Recently, deep learning (DL) became one of the essential tools in bioinformatics. A modified convolutional neural network (CNN) is employed in this paper for building an integrated model for deoxyribonucleic acid (DNA) classification. In any CNN model, convolutional layers are used to extract features followed by max-pooling layers to reduce the dimensionality of features. A novel method based on downsampling and CNNs is introduced for feature reduction. The downsampling is an improved form of the existing pooling layer to obtain better classification accuracy. The two-dimensional discrete transform (2D DT) and two-dimensional random projection (2D RP) methods are applied for downsampling. They convert the high-dimensional data to low-dimensional data and transform the data to the most significant feature vectors. However, there are parameters which directly affect how a CNN model is trained. In this paper, some issues concerned with the training of CNNs have been handled. The CNNs are examined by changing some hyperparameters such as the learning rate, size of minibatch, and the number of epochs. Training and assessment of the performance of CNNs are carried out on 16S rRNA bacterial sequences. Simulation results indicate that the utilization of a CNN based on wavelet subsampling yields the best trade-off between processing time and accuracy with a learning rate equal to 0.0001, a size of minibatch equal to 64, and a number of epochs equal to 20.
Recently, deep learning has opened a remarkable research direction in the track of bioinformatics, especially for the applications that need classification and regression. With deep learning techniques, DNA sequences can be classified with high accuracy. Firstly, a DNA sequence should be represented, numerically. After that, DNA features are extracted from the numerical representations based on deep learning techniques to improve the classification process. Recently, several architectures have been developed based on deep learning for DNA sequence classification. Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) are the default deep learning architectures used for this task. This paper presents a hybrid module that combines a CNN with an RNN for DNA classification. The CNN is used for feature extraction, and this is followed by a subsampling layer, while the RNN is trained for classifying bacteria into taxonomic levels. Besides, a wavelet-based pooling strategy is adopted in the subsampling layer, because the wavelet transform with down-sampling allows signal compression, while maintaining the most discriminative features of the signal. The proposed hybrid module is compared with a CNN based on Random Projection (RP) and an RNN based on histogram-oriented gradient features. The simulation results show that the hybrid module has the best performance among other ones.
High gain antennas are highly desirable for long-range wireless communication systems. In this paper, a compact, low profile, and high gain dielectric resonator antenna is proposed, fabricated, experimentally tested, and verified. The proposed antenna system has a cylindrical dielectric resonator antenna with a height of 9 mm and a radius of 6.35 mm as a radiating element. The proposed dielectric resonator antenna is sourced with a slot while the slot is excited with a rectangular microstrip transmission line. The microstrip transmission line is designed for a 50 impedance to provide maximum power to the slot. As a result, the proposed antenna operates at 5.15 GHz with a 10-dB absolute bandwidth of 430 MHz (4.98 -5.41 GHz). It is important to mention that the gain of the dielectric resonator antenna is enhanced by the introduction of an electromagnetic bandgap (EBG) structure. In fact, EBG units are placed below the antenna, which enhances the realized peak gain from 5.32 dBi to 8.36 dBi at 5.15 GHz. More specifically, a gain enhancement of 3.04 dB is observed with the introduction of the EBG array. This antenna has several good features such as high gain, compact size, large bandwidth, and lower losses which make it a suitable choice for long-range wireless communication systems.
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