scholarly journals Sistem Identifikasi Tingkat Kematangan Buah Nanas Secara Non-Destruktif Berbasis Computer Vision

2021 ◽  
Vol 2 (1) ◽  
pp. 1-10
Author(s):  
Nevalen Aginda Prasetyo ◽  
◽  
Arif Surtono ◽  
Junaidi Junaidi ◽  
Gurum Ahmad Pauzi
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Artificial Neural Network ◽  
Pearson Correlation ◽  
Statistical Parameter ◽  
Identification System ◽  
Maturity Level ◽  
Validation Data ◽  
Artificial Neural ◽  
Level Identification

A computer vision-based non-destructive pineapple maturity level identification system has been realized. This research was conducted to create a system capable of identifying six indexes of pineapple maturity level. An artificial neural network is used as a classifier for the level of maturity pineapples. Artificial neural network input is a statistical parameter consisting of mean, standard deviation, variance, kurtosis, and skewness of RGB and HSV color models pineapple images. Statistical parameters of the color model with a Pearson correlation value greater than 0.5 were used to characterize pineapple images. A total of 360 pineapple images were used in the training process with a percentage of 75% of training data and 25% of validation data. An image segmentation process is applied to separate the pineapple image from the image background. The result of this research is a pineapple maturity level identification system consisting of software and hardware which is able to identify six indexes of pineapple maturity level with average accuracy value of 98,4%.

Improved estimation of energy expenditure by artificial neural network modeling

2008 ◽  
Vol 33 (6) ◽  
pp. 1213-1222 ◽  
Author(s):  
Dean Charles Hay ◽  
Akinobu Wakayama ◽  
Ken Sakamura ◽  
Senshi Fukashiro
Keyword(s):  
Neural Network ◽  
Heart Rate ◽  
Artificial Neural Network ◽  
Energy Expenditure ◽  
Network Modeling ◽  
Neural Network Modeling ◽  
Validation Data ◽  
Artificial Neural Network Modeling ◽  
Feed Forward Back Propagation ◽  
Artificial Neural

Estimation of energy expenditure in daily living conditions can be a tool for clinical assessment of health status, as well as a self-measure of lifestyle and general activity levels. Criterion measures are either prohibitively expensive or restricted to laboratory settings. Portable devices (heart rate monitors, pedometers) have gained recent popularity, but accuracy of the prediction equations remains questionable. This study applied an artificial neural network modeling approach to the problem of estimating energy expenditure with different dynamic inputs (accelerometry, heart rate above resting (HRar), and electromyography (EMG)). Nine feed-forward back-propagation models were trained, with the goal of minimizing the mean squared error (MSE) of the training datasets. Model 1 (accelerometry only) and model 2 (HRar only) performed poorly and had significantly greater MSE than all other models (p < 0.001). Model 3 (combined accelerometry and HRar) had overall performance similar to EMG models. Validation of all models was performed by simulating untrained datasets. MSE of all models increased when tested with validation data. While models 1 and 2 again performed poorly, model 3 MSE was lower than all but 2 EMG models. Squared correlation coefficients of measured and predicted energy expenditure for models 3 to 9 ranged from 0.745 to 0.817. Analysis of mean error within specific movement categories indicates that EMG models may be better at predicting higher-intensity energy expenditure, but combined accelerometry and HRar provides an economical solution, with sufficient accuracy.

Identification of the Maturity Level of Mango “Arumanis” Using Artificial Neural Network

2001 ◽  
Vol 34 (11) ◽  
pp. 325-329
Author(s):  
Atris Suyantohadi ◽  
Guntarti Tatik Mulyati ◽  
Wahyu Supartono ◽  
Titik F. Djafar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Maturity Level ◽  
Artificial Neural

Artificial Neural Network Learning of Nonstationary Behavior in Time Series

2003 ◽  
Vol 13 (02) ◽  
pp. 103-109 ◽  
Author(s):  
María I. Széliga ◽  
Pablo F. Verdes ◽  
Pablo M. Granitto ◽  
H. Alejandro Ceccatto
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Error Function ◽  
Input Parameter ◽  
Input Unit ◽  
Validation Data ◽  
Neural Network Learning ◽  
Secular Evolution ◽  
Artificial Neural

We refine and complement a previously-proposed artificial neural network method for learning hidden signals forcing nonstationary behavior in time series. The method adds an extra input unit to the network and feeds it with the proposed profile for the unknown perturbing signal. The correct time evolution of this new input parameter is learned simultaneously with the intrinsic stationary dynamics underlying the series, which is accomplished by minimizing a suitably-defined error function for the training process. We incorporate here the use of validation data, held out from the training set, to accurately determine the optimal value of a hyperparameter required by the method. Furthermore, we evaluate this algorithm in a controlled situation and show that it outperforms other existing methods in the literature. Finally, we discuss a preliminary application to the real-world sunspot time series and link the obtained hidden perturbing signal to the secular evolution of the solar magnetic field.

