scholarly journals Developing a Simulated Online Model That Integrates GNSS, Accelerometer and Weather Data to Detect Parturition Events in Grazing Sheep: A Machine Learning Approach

Animals ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 303
Author(s):  
Eloise S. Fogarty ◽  
David L. Swain ◽  
Greg M. Cronin ◽  
Luis E. Moraes ◽  
Derek W. Bailey ◽  
...  
Keyword(s):  
Machine Learning ◽  
Field Trials ◽  
Satellite System ◽  
False Positives ◽  
Sensor Data ◽  
Weather Data ◽  
Integrated Sensor ◽  
Detection Model ◽  
Independent Field ◽  
Grazing Sheep

In the current study, a simulated online parturition detection model is developed and reported. Using a machine learning (ML)-based approach, the model incorporates data from Global Navigation Satellite System (GNSS) tracking collars, accelerometer ear tags and local weather data, with the aim of detecting parturition events in pasture-based sheep. The specific objectives were two-fold: (i) determine which sensor systems and features provide the most useful information for lambing detection; (ii) evaluate how these data might be integrated using ML classification to alert to a parturition event as it occurs. Two independent field trials were conducted during the 2017 and 2018 lambing seasons in New Zealand, with the data from each used for ML training and independent validation, respectively. Based on objective (i), four features were identified as exerting the greatest importance for lambing detection: mean distance to peers (MDP), MDP compared to the flock mean (MDP.Mean), closest peer (CP) and posture change (PC). Using these four features, the final ML was able to detect 27% and 55% of lambing events within ±3 h of birth with no prior false positives. If the model sensitivity was manipulated such that earlier false positives were permissible, this detection increased to 91% and 82% depending on the requirement for a single alert, or two consecutive alerts occurring. To identify the potential causes of model failure, the data of three animals were investigated further. Lambing detection appeared to rely on increased social isolation behaviour in addition to increased PC behaviour. The results of the study support the use of integrated sensor data for ML-based detection of parturition events in grazing sheep. This is the first known application of ML classification for the detection of lambing in pasture-based sheep. Application of this knowledge could have significant impacts on the ability to remotely monitor animals in commercial situations, with a logical extension of the information for remote monitoring of animal welfare.

scholarly journals AI Driven Planetary Bot for Future Vision

2021 ◽  
pp. 736-740
Author(s):  
Seema J ◽  
Kunal Kumar Gupta ◽  
Challapalli Balaram ◽  
Akshay Puttu Shetty ◽  
Kamjula Vasudeva Reddy
Keyword(s):  
Machine Learning ◽  
Light Intensity ◽  
Real Time ◽  
Sensor Data ◽  
Weather Data ◽  
Time Data ◽  
Model Training ◽  
Sensor Temperature ◽  
Future Vision ◽  
Future Condition

These days focus is more on technologies like Artificial Intelligence, Machine Learning and IoT. There is lots of platforms available for IOT implementation. ESP8266 chip is among them Here the implementation is about prediction of different aspects of weather data that can be used in many ways like predicting the future condition of different region of earth or predicting future condition of different planets and their different regions. To implement this system, we need different sensors like pressure sensor humidity sensor, temperature sensor and a light intensity sensor i.e DHT11 is utilize for temperature and humidity data together and LDR. Is for light intensity. The data which is sensed by different sensors are than uploaded to Thingspeak which is an API for cloud server by the help of NodeMCU and then converted to csv format. The data can be used for monitoring the real time values too. Machine Learning Environment can be setup by the help of a CNN model. Training of model can be done by recorded values of sensor data. After recording data from sensors to NodeMCU like temperature, pressure, humidity and light intensity and after these values are sent to python environment that is Jupyter notebook for further analysis. Here the data which is used is real time data to predict the particular value and test the model.

