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.
An Elliptic Curve Crypto-Processor (ECCP) is a favorite public-key cryptosystem due to its small key size and its high security arithmetic unit. It is applied in constrained devices which often run on batteries and have limited processing, storage capabilities and low power. This research work presents an effective ECCP architecture for security in IoT and embedded devices. A finite field polynomial multiplier takes the most implementation effort of an ECCP because it is the most consuming operation for time and area. So, the objective is to implement the main operation of Point Multiplication (PM) = using FPGA. The aim is to obtain the optimal registers number for an area optimization of ECCP architecture. Moreover, it proposes a time optimization of ECCP based on the liveness analysis and exploiting forward paths. Also, a comparison between sequential and parallel hardware design of PM based on Montgomery ladder algorithm is provided. The developed ECCP design is implemented over Galois Fields GF (2 163) and GF (2 409) on Xilinx Integrated Synthesizes Environment (ISE) Virtex 6 FPGA. In case of GF (2 163), this work achieved an area saving that uses 2083 Flip Flops (FFs), 40876 Lookup Tables (LUTs) and 19824 occupied slices. The execution time is 1.963 s runs at a frequency of 369.529 MHz and consumes 5237.00 mW. In case of GF (2 409), this work achieved an area saving that uses 8129 Flip Flops (FFs), 42300 Lookup Tables (LUTs) and 18807 occupied slices. The execution time is 29 s runs at a frequency of 253.770 MHz and consumes 2 W. The obtained results are highly comparable with other state-of-the-art crypto-processor designs. The developed ECCP is applied as a case study of a cryptography protocol in ATMs.
Deep learning (DL) methods have been achieving amazing results in solving a variety of problems in many different fields especially in the area of big data. With the advances of the big data era in bioinformatics, applying DL techniques, the DNA sequences can be classified with accurate and scalable prediction. The strength of DL methods come from the development of software and hardware, such as processing abilities graphical processing units (GPU) for the hardware and new learning or inference algorithms for the software, which reducing the main primary difficulties that faced the training process. In This work, we start from the previous classification methods such as alignment methods pointing out the problems, which are face to use these methods.After that, we demonstrate deep learning, from artificial neural networks to hyper parameter tuning, and the most recent state-of-the-art DL architectures used in DNA classification. After that, the paper ended with limitations and suggestions.
An Elliptic Curve Crypto-Processor (ECCP) is a favourite public-key cryptosystem. It is used for embedded systems due to its small key size and its high security arithmetic unit. It is applied in constrained devices which often run on batteries and have limited processing, storage capabilities and low power. A finite field polynomial multiplier takes the most implementation effort of an ECCP because it is the most consuming operation for time and area. So, it is preferable to optimize this operation especially for light devices where the small area is needed. This research introduces a hardware design for parallel ECCP binary implementation that is based on Montgomery ladder algorithm. This implementation is targeted for GF(2163) and GF(2409) where the executed time are 2.9 μs and 29 μs respectively. The implementation is performed on Xilinx ISE Virtex6.
Identifying and classifying Deoxyribonucleic Acid (DNA) sequences and their functions have been considered as the main challenges in bioinformatics. Advances in machine learning and Deep Learning (DL) techniques are expected to improve DNA sequence classification. Since the DNA sequence classification depends on analyzing textual data, Bidirectional Long Short-Term Memory (BLSTM) algorithms are suitable for tackling this task. Generally, classifiers depend on the patterns to be processed and the pre-processing method. This paper is concerned with a new proposed classification framework based on Frequency Chaos Game Representation (FCGR) followed by Discrete Wavelet Transform (DWT) and BLSTM. Firstly, DNA strings are transformed into numerical matrices by FCGR. Then, the DWT is used instead of the pooling layer as a tool of data compression. The benefit of using the DWT is two-fold. It preserves the useful information only that enables the following BLSTM training, effectively. Besides, DWT adds more important details to the encoded sequences due to finding effective features in the DNA fragments. Finally, the BLSTM model is trained to classify the DNA sequences. Evaluation metrics such as F1 score and accuracy show that the proposed framework outperforms the state-of-the-art algorithms. Hence, it can be used in DNA classification applications.
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