The growth state of flowers is affected by many factors such as temperature, humidity, and light. Therefore, the maintenance of flowers often requires more professional knowledge. Ordinary people are often at a loss when face with various flower representations and do not know where the problem is. In response to the above problems, this article proposes the use of deep learning to identify the growth status of flowers to assist people in successfully raising flowers. In this article, we propose that the mainstream convolutional neural network has the limitation of only inputting images. In terms of network input, data of the current growth environment of flowers will also be input to supplement the input data of the network. In view of the lack of information interaction in the network, in terms of network structure, the shallow and deep characteristics of the network are integrated to make the network performance more advantageous. Experiments show that this method can effectively improve the recognition rate of flower growth status, so as to correctly distinguish the current growth status of flowers.
Protein is closely related to life activities. As a kind of protein, DNA-binding protein plays an irreplaceable role in life activities. Therefore, it is very important to study DNA-binding protein, which is a subject worthy of study. Although traditional biotechnology has high precision, its cost and efficiency are increasingly unable to meet the needs of modern society. Machine learning methods can make up for the deficiencies of biological experimental techniques to a certain extent, but they are not as simple and fast as deep learning for data processing. In this paper, a deep learning framework based on parallel long and short-term memory(LSTM) and convolutional neural networks(CNN) was proposed to identify DNA-binding protein. This model can not only further extract the information and features of protein sequences, but also the features of evolutionary information. Finally, the two features are combined for training and testing. On the PDB2272 dataset, compared with PDBP_Fusion model, Accuracy(ACC) and Matthew’s Correlation Coefficient (MCC) increased by 3.82% and 7.98% respectively. The experimental results of this model have certain advantages.
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<p>The study of DNA binding proteins (DBPs) is of great importance in the biomedical field and plays a key role in this field. At present, many researchers are working on the prediction and detection of DBPs. Traditional DBP prediction mainly uses machine learning methods. Although these methods can obtain relatively high pre-diction accuracy, they consume large quantities of human effort and material resources. Transfer learning has certain advantages in dealing with such prediction problems. Therefore, in the present study, two features were extracted from a protein sequence, a transfer learning method was used, and two classical transfer learning algorithms were compared to transfer samples and construct data sets. In the final step, DBPs are detected by building a deep learning neural network model in a way that uses attention mechanisms.</p>
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