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• W3003646942 abstract "Good representations of data eliminate irrelevant variability of the input data, while preserving the information that is useful for the ultimate task. Among the various ways for learning representation is using deep learning methods. Deep feature hierarchies are formed by stacking unsupervised modules on top of each other, forming multiple nonlinear transformations to produce better representations. In this talk, we will first show how deep learning is used for bioactivity prediction of chemical compounds. Molecules are represented as several convolutional neural networks to predict their bioactivity. In addition, a new concept of merging multiple convolutional neural networks and an automatic learning features representation for the chemical compounds was proposed using the values within neurons of the last layer of the CNN architecture. We will also show how the concepts of deep learning is adapted into a deep belief network (DBN) to enhance the molecular similarity searching. The DBN achieves feature abstraction by reconstruction weight for each feature and minimizing the reconstruction error over the whole feature set. The DBN is later enhanced using data fusion to obtain a lower detection error probability and a higher reliability by using data from multiple distributed descriptors. Secondly, we will show how we used deep learning for stock market prediction. Here, we developed a Deep Long Short Term Memory Network model that is able to forecast the crude palm oil price movement with combined factors such as other commodities prices, weather and news sentiments and price movement of crude palm oil. We also show how we combined stock markets price and financial news and deployed the Long Short Term Memory (LSTM), Recurrent Neural Network (RNN), and Word 2 Vector (Word2Vec) to project the stock prices for the following seven days. Finally, we will show how we exploited deep learning method for the opinion mining and later used it to extract the product's aspects from the user textual review for recommendation systems. Specifically, we employ a multichannel convolutional neural network (MCNN) for two different input layers, namely, word embedding layer and Part-of-speech (POS) tag embedding layer. We show effectiveness of the proposed model in terms of both aspect extraction and rating prediction performance." @default.
• W3003646942 created "2020-02-07" @default.
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• W3003646942 date "2019-09-01" @default.
• W3003646942 modified "2023-09-24" @default.
• W3003646942 title "Deep Learning Approaches for Big Data Analysis" @default.
• W3003646942 doi "https://doi.org/10.23919/eecsi48112.2019.8977075" @default.
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