1,720,976 research outputs found
Using online textual data, principal component analysis and artificial neural networks to study business and innovation practices in technology-driven firms
Full text linkIn this paper we introduce a method that combines principal component analysis, correlation analysis, K-means clustering and self organizing maps for the quantitative semantic analysis of textual data focusing on the relationship between firms' co-creation activities, the perception of their innovation and the articulation of the attributes of their product-enabled services. Principal component analysis was used to identify the components of firms' varlue co-creation activities and service value attributes; correlation analysis was used to examine the relationship between the degree of involvement in specific co-creation activities, the online articulation of firms' service value attributes and the perception of their innovativeness. K-means and self organizing map (SOM) are used to cluster firms with regards to their involvement in co-creation and new service development, and, additionally, as complementary tools for studying the relationship between co-creation and new service development. The results show that, first, there is a statistically significant relationship between firms' degree of involvement in co-creation activities and the degree of articulation of their service value attributes; second, the relationship should be considered within the context of firms' innovation activities; third, OS Software-driven firms are the best example in terms of co-creation and new product-enabled service development, i.e. the collaborative principles built in their customer participation platforms should be adopted by other (non-software) firms interested in enhancing their innovation capacity through involvement in co-creation and new product-enabled service development
Response improvement in complex experiments by co-information composite likelihood optimization
Full text linkWe propose an adaptive procedure for improving the response outcomes of complex combinatorial experiments. New experiment batches are chosen by minimizing the co-information composite likelihood (COIL) objective function, which is derived by coupling importance sampling and composite likelihood principles. We show convergence of the best experiment within each batch to the globally optimal experiment in finite time, and carry out simulations to assess the convergence behavior as the design space size increases. The procedure is tested as a new enzyme engineering protocol in an experiment with a design space size of order 107. © 2013 Springer Science+Business Media New York
Bayesian Networks for Detecting Relevant Variable Interactions for Biochemical Experiments
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Design of adaptive Elman networks for credit risk assessment
No full textThe banks' need of quantitative approaches for credit risk assessment is becoming more and more evident, due to the introduction of the Basel agreements. To this extent, we define an Elman network approach to determine the insolvency of a borrower, and compare its performances with classical neural networks approaches for bankruptcy prediction. Then, we devise an adaptive procedure to select the best network topology, by performing a multi-objective analysis to take into account different compromises between conflicting criteria. We apply our procedure to different real and partly-artificial-case scenarios composed of Italian SMEs by using predictors coming from balance-sheet ratios, credit-history ratios and macro-economic indicators; then, we compare our approach to other ones proposed in the literature and to a standard logistic regression tool used by practitioners; last, given the recent research interest towards the use of qualitative predictors in credit risk assessment, we also apply our approaches on qualitative data. The results show that the Elman networks are effective in assessing credit risk and robust with respect to criteria and data, confirming the applicability of neural networks to bankruptcy prediction. Our contribution adds to the discussion of the ongoing debate about comparing neural networks to standard techniques: in particular, we find Elman Networks to lead to lower classification errors than standard feed-forward networks, whilst results from the comparison to logistic regression vary with respect to the error class considered. As for the data, we remark that the use of macro-economics indicators does not lead to particular improvement in classification accuracy, except when used to improve results coming from the use of qualitative variables only.The banks’ need of quantitative approaches for credit risk assessment is becoming more and more evident, due to the introduction of the Basel agreements. To this extent, we define an Elman network approach to determine the insolvency of a borrower, and compare its performances with classical neural networks approaches for bankruptcy prediction. Then, we devise an adaptive procedure to select the best network topology, by performing a multi-objective analysis to take into account different compromises between conflicting criteria. We apply our procedure to different real and partly-artificial-case scenarios composed of Italian SMEs by using predictors coming from balancesheet ratios, credit-history ratios and macro-economic indicators; then, we compare our approach to other ones proposed in the literature and to a standard logistic regression tool used by practitioners; last, given the recent research interest towards the use of qualitative predictors in credit risk assessment, we also apply our approaches on qualitative data. The results show that the Elman networks are effective in assessing credit risk and robust with respect to criteria and data, confirming the applicability of neural networks to bankruptcy prediction. Our contribution adds to the discussion of the ongoing debate about comparing neural networks to standard techniques: in particular, we find Elman Networks to lead to lower classification errors than standard feed-forward networks, whilst results from the comparison to logistic regression vary with respect to the error class considered. As for the data, we remark that the use of macro-economics indicators does not lead to particular improvement in classification accuracy, except when used to improve results coming from the use of qualitative variables only
Neural Networks to model the innovativeness perception of co-creative firms
No full textValue co-creation is an emerging business, marketing and innovation paradigm describing the firms aptitude to adopt practices enabling their customers to become active participants in the design and development of personalised products, services and experiences. The main objective of our contribution is to make a quantitative analysis in order to assess the relationship between value co-creation and innovation in technology-driven firms: we are using Artificial Neural Network (ANN) to investigate the relationship between value co-creation and innovativeness, and Self Organising Map (SOM) models to cluster firms in terms of their degree of involvement in co-creation and innovativeness. Results from the ANN show that a strong relationship exists between value co-creation and innovativeness; furthermore, SOM are well performing in identifying cluster of firms that are more involved in co-creation values. Our work makes a methodological contribution by adopting and validating a combination of techniques that is able to address complexity and emergence in value co-creation systems. © 2012 Elsevier Ltd. All rights reserved
Designing lead optimisation of MMP-12 Inhibitors
Full text linkThe design of new molecules with desired properties is in general a very difficult problem, involving heavy experimentation with high investment of resources and possible negative impact on the environment. The standard approach consists of iteration among formulation, synthesis, and testing cycles, which is a very long and laborious process. In this paper we address the so-called lead optimisation process by developing a new strategy to design experiments and modelling data, namely, the evolutionary model-based design for optimisation (EDO). This approach is developed on a very small set of experimental points, which change in relation to the response of the experimentation according to the principle of evolution and insights gained through statistical models. This new procedure is validated on a data set provided as test environment by Pickett et al. (2011), and the results are analysed and compared to the genetic algorithm optimisation (GAO) as a benchmark. The very good performance of the EDO approach is shown in its capacity to uncover the optimum value using a very limited set of experimental points, avoiding unnecessary experimentation
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