Binary Spectrum Feature for Improved Classifier Performance

2020 ◽  
Author(s):  
Nalika Ulapane ◽  
Karthick Thiyagarajan ◽  
sarath kodagoda
Keyword(s):  
Machine Learning ◽  
Classification Performance ◽  
Feature Reduction ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Monitoring Task ◽  
Classifier Performance ◽  
Spectrum Feature

<div>Classification has become a vital task in modern machine learning and Artificial Intelligence applications, including smart sensing. Numerous machine learning techniques are available to perform classification. Similarly, numerous practices, such as feature selection (i.e., selection of a subset of descriptor variables that optimally describe the output), are available to improve classifier performance. In this paper, we consider the case of a given supervised learning classification task that has to be performed making use of continuous-valued features. It is assumed that an optimal subset of features has already been selected. Therefore, no further feature reduction, or feature addition, is to be carried out. Then, we attempt to improve the classification performance by passing the given feature set through a transformation that produces a new feature set which we have named the “Binary Spectrum”. Via a case study example done on some Pulsed Eddy Current sensor data captured from an infrastructure monitoring task, we demonstrate how the classification accuracy of a Support Vector Machine (SVM) classifier increases through the use of this Binary Spectrum feature, indicating the feature transformation’s potential for broader usage.</div><div><br></div>

scholarly journals Automatic Identification of Upper Extremity Rehabilitation Exercise Type and Dose Using Body-Worn Sensors and Machine Learning: A Pilot Study

2021 ◽  
pp. 158-166
Author(s):  
Noah Balestra ◽  
Gaurav Sharma ◽  
Linda M. Riek ◽  
Ania Busza
Keyword(s):  
Machine Learning ◽  
Upper Extremity ◽  
Sensor Data ◽  
Inpatient Setting ◽  
Accelerometer Data ◽  
Data Set ◽  
Machine Learning Classification ◽  
Exercise Type ◽  
Exercise Dose ◽  
Rehabilitation Exercises

<b><i>Background:</i></b> Prior studies suggest that participation in rehabilitation exercises improves motor function poststroke; however, studies on optimal exercise dose and timing have been limited by the technical challenge of quantifying exercise activities over multiple days. <b><i>Objectives:</i></b> The objectives of this study were to assess the feasibility of using body-worn sensors to track rehabilitation exercises in the inpatient setting and investigate which recording parameters and data analysis strategies are sufficient for accurately identifying and counting exercise repetitions. <b><i>Methods:</i></b> MC10 BioStampRC® sensors were used to measure accelerometer and gyroscope data from upper extremities of healthy controls (<i>n</i> = 13) and individuals with upper extremity weakness due to recent stroke (<i>n</i> = 13) while the subjects performed 3 preselected arm exercises. Sensor data were then labeled by exercise type and this labeled data set was used to train a machine learning classification algorithm for identifying exercise type. The machine learning algorithm and a peak-finding algorithm were used to count exercise repetitions in non-labeled data sets. <b><i>Results:</i></b> We achieved a repetition counting accuracy of 95.6% overall, and 95.0% in patients with upper extremity weakness due to stroke when using both accelerometer and gyroscope data. Accuracy was decreased when using fewer sensors or using accelerometer data alone. <b><i>Conclusions:</i></b> Our exploratory study suggests that body-worn sensor systems are technically feasible, well tolerated in subjects with recent stroke, and may ultimately be useful for developing a system to measure total exercise “dose” in poststroke patients during clinical rehabilitation or clinical trials.

Machine Learning Assisted PUF Calibration for Trustworthy Proof of Sensor Data in IoT

2020 ◽  
Vol 25 (4) ◽  
pp. 1-21
Author(s):  
Urbi Chatterjee ◽  
Soumi Chatterjee ◽  
Debdeep Mukhopadhyay ◽  
Rajat Subhra Chakraborty
Keyword(s):  
Machine Learning ◽  
Sensor Data

scholarly journals Cloud Radar Observations of Diurnal and Seasonal Cloudiness over Reunion Island

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 868
Author(s):  
Jonathan Durand ◽  
Edouard Lees ◽  
Olivier Bousquet ◽  
Julien Delanoë ◽  
François Bonnardot
Keyword(s):  
Sea Breeze ◽  
Satellite System ◽  
Research Infrastructure ◽  
Weather Data ◽  
Reunion Island ◽  
Wet Season ◽  
Trade Winds ◽  
Cloud Radar ◽  
Réunion Island ◽  
Global Navigation Satellite

