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Sahila, T.
- An Ontological Technique to Group Research Projects
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Authors
Affiliations
1 Kalasalingam Institute of Technology, Virudhunagar, IN
1 Kalasalingam Institute of Technology, Virudhunagar, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 5, No 4 (2013), Pagination: 184-188Abstract
Identifying the discipline to which a Project belongs is an important criterion for grouping of Research projects. This can be achieved by the usage of ontology based text mining method. An ontological structure is constructed for classifying into various departments. The keywords used in the Project are compared to the tree structure. Text mining techniques are used to intelligently and automatically extract implicit and potentially useful knowledge from the documents. Clustering is ensured by means of mapping algorithms. Existing methods used in classification are manual matching of key terms and the usage of text mining methods. Hence an ontological text mining method with unsupervised learning is proposed, which matches the index terms and the project title to group the projects in their respective disciplines.Keywords
Clustering, Ontological Structure, Research Projects, Text Mining, Unsupervised.
- Tracing and Recognition of Medicinal Herbs in Marunthuvazh Malai at the Western Ghats Through Feature Extraction
Abstract Views :141 |
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Authors
Affiliations
1 Department of Information Technology, University College of Engineering, Nagercoil, India., IN
2 Department of Mechanical Engineering University College of Engineering, Nagercoil, India., IN
1 Department of Information Technology, University College of Engineering, Nagercoil, India., IN
2 Department of Mechanical Engineering University College of Engineering, Nagercoil, India., IN
Source
ICTACT Journal on Soft Computing, Vol 13, No 3 (2023), Pagination: 2960-2968Abstract
The identification and classification of the herbs using the naked eye is difficult in forest or mountain areas like Marunthuvazh Malai of Kanyakumari district. The difficulties arise because of the variations in the crops identified are inaccurate. Mostly the manual prediction is taken place in those areas which require high expertise and more human resources. In this work both plant identification and tracking system based on fuzzy empowered Hybrid artificial neural networks (FHANN) are proposed. Here the input is taken from the video signals taken by the drone camera. The input video signals are converted into images. The fuzzy logic along with the HANN is used for the classification of the specific herbs from the set of plants. Some of the herbs included in the analysis are Parsley, Dill, Oregano, Chervil, Stevia, Basil, Catnip, Fennel and Lemon Grass. This approach used artificial neural networks (ANN) in combination with the K-Nearest neighbor (KNN) as the hybrid model for the herb prediction and classification in association with the fuzzy logic. The Linear Discriminant Analysis (LDA) and Convolutional Autoencoder are used as a hybrid model for the extraction of the feature from the obtained images. This approach considers various shapes, color features, and textures specifically representing the specific herbs. The experimental results show that the proposed model provides better results in the identification and classification of the various medicinal herbs.Keywords
Artificial Neural Networks, K-Nearest Neighbour, Linear Discriminant Analysis, Convolutional Auto Encoder.References
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