scholarly journals A Gated Recurrent Units (GRU)-Based Model for Early Detection of Soybean Sudden Death Syndrome through Time-Series Satellite Imagery

2020 ◽  
Vol 12 (21) ◽  
pp. 3621
Author(s):  
Luning Bi ◽  
Guiping Hu ◽  
Muhammad Mohsin Raza ◽  
Yuba Kandel ◽  
Leonor Leandro ◽  
...  
Keyword(s):  
Time Series ◽  
Early Detection ◽  
Sudden Death ◽  
Near Infrared ◽  
Time Series Data ◽  
Sudden Death Syndrome ◽  
Plant Diseases ◽  
Series Data ◽  
Detection Accuracy ◽  
Test Accuracy

In general, early detection and timely management of plant diseases are essential for reducing yield loss. Traditional manual inspection of fields is often time-consuming and laborious. Automated imaging techniques have recently been successfully applied to detect plant diseases. However, these methods mostly focus on the current state of the crop. This paper proposes a gated recurrent unit (GRU)-based model to predict soybean sudden death syndrome (SDS) disease development. To detect SDS at a quadrat level, the proposed method uses satellite images collected from PlanetScope as the training set. The pixel image data include the spectral bands of red, green, blue and near-infrared (NIR). Data collected during the 2016 and 2017 soybean-growing seasons were analyzed. Instead of using individual static imagery, the GRU-based model converts the original imagery into time-series data. SDS predictions were made on different data scenarios and the results were compared with fully connected deep neural network (FCDNN) and XGBoost methods. The overall test accuracy of classifying healthy and diseased quadrates in all methods was above 76%. The test accuracy of the FCDNN and XGBoost were 76.3–85.5% and 80.6–89.2%, respectively, while the test accuracy of the GRU-based model was 82.5–90.4%. The calculation results show that the proposed method can improve the detection accuracy by up to 7% with time-series imagery. Thus, the proposed method has the potential to predict SDS at a future time.

Outlier Detection Using Convolutional Neural Network for Wireless Sensor Network

2021 ◽  
Vol 17 (2) ◽  
pp. 0-0
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Deep Learning ◽  
Wireless Sensor Network ◽  
Convolutional Neural Network ◽  
Sensor Network ◽  
Time Series Data ◽  
Wireless Sensor ◽  
Series Data ◽  
Detection Accuracy

Over the recent years, the term deep learning has been considered as one of the primary choice for handling huge amount of data. Having deeper hidden layers, it surpasses classical methods for detection of outlier in wireless sensor network. The Convolutional Neural Network (CNN) is a biologically inspired computational model which is one of the most popular deep learning approaches. It comprises neurons that self-optimize through learning. EEG generally known as Electroencephalography is a tool used for investigation of brain function and EEG signal gives time-series data as output. In this paper, we propose a state-of-the-art technique designed by processing the time-series data generated by the sensor nodes stored in a large dataset into discrete one-second frames and these frames are projected onto a 2D map images. A convolutional neural network (CNN) is then trained to classify these frames. The result improves detection accuracy and encouraging.

scholarly journals TimeTrial: An Interactive Application for Optimizing the Design and Analysis of Transcriptomic Time-Series Data in Circadian Biology Research

2020 ◽  
Vol 35 (5) ◽  
pp. 439-451 ◽  
Author(s):  
Elan Ness-Cohn ◽  
Marta Iwanaszko ◽  
William L. Kath ◽  
Ravi Allada ◽  
Rosemary Braun
Keyword(s):  
Time Series ◽  
Experimental Design ◽  
Time Series Data ◽  
Synthetic Data ◽  
Optimal Choice ◽  
Series Data ◽  
Detection Accuracy ◽  
Periodic Patterns ◽  
Detection Algorithms ◽  
The Impact

The circadian rhythm drives the oscillatory expression of thousands of genes across all tissues, coordinating physiological processes. The effect of this rhythm on health has generated increasing interest in discovering genes under circadian control by searching for periodic patterns in transcriptomic time-series experiments. While algorithms for detecting cycling transcripts have advanced, there remains little guidance quantifying the effect of experimental design and analysis choices on cycling detection accuracy. We present TimeTrial, a user-friendly benchmarking framework using both real and synthetic data to investigate cycle detection algorithms’ performance and improve circadian experimental design. Results show that the optimal choice of analysis method depends on the sampling scheme, noise level, and shape of the waveform of interest and provides guidance on the impact of sampling frequency and duration on cycling detection accuracy. The TimeTrial software is freely available for download and may also be accessed through a web interface. By supplying a tool to vary and optimize experimental design considerations, TimeTrial will enhance circadian transcriptomics studies.

