DeepSeq: Deep Sequential Circuit Learning
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Automated deep learning is promising in artificial intelligence (AI). However, a few applications of automated deep learning networks have been made in the clinical medical fields. Therefore, we studied the application of an open-source automated deep learning framework, Autokeras, for detecting smear blood images infected with malaria parasites. Autokeras is able to identify the optimal neural network to perform the classification task. Hence, the robustness of the adopted model is due to it not needing any prior knowledge from deep learning. In contrast, the traditional deep neural network methods still require more construction to identify the best convolutional neural network (CNN). The dataset used in this study consisted of 27,558 blood smear images. A comparative process proved the superiority of our proposed approach over other traditional neural networks. The evaluation results of our proposed model achieved high efficiency with impressive accuracy, reaching 95.6% when compared with previous competitive models.
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In recent years, view-based 3D model retrieval has become one of the research focuses in the field of computer vision and machine learning. In fact, the 3D model retrieval algorithm consists of feature extraction and similarity measurement, and the robust features play a decisive role in the similarity measurement. Although deep learning has achieved comprehensive success in the field of computer vision, deep learning features are used for 3D model retrieval only in a small number of works. To the best of our knowledge, there is no benchmark to evaluate these deep learning features. To tackle this problem, in this work we systematically evaluate the performance of deep learning features in view-based 3D model retrieval on four popular datasets (ETH, NTU60, PSB, and MVRED) by different kinds of similarity measure methods. In detail, the performance of hand-crafted features and deep learning features are compared, and then the robustness of deep learning features is assessed. Finally, the difference between single-view deep learning features and multi-view deep learning features is also evaluated. By quantitatively analyzing the performances on different datasets, it is clear that these deep learning features can consistently outperform all of the hand-crafted features, and they are also more robust than the hand-crafted features when different degrees of noise are added into the image. The exploration of latent relationships among different views in multi-view deep learning network architectures shows that the performance of multi-view deep learning outperforms that of single-view deep learning features with low computational complexity.
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In recent times, deep learning has emerged as a great resource to help research in medical sciences. A lot of work has been done with the help of computer science to expose and predict different diseases in human beings. This research uses the Deep Learning algorithm Convolutional Neural Network (CNN) to detect a Lung Nodule, which can be cancerous, from different CT Scan images given to the model. For this work, an Ensemble approach has been developed to address the issue of Lung Nodule Detection. Instead of using only one Deep Learning model, we combined the performance of two or more CNNs so they could perform and predict the outcome with more accuracy. The LUNA 16 Grand challenge dataset has been utilized, which is available online on their website. The dataset consists of a CT scan with annotations that better understand the data and information about each CT scan. Deep Learning works the same way our brain neurons work; therefore, deep learning is based on Artificial Neural Networks. An extensive CT scan dataset is collected to train the deep learning model. CNNs are prepared using the data set to classify cancerous and non-cancerous images. A set of training, validation, and testing datasets is developed, which is used by our Deep Ensemble 2D CNN. Deep Ensemble 2D CNN consists of three different CNNs with different layers, kernels, and pooling techniques. Our Deep Ensemble 2D CNN gave us a great result with 95% combined accuracy, which is higher than the baseline method.
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Deep learning is a branch of machine learning that has grown by leaps and bounds since it was first used in computer vision. The "Olympics" of computer vision, ImageNet Classification, was won by a system that used deep learning and convolutional neural networks in December 2012. Because of how important it is in the field, this competition is sometimes called the "Olympics" of computer vision. (CNN). Since then, people in many different fields, such as medical image analysis, have looked into deep learning. We are going to look into whether or not it would be possible to use deep learning algorithms to analyse medical images. This poll asked people what they thought about the four following topics related to machine learning: 1) How it is now used in computer vision, 2) How machine learning has changed before and after deep learning, 3) What role ML models play in deep learning, and 4) How deep learning can be used to analyse medical photos. Before the invention of deep learning, most machine learning systems relied on inputs called "features." This type of machine learning is called feature-based ML by some (also known as feature-based ML). Studying photographic data can be used to learn through deep learning without the need to separate objects or pull out features. The main difference between the two was this. This was pretty clear when we looked at MLs made before and after deep learning became very popular. This part, along with the model's huge scope, makes deep learning work well. Even though the term "deep learning" is still new, a study on the topic found that photo-input deep-learning algorithms have been available in the field of machine learning for a long time. Even though "deep learning" is a term that has only been around for a short time, this was seen. Even though the idea of "deep learning" is still in its early stages, discoveries like this one have been made. Even before the term "deep learning" was invented, machine learning techniques that used pictures as input were already showing promise for solving a wide range of medical image interpretation problems. Even before the term "deep learning" was made up, this was the case. One of these jobs is to Figure out how lesions are different from other organs and tissues. To solve the problem, an approach to machine learning that is based on images was used. In the next few decades, it is expected that deep learning will completely replace all of the traditional ways that medical images are currently interpreted. This is because applying deep learning and other machine learning techniques to the study of picture data could make medical image analysis much better. "Deep learning," which is the process of teaching computers to "learn" from images, is one of the most promising and quickly growing areas of medical image analysis. Traditional ways of figuring out what a medical image means are likely to be replaced in the next few decades by machine learning that works from pictures.
