scholarly journals Factors Affecting the Dynamics of Frosty Pod Rot in the Main Cocoa Areas of Santander State, Colombia

Plant Disease ◽  
2019 ◽  
Vol 103 (7) ◽  
pp. 1665-1673 ◽  
Author(s):  
Yeirme Y. Jaimes ◽  
Fabienne Ribeyre ◽  
Carolina Gonzalez ◽  
Jairo Rojas ◽  
Edson L. Furtado ◽  
...  
Keyword(s):  
Partial Resistance ◽  
Good Practice ◽  
Boosted Regression Trees ◽  
Machine Learning Technique ◽  
Factors Affecting ◽  
Learning Technique ◽  
The Difference ◽  
Moniliophthora Roreri ◽  
Frosty Pod Rot ◽  
Different Levels

Frosty pod rot (FPR) caused by Moniliophthora roreri is the primary disease affecting cacao production in the major producing countries of the Americas and is one of the major threats to cacao worldwide. The incidence of FPR on clones with different levels of resistance was investigated in four localities of Santander State, Colombia, between July 2013 and May 2015. Dynamics of diseased pods were modeled using boosted regression trees, a machine learning technique that allows regressions to be performed without prior statistical assumptions. The results suggested that FPR epidemics varied according to plot location, clone, weeks of observation, and total pods produced. Dynamics in the phenology of pods had an effect on the epidemics, and this dynamic could partially explain the difference in resistance among clones. Although not total, partial resistance of ICS 95 was confirmed. An important wilt effect was observed, particularly in the resistant clones; consequently, differences in harvested pods were not significant among clones. Pod stripping remains a good practice for the management of the disease and this practice could also have an effect on the pod dynamics and wilt phenomenon.

scholarly journals An Empirical Investigation on Evolutionary Algorithm Evolving Developmental Timings

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1866
Author(s):  
Kei Ohnishi ◽  
Kouta Hamano ◽  
Mario Koeppen
Keyword(s):  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Search Strategy ◽  
Empirical Investigation ◽  
Optimization Problems ◽  
Machine Learning Technique ◽  
Model Based ◽  
Fitness Value ◽  
Learning Technique ◽  
The Difference

Recently, evolutionary algorithms that can efficiently solve decomposable binary optimization problems have been developed. They are so-called model-based evolutionary algorithms, which build a model for generating solution candidates by applying a machine learning technique to a population. Their central procedure is linkage detection that reveals a problem structure, that is, how the entire problem consists of sub-problems. However, the model-based evolutionary algorithms have been shown to be ineffective for problems that do not have relevant structures or those whose structures are hard to identify. Therefore, evolutionary algorithms that can solve both types of problems quickly, reliably, and accurately are required. The objective of the paper is to investigate whether the evolutionary algorithm evolving developmental timings (EDT) that we previously proposed can be the desired one. The EDT makes some variables values more quickly converge than the remains for any problems, and then, decides values of the remains to obtain a higher fitness value under the fixation of the variables values. In addition, factors to decide which variable values converge more quickly, that is, developmental timings are evolution targets. Simulation results reveal that the EDT has worse performance than the linkage tree genetic algorithm (LTGA), which is one of the state-of-the-art model-based evolutionary algorithms, for decomposable problems and also that the difference in the performance between them becomes smaller for problems with overlaps among linkages and also that the EDT has better performance than the LTGA for problems whose structures are hard to identify. Those results suggest that an appropriate search strategy is different between decomposable problems and those hard to decompose.

scholarly journals Towards Real-Time Monitoring of the Hajj

2020 ◽  
Vol 5 ◽  
Author(s):  
Muhammad Baqui ◽  
Rainald Löhner
Keyword(s):  
Projective Transformation ◽  
Time Lapse ◽  
High Density ◽  
Boosted Regression Trees ◽  
Machine Learning Technique ◽  
Fundamental Properties ◽  
Image Velocimetry ◽  
Learning Technique ◽  
Pedestrian Traffic ◽  
And Performance

An automated approach to explore the fundamental properties of high-density pedestrian traffic is outlined. The framework operates on video or time lapse images captured from surveillance cameras. For pedestrian velocity extraction, the framework incorporates cross-correlation based Particle Image Velocimetry (PIV) techniques. For pedestrian density estimation, the framework relies on the Machine Learning technique of the Boosted Regression Trees. The information collected from images in pixel coordinates are transformed to world coordinates with a pin-hole camera based projective transformation technique. The framework has been tested with high density crowd images acquired during the Muslim religious event, the Hajj. Accuracy and performance of the framework are reported.

