Classification of the coefficient of variation to variables in beef cattle experiments

Vaz, Marcos André Braz; Pacheco, Paulo Santana; Seidel, Enio Júnior; Ansuj, Ângela Pellegrin

doi:10.1590/0103-8478cr20160946

Cited by 17 publications

(16 citation statements)

References 5 publications

(9 reference statements)

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“…To define the device classes based on the C u index value, we used the method of coefficients of variation classification used in research [29,[39][40][41][42]. It assumes a normal distribution of data.…”

Section: Defining Iot Device Classes Based On Coefficients Of Variationmentioning

confidence: 99%

Ensemble machine learning approach for classification of IoT devices in smart home

Cvitić

Peraković

Periša

et al. 2021

Int. J. Mach. Learn. & Cyber.

141

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The emergence of the Internet of Things (IoT) concept as a new direction of technological development raises new problems such as valid and timely identification of such devices, security vulnerabilities that can be exploited for malicious activities, and management of such devices. The communication of IoT devices generates traffic that has specific features and differences with respect to conventional devices. This research seeks to analyze the possibilities of applying such features for classifying devices, regardless of their functionality or purpose. This kind of classification is necessary for a dynamic and heterogeneous environment, such as a smart home where the number and types of devices grow daily. This research uses a total of 41 IoT devices. The logistic regression method enhanced by the concept of supervised machine learning (logitboost) was used for developing a classification model. Multiclass classification model was developed using 13 network traffic features generated by IoT devices. Research has shown that it is possible to classify devices into four previously defined classes with high performances and accuracy (99.79%) based on the traffic flow features of such devices. Model performance measures such as precision, F-measure, True Positive Ratio, False Positive Ratio and Kappa coefficient all show high results (0.997–0.999, 0.997–0.999, 0.997–0.999, 0–0.001 and 0.9973, respectively). Such a developed model can have its application as a foundation for monitoring and managing solutions of large and heterogeneous IoT environments such as Industrial IoT, smart home, and similar.

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Section: Defining Iot Device Classes Based On Coefficients Of Variationmentioning

confidence: 99%

Ensemble machine learning approach for classification of IoT devices in smart home

Cvitić

Peraković

Periša

et al. 2021

Int. J. Mach. Learn. & Cyber.

141

View full text Add to dashboard Cite

show abstract

“…The coefficients of variation (Table 2) were considered medium-high for the parameters β 1 and β 3 , and low-medium for the parameter β 2 , according to the classification of Vaz et al (24) . By the Tukey's test, it was noticed that the animals which received 70% of roughage in the feed presented a higher value of β 2 and a lower value of than the animals which received only 30% of roughage.…”

Section: Resultsmentioning

confidence: 99%

Mixed nonlinear models in ruminal in situ degradability trials

et al. 2020

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Classical methods of analysis of nonlinear models are widely used in studies of ruminal degradation kinetics. As this type of study involves repeated measurements in the same experimental unit, the use of mixed nonlinear models (MNLM) is proposed, in order to solve problems of heterogeneity of variances of the responses, correlation among repeated measurements and consequent lack of sphericity in the covariance matrix. The aims of this work are to present an evaluation of the applicability of MNLM in the estimation of parameters to describe the in situ ruminal degradation kinetics of the dry matter of Tifton 85 hay and to compare the results with those obtained from the usual analysis in two-phases. The steers used in the trial were fed diets composed of three different combinations of roughage and concentrate and two hays with different nutritional qualities. The proposed approach was proven as effective as the traditional one for estimating model parameters. However, it adequately models the correlation among the longitudinal data, which can affect the estimates obtained, the standard error associated with them and potentially change the results of the inferences. It is quite attractive when the research seeks to understand the behavior of the process of food degradation throughout the incubation times.

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“…4 percent and 17.7 percent indicated high variability, while values above 17.7 percent indicated very high variability. This classification was computed using the mean and standard deviation of coefficient of variability for above ground carbon sequestration in South West Mau forest between 1985 and 2015and based on the criteria proposed by Garcia in 1989 (Ferreira et al 2018;Couto et al 2013;Vaz et al 2017). The criteria are presented in table 1.…”

Section: Discussionmentioning

confidence: 99%

Carbon Sequestration by the Above Ground Biomass Pool in the South West Mau Forest of Kenya, 1985 - 2015

2020

JEES

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Forests are important for regulation of the global carbon balance. Increase in forest biomass enhances atmospheric carbon sequestration while decrease in forest biomass contributes to carbon dioxide emissions. World over, forest biomass has been declining due to forest loss and degradation. The South West Mau has experienced significant forest loss since 1964. The decline is posited to have significant impacts on carbon sequestration, carbon storage, carbon dioxide emissions and status of atmospheric carbon dioxide. This study assessed interannual trend and variability as well as change point detection in carbon sequestration in South West Mau Forest, Kenya between 1985 and 2015. Above ground biomass carbon sequestration was quantified based on the Carnegie-Ames-Stanford Approach (CASA) and carbon fraction for tropical climate domain. Carbon sequestration dynamics were characterized by increase-decrease cycles of approximately 3 years and low interannual variability (CV= 9.13). It emerged that South West Mau Forest was a net carbon emitter with a carbon sequestration balance of-588.40 Kg/ha between 1985 and 2015.

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Classification of the coefficient of variation to variables in beef cattle experiments

Cited by 17 publications

References 5 publications

Ensemble machine learning approach for classification of IoT devices in smart home

Ensemble machine learning approach for classification of IoT devices in smart home

Mixed nonlinear models in ruminal in situ degradability trials

Carbon Sequestration by the Above Ground Biomass Pool in the South West Mau Forest of Kenya, 1985 - 2015

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