Summary of the research

This study is motivated by the fact that there is much research on the new and more accurate method of beehive monitoring algorithms but none of them is testing the noise tolerance of the algorithm. A beehive monitoring algorithm should be able to operate in a noisy environment with a high classification rate. This thesis contains the prof of Fbank feature and neural network based beehive monitoring algorithm have lower classification accuracy when exposed in a noisy environment. Based on this problem statement, this study attempted to demonstrate the weakness of the proposed algo-rithm in noisy environments and proposing a solution as well. The result generated from the simulation is exactly as expected. Classification accuracy of the algorithm

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using the conventional feature extraction method keeps on falling as the noise level increased proving its weakness in noisy environments. Performance of the algorithm using the proposed method of feature extraction remained very high for the same level of noise. The simulation is designed with 4 different noise intensity. Based on the result of the simulation, the methodology of the proposed solution could be concluded to be successful. This implies that using the new method of feature extraction, the beehive monitoring algorithm could perform efficiently in a noisy environment without any additional noise isolation or noise-cancelling mechanism.

Based on the simulation result and for better understanding of the results, future studies could include building a working prototype of the algorithm with single board microcomputers like Raspberry pi demonstrating the real-world implication of the pro-posed algorithm. All the computational process in the propro-posed solution kept simple and it is light enough to run on very low power algorithms. Future research could also include designing similar algorithms with other feature extraction methods like MFCC and compare the findings.

CHAPTER 8. CONCLUSION 65

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