scholarly journals Deteksi Gulma Berdasarkan Warna HSV dan Fitur Bentuk Menggunakan Jaringan Syaraf Tiruan

2021 ◽  
Vol 8 (5) ◽  
pp. 929
Author(s):  
Hurriyatul Fitriyah ◽  
Rizal Maulana
Keyword(s):  
Learning Algorithm ◽  
Color Space ◽  
Outdoor Environment ◽  
Training Data ◽  
Distance Transform ◽  
Validation Data ◽  
Hsv Color Space ◽  
Testing Data ◽  
Artificial Neural Network Ann ◽  
Center Distance

<p class="Abstrak">Gulma merupakan tanaman pengganggu dalam lahan pertanian. Herbisida merupakan obat yang efektif membunuh gulma tersebut. Penyemprotan herbisida harus tepat sasaran kepada gulma saja dan tidak mengenai tanaman. Penelitian ini membuat sistem yang dapat mendeteksi gulma secara otomatis di antara tanaman pada lahan pertanian riil. Sistem ini menggunakan gambar lahan pertanian riil dimana tanaman tampak utuh (daun dapat lebih dari satu) yang diambil menggunakan kamera dengan posisi vertikal menghadap ke bawah. Algoritma yang dibuat menggunakan segmentasi berdasarkan warna hijau dalam ruang warna HSV untuk mendeteksi daun, baik gulma maupun tanaman pada beragam pencahayaan. Sebanyak tiga fitur bentuk domain spasial digunakan untuk membedakan gulma dengan tanaman yang memiliki karakteristik bentuk daun yang berbeda. Fitur bentuk yang digunakan adalah <em>Rectangularity, Edge-to-Center distances function</em>, dan <em>Distance Transform function</em>. Klasifikasi gulma dan tanaman menggunakan metode Jaringan syaraf tiruan (JST) yang dapat dilatih secara <em>offline. </em>Dari 149 tanaman yang terdeteksi dimana 70% sebagai data training, 15% data validasi dan 15% data uji, didapati akurasi pengujian sebesar 95.46%.</p><p class="Abstrak"><em><strong><br /></strong></em></p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstract"><em>Weed is a major challenge in a crop plantation. A herbicide is the most effective substance to kill this unwanted vegetation. Spraying the herbicide must be done carefully to target the weeds only. Here in this research, we develop an algorithm that detects weeds among the plants based on the shape of their leaves. The detection is based on images that were acquired using a camera. The leaves of weeds and plants were detected based on their green color using segmentation in HSV color-space as it is more effective to detect objects in various illumination. Three shape features were extracted, which are Rectangularity that is based on Rectangularity, Edge-to-Center distance function, and Distance Transform function. Those features were fed into a learning algorithm, Artificial Neural Network (ANN), to classify whether it is the plant or the weed. The testing on the weed classification in a real outdoor environment showed 95.46% accuracy using a total of 149 detected plants (70% as training data, 15%  as validation data, and 15% as testing data).<strong></strong></em></p><p class="Abstrak"><em><strong><br /></strong></em></p>

Neural Network MIMO Model for Production of Isopropyl Myristate in a Semibatch Reactive Distillation Column

2013 ◽  
Vol 284-287 ◽  
pp. 403-408
Author(s):  
Nur Alwani Ali Bashah ◽  
Mohd Roslee Othman ◽  
Norashid Aziz
Keyword(s):  
Neural Network ◽  
Reactive Distillation ◽  
Batch Reactor ◽  
Isopropyl Myristate ◽  
Ann Model ◽  
Validation Data ◽  
Testing Data ◽  
Ann Models ◽  
Artificial Neural Network Ann ◽  
Mimo Model

