Informatics Publishing Limited

S. Uma Maheswari ¹, R. Ramakrishnan ²

Affiliations
1 Tamilnadu Physical Education and Sports University, Chennai, IN
2 Department of Advanced Sports Training & Technology, Tamilnadu Physical Education and Sports University, Chennai, IN

Abstract

Objectives: In order to achieve convenient sports video accessing without sequential scanning, automated sports video categorization is presented in this study. Methods/Analysis: In order to build efficient sports video categorizing system, edge features obtained from Non Subsampled Shearlet Transform (NSST) are taken into account. Then, sports genre categorization is done by Nearest Neighbor (NN) classifier due to its discriminative learning approach. The five sports category; tennis, cricket, volleyball, basketball and football are considered. Findings: To validate the proposed system based on NSST, experiments are carried using internal database video at frame level. Totally, 500 video clips are collected in which 100 video clips are gathered for each sports genre. The proposed system achieves maximum average classification accuracy of 94.80% at 4 directions of 2-scale NSST features while using city block distance measure in KNN classifier. For the same NSST features, the Euclidean, cosine and correlation distance measures gives an accuracy of 93.20%, 92.80% and 92% respectively. Conclusion/Application: The effectiveness of the system is clearly demonstrated by the experimental assessment. The proposed framework can adequately classify the sports video into one of the five predefined genre.

Keywords

Edge Features, Nearest Neighbor Classifier, NSST, Shearlet Transform, Sports Video Classification.

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S. Uma Maheswari ¹, L. Uthira ², Aruna ²

Affiliations
1 Department of Biochemistry, PSG Institute of Medical Science and Research, Coimbatore, IN
2 Department of Nutrition and Dietetics, PSG College of Arts and Science, Coimbatore, IN

Abstract

Exercise is accompanied by an elevated metabolic rate, which increases oxygen consumption in the celis leading to production of deleterious species such as reactive oxygen species (ROS) and free radicals (FR). The formation of ROS and FRs is directly related to the intensity and duration of exercise and these elicit a series of chemical reactions resulting in peroxidation of lipids and proteins which is potentially harmful as it disrupts cell membranes and cellular components. These disturbances of intracellular pro-oxidant, antioxidant homeostasis is known as oxidative stress.

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S. Uma Maheswari ¹, A. Shamila Ebenezer ¹

Affiliations
1 Karunya University, Coimbatore, Tamil Nadu, IN

Abstract

Besides the dynamic nature of grids which means that resources may enter and leave the grid at any time, in many cases outside of the applications control, grid resources are also heterogeneous in nature. Many grid applications will be running in environments where interaction faults are Fault more likely to occur between disparate grid nodes. As resources may also be used outside of organizational boundaries, it becomes increasingly difficult to guarantee that a resource being used is not malicious. Due to the diverse faults and failure conditions, developing, deploying, and executing long running applications over the grid remains a challenge. So Fault-tolerant scheduling is an imperative step for large-scale computational Grid systems, as often geographically distributed nodes co-operate to execute a task. One Motivation of Grid computing is to aggregate the power of widely distributed resources, and provide non-trivial services to users. To achieve this goal, an efficient Grid scheduling system is an essential part of the Grid. This paper presents an extensive survey of different fault tolerant scheduling Technique such as Distributed Fault Tolerant Scheduling (DFTS) algorithm, Volunteer Availability based Tolerant Scheduling (VAFTS) algorithm, A Reliability Cost Driven (RCD) Scheduling, A Dynamic Reliability-Cost-Driven (DRCD) Scheduling Algorithm, An Efficient fault-tolerant scheduling algorithm (eFRD), contention-aware fault-tolerant (CAFT) scheduling algorithm, eFRCD (efficient Fault-tolerant Reliability Cost Driven Algorithm).

Keywords

Fault, Fault-Tolerance, Fault Tolerant Scheduling, Single Resource Manager, Job Placement, Replica Management.

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S. Uma Maheswari ¹, S. K. Srivatsa ²

Affiliations
1 Department of Information Technology, SCSVMV University, Enathur, Kancheepuram, Tamilnadu, IN
2 Department of ECE, Prathyusha Institute of Technology and Management, Chennai, Tamilnadu, IN

Abstract

Web usage mining is the application of data mining techniques to discover usage patterns from web data, in order to understand and better serve the needs of web-based applications. Web usage mining is parsed into three distinctive phases such as preprocessing, pattern discovery and pattern analysis. Analyzing data through web usage mining can help effective web site management, creating adaptive Web sites, business and support services, personalization, network traffic flow analysis and etc.The aim of this paper is to give web page recommendations with help of preprocessed, analyzed web log data and user profile. In this paper combined effort of clustering and association rule mining is applied for pattern discovery which helps in finding effective usage patterns. This recommendation system provides recommendations based on user's navigational patterns and gives suitable recommendations to cater to current needs of users. The experimental results performed on real usage data show a significant improvement in the recommendation effectiveness of the proposed system.

Keywords

Web Usage Mining, Web Log, Clustering, Association Rule Mining.

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S. Uma Maheswari ¹, S. K. Srivatsa ²

Affiliations
1 SCSVMV University, Kanchipuram, IN
2 Prathyusha Institute of Tech & Mgnt, Chennai, IN

Abstract

The web access log is the best repositories for the information source. It maintains the entire record of even a tiny low event. The web log updates each time a user starts a new session. Initially the log file contains each and every detail regarding the user, the Ip address, website name, time stamp and other details. The web usage pattern analysis is a method of distinguishing browsing patterns by analyzing the user's navigation and behaviour. The internet server log files that store the knowledge concerning the guests of internet sites is employed as input for the web usage pattern analysis method. It must to trace the visitors' on-line behaviors for website usage analysis. This paper reviews the method of Preprocessing that is helpful to take clear web log data from the online server log file. The preprocessed and analyzed results are used in many areas such as net traffic analysis, economical web site administration, website modifications, system improvement and personalization and business intelligence etc.

Keywords

Web Usage Mining, Web Log Data, Navigation Pattern, Prediction.

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D. Premnath , S. Uma Maheswari

Abstract

Digital Images are generally corrupted by Impulse noise during the image acquisition process, while Gaussian noise is encountered during transmission. These noises seriously affect the quality of images. It causes degradation of image spatial resolution, loss of image details and distortion of important image features. Therefore it is essential to correct corrupted pixels before using them in any applications. There are numerous approaches have been proposed to reduce these noises independently. Recently, a Switching Bilateral Filter algorithm is proposed which filters both noises using a single filter but with parameters different for Impulse and Gaussian noise.

 Switching Bilateral Filter (SBF) performances poorly for Impulse noise densities beyond 35%. In this thesis, the above algorithm has been modified to detect impulse noise even at high noise densities. The proposed filter is found to yield better quality images in terms of subjective quality and PSNR values compared to Switching Bilateral Filter.

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