  Flowers play an essential role in removing the duller from the environment.
The life cycle of the flowering plants involves pollination, fertilization,
flowering, seed-formation, dispersion, and germination. Honeybees pollinate
approximately 75% of all flowering plants. Environmental pollution, climate
change, natural landscape demolition, and so on, threaten the natural habitats,
thus continuously reducing the number of honeybees. As a result, several
researchers are attempting to resolve this issue. Applying acoustic
classification to recordings of beehive sounds may be a way of detecting
changes within them. In this research, we use deep learning techniques, namely
Sequential Neural Network, Convolutional Neural Network, and Recurrent Neural
Network, on the recorded sounds to classify bee sounds from the nonbeehive
noises. In addition, we perform a comparative study among some popular non-deep
learning techniques, namely Support Vector Machine, Decision Tree, Random
Forest, and Na\"ive Bayes, with the deep learning techniques. The techniques
are also verified on the combined recorded sounds (25-75% noises).
