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 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 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 Using Machine Learning for Real-Time BAC Estimation from a New-Generation Transdermal Biosensor in the Laboratory

2020 ◽  
Author(s):  
Catharine Fairbairn ◽  
Dahyeon Kang ◽  
Nigel Bosch
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Real Time ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Laboratory Research ◽  
Time Interval ◽  
Rapid Sampling ◽  
Potential Applications ◽  
New Generation

Background: Transdermal biosensors offer a noninvasive, low-cost technology for the assessment of alcohol consumption with broad potential applications in addiction science. Older-generation transdermal devices feature bulky designs and sparse sampling intervals, limiting potential applications for transdermal technology. Recently a new-generation of transdermal device has become available, featuring smartphone connectivity, compact designs, and rapid sampling. Here we present initial laboratory research examining the validity of a new-generation transdermal sensor prototype. Methods: Participants were young drinkers administered alcohol (target BAC=.08%) or no-alcohol in the laboratory. Participants wore transdermal sensors while providing repeated breathalyzer (BrAC) readings. We assessed the association between BrAC (measured BrAC for a specific time point) and eBrAC (BrAC estimated based only on transdermal readings collected in the immediately preceding time interval). Extra-Trees machine learning algorithms, incorporating transdermal time series features as predictors, were used to create eBrAC. Results: Failure rates for the new-generation prototype sensor were high (16%-34%). Among participants with useable new-generation sensor data, models demonstrated strong capabilities for separating drinking from non-drinking episodes, and significant (moderate) ability to differentiate BrAC levels within intoxicated participants. Differences between eBrAC and BrAC were 60% higher for models based on data from old-generation vs new-generation devices. Model comparisons indicated that both time series analysis and machine learning contributed significantly to final model accuracy. Conclusions: Results provide favorable preliminary evidence for the accuracy of real-time BAC estimates from a new-generation sensor. Future research featuring variable alcohol doses and real-world contexts will be required to further validate these devices.

Fundamentals of Wireless Sensor Networks Using Machine Learning Approaches

2020 ◽  
pp. 233-254
Author(s):  
E. B. Priyanka ◽  
S. Thangavel ◽  
D. Venkatesa Prabu
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Real Time ◽  
Oil And Gas ◽  
Predictive Analytics ◽  
Oil And Gas Industry ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Learning Approaches ◽  
Gas Industry

Big data and analytics may be new to some industries, but the oil and gas industry has long dealt with large quantities of data to make technical decisions. Oil producers can capture more detailed data in real-time at lower costs and from previously inaccessible areas, to improve oilfield and plant performance. Stream computing is a new way of analyzing high-frequency data for real-time complex-event-processing and scoring data against a physics-based or empirical model for predictive analytics, without having to store the data. Hadoop Map/Reduce and other NoSQL approaches are a new way of analyzing massive volumes of data used to support the reservoir, production, and facilities engineering. Hence, this chapter enumerates the routing organization of IoT with smart applications aggregating real-time oil pipeline sensor data as big data subjected to machine learning algorithms using the Hadoop platform.

scholarly journals ANALYSIS OF THE INFLUENCE OF MACHINE LEARNING ALGORITHM PARAMETERS ON THE RESULTS OF TRAFFIC CLASSIFICATION IN REAL TIME

T-Comm ◽  
2021 ◽  
Vol 15 (9) ◽  
pp. 24-35
Author(s):  
Irina A. Krasnova ◽  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Real Time ◽  
Experimental Studies ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Traffic Classification ◽  
Data Set ◽  
Minimum Number ◽  
The Impact

