scholarly journals Pengenalan Spesies Ikan Berdasarkan Kontur Otolith Menggunakan Convolutional Neural Network

2019 ◽  
Vol 2 (1) ◽  
pp. 41
Author(s):  
Heri Darmanto
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Discriminant Analysis ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Gradient Descent ◽  
Stochastic Gradient ◽  
Stochastic Gradient Descent ◽  
Processing Unit ◽  
Graphical Processing

Hasil sensus kehidupan laut pada tahun 2013 di seluruh dunia terdapat lebih dari 23.000 spesies dan masih banyak sekali spesies ikan yang belum diidentifikasi. Otolith merupakan organ yang sangat penting di belakang telinga ikan, karena melalui otolith ini dapat diketahui jenis ikan, pertumbuhan dan lingkungan, serta sejarah kehidupannya,  misalnya, umur, reproduksi, dan migrasi. Dengan semakin  canggihnya komputer dan pengolahan di bidang citra,  diharapkan  kemampuan  mengidentifikasi jenis  ikan  yang dimiliki oleh manusia bisa diadopsi  dan diterapkan pada perangkat komputer. Deep Learning saat ini semakin berkembang memanfaatkan sumber daya perangkat keras yang semakin canggih termasuk penggunaan GPU (Graphical Processing Unit) untuk perhitungan proses komputasi dengan akurasi yang lebih baik dan proses yang lebih cepat. Pada penelitian ini metode yang diusulkan, untuk keperluan klasifikasi ikan menggunakan metode Convolutional Neural Network dengan teknik Transfer Learning dari model Alexnet dan optimasi Momentum Stochastic Gradient Descent. Hasil eksperimen diperoleh akurasi sebesar 95.4% lebih tinggi dibanding metode Discriminant Analysis yang memiliki akurasi sebesar 92%.

scholarly journals Implementation of Deep Learning for Slump Optimization Based on Concrete Quality Using Convolutional Neural Network in PT. SELO PROGO SAKTI

2020 ◽  
Vol 5 (2) ◽  
pp. 83-88
Author(s):  
Hedi Pandowo
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Experimental Design ◽  
Convolutional Neural Network ◽  
Processing Unit ◽  
Training Process ◽  
Average Accuracy ◽  
Graphical Processing ◽  
Intelligence Science

Deep Learning is part of the scientific field of Machine Learning and Machine Learning is part of Artificial Intelligence science. Deep Learning has extraordinary capabilities by using a hardware Graphical Processing Unit (GPU) so that the artificial requirement network can run faster than using a Personal Computer Unit (CPU). Especially in terms of object classification in images using existing methods in the Convolutional Neural Network (CNN). The method used in this research is Preprocessing and Processing of Input Data, Training Process in which CNN is trained to obtain high accuracy from the classification carried out and the Testing Process which is a classification process using weights and bias from the results of the training process. This type of research is a pre experimental design (pre experimental design). The results of the object image classification test with different levels of confusion in the Concrete database with the Mix Design K-125, K-150, K-250 and K-300 produce an average accuracy value. This is also relevant to measuring the failure rate of concrete or slump

scholarly journals Kinerja Algoritma Optimasi Root-Mean-Square Propagation dan Stochastic Gradient Descent pada Klasifikasi Pneumonia Covid-19 Menggunakan CNN

2021 ◽  
Vol 7 (3) ◽  
pp. 420
Author(s):  
Budi Nugroho ◽  
Eva Yulia Puspaningrum ◽  
M. Syahrul Munir
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Root Mean Square ◽  
Gradient Descent ◽  
Stochastic Gradient ◽  
Stochastic Gradient Descent ◽  
Mean Square ◽  
X Ray

Penelitian ini berkaitan dengan proses klasifikasi Pneumonia Covid-19 (radang paru-paru atau pneumonia yang disebabkan oleh virus corona SARS-CoV-2) dari citra hasil foto rontgen / x-ray paru-paru dengan menggunakan pendekatan pembelajaran mesin. Klasifikasi dilakukan untuk menentukan apakah kondisi paru-paru seseorang mengalami Pneumonia Covid-19, Pneumonia biasa, atau Normal / Sehat. Untuk menghasilkan kinerja klasifikasi yang lebih baik, proses optimasi seringkali digunakan pada tahap pelatihan data. Banyak teknik yang digunakan untuk melakukan optimasi tersebut, diantaranya adalah algoritma Root-Mean-Square Propagation (RMSprop) dan Stochastic Gradient Descent (SGD). Pada penelitian ini, pengujian dilakukan terhadap kedua metode tersebut untuk mengetahui kinerjanya pada klasifikasi Pneumonia Covid-19. Metode klasifikasi menggunakan Convolutional Neural Network (CNN) yang menerapkan 5 layer konvolusi dengan nilai filter 16, 32, 64, 128, dan 256. Proses pelatihan menggunakan 3.900 citra yang terdiri atas 1.300 citra pneumonia covid-19, 1.300 citra pneumonia, dan 1.300 citra normal. Sedangkan proses validasi menggunakan 450 citra dan proses pengujian mengunakan 225 citra. Berdasarkan uji coba yang telah dilakukan, implementasi algoritma optimasi RMSprop menghasilkan akurasi 87,99%, presisi 0,88, recall 0,86, dan f1 score 0,87. Sedangkan implementasi algoritma optimasi SGD menghasilkan akurasi 66,22%, presisi 0,69, recall 0,64, dan f1 score 0,67. Hasil ini memberikan informasi penting bahwa algoritma optimasi RMSprop menghasilkan kinerja yang jauh lebih baik daripada SGD pada klasifikasi Pneumonia Covid-19.

