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The search for an unbiased company valuation method to reduce uncertainty, whether or not it is automatic, has been a relevant topic in social sciences and business development for decades. Many methods have been described in the literature, but consensus has not been reached. In the companion paper we aimed to review the assessment capabilities of traditional company valuation model, based on company's intrinsic value using the Discounted Cash Flow (DCF). In this paper, we capitalized on the potential of exogenous information combined with Machine Learning (ML) techniques. To do so, we performed an extensive analysis to evaluate the predictive capabilities with up to 18 different ML techniques. Endogenous variables (features) related to value creation (DCF) were proved to be crucial elements for the models, while the incorporation of exogenous, industry/country specific ones, incrementally improves the ML performance. Bagging Trees, Supported Vector Machine Regression, Gaussian Process Regression methods consistently provided the best results. We concluded that an unbiased model can be created based on endogenous and exogenous information to build a reference framework, to price and benchmark Enterprise Value for valuation and credit risk assessment. [ABSTRACT FROM AUTHOR]

For cleaner and sustainable greenhouse crops production, it is essential to successfully manage the needs and resources. Thus the prediction of the greenhouse microclimate, especially the temperature and relative humidity is of great interest. The research done in this area is, however, still limited, and a number of machine learning techniques have not yet been sufficiently exploited. The objective of this paper is to evaluate two greenhouse modeling techniques (machine learning (Artificial Neural Networks (ANN), Support Vector Machine (SVM), Bagging trees (BG) and Boosting trees (BT)) and Computational Fluid Dynamics (CFD) methods and assess the impact of the seasonal changes on machine learning performances. The study was carried out in a commercial greenhouse located in Agadir, Morocco, and the experimental data were collected during October and March. Results show that all predictive models are capable of predicting the inside air temperature (Tin) and relative humidity (Rhin) of the greenhouse with a quite good precision (R>0.98, nRMSE0.98, nRMSE

The task of modeling the distribution of a large number of tree species under future climate scenarios presents unique challenges. First, the model must be robust enough to handle climate data outside the current range without producing unacceptable instability in the output. In addition, the technique should have automatic search mechanisms built in to select the most appropriate values for input model parameters for each species so that minimal effort is required when these parameters are fine-tuned for individual tree species. We evaluated four statistical models—Regression Tree Analysis (RTA), Bagging Trees (BT), Random Forests (RF), and Multivariate Adaptive Regression Splines (MARS)—for predictive vegetation mapping under current and future climate scenarios according to the Canadian Climate Centre global circulation model. To test, we applied these techniques to four tree species common in the eastern United States: loblolly pine ( Pinus taeda), sugar maple ( Acer saccharum), American beech ( Fagus grandifolia), and white oak ( Quercus alba). When the four techniques were assessed with Kappa and fuzzy Kappa statistics, RF and BT were superior in reproducing current importance value (a measure of basal area in addition to abundance) distributions for the four tree species, as derived from approximately 100,000 USDA Forest Service’s Forest Inventory and Analysis plots. Future estimates of suitable habitat after climate change were visually more reasonable with BT and RF, with slightly better performance by RF as assessed by Kappa statistics, correlation estimates, and spatial distribution of importance values. Although RTA did not perform as well as BT and RF, it provided interpretive models for species whose distributions were captured well by our current set of predictors. MARS was adequate for predicting current distributions but unacceptable for future climate. We consider RTA, BT, and RF modeling approaches, especially when used together to take advantage of their individual strengths, to be robust for predictive mapping and recommend their inclusion in the ecological toolbox. [ABSTRACT FROM AUTHOR]