IJCTA-Volume 8 Issue 5 / September-October 2017
Title/Author Name
Page No
A Survey on Access Control Mechanism used In Cloud Computing
-Anjali soni,Dr.Sanjay Silakari,Prof.Uday Chaurasia
This paper deals with numerous access control mechanisms that square measure present in cloud computing. Cloud computing is that the rising technology wherever resources square measure offered pay as you go basis. Cloud storage technology provides the big pool of storage capability to the cloud users. Providing security to the info keeps in cloud is that the major concern. Thus Security is increased by providing access control to the approved users. Access control provides the authorization to the users which supplies the access privileges on knowledge and different resources. Access control is enabled in most of the computing atmosphere like Peer to look, Grid and Cloud. Cloud storage services square measure accessed through a cloud storage entryway. We have a tendency to present numerous kinds of access control mechanisms that square measure utilized in cloud computing atmosphere.
Keywords—Access control, DAC, MAC, RBAC, ABE and HABE.
An Offline Arabic Handwritten Character Recognition System using Template Matching
-Saud M. Maghrabi
This paper proposes computerized offline handwritten Arabic text recognition method using template matching technique based on 2-D normalized cross-correlation. The objective of this research is to find efficient and accurate handwriting Arabic text recognition algorithm, which can accept handwriting input and recognize handwritten character entered in the computer using template matching technique. The recognition process consists of five stages: input capture, image preprocessing, line segmentation, feature extraction, character recognition. These stages are implemented in MATLAB R2017a version. Experimental results demonstrate the recognition adequacy isolated test dataset with a general exactness of 97% for Arabic handwritten characters.
Keywords : Template matching, Normalized Cross-Correlation, Arabic handwriting recognition
Role of Ethnography in Research in Computer Application
-Dr. Sampada Gulavani,Dr. R.V. Kulkarni
Ethnography is description of people or culture and record data about the culture being studied. Ethnography is undertaken in order to produce a theory or it can be served as a test-bed for theories. For ethnography, researcher uses different data generation methods like interview, observation and field notes. In ethnography research, researcher prefers field notes which contain substance, methodology and analysis which acts as a source of evidence and a basis for data analysis in the writing up of the ethnography. Type of ethnographer to be selected is a choice of researcher. Ethnography is said to be successful, if its readers are able to understand the activities of people in other culture and see that they make sense within the context of that culture. By applying ethnographic methods in computer application requires a careful design so that they are implemented correctly and important aspects are highlighted in the results.
Keywords : Research, Action research, ethnography, types of ethnography, computer application
Object Recoginition using Legendre Moment Invariants for ROBOTIC Applications
-Dr. A. Venkataramana,S. M. Prasad
One of the important research areas in the field of robotics is the recognition of objects which are invariant under object plane transformations such as scale, translation and rotation. This paper presents object recognition system using Legendre moment invariants as features and back propagation neural network classifier for classification. Legendre moment invariants are selected for feature extraction of objects because they are orthogonal and are also invariant with respect to object plane transformations. Due to the invariant properties, the extracted features can be used to identify objects under different changes in scale, translation and rotation. The performance of back-propagation neural network is better than that of linear classifiers. Hence, back propagation neural network is used as classifier in the proposed approach. From the simulation results, it is observed that this approach provide better recognition rate.
Key Words: Object recognition, Legendre moments, Moment invariants, Feature extraction
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