Informatics Publishing Limited

Harini Jayaraman ¹, B. Poornima ², A. Arunkumar ³

Affiliations
1 Language Acquisition, LAMED, Chennai, ID
2 Language Acquisition, LAMED, Chennai, IN
3 LAMED, Chennai, IN

Abstract

Each student of foreign language has a different level of motivation to learn the language. This paper examines the motivation level of 191 students (between the ages of 15 and 55 years) of an international French language and culture institution. A self-reported questionnaire by Vivian Cook was used for the assessment. Five dimensions of language motivation were measured; Self-Image, Inhibition, Risk Taking, Ego Permeability and Ambiguity. The average score that the participant obtained in these five dimensions was termed the 'overall language motivation score'. Motivation levels were classified as high (48-64), above average (36-47), average (16-34) and low (below 15). The data was collected during the period of March-May 2015 and analyzed using SPSS 20. Results showed that motivation levels were; high (n=0), above average (n=35, 18.3%), average (n=155, 81.1%) and low (n=1, 0.5%). The study revealed that the majority of students lack overall motivation in learning the language, which is a factor for increasing attrition rates in higher levels of language learning. Foreign language learning classrooms should concentrate on reinforcing the intrinsic motivation through techniques that extrinsically motivate students. Further studies are required to qualitatively analyze the motivating factors and the reasons for attrition.

Keywords

Second Language, Motivation, Language Acquisition, Chennai.

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Y. Ramadevi ¹, T. Sridevi ¹, B. Poornima ¹, B. Kalyani ¹

Affiliations
1 Department of CSE, Chaitanya Bharathi Institute of Technology, Gandipet, Hyderabad, IN

Abstract

Image segmentation is to partition an image into meaningful regions with respect to a particular application. Object recognition is the task of finding a given object in an image or video sequence. In this paper, interaction between image segmentation (using different edge detection methods) and object recognition are discussed. Edge detection methods such as Sobel, Prewitt, Roberts, Canny, Laplacian of Guassian(LoG) are used for segmenting the image. Expectation-Maximization (EM) algorithm, OSTU and Genetic algorithms were used to demonstrate the synergy between the segmented images and object recognition.

Keywords

EM Algorithm, OSTU, Genetic Algorithm, Image Segmentation, Object Recognition.

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M. P. Pavan Kumar ¹, B. Poornima ², H. S. Nagendraswamy ³, C. Manjunath ⁴, B. E. Rangaswamy ⁵

Affiliations
1 Department of Information Science and Engineering, Jawaharlal Nehru National College of Engineering, IN
2 Department of Information Science and Engineering, Bapuji Institute of Engineering and Technology, IN
3 Department of Studies in Computer Science, University of Mysore, IN
4 Department of Mechanical Engineering, Jawaharlal Nehru National College of Engineering, IN
5 Research and Development, Bapuji Institute of Engineering and Technology, IN

Abstract

The paper deals with NPR filtering and image processing techniques to produce the structure preserving image abstraction and artistic stylization effect from complex background and low illuminated images. Structure preserving image abstraction and stylization is most useful in the animation process, film industry, and artistic illustration and education sectors for innovative teaching. Abstraction concept reduces the image complexity and Stylization produces good visual effect to human’s sense. The work involves combining different NPR filtering techniques to create an effective NPR artistic illustration. The proposed technique consists of adoptive structure tensor flow, difference of Gaussian filter, 2D modified coherence shock filter, order dithering and Mean Curvature Flow (MCF). The work involves applying all these techniques in a series and the proposed scheme is found to give a good rendering effect on images with complex background and low luminance images. Moreover the proposed method does not require any kind of post processing techniques for abstraction and artistic stylization. The applied method produces the best abstraction effect and avoids halo effect. Implementation of proposed work is carried out in the Matlab environment. Efficiency of proposal work has been corroborated by conducting different experiments on various types of images and the results are compared with contemporary works. This approach is found to be computationally efficient in rendering effective structure preserving abstraction and stylization to the Human Visual System (HVS) and this approach opens up new research paths towards image and video stylization.

Keywords

Non-Photorealistic Rendering, Adoptive Structure Tensor Flow, Mean Curvature Flow, Order Dithering, Difference of Gaussian Filter, Shock Filtering.

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B. Poornima ¹

Affiliations
1 Seshachala Degree & P.G. College, Puttur, Andhra Pradesh, IN

Abstract

The development of proficient and successful decision trees stays a key theme in machine learning on account of their effortlessness and adaptability. A great deal of heuristic calculations has been proposed to build close ideal choice trees. The traditional decision tree calculations and the split measures they utilized are entropy, Gain Ratio and Gini list individually. In this paper, we introduced a conventional correlation of the conduct of two of the most well-known split capacities, to be specific the Gini Index and entropy. The target of this paper is to distinguish and investigate these imperative standards’ or elements of decision tree calculation for Wisconsin Breast cancer growth expectation. The significant commitment of this examination work is to give a way to choose a particular parting factor for the development of decision tree calculation according to necessity or issue. Trial results indicated that utilizing the decision tree calculation with the entropy parting technique accomplished higher grouping precision than Gini list strategy.

Keywords

Breast Cancer, Data Mining, Decision Tree, Entropy, Gini.

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