scholarly journals Identification of Disease on Leaves Soybean using Modified Otsu and Learning Vector Quantization Neural Networks

Kursor ◽  
2018 ◽  
Vol 9 (3) ◽  
Author(s):  
Candra Dewi ◽  
Muhammad Sa’idul Umam ◽  
Imam Cholissodin
Keyword(s):  
Vector Quantization ◽  
Threshold Value ◽  
Learning Vector Quantization ◽  
Training Data ◽  
Blue Color ◽  
Phakopsora Pachyrhizi ◽  
Average Value ◽  
Maximum Value ◽  
Otsu Method ◽  
Soybean Leaf

Disease of the soybean crop is one of the obstacles to increase soybean production in Indonesia. Some of these diseases usually are found in the leaves and resulted to the crop become unhealthy. This study aims to identify disease on soybean leaf through leaves image by applying the Learning Vector Quantization (LVQ) algorithm. The identification begins with preprocessing using modified Otsu method to get part of the diseases on the leaves with a certain threshold value. The next process is to identify the type of disease using LVQ. This process uses the minimum value, the maximum value and the average value of the red, green and blue color of the image. The testing conducted in this study is to identify two diseases called Peronospora manshurica (Downy Mildew) and phakopsora pachyrhizi (Karat). The result of testing by using 60 training data and the value of all recommendations parameters obtained the highest accuracy of identification is 95% %, but the more stable accuracy is 90%. This result shows that the method perform quite well identification of two mentioned disease.

scholarly journals Identification of Formaldehyde Bananas using Learning Vector Quantization

2021 ◽  
Vol 3 (2) ◽  
pp. 160
Author(s):  
Rahmat Musa ◽  
Mutaqin Akbar
Keyword(s):  
Vector Quantization ◽  
Visual Recognition ◽  
Texture Features ◽  
Learning Vector Quantization ◽  
Learning Rate ◽  
Training Data ◽  
Blue Color ◽  
Test Results ◽  
Accurate Identification ◽  
Sample Data

Bananas that ripen with chemical process or do not ripen naturally usually, this can be recognized by the presence of blackish patches on the surface of the skin. But visual recognition has its drawbacks, which is that it is difficult to recognize similarities between formalin bananas and natural bananas, resulting in a lack of accurate identification. In this study, a system was built that can determined formalin bananas and natural bananas through digital image identification using supervised classification. The image to be identification previously goes through the process of transforming RGB (Red Green Blue) color to Grayscale, and the process of extracting texture features using statically recognizable features through histograms, in the form of average, standard deviation, skewness, kurtosis, energy, entropy and smoothness. The extraction of texture features is classified with LVQ (Learning Vector Quantization) to determine formalin or natural bananas. The test was conducted with 122 banana imagery sample data, 100 imagery as training data consisting of 50 imagery for natural bananas and 50 imagery for bananas formalin, 22 imagery as test data. The test results showed LVQ method has the best percentage at Learning Rate 0.1, Decreased Learning Rate 0.75 and maximum epoch of 1000 with the smallest epoch of 7, obtained accuracy 90.90%, precision 84.61% and recall 100%.

scholarly journals Klasifikasi Multi Output pada Harga Smartphone Menggunakan Learning Vector Quantization (LVQ) dan Backpropagation (BP)

2021 ◽  
Vol 6 (2) ◽  
pp. 14-19
Author(s):  
Dinita Rahmalia ◽  
Mohammad Syaiful Pradana ◽  
Teguh Herlambang
Keyword(s):  
Vector Quantization ◽  
Error Rate ◽  
Gradient Descent ◽  
Euclidean Distance ◽  
Learning Vector Quantization ◽  
Training Data ◽  
Price Range ◽  
Testing Data ◽  
Internal Storage ◽  
Simulation Results

