neural network IEEE PAPER, IEEE PROJECT
BOND STRENGTH PREDICTION MODEL OF CORRODED REINFORCEMENT IN CONCRETE USING NEURAL NETWORK
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The expansion of corrosion products in the steel-concrete interface offers radial tensile stress resulting in the development of cracks in reinforced concrete structures. This corrosion- induced crack promotes bond reduction involving intricate non-linear interactions. To deeply
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 shallow models and the interpretation of DNNs what do intermediate layers do Despite
Deep convolutional neural network models for the diagnosis of thyroid cancer
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The study by Xiangchun Li and colleagues1 adds to the growing body of evidence that application of the newly developed deep convolutional neural network models on sonographic images can improve accuracy, sensitivity, and specificity in identifying patients
Deep convolutional neural network models for the diagnosis of thyroid cancer Authors reply
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We appreciate the comments from Dan Hu and colleaguesand Eun Ha and colleagues about our Article. 1 We agree with Hu and colleagues regarding the incorporation of demographic features and laboratory test results in the model. Specifically, two neural
Bistable firing pattern in a neural network model
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Detecting Early Stage Lung Cancer using a Neural Network Trained with Patches from Synthetically Generated X-Rays
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The aim of this research is to train a neural network to detect early stage lung cancer with high accuracy. Since X-rays are a relatively cheap and quick procedure that provide a preliminary look into a patients lungs and because real X-rays are often difficult to obtain
A Deep Spatio-Temporal Fuzzy Neural Network for Passenger Demand Prediction
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In spite of its importance, passenger demand prediction is a highly challenging problem, because the demand is simultaneously influenced by the complex interactions among many spatial and temporal factors and other external factors such as weather. To address this
MODELLING LAND COVER CHANGE IN A MEDITERRANEAN ENVIRONMENT USING A MULTI-LAYER NEURAL NETWORK MODEL AND MARKOV CHAIN
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Current rates, extents and intensities of land-use and land-cover change (LULCC) are driving important changes in ecosystems and environmental processes at local, regional and global scales. These changes encompass some of the greatest environmental concerns
Classification of Satellite Images Using Perceptron Neural Network
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Image classification is an important part of digital image analysis and is defined as a process of categorizing the pixels into one of the object classes present in the image. As a prerequisite to image classification, a number of processes such as image enhancement
GRN: Gated Relation Network to Enhance Convolutional Neural Network for Named Entity Recognition
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The dominant approaches for named entity recognition (NER) mostly adopt complex recurrent neural networks (RNN), eg, long-short-term-memory (LSTM). However, RNNs are limited by their recurrent nature in terms of computational efficiency. In contrast
Stock Price Forecast Using Recurrent Neural Network
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Investors and researchers have continuously been trying to predict the behavior of the stock market. The accurate predictions can be helpful in taking timely and correct investment decisions. Many statistical and machine learning based techniques are proposed. Neural
Artificial Neural Network models to predict energy
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Climate change, the decrease in fossil-based energy resources and the need of reducing the greenhouse gas emissions require energy efficient and smart buildings. Moreover, the ratio of renewable energy sources should be increased against traditional energy sources
Artificial Neural Network Based Path Planning of Excavator Arm
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This paper presents a solution in path planning for a robotic arm based on the artificial neural network (ANN) architecture, particularly a Static (Feedforward) Neural Network (SNN). The inputs of the network are the sample sets that are obtained from some specific
Prediction of Sediment Accumulation Model for Trunk Sewer Using Multiple Linear Regression and Neural Network Techniques
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Sewer sediment deposition is an important aspect as it relates to several operational and environmental problems. It concerns municipalities as it affects the sewer system and contributes to sewer failure which has a catastrophic effect if happened in trunks or
Effect of Columnar Neural Grouping on Network Synchronization
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Connectivity in the brain has long been explored on varying scales: from connectivity of large regions down to groups of only a few neurons. In this work we explore how a connectivity scheme inspired by columnar organization in the neocortex effects the
Artificial Neural Network for Diagnose Autism Spectrum Disorder
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Artificial Neural Network for Diagnose Autism Spectrum Disorder
Corrigendum to Optimization of R245fa Flow Boiling Heat Transfer Prediction inside Horizontal Smooth Tubes Based on the GRNN Neural Network
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In the article titled Optimization of R245fa Flow Boiling Heat Transfer Prediction inside Horizontal Smooth Tubes Based on the GRNN Neural Network , , the authors detected some errors in the content of the article where the last sentence in Section 4.2, Although the
Bundling in molecular dynamics simulations to improve generalization performance in high-dimensional neural network potentials
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We examined the influence of using bundling trajectories in molecular dynamics (MD) simulations for predicting energies in high-dimensional neural network potentials. In particular, we focused on the chemical transferability of gold nanoclusters, that is, how well
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
Hierarchical Context enabled Recurrent Neural Network for Recommendation
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A long user history inevitably reflects the transitions of personal interests over time. The analyses on the user history require the robust sequential model to anticipate the transitions and the decays of user interests. The user history is often modeled by various RNN
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