scholarly journals Autocorrelation Analysis of Financial Botnet Traffic

2018 ◽  
Author(s):  
Prathiba Nagarajan ◽  
Fabio Di Troia ◽  
Thomas H. Austin ◽  
Mark Stamp
Keyword(s):  
Autocorrelation Analysis

Tire audio signal processing based on EMD and autocorrelation analysis

2009 ◽  
Vol 2009 (9) ◽  
pp. 33-37
Author(s):  
Jiaoying Huang ◽  
Haiwen Yuan ◽  
Yong Cui ◽  
Chenxi Bi
Keyword(s):  
Signal Processing ◽  
Audio Signal ◽  
Audio Signal Processing ◽  
Autocorrelation Analysis

scholarly journals The Use of Crop Yield Autocorrelation Data as a Sustainable Approach to Adjust Agronomic Inputs

2021 ◽  
Vol 13 (4) ◽  
pp. 2362
Author(s):  
Thomas M. Koutsos ◽  
Georgios C. Menexes ◽  
Andreas P. Mamolos
Keyword(s):  
Decision Making ◽  
Spatial Analysis ◽  
Crop Yield ◽  
Spatial Autocorrelation Analysis ◽  
Autocorrelation Analysis ◽  
Yield Data ◽  
Site Specific ◽  
Production Areas ◽  
Short Time ◽  
Monitor Data

Agricultural fields have natural within-field soil variations that can be extensive, are usually contiguous, and are not always traceable. As a result, in many cases, site-specific attention is required to adjust inputs and optimize crop performance. Researchers, such as agronomists, agricultural engineers, or economists and other scientists, have shown increased interest in performing yield monitor data analysis to improve farmers’ decision-making concerning the better management of the agronomic inputs in the fields, while following a much more sustainable approach. In this case, spatial analysis of crop yield data with the form of spatial autocorrelation analysis can be used as a practical sustainable approach to locate statistically significant low-production areas. The resulted insights can be used as prescription maps on the tractors to reduce overall inputs and farming costs. This aim of this work is to present the benefits of conducting spatial analysis of yield crop data as a sustainable approach. Current work proves that the implementation of this process is costless, easy to perform and provides a better understanding of the current agronomic needs for better decision-making within a short time, adopting a sustainable approach.

scholarly journals Spatial and Temporal Analysis of Lung Cancer in Shenzhen, 2008–2018

2020 ◽  
Vol 18 (1) ◽  
pp. 26
Author(s):  
Lin Lei ◽  
Anyan Huang ◽  
Weicong Cai ◽  
Ling Liang ◽  
Yirong Wang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Spatial Autocorrelation ◽  
Incidence Rate ◽  
Cancer Incidence ◽  
Southern China ◽  
Percentage Change ◽  
Spatial Autocorrelation Analysis ◽  
Lung Cancer Incidence ◽  
Annual Percentage Change ◽  
Autocorrelation Analysis

Lung cancer is the most commonly diagnosed cancer in China. The incidence trend and geographical distribution of lung cancer in southern China have not been reported. The present study explored the temporal trend and spatial distribution of lung cancer incidence in Shenzhen from 2008 to 2018. The lung cancer incidence data were obtained from the registered population in the Shenzhen Cancer Registry System between 2008 and 2018. The standardized incidence rates of lung cancer were analyzed by using the joinpoint regression model. The Moran’s I method was used for spatial autocorrelation analysis and to further draw a spatial cluster map in Shenzhen. From 2008 to 2018, the average crude incidence rate of lung cancer was 27.1 (1/100,000), with an annual percentage change of 2.7% (p < 0.05). The largest average proportion of histological type of lung cancer was determined as adenocarcinoma (69.1%), and an increasing trend was observed in females, with an average annual percentage change of 14.7%. The spatial autocorrelation analysis indicated some sites in Shenzhen as a high incidence rate spatial clustering area. Understanding the incidence patterns of lung cancer is useful for monitoring and prevention.

scholarly journals Autocorrelation analysis of COVID-19 based on hijri calendar

2021 ◽  
Vol 1918 (4) ◽  
pp. 042004
Author(s):  
G Darmawan ◽  
D Rosadi ◽  
BN Ruchjana ◽  
Hermansah
Keyword(s):  
Autocorrelation Analysis

scholarly journals Electrical Load Forecast by Means of LSTM: The Impact of Data Quality

Forecasting ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 91-101
Author(s):  
Alfredo Nespoli ◽  
Emanuele Ogliari ◽  
Silvia Pretto ◽  
Michele Gavazzeni ◽  
Sonia Vigani ◽  
...  
Keyword(s):  
Forecast Accuracy ◽  
Electric Load ◽  
Electrical Load ◽  
Autocorrelation Analysis ◽  
Load Forecast ◽  
Error Metrics ◽  
Input Dataset ◽  
Accurate Forecast ◽  
The Impact

Accurate forecast of aggregate end-users electric load profiles is becoming a hot topic in research for those main issues addressed in many fields such as the electricity services market. Hence, load forecast is an extremely important task which should be understood more in depth. In this research paper, the dependency of the day-ahead load forecast accuracy on the basis of the data typology employed in the training of LSTM has been inspected. A real case study of an Italian industrial load with samples recorded every 15 min for the year 2017 and 2018 was studied. The effect in the load forecast accuracy of different dataset cleaning approaches was investigated. In addition, the Generalised Extreme Studentized Deviate hypothesis testing was introduced to identify the outliers present in the dataset. The populations were constructed on the basis of an autocorrelation analysis that allowed for identifying a weekly correlation of the samples. The accuracy of the prediction obtained from different input dataset has been therefore investigated by calculating the most commonly used error metrics, showing the importance of data processing before employing them for load forecast.

Using local autocorrelation analysis to identify conservation areas: an example considering threatened invertebrate species in Spain

2012 ◽  
Vol 21 (8) ◽  
pp. 2127-2137 ◽  
Author(s):  
Olga Lucía Hernández-Manrique ◽  
David Sánchez-Fernández ◽  
José R. Verdú ◽  
Catherine Numa ◽  
Eduardo Galante ◽  
...  
Keyword(s):  
Autocorrelation Analysis ◽  
Conservation Areas ◽  
Invertebrate Species ◽  
Threatened Invertebrate

Spatial genetic substructure within natural populations of jack pine (Pinus banksiana)

1991 ◽  
Vol 69 (3) ◽  
pp. 547-551 ◽  
Author(s):  
Chang Yi Xie ◽  
Peggy Knowles
Keyword(s):  
Spatial Autocorrelation ◽  
Pinus Banksiana ◽  
Pollen Dispersal ◽  
Natural Populations ◽  
Jack Pine ◽  
Spatial Autocorrelation Analysis ◽  
Autocorrelation Analysis ◽  
Long Distance ◽  
Genetic Substructure ◽  
Good Agreement

Spatial autocorrelation analysis was used to investigate the geographic distribution of allozyme genotypes within three natural populations of jack pine (Pinus banksiana Lamb.). Results indicate that genetic substructuring within these populations is very weak and the extent differs among populations. These results are in good agreement with those inferred from mating-system studies. Factors such as the species' predominantly outbreeding system, high mortality of selfs and inbreds prior to reproduction, long-distance pollen dispersal, and the absence of strong microhabitat selection may be responsible for the observed weak genetic substructuring. Key words: jack pine, Pinus banksiana, genetic substructure, allozyme, spatial autocorrelation analysis.

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