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Jayasimman, L.
- A Study on the User Interface Design for a Multimedia Learning System with Emphasis on 3D Animation
Abstract Views :205 |
PDF Views:2
Authors
Affiliations
1 E.S. Pan Asian College of Art & Animation, Villupuram, TN, IN
2 J.J. College of Engineering & Technology, Trichy, Tamil Nadu, IN
1 E.S. Pan Asian College of Art & Animation, Villupuram, TN, IN
2 J.J. College of Engineering & Technology, Trichy, Tamil Nadu, IN
Source
Programmable Device Circuits and Systems, Vol 4, No 2 (2012), Pagination: 71-75Abstract
This paper presents the experiments and makes a research on Effective User interface design of a Multimedia learning system. An experiment has been conducted to study the use of 3D Animation in the effective user interface design. The purpose of the research reported in this paper was to find out the use of 3D animation for developing an effective user interface design. It has been realized that there is a strong need for voice support, text and 3d animation for the effective user interface design to put the system highly utilizable and practicable. We have used Autodesk MAYA software learning edition to develop the multimedia learning system. This research examines the usability of multimedia learning system improvement with the help of usability analysis checklists. The ultimate objective of this study is to bring out the users understanding level and comforting level as well as satisfaction level due to the presence of 3d animation and attractive graphics for a multimedia learning system.Keywords
Multimedia Learning System, User Interfaces Design, 3D Animation, Usability Analysis, Autodesk MAYA Software.
- A Hybrid Classification Algorithm for Web Based Learning
Abstract Views :327 |
PDF Views:6
Authors
Affiliations
1 Department of Computer Application, J.J.College of Engineering and Technology, Trichy, IN
2 School of Computer Science and Engineering, Bharathidasan University, Trichy, IN
1 Department of Computer Application, J.J.College of Engineering and Technology, Trichy, IN
2 School of Computer Science and Engineering, Bharathidasan University, Trichy, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 5, No 6 (2013), Pagination: 249-253Abstract
The Web Based Learning concept emphasizes the role of the Internet as a general communications infrastructure; the multimedia features of the World Wide Web; and the potential of the web in facilitating the activities of the learners themselves. Web Based Learning (WBL) offers added flexibility in relation to the time and space contexts of students’ learning; enhanced resources for teachers to develop efficient as well as captivating learning experiences; and strategic potentials for universities to improve educational quality and throughput in response to the needs of the societies they serve. The cognitive aspect of the learners plays an important role to improve the learning ability. To enhance the WBL system it is necessary to know the user’s cognitive aspect through questionnaire. In this paper a novel Hybrid approach is implemented to classify the user’s needs.Keywords
Classification Accuracy, Cognitive Approach, Decision Tree Induction, User Interface Design.- IOT Based Environment Comfort Level Prediction Model Using Ensemble Learning
Abstract Views :192 |
PDF Views:1
Authors
Affiliations
1 Department of Computer Science, Bharathidasan University, IN
1 Department of Computer Science, Bharathidasan University, IN
Source
ICTACT Journal on Soft Computing, Vol 12, No 1 (2021), Pagination: 2515-2520Abstract
Predicting indoor environment comfort through machine learning algorithm is considered as an important research topic nowadays. People spend most of the time inside the building by doing some kitchen work, reading, watching TV, work in office building, learning in classroom, patients in hospital, workers in industry etc. Environment should be comfortable for healthy living. Thus, predicting the comfort level of the environment is necessary for keeping good health and wellbeing. Machine learning algorithm play an important role in prediction model. This paper focus on predicting the comfort of environment using machine learning classifier model. This model is used to train and improve the robustness of the model. This ensemble model is applied to reduce bias factor to enhance the stability and accuracy of the result.Keywords
Ensemble, Confusion Matrix, Boosting, Machine Learning, Comfort Level.References
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- Leaf Disease Recognition Using Segmentation With Visual Feature Descriptor
Abstract Views :208 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science, Bharathidasan University, IN
1 Department of Computer Science, Bharathidasan University, IN
Source
ICTACT Journal on Image and Video Processing, Vol 12, No 3 (2022), Pagination: 2624-2629Abstract
Agriculture has become the main sources of the income for many developed countries. The productivity in agriculture can be affected by various diseases present in the plant due to climatic conditions. The key step to improve the productivity of crops are to detect the disease at the preliminary stage. Automation becomes the best solution for this because it is more difficult to observe the disorders in plants parts. For that an image of affected plant leaf is acquired and segments the affected portion and to recognize the disease by using image processing and computer vision and machine learning techniques. The extracted features from the segmented portion are descripted using Global and Local Visual descriptors. Finally, we use the classifier to recognize the disease. Extracting a meaningful feature from an image is a central problem for a variety of computer vision problems like recognition, image retrieval, and classification. In this research, visual feature descriptor that best describe an image with respect to its visual property is explored. It is specifically focusing on recognizing tasks. The experimental results have proved that the combination of visual descriptors with various classifiers such as SVM and Ensemble Classifier produces high quality outcomes when compared to individual descriptors.Keywords
Duck Search Optimization based Image Segmentation, Grey Level CoOccurrence Matrix, Scale-Invariant Feature Transform, Support Vector Machines, Ensemble ClassifiersReferences
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