Document Details

Document Type : Thesis 
Document Title :
Developing “Smartness” in Emerging Environments and Applications using Artificial Intelligence
تطوير ''الذكاء'' في البيئات والتطبيقات الناشئة باستخدام الذكاء الاصطناعي
 
Subject : Faculty of Computing and Information Technology 
Document Language : Arabic 
Abstract : The concept of "Smartness" has emerged as a response to the challenges posed by urbanization and increasing city population density. With the global urban population projected to reach 66% or 70% by 2050, decision-makers are actively pursuing Smartness projects to promote sustainable economic growth and improve the quality of life for inhabitants and visitors. Additionally, the rapid adoption of technologies such as the Internet of Things (IoT) and mobile devices has been a significant factor in advancing the development of smartness. By integrating various devices and systems seamlessly, these technologies allow for the creation of an intelligent network of interconnected entities. The proliferation of IoT devices and the ubiquitous use of mobile devices has provided a wealth of data that can be harnessed for smartness initiatives. Four dimensions of smartness have been identified to gain a comprehensive understanding of smart systems’ capabilities and potential: Sensors, IoT, and Data Generation; Data and Information Processing; Actuation; and Digital Systems and Infrastructure. In order to develop and implement smart systems, these dimensions serve as pillars, facilitating data collection, processing, and real-time decision-making. The outcome of this research initiative has been the development of two innovative models based on data from the Saudi Stock Market (Tadawul) and a new solution architecture framework. Through Vision Transformers (ViT) vand Reinforcement Learning (RL), these models are intended to enhance developing environments and applications. By integrating Vision Transformers with Reinforcement Learning capabilities in the proposed framework, decision-making processes are expected to be revolutionized in smart environments and applications. This synergistic approach can handle irregularities and complex patterns in time series data, enhancing prediction accuracy and optimizing decision-making capabilities, particularly in the context of intelligent robotic trading strategies. The comprehensive solution takes advantage of the unique characteristics and patterns of sequential data, resulting in more accurate predictions, improved decision-making capabilities, and the development of intelligent robotic trading strategies that are tailored to specific markets’ complexities. In employing this tailored approach, the research seeks to contribute to the advancement of smartness in cities and societies through the improvement and effectiveness of decision-making processes. 
Supervisor : Prof. Iyad A. Katib 
Thesis Type : Doctorate Thesis 
Publishing Year : 1445 AH
2023 AD 
Co-Supervisor : Prof. Rashid Mehmood 
Added Date : Tuesday, November 28, 2023 

Researchers

Researcher Name (Arabic)Researcher Name (English)Researcher TypeDr GradeEmail
نديم عبدالله مليباريMalibari, Nadeem AbdullahResearcherDoctorate 

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