FPGA Implementation of Support Vector Machine Using Ising Model for AI on Things

2021 ◽  
Author(s):  
Yudai Taguchi ◽  
Ryoma Iimura ◽  
Takayuki Kawahara
Keyword(s):  
Support Vector Machine ◽  
Ising Model ◽  
Fpga Implementation ◽  
Support Vector

Automatic Prostate Lesions Detection on MR Images Based on the Ising Model

2019 ◽  
Vol 16 (2) ◽  
pp. 341-350
Author(s):  
Artur Bernardo Silva Reis ◽  
Aristófanes Corrêa Silva ◽  
Anselmo Cardoso de Paiva ◽  
Marcelo Gattass
Keyword(s):  
Prostate Cancer ◽  
Support Vector Machine ◽  
Feature Extraction ◽  
Ising Model ◽  
Support Vector ◽  
Mr Images ◽  
Male Population ◽  
Prostate Imaging ◽  
Wolff Algorithm

Prostate cancer is the second most prevalent type of cancer in the male population worldwide. Prostate imaging tests have adopted for the prevention, diagnosis, and treatment. It is known that early detection increases the chances of an effective treatment, improving the prognosis of the disease. This paper proposes an automatic methodology for prostate lesions detection. It consists of the following steps: Extracting candidates for lesions with Wolff algorithm; feature extraction using the Ising model measures and finally the uses support vector machine in the classification of a lesion or healthy tissue. The methodology was validated using a set of 28 exams containing the lesion markings and obtained a sensitivity of 95.92%, specificity of 93.89% and accuracy of 94.16%. These are promising since they were more significant than other methods compared.

Support vector machine? – not only for MRI-images

2020 ◽  
Author(s):  
V Vasilevska ◽  
K Schlaaf ◽  
H Dobrowolny ◽  
G Meyer-Lotz ◽  
HG Bernstein ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

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