scholarly journals Toward automating post processing of aquatic sensor data

2021 ◽  
Author(s):  
Amber Jones ◽  
Tanner Jones ◽  
Jeffery Horsburgh
Keyword(s):  
Anomaly Detection ◽  
Moving Average ◽  
Control Process ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Time Series Regression ◽  
Quality Control Process ◽  
Auto Regressive ◽  
And Performance ◽  
Using Data

Sensors measuring environmental phenomena at high frequency commonly report anomalies related to fouling, sensor drift and calibration, and datalogging and transmission issues. Suitability of data for analyses and decision making often depends on manual review and adjustment of data. Machine learning techniques have potential to automate identification and correction of anomalies, streamlining the quality control process. We explored approaches for automating anomaly detection and correction of aquatic sensor data for implementation in a Python package (PyHydroQC). We applied both classical and deep learning time series regression models that estimate values, identify anomalies based on dynamic thresholds, and offer correction estimates. Techniques were developed and performance assessed using data reviewed, corrected, and labeled by technicians in an aquatic monitoring use case. Auto-Regressive Integrated Moving Average (ARIMA) consistently performed best, and aggregating results from multiple models improved detection. PyHydroQC includes custom functions and a workflow for anomaly detection and correction.

scholarly journals Analisis Proses Pengendalian Mutu dalam Pelaksanaan Standar Pelayanan Minimal (SPM) di Instalasi Farmasi RS Keluarga Sehat

2017 ◽  
Vol 5 (1) ◽  
pp. 35-42
Author(s):  
LIa Alfa Rosida ◽  
Sudiro Sudiro
Keyword(s):  
Quality Control ◽  
Performance Appraisal ◽  
Control Process ◽  
Control Group ◽  
Pharmacy Staff ◽  
Quality Performance ◽  
Quality Control Process ◽  
Performance Report ◽  
Report Data ◽  
And Performance

The indicator achievement of minimum service standard (MSS) for the accuracy of distributingdrugs in Pharmacy Unit of Keluarga Sehat Hospital has not been achieved, even the incidence ofthe distributionerror from 2013 to 2016 continues to increase. The purpose of this researchwas to analyze the quality control process in the implementation of MSS in Pharmacy Unit of Keluarga Sehat Hospital. This was a qualitative research, with research subject 3pharmacy officers and 3 people of pharmacy management services.Data collectedby in-depth interview and observation of pharmaceutical performance report data and analysed by content analysis. The result of the research showed that the evaluation of pharmacy staff performance has not been implemented, because there is no performance appraisal indicator yet. Comparison was done only by comparing reports with general target, medical support manager double job resulted in no feedback to Pharmacy Unit, and so it has not supported the implementation of MSS. The Improvement of performancehas not been implementedand has notfound the concept of improvement. The new management will attempt to conduct a comparative study, including pharmacy installation into the Quality Control Group (GKM) or Problem Solving for Better Health (PSBH), find the cause of the problem and develop the policy. It can be concluded that the quality control of the MSS in the Pharmacy Unit still not going well and need to be improved especially related to quality performance appraisal and performance improvement based on the SOP.

scholarly journals Peramalan Data Runtun Waktu menggunakan Model Hybrid Time Series Regression – Autoregressive Integrated Moving Average

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Melisa Arumsari ◽  
◽  
Andrea Dani ◽  
Keyword(s):  
Time Series ◽  
Hybrid Method ◽  
Time Series Data ◽  
Moving Average ◽  
Series Data ◽  
Time Series Regression ◽  
Accurate Forecast ◽  
Using Data ◽  
The Individual ◽  
Time Series Data Analysis

Forecasting is a method used to estimate or predict a value in the future using data from the past. With the development of methods in time series data analysis, a hybrid method was developed in which a combination of several models was carried out in order to produce a more accurate forecast. The purpose of this study was to determine whether the TSR-ARIMA hybrid method has a better level of accuracy than the individual TSR method so that more accurate forecasting results are obtained. The data in this study are monthly data on the number of passengers on American airlines for the period January 1949 to December 1960. Based on the analysis, the TSR-ARIMA hybrid method produces a MAPE of 3,061% and the TSR method produces an MAPE of 7,902%.

scholarly journals Modeling and Forecasting Under-Five Mortality Rate in Nigeria using Auto-Regressive Integrated Moving Average Approach

