scholarly journals Kayu7net: Identifikasi dan Evaluasi F-Measure Citra Kayu berbasis Deep Convolutional Neural Network (DCNN)

2020 ◽  
Vol 7 (6) ◽  
pp. 1089
Author(s):  
Iwan Muhammad Erwin ◽  
Risnandar Risnandar ◽  
Esa Prakarsa ◽  
Bambang Sugiarto
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Computer Vision ◽  
Convolutional Neural Networks ◽  
Iteration Process ◽  
Batch Size ◽  
Wood Identification ◽  
Deep Convolutional Neural Networks ◽  
Fully Connected ◽  
F Measure

<p class="Abstrak">Identifikasi kayu salah satu kebutuhan untuk mendukung pemerintah dan kalangan bisnis kayu untuk melakukan perdagangan kayu secara legal. Keahlian khusus dan waktu yang cukup dibutuhkan untuk memproses identifikasi kayu di laboratorium. Beberapa metodologi penelitian sebelumnya, proses identifikasi kayu masih dengan cara menggabungkan sistem manual menggunakan anatomi DNA kayu. Sedangkan penggunaan sistem komputer diperoleh dari citra penampamg melintang kayu secara proses mikrokopis dan makroskopis. Saat ini, telah berkembang teknologi computer vision dan machine learning untuk mengidentifikasi berbagai jenis objek, salah satunya citra kayu. Penelitian ini berkontribusi dalam mengklasifikasi beberapa spesies kayu yang diperdagangkan menggunakan Deep Convolutional Neural Networks (DCNN). Kebaruan penelitian ini terletak pada arsitektur DCNN yang bernama Kayu7Net. Arsitektur Kayu7Net yang diusulkan memiliki tiga lapisan konvolusi terhadap tujuh spesies dataset citra kayu. Pengujian dengan merubah citra input menjadi berukuran 600×600, 300×300, dan 128×128 piksel serta masing-masing diulang pada epoch 50 dan 100. DCNN yang diusulkan menggunakan fungsi aktivasi ReLU dengan batch size 32. ReLU bersifat lebih konvergen dan cepat saat proses iterasi. Sedangkan Fully-Connected (FC) berjumlah 4 lapisan akan menghasilkan proses training yang lebih efisien. Hasil eksperimen memperlihatkan bahwa Kayu7Net yang diusulkan memiliki nilai akurasi sebesar 95,54%, precision sebesar 95,99%, recall sebesar 95,54%, specificity sebesar 99,26% dan terakhir, nilai F-measure sebesar 95,46%. Hasil ini menunjukkan bahwa arsitektur Kayu7Net lebih unggul sebesar 1,49% pada akurasi, 2,49% pada precision, dan 5,26% pada specificity dibandingkan penelitian sebelumnya.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstrak"><em>Wood identification is one of the needs to support the government and the wood business community for a legally wood trading system. Special expertise and sufficient time are needed to process wood identification in the laboratory. Some previous research works show that the process of identifying wood combines a manual system using a wood DNA anatomy. While, the use of a computer system is obtained from the wood image of microscopic and macroscopic process. Recently, the latest technology has developed by using the machine learning and computer vision to identify many objects, the one of them is wood image. This research contributes to classify several the traded wood species by using Deep Convolutional Neural Networks (DCNN). The novelty of this research is in the DCNN architecture, namely Kayu7Net. The proposed of Kayu7Net Architecture has three convolution layers of the seven species wood image dataset. The testing changes the wood image input to 600×600, 300×300, and 128×128 pixel, respectively, and each of them repeated until 50 and 100 epoches, respectively. The proposed DCNN uses the ReLU activation function and batch size 32. The ReLU is more convergent and faster during the iteration process. Whereas, the 4 layers of Fully-Connected (FC) will produce a more efficient training process. The experimental results show that the proposed Kayu7Net has an accuracy value of 95.54%, a precision of 95.99%, a recall of 95.54%, a specificity of 99.26% and finally, an F-measure value of 95.46%. These results indicate that Kayu7Net is superior by 1.49% of accuracy, 2.49% of precision, and 5.26% of specificity compared to the previous work. </em></p><p class="Abstrak"> </p>

scholarly journals Image classification using Deep learning

2018 ◽  
Vol 7 (2.7) ◽  
pp. 614 ◽  
Author(s):  
M Manoj krishna ◽  
M Neelima ◽  
M Harshali ◽  
M Venu Gopala Rao
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Image Processing ◽  
Computer Vision ◽  
Deep Learning ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Classical Problem

