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Ponmuthuramalingam, P.
- Students and Software Testing Professional’s Web Based Learning Standards, Styles and UI:A Case Study
Abstract Views :123 |
PDF Views:4
Authors
Affiliations
1 Government Arts College, Coimbatore, IN
2 Department of Computer Science, Government Arts College (Autonomous), Coimbatore, IN
1 Government Arts College, Coimbatore, IN
2 Department of Computer Science, Government Arts College (Autonomous), Coimbatore, IN
Source
Software Engineering, Vol 4, No 5 (2012), Pagination: 198-201Abstract
The World Wide Web has become firmly entrenched in our common life. Not only it is a space where people can speak, employment, and trade but it has also increasingly become a place to learn. The purpose of this paper was to examine how a particular technique of education can impact the helpfulness of an on-line or web based course in delivering subject content. The learning is supported into software testing professionals and students. The learning interfaces are spitted into six factors. Also the data show the infesting phenomena with respect to motivation, research and social aspects. The system filtered web pages based on the relevance of their contents and assisted to users for their learning style and recommending pages based on the page relevancy. This paper proposes the methodology of conducting a wiki websites in an improvement process.Keywords
Web Based Learning, Web Learning Standards, Web Learning Styles, User Interface, Wiki Web Site, Web Learning Functionalities.
- Interactive Image Segmentation using Improved Adaptive Markov Random Field Approach
Abstract Views :175 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science, Governments Arts College, Coimbatore – 641046, Tamil Nadu, IN
1 Department of Computer Science, Governments Arts College, Coimbatore – 641046, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 10, No 6 (2017), Pagination:Abstract
Background/Objectives: To interactively split an object of interest from the remaining image with better smoothing by introducing improved adaptive Markov Random field resolving the problem of much noise. Methods/Statistical analysis: By utilizing a Dirichlet process multiple - view learning scheme, the unlabelled pixel labels are calculated by using the seed pixels. It is used for supporting the multiple-view learning in order to incorporate the constraints of both appearance and boundary constraint, and the Dirichlet process mixture-based nonlinear classification for concurrently modelling the image features and distinguishing the differences between the classes of object and background. The MRF field is utilized for providing the smoothness in segment labels. Findings: In Markov Random Field (MRF) based scheme, only the pixel and the surrounding pixels relationships are considered. The microscopic image processing result along with low noise is bad. To solve this problem, the adaptive MRF method based on region; it exploits Graph Cuts for inference. The different type of images produces the different pre-segmentation results. The connection degree between current and its linked regional blocks is denoted by connection parameter. If the connection parameter value is high, large connection degree between regional block and neighbouring regional blocks is defined. Otherwise lower connection degree between regional block and neighbourhood regional blocks. The adaptive MRF smoothing is more accurate and efficient segmentation result than the traditional MRF based Smoothing method. However, the appropriate parameter selection is a difficult task for practical image segmentation which can be solved by introducing an improved adaptive MRF model by using a modified graph cutter. Thus, the image segmentation is achieved based on modified graph-cut model using a novel energy function without the regularizing parameter. Improvements/Applications: The improved adaptive Markov Random Field approach interactively segment images with better smoothness than most of the current approaches.Keywords
Boundary Constraints, Interactive Segmentation, Markov Random Field.Full Text
- Erudite Fish Swarm Optimization Based Routing Protocol to Maximize Wireless Sensor Network Lifetime
Abstract Views :183 |
PDF Views:1
Authors
Affiliations
1 Department of Computer Science, Government Arts College, Coimbatore, Tamil Nadu, IN
2 Regional Joint Directorate of Collegiate Education, Madurai Region, Madurai, Tamil Nadu, IN
1 Department of Computer Science, Government Arts College, Coimbatore, Tamil Nadu, IN
2 Regional Joint Directorate of Collegiate Education, Madurai Region, Madurai, Tamil Nadu, IN
Source
International Journal of Computer Networks and Applications, Vol 9, No 3 (2022), Pagination: 305-315Abstract
Wireless Sensor Networks (WSNs) are an influential network form that comprises remote nodes having sensing, processing, and communication capabilities. WSN is a unique ad-hoc network with a wireless telecommunications infrastructure that effectively supports, observes, and responds to natural and artificial events. It is impossible to employ the ad-hoc network routing methods in sensor networks since they are not scalable. WSN relies on the routing protocol to get data from sensors to their final destination in a timely way. If the routing protocol fails to work, then it is expected that a significant amount of time and effort will be spent finding the most efficient route, increasing the likelihood that the worst possible option will be selected. Because of this, WSN routing protocols must include the concept of "erudite" features, which refers to a high degree of sensing of the nodes around them to determine the optimum path. Fish swarm optimization is the basis of the new WSN routing protocol proposed in this paper, namely the Erudite Fish Swarm Optimization Based Routing Protocol (EFSORP). In EFSORP, nodes are treated as fishes. Nodes having prior knowledge about routes are selected at random. Foraging, following, swarming, and random movement is four of the most common behaviors of fishes while seeking food. These behaviors are mimicked to identify the best routes in WSN. EFSORP’s performance is evaluated in NS3. A wide range of necessary computer network performance measures are used to assess EFSORP against existing routing protocols. EFSORP's results show that it outperforms the current routing protocols on all measures.Keywords
Routing, WSN, Energy, Delay, Fish, Optimization.Full Text
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