An Ensemble Machine Learning Model for Enhancing the Prediction Accuracy of Energy Consumption in Buildings

Abstract Background. Assembly and function of neuronal synapses require the coordinated expression of a yet undetermined set of genes. Previously, we had trained an ensemble machine learning model to assign a probability of having synaptic function to every protein-coding gene in Drosophila melanogaster. This approach resulted in the publication of a catalogue of 893 genes that was postulated to be very enriched in genes with still undocumented synaptic functions. Since then, the scientific community has experimentally identified 79 new synaptic genes. Here we used these new empirical data to evaluate the predictive power of the catalogue. Then we implemented a series of improvements to the training scheme and the ensemble rules of our model and added the new synaptic genes to the training set, to obtain a new, enhanced catalogue of putative synaptic genes. Results. The retrospective analysis demonstrated that our original catalogue was indeed highly enriched in genes with unknown synaptic function. The changes to the training scheme and the ensemble rules resulted in a catalogue with better predictive power. Finally, training this improved model with an updated training set, that includes all the new synaptic genes, we obtained a new, enhanced catalogue of putative synaptic genes, which we present here announcing a regularly updated version that will be available online at: http://synapticgenes.bnd.edu.uy Conclusions. We show that training a machine learning model solely with the whole-body temporal transcription profiles of known synaptic genes resulted in a catalogue with a significant enrichment in undiscovered synaptic genes. Using new empirical data, we validated our original approach, improved our model an obtained a better catalogue. The utility of this approach is that it reduces the number of genes to be tested through hypothesis-driven experimentation.

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Predicting Sentiment Polarity of Microblogs using an LSTM – CNN Deep Learning Model

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f8933.088619 ◽

2019 ◽

Vol 8 (6) ◽

pp. 4368-4373

Keyword(s):

Neural Network ◽

Machine Learning ◽

Prediction Accuracy ◽

Short Term Memory ◽

Learning Model ◽

Supervised Machine Learning ◽

Machine Learning Model ◽

Proposed Model ◽

Network Approaches

In this paper we propose a novel supervised machine learning model to predict the polarity of sentiments expressed in microblogs. The proposed model has a stacked neural network structure consisting of Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) layers. In order to capture the long-term dependencies of sentiments in the text ordering of a microblog, the proposed model employs an LSTM layer. The encodings produced by the LSTM layer are then fed to a CNN layer, which generates localized patterns of higher accuracy. These patterns are capable of capturing both local and global long-term dependences in the text of the microblogs. It was observed that the proposed model performs better and gives improved prediction accuracy when compared to semantic, machine learning and deep neural network approaches such as SVM, CNN, LSTM, CNN-LSTM, etc. This paper utilizes the benchmark Stanford Large Movie Review dataset to show the significance of the new approach. The prediction accuracy of the proposed approach is comparable to other state-of-art approaches.

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