Development of a Quantum-Inspired Neural Network for Image Recognition in Federal University, Lokoja, Kogi State
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- NGN 5000
Background of the Study
Artificial intelligence (AI) has seen remarkable progress in recent years, particularly in the field of machine learning and image recognition. Neural networks, which are at the core of AI, have been successfully applied to a range of image processing tasks, from facial recognition to medical imaging analysis (LeCun et al., 2023). However, traditional neural networks, while effective, can be computationally expensive and limited in their processing capabilities. Quantum computing, with its ability to handle large datasets and complex calculations more efficiently than classical computers, offers the potential to enhance neural networks, particularly for tasks like image recognition. Quantum-inspired neural networks, which integrate quantum computing principles into classical machine learning models, can help overcome these limitations and improve the accuracy and efficiency of image recognition systems.
At Federal University, Lokoja, Kogi State, image recognition plays a crucial role in various academic and administrative applications, including campus security and document management. This study aims to develop a quantum-inspired neural network for image recognition, leveraging quantum algorithms to enhance the university's image processing capabilities. The research will evaluate the effectiveness of quantum-inspired neural networks in comparison to traditional models, specifically focusing on their application in the university's administrative and security systems.
Statement of the Problem
Despite advancements in image recognition technology, current systems at Federal University, Lokoja, face challenges such as slow processing speeds and inaccuracies in recognition. Traditional neural networks are often computationally expensive and do not always achieve the desired level of performance, especially when handling large and complex datasets. There is a need to explore more advanced techniques, such as quantum-inspired neural networks, to improve the accuracy and efficiency of image recognition systems in the university.
Objectives of the Study
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To develop a quantum-inspired neural network for image recognition at Federal University, Lokoja.
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To compare the performance of quantum-inspired neural networks with traditional image recognition systems.
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To assess the feasibility and effectiveness of implementing the quantum-inspired neural network for university security and administrative purposes.
Research Questions
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How can a quantum-inspired neural network improve image recognition capabilities at Federal University, Lokoja?
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How does the performance of a quantum-inspired neural network compare to traditional image recognition systems in terms of speed and accuracy?
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What are the practical challenges of implementing a quantum-inspired neural network for image recognition at the university?
Significance of the Study
This study will contribute to the development of more efficient and accurate image recognition systems at Federal University, Lokoja. By adopting quantum-inspired neural networks, the university can improve its administrative operations, campus security, and overall data management processes. The research could also provide insights into the broader applications of quantum computing in AI and machine learning.
Scope and Limitations of the Study
The study will focus on the development and implementation of a quantum-inspired neural network for image recognition at Federal University, Lokoja, Kogi State. The research will be limited to evaluating the system's effectiveness in image recognition tasks, and will not explore the broader implementation of quantum computing in other areas of university operations.
Definitions of Terms
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Quantum-Inspired Neural Network: A machine learning model that integrates quantum computing principles into classical neural networks to enhance their performance, especially in tasks like image recognition.
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Image Recognition: The ability of a computer system to identify and classify objects, people, or patterns within images.
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Neural Network: A computational model inspired by the human brain, used for tasks like pattern recognition and data classification.
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