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Deep learning is a multilayer neural network learning algorithm which emerged in recent years. It has brought a new wave to machine learning, and making artificial intelligence and human-computer interaction advance with big strides. We applied deep learning to handwritten character recognition, and explored the two mainstream algorithm of deep learning: the Convolutional Neural Network (CNN) and the Deep Belief NetWork (DBN). We conduct the performance evaluation for CNN and DBN on the MNIST database and the real-world handwritten character database. The classification accuracy rate of CNN and DBN on the MNIST database is 99.28% and 98.12% respectively, and on the real-world handwritten character database is 92.91% and 91.66% respectively. The experiment results show that deep learning does have an excellent feature learning ability. It don't need to extract features manually. Deep learning can learn more nature features of the data.
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Abstract: Recently, a machine learning (ML) area called deep learning emerged in the computer-vision field and became very popular in many fields. It started from an event in late 2012, when a deep-learning approach based on a convolutional neural network (CNN) won an overwhelming victory in the best-known worldwide computer vision competition, ImageNet Classification. Since then, researchers in many fields, including medical image analysis, have started actively participating in the explosively growing field of deep learning. In this paper, deep learning techniques and their applications to medical image analysis are surveyed. This survey overviewed 1) standard ML techniques in the computer-vision field, 2) what has changed in ML before and after the introduction of deep learning, 3) ML models in deep learning, and 4) applications of deep learning to medical image analysis. The comparisons between MLs before and after deep learning revealed that ML with feature input (or feature-based ML) was dominant before the introduction of deep learning, and that the major and essential difference between ML before and after deep learning is learning image data directly without object segmentation or feature extraction; thus, it is the source of the power of deep learning, although the depth of the model is an important attribute. The survey of deep learningalso revealed that there is a long history of deep-learning techniques in the class of ML with image input, except a new term, “deep learning”. “Deep learning” even before the term existed, namely, the class of ML with image input was applied to various problems in medical image analysis including classification between lesions and nonlesions, classification between lesion types, segmentation of lesions or organs, and detection of lesions. ML with image input including deep learning is a verypowerful, versatile technology with higher performance, which can bring the current state-ofthe-art performance level of medical image analysis to the next level, and it is expected that deep learning will be the mainstream technology in medical image analysis in the next few decades. “Deep learning”, or ML with image input, in medical image analysis is an explosively growing, promising field. It is expected that ML with image input will be the mainstream area in the field of medical image analysis in the next few decades. Keywords: Deep learning, Convolutional neural network, Massive-training artificial neural network, Computer-aided diagnosis, Medical image analysis, Classification (key words)
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Deep Learning is an effective technique and used in various fields of natural language processing, computer vision, image processing and machine vision. Deep fakes uses deep learning technique to synthesis and manipulate image of a person in which human beings cannot distinguish the fake one. By using generative adversarial neural networks (GAN) deep fakes are generated which may threaten the public. Detecting deep fake image content plays a vital role. Many research works have been done in detection of deep fakes in image manipulation. The main issues in the existing techniques are inaccurate, consumption time is high. In this work we implement detecting of deep fake face image analysis using deep learning technique of fisherface using Local Binary Pattern Histogram (FF-LBPH). Fisherface algorithm is used to recognize the face by reduction of the dimension in the face space using LBPH. Then apply DBN with RBM for deep fake detection classifier. The public data sets used in this work are FFHQ, 100K-Faces DFFD, CASIA-WebFace.
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Deep belief network
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