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This work presents a suite of hybrid intelligent techniques helpful in decision making, the Hybrid Intelligent Decision Suite (HIDS). The system is composed of two complementary modules, one for forecasting new decision variables and the other, for searching among generated results of candidate decisions. Using this synergistic approach, HIDS is also suitable to obtain conditioning factors leading to desired decision, thus, overcoming some of the challenges posed by the 'Inverse Problem'. To test this concept we have applied our approach on two distinct problems: (1) diagnosis of cardiologic diseases (of the proben-1 data-set) and (2) automobile feature selection (of UCI data-set). In the simulations carried out here, the HIDS comprised Artificial Neural Networks (ANNs) and Fuzzy Logic Controllers. Results proved that the ideas presented here can be effective to assemble tools which reduce uncertainty and improve quality in decision making about future scenarios.

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Vale do São Francisco in Pernambuco is one of the most economically important poles in the state and among its cultivars, it is worth mentioning the grape culture. This sector faces challenges related to the response time between identifying a field infestation and taking corrective actions, in order to minimize losses. This work comprises a comparative analysis between deep learning architectures, applied to identification of diseases in grape cultivars. Results suggest that the use of these technologies is plausible to differentiate healthy grape leaves from leaves presenting one of three different types of diseases, obtaining near 100% accuracy in studied database using an architecture that can be employed in embedded devices.

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