Journal ArticleDOI
A CLSTM-TMN for marketing intention detection
Yufeng Wang,Kun Ma,Laura García-Hernández,Jing Chen,Zhihao Hou,Ke Ji,Zhenxiang Chen,Ajith Abraham +7 more
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TLDR
A CLSTM-based topic memory network for marketing intention detection and a new combination ensemble both long and short term memory (LSTM) and convolution neural network (CNN) is proposed.About:
This article is published in Engineering Applications of Artificial Intelligence.The article was published on 2020-05-01. It has received 10 citations till now. The article focuses on the topics: Deep learning & Topic model.read more
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CatBoost for big data: an interdisciplinary review
TL;DR: This survey takes an interdisciplinary approach to cover studies related to CatBoost in a single work, and provides researchers an in-depth understanding to help clarify proper application of Cat boost in solving problems.
Journal ArticleDOI
Tacho-less sparse CNN to detect defects in rotor-bearing systems at varying speed
TL;DR: In this article, a tacho-less deep learning model is developed which can effectively learn, even from small data set, by adding newly developed sparsity cost in the existing cost function of CNN.
Journal ArticleDOI
Spatio-Temporal Prediction of Baltimore Crime Events Using CLSTM Neural Networks
TL;DR: A Convolutional Neural Network (CNN) together with a Long-Short Term Memory (LSTM) network (thus CLSTM-NN) are proposed to predict the presence of crime events over the city of Baltimore (USA).
Journal ArticleDOI
Attention-based learning of self-media data for marketing intention detection
TL;DR: An attention-based convolutional neural network for self-media data learning (called A-CNN) for marketing intention, which can not only solve the problem of local and global features disconnection caused by the CNN pooling layer, but also avoid the increase of algorithm complexity.
Journal ArticleDOI
Machine learning in marketing: A literature review, conceptual framework, and research agenda
Eric W.T. Ngai,Yuanyuan Wu +1 more
TL;DR: This paper conducted a literature review of academic journal studies on ML in marketing applications and proposed a conceptual framework highlighting the main ML tools and technologies that serve as the foundation of ML applications in marketing.
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