scholarly journals Multiclass Recognition of Offline Handwritten Devanagari Characters using CNN

2020 ◽  
Vol 5 (6) ◽  
pp. 1429-1439
Author(s):  
Mamta Bisht ◽  
Richa Gupta
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Character Recognition ◽  
Gradient Descent ◽  
Recognition Task ◽  
Stochastic Gradient ◽  
Stochastic Gradient Descent ◽  
Handwritten Character Recognition ◽  
Handwritten Documents ◽  
Acceptable Accuracy

The handwriting style of every writer consists of variations, skewness and slanting nature and therefore, it is a stimulating task to recognise these handwritten documents. This article presents a study on various methods available in literature for Devanagari handwritten character recognition and performs its implementation using Convolutional neural network (CNN). Available methods are studied on different parameters and a tabular comparison is also presented which concludes superiority of CNN model in character recognition task. The proposed CNN model results in well acceptable accuracy using dropout and stochastic gradient descent (SGD) optimizer.

Deep Learning With TensorFlow: A Review

2019 ◽  
Vol 45 (2) ◽  
pp. 227-248 ◽  
Author(s):  
Bo Pang ◽  
Erik Nijkamp ◽  
Ying Nian Wu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Network Models ◽  
Optimization Method ◽  
Stochastic Gradient Descent ◽  
Processing Unit ◽  
Neural Network Models ◽  
Core Concepts ◽  
High Level

This review covers the core concepts and design decisions of TensorFlow. TensorFlow, originally created by researchers at Google, is the most popular one among the plethora of deep learning libraries. In the field of deep learning, neural networks have achieved tremendous success and gained wide popularity in various areas. This family of models also has tremendous potential to promote data analysis and modeling for various problems in educational and behavioral sciences given its flexibility and scalability. We give the reader an overview of the basics of neural network models such as the multilayer perceptron, the convolutional neural network, and stochastic gradient descent, the most commonly used optimization method for neural network models. However, the implementation of these models and optimization algorithms is time-consuming and error-prone. Fortunately, TensorFlow greatly eases and accelerates the research and application of neural network models. We review several core concepts of TensorFlow such as graph construction functions, graph execution tools, and TensorFlow’s visualization tool, TensorBoard. Then, we apply these concepts to build and train a convolutional neural network model to classify handwritten digits. This review is concluded by a comparison of low- and high-level application programming interfaces and a discussion of graphical processing unit support, distributed training, and probabilistic modeling with TensorFlow Probability library.

Share Market Data Prediction Strategies using Deep Learning Algorithm

2019 ◽  
Vol 13 ◽  
Author(s):  
A John. ◽  
D. Praveen Dominic ◽  
M. Adimoolam ◽  
N. M. Balamurugan
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Stock Market ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Market Price ◽  
Stochastic Gradient ◽  
Stochastic Gradient Descent ◽  
Mining Machine ◽  
Gradient Descent Algorithm

Background:: Predictive analytics has a multiplicity of statistical schemes from predictive modelling, data mining, machine learning. It scrutinizes present and chronological data to make predictions about expectations or if not unexplained measures. Most predictive models are used for business analytics to overcome loses and profit gaining. Predictive analytics is used to exploit the pattern in old and historical data. Objective: People used to follow some strategies for predicting stock value to invest in the more profit-gaining stocks and those strategies to search the stock market prices which are incorporated in some intelligent methods and tools. Such strategies will increase the investor’s profits and also minimize their risks. So prediction plays a vital role in stock market gaining and is also a very intricate and challenging process. Method: The proposed optimized strategies are the Deep Neural Network with Stochastic Gradient for stock prediction. The Neural Network is trained using Back-propagation neural networks algorithm and stochastic gradient descent algorithm as optimal strategies. Results: The experiment is conducted for stock market price prediction using python language with the visual package. In this experiment RELIANCE.NS, TATAMOTORS.NS, and TATAGLOBAL.NS dataset are taken as input dataset and it is downloaded from National Stock Exchange site. The artificial neural network component including Deep Learning model is most effective for more than 100,000 data points to train this model. This proposed model is developed on daily prices of stock market price to understand how to build model with better performance than existing national exchange method.

scholarly journals Effectiveness of transfer learning for enhancing tumor classification with a convolutional neural network on frozen sections

