Artificial Neural Network Modelling: An Introduction

Optimasi Algoritma Pelatihan Levenberg–Marquardt Berdasarkan Variasi Nilai Learning-Rate dan Jumlah Neuron dalam Lapisan Tersembunyi

“…PENDAHULUAN Jaringan syaraf tiruan (artificial neural network/JST) adalah model komputasi yang terinspirasi secara biologis. JST terdiri dari beberapa elemen pengolahan (neuron) dan ada hubungan antara neuron [1]. Setiap pengolahan elemen membuat perhitungan berdasarkan pada jumlah masukan (input).…”

Classification of breast lesions in ultrasonography using sparse logistic regression and morphology‐based texture features

“…Two main types of features are extracted for BUS image analysis; morphological and textural features. Morphological features represent local characteristics of the tumor lesion and are used to quantitate malignancy using shape, contour,or boundary characteristics . Textural features explain internal echo patterns and the composition of the encompassing tissues and are selected from the original or preprocessed images .…”

“…Morphological features represent local characteristics of the tumor lesion and are used to quantitate malignancy using shape, contour,or boundary characteristics. [1][2][3]5,11,18 Textural features explain internal echo patterns and the composition of the encompassing tissues and are selected from the original or preprocessed images. [1][2][3]5,6,11 For feature extraction, Genetic algorithm, 19 mutual information, 11 statistical tests, 20 Relief and FOCUS techniques 21 have been utilized for breast ultrasound images.…”

An Approach to Teaching Quality Evaluation Based on ANN and Boosting

“…The artificial neural network is a non-linear mechanism to realize the mapping function of arbitrary functions and a powerful tool for solving non-linear and dynamic characteristics [5,6]. Boosting is a kind of effective method to improve the accuracy of any given learning algorithm [7,8], can be used to produce accurate evaluation results.…”