The paper analyzes the impact of setting the parameters of Machine Learning algorithms on the results of traffic classification in real-time. The Random Forest and XGBoost algorithms are considered. A brief description of the work of both methods and methods for evaluating the results of classification is given. Experimental studies are conducted on a database obtained on a real network, separately for TCP and UDP flows. In order for the results of the study to be used in real time, a special feature matrix is created based on the first 15 packets of the flow. The main parameters of the Random Forest (RF) algorithm for configuration are the number of trees, the partition criterion used, the maximum number of features for constructing the partition function, the depth of the tree, and the minimum number of samples in the node and in the leaf. For XGBoost, the number of trees, the depth of the tree, the minimum number of samples in the leaf, for features, and the percentage of samples needed to build the tree are taken. Increasing the number of trees leads to an increase in accuracy to a certain value, but as shown in the article, it is important to make sure that the model is not overfitted. To combat overfitting, the remaining parameters of the trees are used. In the data set under study, by eliminating overfitting, it was possible to achieve an increase in classification accuracy for individual applications by 11-12% for Random Forest and by 12-19% for XGBoost. The results show that setting the parameters is a very important step in building a traffic classification model, because it helps to combat overfitting and significantly increases the accuracy of the algorithm’s predictions. In addition, it was shown that if the parameters are properly configured, XGBoost, which is not very popular in traffic classification works, becomes a competitive algorithm and shows better results compared to the widespread Random Forest.

scholarly journals A Survey on Distributed Fibre Optic Sensor Data Modelling Techniques and Machine Learning Algorithms for Multiphase Fluid Flow Estimation

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2801
Author(s):  
Hasan Asy’ari Arief ◽  
Tomasz Wiktorski ◽  
Peter James Thomas
Keyword(s):  
Machine Learning ◽  
Fluid Flow ◽  
Data Analysis ◽  
Real Time ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Support Vector ◽  
Measurement Technology ◽  
Fibre Optic ◽  
Multiphase Fluid

Real-time monitoring of multiphase fluid flows with distributed fibre optic sensing has the potential to play a major role in industrial flow measurement applications. One such application is the optimization of hydrocarbon production to maximize short-term income, and prolong the operational lifetime of production wells and the reservoir. While the measurement technology itself is well understood and developed, a key remaining challenge is the establishment of robust data analysis tools that are capable of providing real-time conversion of enormous data quantities into actionable process indicators. This paper provides a comprehensive technical review of the data analysis techniques for distributed fibre optic technologies, with a particular focus on characterizing fluid flow in pipes. The review encompasses classical methods, such as the speed of sound estimation and Joule-Thomson coefficient, as well as their data-driven machine learning counterparts, such as Convolutional Neural Network (CNN), Support Vector Machine (SVM), and Ensemble Kalman Filter (EnKF) algorithms. The study aims to help end-users establish reliable, robust, and accurate solutions that can be deployed in a timely and effective way, and pave the wave for future developments in the field.

scholarly journals An Efficient Fingertip Photoplethysmographic Signal Artifact Detection Method: A Machine Learning Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Tasbiraha Athaya ◽  
Sunwoong Choi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Random Forest ◽  
Real Time ◽  
Time Algorithm ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Public Healthcare ◽  
Artifact Detection ◽  
Efficient System

A photoplethysmography method has recently been widely used to noninvasively measure blood volume changes during a cardiac cycle. Photoplethysmogram (PPG) signals are sensitive to artifacts that negatively impact the accuracy of many important measurements. In this paper, we propose an efficient system for detecting PPG signal artifacts acquired from a fingertip in the public healthcare database named Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) by using 11 features as the input of the random forest algorithm and classified the signals into two classes: acceptable and anomalous. A real-time algorithm is proposed to identify artifacts by using the method. The efficient Fisher score feature selection algorithm was used to order and select 11 relevant features from 19 available features that represented the PPG signal very effectively. Six machine learning algorithms (random forest, decision tree, Gaussian naïve Bayes, linear support vector machine, artificial neural network, and probabilistic neural network) were applied with the extracted features, and their classification accuracy was measured. Among them, the random forest had the best performance using only 11 out of 19 features with an accuracy of 85.68%. Our proposed method also achieved good sensitivity and specificity value of 86.57% and 85.09%, respectively. The proposed real-time algorithm can be an easy and convenient way for real-time PPG signal artifact detection using smartphones and wearable devices.

scholarly journals Air-to-Air Path Loss Prediction Based on Machine Learning Methods in Urban Environments

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Yan Zhang ◽  
Jinxiao Wen ◽  
Guanshu Yang ◽  
Zunwen He ◽  
Xinran Luo
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Prediction Models ◽  
Low Cost ◽  
Path Loss ◽  
Absolute Error ◽  
Urban Environments ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Prediction Errors