2020 ◽  
pp. 347-360 ◽  
Author(s):  
Donalben Onome Eke ◽  
Friday Ewere
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Childhood Mortality ◽  
Live Births ◽  
Under Five ◽  
Child Deaths ◽  
Mortality Estimate ◽  
Modeling And Forecasting ◽  
Auto Regressive ◽  
Using Data

Nigeria’s efforts aimed at reducing avoidable child deaths have been met with gradual and sustained progress. Despite the decline in childhood mortality in Nigeria in the last two decades, its prevalence still remain high in comparison to the global standard of mortality for children under the age of five which stands at 25 deaths per 1000 live births. Knowledge of the chances of Nigeria achieving this goal for childhood mortality will aid proper interventions needed to reduce the occurrence. Therefore, this paper employed the Auto-Regressive Integrated Moving Average (ARIMA) model for time series analysis to make forecast of under-five mortality in Nigeria up to 2030 using data obtained from the United Nation’s Inter Agency Group for Childhood Mortality Estimate (UN-IGME). The ARIMA (2, 1, 1) model predicted a reduction of up to 37.3% by 2030 at 95% confidence interval. Results from the study also showed that a reduction of over 300% in under-five mortality is required for Nigeria to be able to achieve the SDG goal for under-five mortality.

scholarly journals Online Forecasting and Anomaly Detection Based on the ARIMA Model

2021 ◽  
Vol 11 (7) ◽  
pp. 3194
Author(s):  
Viacheslav Kozitsin ◽  
Iurii Katser ◽  
Dmitry Lakontsev
Keyword(s):  
Anomaly Detection ◽  
Power Plants ◽  
Moving Average ◽  
Arima Model ◽  
Diagnostic System ◽  
Simple Algorithm ◽  
Diagnostic Systems ◽  
Predictive Algorithms ◽  
Moving Average Model ◽  
Auto Regressive

Real-time diagnostics of complex technical systems such as power plants are critical to keep the system in its working state. An ideal diagnostic system must detect any fault in advance and predict the future state of the technical system, so predictive algorithms are used in the diagnostics. This paper proposes a novel, computationally simple algorithm based on the Auto-Regressive Integrated Moving Average model to solve anomaly detection and forecasting problems. The good performance of the proposed algorithm was confirmed in numerous numerical experiments for both anomaly detection and forecasting problems. Moreover, a description of the Autoregressive Integrated Moving Average Fault Detection (ARIMAFD) library, which includes the proposed algorithms, is provided in this paper. The developed algorithm proves to be an efficient algorithm and can be applied to problems related to anomaly detection and technological parameter forecasting in real diagnostic systems.

scholarly journals Intelligent Controller Design by the Artificial Intelligence Methods

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4454 ◽  
Author(s):  
Jana Nowaková ◽  
Miroslav Pokorný
Keyword(s):  
Artificial Intelligence ◽  
Control System ◽  
Continuous Monitoring ◽  
Controller Design ◽  
Control Process ◽  
Intelligent Controller ◽  
Intelligent Control System ◽  
Quality Control Process ◽  
Using Data ◽  
Takagi Sugeno

With the rapid growth of sensor networks and the enormous, fast-growing volumes of data collected from these sensors, there is a question relating to the way it will be used, and not only collected and analyzed. The data from these sensors are traditionally used for controlling and influencing the states and processes. Standard controllers are available and successfully implemented. However, with the data-driven era we are facing nowadays, there is an opportunity to use controllers, which can include much information, elusive for common controllers. Our goal is to propose a design of an intelligent controller–a conventional controller, but with a non-conventional method of designing its parameters using approaches of artificial intelligence combining fuzzy and genetics methods. Intelligent adaptation of parameters of the control system is performed using data from the sensors measured in the controlled process. All parts designed are based on non-conventional methods and are verified by simulations. The identification of the system’s parameters is based on parameter optimization by means of its difference equation using genetic algorithms. The continuous monitoring of the quality control process and the design of the controller parameters are conducted using a fuzzy expert system of the Mamdani type, or the Takagi–Sugeno type. The concept of the intelligent control system is open and easily expandable.

scholarly journals Forecasting daily meteorological time series using ARIMA and regression models

