scholarly journals Real-time solar image classification: Assessing spectral, pixel-based approaches

2019 ◽  
Vol 9 ◽  
pp. A38
Author(s):  
J. Marcus Hughes ◽  
Vicki W. Hsu ◽  
Daniel B. Seaton ◽  
Hazel M. Bain ◽  
Jonathan M. Darnel ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Random Forest ◽  
Image Classification ◽  
Real Time ◽  
Data Science ◽  
Extreme Ultraviolet ◽  
Machine Learning Algorithms ◽  
The Sun ◽  
Solar Image ◽  
Likelihood Model

In order to utilize solar imagery for real-time feature identification and large-scale data science investigations of solar structures, we need maps of the Sun where phenomena, or themes, are labeled. Since solar imagers produce observations every few minutes, it is not feasible to label all images by hand. Here, we compare three machine learning algorithms performing solar image classification using Extreme Ultraviolet (EUV) and Hα images: a maximum likelihood model assuming a single normal probability distribution for each theme from Rigler et al. (2012) [Space Weather 10(8): 1–16], a maximum-likelihood model with an underlying Gaussian mixtures distribution, and a random forest model. We create a small database of expert-labeled maps to train and test these algorithms. Due to the ambiguity between the labels created by different experts, a collaborative labeling is used to include all inputs. We find the random forest algorithm performs the best amongst the three algorithms. The advantages of this algorithm are best highlighted in: comparison of outputs to hand-drawn maps; response to short-term variability; and tracking long-term changes on the Sun. Our work indicates that the next generation of solar image classification algorithms would benefit significantly from using spatial structure recognition, compared to only using spectral, pixel-by-pixel brightness distributions.

scholarly journals Machine learning algorithms can predict tail biting outbreaks in pigs using feeding behaviour records

2021 ◽  
Author(s):  
Catherine Ollagnier ◽  
Claudia Kasper ◽  
Anna Wallenbeck ◽  
Linda Keeling ◽  
Siavash A Bigdeli
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Real Time ◽  
Feeding Behaviour ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Algorithm ◽  
Tail Biting ◽  
Testing Set

Tail biting is a detrimental behaviour that impacts the welfare and health of pigs. Early detection of tail biting precursor signs allows for preventive measures to be taken, thus avoiding the occurrence of the tail biting event. This study aimed to build a machine-learning algorithm for real time detection of upcoming tail biting outbreaks, using feeding behaviour data recorded by an electronic feeder. Prediction capacities of seven machine learning algorithms (e.g., random forest, neural networks) were evaluated from daily feeding data collected from 65 pens originating from 2 herds of grower-finisher pigs (25-100kg), in which 27 tail biting events occurred. Data were divided into training and testing data, either by randomly splitting data into 75% (training set) and 25% (testing set), or by randomly selecting pens to constitute the testing set. The random forest algorithm was able to predict 70% of the upcoming events with an accuracy of 94%, when predicting events in pens for which it had previous data. The detection of events for unknown pens was less sensitive, and the neural network model was able to detect 14% of the upcoming events with an accuracy of 63%. A machine-learning algorithm based on ongoing data collection should be considered for implementation into automatic feeder systems for real time prediction of tail biting events.

scholarly journals Machine Learning Algorithms for Visualization and Prediction Modeling of Boston Crime Data

2020 ◽  
Author(s):  
Jiarui Yin ◽  
Inikuro Afa Michael ◽  
Iduabo John Afa
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Data Science ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Crime Data ◽  
Detection Analysis ◽  
Supervised Learning Algorithms ◽  
Supervised Methods

Machine learning plays a key role in present day crime detection, analysis and prediction. The goal of this work is to propose methods for predicting crimes classified into different categories of severity. We implemented visualization and analysis of crime data statistics in recent years in the city of Boston. We then carried out a comparative study between two supervised learning algorithms, which are decision tree and random forest based on the accuracy and processing time of the models to make predictions using geographical and temporal information provided by splitting the data into training and test sets. The result shows that random forest as expected gives a better result by 1.54% more accuracy in comparison to decision tree, although this comes at a cost of at least 4.37 times the time consumed in processing. The study opens doors to application of similar supervised methods in crime data analytics and other fields of data science

scholarly journals Sustainable Irrigation System for Farming Supported by Machine Learning and Real-Time Sensor Data

