Human action is not merely a matter of presenting patterns of motion of different parts of the body, in addition, it is also a description of intention, emotion and thoughts of the person. Hence, it has become a crucial component in human behavior analysis and…
This project focuses on semi-supervised human activity recognition (HAR) tasks, in which the inputs are partly labeled time series data acquired from sensors such as accelerometer data, and the outputs are predefined human activities. Most state-of-the-art exi…
Being able to generate summaries automatically could speed up the spread and retention of information and potentially increase productivity in several fields. Using RNN-based encoder-decoder models with attention have been successful on a variety of language-r…
El aumento de la población de las últimas décadas y el flujo de personas de las zonas rurales a las grandes ciudades ha ocasionado un incremento del volumen de desplazamientos de pasajeros y mercancías, llevando a la sobresaturación de la circulación vehicular…
Natural language processing (NLP) has come of age. For example, semantic role labeling (SRL), which automatically annotates sentences with a labeled graph representing who did what to whom, has in the past ten years seen nearly 40% reduction in error, bringing…
Attempting to predict the future long precedes the time where we could first quantify much of our present. But nowadays, performing long-term and reliable time series forecasting remains as relevant as ever across a vast number of application domains, includin…
Understanding the behavior of financial time series is very importance to forecast future values and make efficient decisions. This area is often the target of studies and new proposals in the attempt of its modeling, since it is very volatile in response to t…
There are several tools and models found in machine learning that can be used to forecast a certain time series; however, it is not always clear which model is appropriate for selection, as different models are suited for different types of data, and domain-sp…
Η παρούσα εργασία αφορά την υλοποίηση ενός συστήματος αυτόματης αναγνώρισης ομιλίας με την χρήση Deep Neural Networks. Βασίζεται στην εργασία του BAIDU “Deep Speech 2” και έχει υλοποιηθεί με python και TensorFlow. Ξεκινάει με μια μικρή εισαγωγή στα συστήματα α…
This thesis presents detailed observation of different Convolutional Neural Network (CNN) architecture which had assisted Computer Vision researchers to achieve state-of-the-art performance on classification, detection, segmentation and much more to name image…
This thesis aims to provide a reasonable solution for categorizing automatically sentences into types of toxicity using different types of neural networks. There are six types of categories: Toxic, severe toxic, obscene, threat, insult and identity hate. Three…
This research project evaluates the suitability of machine learning methods for early fault prediction and predictive maintenance in petrochemical refineries based on real- life use cases at Shell Pernis. Refineries are mature industrial installations, however…
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