Batch reactive distillation is an integrated unit of batch reactor and distillation. It provides benefits of having higher conversion and yield by continuous removal of side product. The aim of this paper is to develop an artificial neural network (ANN) based model for production of isopropyl myristate in an industrial scaled semibatch reactive distillation. Two cases of the MIMO model were developed. Case 1 does not consider historical data as inputs while case 2 does. The trained ANN for both cases was validated with independent validation data and the best architecture was optimized. Case 1 resulted to 8 inputs, 14 hidden nodes and 2 outputs [8-14-2] ANN while Case 2 resulted to [12-13-2] ANN. The results show that both ANN models have ability to predict the unknown validation and testing data very well. However, the [8-14-2] ANN model produce higher accuracy than [12-13-2] ANN model with MSE of 0.00094 and 0.0013, respectively.

scholarly journals Evaluasi Performasi Ruang Warna pada Klasifikasi Diabetic Retinophaty Menggunakan Convolution Neural Network

2021 ◽  
Vol 8 (3) ◽  
pp. 619
Author(s):  
Candra Dewi ◽  
Andri Santoso ◽  
Indriati Indriati ◽  
Nadia Artha Dewi ◽  
Yoke Kusuma Arbawa
Keyword(s):  
Neural Network ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Learning Algorithm ◽  
Color Space ◽  
Training Data ◽  
Color Spaces ◽  
Deep Learning Algorithm ◽  
Optimal Accuracy

<p>Semakin meningkatnya jumlah penderita diabetes menjadi salah satu faktor penyebab semakin tingginya penderita penyakit <em>diabetic retinophaty</em>. Salah satu citra yang digunakan oleh dokter mata untuk mengidentifikasi <em>diabetic retinophaty</em> adalah foto retina. Dalam penelitian ini dilakukan pengenalan penyakit diabetic retinophaty secara otomatis menggunakan citra <em>fundus</em> retina dan algoritme <em>Convolutional Neural Network</em> (CNN) yang merupakan variasi dari algoritme Deep Learning. Kendala yang ditemukan dalam proses pengenalan adalah warna retina yang cenderung merah kekuningan sehingga ruang warna RGB tidak menghasilkan akurasi yang optimal. Oleh karena itu, dalam penelitian ini dilakukan pengujian pada berbagai ruang warna untuk mendapatkan hasil yang lebih baik. Dari hasil uji coba menggunakan 1000 data pada ruang warna RGB, HSI, YUV dan L*a*b* memberikan hasil yang kurang optimal pada data seimbang dimana akurasi terbaik masih dibawah 50%. Namun pada data tidak seimbang menghasilkan akurasi yang cukup tinggi yaitu 83,53% pada ruang warna YUV dengan pengujian pada data latih dan akurasi 74,40% dengan data uji pada semua ruang warna.</p><p> </p><p><em><strong>Abstract</strong></em></p><p class="Abstract"><em>Increasing the number of people with diabetes is one of the factors causing the high number of people with diabetic retinopathy. One of the images used by ophthalmologists to identify diabetic retinopathy is a retinal photo. In this research, the identification of diabetic retinopathy is done automatically using retinal fundus images and the Convolutional Neural Network (CNN) algorithm, which is a variation of the Deep Learning algorithm. The obstacle found in the recognition process is the color of the retina which tends to be yellowish red so that the RGB color space does not produce optimal accuracy. Therefore, in this research, various color spaces were tested to get better results. From the results of trials using 1000 images data in the color space of RGB, HSI, YUV and L * a * b * give suboptimal results on balanced data where the best accuracy is still below 50%. However, the unbalanced data gives a fairly high accuracy of 83.53% with training data on the YUV color space and 74,40% with testing data on all color spaces.</em></p><p><em><strong><br /></strong></em></p>

scholarly journals Sensor Number Optimization Using Neural Network for Ankle Foot Orthosis Equipped with Magnetorheological Brake

2020 ◽  
Vol 11 (1) ◽  
pp. 91-101
Author(s):  
Dimas Adiputra ◽  
Mohd Azizi Abdul Rahman ◽  
Irfan Bahiuddin ◽  
Ubaidillah ◽  
Fitrian Imaduddin ◽  
...  
Keyword(s):  
Neural Network ◽  
Training Data ◽  
Bayesian Regularization ◽  
Training Algorithms ◽  
Validation Data ◽  
Ankle Foot Orthosis ◽  
Emg Signal ◽  
Testing Data ◽  
Foot Orthosis ◽  
Narx Neural Network

