scholarly journals Applying machine learning models for predicting forest fires in Australia and the influence of weather on the spread of fires based on satellite and weather forecast data.

2020 ◽  
Vol 9 ◽  
Author(s):  
Bakhtiyor Askarovich Meraliyev ◽  
Kurmangazy Sakenuly Kongratbayev
Keyword(s):  
Machine Learning ◽  
Forest Fires ◽  
Learning Algorithms ◽  
Weather Forecast ◽  
Weather Conditions ◽  
Machine Learning Algorithms ◽  
Multidimensional Analysis ◽  
Natural Habitats ◽  
Meteorological Observations ◽  
High Level

What shall we expect from the year 2020? The coronavirus pandemic is not the worst thing that humanity can face in the near future. According to the observations of the scientists, in March, 2020, the planet temperature warmed up to the record-high level. Also, the temperature of the world’s oceans exceeded its average temperatures by 80%, and prognosis of the meteorological observations is not good. The warming seas had already led to catastrophic disaster. The average temperature increase can also lead to hurricanes, drought, invasion of locusts and, the worst, to forest fires. Natural disasters lead to loss of life, destruction of properties and infrastructure, loss of animal natural habitats, displacement of humans. And the results of these all lead to humanitarian catastrophes, including social and economic.The situations related to the nature are always very serious, as the whole world is involved. This is like butterfly effect, i.e., the natural disaster in Australia affect the economic and ecologic situation in USA and England. Taking the Australia, they faced problem that cannot be avoided. Nevertheless, the world can be prepared and prevent from the huge disasters. The forecasting of forest fires can really be helpful, as well as the inquiry of the weather impact on fires. The current paper is focused on the study of fire forecasting and weather influence on fire. The relevance of the study is important, as the global warming and human caused fires are increasing and there is a trend that Australia’s fires became more dangerous and longer lasting. The artificial intelligence, particularly machine learning algorithms, can help to make appropriate calculations and predictions to safe the ecosystem and human lives.According to the preliminary research results we acquire; in-depth multidimensional analysis confirms almost 100 percent dependence of bushfires on the weather conditions. Using the machine learning algorithms, it would be possible to predict the time and positioning of inflammation source.

Landslide detection by machine learning on high-resolution DEMs

2021 ◽  
Author(s):  
Mihai Niculita
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Binary Classification ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Single Event ◽  
High Resolution Data ◽  
Susceptibility Modeling ◽  
Study Case ◽  
High Level

<p>Machine learning algorithms are increasingly used in geosciences for the detection of susceptibility modeling of certain landforms or processes. The increased availability of high-resolution data and the increase of available machine learning algorithms opens up the possibility of creating datasets for the training of models for automatic detection of specific landforms. In this study, we tested the usage of LiDAR DEMs for creating a dataset of labeled images representing shallow single event landslides in order to use them for the detection of other events. The R stat implementation of the keras high-level neural networks API was used to build and test the proposed approach. A 5m LiDAR DEM was cut in 25 by 25 pixels tiles, and the tiles that overlayed shallow single event landslides were labeled accordingly, while the tiles that did not contain landslides were randomly selected to be labeled as non-landslides. The binary classification approach was tested with 255 grey levels elevation images and 255 grey levels shading images, the shading approach giving better results. The presented study case shows the possibility of using machine learning in the landslide detection on high-resolution DEMs.</p>

scholarly journals Review Paper on Prediction of Heart Disease using Machine Learning Algorithms

2021 ◽  
Vol 9 (VI) ◽  
pp. 2937-2940
Author(s):  
Aadar Pandita
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Review Paper ◽  
Heart Diseases ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
The World ◽  
Medical Researchers ◽  
High Level ◽  
The Impact

: Heart disease has been one of the ruling causes for death for quite some time now. About 31% of all deaths every year in the world take place as a result of cardiovascular diseases [1]. A majority of the patients remain uninformed of their symptoms until quite late while others find it difficult to minimise the effects of risk factors that cause heart diseases. Machine Learning Algorithms have been quite efficacious in producing results with a high level of correctness thereby preventing the onset of heart diseases in many patients and reducing the impact in the ones that are already affected by such diseases. It has helped medical researchers and doctors all over the world in recognising patterns in the patients resulting in early detections of heart diseases.

scholarly journals Comparative study on machine learning algorithms for early fire forest detection system using geodata.

