Evaluation of image processing technique as an expert system in mulberry fruit grading based on ripeness level using artificial neural networks (ANNs) and support vector machine (SVM)

A real-time white ginseng quality evaluation system based on a machine vision technique and artificial neural networks was developed to replace the current manual grading and its efficiency was tested. The system consisted of conveyor, image acquisition system synchronized with a sample-detecting sensor, and image processing and decision-making system. Software running under Windows system was developed. The algorithm included three consecutive stages of (a) image acquisition and preprocessing, (b) mathematical feature extraction, and (c) grade decision using artificial neural networks. Mathematical features such as area ratio, mean and standard deviation of gray level, skewness of gray level histogram, and the number of run segment, were extracted from five equally divided parts of a specimen. An artificial neural network model was used to classify samples into three grading categories. The grading error of the system was about 26%, which is comparable to the 30% in case of manual grading. The grading rate was one sample per a second.

Download Full-text

Intelligent Prediction of Differential Pipe Sticking by Support Vector Machine Compared With Conventional Artificial Neural Networks: An Example of Iranian Offshore Oil Fields

SPE Drilling & Completion ◽

10.2118/163062-pa ◽

2012 ◽

Vol 27 (04) ◽

pp. 586-595 ◽

Cited By ~ 18

Author(s):

Reza Jahanbakhshi ◽

Reza Keshavarzi ◽

Mahdi Aliyari Shoorehdeli ◽

Abolqasem Emamzadeh

Keyword(s):

Neural Networks ◽

Support Vector Machine ◽

Artificial Neural Networks ◽

Oil Fields ◽

Support Vector ◽

Artificial Neural ◽

Offshore Oil

Download Full-text

IMPROVEMENT OF DOSE ESTIMATION PROCESS USING ARTIFICIAL NEURAL NETWORKS

Radiation Protection Dosimetry ◽

10.1093/rpd/ncy185 ◽

2018 ◽

Vol 184 (1) ◽

pp. 36-43 ◽

Cited By ~ 3

Author(s):

Gal Amit ◽

Hanan Datz

Keyword(s):

Neural Networks ◽

Support Vector Machine ◽

Artificial Neural Networks ◽

Support Vector ◽

Physical Parameters ◽

Machine Method ◽

Algorithm Performance ◽

Artificial Neural ◽

First Time ◽

Automatic Algorithm

Abstract We present here for the first time a fast and reliable automatic algorithm based on artificial neural networks for the anomaly detection of a thermoluminescence dosemeter (TLD) glow curves (GCs), and compare its performance with formerly developed support vector machine method. The GC shape of TLD depends on numerous physical parameters, which may significantly affect it. When integrated into a dosimetry laboratory, this automatic algorithm can classify ‘anomalous’ (having any kind of anomaly) GCs for manual review, and ‘regular’ (acceptable) GCs for automatic analysis. The new algorithm performance is then compared with two kinds of formerly developed support vector machine classifiers—regular and weighted ones—using three different metrics. Results show an impressive accuracy rate of 97% for TLD GCs that are correctly classified to either of the classes.

Download Full-text

Predicting Students Grades Using Artificial Neural Networks and Support Vector Machine

Advanced Methodologies and Technologies in Modern Education Delivery - Advances in Educational Technologies and Instructional Design ◽

10.4018/978-1-5225-7365-4.ch059 ◽

2019 ◽

pp. 751-766

Author(s):

Sajid Umair ◽

Muhammad Majid Sharif

Keyword(s):

Neural Networks ◽

Support Vector Machine ◽

Artificial Neural Networks ◽

Student Performance ◽

Semantic Analysis ◽

Vital Role ◽

Support Vector ◽

Data Set ◽

Important Research Topic ◽

Artificial Neural

Prediction of student performance on the basis of habits has been a very important research topic in academics. Studies show that selection of the correct data set also plays a vital role in these predictions. In this chapter, the authors took data from different schools that contains student habits and their comments, analyzed it using latent semantic analysis to get semantics, and then used support vector machine to classify the data into two classes, important for prediction and not important. Finally, they used artificial neural networks to predict the grades of students. Regression was also used to predict data coming from support vector machine, while giving only the important data for prediction.

Download Full-text