1,721,023 research outputs found
Evolutionary Bayesian Network Design for High Dimensional Experiments
No full textLaboratory experimentation is increasingly concerned with systems whose dynamical behaviour can be affected by a very large number of variables. Objectives of experimentation on such systems are generally both the optimisation of some experimental responses and efficiency of experimentation in terms of low investment of resources and low impact on the environment. Design and modelling for high dimensional systems with these objectives present hard and challenging problems, to which much current research is devoted. In this paper, we introduce a novel approach based on the evolutionary principle and Bayesian network models. This approach can discover optimum values while testing just a very limited number of experimental points. The very good performance of the approach is shown both in a simulation analysis and biochemical study concerning the emergence of new functional bio-entities
Tendenze dei consumi e innovazione nell’industria agroalimentare
Full text linkAnalysing and understanding the evolution of the demand for food and
beverage allows to make effective decisions conducive to product, process and service innovations and to novel strategies. Moving from the analysis of fifty years of expenses in food and beverages, the chapter singles out some macro trends that shape current consumption behaviours. In particular, the chapter focuses on the role of health, sustainability, tradition and authenticity in the evolution of markets
On using Bayesian networks for complexity reduction in decision trees
Full text linkBayesian networks, Decision trees, Markov blanket, Complexity reduction, Classification,
Using Twitter Data to Monitor Political Campaigns and Predict Election Results
No full textIn recent years social networks have increasingly been used to study political opinion formation, monitor electoral campaigns and predict electoral outcomes as they are able to generate huge amount of data, usually in textual and non structured form. In this paper we aim at collecting and analysing data from Twitter posts identifying emerging patterns of topics related to a constitutional referendum that recently took place in Italy to better understand and nowcast its outcome. Using the Twitter API we collect tweets expressing voting intentions in the four weeks before the elections obtaining a database of approximately one million tweets. We restrict the data collection to tweets that contain hashtags referring to the referendum, therefore we are sure to include in the analysis only relevant text. On this huge volume of data, we perform a topic modelling analysis using a Latent Dirichelet Allocation model (LDA) to extract frequent topics and keywords. Analysing the behaviour of frequent words we find that connected to voting in favour of the constitutional reform there are positive words such as future and change while connected to voting against it there are words such ad fear and risk
High dimensional design of experiments: The combination of evolution and statistical models.
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