Abhishek Vaish

3 dimensional security in cloud computing

Cloud computing is emerging field because of its performance, high availability, least cost and many others. Besides this companies are binding there business from cloud computing because the fear of data leakage. Due lack of proper security control policy and weakness in safeguard which lead to many vulnerability in cloud computing.

Brainwave based user identification system

The world is seeing the emergence of brainwave computing and its application in our daily communication and security is on the horizon. One of the interesting areas of interaction modality is robotic environment where involvement of illegitimate users may cause severe threat to environment thereby, a continuous monitoring is mandatory. In this paper, we have presented a study of potential of brainwaves and their computing for providing higher security. This work utilizes EEG signal generated by visual stimuli for keying in the password. In order to construct a model for user authentication, we have selected the time-frequency analysis method called wavelet analysis to decompose original signal corresponding to EEG sub-band frequency, thereby extracting statistical measures and energy calculation of each decomposed waves. Furthermore, we have selected and employed the neural network based on learning vector quantization for correct classification. Classification rate has been calculated over the different combination of channels. User authentication in robotics environment.EEG signal feature extracting using wavelet transform.LVQ-neural network for classification.Performance calculation based on the number of channels.

Brainwave's energy feature extraction using wavelet transform

Brainwaves has an important security and communication implication. Nevertheless, the reliability of the methods for discrimination between genuine and imposter is still in infancy. Efforts to enhance the reliability have exact identification using EEG. As EEG is non-stationary signal, the use of the joint time-frequency feature may be yield more reliable results. In this paper we have used cerebral region of central channel of brain waves named as CZ and used three wavelet decomposition functions Symlet, Daubechies and Coifet to investigate the potential of energy features such as Recoursing Energy Efficient (REE), Logarithmic REE and Absolute LREE for subject differentiation.

Information-Theoretic Measures on Intrinsic Mode Function for the Individual Identification Using EEG Sensors

In spite of recent advances, the interest in extracting knowledge hidden in the electroencephalogram (EEG) signals is rapidly growing, as well as their application in the computational neuroengineering field, such as mobile robot control, wheelchair control, and person identification using brainwaves. The large number of methods for the EEG feature extraction demands a good feature for every task. Digging up the most unique feature would be worthy for the identification of individual using EEG signal. This research presents a novel approach for feature extraction of EEG signal using the empirical mode decomposition (EMD) and information-theoretic method. The EMD technique is applied to decompose an EEG signal into a set of intrinsic mode function. These decomposed signals are of the same length and in the same time domain as the original signal. Hence, the EMD method preserves varying frequencies in time. To measure the performance of the features, we have used hybrid learning for classification where we have selected learning vector quantization neural network with fuzzy algorithm. In order to test the performance of proposed classifier based on fuzzy theory, we have tested classification accuracy of each cognitive task over all participated subjects. The results are compared with the past methods in the literature for feature extraction and classification methods. Results confirm that the proposed features present a satisfactory performance.

Feature extraction using emprical mode decomposition for biometric system

Biometric system is one of the most popular tool for user identification where security is primary concern such as Banks, airports and companies etc. Few of biometrics tool such as fingureprints, face and Iris are frequently using for user identification, nevertheless its flaw like mimic the original data makes it weak. Hence, there is another biometric has been emerged based on EEG where mimicry of data is quite impossible because it depends on users thought. The following article presents a new and unique template using empirical mode decomposition as a feature for classification each from another one. In order to do this work we have taken four regions of the brain such as frontal, cerebral, parietal, and occipital and we have observed that each and every individual has unique pattern.

Feature-level fusion of mental task's brain signal for an efficient identification system

Abstract In this research, we have explored the canonical correlation analysis (CCA) to improve the performance of the identification system that involves multiple correlated modalities. In particular, we consider the electroencephalogram signal of different mental task performed by the subject such as breathing, mental mathematics, and geometric figure rotation, visual counting and mental letter composing. Our motivation based on the fusion of feature vector of mental task using canonical correlation analysis, where feature set extraction using empirical mode decomposition and information theoretic measure and statistical measurement. In order to classify the fused vector from different mental, we have used linear vector quantization (LVQ) neural network and its extension LVQ2. The results of the experiments testing the performance have been evaluated with two profiles of the database. We have observed canonical correlation-based fusion providing the better results in comparison with simple fusion rule. The novelty of this research is the new feature generation using fused feature of distinct mental task based on CCA.

