Deep Neural Network Model for Evaluating and Achieving the Sustainable Development Goal 16

The decision-making process for attaining Sustainable Development Goals (SDGs) can be enhanced through the use of predictive modelling. The application of predictive tools like deep neural networks (DNN) empowers stakeholders with quality information and promotes open data policy for curbing corruption. The anti-corruption drive is a cardinal component of SDG 16 which is aimed at strengthening state institutions and promoting social justice for the attainment of all 17 SDGs. This study examined the implementation of the SDGs in Nigeria and modelled the 2017 national corruption survey data using a DNN. We experimentally tested the efficacy of DNN optimizers using a standard image dataset from the Modified National Institute of Standards and Technology (MNIST). The outcomes validated our claims that predictive analytics could enhance decision-making through high-level accuracies as posted by the optimizers: Adam 98.2%; Adadelta 98.4%; SGD 94.9%; RMSProp 98.1%; Adagrad 98.1%.publishedVersio

Developing a long short-term memory-based model for forecasting the daily energy consumption of heating, ventilation, and air conditioning systems in buildings

Forecasting the energy consumption of heating, ventilating, and air conditioning systems is important for the energy efficiency and sustainability of buildings. In fact, conventional models present limitations in these systems due to their complexity and unpredictability. To overcome this, the long short-term memory-based model is employed in this work. Our objective is to develop and evaluate a model to forecast the daily energy consumption of heating, ventilating, and air conditioning systems in buildings. For this purpose, we apply a comprehensive methodology that allows us to obtain a robust, generalizable, and reliable model by tuning different parameters. The results show that the proposed model achieves a significant improvement in the coefficient of variation of root mean square error of 9.5% compared to that proposed by international agencies. We conclude that these results provide an encouraging outlook for its implementation as an intelligent service for decision making, capable of overcoming the problems of other noise-sensitive models affected by data variations and disturbances without the need for expert knowledge in the domain.Se buscó pronosticar el consumo de energía de los sistemas de calefacción Heating, ventilating y aire acondicionado (HVAC) para la eficiencia energética de los edificios. En este estudio, se desarrolla un modelo de red neuronal artificial (RNA) recurrente del tipo Long short-term memory (LSTM) destinada a pronosticar el consumo de energía de un sistema HVAC en los edificios, en concreto una bomba de calor del Teatro Real de España. El trabajo comparó diferentes configuraciones del modelo con respecto a los datos reales proporcionados por el BMS del edificio y se identificó los hiperparámetros adecuados para el LSTM. El objetivo fue desarrollar y evaluar el modelo para pronosticar el consumo diario de energía de los sistemas HVAC, lográndose una predicción del uso de la energía según los criterios indicados por las directrices de American Society of Heating, Refrigerating and Air-Conditioning Engineers ASHRAE, The International Performance Measurement and Verification Protocol IPMVP y The Federal Energy Management Program organismos que validan un modelo HVAC. La contribución del solicitante se centró en el diseño del LSTM, y en la validación de las pruebas con los datos experimentales, así como en el análisis de los resultados obtenidos

Comparative study of NER using Bi-LSTM-CRF with different word vectorisation techniques on DNB documents

The presence of huge volumes of unstructured data in the form of pdf documents poses a challenge to the organizations trying to extract valuable information from it. In this thesis, we try to solve this problem as per the requirement of DNB by building an automatic information extraction system to get only the key information in which the company is interested in from the pdf documents. This is achieved by comparing the performance of named entity recognition models for automatic text extraction, built using Bi-directional Long Short Term Memory (Bi-LSTM) with a Conditional Random Field (CRF) in combination with three variations of word vectorization techniques. The word vectorisation techniques compared in this thesis include randomly generated word embeddings by the Keras embedding layer, pre-trained static word embeddings focusing on 100-dimensional GloVe embeddings and, finally, deep-contextual ELMo word embeddings. Comparison of these models helps us identify the advantages and disadvantages of using different word embeddings by analysing their effect on NER performance. This study was performed on a DNB provided data set. The comparative study showed that the NER systems built using Bi-LSTM-CRF with GloVe embeddings gave the best results with a micro F1 score of 0.868 and a macro-F1 score of 0.872 on unseen data, in comparison to a Bi-LSTM-CRF based NER using Keras embedding layer and ELMo embeddings which gave micro F1 scores of 0.858 and 0.796 and macro F1 scores of 0.848 and 0.776 respectively. The result is in contrary to our assumption that NER using deep contextualised word embeddings show better performance when compared to NER using other word embeddings. We proposed that this contradicting performance is due to the high dimensionality, and we analysed it by using a lower-dimensional word embedding. It was found that using 50-dimensional GloVe embeddings instead of 100-dimensional GloVe embeddings resulted in an improvement of the overall micro and macro F1 score from 0.87 to 0.88. Additionally, optimising the best model, which was the Bi-LSTM-CRF using 100-dimensional GloVe embeddings, by tuning in a small hyperparameter search space did not result in any improvement from the present micro F1 score of 0.87 and macro F1 score of 0.87.M30-DV Master's ThesisM-D