scholarly journals Human Activity Prediction in Smart Home Time Series Data by using Change Point Detection

2020 ◽  
Vol 9 (5) ◽  
pp. 1268-1271
Keyword(s):  
Time Series ◽  
Change Point ◽  
Time Series Data ◽  
Proper Time ◽  
Arima Model ◽  
Computational Cost ◽  
Change Point Detection ◽  
Series Data ◽  
Detection Accuracy ◽  
Point Detection

A Sensor is attached to a house which send message now and then about the housemates or the old people activities. Whether they had taken food in the correct time or not and other activities they perform in the proper time or not. Change Point Detection (CPD) is a matter of perspective which discovers deviating from what are normal or usual changes within the housemates. Any abnormal changes in the housemates have been identifying the presence of time points. The dissimilar changes occurs called SEPERATION Change Point Detection. It will not coincide when a remarkable occurrence of events at any points. Change Point Detection (CPD) occurs at the same time and in the problem of finding unexpected changes in facts and statistics collected together for references and analysis and in the property of the time series changes. An unusual real-time not involving any assumptions as to the form or in the parameters of a frequency distribution change point detection algorithm called Separation , It is used to calculate as a parting to recognize change points in fully measurement characteristics relating to measurements having sufficient depth and substance to be in time series. To ameliorate the order of this algorithm used in ARIMA with SEP algorithm. ARIMA model is used for predicting the Time series forecasting result. If emergency is occur then automatically send notification to caring person. The proposed work can decreasing computational cost and also improves the detection accuracy in the quality or fact of being useful of proposed technique.

scholarly journals ESTIMATING RELIABILITY OF DISTURBANCES IN SATELLITE TIME SERIES DATA BASED ON STATISTICAL ANALYSIS

2016 ◽  
Vol XLI-B3 ◽  
pp. 549-554
Author(s):  
Z.-G. Zhou ◽  
P. Tang ◽  
M. Zhou
Keyword(s):  
Time Series ◽  
Statistical Analysis ◽  
Land Cover ◽  
Time Series Data ◽  
Estimation Error ◽  
Satellite Image ◽  
Plant Diseases ◽  
Series Data ◽  
The Status ◽  
Statistical Analysis Method

Normally, the status of land cover is inherently dynamic and changing continuously on temporal scale. However, disturbances or abnormal changes of land cover — caused by such as forest fire, flood, deforestation, and plant diseases — occur worldwide at unknown times and locations. Timely detection and characterization of these disturbances is of importance for land cover monitoring. Recently, many time-series-analysis methods have been developed for near real-time or online disturbance detection, using satellite image time series. However, the detection results were only labelled with “Change/ No change” by most of the present methods, while few methods focus on estimating reliability (or confidence level) of the detected disturbances in image time series. To this end, this paper propose a statistical analysis method for estimating reliability of disturbances in new available remote sensing image time series, through analysis of full temporal information laid in time series data. The method consists of three main steps. (1) Segmenting and modelling of historical time series data based on Breaks for Additive Seasonal and Trend (BFAST). (2) Forecasting and detecting disturbances in new time series data. (3) Estimating reliability of each detected disturbance using statistical analysis based on Confidence Interval (CI) and Confidence Levels (CL). The method was validated by estimating reliability of disturbance regions caused by a recent severe flooding occurred around the border of Russia and China. Results demonstrated that the method can estimate reliability of disturbances detected in satellite image with estimation error less than 5% and overall accuracy up to 90%.

scholarly journals Deep Learning-Based Drivers Emotion Classification System in Time Series Data for Remote Applications

2020 ◽  
Vol 12 (3) ◽  
pp. 587 ◽  
Author(s):  
Rizwan Ali Naqvi ◽  
Muhammad Arsalan ◽  
Abdul Rehman ◽  
Ateeq Ur Rehman ◽  
Woong-Kee Loh ◽  
...  
Keyword(s):  
Time Series ◽  
Computer Games ◽  
Large Scale ◽  
Traffic Accidents ◽  
Near Infrared ◽  
Time Series Data ◽  
Series Data ◽  
Emotion Classification ◽  
Facial Emotions ◽  
Left And Right

