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This work presents a neural network used to diagnosis patients with benign or malignant breast cancer. The study is carried out using the Breast Cancer Wisconsin dataset. To solve the problem a feedforward neural network (NN) with multilayers was used. In the work, the implementation was made in Python, using two different libraries (sklearn and keras). Experimental results were obtained by performing simulations in both developed applications, and the performance of the neural classifier was evaluated through the performance measures of the classification systems and the ROC curve. The results were promising, since the NN was able to discriminate with high accuracy the two separable sets discriminating the benign or malignant tumor patients.

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The purpose of this special issue is to assemble high quality papers that deepen and boost understanding the role of open innovation and knowledge on business ecosystems development. This special issue includes ten papers specific related to the special issue topic: Open Innovation and Knowledge for Fostering Business Ecosystems. Jointly, the papers scrutinize and explore this subject using different theoretical backgrounds and methodologies. Individually, each paper provides interesting insights concerning the singularities they explore. (C) 2018 Journal of Innovation & Knowledge. Published by Elsevier Espana, S.L.U.

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Google Ads is a powerful tool for companies wishing to gain visibility on Google searches, as it offers impression privileges for advertisers, by featuring the ad above the organic results listing. This chapter contributes to the optimization of Google Ads campaigns. It includes an empirical study with a sample of marketing professionals exploring their views on the challenges of Google Ads as a digital marketing tool. According to the participants in this study, Google Ads campaign profitability depends, largely, on the ability to choose a keyword pool that achieves the company's goals. Moreover, the complexity of these pay-per-click decisions, the costs involved, and its business implications demand more reasoned, quantified, and, if possible, optimized solutions. The chapter develops linear programming optimization models based on impressions, clicks, conversions, and billing. The models are tested on a real example using Excel optimization add-ins.

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Futures contracts are a valuable market option for electricity negotiating players, as they enable reducing the risk associated to the day-ahead market volatility. The price defined in these contracts is, however, itself subject to a degree of uncertainty; thereby turning price forecasting models into attractive assets for the involved players. This paper proposes a model for futures contracts price forecasting, using artificial neural networks. The proposed model is based on the results of a data analysis using the spearman rank correlation coefficient. From this analysis, the most relevant variables to be considered in the training process are identified. Results show that the proposed model for monthly average electricity price forecast is able to achieve very low forecasting errors.

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Common natural walking techniques for navigating in virtual environments feature constraints that make it difficult to use those methods in cramped home environments. Indeed, natural walking requires unobstructed and open space, to allow users to roam around without fear of stumbling on obstacles while immersed in a virtual world. In this work, we propose a new virtual locomotion technique, CWIP-AVR, that allows people to take advantage of the available physical space and empowers them to use natural walking to navigate in the virtual world. To inform users about real world hazards our approach uses augmented virtual reality visual indicators. A user evaluation suggests that CWIP-AVR allows people to navigate safely, while switching between locomotion modes flexibly and maintaining a adequate degree of immersion.