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Abstract
Up to one third of the global food production depends on the pollination of honey bees, making them vital. This study defines a methodology to create a bee hive health monitoring system through image processing techniques. The approach consists of two models, where one performs the detection of bees in an image and the other classifies the detected bee’s health. The main contribution of the defined methodology is the increased efficacy of the models, whilst maintaining the same efficiency found in the state of the art. Two databases were used to create models based on Convolutional Neural Network (CNN). The best results consist of 95% accuracy for health classification of a bee and 82% accuracy in detecting the presence of bees in an image, higher than those found in the state-of-the-art.

Abstract
In real manufacturing environments, scheduling can be defined as the problem of effectively and efficiently assigning tasks to specific resources. Metaheuristics are often used to obtain near-optimal solutions in an efficient way. The parameter tuning of metaheuristics allows flexibility and leads to robust results, but requires careful specifications. The a priori definition of parameter values is complex, depending on the problem instances and resources. This paper implements a novel approach to the automatic specification of metaheuristic parameters, for solving the scheduling problem. This novel approach incorporates two learning techniques, namely, racing and case-based reasoning (CBR), to provide the system with the ability to learn from previous cases. In order to evaluate the contributions of the proposed approach, a computational study was performed, focusing on comparing our results previous published results. All results were validated by analyzing the statistical significance, allowing us to conclude the statistically significant advantage of the use of the novel proposed approach.

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Abstract

Abstract