Informatics Publishing Limited

S. Muruganandam ¹, N. Srinivasan ¹

Affiliations
1 Sathyabama University, Rajiv Gandhi Road, Jeppiaar Nagar, Chennai – 600119, Tamil Nadu, IN

Abstract

Objectives: E-learning is adapted to suit the different heterogeneous learners with different leaning styles. Felder and Silverman have given a catalogue of learning styles (FSLS). This paper is indented to analyze the reliability of their recommended learning styles. Method: Personalization is the latest improvement in E-learning system. Personalized E-learning system (PES) is suggested as the next generation of E-learning system. Various factors are analyzed to address the prediction of user's preferences. Learning styles is main deciding factor of personalizing E-leaning system. Most of the personalized models personalizing on learning styles have used FSLS. Previous research works performs the analysis of FSLS with single set of data. In this paper, the model is tested with two distinct of data using Karl's Pearson Coefficient method. To carry out analysis, adaption model is developed with FSLS using JAVA language. Findings: The observation of Appraisal of FSLS model states that it is strongly accepted by one set of data and there is small divergence in another set of data. Applications/Improvement: The study can be expanded with larger set of data and more than two distinct set of data.

Keywords

Learning Styles, Learning Style Preferences, Pearson Correlation Coefficient, Technology-Enhanced Learning

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S. Muruganandam ¹, S. Pugazhenthi ²

Affiliations
1 School of Mechanical Engg., SASTRA University, Thanjavur, IN
2 School of Mechanical Engg., SASTRA University, Thanjavur, IN

Abstract

The workspace and stiffness of hexapod machine tool are configuration dependent and vary dynamically. Hence, work piece is to be located in such a way that the hexapod will have high stiffness and required workspace area. This paper presents a methodology for finding the maximum stiffness of the hexapod machine tool, which in turn can be used to find the optimal location of the work piece. For finding the location of maximum stiffness, Particle Swarm Optimization (PSO) algorithm is used.

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K. Mohan ¹, S. Lakshmi ², M. Sumithra ³, S. Muruganandam ¹

Affiliations
1 Department of Computer Science & Engg., Thirumalai Engineering College, Kanchipuram, IN
2 Department of Electronics and Comm. Engg., Thirumalai Engineering College, Kanchipuram, IN
3 Department of Computer Science & Engg., Jei Mathaajee College of Engineering, Kanchipuram, IN

Abstract

Steganography is the art of hiding the fact that communication is taking place, by hiding information in other information. Many different carrier file formats can be used, but digital images are the most popular because of their frequency on the Internet. For hiding secret information in images, there exists a large variety of steganographic techniques some are more complex than others and all of them have respective strong and weak points. Different applications have different requirements of the steganography technique used. In this paper presents hiding the data behind the image then encrypt the image by using hyper-chaos encryption algorithm. In this algorithm, shuffling matrix and diffusing matrix are generated based on Chen’s hyper-chaotic system. Firstly, the Chen’s hyper-chaotic system is used to shuffle the position of the image pixels, and then use Chen’s hyper-chaotic system to confuse the relationship between the original image and the cipher image.

Keywords

Hyper-Chaos, Steganography, Hiding Data.

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