A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Ramakrishnan, T.
- Role of Computer in Learning English
Authors
1 Vinayaka Missions University, Salem, IN
2 Department of English, Vinayaka Missions Kripananda Variyar Engineering College, Vinayaka Missions University, Salem, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 6, No 8 (2014), Pagination: 297-300Abstract
In the present computer age teaching technology should have to be adapted in tune with introduction of computers in teaching English. Programmed learning, computer assisted instruction, computer and internet sites,etc. play a very important role in instructional strategy for classroom instruction as well as self-learning. ICT is now considered as one of the most important inventions in the field of education. English is an important language. It is non-phonetic language and there are no proper models to imitate. Hence it must be taught and learnt properly. It plays an important role in higher education and it stands on the way of employability. This paper focuses on importance of ICT in learning English in higher education and the duties of faculties to improve learners' knowledge in English. ICT should be implemented in classroom teaching to become a successful person.
Keywords
ICT in Learning English, Language Skills, Role of Computers, Role of ICT.- An Efficient Approach for the Classification of Medicinal Leaves using BFO and FRVM
Authors
1 Dept. of Electronics and Instrumentation Engg. National Engineering College, Kovilpatti, Tamil Nadu -628 503, IN
2 Dept. of Bio medical Engineering Saveetha Engineering College, Thandalam Chennai – 602 105, IN
3 Dept. of Electronics and Instrumentation Engg., National Engineering College, Kovilpatti, Tamil Nadu -628 503, IN
4 Siddha Clinical Research Unit (SCRU) Government Siddha Medical College Campus, Palayamkottai, Tamil Nadu-627002, IN
Source
International Journal of Advanced Networking and Applications, Vol 10, No 6 (2019), Pagination: 4105-4112Abstract
Herbal plants have been used for medicinal purposes since the ages. These plants also play a major role in medicines, food, perfumes and cosmetics. At present, the identification of herbal plants is purely based on the human perception of their knowledge. It may be probability of human error occurring. An efficient herb species classification system should be automatic and a convenient recognition of herbal plants which reduces the human error. The present research aims to predict the herbal plants in a very convenient and accurate way. This approach is based on the leaf shape, texture, color and its feature. Bacteria Foraging Optimization (BFO) for feature selection and Fuzzy Relevance Vector Machine (FRVM) for the classification of herbal plants are used in the proposed system. The data required for classification are computed using the MATLAB software. In the present work, ten different types of herbal leaves and twenty samples of each have been considered for the process and the classification accuracy is achieved as maximum with an efficient intelligence technique. The efficiency of the proposed method of classifying the different herbal plants gives better performance.Keywords
Detection, GLCM Texture Feature Extraction, BFO, FRVM Classifier.References
- YG Naresh, H.S. Nagendraswamy, ‘Classification of medicinal plants: An approach using modified LBP with symbolic representation’, Neurocomputing, (17), 2016, 89-97.
- Ji-Xiang Du, Mei-Wen Shao, ‘Recognition of leaf image set based on manifold–manifold distance’, Neurocomputing, (188), 2016, 131-138.
- Jyotismita Chaki, Ranjan Parekh, ‘Plant leaf recognition using texture and shape with neural classifiers, Pattern Recognition Letters, (58),2015, 61-68.
- Aimen Aakif, Muhammad Faisal Khan, ‘Automatic classification of plants based on their leaves’, Science Direct, (139),2015, 66-75.
- Sinan Kayaligil, Tulin Inkaya,’Ant colony optimization based clustering methodology’Appl.Soft.Comp,(28),2015,301-311.
- Mohammad Ali Jan Ghasab, Shamsul Khamis, ‘Feature decision-making ant colony optimization system for an automated recognition of plant species’, Expert systems with applications, (23), 2015, 61-70.
- Balasubramanian Vijayalakshmi, Vasudev Mohan, ‘Kernel-based PSO and FRVM: An automatic plant leaf detection using texture, shape and color features’, Computers and Electronics in Agriculture, (125), 2016, 99-112.
- Ji-xiang du, Chuan-Min, ‘Recognition of plant leaf image based on fractal dimension features’,Neurocomputing, (116), 2013,150-156.
- G.Monica, Larese, Rafael Namias, Automatic classification of legumes using leaf vein image features, Pattern Recognition, (47),2014, 158-168.
- K. Prakash, Saravanamoorthi P, Sathiskumar R, Parimala M, A study of image processing in agriculture. Int. J. Advanced Networking and applications, (9):1, 2017, 3311-3315.