It is the information age thus;it is very important to understand how to personalize content in accordance with what appeals to a user. This work is conceptualized to be labeled 'Tailoring Content with Keyword-Bas...
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Cattles are one of the most important components of the global agriculture industry as they provide essential resources like meat and dairy products. This is the reason cattle health is very crucial to ensure animal w...
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The proceedings contain 102 papers. The topics discussed include: phishing detection using machine learning technique;analysis of SDLC models with web engineering principles;new era of IoT with cloud computing: a revi...
ISBN:
(纸本)9798350390179
The proceedings contain 102 papers. The topics discussed include: phishing detection using machine learning technique;analysis of SDLC models with web engineering principles;new era of IoT with cloud computing: a review of technology, issues and challenges;multi-role unmanned aerial vehicle;advanced microstrip patch antenna sensor design for salt and sugar detection in liquids to support health monitoring;techno-economic and environmental analysis of Amity University Noida rooftop SPV generation: towards a net zero future;AmiPi: artificial intelligence based interactive humanoid interface;AAWAS: an application and workload-aware scheduling technique for efficient task allocation in cloud computing environments;and exploring different bypass schemes for photovoltaic modules in partial shading conditions: a comprehensive analysis.
In healthcare, disease diagnosis is essential because it allows for timely and accurate treatment decisions. Machine learning techniques have emerged as promising tools for disease diagnosis due to the increasing amou...
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This research study presents an optimized wind energy conversion system for increased energy output through the use of Graph Neural Networks (GNNs) and machine learning. In order to increase efficiency, the system inc...
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This paper introduces a pioneering approach integrating Advanced Encryption Standard (AES) security algorithms with multi-objective drug design, aimed at personalized medicine and optimized drug discovery. By leveragi...
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In today's world, it's common for people to apply loans from banks and financial institutions for various reasons. But not everyone who applies can be approved. We often hear about cases where individuals fail...
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The loss of melanocytes caused by the chronic autoimmune disease vitiligo leads in depigmented patches of skin that are frequently hard to distinguish from other hypopigmented disorders. Effective therapy depends on e...
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The title is 'Rainfall Prediction Using Machine learning'. The initiative's dataset is written in Python and stored in Microsoft Excel. A wide range of machine learning algorithms are used to discover whic...
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Quantum computers have the potential to outperform classical computers for some complex computational problems. However, current quantum computers (e.g., from IBM and Google) have inherent noise that results in errors...
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ISBN:
(纸本)9798400706585
Quantum computers have the potential to outperform classical computers for some complex computational problems. However, current quantum computers (e.g., from IBM and Google) have inherent noise that results in errors in the outputs of quantum software executing on the quantum computers, affecting the reliability of quantum software development. The industry is increasingly interested in machine learning (ML)-based error mitigation techniques, given their scalability and practicality. However, existing ML-based techniques have limitations, such as only targeting specific noise types or specific quantum circuits. This paper proposes a practical ML-based approach, called Q-LEAR, with a novel feature set, to mitigate noise errors in quantum software outputs. We evaluated Q-LEAR on eight quantum computers and their corresponding noisy simulators, all from IBM, and compared Q-LEAR with a state-of-the-art ML-based approach taken as baseline. Results show that, compared to the baseline, Q-LEAR achieved a 25% average improvement in error mitigation on both real quantum computers and simulators. We also discuss the implications and practicality of Q-LEAR, which, we believe, is valuable for practitioners.
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