An Efficient Image Forgery Detection Using Biorthogonal Wavelet Transform and Improved Relevance Vector Machine

Nowadays, the development of refined image processing and software editing tools has finish the exploitation of digital images easily and invisible the image to the normal eyes and this process known as image fakery. Image security is one of the key issues in any field that makes use of digital images. Copy-move forgery (CMF) is the most effective and simple scheme to create forged digital images. In general, the methodologies based on Scale Invariant Feature Transform (SIFT) are widely used to detect CMF. Unfortunately, the detection performance of all SIFT based CMF detection approaches are extremely dependent on the selection of feature vectors. The values of these parameters are often determined through experience or some experiments on a number of forgery images. However, these experience parameter values are not applicable to every image thereby offers a limited usefulness. This paper deals the CMF problem using improved Relevance Vector Machine technique. The key idea of the IVRM is to apply Biorthogonal Wavelet Transform based scheme on image for feature extraction. The feature vectors are then stored lexicographically and similarity of vectors is decided using Minkowski distance and threshold value. The simulation results of proposed technique show a significant improvement in accuracy, sensitivity, and specificity rates over others existing schemes.     

Providing security and privacy to huge and vulnerable songs repository using visual cryptography

Multimedia Tools and Applications - In the today’s scenario, the number of online song repositories such as iTunes, Hungama.com , etc. is increasing day-by-day. The reason for this can be...     

Guest editorial–Special issue on ground control in mining in 2017

正Ground control is the science of studying and controlling the behavior of rock strata in response to mining operations.Ground control-related research has seen significant advancements over the last 36 years,and these accomplishments are well documented in the proceedings of the annual International Conference on Ground Control in Mining(ICGCM)[1].The ICGCM is a forum to promote closer communication among researchers,consultants,     

Multi-resolution texture analysis for fingerprint based age-group estimation

Multimedia Tools and Applications - In this paper the possibility of using digital fingerprints to estimate age-groups of human being, particularly children is investigated. To our knowledge,...     

Energy efficient cluster head formation in wireless sensor network

Microsystem Technologies - In clarification of wireless sensor network (WSN) clustering process at household and overseas, an energy efficient cluster head formation technique for WSN are set forth...     

Role of digital,hyper spectral,and SAR images in detection of plant disease with deep learning network

In agriculture, plants plays a major role and taking attention of plants is very critical. Generally, the plant are affected through various diseases like fungi, virus and bacteria. Finding of these diseases are main challenging task for a plant disease identification and classification. In the past few years, machine learning (ML) methods have been developed for the plant disease detection. But, the advancement in a subsection of ML, that is, DL (deep learning) models provide a great solution in the agricultural areas in the recent decades. The main objective of the paper is to provide the survey of numerous DL classification models for the plant disease detection by analysing the digital, hyper spectral and SAR images. This paper provide the review of different deep learning architectures which is utilized for plant disease identification and classification. The role of digital, hyper spectral and SAR images with deep learning models for plant disease detection is reviewed. Further, the different well-known DL architecture for plant disease classification is studied. In addition, the current challenges and their solutions of plant disease identification are discussed. Also, the application of DL and advantages/disadvantages of DL structure in plant domain are presented. Finally, the future scope of DL structure for plant domain is discussed. The preparation of this review is to permit future research to learn higher competences of deep learning while identifying plant diseases by enhancing system performance and accuracy.

     

Numerical Study of a Transonic Aircraft Wing for the Prediction of Flutter Failure

In the present paper, computational analysis has been carried out to assess the coupled fluid–structure interaction using NASTRAN finite element approach. A straight swept wing of aluminum material is studied at transonic zone. Analysis has been carried out to find the natural frequency by fluid–structure interaction, then adopting its natural frequency to calculate the reduced frequency for analyzing the flutter effectiveness. A typical case study of plate has been carried out for better understanding the flutter which was then adopted for the swept wing. A fluid–structure interaction phenomenon provides an additional energy to the moving object in terms of frequency in transonic zone. In this speed zone, the divergence speed results a drag that leads to the object to be in a stronger twisting mode resulting in catastrophic failure of the aircraft. The study has defined the flutter boundary of the wing in terms of velocity and frequency which will be very useful in preventing the flutter failure of the aircraft wing through appropriate design improvement or through restriction operational regime.     

Corrosion Inhibition effect of Clerodendron Colebrookianum Walp Leaves (Phuinam) Extract on the Acid Corrosion of Mild Steel

Protection of Metals and Physical Chemistry of Surfaces - The adsorption and anticorrosion effect of crude methanolic extract of Clerodendron colebrookianum walp leaves (CCWL) were investigated...