There are many smartphones with various price sold in market. The price of smartphone is affected by some components such as weight, internal storage, memory (RAM), rear camera, front camera and brands. There are two methods for classifying price class of smartphone in market such as Learning Vector Quantization (LVQ) and Backpropagation (BP). From classifying price class of smartphone in market using LVQ and BP, there are the differences on the both of them. LVQ classifies price range of smartphone by euclidean distance of weight and data on its iteration. BP classifies price range of smartphone by gradient descent of target and output on its iteration. In multi output classification, one object may have multi output. Based on simulation results, BP gives the better accuracy and error rate in training data and testing data than LVQ.  

scholarly journals Cataract Disease Diagnosis System Using Artificial Neural Network Learning Vector Quantization Method

2020 ◽  
Vol 4 (2) ◽  
pp. 75-85
Author(s):  
Chrisani Waas ◽  
D. L. Rahakbauw ◽  
Yopi Andry Lesnussa
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Vector Quantization ◽  
Processing System ◽  
Disease Diagnosis ◽  
Learning Vector Quantization ◽  
Learning Rate ◽  
Training Data ◽  
Neural Network Learning ◽  
Artificial Neural

Artificial Neural Network (ANN) is an information processing system that has certain performance characteristics that are artificial representatives based on human neural networks. ANN method has been widely applied to help human performance, one of which is health. In this research, ANN will be used to diagnose cataracts, especially Congenital Cataracts, Juvenile Cataracts, Senile Cataracts and Traumatic Cataracts based on the symptoms of the disease. The ANN method used is the Learning Vector Quantization (LVQ) method. The data used in this research were 146 data taken from the medical record data of RSUD Dr. M. Haulussy, Ambon. The data consists of 109 data as training data and 37 data as testing data. By using learning rate (α) = 0.1, decrease in learning rate (dec α) = 0.0001 and maximum epoch (max epoch) = 5, the accuracy rate obtained is 100%.

scholarly journals Cervical Cell Classification using Learning Vector Quantization (LVQ) Based on Shape and Statistical Features

2019 ◽  
Vol 15 (02) ◽  
pp. 91
Author(s):  
Erlinda Metta Dewi ◽  
Endah Purwanti ◽  
Retna Apsari
Keyword(s):  
Vector Quantization ◽  
Optimal Parameter ◽  
Learning Vector Quantization ◽  
Learning Rate ◽  
Training Data ◽  
Statistical Features ◽  
Shape Features ◽  
Image Histogram ◽  
Cervical Cell ◽  
Cervical Cells

This research was conducted to design a system that is able to classify cervical cells into two classes, namely normal cells or abnormal cells. We use digital images of single cervical as research materials and Learning Vector Quantization (LVQ) as classification method.  Prior to classification, the nucleus areas of single cervical cell images were segmented and features were extracted. The features used in this study are 7 kinds of which consist of 2 types of feature, namely shape features and statistical features. The shape features used are area, perimeter, shape factor, and roundness of the nucleus, while the statistical features of the grayscale image histogram used are mean, standard deviation, and entropy. LVQ optimal parameter values based on the highest accuracy of training data, are learning rate 0.1 and learning rate reduction 0.5. The highest accuracy of system obtained from 45 testing data is 93.33%.

scholarly journals Perbandingan Metode Backpropagation dan Learning Vector Quantization (LVQ) Dalam Menggali Potensi Mahasiswa Baru di STMIK PalComTech

2019 ◽  
Vol 18 (2) ◽  
pp. 294-301
Author(s):  
Yarza Aprizal ◽  
Rabin Ibnu Zainal ◽  
Afriyudi Afriyudi
Keyword(s):  
Information Systems ◽  
Vector Quantization ◽  
Learning Vector Quantization ◽  
Learning Rate ◽  
Training Data ◽  
Rate Variation ◽  
New Students ◽  
Backpropagation Method ◽  
Input Variables ◽  
The Right

