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Titlebook: Recent Developments in Machine Learning and Data Analytics; IC3 2018 Jugal Kalita,Valentina Emilia Balas,Ratika Pradhan Conference proceedi

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樓主: Filament
41#
發(fā)表于 2025-3-28 17:04:30 | 只看該作者
42#
發(fā)表于 2025-3-28 22:12:32 | 只看該作者
43#
發(fā)表于 2025-3-28 23:39:10 | 只看該作者
An Overview of Hadoop MapReduce, Spark, and Scalable Graph Processing Architecture,re open source framework to process Big Data. Hadoop provides batch processing while Spark supports both batch as well as stream processing, i.e., it is a hybrid processing framework. Both frameworks have their own advantages and drawback. The contribution of this paper is to provide a comparative a
44#
發(fā)表于 2025-3-29 03:49:56 | 只看該作者
45#
發(fā)表于 2025-3-29 09:49:13 | 只看該作者
Improvement in Boosting Method by Using RUSTBoost Technique for Class Imbalanced Data,es in one class is more or less compared to another class. Data mining algorithms may generate suboptimal classification models when trained with imbalanced datasets. Several techniques have been proposed to solve the class imbalance problem. One of them includes boosting which is combined with resa
46#
發(fā)表于 2025-3-29 11:44:51 | 只看該作者
A Survey on Medical Diagnosis of Diabetes Using Machine Learning Techniques,uccessfully employed in assorted applications including medical diagnosis. By developing classifier system, machine learning algorithm may immensely help to solve the health-related issues which can assist the physicians to predict and diagnose diseases at an early stage. We can ameliorate the speed
47#
發(fā)表于 2025-3-29 18:00:48 | 只看該作者
How Effective Is the Moth-Flame Optimization in Diabetes Data Classification,e science approaches like machine intelligence are used to detect the type of diabetes in a patient for getting correct accuracy. This paper uses a metaheuristic algorithm named Moth-Flame Optimization for diabetes data classification. The MFO is used to update the weights of the feed foreword neura
48#
發(fā)表于 2025-3-29 20:38:32 | 只看該作者
49#
發(fā)表于 2025-3-30 02:48:35 | 只看該作者
50#
發(fā)表于 2025-3-30 05:35:16 | 只看該作者
Recommending Top , Movies Using Content-Based Filtering and Collaborative Filtering with Hadoop androm a large set of movies list and their ratings based on different users. Since the number of users and the movies are increasing day by day, computing the recommended movies list in a single node machine takes a very large time. Hence to reduce the computation time, we are using Hadoop framework t
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