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3079
Author(s):  
André Glória ◽  
João Cardoso ◽  
Pedro Sebastião
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Real Time ◽  
New Technologies ◽  
Irrigation System ◽  
Low Cost ◽  
Time Of Day ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Sensors And Actuators

Presently, saving natural resources is increasingly a concern, and water scarcity is a fact that has been occurring in more areas of the globe. One of the main strategies used to counter this trend is the use of new technologies. On this topic, the Internet of Things has been highlighted, with these solutions being characterized by offering robustness and simplicity, while being low cost. This paper presents the study and development of an automatic irrigation control system for agricultural fields. The developed solution had a wireless sensors and actuators network, a mobile application that offers the user the capability of consulting not only the data collected in real time but also their history and also act in accordance with the data it analyses. To adapt the water management, Machine Learning algorithms were studied to predict the best time of day for water administration. Of the studied algorithms (Decision Trees, Random Forest, Neural Networks, and Support Vectors Machines) the one that obtained the best results was Random Forest, presenting an accuracy of 84.6%. Besides the ML solution, a method was also developed to calculate the amount of water needed to manage the fields under analysis. Through the implementation of the system it was possible to realize that the developed solution is effective and can achieve up to 60% of water savings.

scholarly journals Real-Time Inferential Analytics Based on Online Databases of Trends: A Breakthrough within the Discipline of Digital Epidemiology in Dentistry and Dental Anatomy

2018 ◽  
Author(s):  
Ahmed Al-Imam ◽  
Usama Khalid ◽  
Shahad Al-Qaisi ◽  
Nawfal Al-Hadithi ◽  
Dawoude Kaouche
Keyword(s):  
Statistical Analysis ◽  
Statistical Inference ◽  
Real Time ◽  
Data Science ◽  
Machine Learning Algorithms ◽  
Google Trends ◽  
Cochrane Library ◽  
Dental Anatomy ◽  
Digital Epidemiology ◽  
The Cochrane Library

BACKGROUND Epidemiological sciences have been evolving at an exponential rate paralleled only by the comparable growth within the discipline of data science. Digital epidemiological studies are playing a vital role in medical science analytics for the past few decades. To date, there are no published attempts at deploying the use of real-time analytics in connection with the disciplines of Dentistry or Medicine. AIMS AND OBJECTIVES We deployed a real-time statistical analysis in connection with topics in Dental Anatomy and Dental Pathology represented by the maxillary sinus, posterior maxillary teeth, related oral pathology. The purpose is to infer the digital epidemiology based on a continuous stream of raw data retrieved from Google Trends database. MATERIALS AND METHODS Statistical analysis was carried out via Microsoft Excel 2016 and SPSS version 24. Google Trends database was used to retrieve data for digital epidemiology. Real-time analysis and the statistical inference were based on encoding a programming script using Python high-level programming language. A systematic review of the literature was carried out via PubMed-NCBI, the Cochrane Library, and Elsevier databases. RESULTS The comprehensive review of databases of the literature, based on specific keywords search, yielded 491813 published studies. These were distributed as 488884 (PubMed-NCBI), 1611 (the Cochrane Library), and 1318 (Elsevier). However, there was no single study attempting real-time analytics. Nevertheless, we succeeded in achieving an automated real-time stream of data accompanied by a statistical inference based on data extrapolated from Google Trends. CONCLUSION Real-time analytics are of considerable impact when implemented in biological and life sciences as they will tremendously reduce the required resources for research. Predictive analytics, based on artificial neural networks and machine learning algorithms, can be the next step to be deployed in continuation of the real-time systems to prognosticate changes in the temporal trends and the digital epidemiology of phenomena of interest.