2020 ◽  
Vol 10 (5) ◽  
pp. 5507
Author(s):  
Zouiten Mohammed ◽  
Chaaouan Hanae ◽  
Setti Larbi
Keyword(s):  
Machine Learning ◽  
Forest Fire ◽  
Forest Fires ◽  
Learning Algorithm ◽  
Detection System ◽  
Learning Algorithms ◽  
Spatial Prediction ◽  
Machine Learning Algorithms ◽  
Geographical Information ◽  
Deep Learning Algorithm

Forest fires have caused considerable losses to ecologies, societies and economies worldwide. To minimize these losses and reduce forest fires, modeling and predicting the occurrence of forest fires are meaningful because they can support forest fire prevention and management. In recent years, the convolutional neural network (CNN) has become an important state-of-the-art deep learning algorithm, and its implementation has enriched many fields. Therefore, a competitive spatial prediction model for automatic early detection of wild forest fire using machine learning algorithms can be proposed. This model can help researchers to predict forest fires and identify risk zonas. System using machine learning algorithm on geodata will be able to notify in real time the interested parts and authorities by providing alerts and presenting on maps based on geographical treatments for more efficacity and analyzing of the situation. This research extends the application of machine learning algorithms for early fire forest prediction to detection and representation in geographical information system (GIS) maps.

Predictive Strength of Ensemble Machine Learning Algorithms for the Diagnosis of Large Scale Medical Datasets

2021 ◽  
pp. 260-281
Author(s):  
Elangovan Ramanujam ◽  
L. Rasikannan ◽  
S. Viswa ◽  
B. Deepan Prashanth
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Nearest Neighbor ◽  
Learning Algorithms ◽  
Weather Forecast ◽  
Machine Learning Algorithms ◽  
K Nearest Neighbor ◽  
Ensemble Machine Learning ◽  
Simple Technology ◽  
Sensitivity Specificity

Machine learning is not a simple technology but an amazing field having more and more to explore. It has a number of real-time applications such as weather forecast, price prediction, gaming, medicine, fraud detection, etc. Machine learning has an increased usage in today's technological world as data is growing in volumes and machine learning is capable of producing mathematical and statistical models that can analyze complex data and generate accurate results. To analyze the scalable performance of the learning algorithms, this chapter utilizes various medical datasets from the UCI Machine Learning repository ranges from smaller to large datasets. The performance of learning algorithms such as naïve Bayes, decision tree, k-nearest neighbor, and stacking ensemble learning method are compared in different evaluation models using metrics such as accuracy, sensitivity, specificity, precision, and f-measure.

scholarly journals The FluPRINT dataset, a multidimensional analysis of the influenza vaccine imprint on the immune system

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Adriana Tomic ◽  
Ivan Tomic ◽  
Cornelia L. Dekker ◽  
Holden T. Maecker ◽  
Mark M. Davis
Keyword(s):  
Machine Learning ◽  
Influenza Vaccine ◽  
Large Scale ◽  
Age Groups ◽  
Small Sample ◽  
Machine Learning Algorithms ◽  
Antibody Responses ◽  
Multidimensional Analysis ◽  
Influenza Vaccines ◽  
High Level

Abstract Machine learning has the potential to identify novel biological factors underlying successful antibody responses to influenza vaccines. The first attempts have revealed a high level of complexity in establishing influenza immunity, and many different cellular and molecular components are involved. Of note is that the previously identified correlates of protection fail to account for the majority of individual responses across different age groups and influenza seasons. Challenges remain from the small sample sizes in most studies and from often limited data sets, such as transcriptomic data. Here we report the creation of a unified database, FluPRINT, to enable large-scale studies exploring the cellular and molecular underpinnings of successful antibody responses to influenza vaccines. Over 3,000 parameters were considered, including serological responses to influenza strains, serum cytokines, cell phenotypes, and cytokine stimulations. FluPRINT, facilitates the application of machine learning algorithms for data mining. The data are publicly available and represent a resource to uncover new markers and mechanisms that are important for influenza vaccine immunogenicity.

Neuronal Communication Genetic Algorithm-Based Inductive Learning

2020 ◽  
Vol 13 (2) ◽  
pp. 141-154
Author(s):  
Abdiya Alaoui ◽  
Zakaria Elberrichi
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Learning Strategies ◽  
Predictive Accuracy ◽  
Inductive Learning ◽  
Learning Algorithms ◽  
Hybrid Approach ◽  
Machine Learning Algorithms ◽  
Rule Based ◽  
High Level

The development of powerful learning strategies in the medical domain constitutes a real challenge. Machine learning algorithms are used to extract high-level knowledge from medical datasets. Rule-based machine learning algorithms are easily interpreted by humans. To build a robust rule-based algorithm, a new hybrid metaheuristic was proposed for the classification of medical datasets. The hybrid approach uses neural communication and genetic algorithm-based inductive learning to build a robust model for disease prediction. The resulting classification models are characterized by good predictive accuracy and relatively small size. The results on 16 well-known medical datasets from the UCI machine learning repository shows the efficiency of the proposed approach compared to other states-of-the-art approaches.

