國立虎尾科技大學 |

Hybrid Comparative Predictive Modeling.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Hybrid Comparative Predictive Modeling./
作者:	Nawaz, Mohammad Asif.
面頁冊數:	1 online resource (242 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-02(E), Section: B.
標題:	Information technology. -
電子資源:	click for full text (PQDT)
ISBN:	9780355328493

Hybrid Comparative Predictive Modeling.
Nawaz, Mohammad Asif.

Hybrid Comparative Predictive Modeling. - 1 online resource (242 pages)

Source: Dissertation Abstracts International, Volume: 79-02(E), Section: B.

Thesis (Ph.D.)--The University of North Carolina at Charlotte, 2017.

Includes bibliographical references

In this research, a hybrid predictive model was proposed for the decision-making process. Predictive models can be built through the use of Machine Learning using different classifiers/algorithms to predict results as well as provide recommendations to the management for the student placement in appropriate programs of study and to the students for the adoption of appropriate study strategies and habits. A predictive model through Machine Learning was used in conjunction with probabilistic classification and clustering of specific segments within the data to increase the rate of success for an improved decision-making process. Variance in the actual and predicted results with respect to the difference in success rates can assist the decision makers in student placement. An aggregate of all the processes with the help of Cobb-Douglas utility function leads to a Hybrid Predictive Model, which combined two different phases for better placement, an increased rate of success, and an overall improved decision-making process. The introduction of Cobb-Douglas utility function can further streamline the process to check any external factors that may have influenced the predicted results. This model can also be applied in the corporate sector to maximize any program or individual efficiency by placing and training individuals with respect to individuals/employees aptitude, background, and personality type and learning styles. This type of model can also be applied in the same capacity in different organizations to maximize the program efficiency, placement, and employees' capabilities.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355328493Subjects--Topical Terms:

559429
Information technology.
Index Terms--Genre/Form:

554714
Electronic books.

Hybrid Comparative Predictive Modeling.
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500 $a Source: Dissertation Abstracts International, Volume: 79-02(E), Section: B.
500 $a Adviser: Mirsad Hadzikadic.
502 $a Thesis (Ph.D.)--The University of North Carolina at Charlotte, 2017.
504 $a Includes bibliographical references
520 $a In this research, a hybrid predictive model was proposed for the decision-making process. Predictive models can be built through the use of Machine Learning using different classifiers/algorithms to predict results as well as provide recommendations to the management for the student placement in appropriate programs of study and to the students for the adoption of appropriate study strategies and habits. A predictive model through Machine Learning was used in conjunction with probabilistic classification and clustering of specific segments within the data to increase the rate of success for an improved decision-making process. Variance in the actual and predicted results with respect to the difference in success rates can assist the decision makers in student placement. An aggregate of all the processes with the help of Cobb-Douglas utility function leads to a Hybrid Predictive Model, which combined two different phases for better placement, an increased rate of success, and an overall improved decision-making process. The introduction of Cobb-Douglas utility function can further streamline the process to check any external factors that may have influenced the predicted results. This model can also be applied in the corporate sector to maximize any program or individual efficiency by placing and training individuals with respect to individuals/employees aptitude, background, and personality type and learning styles. This type of model can also be applied in the same capacity in different organizations to maximize the program efficiency, placement, and employees' capabilities.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Information technology. $3 559429
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710 2 $a The University of North Carolina at Charlotte. $b Information Technology. $3 1185450
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