scholarly journals Real-Time Inferential Analytics Based on Online Databases of Trends: A Breakthrough within the Discipline of Digital Epidemiology in Dentistry and Dental Anatomy

2018 ◽  
Author(s):  
Ahmed Al-Imam ◽  
Usama Khalid ◽  
Nawfal Al-Hadithi ◽  
Dawoude Kaouche
Keyword(s):  
Statistical Analysis ◽  
Statistical Inference ◽  
Real Time ◽  
Data Science ◽  
Machine Learning Algorithms ◽  
Google Trends ◽  
Cochrane Library ◽  
Dental Anatomy ◽  
Digital Epidemiology ◽  
The Cochrane Library

BACKGROUND Epidemiological sciences have been evolving at an exponential rate paralleled only by the comparable growth within the discipline of data science. Digital epidemiological studies are playing a vital role in medical science analytics for the past few decades. To date, there are no published attempts at deploying the use of real-time analytics in connection with the disciplines of Dentistry or Medicine. AIMS AND OBJECTIVES We deployed a real-time statistical analysis in connection with topics in Dental Anatomy and Dental Pathology represented by the maxillary sinus, posterior maxillary teeth, related oral pathology. The purpose is to infer the digital epidemiology based on a continuous stream of raw data retrieved from Google Trends database. MATERIALS AND METHODS Statistical analysis was carried out via Microsoft Excel 2016 and SPSS version 24. Google Trends database was used to retrieve data for digital epidemiology. Real-time analytics and the statistical inference were based on encoding a programming script using Python high-level programming language. A systematic review of the literature was carried out via PubMed-NCBI, the Cochrane Library, and Elsevier databases. RESULTS The comprehensive review of databases of the literature, based on specific keywords search, yielded 491813 published studies. These were distributed as 488884 (PubMed-NCBI), 1611 (the Cochrane Library), and 1318 (Elsevier). However, there was no single study attempting real-time analytics. Nevertheless, we succeeded in achieving an automated real-time stream of data accompanied by a statistical inference based on data extrapolated from Google Trends. CONCLUSION Real-time analytics are of considerable impact when implemented in biological and life sciences as they will tremendously reduce the required resources for research. Predictive analytics, based on artificial neural networks and machine learning algorithms, can be the next step to be deployed in continuation of the real-time systems to prognosticate changes in the temporal trends and the digital epidemiology of phenomena of interest.

scholarly journals Real Time Efficient Accident Predictor System using Machine Learning Techniques (kNN, RF, LR, DT)

2020 ◽  
Vol 10 (2) ◽  
pp. 108-111
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Real Time ◽  
Classification Accuracy ◽  
Nearest Neighbors ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Classification Methods ◽  
K Nearest Neighbors ◽  
Learning Techniques

Real time crash predictor system is determining frequency of crashes and also severity of crashes. Nowadays machine learning based methods are used to predict the total number of crashes. In this project, prediction accuracy of machine learning algorithms like Decision tree (DT), K-nearest neighbors (KNN), Random forest (RF), Logistic Regression (LR) are evaluated. Performance analysis of these classification methods are evaluated in terms of accuracy. Dataset included for this project is obtained from 49 states of US and 27 states of India which contains 2.25 million US accident crash records and 1.16 million crash records respectively. Results prove that classification accuracy obtained from Random Forest (RF) is96% compared to other classification methods.

scholarly journals Nowcasting Indonesia’s GDP Growth Using Machine Learning Algorithms

2021 ◽  
Vol 5 (2) ◽  
pp. 355-368
Author(s):  
Nadya Dwi Muchisha ◽  
Novian Tamara ◽  
Andriansyah Andriansyah ◽  
Agus M Soleh
Keyword(s):  
Random Forest ◽  
Real Time ◽  
Pearson Correlation ◽  
Forecast Accuracy ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Gdp Growth ◽  
Forecast Combination ◽  
Lasso Regression ◽  
The Individual

