An Introduction to MultiAgent Systems.

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A Break in the Clouds: Towards a Cloud Definition

Exploring and identifying structure is even more important for multivariate data than univariate data, given the difficulties in graphically presenting multivariate data and the comparative lack of parametric models to represent it. Unfortunately, such exploration is also inherently more difficult.

Multivariate Density Estimation

Computational Analysis of Microarray Data

https://cronfa.swan.ac.uk/Record/cronfa54400/Download/54400__17763__d6602f13cc44449b8145bc15173fc903.pdf

Setting the future of digital and social media marketing research: Perspectives and research propositions

How blockchain improves the supply chain: case study alimentary supply chain

The study of public opinion can provide us with valuable information. The analysis of sentiment on social networks, such as Twitter or Facebook, has become a powerful means of learning about the users’ opinions and has a wide range of applications. However, the efficiency and accuracy of sentiment analysis is being hindered by the challenges encountered in natural language processing (NLP). In recent years, it has been demonstrated that deep learning models are a promising solution to the challenges of NLP. This paper reviews the latest studies that have employed deep learning to solve sentiment analysis problems, such as sentiment polarity. Models using term frequency-inverse document frequency (TF-IDF) and word embedding have been applied to a series of datasets. Finally, a comparative study has been conducted on the experimental results obtained for the different models and input features.

Sentiment analysis based on deep learning: A comparative study

Artificial neural networks used in optimization problems

At present, the domotization of homes and public buildings is becoming increasingly popular. Domotization is most commonly applied to the field of energy management, since it gives the possibility of managing the consumption of the devices connected to the electric network, the way in which the users interact with these devices, as well as other external factors that influence consumption. In buildings, Heating, Ventilation and Air Conditioning (HVAC) systems have the highest consumption rates. The systems proposed so far have not succeeded in optimizing the energy consumption associated with a HVAC system because they do not monitor all the variables involved in electricity consumption. For this reason, this article presents an agent approach that benefits from the advantages provided by a Multi-Agent architecture (MAS) deployed in a Cloud environment with a wireless sensor network (WSN) in order to achieve energy savings. The agents of the MAS learn social behavior thanks to the collection of data and the use of an artificial neural network (ANN). The proposed system has been assessed in an office building achieving an average energy savings of 41% in the experimental group offices.

Fernando De la Prieta

Papers

How blockchain improves the supply chain: case study alimentary supply chain

Sentiment analysis based on deep learning: A comparative study

Artificial neural networks used in optimization problems

Energy Optimization Using a Case-Based Reasoning Strategy.

Non-linear adaptive closed-loop control system for improved efficiency in IoT-blockchain management