The Research aimst to compare backpropagation and Learning Vector Quantization (LVQ) methods in exploring the potential of new students at STMIK PalComTech. Comparisons in this study involve four input variables used which consist of four basic subjects of informatics engineering and information systems (math, basic programming, computer networks and management bases) which then make informatics techniques and information systems as outputs, to get the accuracy level high in this study, the researchers used several variations of parameters which eventually produced the best accuracy of the two methods. From 120 data tested using variations in test data and training data which are then processed using variations in the learning rate parameters and epochs. From the test results obtained the level of accuracy of pattern recognition in the backpropagation method is 99.17% with a learning rate variation of 0.1 and epoch 100, the learning vector quantization method has an accuracy rate of 96.67% with a variation of learning rate 1 and epoch 20 From the results of the comparison the Backpropagation method is superior in terms of accuracy so that it becomes the right method to use in exploring the potential of new students at STMIK PalComTech.

MLVQ

2011 ◽  
pp. 485-509
Author(s):  
Kai Keng Ang ◽  
Chai Quek
Keyword(s):  
Vector Quantization ◽  
Time Series Prediction ◽  
Learning Vector Quantization ◽  
Training Data ◽  
Forced Termination ◽  
Membership Functions ◽  
Number Of Clusters ◽  
One Dimensional ◽  
Fuzzy Neural ◽  
Proper Training

The Learning Vector Quantization (LVQ) algorithm and its variants have been employed in some fuzzy neural networks to automatically derive membership functions from training data. Although several improvements to the LVQ algorithm have been proposed, problematic areas of the LVQ algorithm include: the selection of number of clusters, initial weights, proper training parameters, and forced termination. These problematic areas in the derivation of centroids of one-dimensional data are illustrated with an artificially generated experimental data set on LVQ, GLVQ, and FCM. A Modified Learning Vector Quantization (MLVQ) algorithm is presented in this chapter to address these problematic areas for one-dimensional data. MLVQ models the development of the nervous system in two stages: a first stage where the basic architecture and coarse connections patterns are laid out, and a second stage where the initial architecture is refined in activity-dependent ways. MLVQ determines the learning constant parameter and modifies the terminating condition of the LVQ algorithm so that convergence can be achieved and easily detected. Experiments on the MLVQ algorithm are performed and contrasted against LVQ, GLVQ, and FCM. Results show that MLVQ determines the number of clusters and converges to the centroids. Results also show that MLVQ is insensitive to the sequence of the training data, able to identify centroids of overlapping clusters, and able to ignore outliners without identifying them as separate clusters. Results using MLVQ algorithm and Gaussian membership functions with Pseudo Outer-Product Fuzzy Neural Network using Compositional Rule of Inference and Singleton fuzzifier (POPFNN-CRI(S)) on pattern classification and time series prediction are also provided to demonstrate the effectiveness of the fuzzy membership functions derived using MLVQ.

Classification of Postural Profiles among Mouth-breathing Children by Learning Vector Quantization

2011 ◽  
Vol 50 (04) ◽  
pp. 349-357 ◽  
Author(s):  
F. Mancini ◽  
F. S. Sousa ◽  
A. D. Hummel ◽  
A. E. J. Falcão ◽  
L. C. Yi ◽  
...  
Keyword(s):  
Vector Quantization ◽  
Learning Vector Quantization ◽  
Mouth Breathing ◽  
Training Data ◽  
Validation Dataset ◽  
Multilayer Perceptrons ◽  
Kappa Statistics ◽  
Validation Data ◽  
Abnormal Posture ◽  
Postural Changes