Abstract A passive controlled ankle foot orthosis (PICAFO) used a passive actuator such as Magnetorheological (MR) brake to control the ankle stiffness. The PICAFO used two kinds of sensors, such as Electromyography (EMG) signal and ankle position (two inputs) to determine the amount of stiffness (one output) to be generated by the MR brake. As the overall weight and design of an orthotic device must be optimized, the sensor numbers on PICAFO wanted to be reduced. To do that, a machine learning approach was implemented to simplify the previous stiffness function. In this paper, Non-linear Autoregressive Exogeneous (NARX) neural network were used to generate the simplified function. A total of 2060 data were used to build the network with detail such as 1309 training data, 281 validation data, 281 testing data 1, and 189 testing data 2. Three training algorithms were used such as Levenberg-Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient. The result shows that the function can be simplified into one input (ankle position) – one output (stiffness). Optimized result was shown by the NARX neural network with 15 hidden layers and trained using Bayesian Regularization with delay 2. In this case, the testing data shows R-value of 0.992 and MSE of 19.16.

scholarly journals Artificial neural network (ANN) approach to predicting micro hardness profile values of iron-based sintered alloys

2017 ◽  
Vol 1 (1) ◽  
pp. 1-5
Author(s):  
Desislava Yordanova Mincheva ◽  
Georgi Stefanov Antonov
Keyword(s):  
Neural Network ◽  
Learning Algorithm ◽  
Experimental Testing ◽  
Training Data ◽  
Hardness Profile ◽  
Micro Hardness ◽  
Iron Based ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Sintered Alloys

Recent interest in artificial neural networks has considerably extended their use in the field of powder metallurgy. Advanced in the paper is a model for predicting the micro hardness of sintered compacts made from iron powders and powder mixtures through the process of sintering performed in different atmospheres. The proposed model is based on three layer neural network with backpropagation learning algorithm. Specially developed software has been used to provide for the proper functioning of the neural network. Moreover, it should also be noted that the training data used to carry out the research has been collected by a laboratory controlled experimental testing. Finally, the paper concludes that the presented neural network model is applicable for hardness profile prediction of iron-based sintered alloys as confirmed by the experimental results.

scholarly journals Pemodelan Generator Uap Berbasis Jaringan Saraf Tiruan dengan Algoritme Pelatihan BPGD-ALAM

2016 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
Fadhlia Annisa ◽  
Agfianto Eko Putra
Keyword(s):  
Neural Network ◽  
Steam Generator ◽  
Multiple Input Multiple Output ◽  
Network Models ◽  
Training Data ◽  
Training Process ◽  
Training Simulator ◽  
Neural Network Models ◽  
Validation Data ◽  
Testing Data

Steam generator is unit plant which has nonlinear and complex system with multiple-input-multiple-output (MIMO) configuration which is hard to be modeled. Whereas, steam generator model is very useful to create simulation such as operator training simulator (OTS). The purpose of this research is to obtain model of steam generator which has 8 output parameters and 9 input parameters based neural network (NN) with BPGD-ALAM training algorithm. Data had been taken from steam generator of PT. Chevron Pacific Indonesia, Duri and it is divided into three types, i.e training data, validation data and testing data. Training data was used to obtain model for each ouput through training process. Verification model is also done for each epoch using validation data to monitor training process whether overfitting occurs or not. Eight NN model of each output which is obtained from training and verification, is tested using testing data for getting its performance. From the reseach results, architecture of neural network models are obtained with various configuration for each output with RMSE value under 9.71 %. It shows that model which has been obtained, close with steam generator real system.

