A novel Analysis of Image Forgery Detection Using SVM

This paper deals with basic information regarding the face recognition and whole parameters that effects the face structure and face shape. For the calculation of age, clients utilize age function combined with aging way. Face recognition is most difficult field of pattern recognition, however research in this field almost attains constancy with new difficulties emerges with time, and the research again towards the problem encounters due to aging, an automatic age technique utilized for strong face recognition is given briefly. Then user use age, commonly vector generating function or feature vector of real image to create synthesized feature vectors at target age. User uses a structure and texture vectors to show a facial image by projecting it in Eigen space of shape or texture. Images in courtrooms for evidence, graphics in newspapers and magazines, and digital graphics used by doctors are few instances that needs for pictures and not using a manipulation. Earlier, SVM algorithm failed in many instances in detection of forged picture. For the reason that single characteristic extraction algorithm, just isn#39t capable to include the certain function of the pictures. So you can overcome drawbacks of existing algorithm. We can use meta-fusion technique of HOG and Sasi elements classifier also to beat the drawback of SVM classifier.nbs

A machine learning based personalized system for driving state recognition

Reliable driving state recognition (e.g. normal, drowsy, and aggressive) plays a significant role in improving road safety, driving experience and fuel efficiency. It lays the foundation for a number of advanced functions such as driver safety monitoring systems and adaptive driving assistance systems. In these applications, state recognition accuracy is of paramount importance to guarantee user acceptance. This paper is mainly focused on developing a personalized driving state recognition system by learning from non-intrusive, easily accessible vehicle related measurements and its validation using real-world driving data. Compared to conventional approaches, this paper first highlights the necessities of adopting a personalized system by analysing feature distribution of individual driver’s data and all drivers’ data via advanced data visualization and statistical analysis. If significant differences are identified, a dedicated personalized model is learnt to predict the driver’s driving state. Spearman distance is also drawn to evaluate the differences between individual driver’s data and all drivers’ data in a quantitative manner. In addition, five categories of classifiers are tested and compared to identify a suitable one for classification, where random forest with Bayesian parameter optimization outperforms others and therefore is adopted in this paper. A recently collected dataset from real-world driving experiments is adopted to evaluate the proposed system. Comparative experimental results indicate that the personalized learning system with road information significantly outperforms conventional approaches without considering personalized characteristics or road information, where the overall accuracy increases from 81.3% to 91.6%. It is believed that the newly developed personalized learning system can find a wide range of applications where diverse behaviours exist

Comprehensive Review on Detection and Classification of Power Quality Disturbances in Utility Grid With Renewable Energy Penetration

The global concern with power quality is increasing due to the penetration of renewable energy (RE) sources to cater the energy demands and meet de-carbonization targets. Power quality (PQ) disturbances are found to be more predominant with RE penetration due to the variable outputs and interfacing converters. There is a need to recognize and mitigate PQ disturbances to supply clean power to the consumer. This article presents a critical review of techniques used for detection and classification PQ disturbances in the utility grid with renewable energy penetration. The broad perspective of this review paper is to provide various concepts utilized for extraction of the features to detect and classify the PQ disturbances even in the noisy environment. More than 220 research publications have been critically reviewed, classified and listed for quick reference of the engineers, scientists and academicians working in the power quality area