Convolutional Neural Networks (CNNs) have been established as a powerful class of models for image recognition problems. Despite their success in other areas, CNNs have been applied only for very limited agricultural applications due to the need for large datasets. The aim of this research is to design a robust CNN model that classifies raw coffee beans into their 12 quality grades using small datasets which have high data variability. The dataset contains images of raw coffee beans acquired in two sets using different acquisition technique under varying illuminations which poses a complex challenge to designing a robust model. To design the model, preprocessing techniques were applied to the input in order to reduce task irrelevant features. But adding the preprocessing techniques did not improve the performance of the CNN model for our dataset. We have also used ensemble methods to solve the high variance that exists in networks when working with small datasets. Finally, we were able to design a model that classifies the beans into their quality grades with an accuracy of 89.01% on the test dataset.
Abstract-Believability of automated characters in virtual worlds has posed a challenge for many years. In this paper, the author discusses a novel approach of using human-inspired mirroring behavior in MirrorBot, an Unreal Tournament 2004 game bot which crossed the humanness barrier and won the 2K BotPrize 2012 competition with the score of 52.2%, a record in the five year history of this contest. A comparison with past contest entries is presented and the relevance of the mirroring behavior as a humanness improvement factor is argued. The modules that compose MirrorBot's architecture are presented along with a discussion of the advantages of this approach and proposed solutions for its drawbacks. The contribution continues with a discussion of the bot's results in humanness and judging accuracy.
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