ОБНАРУЖЕНИЕ ПАТОЛОГИИ РЕЧЕВОГО ТРАКТА НА ОСНОВЕ ГЕНЕТИЧЕСКОГО АЛГОРИТМА И АНСАМБЛЯ ДЕРЕВА РЕШЕНИЙ

A combination of decision tree ensemble and genetic algorithm is proposed for the vocal fold pathology diagnosis by acoustic signals. The experimental results show a better performance (higher classification accuracy) of the proposed method in comparison with the others.Для диагноза патологии речевого тракта по акустическому сигналу предлагается соединение ансамбля дерева принятия решений и генетического алгоритма. Результаты экспериментов подтверждают лучшие показатели (более высокую точность классификации) предложенного подхода по сравнению с другими методами

СОВРЕМЕННЫЕ МЕТОДЫ АВТОМАТИЧЕСКОГО ОБНАРУЖЕНИЯ ПРЯМОУГОЛЬНЫХ ОБЪЕКТОВ НА ИЗОБРАЖЕНИЯХ

Low-level and high-level feature extraction methods and algorithms for the image formation of a rectangular object were considered. The algorithm for object detection based on correlation analysis, as well as the algorithm containing the use of Canny edge detector, Hough and Radon transform for lines detection, and then, depending on the properties of the object lines combining in the rectangular area, were explored. The algorithms were tested on the base of 1000 passports for the problem of accurate photo edges detection.Рассмотрены низкоуровневые и высокоуровневые методы выделения признаков на изображении и алгоритмы формирования прямоугольного объекта. Были исследованы и протестированы алгоритм обнаружения объекта на основе корреляционного анализа, а также алгоритм, содержащий в себе применение детектора границ Канни, обнаружение линий с помощью преобразования Хафа и преобразования Радона и далее, в зависимости от свойств объекта, объединением линий в прямоугольную область. Алгоритмы были протестированы на базе из 1000 паспортов для задачи точного обнаружения границ фотографии

Диагностика патологии голосового тракта на основе нейронных сетей

There are different algorithms for vocal fold pathology diagnosis. These algorithms usually have three stages which are Feature Extraction, Feature Reduction and Classification. While the third stage implies a choice of a variety of machine learning methods, the first and second stages play a critical role in performance and accuracy of the classification system. In this paper we present initial study of feature extraction and feature reduction in the task of vocal fold pathology diagnosis. A new type of feature vector, based on wavelet packet decomposition and Mel- Frequency-Cepstral-Coefficients (MFCCs), is proposed. Also Principal-Component Analysis (PCA) is used for feature reduction. An Artificial Neural Network is used as a classifier for evaluating the performance of our proposed method.В этой статье представляется метод искусственных нейронных сетей для решения задач диагностики патологии голосового тракта

MODERN METHODS OF AUTOMATIC RECTANGLE OBJECTS DETECTION

Low-level and high-level feature extraction methods and algorithms for the image formation of a rectangular object were considered. The algorithm for object detection based on correlation analysis, as well as the algorithm containing the use of Canny edge detector, Hough and Radon transform for lines detection, and then, depending on the properties of the object lines combining in the rectangular area, were explored. The algorithms were tested on the base of 1000 passports for the problem of accurate photo edges detection

Диагностика патологии голосового тракта на основе нейронных сетей

There are different algorithms for vocal fold pathology diagnosis. These algorithms usually have three stages which are Feature Extraction, Feature Reduction and Classification. While the third stage implies a choice of a variety of machine learning methods, the first and second stages play a critical role in performance and accuracy of the classification system. In this paper we present initial study of feature extraction and feature reduction in the task of vocal fold pathology diagnosis. A new type of feature vector, based on wavelet packet decomposition and Mel- Frequency-Cepstral-Coefficients (MFCCs), is proposed. Also Principal-Component Analysis (PCA) is used for feature reduction. An Artificial Neural Network is used as a classifier for evaluating the performance of our proposed method