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Register a new userConvolutional Neural Networks for Photo Features Extraction
استخدام الشبكات العصبونية الإلتفافية في تحديد الميزات الأساسية للصور
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Added by
Damascus University حلقة بحث
Publication date
2018
fields
Informatics Engineering
and research's language is
العربية
Created by
محمد سليمان
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يهدف البحث إلى تقديم دراسة مرجعيّة مفصلة عن استخدام الشبكات العصبونية الإلتفافية (CNNs) في استخراج الميزات (Features) من الصور. وسيتطرق البحث إلى التعريف بمعنى الميزات (Features) الخاصة بالصور وأهميتها في تطبيقات معالجة الصورة. وسيتم أيضاً التعريف بالشبكات العصبونية الإلتفافية (CNNs) وبنيتها و طريقة عملها وأنواع المقاربات والمنهجيات المستخدمة في تدريبها لاستخراج الميزات (Features) من الصور.
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References used
http://deeplearning.net/tutorial/lenet.html
https://en.wikipedia.org/wiki/Convolutional_neural_network
https://www.datascience.com/blog/supervised-and unsupervised-machine-learning-algorithms
مقرر الرؤية الحاسوبية في جامعة دمشق
https://medium.com/ai%C2%B3-theory-practice-business/understanding-hintons-capsule-networks-part-i-intuition-b4b559d1159b
https://hackernoon.com/what-is-a-capsnet-or-capsule-network-2bfbe48769cc
https://blog.athelas.com/a-brief-history-of-cnns-in-image-segmentation-from-r-cnn-to-mask-r-cnn-34ea83205de4
A.Krizhevsky, I.Sutskever, and G.E.Hinton. ImageNet classification with deep convolutional neural networks. In NIPS, pages 1106–1114, 2012
M.D.Zeiler and R.Fergus. Visualizing and understanding convolutional networks. In ECCV, 2014
W.Y.Zou, A.Y.Ng, S.Zhu, and K.Yu. Deep learning of invariant features via simulated fixations in video. In NIPS, pages 3212–3220, 2012.
Discriminative Unsupervised Feature Learning with Convolutional Neural Networks by Alexey Dosovitskiy, Jost Tobias Springenberg, Martin Riedmiller and Thomas Brox
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In recent years, the problem of classifying objects in images has increased by using deep learning as a result of the industrial sector requirements. Despite of many algorithms used in this field, such as Deep Learning Neural Network DNN and Convolut
ional Neural Network CNN, the proposed systems to address this problem Lack of comprehensive solution to the difficulties of long training time and floating memory during the training process, low rating classification. Convolutional Neural Networks (CNNs), which are the most used algorithms for this task, were a mathematical pattern for analyzing images data. A new deep-traversal network pattern was proposed to solve the above problems. The aim of the research is to demonstrate the performance of the recognition system using CNNs networks on the available memory and training time by adapting appropriate variables for the bypass network. The database used in this research is CIFAR10, which consists of 60000 colorful images belonging to ten categories, as every 6,000 images are for a class of these items. Where there are 50,000 training images and 10,000 test tubes. When tested on a sample of selected images from the CIFAR10 database, the model achieved a rating classification of 98.87%.
A reliable and continuous supply of electrical energy is necessary for the functioning
of today’s complex society. Because of the increasing consumption and the extension of
existing electrical transmission networks and these power systems are oper
ated closer and
closer to their limits accordingly the possibilities of overloading, equipment failures and
blackout are also increasing, furthermore, we have an additional obstacle which is that
electrical energy cannot be stored efficiently, so, electrical energy should be generated only
when it's needed.
Due to the fact that world is facing a lack of oil reserves and the difficulties related to
have alternative sources to generate electrical power, then, electrical load forecasting is
considered as a crucial factor in electrical power system either from economical or
technical point of view on both planning and operating levels.
This research introduces a short term electrical load forecasting system by using
artificial neural networks with a simulation in Matlab environment in addition to an
interface for the system and all that is depending on previous load data and weather
parameters in Tartous province.
This paper presents a new technique based on artificial neural networks (ANNs) to
correct power factor. A synchronous motor controlled by the neural controller was used to
handle the problem of reactive power compensation of the system, in order to
correct
power factor.
In this paper, the electrical system and the neural controller were simulated using
MATLAB. The results have shown that the presented technique overcomes the problems
in conventional compensators (using static capacitors) such as time delay and step changes
of reactive power besides to the fast compensation compared to the technique with
capacitors groups.
Text classifiers are regularly applied to personal texts, leaving users of these classifiers vulnerable to privacy breaches. We propose a solution for privacy-preserving text classification that is based on Convolutional Neural Networks (CNNs) and Se
cure Multiparty Computation (MPC). Our method enables the inference of a class label for a personal text in such a way that (1) the owner of the personal text does not have to disclose their text to anyone in an unencrypted manner, and (2) the owner of the text classifier does not have to reveal the trained model parameters to the text owner or to anyone else. To demonstrate the feasibility of our protocol for practical private text classification, we implemented it in the PyTorch-based MPC framework CrypTen, using a well-known additive secret sharing scheme in the honest-but-curious setting. We test the runtime of our privacy-preserving text classifier, which is fast enough to be used in practice.
In this paper, we presented a scientific methodicalness in
very short term load forecasting depends on back propagation
artificial neural networks, and we relied upon real data of Syrian
electrical power system.
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