Retinal diseases classification based on hybrid ensemble deep learning and optical coherence tomography images

Optical coherence tomography (OCT) is a noninvasive, high-resolution imaging technique widely used in clinical practice to depict the structure of the retina. Over the past few decades, ophthalmologists have used OCT to diagnose, monitor, and treat retinal diseases. However, manual analysis of the complicated retinal layers using two colors, black and white, is time consuming. Although ophthalmologists have more experience, their results may be prone to erroneous diagnoses. Therefore, in this study, we propose an automatic method for diagnosing five retinal diseases based on the use of hybrid and ensemble deep learning (DL) methods. DL extracts a thousand constitutional features from images as features for training classifiers. The machine learning method classifies the extracted features and fuses the outputs of the two classifiers to improve classification performance. The distribution probabilities of two classifiers of the same class are aggregated; then, class prediction is made using the class with the highest probability. The limited dataset is resolved by the fine-tuning of classification knowledge and generating augmented images using transfer learning and data augmentation. Multiple DL models and machine learning classifiers are used to access a suitable model and classifier for the OCT images. The proposed method is trained and evaluated using OCT images collected from a hospital and exhibits a classification accuracy of 97.68% (InceptionResNetV2, ensemble: Extreme gradient boosting (XG-Boost) and k-nearest neighbor (k-NN). The experimental results show that our proposed method can improve the OCT classification performance; moreover, in the case of a limited dataset, the proposed method is critical to develop accurate classifications

Uso de técnicas de soft computing en problemas energéticos en edificios inteligentes

Este Trabajo de Fin de Grado (TFG) explora el proceso de predicción del consumo de energía en edificios inteligentes en función del consumo de los dispositivos y aparatos. Para ello, se generan modelos de predicción empleando técnicas de Aprendizaje Automático (AA), teniendo en cuenta que se trabaja con series temporales, ya que el consumo en cierto momento puede estar relacionado con el consumo en momentos anteriores. El trabajo detalla los pasos del proceso de predicción, presentando en primer lugar los conceptos teóricos necesarios, para luego aplicarlos a un conjunto de datos reales.This Final Degree project (TFG) explores the process of prediction of energy consumption in Smart Buildings based on the devices’ consumption. To do so, forecasting models are generated using Machine Learning (ML) techniques, taking into consideration that we are working with time series since the consumption at a given time could be related to the consumption at previous times. The project details each step of the prediction process, presenting first the necessary theoretical concepts, so then, they are applied to a real dataset.Grado en Ingeniería Informátic

A systematic literature review on the use of artificial intelligence in energy self-management in smart buildings

Buildings are one of the main consumers of energy in cities, which is why a lot of research has been generated around this problem. Especially, the buildings energy management systems must improve in the next years. Artificial intelligence techniques are playing and will play a fundamental role in these improvements. This work presents a systematic review of the literature on researches that have been done in recent years to improve energy management systems for smart building using artificial intelligence techniques. An originality of the work is that they are grouped according to the concept of "Autonomous Cycles of Data Analysis Tasks", which defines that an autonomous management system requires specialized tasks, such as monitoring, analysis, and decision-making tasks for reaching objectives in the environment, like improve the energy efficiency. This organization of the work allows us to establish not only the positioning of the researches, but also, the visualization of the current challenges and opportunities in each domain. We have identified that many types of researches are in the domain of decision-making (a large majority on optimization and control tasks), and defined potential projects related to the development of autonomous cycles of data analysis tasks, feature engineering, or multi-agent systems, among others.European Commissio

Multiple Electric Energy Consumption Forecasting Using a Cluster-Based Strategy for Transfer Learning in Smart Building

Electric energy consumption forecasting is an interesting, challenging, and important issue in energy management and equipment efficiency improvement. Existing approaches are predictive models that have the ability to predict for a specific profile, i.e., a time series of a whole building or an individual household in a smart building. In practice, there are many profiles in each smart building, which leads to time-consuming and expensive system resources. Therefore, this study develops a robust framework for the Multiple Electric Energy Consumption forecasting (MEC) of a smart building using Transfer Learning and Long Short-Term Memory (TLL), the so-called MEC-TLL framework. In this framework, we first employ a k-means clustering algorithm to cluster the daily load demand of many profiles in the training set. In this phase, we also perform Silhouette analysis to specify the optimal number of clusters for the experimental datasets. Next, this study develops the MEC training algorithm, which utilizes a cluster-based strategy for transfer learning the Long Short-Term Memory models to reduce the computational time. Finally, extensive experiments are conducted to compare the computational time and different performance metrics for multiple electric energy consumption forecasting on two smart buildings in South Korea. The experimental results indicate that our proposed approach is capable of economical overheads while achieving superior performances. Therefore, the proposed approach can be applied effectively for intelligent energy management in smart buildings