In November 2016, a 95 GHz cloud radar was permanently deployed in Reunion Island to investigate the vertical distribution of tropical clouds and monitor the temporal variability of cloudiness in the frame of the pan-European research infrastructure Aerosol, Clouds and Trace gases Research InfraStructure (ACTRIS). In the present study, reflectivity observations collected during the two first years of operation (2016–2018) of this vertically pointing cloud radar are relied upon to investigate the diurnal and seasonal cycle of cloudiness in the northern part of this island. During the wet season (December–March), cloudiness is particularly pronounced between 1–3 km above sea level (with a frequency of cloud occurrence of 45% between 12:00–19:00 LST) and 8–12 km (with a frequency of cloud occurrence of 15% between 14:00–19:00 LST). During the dry season (June–September), this bimodal vertical mode is no longer observed and the vertical cloud extension is essentially limited to a height of 3 km due to both the drop-in humidity resulting from the northward migration of the ITCZ and the capping effect of the trade winds inversion. The frequency of cloud occurrence is at its maximum between 13:00–18:00 LST, with a probability of 35% at 15 LST near an altitude of 2 km. The analysis of global navigation satellite system (GNSS)-derived weather data also shows that the diurnal cycle of low- (1–3 km) and mid-to-high level (5–10 km) clouds is strongly correlated with the diurnal evolution of tropospheric humidity, suggesting that additional moisture is advected towards the island by the sea breeze regime. The detailed analysis of cloudiness observations collected during the four seasons sampled in 2017 and 2018 also shows substantial differences between the two years, possibly associated with a strong positive Indian Ocean Southern Dipole (IOSD) event extending throughout the year 2017.

Prediction of aircraft estimated time of arrival using machine learning methods

2021 ◽  
pp. 1-15
Author(s):  
O. Basturk ◽  
C. Cetek
Keyword(s):  
Machine Learning ◽  
Web Application ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Weather Data ◽  
Time Of Arrival ◽  
Learning Models ◽  
Trajectory Data ◽  
Different Sources ◽  
Machine Learning Models

ABSTRACT In this study, prediction of aircraft Estimated Time of Arrival (ETA) is proposed using machine learning algorithms. Accurate prediction of ETA is important for management of delay and air traffic flow, runway assignment, gate assignment, collaborative decision making (CDM), coordination of ground personnel and equipment, and optimisation of arrival sequence etc. Machine learning is able to learn from experience and make predictions with weak assumptions or no assumptions at all. In the proposed approach, general flight information, trajectory data and weather data were obtained from different sources in various formats. Raw data were converted to tidy data and inserted into a relational database. To obtain the features for training the machine learning models, the data were explored, cleaned and transformed into convenient features. New features were also derived from the available data. Random forests and deep neural networks were used to train the machine learning models. Both models can predict the ETA with a mean absolute error (MAE) less than 6min after departure, and less than 3min after terminal manoeuvring area (TMA) entrance. Additionally, a web application was developed to dynamically predict the ETA using proposed models.

Using Machine Learning for Dependable Outlier Detection in Environmental Monitoring Systems

2021 ◽  
Vol 5 (3) ◽  
pp. 1-30
Author(s):  
Gonçalo Jesus ◽  
António Casimiro ◽  
Anabela Oliveira
Keyword(s):  
Machine Learning ◽  
Environmental Monitoring ◽  
Data Quality ◽  
Outlier Detection ◽  
Prediction Models ◽  
Sensor Data ◽  
Natural Phenomenon ◽  
Monitoring Systems ◽  
Data Errors ◽  
Redundant Data