GDP is very important to be monitored in real time because of its usefulness for policy making. We built and compared the ML models to forecast real-time Indonesia's GDP growth. We used 18 variables that consist a number of quarterly macroeconomic and financial market statistics. We have evaluated the performance of six popular ML algorithms, such as Random Forest, LASSO, Ridge, Elastic Net, Neural Networks, and Support Vector Machines, in doing real-time forecast on GDP growth from 2013:Q3 to 2019:Q4 period. We used the RMSE, MAD, and Pearson correlation coefficient as measurements of forecast accuracy. The results showed that the performance of all these models outperformed AR (1) benchmark. The individual model that showed the best performance is random forest. To gain more accurate forecast result, we run forecast combination using equal weighting and lasso regression. The best model was obtained from forecast combination using lasso regression with selected ML models, which are Random Forest, Ridge, Support Vector Machine, and Neural Network.

scholarly journals Using Machine Learning to Predict Heart Disease

2022 ◽  
Vol 19 ◽  
pp. 1-9
Author(s):  
Nikhil Bora ◽  
Sreedevi Gutta ◽  
Ahmad Hadaegh
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Heart Disease ◽  
Random Forest ◽  
Data Science ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbor

Heart Disease has become one of the most leading cause of the death on the planet and it has become most life-threatening disease. The early prediction of the heart disease will help in reducing death rate. Predicting Heart Disease has become one of the most difficult challenges in the medical sector in recent years. As per recent statistics, about one person dies from heart disease every minute. In the realm of healthcare, a massive amount of data was discovered for which the data-science is critical for analyzing this massive amount of data. This paper proposes heart disease prediction using different machine-learning algorithms like logistic regression, naïve bayes, support vector machine, k nearest neighbor (KNN), random forest, extreme gradient boost, etc. These machine learning algorithm techniques we used to predict likelihood of person getting heart disease on the basis of features (such as cholesterol, blood pressure, age, sex, etc. which were extracted from the datasets. In our research we used two separate datasets. The first heart disease dataset we used was collected from very famous UCI machine learning repository which has 303 record instances with 14 different attributes (13 features and one target) and the second dataset that we used was collected from Kaggle website which contained 1190 patient’s record instances with 11 features and one target. This dataset is a combination of 5 popular datasets for heart disease. This study compares the accuracy of various machine learning techniques. In our research, for the first dataset we got the highest accuracy of 92% by Support Vector Machine (SVM). And for the second dataset, Random Forest gave us the highest accuracy of 94.12%. Then, we combined both the datasets which we used in our research for which we got the highest accuracy of 93.31% using Random Forest.

scholarly journals Revealing the Unknown: Real-Time Recognition of Galápagos Snake Species Using Deep Learning

Animals ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 806
Author(s):  
Anika Patel ◽  
Lisa Cheung ◽  
Nandini Khatod ◽  
Irina Matijosaitiene ◽  
Alejandro Arteaga ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Classification ◽  
Real Time ◽  
Mobile Application ◽  
Galapagos Islands ◽  
Machine Learning Algorithms ◽  
Galápagos Islands ◽  
Snake Species ◽  
Promising Tool ◽  
Real Time Identification

Real-time identification of wildlife is an upcoming and promising tool for the preservation of wildlife. In this research project, we aimed to use object detection and image classification for the racer snakes of the Galápagos Islands, Ecuador. The final target of this project was to build an artificial intelligence (AI) platform, in terms of a web or mobile application, which would serve as a real-time decision making and supporting mechanism for the visitors and park rangers of the Galápagos Islands, to correctly identify a snake species from the user’s uploaded image. Using the deep learning and machine learning algorithms and libraries, we modified and successfully implemented four region-based convolutional neural network (R-CNN) architectures (models for image classification): Inception V2, ResNet, MobileNet, and VGG16. Inception V2, ResNet and VGG16 reached an overall accuracy of 75%.

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