Title: Enhancement over Learning Vector Quantization through Distance Function


Volume 10 Issue 1 Year 2020

Authors:

Preeti Jorwal

ptpkm2013@gmail.com
Department of Computer Science & Engineering, Swami Keshvanand Institute of Technology, Management & Gramothan Jaipur-302017 (INDIA),

Vijeta Khicha

ijskit@skit.ac.in
Department of Computer Science & Engineering, Swami Keshvanand Institute of Technology, Management & Gramothan Jaipur-302017 (INDIA)ptpkm2013@gmail.com,

Vipin Jain

ervipin85@gmail.com
Department of Information Technology, Swami Keshvanand Institute of Technology, Management & Gramothan Jaipur-302017 (INDIA)

Pages: 10-15


Abstract:

Artificial Neural Network (ANN) represents the scientific similarity between neuron of biological elements. These are computational models, which lightly stimulated by their biological equivalents. AI and ANNs are two stimulating and intertwined arenas in computer science. ANNs are hominid ready information handling systems that are grown up extensively in last thirty years. Researchers have used ANN for detection of different types of diseases. In this paper, we have proposed a hybrid function used with ANN for detection of cancer and diabetes diseases. Basically, this approach modifies the existing distance function and proposed a hybrid distance function. We have used this modified distance function for training and testing of model in supervised learning vector quantization for detection of diseases. The data sets have been taken form Medical Science for providing learning and examining. The various experiments were performed using MATLAB tool. The results show that the performance of enhanced ANN algorithm is far better than existing ANN for detection of cancer and diabetes diseases.

Keywords:
Artificial Neural Network, Artificial Intelligence, Learning Vector Quantization