Sensor platforms used in environmental monitoring applications are often subject to harsh environmental conditions while monitoring complex phenomena. Therefore, designing dependable monitoring systems is challenging given the external disturbances affecting sensor measurements. Even the apparently simple task of outlier detection in sensor data becomes a hard problem, amplified by the difficulty in distinguishing true data errors due to sensor faults from deviations due to natural phenomenon, which look like data errors. Existing solutions for runtime outlier detection typically assume that the physical processes can be accurately modeled, or that outliers consist in large deviations that are easily detected and filtered by appropriate thresholds. Other solutions assume that it is possible to deploy multiple sensors providing redundant data to support voting-based techniques. In this article, we propose a new methodology for dependable runtime detection of outliers in environmental monitoring systems, aiming to increase data quality by treating them. We propose the use of machine learning techniques to model each sensor behavior, exploiting the existence of correlated data provided by other related sensors. Using these models, along with knowledge of processed past measurements, it is possible to obtain accurate estimations of the observed environment parameters and build failure detectors that use these estimations. When a failure is detected, these estimations also allow one to correct the erroneous measurements and hence improve the overall data quality. Our methodology not only allows one to distinguish truly abnormal measurements from deviations due to complex natural phenomena, but also allows the quantification of each measurement quality, which is relevant from a dependability perspective. We apply the methodology to real datasets from a complex aquatic monitoring system, measuring temperature and salinity parameters, through which we illustrate the process for building the machine learning prediction models using a technique based on Artificial Neural Networks, denoted ANNODE ( ANN Outlier Detection ). From this application, we also observe the effectiveness of our ANNODE approach for accurate outlier detection in harsh environments. Then we validate these positive results by comparing ANNODE with state-of-the-art solutions for outlier detection. The results show that ANNODE improves existing solutions regarding accuracy of outlier detection.

scholarly journals Detecting Video Game Player Burnout with the Use of Sensor Data and Machine Learning

2021 ◽  
pp. 1-1
Author(s):  
Anton Smerdov ◽  
Andrey Somov ◽  
Evgeny Burnaev ◽  
Bo Zhou ◽  
Paul Lukowicz
Keyword(s):  
Machine Learning ◽  
Video Game ◽  
Sensor Data ◽  
Game Player ◽  
Video Game Player

scholarly journals NLOS Multipath Classification of GNSS Signal Correlation Output Using Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2503
Author(s):  
Taro Suzuki ◽  
Yoshiharu Amano
Keyword(s):  
Machine Learning ◽  
Satellite System ◽  
Training Data ◽  
Support Vector ◽  
Positioning Errors ◽  
Automated Method ◽  
Global Navigation Satellite ◽  
Better Than ◽  
Signal Correlation

This paper proposes a method for detecting non-line-of-sight (NLOS) multipath, which causes large positioning errors in a global navigation satellite system (GNSS). We use GNSS signal correlation output, which is the most primitive GNSS signal processing output, to detect NLOS multipath based on machine learning. The shape of the multi-correlator outputs is distorted due to the NLOS multipath. The features of the shape of the multi-correlator are used to discriminate the NLOS multipath. We implement two supervised learning methods, a support vector machine (SVM) and a neural network (NN), and compare their performance. In addition, we also propose an automated method of collecting training data for LOS and NLOS signals of machine learning. The evaluation of the proposed NLOS detection method in an urban environment confirmed that NN was better than SVM, and 97.7% of NLOS signals were correctly discriminated.

scholarly journals Machine Learning-Based Malicious X.509 Certificates’ Detection

2021 ◽  
Vol 11 (5) ◽  
pp. 2164
Author(s):  
Jiaxin Li ◽  
Zhaoxin Zhang ◽  
Changyong Guo
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Ensemble Learning ◽  
Traffic Analysis ◽  
Learning Models ◽  
Detection Model ◽  
Analysis Tools ◽  
Average Accuracy ◽  
Machine Learning Models

X.509 certificates play an important role in encrypting the transmission of data on both sides under HTTPS. With the popularization of X.509 certificates, more and more criminals leverage certificates to prevent their communications from being exposed by malicious traffic analysis tools. Phishing sites and malware are good examples. Those X.509 certificates found in phishing sites or malware are called malicious X.509 certificates. This paper applies different machine learning models, including classical machine learning models, ensemble learning models, and deep learning models, to distinguish between malicious certificates and benign certificates with Verification for Extraction (VFE). The VFE is a system we design and implement for obtaining plentiful characteristics of certificates. The result shows that ensemble learning models are the most stable and efficient models with an average accuracy of 95.9%, which outperforms many previous works. In addition, we obtain an SVM-based detection model with an accuracy of 98.2%, which is the highest accuracy. The outcome indicates the VFE is capable of capturing essential and crucial characteristics of malicious X.509 certificates.

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