  
Author(s):  
Julieta G. Rodríguez-Ruiz ◽  
Carlos E. Galván-Tejada ◽  
Laura A. Zanella-Calzada ◽  
José M. Celaya-Padilla ◽  
Jorge I. Galván-Tejada ◽  
...  
Keyword(s):  
Data Mining ◽  
Motor Activity ◽  
Random Forest Classifier ◽  
Machine Learning Algorithms ◽  
World Population ◽  
Activity Data ◽  
The World ◽  
Depressive Episodes ◽  
Full Day

Major Depression Disease has been increasing in the last few years, affecting around 7 percent of the world population, but nowadays techniques to diagnose it are outdated and inefficient. Motor activity data in the last decade is presented as a better way to diagnose, treat and monitor patients suffering from this illness, this is achieved through the use of machine learning algorithms. Disturbances in the circadian rhythm of mental illness patients increase the effectiveness of the data mining process. In this paper, a comparison of motor activity data from the night, day and full day is carried out through a data mining process using the Random Forest classifier to identified depressive and non-depressive episodes. Data from Depressjon dataset is split into three different subsets and 24 features in time and frequency domain are extracted to select the best model to be used in the classification of depression episodes. The results showed that the best dataset and model to realize the classification of depressive episodes is the night motor activity data with 99.37% of sensitivity and 99.91% of specificity.

E-Commerce product comparison portal for classification of customer data based on data mining

2021 ◽  
Author(s):  
Dilip Kumar Sharma ◽  
Sarika Lohana ◽  
Saurabh Arora ◽  
Ashutosh Dixit ◽  
Mohit Tiwari ◽  
...  
Keyword(s):  
Data Mining ◽  
Customer Data ◽  
Product Comparison

CLASSIFICATION OF HIGH-DIMENSIONAL MICROARRAY DATA WITH A TWO-STEP PROCEDURE VIA A WILCOXON CRITERION AND MULTILAYER PERCEPTRON

2011 ◽  
Vol 10 (01) ◽  
pp. 1-14
Author(s):  
VLADIMIR NIKULIN ◽  
TIAN-HSIANG HUANG ◽  
GEOFFREY J. MCLACHLAN
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
High Dimensional ◽  
Second Step ◽  
Support Vector ◽  
Step Procedure ◽  
Leave One Out ◽  
Natural Combination ◽  
Feature Selection Techniques

The method presented in this paper is novel as a natural combination of two mutually dependent steps. Feature selection is a key element (first step) in our classification system, which was employed during the 2010 International RSCTC data mining (bioinformatics) Challenge. The second step may be implemented using any suitable classifier such as linear regression, support vector machine or neural networks. We conducted leave-one-out (LOO) experiments with several feature selection techniques and classifiers. Based on the LOO evaluations, we decided to use feature selection with the separation type Wilcoxon-based criterion for all final submissions. The method presented in this paper was tested successfully during the RSCTC data mining Challenge, where we achieved the top score in the Basic track.

Improved differentiation classification of variable precision artificial intelligence higher education management

2021 ◽  
pp. 1-10
Author(s):  
Chao Dong ◽  
Yan Guo
Keyword(s):  
Artificial Intelligence ◽  
Higher Education ◽  
Data Mining ◽  
Decision Tree ◽  
Classification Accuracy ◽  
Attribute Selection ◽  
Higher Education Management ◽  
Education Management ◽  
Decision Tree Classification

The wide application of artificial intelligence technology in various fields has accelerated the pace of people exploring the hidden information behind large amounts of data. People hope to use data mining methods to conduct effective research on higher education management, and decision tree classification algorithm as a data analysis method in data mining technology, high-precision classification accuracy, intuitive decision results, and high generalization ability make it become a more ideal method of higher education management. Aiming at the sensitivity of data processing and decision tree classification to noisy data, this paper proposes corresponding improvements, and proposes a variable precision rough set attribute selection standard based on scale function, which considers both the weighted approximation accuracy and attribute value of the attribute. The number improves the anti-interference ability of noise data, reduces the bias in attribute selection, and improves the classification accuracy. At the same time, the suppression factor threshold, support and confidence are introduced in the tree pre-pruning process, which simplifies the tree structure. The comparative experiments on standard data sets show that the improved algorithm proposed in this paper is better than other decision tree algorithms and can effectively realize the differentiated classification of higher education management.

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