AI algorithms for the future world

This paper will primarily focus on the use of Artificial Intelligence (AI) through multiple supervised machine learning techniques such as the Decision Tree algorithm, Random Forests algorithm, Support Vector Machines (SVM) as well as Neural Network algorithm as classifiers to predict the results of...

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Main Author: Mohammed Yakoob Siyal
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2021
Subjects:
Online Access:https://hdl.handle.net/10356/153781
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1537812023-07-07T18:18:52Z AI algorithms for the future world Mohammed Yakoob Siyal Mohammed Yakoob Siyal School of Electrical and Electronic Engineering EYAKOOB@ntu.edu.sg Engineering::Electrical and electronic engineering This paper will primarily focus on the use of Artificial Intelligence (AI) through multiple supervised machine learning techniques such as the Decision Tree algorithm, Random Forests algorithm, Support Vector Machines (SVM) as well as Neural Network algorithm as classifiers to predict the results of randomly chosen actual England Championship matches in the 2020/2021 Season. Prior to the prediction, the selected datasets will be subjected to feature extraction and attribute measure selection techniques, and various types of classifiers will be trained and tested to fit the best model for predicting the results of a match based on the last five years (2016-2020) datasets of the England Championship Season matches statistics. Following that, the predicted accuracy of different types of classifiers will be evaluated and compared, as well as their advantages and drawbacks. Finally, we will foresee the future of Artificial Intelligence and machine learning with their practical applications. Bachelor of Engineering (Electrical and Electronic Engineering) 2021-12-13T02:40:16Z 2021-12-13T02:40:16Z 2021 Final Year Project (FYP) Mohammed Yakoob Siyal (2021). AI algorithms for the future world. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/153781 https://hdl.handle.net/10356/153781 en P3024-201 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
spellingShingle Engineering::Electrical and electronic engineering
Mohammed Yakoob Siyal
AI algorithms for the future world
description This paper will primarily focus on the use of Artificial Intelligence (AI) through multiple supervised machine learning techniques such as the Decision Tree algorithm, Random Forests algorithm, Support Vector Machines (SVM) as well as Neural Network algorithm as classifiers to predict the results of randomly chosen actual England Championship matches in the 2020/2021 Season. Prior to the prediction, the selected datasets will be subjected to feature extraction and attribute measure selection techniques, and various types of classifiers will be trained and tested to fit the best model for predicting the results of a match based on the last five years (2016-2020) datasets of the England Championship Season matches statistics. Following that, the predicted accuracy of different types of classifiers will be evaluated and compared, as well as their advantages and drawbacks. Finally, we will foresee the future of Artificial Intelligence and machine learning with their practical applications.
author2 Mohammed Yakoob Siyal
author_facet Mohammed Yakoob Siyal
Mohammed Yakoob Siyal
format Final Year Project
author Mohammed Yakoob Siyal
author_sort Mohammed Yakoob Siyal
title AI algorithms for the future world
title_short AI algorithms for the future world
title_full AI algorithms for the future world
title_fullStr AI algorithms for the future world
title_full_unstemmed AI algorithms for the future world
title_sort ai algorithms for the future world
publisher Nanyang Technological University
publishDate 2021
url https://hdl.handle.net/10356/153781
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