Steganography is the art and science of writing hidden messages such that the existence of a secret communication is known only to the sender and receiver. For hiding messages different types of media are used. Audio steganography uses audio as the cover media. Commonly used techniques for audio steganography are temporal domain and transform domain techniques, where the frequency domain techniques and wavelet domain techniques come under transform domain. Under temporal domain the techniques include LSB encoding, parity coding and echo hiding. Under frequency domain the different techniques are tone insertion, phase coding and spread spectrum technique. This paper makes a discussion on audio steganography techniques. Among the techniques studied wavelet domain shows high hiding capacity and transparency. In wavelet domain different techniques are applied on the wavelet coefficients to increase the hiding capacity and perceptual transparency. The paper mainly concentrates on a survey on audio steganography in wavelet domain.

Knowledge about protein structure assignment enriches the structural and functional understanding of proteins. Accurate and reliable structure assignment data is crucial for secondary structure prediction systems. Since the '80s various methods based on hydrogen bond analysis and atomic coordinate geometry, followed by Machine Learning, have been employed in protein structure assignment. However, the assignment process becomes challenging when missing atoms are present in protein files. Our model develops a multi-class classifier program named DLFSA for assigning protein Secondary Structure Elements(SSE) using Convolutional Neural Networks(CNN). A fast and efficient GPU based parallel procedure extracts fragments from protein files. The model implemented in this work is trained with a subset of protein fragments and achieves 88.1% and 82.5% train and test accuracy, respectively. Our model uses only Cα coordinates for secondary structure assignments. The model is successfully tested on a few full-length proteins also. Results from the fragment-based studies demonstrate the feasibility of applying deep learning solutions for structure assignment problems.

No abstract

No abstract

No abstract