Automatic face recognition system is one of the core technologies in computer vision, machine learning, and biometrics. The present study presents a novel and improved way for face recognition. In the suggested approach, first, the place of face is extracted from the original image and then is sent to feature extraction stage, which is based on Principal Component Analysis (PCA) technique. In t...

Machine learning is the core of artificial intelligence. It is a fundamental way to the computer intelligence. Support vector machine is one of the important methods in the field of machine learning. It has the advantages of global optimization and strong generalization ability. It has been successfully applied to face recognition, fault diagnosis, financial forecasting and other fields. In thi...

Background: Migraine headache without aura is the most common type of migraine especially among pediatric patients. It has always been a great challenge of migraine diagnosis using quantitative electroencephalography measurements through feature classification. It has been proven that different feature extraction and classification methods vary in terms of performance regarding detection and di...

Although fuzzy support vector machine introduces the fuzzy membership degree in maximizing the margin and improves performance of classifier, it has not fully considered the position of training samples in the margin. In this paper, a double margin (rough margin) based fuzzy support vector machine (RFSVM) algorithm is presented by introducing rough set into fuzzy support vector machine. Firstly...

background and aims: dna constructs containing hcv antigens have become one of the vaccine candidates for induction of anti-hcv cellular and humoral immunity. in this study, we constructed a novel expressing vector harboring a fusion sequence derived from an overlapping fragment in the middle of ns3 and a truncated core fragment to avoid troubles reported to be associated with full gene express...

Introduction: In this paper, a method is presented to classify the breast cancer masses according to new geometric features. Methods: After obtaining digital breast mammogram images from the digital database for screening mammography (DDSM), image preprocessing was performed. Then, by using image processing methods, an algorithm was developed for automatic extracting of masses from other norma...