2018 ◽  
Vol 32 (2) ◽  
pp. 253-264 ◽  
Author(s):  
Małgorzata Murat ◽  
Iwona Malinowska ◽  
Magdalena Gos ◽  
Jaromir Krzyszczak
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Series Data ◽  
R Software ◽  
Time Series Regression ◽  
Climatic Zones ◽  
Autoregressive Integrated Moving Average ◽  
Temperature And Precipitation ◽  
Auto Regressive

Abstract The daily air temperature and precipitation time series recorded between January 1, 1980 and December 31, 2010 in four European sites (Jokioinen, Dikopshof, Lleida and Lublin) from different climatic zones were modeled and forecasted. In our forecasting we used the methods of the Box-Jenkins and Holt- Winters seasonal auto regressive integrated moving-average, the autoregressive integrated moving-average with external regressors in the form of Fourier terms and the time series regression, including trend and seasonality components methodology with R software. It was demonstrated that obtained models are able to capture the dynamics of the time series data and to produce sensible forecasts.

scholarly journals Comparison of Rice Price Forecasting Using the ARIMA Method on Amazon Forecast and Sagemaker

2020 ◽  
Vol 4 (3) ◽  
pp. 537-543
Author(s):  
Is Mardianto ◽  
Muhamad Ichsan Gunawan ◽  
Dedy Sugiarto ◽  
Abdul Rochman
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Price Forecasting ◽  
Price Prediction ◽  
Rice Price ◽  
Moving Average Processes ◽  
Auto Regressive ◽  
Independent Variable ◽  
Rice Prices ◽  
Using Data

Rice is one of the main commodities of trade in Indonesia. PT Food Station as the management company of Cipinang Rice Main Market every day publishes data on price, type of rice and the amount of rice that enters and exits Jakarta area. This study aims to forecast rice prices in the Jakarta area using data held by PT FoodStation during the 2016-2018 data period. Rice price prediction is carried out for the next 30 days using the Auto Regressive Integrated Moving Average (ARIMA) method on the Amazon Forecast and Amazon Sagemaker platforms. The ARIMA model is a form of regression analysis that measures the strength of one dependent variable that is relatively influential on other change variables. The ARIMA model is a special type of regression model in which the dependent variable is considered stationary and the independent variable is the lag or previous value of the dependent variable itself and the error lag. ARIMA is a combination of auto-regressive and moving average processes. The final result obtained in this experiment is that the ARIMA model on Amazon Sagemaker cloud computing is superior when compared to Amazon Forecast. From the experimental results obtained the results of Amazon Sagemaker RMSE (313.379941) are smaller than Amazon Forecast (322.4118029). So it can be concluded that the ARIMA model run at Amazon Sagemaker is more accurate than Amazon Forecast for forecasting the price of rice for 30 days at the Cipinang Rice Main Market

scholarly journals Prediction of Students’ Performance in e-Learning Environment using Data Mining/ Machine Learning Techniques

2021 ◽  
Vol 23 (05) ◽  
pp. 596-593
Author(s):  
Brijesh Kumar Verma ◽  
◽  
Dr. Nidhi Srivastava ◽  
Hemant Kumar Singh ◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Experimental Analysis ◽  
Relevant Information ◽  
Machine Learning Techniques ◽  
Mining Machine ◽  
Machine Leaning ◽  
E Learning ◽  
And Performance ◽  
Using Data ◽  
Teaching Learning

The COVID-19 pandemic has drastically changed the way od of learning. During this pandemic the learning has shifted from offline to online. student’s performance prediction based on their relevant information has emerged new area for educational institutions for improving teaching learning process, changes in course curriculum. Machine leaning technology can be helpful in predicting the performance of student and accordingly the institutions can make required changes in in their lecture delivery and curriculum. This paper utilized some machine learning methodologies to predict the students’ performance. Educational data of open University(OU) is analyzed Based on parameters that are demographic, engagement and performance. In the experimental analysis. In the experimental analysis, the k-NN approach performed best in some cases and ANN performed best in other cases among all compared algorithms on OU dataset.

Anomaly Detection in Sensor Data

2018 ◽  
Vol 18 (1) ◽  
pp. 20-32 ◽  
Author(s):  
Jong-Min Kim ◽  
Jaiwook Baik
Keyword(s):  
Anomaly Detection ◽  
Sensor Data
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