scholarly journals On the Application of Advanced Machine Learning Methods to Analyze Enhanced, Multimodal Data from Persons Infected with COVID-19

Computation ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 4
Author(s):  
Wenhuan Zeng ◽  
Anupam Gautam ◽  
Daniel H. Huson
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Climatic Factors ◽  
Sequence Data ◽  
Learning Algorithms ◽  
Weather Conditions ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Extreme Gradient Boosting ◽  
The Impact

The current COVID-19 pandemic, caused by the rapid worldwide spread of the SARS-CoV-2 virus, is having severe consequences for human health and the world economy. The virus affects different individuals differently, with many infected patients showing only mild symptoms, and others showing critical illness. To lessen the impact of the epidemic, one problem is to determine which factors play an important role in a patient’s progression of the disease. Here, we construct an enhanced COVID-19 structured dataset from more than one source, using natural language processing to add local weather conditions and country-specific research sentiment. The enhanced structured dataset contains 301,363 samples and 43 features, and we applied both machine learning algorithms and deep learning algorithms on it so as to forecast patient’s survival probability. In addition, we import alignment sequence data to improve the performance of the model. Application of Extreme Gradient Boosting (XGBoost) on the enhanced structured dataset achieves 97% accuracy in predicting patient’s survival; with climatic factors, and then age, showing the most importance. Similarly, the application of a Multi-Layer Perceptron (MLP) achieves 98% accuracy. This work suggests that enhancing the available data, mostly basic information on patients, so as to include additional, potentially important features, such as weather conditions, is useful. The explored models suggest that textual weather descriptions can improve outcome forecast.

scholarly journals Natural Gas Consumption Forecasting Based on the Variability of External Meteorological Factors Using Machine Learning Algorithms

Energies ◽  
2022 ◽  
Vol 15 (1) ◽  
pp. 348
Author(s):  
Wojciech Panek ◽  
Tomasz Włodek
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Natural Gas ◽  
Learning Algorithms ◽  
Weather Conditions ◽  
Machine Learning Algorithms ◽  
Forecast Horizon ◽  
Gas Consumption ◽  
Natural Gas Consumption ◽  
The Impact

Natural gas consumption depends on many factors. Some of them, such as weather conditions or historical demand, can be accurately measured. The authors, based on the collected data, performed the modeling of temporary and future natural gas consumption by municipal consumers in one of the medium-sized cities in Poland. For this purpose, the machine learning algorithms, neural networks and two regression algorithms, MLR and Random Forest were used. Several variants of forecasting the demand for natural gas, with different lengths of the forecast horizon are presented and compared in this research. The results obtained using the MLR, Random Forest, and DNN algorithms show that for the tested input data, the best algorithm for predicting the demand for natural gas is RF. The differences in accuracy of prediction between algorithms were not significant. The research shows the differences in the impact of factors that create the demand for natural gas, as well as the accuracy of the prediction for each algorithm used, for each time horizon.

scholarly journals Machine learning goes wild: Using data from captive individuals to infer wildlife behaviour

2019 ◽  
Author(s):  
W. Rast ◽  
S. E. Kimmig ◽  
L. Giese ◽  
A. Berger
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
List Type ◽  
Moving Window ◽  
Classification Models ◽  
Red Foxes ◽  
Natural Habitats ◽  
Acceleration Data

ABSTRACTRemotely tracking distinct behaviours of animals using acceleration data and machine learning has been carried out successfully in several species in captive settings. In order to study the ecology of animals in natural habitats, such behaviour classification models need to be transferred to wild individuals. However, at present the development of those models usually requires direct observation of the target animals.The goal of this study was to infer behaviour of wild, free roaming animals from acceleration data by training behaviour classification models on captive individuals, without the necessity to observe their wild conspecifics. We further sought to develop methods to validate the credibility of the resulting behaviour extrapolations.We trained two machine learning algorithms proposed by the literature, Random Forest (RF) and Support Vector Machine (SVM), on data from captive red foxes (Vulpes vulpes) and later applied them to data from wild foxes. We also tested a new advance for behaviour classification, by applying a moving window to an Artificial Neural Network (ANN). Finally, we investigated four strategies to validate our classification output.While all three machine learning algorithms performed well under training conditions, the established methods, RF and SVM, failed in classifying distinct behaviours when transferred from captive to wild foxes. Behaviour classification with the ANN and a moving window, in contrast, inferred distinct behaviours and showed consistent results for most individuals.Our approach is a substantial improvement over the methods previously proposed in the literature as it generated plausible results for wild fox behaviour. We were able to infer the behaviour of wild animals that have never been observed in the wild and to further illustrate the outputs credibility. This framework is not restricted to foxes but can be applied to infer the behaviour of many other species and thus empowers new advances in behavioural ecology.

Sign in / Sign up

Export Citation Format

Share Document