Identification of the exact location of an exon in a DNA sequence is an important research area of bioinformatics. The main issues of the previous signal processing techniques are accuracy and robustness for the exact locating of exons. To address the mentioned issues, in this study, a method has been proposed based on deep learning. The proposed method includes a new preprocessing, a new mapping method, and a multi-scale modified and Hybrid deep neural network. The proposed preprocessing method enriches the network to accept and encode genes at any length in a new mapping method. The proposed multi-scale deep neural network uses a combination of an embedding layer, a modified CNN, and an LSTM network. In this study, HMR195, BG570, and F56F11.4 datasets have been used to compare this work with previous studies. The accuracies of the proposed method have been 0.982, 0.966, and 0.965 on HMR195, BG570, and F56F11.4 databases, respectively. The results reveal the superiority and effectiveness of the proposed Hybrid multi-scale CNN-LSTM network.

Rosa Hybrida is a perennial plant that after cut off head of the stem, lateral buds become free from apical dominance and begin to growth. In each new period of growth, new and young leaves can be formed. The four leaf samples were numbered from apex to base, showing young to mature leaf stages and some biochemical pigment markers changes were studied. The results suggest that during maturation, in addition to morphological differences, some physiological and biochemical markers are change. Data showed age dependent increase in the amount of pigments such as chlorophylls and carotenoids to reach the stage of leaf maturity to improve the leaf photosynthetic system efficiency. Gradually reducing in content of phenolic compounds, anthocyanins and flavonoids suggest reduction on oxidative damage.

In this article, a new stream steganography method for real time digital data network environment with the focus on VoIP is presented. This method consists of a combination of Least Signi cant Bits (LSB) algorithm and an algorithm called LACK, which is based on the delayed packets in VoIP streams, and forms a new algorithm called LASB (LACK-LSB combination). The combination allows both the reduction of detection probability of stegano data and the use of LSB high capacity as well as being more robust because of diversifying the source of data embedding. That is, by combing these two methods the probability of detection existing steganography data is reduced extensively. Moreover, in the case of detecting stegano data, the potential eavesdropper is not able to easily reconstruct it. To show the performance, both LACK and LSB algorithms are implemented and are compared with the new LASB algorithm in the VoIP environment in terms of capacity, eavesdropping and robustness under several attacks. The experimental results show that LASB is superior to both LACK and LSB in many aspects.

Data security is an important area of concern for every computer system owner. An intrusion detection system is a device or software application that monitors a network or systems for malicious activity or policy violations. Already various techniques of artificial intelligence have been used for intrusion detection. The main challenge in this area is the running speed of the available implementations. In this research work, we present a Hybrid approach which is based on the “ linear discernment analysis” and the “ extreme learning machine” to build a tool for intrusion detection. In the proposed method, the linear discernment analysis is used to reduce the dimensions of data and the extreme learning machine neural network is used for data classification. This idea allowed us to benefit from the advantages of both methods. We implemented the proposed method on a microcomputer with core i5 1. 6 GHz processor by using machine learning toolbox. In order to evaluate the performance of the proposed method, we run it on a comprehensive data set concerning intrusion detection. The data set is called KDD, which is a version of the data set DARPA presented by MIT Lincoln Labs. The experimental results were organized in related tables and charts. Analysis of the results show meaningful improvements in intrusion detection. In general, compared to the existing methods, the proposed approach works faster with higher accuracy.

Traditional noise removal methods like Non-Local Means create spurious boundaries inside regular zones. Visushrink removes too many coefficients and yields recovered images that are overly smoothed. In Bayesshrink method, sharp features are preserved. However, PSNR (Peak Signal-to-Noise Ratio) is considerably low. BLS-GSM generates some discontinuous information during the course of denoising and destroys the flatness of homogenous area. Wavelets are not very effective in dealing with multidimensional signals containing distributed discontinuities such as edges. This paper develops an effective shearlet-based denoising method with a strong ability to localize distributed discontinuities to overcome this limitation. The approach introduced here presents two major contributions: (a) Shearlet Transform is designed to get more directional subbands which helps to capture the anisotropic information of the image; (b) coefficients are divided into low frequency and high frequency subband. Then, the low frequency band is refined by Wiener filter and the high-pass bands are denoised via NeighShrink model.Our framework outperforms the wavelet transform denoising by %7.34 in terms of PSNR (peak signal-to-noise ratio) and %13.42 in terms of SSIM (Structural Similarity Index) for‘Lena’ image. Our results in standard images show the good performance of this algorithm, and prove that the algorithm proposed is robust to noise.