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Machine Learning Techniques for Prediction of Age: From Eruption Status Of Permanent Teeth In Sri Lankan Children - Softcover
Synopsis
Prediction of age is a predominant facet in forensic and clinical fields. Forensic odontology is used to predict an age by using permanent teeth resistant to high temperatures and any mass disaster than other parts of the body. Age-related memory loss, memory loss associated with dementia, and the absence of official documents to verify their age are the main reasons people have no knowledge about their age. Therefore, age prediction is used in various situations, such as identification, admission purposes, employment, criminal issues and judicial punishments. The main objective of this study is to predict the age of a child using the eruption status of permanent teeth. This cross-sectional study was conducted on 3321 individuals (1681 males and 1640 females) from 7 provinces and 20 schools in Sri Lanka. Regression tree algorithms in Machine learning, namely Classification and regression trees (CART), gradient boosting (GB) classifier and extreme gradient boost (XGBoost) classifier, were used to make predictions for the age of a child. The study results provide an XGBoost machine learning classifier as the most suitable method for age prediction with higher accuracy.
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Machine Learning Techniques for Prediction of Age
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LAP LAMBERT Academic Publishing, 2022
ISBN 10: 6200311021
ISBN 13: 9786200311023
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Machine Learning Techniques for Prediction of Age
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Machine Learning Techniques for Prediction of Age
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Prediction of age is a predominant facet in forensic and clinical fields. Forensic odontology is used to predict an age by using permanent teeth resistant to high temperatures and any mass disaster than other parts of the body. Age-related memory loss, memory loss associated with dementia, and the absence of official documents to verify their age are the main reasons people have no knowledge about their age. Therefore, age prediction is used in various situations, such as identification, admission purposes, employment, criminal issues and judicial punishments. The main objective of this study is to predict the age of a child using the eruption status of permanent teeth. This cross-sectional study was conducted on 3321 individuals (1681 males and 1640 females) from 7 provinces and 20 schools in Sri Lanka. Regression tree algorithms in Machine learning, namely Classification and regression trees (CART), gradient boosting (GB) classifier and extreme gradient boost (XGBoost) classifier, were used to make predictions for the age of a child. The study results provide an XGBoost machine learning classifier as the most suitable method for age prediction with higher accuracy. 80 pp. Englisch. Seller Inventory # 9786200311023
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Machine Learning Techniques for Prediction of Age
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Machine Learning Techniques for Prediction of Age
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Prediction of age is a predominant facet in forensic and clinical fields. Forensic odontology is used to predict an age by using permanent teeth resistant to high temperatures and any mass disaster than other parts of the body. Age-related memory loss, memo. Seller Inventory # 593980924
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Machine Learning Techniques for Prediction of Age
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ISBN 10: 6200311021
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Prediction of age is a predominant facet in forensic and clinical fields. Forensic odontology is used to predict an age by using permanent teeth resistant to high temperatures and any mass disaster than other parts of the body. Age-related memory loss, memory loss associated with dementia, and the absence of official documents to verify their age are the main reasons people have no knowledge about their age. Therefore, age prediction is used in various situations, such as identification, admission purposes, employment, criminal issues and judicial punishments. The main objective of this study is to predict the age of a child using the eruption status of permanent teeth. This cross-sectional study was conducted on 3321 individuals (1681 males and 1640 females) from 7 provinces and 20 schools in Sri Lanka. Regression tree algorithms in Machine learning, namely Classification and regression trees (CART), gradient boosting (GB) classifier and extreme gradient boost (XGBoost) classifier, were used to make predictions for the age of a child. The study results provide an XGBoost machine learning classifier as the most suitable method for age prediction with higher accuracy.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 80 pp. Englisch. Seller Inventory # 9786200311023
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Machine Learning Techniques for Prediction of Age : From Eruption Status Of Permanent Teeth In Sri Lankan Children
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Prediction of age is a predominant facet in forensic and clinical fields. Forensic odontology is used to predict an age by using permanent teeth resistant to high temperatures and any mass disaster than other parts of the body. Age-related memory loss, memory loss associated with dementia, and the absence of official documents to verify their age are the main reasons people have no knowledge about their age. Therefore, age prediction is used in various situations, such as identification, admission purposes, employment, criminal issues and judicial punishments. The main objective of this study is to predict the age of a child using the eruption status of permanent teeth. This cross-sectional study was conducted on 3321 individuals (1681 males and 1640 females) from 7 provinces and 20 schools in Sri Lanka. Regression tree algorithms in Machine learning, namely Classification and regression trees (CART), gradient boosting (GB) classifier and extreme gradient boost (XGBoost) classifier, were used to make predictions for the age of a child. The study results provide an XGBoost machine learning classifier as the most suitable method for age prediction with higher accuracy. Seller Inventory # 9786200311023
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Machine Learning Techniques for Prediction of Age | From Eruption Status Of Permanent Teeth In Sri Lankan Children
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Taschenbuch. Condition: Neu. Machine Learning Techniques for Prediction of Age | From Eruption Status Of Permanent Teeth In Sri Lankan Children | Lakshika S. Nawarathna. (u. a.) | Taschenbuch | Englisch | 2022 | LAP LAMBERT Academic Publishing | EAN 9786200311023 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Seller Inventory # 121662054
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