The image classification is a classical problem of image processing, computer vision and machine learning fields. In this paper we study the image classification using deep learning. We use AlexNet architecture with convolutional neural networks for this purpose. Four test images are selected from the ImageNet database for the classification purpose. We cropped the images for various portion areas and conducted experiments. The results show the effectiveness of deep learning based image classification using AlexNet.  

scholarly journals A primer on deep learning and convolutional neural networks for clinicians

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Lara Lloret Iglesias ◽  
Pablo Sanz Bellón ◽  
Amaia Pérez del Barrio ◽  
Pablo Menéndez Fernández-Miranda ◽  
David Rodríguez González ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Learning Capabilities ◽  
Computer Vision Applications ◽  
Fully Connected ◽  
Imaging Perspective ◽  
Adaptive Momentum

AbstractDeep learning is nowadays at the forefront of artificial intelligence. More precisely, the use of convolutional neural networks has drastically improved the learning capabilities of computer vision applications, being able to directly consider raw data without any prior feature extraction. Advanced methods in the machine learning field, such as adaptive momentum algorithms or dropout regularization, have dramatically improved the convolutional neural networks predicting ability, outperforming that of conventional fully connected neural networks. This work summarizes, in an intended didactic way, the main aspects of these cutting-edge techniques from a medical imaging perspective.

scholarly journals COVID-19 Face Mask Detection using Deep Convolutional Neural Networks & Computer Vision

2021 ◽  
Vol 14 (38) ◽  
pp. 2899-2915
Author(s):  
Premanand Ghadekar ◽  
◽  
Gurdeep Singh ◽  
Joydeep Datta ◽  
Aryan Kumar Gupta ◽  
...  
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Convolutional Neural Networks ◽  
Face Mask ◽  
Deep Convolutional Neural Networks

scholarly journals Not so fast: Limited validity of deep convolutional neural networks as in silico models for human naturalistic face processing

2021 ◽  
Author(s):  
Guo Jiahui ◽  
Ma Feilong ◽  
Matteo Visconti di Oleggio Castello ◽  
Samuel A Nastase ◽  
James V Haxby ◽  
...  
Keyword(s):  
Neural Networks ◽  
Human Brain ◽  
Convolutional Neural Networks ◽  
Face Processing ◽  
In Silico ◽  
Processing System ◽  
Human Face ◽  
Deep Convolutional Neural Networks ◽  
Neural Representations ◽  
Fully Connected

Deep convolutional neural networks (DCNNs) trained for face identification can rival and even exceed human-level performance. The relationships between internal representations learned by DCNNs and those of the primate face processing system are not well understood, especially in naturalistic settings. We developed the largest naturalistic dynamic face stimulus set in human neuroimaging research (700+ naturalistic video clips of unfamiliar faces) and used representational similarity analysis to investigate how well the representations learned by high-performing DCNNs match human brain representations across the entire distributed face processing system. DCNN representational geometries were strikingly consistent across diverse architectures and captured meaningful variance among faces. Similarly, representational geometries throughout the human face network were highly consistent across subjects. Nonetheless, correlations between DCNN and neural representations were very weak overall—DCNNs captured 3% of variance in the neural representational geometries at best. Intermediate DCNN layers better matched visual and face-selective cortices than the final fully-connected layers. Behavioral ratings of face similarity were highly correlated with intermediate layers of DCNNs, but also failed to capture representational geometry in the human brain. Our results suggest that the correspondence between intermediate DCNN layers and neural representations of naturalistic human face processing is weak at best, and diverges even further in the later fully-connected layers. This poor correspondence can be attributed, at least in part, to the dynamic and cognitive information that plays an essential role in human face processing but is not modeled by DCNNs. These mismatches indicate that current DCNNs have limited validity as in silico models of dynamic, naturalistic face processing in humans.