Flood Risk Pattern Recognition Using Chemometric Technique: A Case Study In Kuantan River Basin

2014 ◽  
Vol 72 (1) ◽  
Author(s):  
Ahmad Shakir Mohd Saudi ◽  
Hafizan Juahir ◽  
Azman Azid ◽  
Mohd Khairul Amri Kamarudin ◽  
Mohd Fadhil Kasim ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Level ◽  
River Basin ◽  
Stream Flow ◽  
Pearson Correlation ◽  
Suspended Solid ◽  
Test Analysis ◽  
Risk Class ◽  
Artificial Neural

Integrated Chemometric and Artificial Neural Network were being applied in this study to identify the main contributor for flood, predicting hydrological modelling and risk of flood occurrence at the Kuantan river basin. Based on the Correlation Test analysis, the relationship for Suspended Solid and Stream Flow with Water Level were very high with Pearson correlation of coefficient value more than 0.5. Factor Analysis had been carried out and based on the result, variables such as Stream Flow, Suspended Solid and Water Level turned out to be the major factors and had a strong factor pattern with the results of factor score with >0.7 respectively. Time series analysis was being employed and the limitation had been set up where the Upper Control Limit for Stream Flow, Suspended Solid and Water Level where at this level, it was predicted by using Artificial Neural Network (ANN) to be High Risk Class. The accuracy of prediction from this method stood at 97.8%.

scholarly journals RANCANG BANGUN APLIKASI PENGENALAN WAJAH UNTUK PASSWORD FILE DENGAN METODE JARINGAN SYARAF TIRUAN

2019 ◽  
Vol 21 (1) ◽  
pp. 11-21
Author(s):  
Genrawan Hoendarto ◽  
Vicni Iskandar
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Artificial Neural Network ◽  
Data Security ◽  
Facial Recognition ◽  
Back Propagation ◽  
Computer Users ◽  
Artificial Neural ◽  
Access Rights ◽  
Facial Images

Data security for computer users is increasingly becoming a concern because it is increasingly vulnerable to illegal access even though the file has been protected with a password. This is possible with the increasing number of applications aimed at hacking owner protection. Artificial neural network that was appointed in this study is one part of computer vision, which in this study is intended to make computers able to "see" through a webcam and recognize that face has access rights to the selected file. So that computers can distinguish facial images, it needs to be trained by applying the back propagation method. The reason for choosing facial recognition is because each person has a different face, so that it can be a more effective security key than conventional methods of making or accessing files that are on a computer.

scholarly journals SPEAKER IDENTIFICATION MENGGUNAKAN TRANSFORMASI WAVELET DISKRIT DAN JARINGAN SARAF TIRUAN BACK-PROPAGATION

2008 ◽  
Vol 2 (1) ◽  
pp. 1
Author(s):  
Anny Tandyo ◽  
Martono Martono ◽  
Adi Widyatmoko
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Speaker Recognition ◽  
Speaker Identification ◽  
Back Propagation ◽  
Identification Accuracy ◽  
Identification System ◽  
Accuracy Rate ◽  
Discrete Transformation ◽  
Artificial Neural

Article discussed a speaker identification system. Which was a part of speaker recognition. The system identified asubject based on the voice from a group of pattern had been saved before. This system used a wavelet discrete transformationas a feature extraction method and an artificial neural network of back-propagation as a classification method. The voiceinput was processed by the wavelet discrete transformation in order to obtain signal coefficient of low frequency as adecomposition result which kept voice characteristic of everyone. The coefficient then was classified artificial neural networkof back-propagation. A system trial was conducted by collecting voice samples directly by using 225 microphones in nonsoundproof rooms; contained of 15 subjects (persons) and each of them had 15 voice samples. The 10 samples were used as atraining voice and 5 others as a testing voice. Identification accuracy rate reached 84 percent. The testing was also done onthe subjects who pronounced same words. It can be concluded that, the similar selection of words by different subjects has noinfluence on the accuracy rate produced by system.Keywords: speaker identification, wavelet discrete transformation, artificial neural network, back-propagation.

scholarly journals A Computer Vision System Based on Majority-Voting Ensemble Neural Network for the Automatic Classification of Three Chickpea Varieties

Foods ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 113 ◽  
Author(s):  
Razieh Pourdarbani ◽  
Sajad Sabzi ◽  
Davood Kalantari ◽  
José Luis Hernández-Hernández ◽  
Juan Ignacio Arribas
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Artificial Neural Network ◽  
Vision System ◽  
Color Space ◽  
Majority Voting ◽  
Hsi Color Space ◽  
Artificial Neural ◽  
Hybrid Artificial Neural Network

Since different varieties of crops have specific applications, it is therefore important to properly identify each cultivar, in order to avoid fake varieties being sold as genuine, i.e., fraud. Despite that properly trained human experts might accurately identify and classify crop varieties, computer vision systems are needed since conditions such as fatigue, reproducibility, and so on, can influence the expert’s judgment and assessment. Chickpea (Cicer arietinum L.) is an important legume at the world-level and has several varieties. Three chickpea varieties with a rather similar visual appearance were studied here: Adel, Arman, and Azad chickpeas. The purpose of this paper is to present a computer vision system for the automatic classification of those chickpea varieties. First, segmentation was performed using an Hue Saturation Intensity (HSI) color space threshold. Next, color and textural (from the gray level co-occurrence matrix, GLCM) properties (features) were extracted from the chickpea sample images. Then, using the hybrid artificial neural network-cultural algorithm (ANN-CA), the sub-optimal combination of the five most effective properties (mean of the RGB color space components, mean of the HSI color space components, entropy of GLCM matrix at 90°, standard deviation of GLCM matrix at 0°, and mean third component in YCbCr color space) were selected as discriminant features. Finally, an ANN-PSO/ACO/HS majority voting (MV) ensemble methodology merging three different classifier outputs, namely the hybrid artificial neural network-particle swarm optimization (ANN-PSO), hybrid artificial neural network-ant colony optimization (ANN-ACO), and hybrid artificial neural network-harmonic search (ANN-HS), was used. Results showed that the ensemble ANN-PSO/ACO/HS-MV classifier approach reached an average classification accuracy of 99.10 ± 0.75% over the test set, after averaging 1000 random iterations.

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