- STYLE CODING PREDICTION BASED ON USING N-GRAMS
Sergio Rodríguez1, Luis Villanueva2 and Sergio Ledesma3, 1Instituto Tecnológico Superior de Purísima del Rincón, Purísima del Rincón, Gto.Mexico 2Instituto Tecnológico Superior de Purísima del Rincón, Purísima del Rincón, Gto.Mexico 3Universidad de Guanajuato,Campus Irapuato-Salamanca,Salamanca, Mexico
The use of artificial intelligence algorithms provides programming styles prediction. In this work, we present an analysis of algorithms based on N-grams to make modeling and prediction of programming styles in C++ language. The Markov chains are used to modeling and generate the N-grams, these offer a mathematical model of the writing behavior of coding. We center this investigation on bigrams and trigrams. The trigrams are the most precise on modeling and prediction.
- MULTIPLE SCLEROSIS DIAGNOSIS WITH FUZZY C-MEANS
Saba Heidari Gheshlaghi1 , Abolfazl Madani2, AmirAbolfazl Suratgar3 and Fardin Faraji4 1Department of Electrical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran, 2Department of Control Engineering, South Tehran Branch Islamic Azad University (IAU) Tehran, Iran 3Department of Electrical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran ,4Neurology Department, Arak University of Medical Sciences, Arak, Iran
MR images can support and substitute clinical information in the diagnosis of multiple sclerosis (MS) by presenting lesion. In this paper, we present an algorithm for MS lesion segmentation. We revisit the modification of properties of fuzzy c means algorithms and the canny edge detection. Using reformulated fuzzy c means algorithms, apply canny contraction principle, and establish a relationship between MS lesions and edge detection. For the special case of FCM, we derive a sufficient condition for fixed lesions, allowing identification of them as (local) minima of the objective function.