Artificial intelligence accurately identifies total hip arthroplasty implants: a tool for revision surgery

2021 ◽  
pp. 112070002098752
Author(s):  
Michael Murphy ◽  
Cameron Killen ◽  
Robert Burnham ◽  
Fahad Sarvari ◽  
Karen Wu ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Learning Algorithm ◽  
Revision Arthroplasty ◽  
Femoral Stem ◽  
Training Data ◽  
Consecutive Series ◽  
Validation Data ◽  
Total Hip ◽  
Artificial Neural

Background: A critical part in preoperative planning for revision arthroplasty surgery involves the identification of the failed implant. Using a predictive artificial neural network (ANN) model, the objectives of this study were: (1) to develop a machine-learning algorithm using operative big data to identify an implant from a radiograph; and (2) to compare algorithms that optimise accuracy in a timely fashion. Methods: Using 2116 postoperative anteroposterior (AP) hip radiographs of total hip arthroplasties from 2002 to 2019, 10 artificial neural networks were modeled and trained to classify the radiograph according to the femoral stem implanted. Stem brand and model was confirmed with 1594 operative reports. Model performance was determined by classification accuracy toward a random 706 AP hip radiographs, and again on a consecutive series of 324 radiographs prospectively collected over 2019. Results: The Dense-Net 201 architecture outperformed all others with 100.00% accuracy in training data, 95.15% accuracy on validation data, and 91.16% accuracy in the unique prospective series of patients. This outperformed all other models on the validation ( p < 0.0001) and novel series ( p < 0.0001). The convolutional neural network also displayed the probability (confidence) of the femoral stem classification for any input radiograph. This neural network averaged a runtime of 0.96 (SD 0.02) seconds for an iPhone 6 to calculate from a given radiograph when converted to an application. Conclusions: Neural networks offer a useful adjunct to the surgeon in preoperative identification of the prior implant.

scholarly journals COMPARITIVE STUDY OF TREE COUNTING ALGORITHMS IN DENSE AND SPARSE VEGETATIVE REGIONS

2018 ◽  
Vol XLII-5 ◽  
pp. 801-808
Author(s):  
S. Khan ◽  
P. K. Gupta
Keyword(s):  
Deep Learning ◽  
Color Space ◽  
Watershed Algorithm ◽  
Tree Crown ◽  
Validation Data ◽  
Hsv Color Space ◽  
Sparse Vegetation ◽  
Detection And Counting ◽  
Counting Algorithms

<p><strong>Abstract.</strong> Tree counting can be a challenging and time consuming task, especially if done manually. This study proposes and compares three different approaches for automatic detection and counting of trees in different vegetative regions. First approach is to mark extended minima’s, extended maxima’s along with morphological reconstruction operations on an image for delineation and tree crown segmentation. To separate two touching crowns, a marker controlled watershed algorithm is used. For second approach, the color segmentation method for tree identification is used. Starting with the conversion of an RGB image to HSV color space then filtering, enhancing and thresholding to isolate trees from non-trees elements followed by watershed algorithm to separate touching tree crowns. Third approach involves deep learning method for classification of tree and non-tree, using approximately 2268 positive and 1172 negative samples each. Each segment of an image is then classified and sliding window algorithm is used to locate each tree crown. Experimentation shows that the first approach is well suited for classification of trees is dense vegetation, whereas the second approach is more suitable for detecting trees in sparse vegetation. Deep learning classification accuracy lies in between these two approaches and gave an accuracy of 92% on validation data. The study shows that deep learning can be used as a quick and effective tool to ascertain the count of trees from airborne optical imagery.</p>

scholarly journals Implementasi Jaringan Syaraf Tiruan Recurrent Dengan Metode Pembelajaran Gradient Descent Adaptive Learning Rate Untuk Pendugaan Curah Hujan Berdasarkan Peubah Enso

2010 ◽  
Vol 1 (2) ◽  
pp. 418
Author(s):  
Afan Galih Salman ◽  
Yen Lina Prasetio
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Adaptive Learning ◽  
Gradient Descent ◽  
Learning Rate ◽  
Training Data ◽  
Testing Data ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Data Group

