scholarly journals Broiler Behaviour Differs From Males to Females When Under Different Light Wavelengths

2021 ◽  
Author(s):  
Sandro José Paixão ◽  
Angélica Signor Mendes ◽  
Marco Antonio Possenti ◽  
Rosana Reffatti Sikorski ◽  
Marcos Martinez do Vale ◽  
...  
Keyword(s):  
Data Mining ◽  
Experimental Design ◽  
Age Groups ◽  
Stocking Density ◽  
Idle Period ◽  
Natural Behavior ◽  
Data Mining Approach ◽  
Behavioral Variables ◽  
Bird Feeder

Abstract It is well established that different light wavelengths affect broiler behavior. The present study aims to evaluate the effect of four light wavelengths on broiler behavior from 1 to 42-days of age. Birds were housed at a stocking density of 13 birds/m2, in 32 boxes of 1.56 m2. The experimental design was a completely randomized factorial of 4x2 (four colors x two sexes), with four replicates. Behavioral variables were accessed through cameras and observed in person thrice a week for 30 min per day in three different periods. Data was organized according to age groups and analyzed by data mining approach with the different light wavelengths as the classes. Natural behavior of male broilers reared in environments with green. Blue light was more relevant to the classification of male broilers behavior (96.9 and 96.9% accuracy and 0.8 and 1.0 of class precision of behavior classification, respectively). Blue and green lights affected the behavior of male broilers starting at 7-days of age, increasing the presence at the bird feeder, and reducing the idle period.

Pattern Based Feature Construction in Semantic Data Mining

2014 ◽  
Vol 10 (1) ◽  
pp. 27-65 ◽  
Author(s):  
Agnieszka Ławrynowicz ◽  
Jędrzej Potoniec
Keyword(s):  
Data Mining ◽  
Pattern Mining ◽  
Semantic Features ◽  
Semantic Data ◽  
Data Mining Approach ◽  
Meta Learning ◽  
New Type ◽  
Domain Ontologies ◽  
Semantic Data Mining

The authors propose a new method for mining sets of patterns for classification, where patterns are represented as SPARQL queries over RDFS. The method contributes to so-called semantic data mining, a data mining approach where domain ontologies are used as background knowledge, and where the new challenge is to mine knowledge encoded in domain ontologies, rather than only purely empirical data. The authors have developed a tool that implements this approach. Using this the authors have conducted an experimental evaluation including comparison of our method to state-of-the-art approaches to classification of semantic data and an experimental study within emerging subfield of meta-learning called semantic meta-mining. The most important research contributions of the paper to the state-of-art are as follows. For pattern mining research or relational learning in general, the paper contributes a new algorithm for discovery of new type of patterns. For Semantic Web research, it theoretically and empirically illustrates how semantic, structured data can be used in traditional machine learning methods through a pattern-based approach for constructing semantic features.

Finding the Most Significant Elements for the Classification of Organic Orange Leaves: A Data Mining Approach

2017 ◽  
Vol 50 (14) ◽  
pp. 2292-2307 ◽  
Author(s):  
Camila Maione ◽  
Christian Turra ◽  
Elisabete A. De Nadai Fernandes ◽  
Márcio Arruda Bacchi ◽  
Fernando Barbosa ◽  
...  
Keyword(s):  
Data Mining ◽  
Data Mining Approach

scholarly journals A Data Mining Approach to In Vivo Classification of Psychopharmacological Drugs

2008 ◽  
Vol 34 (3) ◽  
pp. 607-623 ◽  
Author(s):  
Neri Kafkafi ◽  
Daniel Yekutieli ◽  
Greg I Elmer
Keyword(s):  
Data Mining ◽  
Data Mining Approach

Multiple Trait Selection and Classification of Genotype — A Data Mining Approach

2003 ◽  
Vol 02 (03) ◽  
pp. 445-457 ◽  
Author(s):  
Chien-Hsiung Lin ◽  
Yi-Hsin Liu
Keyword(s):  
Data Mining ◽  
Mathematical Programming ◽  
Programming Approach ◽  
Fuzzy Mathematical Programming ◽  
Real Numbers ◽  
Multiple Trait ◽  
Data Mining Approach ◽  
Mathematical Programming Approach ◽  
Genotype A

A set of data represented by a set of real numbers can be handled by the computer much easier than non-real valued data. This paper develops bicriteria linear program solution through a fuzzy mathematical programming approach which assigns a real number to each member of the data. This method integrates data information and the decision maker's objective opinion to construct a tool (function) of selection and classification.

Pattern Based Feature Construction in Semantic Data Mining

2016 ◽  
pp. 823-864
Author(s):  
Agnieszka Ławrynowicz ◽  
Jędrzej Potoniec
Keyword(s):  
Data Mining ◽  
Pattern Mining ◽  
Semantic Features ◽  
Semantic Data ◽  
Data Mining Approach ◽  
Meta Learning ◽  
New Type ◽  
Domain Ontologies ◽  
Semantic Data Mining

The authors propose a new method for mining sets of patterns for classification, where patterns are represented as SPARQL queries over RDFS. The method contributes to so-called semantic data mining, a data mining approach where domain ontologies are used as background knowledge, and where the new challenge is to mine knowledge encoded in domain ontologies, rather than only purely empirical data. The authors have developed a tool that implements this approach. Using this the authors have conducted an experimental evaluation including comparison of our method to state-of-the-art approaches to classification of semantic data and an experimental study within emerging subfield of meta-learning called semantic meta-mining. The most important research contributions of the paper to the state-of-art are as follows. For pattern mining research or relational learning in general, the paper contributes a new algorithm for discovery of new type of patterns. For Semantic Web research, it theoretically and empirically illustrates how semantic, structured data can be used in traditional machine learning methods through a pattern-based approach for constructing semantic features.

Establishment of Reference Intervals for Alkaline Phosphatase in Pakistani Children Using a Data Mining Approach

2019 ◽  
Vol 51 (5) ◽  
pp. 484-490 ◽  
Author(s):  
Sibtain Ahmed ◽  
Jakob Zierk ◽  
Aysha Habib Khan
Keyword(s):  
Data Mining ◽  
Alkaline Phosphatase ◽  
Clinical Decision Making ◽  
Clinical Chemistry ◽  
Age Groups ◽  
Clinical Decision ◽  
German Society ◽  
Reference Intervals ◽  
Photometric Method ◽  
Data Mining Approach

Abstract Objective To establish reference intervals (RIs) for alkaline phosphatase (ALP) levels in Pakistani children using an indirect data mining approach. Methods ALP levels analyzed on a Siemens Advia 1800 analyzer using the International Federation of Clinical Chemistry’s photometric method for both inpatients and outpatients aged 1 to 17 years between January 2013 and December 2017, including patients from intensive care units and specialty units, were retrieved. RIs were calculated using a previously validated indirect algorithm developed by the German Society of Clinical Chemistry and Laboratory Medicine’s Working Group on Guide Limits. Results From a total of 108,845 results, after the exclusion of patients with multiple specimens, RIs were calculated for 24,628 males and 18,083 females with stratification into fine-grained age groups. These RIs demonstrate the complex age- and sex-related ALP dynamics occurring during physiological development. Conclusion The population-specific RIs serve to allow an accurate understanding of the fluctuations in analyte activity with increasing age and to support clinical decision making.

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