Novel mechanism for evaluating feedback in the grid environment on resource allocation

The primary concern in proffering an infrastructure for general purpose computational grids formation is security. Grid implementations have been devised to deal with the security concerns. The chief factors that can be problematic in the secured selection of grid resources are the wide range of selection and the high degree of strangeness. Moreover, the lack of a higher degree of confidence relationship is likely to prevent efficient resource allocation and utilization. In this paper, we propose an efficient approach for the secured selection of grid resources, so as to achieve secure execution of the jobs. The presented approach utilizes trust and reputation for securely selecting the grid resources by also evaluation user’s feedback on the basis of the feedback already available about the entities. The proposed approach is scalable for an increased number of resources

Securing XML Web Services using enhanced Elliptic Curve Cryptographic signature for e-business transactions

Web security using Extended Markup Language (XML) has become a standard for Ebusiness processing. Though XML Keyword Search (XML-KS) in web identifies the user search intention via node query and ranked the results of the queries in an efficient manner, but it failed to handle web security by conforming to a highly recursive schema. Personalized Ontology Model (POM-WIG) learns ontological user profiles for personalized Web Information Gathering using multidimensional ontology mining method, but failed to attain XML security on existing web documents. The XML Signature is also not covered with ECC (Elliptic Curve Cryptography) based algorithms in XML-KS. To address the problem, we present an XML based web security with key values, Enhanced ECC (XML-Enhanced ECC) mechanism in this paper. Initially, XML-Enhanced ECC allows signing in multiple tags for a specific XML document. Compound (i.e.,) multiple signature processing is carried out using the hash values of the information with the procedures, structure and processing resulting in the increased system utility ratio. The signature processing with the key values handle the web security using the high recursive schema resulting in higher precision rate. Secondly to compute the hash value for multiple signatures, Enhanced Elliptic Curve Cryptography Algorithm is developed in the encryption side. The ECC algorithm is enhanced by extracting the palm print as the primary signature information. XML-Enhanced ECC sign takes the information as the child element and XML signature group as the parent element. The information is enveloped with the "Enhanced ECC Palm print Signature Tag". Finally, Enhanced ECC Signature provide optimal element at the receiver (i.e.,) decryption side with appropriate key value that efficiently validates the signature and fetch the original information by minimizing the decryption time. XML-Enhanced ECC Sign attains improved security on web while performing e-business transactions. Web security using XML attains effective result on several metrics such as web security level, precision, recall, system utility ratio, and decryption time

EFFICIENT SCHEDULING STRATEGY USING COMMUNICATION AWARE SCHEDULING FOR PARALLEL JOBS IN CLUSTERS

ABSTRAC

ABNORMAL GAIT CLASSIFICATION USING SILHOUETTES

ABSTRACT This paper proposes a new methodology to classify the person with normal walk or abnormal walk for surveillance purposes. Recognizing human walk is emerging as a critically important biometrics, challenging computer vision problem. However, the inclusion of abnormal gait dataset with normal gait databases has to be very useful to classify the normal and abnormal walking style of a person. The silhouettes are trained and tested with K nearest neighbor classifier. We introduce a more challenging abnormal walk patterns like Antalgic gait, Charlie chaplin gait, steppage gait, scissor gait, circumduction gait, inclusive with normal gait data base. The database consists of about 5000 frames with 5 different walk styles. Manual selection of persons with different walking styles resulted in high degree of variability in pose and illumination. The method starts with the extraction of human silhouettes from input videos. Initially the continuous input videos are converted into frame-by-frame by means of conversion algorithm. Each frame consists of noises and shadows. Then silhouettes are removed from noises and discontinuities to produce an abnormal gait database. From the gait data base, parameters are measured by segmenting into six portions from head to neck, neck to torso, hip to knee of both right and left leg, knee to toe of both legs, height of the blob and width has also taken as features for training. The same features extracted with test data has to be compared with trained data for classification. The proposed methodology achieves 77% classification rate for abnormal gait