Search results for "deep learning"
showing 7 items of 337 documents
Contributions and applications around low resource deep learning modeling
2023
El aprendizaje profundo representa la vanguardia del aprendizaje automático en multitud de aplicaciones. Muchas de estas tareas requieren una gran cantidad de recursos computacionales, lo que limita su adopción en dispositivos integrados. El objetivo principal de esta tesis es estudiar métodos y algoritmos que permiten abordar problemas utilizando aprendizaje profundo con bajos recursos computacionales. Este trabajo también tiene como objetivo presentar aplicaciones de aprendizaje profundo en la industria. La primera contribución es una nueva función de activación para redes de aprendizaje profundo: la función de módulo. Los experimentos muestran que la función de activación propuesta logra…
Análisis de técnicas de “aggregation”/“disaggregation” aplicadas a imágenes satélite para la estimación de parámetros térmicos superficiales a difere…
2023
Las aplicaciones que implican la observación de la superficie terrestre desde plataformas satélites a escala inferior a la regional, como por ejemplo, el caso del seguimiento de cultivos, requieren de una mayor disponibilidad de información térmica, en particular de la temperatura de la superficie terrestre (LST), con resoluciones espaciales apropiadas para un alcance local. Por ello, numerosos autores han propuesto y desarrollado métodos para extraer la LST a nivel “subpíxel”, mediante el empleo de productos complementarios de teledetección, con resultados adecuados para su uso en resoluciones superiores. La mayoría de estos métodos se basan en la correlación entre índices de vegetación, c…
Signal processing techniques for robust sound event recognition
2019
The computational analysis of acoustic scenes is today a topic of major interest, with a growing community focused on designing machines capable of identifying and understanding the sounds produced in our environment, similar to how humans perform this task. Although these domains have not reached the industrial popularity of other related audio domains, such as speech recognition or music analysis, applications designed to identify the occurrence of sounds in a given scenario are rapidly increasing. These applications are usually limited to a set of sound classes, which must be defined beforehand. In order to train sound classification models, representative sets of sound events are record…
Recommending Serendipitous Items using Transfer Learning
2018
Most recommender algorithms are designed to suggest relevant items, but suggesting these items does not always result in user satisfaction. Therefore, the efforts in recommender systems recently shifted towards serendipity, but generating serendipitous recommendations is difficult due to the lack of training data. To the best of our knowledge, there are many large datasets containing relevance scores (relevance oriented) and only one publicly available dataset containing a relatively small number of serendipity scores (serendipity oriented). This limits the learning capabilities of serendipity oriented algorithms. Therefore, in the absence of any known deep learning algorithms for recommend…
Anonymization as homeomorphic data space transformation for privacy-preserving deep learning
2021
Industry 4.0 is largely data-driven nowadays. Owners of the data, on the one hand, want to get added value from the data by using remote artificial intelligence tools as services, on the other hand, they concern on privacy of their data within external premises. Ideal solution for this challenge would be such anonymization of the data, which makes the data safe in remote servers and, at the same time, leaves the opportunity for the machine learning algorithms to capture useful patterns from the data. In this paper, we take the problem of supervised machine learning with deep feedforward neural nets and provide an anonymization algorithm (based on the homeomorphic data space transformation),…
Are customer star ratings and sentiments aligned? A deep learning study of the customer service experience in tourism destinations
2023
AbstractThis study explores the consistency between star ratings and sentiments expressed in online reviews and how they relate to the different components of the customer experience. We combine deep learning applied to natural language processing, machine learning and artificial neural networks to identify how the positive and negative components of 20,954 online reviews posted on TripAdvisor about tourism attractions in Venice impact on their overall polarity and star ratings. Our findings showed that sentiment valence is aligned with star ratings. A cancel-out effect operates between the positive and negative sentiments linked to the service experience dimensions in mixed-neutral reviews.
AnatomySketch : An Extensible Open-Source Software Platform for Medical Image Analysis Algorithm Development
2021
AbstractThe development of medical image analysis algorithm is a complex process including the multiple sub-steps of model training, data visualization, human–computer interaction and graphical user interface (GUI) construction. To accelerate the development process, algorithm developers need a software tool to assist with all the sub-steps so that they can focus on the core function implementation. Especially, for the development of deep learning (DL) algorithms, a software tool supporting training data annotation and GUI construction is highly desired. In this work, we constructed AnatomySketch, an extensible open-source software platform with a friendly GUI and a flexible plugin interfac…