This comprehensive report integrates findings from two separate but connected projects: a Power BI dashboard that shows historical rainfall patterns and trends, and a machinelearning prediction model that estimates f...
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The escalating demand for skilled IT professionals underscores the increasing significance of the recruitment process. Traditional methods often fall short in identifying individuals poised for success in the dynamic ...
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Modern local navigation systems have a large number of data sources, which often have low accuracy and a large amount of noise. The Kalman filter can minimize the impact of these noises on the data, but for nonlinear ...
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This study examines the intersection of quantum computing with machinelearning, focusing on the potential, difficulties, and present progress in this emerging topic. The study explores the theoretical foundations of ...
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ISBN:
(数字)9783031746826
ISBN:
(纸本)9783031746819;9783031746826
This study examines the intersection of quantum computing with machinelearning, focusing on the potential, difficulties, and present progress in this emerging topic. The study explores the theoretical foundations of quantum machinelearning algorithms, explaining the core ideas that utilize quantum mechanics to improve computing skills. The text explores many quanta computing paradigms, including quantum annealing, quantum circuits, and quantum-inspired algorithms. It evaluates their suitability and effectiveness in addressing intricate machinelearning challenges. The study provides a thorough examination of current literature and recent advancements to explain the changing field of quantum machinelearning frameworks. It emphasizes their potential to transform data analysis, optimization, and pattern identification. Furthermore, it discusses the practical factors and technological challenges related to the implementation of quantum machinelearning algorithms on current quantum hardware, highlighting the need of reliable error correction, qubit coherence, and scalable architectures. The study offers valuable insights into the significant influence of quantum machinelearning on diverse fields such as banking, healthcare, and cybersecurity by connecting theoretical principles with real-world applications. Furthermore, it delineates prospective areas of study and possible methods for fully utilizing quantum computing in machinelearning applications, thus promoting cooperation among researchers, practitioners, and stakeholders in shaping the future of this dynamic and interdisciplinary domain.
Cryptocurrencies, such as Bitcoin, Binance, Ethereum, FTX, and XRP, are decentralized digital assets known for their volatile nature and potential as investment instruments. Accurate price prediction is crucial for in...
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With the widespread use of big datascience, there is a growing need for data-driven decision-making in the retail industry. As one of the largest retailers in the world, Walmart's sales forecasting through machin...
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Existing portrait segmentation methods are easily affected by the background. To address this challenge, we propose a simple convolution-based portrait segmentation algorithm to solve the problems of complex backgroun...
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Alzheimer's Disease (AD) poses significant challenges in research due to limited access to longitudinal patient data caused by privacy constraints. This study uses deep learning, specifically Variational Autoencod...
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machinelearning methods have made huge improvements to medical imaging, particularly in the utilization of CT scans for intricate trauma cases. This paper initiates an exploratory journey, employing a hybrid quantum ...
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Agricultural technology, ensuring reliability, and optimizing processes are paramount. This work explores the application of machinelearning algorithms (ML) to two critical areas: agricultural advisory systems (AAS) ...
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