Machine Learning for Short-Term Water Demand Predictions

Urban water supply is coming under increased pressure due to urbanisation, water scarcity and climate change. Efficient urban water management can help alleviate this pressure by improving service quality and reducing water loss. Accurate demand and consumption forecasting enables expansion planning, financing, and operation of water distribution systems. Current research often focuses on model-centric approaches where the model is improved to drive forecast accuracy; however, more efficient data usage could be realised as an alternative to model-centric approaches, without incurring additional computation costs. This work investigates the potential of data-centric forecasting approaches, focusing on ways to improve the efficiency of data and computation resource usage for short-term water demand forecasting. To initiate the investigation, several intrinsically different forecasting models are analysed. A total of four different forecasting models, i.e., Prophet, Autoregressive Integrated Moving Average, Neural Network (NN) and Random Forest (RF) are applied to four demand datasets, i.e., one Chinese hourly demand dataset and three UK 15-minute demand datasets. Various aspects of data and model requirements for optimal performance are investigated. Results obtained from the case studies show that prolonging training data may not be necessary, and that accurate sub-daily water demand forecasting is possible with 10 days of past data for model training. In terms of accuracy, neural network and random forest tend to be better suited towards short-term water demand forecasting over statistical models. The second part of the work aims to unbox the four black-box machine learning methods – NN, Long Short-Term Memory (LSTM), RF, Extreme Gradient Boosting (XGB) and explain their inner workings using SHapley Additive exPlanations and Local Interpretable Model-Agnostic Explanations, Prophet and ARIMA are excluded due to inferior forecasting accuracy. Results have found that feature requirement depends on data resolution, the forecasting model used and the forecast time of day. Network-based models (NN and LSTM) are more temporally dependent and feature intensive, indicating that they require more feature inputs to produce equal accuracy compared to tree-based models (RF and XGB). High-resolution forecasts can maintain a high level of accuracy with only one feature at the previous point. The final part of the work analyses the possibility of incorporating Transfer Learning (TL) into the context of water demand forecasting. To evaluate the potential of TL, 18 UK DMAs water demand datasets are used. Experiments are designed to predict water demands in one DMA that has limited or unavailable data, with an aim to anaysing the forecasting ability of models built with alternative DMA data. Results have found that four and eight external DMA datasets are respectively suitable for 15-minute and hourly demand and that limited accuracy gains are achieved from samples size larger than 20,000. Finally, TL-incorporated methods can improve machine learning forecasting accuracy when there is limited data availability. The results obtained in this study prove the usefulness of data-centric approaches’ ability to improve forecasting accuracy. The data-centric approaches explored in this thesis can be used to guide the development of machine learning-based short-term demand forecasting models for researchers, operators, and utilities. Efficient use of forecasting models and demand data holds further potential in improving forecast accuracy, reducing computation cost, and bettering user confidence in the application of machine learning models.EPSR

Deep Learning Tools for Yield and Price Forecasting Using Satellite Images

The ability to forecast crop yields and prices is vital to secure global food availability and provide farmers, retailers, and consumers with valuable information to maximize effectiveness. Conventional approaches used to tackle this often use localized methods that are expensive and limited in generalizability. To tackle some of these known issues and to benefit from recently developed advanced tools of machine learning, this thesis explores the use of deep learning models as well as satellite images to forecast various crop yields and prices across the USA. The special case of the USA was chosen given the abundance of datasets pertaining to weather and agricultural information. Moreover, the thesis explores Transfer Learning (TL) and incremental learning applications in the field for generalizability. In addition, a web application along with a user-friendly interface are designed and implemented to facilitate the ease of user application of the proposed models and approaches. Multiple machine learning models, specifically those based on artificial neural networks, are deployed and tested, along with several voting regressor ensembles. The models are tested using satellite images for California and the Midwest in USA to predict soybean yield and forecast strawberry and raspberry yield and price. Dimensionality reduction is applied by converting those satellite images into histograms that represent the pixel value frequency count. To gauge the performance of the deployed models, several evaluations metrics are used including Mean Absolute Error (MAE), Root Mean-Squared Error (RMSE), R-Squared Coefficient (R^2), as well as Aggregated Measure (AGM) and their Average Aggregated Measure (AAGM). The potential of using deep learning based models in real-life applications which provides crucial insight for all stakeholders in the field of agriculture is demonstrated in this work. The deployed multi-module based models and voting regressors ensembles proved to have higher performance compared to the single module models. The proposed CNN-LSTM is found to outperform Convolutional Neural Network (CNN) models proposed in the literature by an average RMSE percentage improvement of 31% while the inclusion of the satellite images of surface and subsurface moisture levels enhances the prediction performance. In addition, it is observed that all deployed models consistently lose forecasting performance the further they forecast in the future, with the CNN-LSTM Ensemble outperforming each of its components as well as the LSTM in yield forecasting while the CNN-LSTM outperforms the LSTM in price forecasting. Moreover, the proposed CNN-LSTM-SAE Ensemble outperforms the deployed CNN-LSTM, VAE, and SAE models including the literature CNN model by 70% AGM improvement for yield forecasting and 66% for price forecasting. The deployment of incremental learning with the CNN-LSTM Ensemble for yield forecasting without drastic loss in performance is achieved. Finally, based on the AGM metric, it is found that the TL CNN-LSTM outperforms the non-TL CNN-LSTM model by almost 28% AGM with reduction of 49% in computational time. For future work, there is potential in expanding the utilized datasets and models to verify and improve the obtained results as well as investigating the performance on additional fresh produce and counties to better gauge and enhance the effectiveness of the models and application