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Data Science Senior Specialist | PwC Bulgaria | Обяви

Data Science Senior Specialist | PwC Bulgaria | Обяви
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United States , United Kingdom , Risk Assurance Services , Recurrent Neural Network , Term Memory Network , Convolutional Neural Network , Data Science , Eastern European , Central Europe , North America , Linear Regression , Generalized Linear Model , Support Vector Machine , Nearest Neighbors , Density Estimation , Means Clustering , Gaussian Mixture Model , Random Forest , Stacking Regressor , Deep Learning , Multilayer Perceptron , Long Short Term Memory Network ,

"A novel ultra-short-term wind power prediction method based on XA mech" by Cheng Peng, Yiqin Zhang et al.

A major difficulty in integrating large scale wind power generation in an electrical power system is that wind generated power appears to be erratic, intermittent, and volatile. In this paper, we demonstrate the efficacies of a novel ultra short term 1-step ahead wind generated power prediction model, by combining two best of breed machine learning models in their respective areas of applications: a deep convolutional neural network (CNN) model, known to be effective in classification problems, and a bi-directional long short term memory (Bi-LSTM) model, known to be effective in 1-step ahead time series prediction problems, using a cross attention (XA) mechanism on three challenging practical datasets: the East-China dataset, the Yalova (Turkey) dataset, and the 16 MW dataset. There are two alternative cross attention models: (1) using the CNN features as the key–value pair, and the Bi-LSTM features as the query, this we called a XA model, and (2) using the Bi-LSTM features as the ke ....

Cross Attention , Deep Convolutional Neural Network , Long Short Term Memory Network , Ower Prediction , Time Series , Wind Power ,

"Optimising Automatic Text Classification Approach in Adaptive Online C" by Ya feng Zheng, Zhang hao Gao et al.

A text semantic classification is an essential approach to recognising the verbal intention of online learners, empowering reliable understanding and inquiry for the regulations of knowledge construction amongst students. However, online learning is increasingly switching from static watching patterns to the collaborative discussion. The current deep learning models, such as CNN and RNN, are ineffective in classifying verbal content contextually. Moreover, the contribution of verbal elements to semantics is often considerably varied, requiring the attachment of weights to these elements to increase verbal recognition precision. The Bi-LSTM is considered to be an adaptive model to investigate semantic relations according to the context. Moreover, the attention mechanism in deep learning simulating human vision could assign weights to target texts effectively. This study proposed to construct a deep learning model combining Bi-LSTM and attention mechanism, in which Bi-LSTM obtained the v ....

Adaptation Models , Attention Mechanism , Deep Learning , Feature Extraction , Long Short Term Memory Network , Nline Collaborative Discussion , Task Analysis , Ext Categorization , Text Classification ,