scholarly journals APLICAÇÃO DE APRENDIZADO DE MÁQUINA COM DADOS DE SENSORIAMENTO REMOTO PARA O MAPEAMENTO DE FLORESTAS URBANAS

2021 ◽  
Vol 20 (2) ◽  
pp. 16
Author(s):  
Priscila Lôpo Guimarães Cano ◽  
José Marcato Junior
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Mean Shift ◽  
Google Earth ◽  
Mato Grosso ◽  
The World ◽  
Mato Grosso Do Sul

As florestas urbanas fornecem vários benefícios para as cidades, incluindo redução das temperaturas, melhorias na qualidade do ar, saúde e lazer da população e proteção de bacias hidrográficas, tornando assim um dos indicadores mais importantes da qualidade ambiental e sustentabilidade urbana. Campo Grande, no Mato Grosso do Sul, possui o título de "Tree Cities of the World", que reconhece as cidades mais comprometidas com a preservação das florestas urbanas e o desenvolvimento sustentável, portanto o mapeamento e monitoramento servem como ferramenta de auxílio para os governos e tomadores de decisão. O presente trabalho consistiu em combinar imagens de sensoriamento remoto de alta resolução e algoritmo de aprendizado de máquina (machine learning) para mapear florestas urbanas. O estudo foi realizado na Bacia hidrográfica do Prosa, Campo Grande, Mato Grosso do Sul, Brazil, considerando imagens do Google Earth de 14 de maio de 2020. Para fins de classificação, adotou-se o algoritmo Random Forest associado com segmentação prévia da imagem com a técnica mean shift. Como resultado, obteve-se um percentual de 18,31% de vegetação arbórea na bacia e a métrica F1 superior a 85%, possibilitando, assim, um mapeamento acurado e atualizado de florestas urbanas. PALAVRAS-CHAVE: árvores. random forest. classificação supervisionada. arcgis pro.

scholarly journals Exploratory Analysis of Driving Force of Wildfires in Australia: An Application of Machine Learning within Google Earth Engine

2020 ◽  
Vol 13 (1) ◽  
pp. 10
Author(s):  
Andrea Sulova ◽  
Jamal Jokar Arsanjani
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Random Forest ◽  
Google Earth ◽  
Summer Season ◽  
Driving Factors ◽  
Machine Learning Algorithms ◽  
Classification And Regression Tree ◽  
Training Dataset ◽  
Google Earth Engine

Recent studies have suggested that due to climate change, the number of wildfires across the globe have been increasing and continue to grow even more. The recent massive wildfires, which hit Australia during the 2019–2020 summer season, raised questions to what extent the risk of wildfires can be linked to various climate, environmental, topographical, and social factors and how to predict fire occurrences to take preventive measures. Hence, the main objective of this study was to develop an automatized and cloud-based workflow for generating a training dataset of fire events at a continental level using freely available remote sensing data with a reasonable computational expense for injecting into machine learning models. As a result, a data-driven model was set up in Google Earth Engine platform, which is publicly accessible and open for further adjustments. The training dataset was applied to different machine learning algorithms, i.e., Random Forest, Naïve Bayes, and Classification and Regression Tree. The findings show that Random Forest outperformed other algorithms and hence it was used further to explore the driving factors using variable importance analysis. The study indicates the probability of fire occurrences across Australia as well as identifies the potential driving factors of Australian wildfires for the 2019–2020 summer season. The methodical approach and achieved results and drawn conclusions can be of great importance to policymakers, environmentalists, and climate change researchers, among others.

Brazilian National Household Health Survey and PCAT: the case of Mato Grosso do Sul, Brazil

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
L F Pinto ◽  
D Soranz ◽  
L J Santos ◽  
M S Paranhos ◽  
L S Malta ◽  
...  
Keyword(s):  
Primary Care ◽  
Health Care ◽  
Primary Health Care ◽  
Data Collection ◽  
Health Survey ◽  
Household Surveys ◽  
Mato Grosso ◽  
Primary Health ◽  
The World ◽  
Mato Grosso Do Sul

