Titlebook: Data Engineering and Applications; Proceedings of the I Jitendra Agrawal,Rajesh K. Shukla,Chin-Shiuh Shieh Conference proceedings 2024 The

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书目名称Data Engineering and Applications影响因子(影响力)




书目名称Data Engineering and Applications影响因子(影响力)学科排名




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书目名称Data Engineering and Applications网络公开度学科排名




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书目名称Data Engineering and Applications被引频次学科排名




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书目名称Data Engineering and Applications年度引用学科排名




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书目名称Data Engineering and Applications读者反馈学科排名

WATER

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Nuance

An Efficient Hybrid Model to Summarize the Text Using Transfer Learningt a more useful and important part of that information. In this study, we propose a design to generate a short and coherent version of a longer document. We can achieve this by two methods first one is extractive means it will take out the important words from the same document and the second one is

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Automatic Detection of Learner’s Learning Styleilising surveys, learning styles in traditional and online environments are identified. To automatically determine a learner’s preferred learning style without upsetting them, it is becoming more popular. Machine learning is a branch of artificial intelligence that allows systems to learn and develo

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Construction of an Intelligent Knowledge-Based System Using Transformer Modelion-making. The process of populating a knowledge base by extracting information from unstructured sources such as pdfs, text, images, and so on is known as knowledge base construction (KBC). It is frequently difficult to deal directly with unstructured data, such as documents. KBC systems assist in

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Bereavement

Finite Difference and Finite Volume 1D Steady-State Heat Conduction Model for Machine Learning Algorical models available for machine learning (ML). In thermodynamics field, there are many complex problems like fluid flow, heat transfer, etc. A good mathematical model is needed to solve such problem and to develop ML algorithms. To solve such kinds of problems, there are some mathematical methods

Arrhythmia

Sign Language Detection Through PCANet and SVMhe alphabet in American Sign Language (ASL) using a marker-less vision sensor. Using photos obtained from the color images, this model presents a novel user-independent identification approach for the American Sign Language alphabet. Convolutional neural network designs are utilized for feature lear