SummaryBackground: Mouth breathing is a chronic syndrome that may bring about postural changes. Finding characteristic patterns of changes occurring in the complex musculoskeletal system of mouth-breathing children has been a challenge. Learning vector quantization (LVQ) is an artificial neural network model that can be applied for this purpose.Objectives: The aim of the present study was to apply LVQ to determine the characteristic postural profiles shown by mouth-breathing children, in order to further understand abnormal posture among mouth breathers.Methods: Postural training data on 52 children (30 mouth breathers and 22 nose breathers) and postural validation data on 32 children (22 mouth breathers and 10 nose breathers) were used. The performance of LVQ and other classification models was compared in relation to self-organizing maps, back-propagation applied to multilayer perceptrons, Bayesian networks, naive Bayes, J48 decision trees, k*, and k-nearest-neighbor classifiers. Classifier accuracy was assessed by means of leave-one-out cross-validation, area under ROC curve (AUC), and inter-rater agreement (Kappa statistics).Results: By using the LVQ model, five postural profiles for mouth-breathing children could be determined. LVQ showed satisfactory results for mouth-breathing and nose-breathing classification: sensitivity and specificity rates of 0.90 and 0.95, respectively, when using the training dataset, and 0.95 and 0.90, respectively, when using the validation dataset.Conclusions: The five postural profiles for mouth-breathing children suggested by LVQ were incorporated into application software for classifying the severity of mouth breathers’ abnormal posture.

scholarly journals Prediksi Jenis Cacing Nematoda Usus Yang Menginfeksi Siswa Dengan Menggunakan Metoda LVQ

2017 ◽  
Vol 8 (2) ◽  
pp. 170-184
Author(s):  
Erni Rouza
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Vector Quantization ◽  
Learning Vector Quantization ◽  
Training Data ◽  
Neural Network Learning ◽  
Intestinal Nematode ◽  
Artificial Neural ◽  
Level Of Training ◽  
Artificial Neural Network Ann

Abstrak-Pada saat ini, Jaringan Syaraf Tiruan (JST) telah banyak menjadi objek penelitian yang menarik, karena penerapannya sangat potensial dalam berbagai bidang sains, salah satu penerapannya didalam memprediksi penyakit. Penelitian ini bertujuan untuk mencoba menerapkan metode Learning vector Quantization (LVQ) dalam memprediksi jenis cacing Nematoda usus yang menginfeksi siswa dari nilai akurasi yang dihasilkan, karena beberapa penelitian menunjukkan bahwa anak usia sekolah dasar merupakan golongan yang sering terkena infeksi cacing usus. Dari hasil pelatihan dan pengujian menggunakan metode Learning Vector Quantization (LVQ) diketahui bahwa tingkat akurasi sesuai dengan hasil sebenarnya dan nilainya konstan, proses cepat hanya membutuhkan waktu paling lama 3 menit dan memberikan hasil yang optimal yaitu tingkat akurasi data latih sebesar 78,6885%, serta 80% untuk data uji. Hal ini menunjukkan bahwa jaringan yang terbentuk sudah cukup baik, akurat dan cepat dalam melakukan pembelajaran terhadap data input yang diberikan dalam memprediksi jenis cacing Nematoda Usus yang menginfeksi siswa. Kata kunci : Cacing Nematoda Usus, Jaringan Syaraf Tiruan, Learning Vector Quantization Abstract- At this time, an Artificial Neural Network (ANN) has been an interesting objects of research, because of application has potential in various fields of science, one application was used to predict diseases. This study aims to try to implement methods Learning vector quantization (LVQ) in predicting the type of Nematode worms that infect the intestines of students from the resulting accuracy value, because some studies show that children of primary school age are often exposed to a class of intestinal worm infections. From the results of the training and testing using methods Learning Vector Quantization (LVQ) note that the level of accuracy in accordance with the actual results and the value of the constant, quick process only takes a maximum of 3 minutes and provide optimal results is the level of training data accuracy of 78.6885%, and 80% for the test data. This indicates that the network is formed is quite good, accurate and fast in doing the learning on the input data given in predicting Intestinal Nematode worm species that infect students. Keywords: Intestinal Netamoda Worms, Artificial Neural Network, Learning Vector Quantization

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