The Amazon Basin features a vast network of healthy, free-flowing rivers, which provides habitat for the most biodiverse freshwater fauna of any basin globally. However, existing and future infrastructure developments, including dams, threaten its integrity by diminishing river connectivity, altering flows, or changing sediment regimes, which can impact freshwater species. In this study, we assess critical rivers that need to be maintained as freshwater Bernardo Caldas and Michele L. Thieme are joint first authors.
This paper presents a novel approach to support appropriateness of executive decisions by using combined principles of Artificial Immune System (AIS) and Fuzzy Logic (FL). The main goal is to show that more Appropriate Executive Decisions (AED) may be obtained if strategic decision makers are equipped with supportive tools based on AIS and FL (Fuzzy-AED). In addition, this work aims at improving the quality of the system response by adding new features to the original version of the AED model. A proof of concept for Fuzzy-AED is also includedhere along with experiments carried out within a real business environment. Experimental results suggest that this hybrid approach to executive decision making could be used to assemble helpful executive decision systems that may be easily deployed to reduce some of the risks inherent in strategic decision making.
This paper presents an enhanced version of the AED (Appropriate Executive Decisions) algorithm, which is based on biological immune system (BIS) and whose purpose is the generation of appropriate executive decisions aimed at business environments. A new metric has been incorporated to the algorithm and a larger and more representative database was used to train and validate results. Moreover, this paper offers better directions on how to apply AED in executive decisions, affording the learning process quality improvement through immunoinformatics concepts, namely decision cells, thereby producing more appropriate executive decisions. Experiments were carried out with executive officers experienced in executive decisions in order to suitably validate the appropriateness of responses generated by the enhanced AED algorithm.
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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