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Young-Gon Kim ◽  
Sungchul Kim ◽  
Cristina Eunbee Cho ◽  
In Hye Song ◽  
Hee Jin Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Frozen Section ◽  
Medical Center ◽  
External Validation ◽  
Model Performance ◽  
Classification Model ◽  
Training Dataset

AbstractFast and accurate confirmation of metastasis on the frozen tissue section of intraoperative sentinel lymph node biopsy is an essential tool for critical surgical decisions. However, accurate diagnosis by pathologists is difficult within the time limitations. Training a robust and accurate deep learning model is also difficult owing to the limited number of frozen datasets with high quality labels. To overcome these issues, we validated the effectiveness of transfer learning from CAMELYON16 to improve performance of the convolutional neural network (CNN)-based classification model on our frozen dataset (N = 297) from Asan Medical Center (AMC). Among the 297 whole slide images (WSIs), 157 and 40 WSIs were used to train deep learning models with different dataset ratios at 2, 4, 8, 20, 40, and 100%. The remaining, i.e., 100 WSIs, were used to validate model performance in terms of patch- and slide-level classification. An additional 228 WSIs from Seoul National University Bundang Hospital (SNUBH) were used as an external validation. Three initial weights, i.e., scratch-based (random initialization), ImageNet-based, and CAMELYON16-based models were used to validate their effectiveness in external validation. In the patch-level classification results on the AMC dataset, CAMELYON16-based models trained with a small dataset (up to 40%, i.e., 62 WSIs) showed a significantly higher area under the curve (AUC) of 0.929 than those of the scratch- and ImageNet-based models at 0.897 and 0.919, respectively, while CAMELYON16-based and ImageNet-based models trained with 100% of the training dataset showed comparable AUCs at 0.944 and 0.943, respectively. For the external validation, CAMELYON16-based models showed higher AUCs than those of the scratch- and ImageNet-based models. Model performance for slide feasibility of the transfer learning to enhance model performance was validated in the case of frozen section datasets with limited numbers.

Deep learning for denoising

Geophysics ◽  
2019 ◽  
Vol 84 (6) ◽  
pp. V333-V350 ◽  
Author(s):  
Siwei Yu ◽  
Jianwei Ma ◽  
Wenlong Wang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Graphics Processing Unit ◽  
Random Noise ◽  
Stochastic Gradient Descent ◽  
Processing Unit ◽  
Noise Attenuation ◽  
Optimal Parameters ◽  
Training Set ◽  
Unknown Variance

Compared with traditional seismic noise attenuation algorithms that depend on signal models and their corresponding prior assumptions, removing noise with a deep neural network is trained based on a large training set in which the inputs are the raw data sets and the corresponding outputs are the desired clean data. After the completion of training, the deep-learning (DL) method achieves adaptive denoising with no requirements of (1) accurate modelings of the signal and noise or (2) optimal parameters tuning. We call this intelligent denoising. We have used a convolutional neural network (CNN) as the basic tool for DL. In random and linear noise attenuation, the training set is generated with artificially added noise. In the multiple attenuation step, the training set is generated with the acoustic wave equation. The stochastic gradient descent is used to solve the optimal parameters for the CNN. The runtime of DL on a graphics processing unit for denoising has the same order as the [Formula: see text]-[Formula: see text] deconvolution method. Synthetic and field results indicate the potential applications of DL in automatic attenuation of random noise (with unknown variance), linear noise, and multiples.

scholarly journals Electrocardiogram Classification for Arrhythmia using Convolutional Neural Network 2D and Adabound Optimizer

2020 ◽  
Vol 8 (5) ◽  
pp. 1277-1284
Keyword(s):  
Neural Network ◽  
Cardiovascular Disease ◽  
Convolutional Neural Network ◽  
Gradient Descent ◽  
Stochastic Gradient Descent ◽  
Transform Method ◽  
The World ◽  
Optimal Accuracy ◽  
Deadly Disease ◽  
Electrocardiogram Ecg

Cardiovascular disease is the number one deadly disease in the world. Arrhythmia is one of the types of cardiovascular disease which is hard to detect but by using the routine electrocardiogram (ECG) recording. Due to the variety and the noise of ECG, it is very time consuming to detect it only by experts using bare eyes.Learning from the previous research in order to help the experts, this research develop 11 layers Convolutional Neural Network 2D (CNN 2D) using MITBIH Arrhythmia Dataset. The dataset is firstly preprocessed by using wavelet transform method, then being segmented by R-peak method. The challenge is how to conquer the imbalance and small amount of data but still get the optimal accuracy. This research can be helpful in helping the doctors figure out the type of arrhythmia of the patient. Therefore, this research did the comparison of various optimizers attach in CNN 2D namely, Adabound, Adadelta, Adagrad, Amsbound, Adam and Stochastic Gradient Descent (SGD). The result is Adabound get the highest performance with 91% accuracy and faster 1s training duration than Adam which is approximately 18s per epoch.

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