Abstract Brazil is divided into five administrative regions, 27 federation units and 5,570 municipalities. Mato Grosso do Sul is one of the states located in the Midwest region and has 1.6 million km2 and a resident population of 2.8 million inhabitants, that is, it has an even lower demographic density than its region - only 7.8 inhabitants/km2. Mato Grosso do Sul has part of the Pantanal, a biome considered the largest continuous floodplain in the world, rich in biodiversity. For this reason, displacements for data collection in household surveys combine roads and rivers. In 2019, the Brazilian National Institute of Geography and Statistics (Istituto Nazionale di Statistica del Brasile) in partnership with the Ministry of Health launched the world's largest household sample survey, the National Health Survey (PNS-2019), in which part of its questions included the use of Primary Care Assessment Tool (PCAT, adult version), created by professors Barbara Starfield and Leiyu Shi in the 2000s. IBGE interviewers visited more than 100,000 households across the country. In Mato Grosso do Sul, more than 3,000 households were surveyed. In this work, we present the data collection instrument used by IBGE and its multiple analysis possibilities in the scope of primary health care, crossing the variables from other questionnaire modules in order to compare the results from Brazil with the state of Mato Grosso do Sul and its capital, Campo Grande. Developing a baseline and measuring the attributes of primary health care in each of the Brazilian states is another step towards giving health policy accountability, towards strong primary care. IBGE's experience in household surveys and innovation in data collection in primary care is an example for the world that yes, it is possible to develop statistically representative national sample surveys and make them perennial in their regular household surveys, by the time World Health Organization (WHO) discusses universal health coverage. Key messages Evaluation of primary care using an internationally validated instrument is possible on national bases with random household sample surveys. A questionnaire elaborated academically can be used as an instrument of public policy to evaluate nationwide health services.

PENERAPAN MACHINE LEARNING BERBASIS DATA GEOSPASIAL UNTUK OPTIMALISASI LAHAN PERTANIAN PADA MASA PANDEMI DAN PASCA PANDEMI

2021 ◽  
pp. 161
Author(s):  
Royyannuur Kurniawan Endrayanto ◽  
Adharul Muttaqin
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Early Warning Systems ◽  
Google Earth ◽  
Warning Systems ◽  
Land Data Assimilation ◽  
Google Earth Engine ◽  
Land Data Assimilation System ◽  
Data Assimilation System ◽  
Assimilation System

Pertanian merupakan salah satu sektor penting karena dapat memenuhi kebutuhan pangan sebagai kebutuhan pokok. Kebutuhan pangan masih menjadi salah satu isu hangat terlebih di masa pandemi COVID- 19 seperti saat ini. Pemenuhan kebutuhan pangan juga berkaitan erat dengan jumlah bahan pangan yang diproduksi oleh petani. Lingkungan merupakan salah satu faktor keberhasilan dalam kegiatan pertanian. Kondisi lingkungan Indonesia yang beragam seperti suhu dan tingkat presipitasi menyebabkan adanya perbedaan jenis tanaman pangan potensial setiap daerah di Indonesia. Oleh karena itu perlu upaya untuk mengoptimalkan produksi lahan pertanian berdasarkan faktor lingkungan di setiap daerah. Upaya ini diharapkan dapat membantu menjaga ketahanan pangan baik di masa pandemi dan pasca pandemi. Pada penelitian ini diperkenalkan pemanfaatan data geospasial untuk klasifikasi jenis tanaman pangan menggunakan algoritma machine learning sebagai upaya optimalisasi lahan pertanian. Data yang digunakan adalah Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS). Algoritma machine learning yang digunakan adalah algoritma klasifikasi Random Forest. Teknologi yang digunakan adalah Google Colab, Google Earth Engine dan Python. Tujuan dari penelitian ini adalah untuk mengklasifikasikan tanaman pangan yang memiliki potensi paling baik untuk ditanam di suatu daerah berdasarkan kondisi lingkungan yang ada.

scholarly journals First record of pleasing lacewings (Neuroptera: Dilaridae) in São Paulo state, Brazil

Check List ◽  
2015 ◽  
Vol 11 (1) ◽  
pp. 1538 ◽  
Author(s):  
Caleb Califre Martins ◽  
Dalton De Souza Amorim
Keyword(s):  
Rio Grande ◽  
First Record ◽  
Sao Paulo ◽  
São Paulo ◽  
Mato Grosso ◽  
Paulo State ◽  
Small Family ◽  
The Family ◽  
The World ◽  
Mato Grosso Do Sul

The diversity of the small family Dilaridae in the world includes less than 80 described species, 10 of which known for Brazil. Representatives of the family in Brazil are known for the states of Amazonas, Rondônia, Rio Grande do Norte, Pernambuco, Mato Grosso, Goiás, Mato Grosso do Sul, Rio de Janeiro, Paraná and Santa Catarina. This note includes the first record of the family for the state of São Paulo, with the report of Nallachius limai Adams, 1970 in the Parque Estadual Horto Florestal, Campos do Jordão.