A Comparative Study on Machine Learning Algorithms in Emotion State Recognition Using ECG

Human-Computer-Interface (HCI) has become an emerging area of research among the scientific community. The uses of machine learning algorithms are dominating the subject of data mining, to achieve the optimized result in various areas. One such area is related with emotional state classification using bio-electrical signals. The aim of the paper is to investigate the efficacy, efficiency and computational loads of different algorithms scientific comparisons that are used in recognizing emotional state through cardiovascular physiological signals. In this paper, we have used Decision tables, Neural network, C4.5 and Naive Bayes as a subject under study, the classification is done into two domains: High Arousal and Low Arousal.

ARIMA model based breast cancer detection and classification through image processing

Computer Aided Diagnosis (CAD) has changed the way of medical diagnostics. As similar to other walk of diagnostics field, CAD is having high potential in breast cancer prognosis because of its highest accuracy. CAD may play a very important role in developing countries i.e. EIT-MEM (Electrical Impedance Tomography - Multi-frequency Electrical Impedance Mammography) device being used for breast cancer defection. MEM-EIT produces tomography based mammograms which are considered most reliable method of early detection of breast cancer. Cancer diagnostic expert all over the world find this noninvasive technique very accurate as it is one dimensional representation of images in terms of temperature however the accuracy is limited and investigator fail to take into account the spatial co-ordination between the pixels which is crucial in cancerous tumour detection and their classification (cancerous or normal) in EIT (Electrical Impedance Tomography) - based mammogram images. In this study, we are trying to focus an algorithms based CAD (Computer Aided Diagnosis) model for tumour detection and classification. We model it by ARIMA model (autoregressive integrated moving average (ARIMA) model) and parameter estimation will be performed using leas-square method. Our system classifies the tumour into three categories - (i) healthy tissue (ii) benign tissue (iii) cancerous tissue along with above three segments the performance analysis between 2D image and 1D image will be done for better accuracy and sensitivity detection.

Quantifying Virality of Information in Online Social Networks

The aim of this research is to propose a model through which the viral nature of an information item in an online social network can be quantified. Further we propose an alternate technique for information asset valuation by accommodating virality in it which not only complements the existing valuation system, but also improves the accuracy of the results. We used a popularly available YouTube dataset to collect attributes and used it to measure critical factors such as share-count, Appreciation, User rating, Controversiality and Comment rate. These variables are then used with a proposed formula to obtain viral index of each video on a given date. We then identify a conventional and a hybrid asset valuation technique to demonstrate how virality can fit in to provide accurate results. The research demonstrates the dependency of virality on critical social network factors. With the help of second dataset acquired by us, we determine the pattern virality of an information item takes over time. The findings provide a clear cut manifestation for the practitioner or researcher to utilize the model in real-world scenario.

Child Security in Cyberspace Through Moral Cognition

The increasing number of threats in cyberspace has meant that every internet user is at a greater risk than ever before. Children are no exception to this exploitation, incurring psychological and financial stress. Technology is on a persistent pursuit of offering exquisite solution to address the problems associated with children on the cyberspace. With every new product for parental control to secure children, comes a new technique to trespass the same. Consequently it summons an approach to look beyond technology; this paper aims to explore the relevance of moral cognition to decision making capability of children on the internet & the possibility of minimizing related risks using the observation. The authors establish a correlation between cognitive moral development and the cyber vulnerability level of children of age between 12 and 16 years, based on an empirical research using a comprehensive set of questionnaires and standard tests. The findings also paves path for future researchers to further analyze and implant features in the parental control software that would stimulate moral cognition, thereby redefining parental control software as parental care software.