Aggressive driving emotions is indeed one of the major causes for traffic accidents throughout the world. Real-time classification in time series data of abnormal and normal driving is a keystone to avoiding road accidents. Existing work on driving behaviors in time series data have some limitations and discomforts for the users that need to be addressed. We proposed a multimodal based method to remotely detect driver aggressiveness in order to deal these issues. The proposed method is based on change in gaze and facial emotions of drivers while driving using near-infrared (NIR) camera sensors and an illuminator installed in vehicle. Driver’s aggressive and normal time series data are collected while playing car racing and truck driving computer games, respectively, while using driving game simulator. Dlib program is used to obtain driver’s image data to extract face, left and right eye images for finding change in gaze based on convolutional neural network (CNN). Similarly, facial emotions that are based on CNN are also obtained through lips, left and right eye images extracted from Dlib program. Finally, the score level fusion is applied to scores that were obtained from change in gaze and facial emotions to classify aggressive and normal driving. The proposed method accuracy is measured through experiments while using a self-constructed large-scale testing database that shows the classification accuracy of the driver’s change in gaze and facial emotions for aggressive and normal driving is high, and the performance is superior to that of previous methods.

scholarly journals ESTIMATING RELIABILITY OF DISTURBANCES IN SATELLITE TIME SERIES DATA BASED ON STATISTICAL ANALYSIS

2016 ◽  
Vol XLI-B3 ◽  
pp. 549-554 ◽  
Author(s):  
Z.-G. Zhou ◽  
P. Tang ◽  
M. Zhou
Keyword(s):  
Time Series ◽  
Statistical Analysis ◽  
Land Cover ◽  
Time Series Data ◽  
Estimation Error ◽  
Satellite Image ◽  
Plant Diseases ◽  
Series Data ◽  
The Status ◽  
Statistical Analysis Method

Normally, the status of land cover is inherently dynamic and changing continuously on temporal scale. However, disturbances or abnormal changes of land cover — caused by such as forest fire, flood, deforestation, and plant diseases — occur worldwide at unknown times and locations. Timely detection and characterization of these disturbances is of importance for land cover monitoring. Recently, many time-series-analysis methods have been developed for near real-time or online disturbance detection, using satellite image time series. However, the detection results were only labelled with “Change/ No change” by most of the present methods, while few methods focus on estimating reliability (or confidence level) of the detected disturbances in image time series. To this end, this paper propose a statistical analysis method for estimating reliability of disturbances in new available remote sensing image time series, through analysis of full temporal information laid in time series data. The method consists of three main steps. (1) Segmenting and modelling of historical time series data based on Breaks for Additive Seasonal and Trend (BFAST). (2) Forecasting and detecting disturbances in new time series data. (3) Estimating reliability of each detected disturbance using statistical analysis based on Confidence Interval (CI) and Confidence Levels (CL). The method was validated by estimating reliability of disturbance regions caused by a recent severe flooding occurred around the border of Russia and China. Results demonstrated that the method can estimate reliability of disturbances detected in satellite image with estimation error less than 5% and overall accuracy up to 90%.

scholarly journals Prevention of Unintended Appearance in Photos Based on Human Behaviors Analysis

2020 ◽  
Author(s):  
Yuhi Kaihoko ◽  
Phan Xuan Tan ◽  
Eiji Kamioka
Keyword(s):  
Time Series ◽  
Cross Validation ◽  
Time Series Data ◽  
Video Data ◽  
Network Services ◽  
Series Data ◽  
Detection Accuracy ◽  
Human Behaviors ◽  
Wearable Camera ◽  
Social Network Services

Many people can take photos with smartphones and easily post photos via SNS (Social Network Services). This has caused a social problem that unintended appearance in photos may threaten the privacy of photographed persons. For this issue, numerous studies have already been introduced to prevent the unintended appearance in photos from the photographer’s side, but only a few methods tackled this from the photographed person's side. Therefore, we considered calling attention to a situation that a photo-taking behavior by a photographer can be automatically detected by using a wearable camera worn by a photographed person. In this paper, we propose an approach to detect photo-taking behaviors in video data taken from the wearable camera, analyzing specific human skeleton information. OpenPose is utilized to obtain the human’s skeleton information and the time-series data are analyzed. In addition, we compare two similar behaviors which are photo-taking behaviors and net-surfing behaviors. These video data are distinguished by DP matching and cross-validation. Finally, it is concluded that the detection accuracy of photo-taking behaviors is about 92.5%, which is satisfactory enough for this research purpose.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

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