Web-Based Machine Learning Application for Heart Disease Prediction

2022 ◽  
pp. 383-393
Author(s):  
Lokesh M. Giripunje ◽  
Tejas Prashant Sonar ◽  
Rohit Shivaji Mali ◽  
Jayant C. Modhave ◽  
Mahesh B. Gaikwad
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Random Forest ◽  
Web Application ◽  
Machine Learning Algorithms ◽  
World Health ◽  
Web Based ◽  
The World ◽  
Comprehensive Survey ◽  
Health Organization

Risk because of heart disease is increasing throughout the world. According to the World Health Organization report, the number of deaths because of heart disease is drastically increasing as compared to other diseases. Multiple factors are responsible for causing heart-related issues. Many approaches were suggested for prediction of heart disease, but none of them were satisfactory in clinical terms. Heart disease therapies and operations available are so costly, and following treatment, heart disease is also costly. This chapter provides a comprehensive survey of existing machine learning algorithms and presents comparison in terms of accuracy, and the authors have found that the random forest classifier is the most accurate model; hence, they are using random forest for further processes. Deployment of machine learning model using web application was done with the help of flask, HTML, GitHub, and Heroku servers. Webpages take input attributes from the users and gives the output regarding the patient heart condition with accuracy of having coronary heart disease in the next 10 years.

scholarly journals Mapeamento digital de areia, argila e carbono orgânico por modelos Random Forest sob diferentes resoluções espaciais

2016 ◽  
Vol 51 (9) ◽  
pp. 1359-1370 ◽  
Author(s):  
Silvio Barge Bhering ◽  
César da Silva Chagas ◽  
Waldir de Carvalho Junior ◽  
Nilson Rendeiro Pereira ◽  
Braz Calderano Filho ◽  
...  
Keyword(s):  
Random Forest ◽  
Mato Grosso ◽  
Mato Grosso Do Sul

Resumo O objetivo deste trabalho foi avaliar a influência da resolução espacial do modelo digital de elevação e da eficiência de modelos Random Forest sobre a predição dos teores de areia, argila e carbono orgânico, com uso de número reduzido de amostras. O trabalho foi realizado em área de Cerrado com diversidade litológica, no Estado do Mato Grosso do Sul, tendo-se utilizado atributos morfométricos, dados do sensor TM Landsat 5 e litologia como covariáveis preditoras. Dados da camada superficial (0,0-0,2 m) de 175 perfis de solos (0,009 perfis km-2) e de 26 covariáveis preditoras foram utilizados com resolução espacial de 30 (conjunto 1) e 90 m (conjunto 2). A análise realizada pelo Random Forest mostrou que as covariáveis de nível de base do canal de drenagem, da elevação e da litologia foram as mais importantes para explicar a variabilidade. A validação dos modelos apresentou similaridade entre os conjuntos quanto à predição de areia, argila e carbono orgânico, o que explica os seguintes valores de variabilidade espacial, respectivamente: 44, 40 e 33%, para a resolução de 30 m; e de 45, 46 e 33%, para a resolução de 90 m. A resolução espacial das covariáveis preditoras tem pouca influência sobre a predição dos atributos, e a abordagem por Random Forest apresenta potencial de utilização para estimar atributos do solo.

scholarly journals Neuro-Based Prognosticative Analytics for Parkinson Disease using Random Forest Approach

2020 ◽  
Vol 9 (11) ◽  
pp. 11-15
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Parkinson Disease ◽  
Neurodegenerative Disorder ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Single Test ◽  
Specificity And Sensitivity ◽  
The World ◽  
The Given

