deep neural network 2019


deep neural network (DNN) is an artificial neural network (ANN) with multiple layers between the input and output layers. The DNN finds the correct mathematical manipulation to turn the input into the output, whether it be a linear relationship or a non-linear relationship.

Transport analysis of infinitely deep neural network
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We investigated the feature map inside deep neural networks (DNNs) by tracking the transport map. We are interested in the role of depth why do DNNs perform better than hallow models and the interpretation of DNNs what do intermediate layers do Despite

A Deep Spatio-Temporal Fuzzy Neural Network for Passenger Demand Prediction
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age 1. A Deep Spatio-Temporal Fuzzy Neural Network for Passenger Demand Prediction Xiaoyuan Liang, Guiling Wang∗ Martin Renqiang Min† Yi Qi‡ Zhu Han§ abstract In spite of its importance, passenger demand prediction

A Deep Neural Network for Automated Detection and Mapping of lunar Rockfalls
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Since its launch in 2009, NASAs Lunar Reconnaissance Orbiter Narrow Angle Camera NAC) has taken more than 1.6 million high-resolution images of the lunar surface. This Dataset contains a wealth of potentially significant geomorphological information, including

Real-Time Wheat Classification System for Selective Herbicides Using Broad Wheat Estimation in Deep Neural Network
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identifying seed manually in agriculture takes a long time for practical applications. here fore, an automatic and reliable plant seeds identification is effectively, technically and economically importance in agricultural industry. In addition, the current trend on big data

Sensing and Compensating the Thermal Deformation of a Computer-numerical-control Grinding Machine Using a Hybrid Deep-learning Neural Network
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Performance degradation. This disadvantage obstructs the application and Development of ANN. However, this defect has recently been overcome through he use of the so-called deep neural network (DNN) . Regarding the

Image Saliency Prediction in Transformed Domain: A Deep Complex Neural Network Method
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Most recently, instead of hand-designed features, deep neural network (DNN) based methods

ExNET: Deep Neural Network for Exercise Pose Detection
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detection estimate human activity in images or video frames using computer vision Technique. Pose detection has many applications, such as body to augmented reality, witness, animation etc. ExNET represents a way to detect human pose from 2D human

FACE DETECTION USING DEEP NEURAL NETWORK FOR BEHAVIOUR ANALYSIS
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future Smart Classroom that we envision will significantly enhance learning experience and seamless communication among students and teachers using realtime sensing and machine intelligence. In that, we research a Smart Classroom system that consists of these

Deep Neural Network for Fuzzy Automatic Melanoma Diagnosis
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is the most serious type of skin cancer. We consider in this paper diagnosing elanoma based on skin lesion images obtained by common optical cameras. Given the Power quality of such images, we should cope with the imprecision of image data. This paper

Data Extraction from Charts via Single Deep Neural Network
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automatic data extraction from charts is challenging for two reasons: there exist many elations among objects in a chart, which is not a common consideration in general computer vision problems; and different types of charts may not be processed by the same

Deep Neural Network Attribution Methods for Leakage Analysis and Symmetric Key Recovery
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Weep Neural Networks (DNNs) have recently received significant attention in the side- hannel community due to their state-ofthe-art performance in security testing of embedded Systems. However, research on the subject mostly focused on techniques to improve the

Data Descriptor: Characterization of deep neural network features by decodability from human brain activity
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Methods The data used in this study comes from a previous study performed in our laboratory 6. According to the journal policy, we here provide a self-contained description of he subjects, datasets, and preprocessing of the MRI data for the main experiments to make

FAST ESTIMATION OF GRAVITATIONAL FIELD OF IRREGULAR ASTEROIDS BASED ON DEEP NEURAL NETWORK AND ITS APPLICATION
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novel approach is proposed to fast estimate the gravitational field of the irregular asteroid y the DNN, and its application in the study of asteroid landing problem is investigated in his work. The traditional method of describing the gravitational field of irregular asteroids

A DEEP NEURAL NETWORK BASED END TO END MODEL FOR JOINT HEIGHT AND AGE ESTIMATION FROM SHORT DURATION SPEECH
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automatic height and age prediction of a speaker has a wide variety of applications in peaker profiling, forensics etc. Often in such applications only a few seconds of speech Data is available to reliably estimate the speaker parameters. Traditionally, age and height

Advanced payload architecture for a hyperspectral earth imaging CubeSat based on Software Defined Radio and Deep Neural Network
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earth observation nanosatellites are increasingly popular since they can be deployed quickly and inexpensively. In this paper we have designed an innovative payload for imaging targeted locations on earth from a nanosatellite, for example a 3U or 6U CubeSat

AI-OBC: Conceptual Design of a Deep Neural Network based Next Generation Onboard Computing Architecture for Satellite Systems
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the latest developments in embedded processing and hardware based low powered Artificial intelligence (AI) accelerators employing Deep Neural Network (DNN) have not been implemented in existing real-time computing architecture designs for Nano and

High-resolution palaeovalley classification from airborne electromagnetic imaging and deep neural network training using digital elevation model data
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alaeovalleys are buried ancient river valleys that often form productive aquifers, especially n the semi-arid and arid areas of Australia. Delineating their extent and hydrostratigraphy is However a challenging task in groundwater system characterization. This study developed a

Deep Convolutional Neural Network with Multi-Task Learning Scheme for Modulations Recognition
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results on the RadioML dataset show that the suggested architecture achieves higher overall classification accuracy compared to the recently proposed Convolutional, Long short Term Memory (LSTM), Deep Neural Network (CLDNN)

DETECTION OF ACCURATE FACIAL DETECTION USING HYBRID DEEP CONVOLUTIONAL RECURRENT NEURAL NETWORK
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In this paper, we propose a technique which uses RNN and Deep Neural Network (DNN) o take in the face shape Keywords: Facial landmark, Deep Neural Network , Recurrent neural Network, Convolutional Neural Network 1. INTRODUCTION

Development of daily PM10 and PM2. 5 prediction system using a deep long short-term memory neural network model
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Model development Fig. 1 shows the schematic procedures for the deep LSTM model-based M predictions. There were two main processes in 10 developing this prediction system: (i) data reprocessing and (ii) structure design and optimization of the deep neural network







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