Future Intelligent Systems and Networks

The purpose of this Special Issue is to collect current developments and future directions of Future Intelligent Systems and Networks. This issue is motivated by the progressive implementation of innovative technologies and business models at firms and public organizations. This special issue is a second part of a previous one on the same topic, already published in Future Internet in 2016.

The articles are real experiences that can inspire firms to build more innovative models by properly applying Intelligent Systems and Networks.

The first article, Analysis of the Structure and Use of Digital Resources on the Websites of the Main Football Clubs in Europe, authored by Santiago Tejedor, Laura Cervi, and Gerard Gordon [1], offers a descriptive and comparative analysis of web pages from fifteen best teams in the UEFA ranking. The main objective is analyzing the effectiveness in the management of communication of football clubs. The study concludes, that although the management of communication is effective, none of the analyzed teams have taken full advantage of the possibilities of interaction with the user offered by the digital scenario.

The second article, Tax Fraud Detection through Neural Networks: An Application Using a Sample of Personal Income Taxpayers, authored by César Pérez López, María Jesús Delgado Rodríguez, and Sonia de Lucas Santos [2], is focused on detection of tax fraud concerning personal income tax returns (IRPF, in Spanish) filed in Spain, through the use of Machine Learning advanced predictive tools, by applying Multilayer Perceptron neural network (MLP) models. Results show how the use of neural networks enables taxpayer segmentation and allows the calculation of the probability concerning an individual taxpayer’s propensity to attempt to evade taxes. The model presented shows an efficiency rate of 84.3% over other models used to diagnose tax fraud detection.

The third article, Sentiment Analysis Based Requirement Evolution Prediction, authored by Lingling Zhao and Anping Zhao [3], presents a framework that combines a supervised deep learning neural network with an unsupervised hierarchical topic model to analyze user reviews automatically for product feature requirements evolution prediction. The framework allows discovering hierarchical product feature requirements from the hierarchical topic model and identifying their sentiment by the Long Short-term Memory (LSTM) with word embedding. The application of the model to different experiments evidence that effectiveness and feasibility.

The fourth article, Audio-Visual Genres and Polymediation in Successful Spanish YouTubers, authored by Lorenzo J. Torres Hortelano [4], determines the predominant audio-visual genres among the 10 most influential Spanish YouTubers in 2018. Data are extracted from SocialBlade, an independent website, whose main objective is to track YouTube statistics. Results show that polymediation may present an opportunity that has not yet been fully exploited by successful Spanish YouTubers.

The fifth article, Open Data for Open Innovation: An Analysis of Literature Characteristics, authored by Diego Corrales-Garay, Eva-María Mora-Valentín, and Marta Ortiz-de-Urbina-Criado [5], analyzes the journals, conferences, and authors that have published papers about the use of open data for open innovation, the knowledge areas have that have done research on open data for open innovation and the methodological characteristics of articles centered on open data oriented to open innovation. Results show that there is interesting area of research focused in the development of applications of open data for open innovation practices.

These five contributions provide Internet Systems and Networks applications of interest that can become good examples for promote more innovative business models in a variety of industries.