Parkinson’s malady is the most current neurodegenerative disorder poignant quite ten million folks across the world. There's no single test at which may be administered for diagnosis Parkinson’s malady. Our aim is to analyze machine learning based mostly techniques for Parkinson malady identification in patients. Our machine learning-based technique is employed to accurately predict the malady by speech and handwriting patterns of humans and by predicting leads to the shape of best accuracy and in addition compare the performance of assorted machine learning algorithms from the given hospital dataset with analysis and classification report and additionally determine the result and prove against with best accuracy and exactness, Recall ,F1 Score specificity and sensitivity.

scholarly journals A Pinnacle Technique for Detection of COVID-19 Fake News in Social Media

2020 ◽  
Vol 10 (1) ◽  
pp. 256-261
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Random Forest ◽  
General Public ◽  
Classification Model ◽  
Fake News ◽  
Random Forest Classification ◽  
Forest Classification ◽  
The World ◽  
The Impact

Today the world is gripped with fear of the most infectious disease which was caused by a newly discovered virus namely corona and thus termed as COVID-19. This is a large group of viruses which severely affects humans. The world bears testimony to its contagious nature and rapidity of spreading the illness. 50l people got infected and 30l people died due to this pandemic all around the world. This made a wide impact for people to fear the epidemic around them. The death rate of male is more compared to female. This Pandemic news has caught the attention of the world and gained its momentum in almost all the media platforms. There was an array of creating and spreading of true as well as fake news about COVID-19 in the social media, which has become popular and a major concern to the general public who access it. Spreading such hot news in social media has become a new trend in acquiring familiarity and fan base. At the time it is undeniable that spreading of such fake news in and around creates lots of confusion and fear to the public. To stop all such rumors detection of fake news has become utmost important. To effectively detect the fake news in social media the emerging machine learning classification algorithms can be an appropriate method to frame the model. In the context of the COVID-19 pandemic, we investigated and implemented by collecting the training data and trained a machine learning model by using various machine learning algorithms to automatically detect the fake news about the Corona Virus. The machine learning algorithm used in this investigation is Naïve Bayes classifier and Random forest classification algorithm for the best results. A separate model for each classifier is created after the data preparation and feature extraction Techniques. The results obtained are compared and examined accurately to evaluate the accurate model. Our experiments on a benchmark dataset with random forest classification model showed a promising results with an overall accuracy of 94.06%. This experimental evaluation will prevent the general public to keep themselves out of their fear and to know and understand the impact of fast-spreading as well as misleading fake news.

Predicting the Linguistic Accessibility of Chinese Health Translations: Using Machine Learning Algorithms (Preprint)

2021 ◽  
Author(s):  
Meng Ji ◽  
Pierrette Bouillon
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Health Resources ◽  
Machine Learning Algorithms ◽  
World Health ◽  
Learning Models ◽  
The World ◽  
Health Organization ◽  
C5.0 Decision Tree ◽  
Machine Learning Models

BACKGROUND Linguistic accessibility has important impact on the reception and utilization of translated health resources among multicultural and multilingual populations. Linguistic understandability of health translation has been under-studied. OBJECTIVE Our study aimed to develop novel machine learning models for the study of the linguistic accessibility of health translations comparing Chinese translations of the World Health Organization health materials with original Chinese health resources developed by the Chinese health authorities. METHODS Using natural language processing tools for the assessment of the readability of Chinese materials, we explored and compared the readability of Chinese health translations from the World Health Organization with original Chinese materials from China Centre for Disease Control and Prevention. RESULTS Pairwise adjusted t test showed that three new machine learning models achieved statistically significant improvement over the baseline logistic regression in terms of AUC: C5.0 decision tree (p=0.000, 95% CI: -0.249, -0.152), random forest (p=0.000, 95% CI: 0.139, 0.239) and XGBoost Tree (p=0.000, 95% CI: 0.099, 0.193). There was however no significant difference between C5.0 decision tree and random forest (p=0.513). Extreme gradient boost tree was the best model having achieved statistically significant improvement over the C5.0 model (p=0.003) and the Random Forest model (p=0.006) at the adjusted Bonferroni p value at 0.008. CONCLUSIONS The development of machine learning algorithms significantly improved the accuracy and reliability of current approaches to the evaluation of the linguistic accessibility of Chinese health information, especially Chinese health translations in relation to original health resources. Although the new algorithms developed were based on Chinese health resources, they can be adapted for other languages to advance current research in accessible health translation, communication, and promotion.

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