scholarly journals The Development of a Quantitative Precipitation Forecast Correction Technique Based on Machine Learning for Hydrological Applications

Atmosphere ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 111 ◽  
Author(s):  
Chul-Min Ko ◽  
Yeong Yun Jeong ◽  
Young-Mi Lee ◽  
Byung-Sik Kim
Keyword(s):  
Machine Learning ◽  
Heavy Rainfall ◽  
Extreme Rainfall ◽  
Machine Learning Techniques ◽  
Precipitation Forecast ◽  
Machine Learning Technique ◽  
Rainfall Forecast ◽  
Quantitative Precipitation Forecast ◽  
Correction Technique ◽  
Learning Technique

This study aimed to enhance the accuracy of extreme rainfall forecast, using a machine learning technique for forecasting hydrological impact. In this study, machine learning with XGBoost technique was applied for correcting the quantitative precipitation forecast (QPF) provided by the Korea Meteorological Administration (KMA) to develop a hydrological quantitative precipitation forecast (HQPF) for flood inundation modeling. The performance of machine learning techniques for HQPF production was evaluated with a focus on two cases: one for heavy rainfall events in Seoul and the other for heavy rainfall accompanied by Typhoon Kong-rey (1825). This study calculated the well-known statistical metrics to compare the error derived from QPF-based rainfall and HQPF-based rainfall against the observational data from the four sites. For the heavy rainfall case in Seoul, the mean absolute errors (MAE) of the four sites, i.e., Nowon, Jungnang, Dobong, and Gangnam, were 18.6 mm/3 h, 19.4 mm/3 h, 48.7 mm/3 h, and 19.1 mm/3 h for QPF and 13.6 mm/3 h, 14.2 mm/3 h, 33.3 mm/3 h, and 12.0 mm/3 h for HQPF, respectively. These results clearly indicate that the machine learning technique is able to improve the forecasting performance for localized rainfall. In addition, the HQPF-based rainfall shows better performance in capturing the peak rainfall amount and spatial pattern. Therefore, it is considered that the HQPF can be helpful to improve the accuracy of intense rainfall forecast, which is subsequently beneficial for forecasting floods and their hydrological impacts.

A quantitative evaluation of some factors affecting the non-fatty solids of cow's milk

1960 ◽  
Vol 27 (1) ◽  
pp. 19-32 ◽  
Author(s):  
W. H. Alexander ◽  
F. B. Leech
Keyword(s):  
Cell Count ◽  
Residual Effect ◽  
Clinical Mastitis ◽  
High Cell ◽  
Factors Affecting ◽  
Stage Of Lactation ◽  
Cell Counts ◽  
Satisfactory Analysis ◽  
The Difference ◽  
Image Position

SummaryTen farms in the county of Durham took part in a field study of the effects of feeding and of udder disease on the level of non-fatty solids (s.n.f.) in milk. Statistical analysis of the resulting data showed that age, pregnancy, season of the year, and total cell count affected the percentage of s.n.f. and that these effects were additive and independent of each other. No effect associated with nutritional changes could be demonstrated.The principal effects of the factors, each one freed from effects of other factors, were as follows:Herds in which s.n.f. had been consistently low over a period of years were compared with herds in which s.n.f. had been satisfactory. Analysis of the data showed that about 70% of the difference in s.n.f. between these groups could be accounted for by differences in age of cow, stage of lactation, cell count and breed.There was some evidence of a residual effect following clinical mastitis that could not be accounted for by residual high cell counts.The within-cow regression of s.n.f. on log cell count calculated from the Durham data and from van Rensburg's data was on both occasions negative.The implications of these findings are discussed, particularly in relation to advisory work.

Voice Pathology Detection Using Machine Learning Technique

2020 ◽  
Author(s):  
Fahad Taha AL-Dhief ◽  
Nurul Mu'azzah Abdul Latiff ◽  
Nik Noordini Nik Abd. Malik ◽  
Naseer Sabri ◽  
Marina Mat Baki ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Technique ◽  
Learning Technique ◽  
Voice Pathology Detection
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