scholarly journals Learning Cartographic Building Generalization with Deep Convolutional Neural Networks

2019 ◽  
Vol 8 (6) ◽  
pp. 258 ◽  
Author(s):  
Yu Feng ◽  
Frank Thiemann ◽  
Monika Sester
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Physical Reality ◽  
Learning Approaches ◽  
Deep Convolutional Neural Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Cartographic Generalization

Cartographic generalization is a problem, which poses interesting challenges to automation. Whereas plenty of algorithms have been developed for the different sub-problems of generalization (e.g., simplification, displacement, aggregation), there are still cases, which are not generalized adequately or in a satisfactory way. The main problem is the interplay between different operators. In those cases the human operator is the benchmark, who is able to design an aesthetic and correct representation of the physical reality. Deep learning methods have shown tremendous success for interpretation problems for which algorithmic methods have deficits. A prominent example is the classification and interpretation of images, where deep learning approaches outperform traditional computer vision methods. In both domains-computer vision and cartography-humans are able to produce good solutions. A prerequisite for the application of deep learning is the availability of many representative training examples for the situation to be learned. As this is given in cartography (there are many existing map series), the idea in this paper is to employ deep convolutional neural networks (DCNNs) for cartographic generalizations tasks, especially for the task of building generalization. Three network architectures, namely U-net, residual U-net and generative adversarial network (GAN), are evaluated both quantitatively and qualitatively in this paper. They are compared based on their performance on this task at target map scales 1:10,000, 1:15,000 and 1:25,000, respectively. The results indicate that deep learning models can successfully learn cartographic generalization operations in one single model in an implicit way. The residual U-net outperforms the others and achieved the best generalization performance.

Application of convolutional neural networks for monitoring of marine objects

2020 ◽  
pp. 53-61
Author(s):  
Н.А. Полковникова ◽  
Е.В. Тузинкевич ◽  
А.Н. Попов
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Computer Vision ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Network Architecture ◽  
Research Process ◽  
Probability Of Detection ◽  
Neural Network Architecture ◽  
Deep Convolutional Neural Networks

В статье рассмотрены технологии компьютерного зрения на основе глубоких свёрточных нейронных сетей. Применение нейронных сетей особенно эффективно для решения трудно формализуемых задач. Разработана архитектура свёрточной нейронной сети применительно к задаче распознавания и классификации морских объектов на изображениях. В ходе исследования выполнен ретроспективный анализ технологий компьютерного зрения и выявлен ряд проблем, связанных с применением нейронных сетей: «исчезающий» градиент, переобучение и вычислительная сложность. При разработке архитектуры нейросети предложено использовать функцию активации RELU, обучение некоторых случайно выбранных нейронов и нормализацию с целью упрощения архитектуры нейросети. Сравнение используемых в нейросети функций активации ReLU, LeakyReLU, Exponential ReLU и SOFTMAX выполнено в среде Matlab R2020a. На основе свёрточной нейронной сети разработана программа на языке программирования Visual C# в среде MS Visual Studio для распознавания морских объектов. Программапредназначена для автоматизированной идентификации морских объектов, производит детектирование (нахождение объектов на изображении) и распознавание объектов с высокой вероятностью обнаружения. The article considers computer vision technologies based on deep convolutional neural networks. Application of neural networks is particularly effective for solving difficult formalized problems. As a result convolutional neural network architecture to the problem of recognition and classification of marine objects on images is implemented. In the research process a retrospective analysis of computer vision technologies was performed and a number of problems associated with the use of neural networks were identified: vanishing gradient, overfitting and computational complexity. To solve these problems in neural network architecture development, it was proposed to use RELU activation function, training some randomly selected neurons and normalization for simplification of neural network architecture. Comparison of ReLU, LeakyReLU, Exponential ReLU, and SOFTMAX activation functions used in the neural network implemented in Matlab R2020a.The computer program based on convolutional neural network for marine objects recognition implemented in Visual C# programming language in MS Visual Studio integrated development environment. The program is designed for automated identification of marine objects, produces detection (i.e., presence of objects on image), and objects recognition with high probability of detection.

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