scholarly journals DETERMINATION OF PROTEIN-PROTEIN INTERACTION THROUGH ARTIFICIAL NEURAL NETWORK AND SUPPORT VECTOR MACHINE: A COMPARATIVE STUDY

2014 ◽  
Vol 3 (2) ◽  
pp. 37 ◽  
Author(s):  
Himansu Kumar ◽  
Swati Srivastava ◽  
Pritish Varadwaj
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Comparative Study ◽  
Protein Interaction ◽  
Support Vector ◽  
Protein Protein Interaction ◽  
Artificial Neural

scholarly journals A COMPARATIVE STUDY BETWEEN ARTIFICIAL NEURAL NETWORK AND SUPPORT VECTOR MACHINE FOR ACUTE CORONARY SYNDROME PROGNOSIS

2016 ◽  
Vol 36 (2) ◽  
pp. 321-343 ◽  
Author(s):  
Rodrigo Abrunhosa Collazo ◽  
Leonardo Antonio Monteiro Pessôa ◽  
Laura Bahiense ◽  
Basílio de Bragança Pereira ◽  
Amália Faria dos Reis ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Acute Coronary Syndrome ◽  
Comparative Study ◽  
Support Vector ◽  
Coronary Syndrome ◽  
Artificial Neural

scholarly journals A Comparative Study of Support Vector Machine and Artificial Neural Network for Option Price Prediction

2021 ◽  
Vol 09 (05) ◽  
pp. 78-91
Author(s):  
Biplab Madhu ◽  
Md. Azizur Rahman ◽  
Arnab Mukherjee ◽  
Md. Zahidul Islam ◽  
Raju Roy ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Comparative Study ◽  
Option Price ◽  
Support Vector ◽  
Price Prediction ◽  
Artificial Neural

scholarly journals Pioneer dataset and recognition of Handwritten Pashto characters using Convolution Neural Networks

2020 ◽  
pp. 002029402096482
Author(s):  
Sulaiman Khan ◽  
Abdul Hafeez ◽  
Hazrat Ali ◽  
Shah Nazir ◽  
Anwar Hussain
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Research Work ◽  
Recognition Rate ◽  
Recognition Task ◽  
Convolution Neural Network ◽  
Medium Size ◽  
Support Vector ◽  
Artificial Neural

This paper presents an efficient OCR system for the recognition of offline Pashto isolated characters. The lack of an appropriate dataset makes it challenging to match against a reference and perform recognition. This research work addresses this problem by developing a medium-size database that comprises 4488 samples of handwritten Pashto character; that can be further used for experimental purposes. In the proposed OCR system the recognition task is performed using convolution neural network. The performance analysis of the proposed OCR system is validated by comparing its results with artificial neural network and support vector machine based on zoning feature extraction technique. The results of the proposed experiments shows an accuracy of 56% for the support vector machine, 78% for artificial neural network, and 80.7% for the proposed OCR system. The high recognition rate shows that the OCR system based on convolution neural network performs best among the used techniques.

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