Recently, unmanned aerial vehicle (UAV) plays an important role in many applications because of its high flexibility and low cost. To realize reliable UAV communications, a fundamental work is to investigate the propagation characteristics of the channels. In this paper, we propose path loss models for the UAV air-to-air (AA) scenario based on machine learning. A ray-tracing software is employed to generate samples for multiple routes in a typical urban environment, and different altitudes of Tx and Rx UAVs are taken into consideration. Two machine-learning algorithms, Random Forest and KNN, are exploited to build prediction models on the basis of the training data. The prediction performance of trained models is assessed on the test set according to the metrics including the mean absolute error (MAE) and root mean square error (RMSE). Meanwhile, two empirical models are presented for comparison. It is shown that the machine-learning-based models are able to provide high prediction accuracy and acceptable computational efficiency in the AA scenario. Moreover, Random Forest outperforms other models and has the smallest prediction errors. Further investigation is made to evaluate the impacts of five different parameters on the path loss. It is demonstrated that the path visibility is crucial for the path loss.

scholarly journals Water Quality Monitoring and Prediction of Water Quality at College Premises using Internet of Things

2019 ◽  
Vol 8 (5S3) ◽  
pp. 53-57
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Random Forest ◽  
Real Time ◽  
Multilayer Perceptron ◽  
Low Cost ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Machine Learning Techniques ◽  
Learning Techniques

IoT is becoming more popular and effective tool for any real time application. It has been involved for various water quality monitoring system to maintain the water hygiene level. The main objective is to build a system that regularly monitors the water quality and manages the sustainability. This system deals with specific standards like low cost background and system efficiency when compared to other studies. In this paper, IoT based real time monitoring of water quality system is implemented along with Machine learning techniques such as J48, Multilayer Perceptron (MLP), and Random Forest. These machine learning techniques are compared based on the hyper-parameters and the results were obtained. The attributes such as pH, Dissolved Oxygen (DO), turbidity, conductivity obtained from the corresponding sensors are used to create a prediction model which classifies the quality of water. Measurement of water quality and reporting system is implemented by using Arduino controller, GSM/GPRS module for gathering data in real time. The collected data are then analyzed using WEKA interface which is a visualization tool used for the analysis of data and prediction modeling.The Random forest technique outperforms J48 and Multilayer perceptron by giving 98.89% of correctly classified instances.

Development of Prediction Models Using Machine Learning Algorithms for Girls with Suspected Central Precocious Puberty: Retrospective Study (Preprint)

2018 ◽  
Author(s):  
Liyan Pan ◽  
Guangjian Liu ◽  
Xiaojian Mao ◽  
Huixian Li ◽  
Jiexin Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Retrospective Study ◽  
Random Forest ◽  
Precocious Puberty ◽  
Prediction Models ◽  
Central Precocious Puberty ◽  
Machine Learning Algorithms ◽  
Stimulation Test ◽  
Gnrh Analogue ◽  
Prediction Probability

BACKGROUND Central precocious puberty (CPP) in girls seriously affects their physical and mental development in childhood. The method of diagnosis—gonadotropin-releasing hormone (GnRH)–stimulation test or GnRH analogue (GnRHa)–stimulation test—is expensive and makes patients uncomfortable due to the need for repeated blood sampling. OBJECTIVE We aimed to combine multiple CPP–related features and construct machine learning models to predict response to the GnRHa-stimulation test. METHODS In this retrospective study, we analyzed clinical and laboratory data of 1757 girls who underwent a GnRHa test in order to develop XGBoost and random forest classifiers for prediction of response to the GnRHa test. The local interpretable model-agnostic explanations (LIME) algorithm was used with the black-box classifiers to increase their interpretability. We measured sensitivity, specificity, and area under receiver operating characteristic (AUC) of the models. RESULTS Both the XGBoost and random forest models achieved good performance in distinguishing between positive and negative responses, with the AUC ranging from 0.88 to 0.90, sensitivity ranging from 77.91% to 77.94%, and specificity ranging from 84.32% to 87.66%. Basal serum luteinizing hormone, follicle-stimulating hormone, and insulin-like growth factor-I levels were found to be the three most important factors. In the interpretable models of LIME, the abovementioned variables made high contributions to the prediction probability. CONCLUSIONS The prediction models we developed can help diagnose CPP and may be used as a prescreening tool before the GnRHa-stimulation test.

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