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Titlebook: Data Analytics and Machine Learning; Navigating the Big D Pushpa Singh,Asha Rani Mishra,Payal Garg Book 2024 The Editor(s) (if applicable)

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發(fā)表于 2025-3-26 22:06:58 | 只看該作者
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發(fā)表于 2025-3-27 03:49:39 | 只看該作者
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發(fā)表于 2025-3-27 05:29:46 | 只看該作者
Real-World Applications of Data Analytics, Big Data, and Machine Learning,a, e-commerce, healthcare records, and more, collectively known as Big Data, has inundated our world. Concerning the intelligent analysis of extensive datasets and the development of advanced applications for diverse domains, the crucial foundation lies in artificial intelligence (AI), placing speci
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發(fā)表于 2025-3-27 10:38:32 | 只看該作者
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發(fā)表于 2025-3-27 17:00:23 | 只看該作者
Lung Nodule Segmentation Using Machine Learning and Deep Learning Techniques,h, surgical planning, and diagnostic decision support. All are complex issues with important applications. Machines and humans struggle to split non solitary nodules with uncertain boundaries. Since segmentation has distinct limits, single nodules are easier to divide. Several researchers have propo
36#
發(fā)表于 2025-3-27 20:34:59 | 只看該作者
Convergence of Data Analytics, Big Data, and Machine Learning: Applications, Challenges, and FutureThis chapter offers a brief overview of their practical uses, illuminating how these technologies are reshaping markets and driving creativity. The cornerstone, data analytics, is studied first, emphasizing its capacity to extract useful insights from a variety of sources. To demonstrate how Data An
37#
發(fā)表于 2025-3-27 23:08:52 | 只看該作者
Business Transformation Using Big Data Analytics and Machine Learning,today, and they have garnered a lot of attention for their ability to influence organizational decision-making. With the use of these technologies, firms are able to provide valuable data and obtain answers that will improve their performance and provide them with a competitive advantage. A customer
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發(fā)表于 2025-3-28 04:05:07 | 只看該作者
39#
發(fā)表于 2025-3-28 06:38:36 | 只看該作者
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發(fā)表于 2025-3-28 10:40:57 | 只看該作者
Predictive Algorithms for Smart Agriculture,es for the precision yield, and irrigation needs by looking at different parameters like climatic conditions, soil type, and previous crops grown in the field. The accuracy of algorithms comes out to be more than 90% depending on some uncertainties in the collection of data from different sensors. T
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