The use of technology of technology Artificial Neural Network (ANN) in prediction of rainfall can be done using the learning approach. ANN prediction accuracy measured by the coefficient of determination (R2) and Root Mean Square Error (RMSE).This research employ a recurrent optimized heuristic Artificial Neural Network (ANN) Recurrent Elman gradient descent adaptive learning rate approach using El-Nino Southern Oscilation (ENSO) variable, namely Wind, Southern Oscillation Index (SOI), Sea Surface Temperatur (SST) dan Outgoing Long Wave Radiation (OLR) to forecast regional monthly rainfall. The patterns of input data affect the performance of Recurrent Elman neural network in estimation process. The first data group that is 75% training data and 25% testing data produce the maximum R2 69.2% at leap 0 while the second data group that is 50% training data & 50% testing data produce the maximum R2 53.6%.at leap 0 Our result on leap 0 is better than leap 1,2 or 3. 

scholarly journals Penerapan Artificial Neural Network (ANN) untuk Memprediksi Perubahan Derajat Miopia pada Manusia

2020 ◽  
Vol 10 (1) ◽  
pp. 53
Author(s):  
Ni Kadek Emik Sapitri ◽  
I Putu Eka N. Kencana ◽  
Luh Putu Ida Harini
Keyword(s):  
Training Data ◽  
Fuzzy Membership ◽  
Fuzzy Membership Function ◽  
Individual Identity ◽  
Vision Disorder ◽  
Testing Data ◽  
Visual Disorders ◽  
Artificial Neural Network Ann ◽  
The Right ◽  
And Behavior

Myopia is a vision disorder that causes the sufferers unable to see distant objects. The degree of myopia in humans can changes, both increasing and decreasing. The increasing of myopia degree is proportional to the potential of other visual disorders, such as cataracts, retinal detachment, and glaucoma. Therefore, the increasing of myopia degree needs to be watched out. Several previous studies only considered the time factor in predicting the changes of myopia degree. In fact, the changes of myopia degree also influenced by some factors that related to individual identity and behavior. This study aims to predict the changes of myopia degree in humans based on some factors that causes myopia.. This study uses data that has been scaled with the fuzzy membership function to be processed with ANN for predicting the changes of myopia degree. By ANN 6-2-3 architecture that uses 80 training data, 20 testing data, and 1 predictive data, the prediction result of the changes of  myopia degree in the right eye is 1.1 dioptri, in the left eye is 1.2 dioptri and the accumulated of both is 2.3 dioptri with accuration values 87.79%, 78.47%, and 83.21%.

scholarly journals Optimization of Support Vector Machine Method Using Feature Selection to Improve Classification Results

2021 ◽  
Vol 4 (1) ◽  
pp. 22-27
Author(s):  
Saikin Saikin ◽  
◽  
Sofiansyah Fadli ◽  
Maulana Ashari ◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Training Data ◽  
Support Vector ◽  
Feature Selection Technique ◽  
Validation Data ◽  
Machine Method ◽  
Testing Data ◽  
The Impact

The performance of the organizations or companiesare based on the qualities possessed by their employee. Both of good or bad employee performance will have an impact on productivity and the impact of profits obtained by the company. Support Vector Machine (SVM) is a machine learning method based on statistical learning theory and can solve high non-linearity, regression, etc. In machine learning, the optimization model is a part for improving the accuracy of the model for data learning. Several techniques are used, one of which is feature selection, namely reducing data dimensions so that it can reduce computation in data modeling. This study aims to apply the method of machine learning to the employee data of the Bank Rakyat Indonesia (BRI) company. The method used is SVM method by increasing the accuracy of learning data by using a feature selection technique using a wrapper algorithm. From the results of the classification test, the average accuracy obtained is 72 percent with a precision value of 71 and the recall value is rounded off to 72 percent, with a combination of SVM and cross-validation. Data obtained from Kaggle data, which consists of training data and testing data. each consisting of 30 columns and 22005 rows in the training data and testing data consisting of 29 col-umns and 6000 rows. The results of this study get a classification score of 82 percent. The precision value obtained is rounded off to 82 percent, a recall of 86 percent and an f1-score of 81 percent.

Sign in / Sign up

Export Citation Format

Share Document