Deep learning 2021
Deep learning based respiratory sound analysis for detection of chronic obstructive pulmonary disease
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In recent times, technologies such as machine learning and deep learning have played a vital role in providing assistive solutions to a medical domains challenges. They also improve predictive accuracy for early and timely disease detection using medical imaging
Scalable second order optimization for deep learning
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Optimization in machine learning, both theoretical and applied, is presently dominated by firstorder gradient methods such as stochastic gradient descent. Second-order optimization methods, that involve second derivatives and/or second order statistics of the data, are far
Fault Detection in Wireless Sensor Network Based on Deep Learning Algorithms
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This paper is about Fault detection over a wireless sensor network in a fully distributed manner. First, we proposed the Convex hull algorithm to calculate a set of extreme points with the neighbouring nodes and the duration of the message remains restricted as the In recent years, Iris recognition has emerged as an important and trustworthy biometric model to recognize humans. The application of automatic iris recognition models find useful in different fields namely border control, citizen confirmation, and criminal to commercial
Deep Learning Based Optimal Multimodal Fusion Framework for Intrusion Detection Systems for Healthcare Data
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Data fusion is a multidisciplinary research area that involves different domains. It is used to attain minimum detection error probability and maximum reliability with the help of data retrieved from multiple healthcare sources. The generation of huge quantity of data from
A sequence-based deep learning approach to predict CTCF-mediated chromatin loop
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Abstract Three-dimensional (3D) architecture of the chromosomes is of crucial importance for transcription regulation and DNA replication. Various high-throughput chromosome conformation capture-based methods have revealed that CTCF-mediated chromatin loops
Physicsinformed deep learning for traffic state estimation: A hybrid paradigm informed by second-order traffic models
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Traffic state estimation (TSE) reconstructs the traffic variables (eg, density or average velocity) on road segments using partially observed data, which is important for traffic managements. Traditional TSE approaches mainly bifurcate into two categories: model
Reinforcement Learning for Hyperparameter Tuning in Deep Learningbased Side-channel Analysis.
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Deep learning represents a powerful set of techniques for profiling sidechannel analysis. The results in the last few years show that neural network architectures like multilayer perceptron and convolutional neural networks give strong attack performance where it is
Feasibility of Deep LearningGuided Attenuation and Scatter Correction of Whole-Body 68 Ga-PSMA PET Studies in the Image Domain
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Objective: This study evaluates the feasibility of direct scatter and attenuation correction of whole-body 68Ga-PSMA PET images in the image domain using deep learning . Methods: Whole-body 68Ga-PSMA PET images of 399 subjects were used to train a residual deep
Recent advances in deep learning based dialogue systems: A systematic survey
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Dialogue systems are a popular Natural Language Processing (NLP) task as it is promising in real-life applications. It is also a complicated task since many NLP tasks deserving study are involved. As a result, a multitude of novel works on this task are carried out, and most of
Diagnosis of COVID-19 pneumonia via a novel deep learning architecture
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COVID-19 is a contagious infection that has severe effects on the global economy and our daily life. Accurate diagnosis of COVID-19 is of importance for consultants, patients, and radiologists. In this study, we use the deep learning network AlexNet as the backbone, and
Arctic sea ice cover data from spaceborne synthetic aperture radar by deep learning
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Widely used sea ice concentration and sea ice cover in polar regions are derived mainly from spaceborne microwave radiometer and scatterometer data, and the typical spatial resolution of these products ranges from several to dozens of kilometers. Due to dramatic
Cerebro: A Layered Data Platform for Scalable Deep Learning .
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Deep learning (DL) is gaining popularity across many domains thanks to tools such as TensorFlow and easier access to GPUs. But building large-scale DL applications is still too resource-intensive and painful for all but the big tech firms. A key reason for this pain is the
of protein secondary structure, backbone angles, solvent accessibility and half-sphere exposures using a large training set and ensembled deep learning
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Motivation: Knowing protein secondary and other one-dimensional structural properties are essential for accurate protein structure and function prediction. As a result, many methods have been developed for predicting these one-dimensional structural properties. However
Cryptanalysis of Round-Reduced SIMON32 Based on Deep Learning .
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Deep learning has played an important role in many fields. It shows significant potential to cryptanalysis. Differential cryptanalysis is an important method in the field of block cipher cryptanalysis. The key point of differential cryptanalysis is to find a differential distinguisher
Knowledge distillation in deep learning and its applications
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Deep learning based models are relatively large, and it is hard to deploy such models on resource-limited devices such as mobile phones and embedded devices. One possible solution is knowledge distillation whereby a smaller model (student model) is trained byIn this paper, digital images related to five kinds of leaves which are available at New Zealand are collected as our dataset, two deep learning models, namely, Faster R-CNN and YOLOv representing two-stage and one-stage algorithms, are employed to conduct tree
Analysis of Deep Learning Techniques for Early Detection of Depression on Social Media Network-A Comparative Study
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The early detection or identification of emotional states plays a vital role in todays world, where the number of internet and social media users are increasing at an unprecedented rate. The psychiatric disorders are very dangerous and it is affecting 300 million people. This Todays popularity of the internet has since proven an effective and efficient means of information sharing. However, this has consequently advanced the proliferation of adversaries who aim at unauthorized access to information being shared over the internet
Gambling for Success: The Lottery Ticket Hypothesis in Deep Learningbased SCA.
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Deep learningbased side-channel analysis (SCA) represents the de facto standard in the profiling SCA. Still, this does not mean it is trivial to find neural networks that perform well for any setting. Based on the developed neural network architectures, we can distinguish Traffic-sign recognition (TSR) has been an essential part of driver-assistance systems, which is able to assist drivers in avoiding a vast number of potential hazards and improve the experience of driving. However, the TSR is a realistic task that is full of constraints, such asDeep learning (DL) is a machine learning (ML) subdomain that involves algorithms taken from the brain function named artificial neural networks (ANNs). Recently, DL approaches have gained major accomplishments across various Arabic natural language processing
Deep ‐ learning based method for faults classification of PV system
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The installation of photovoltaic (PV) system, as a renewable energy source, has significantly increased. Therefore, fast and efficient fault detection and diagnosis technique is highly needed to prevent unpredicted power interruptions. This is obtained in this study in the
A comparative study of using various machine learning and deep learning ‐based fraud detection models for universal health coverage schemes
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Fraud detection is an important area of research in the healthcare systems due to its financial consequences arising mainly from investigation costs, revenue losses, and reputational risk. To mitigate this, most of the companies adopt Machine Learning and/or
Using deep learning for automatic water stage measurements
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Image-based gauging stations can allow for significant densification of monitoring networks of river water stages. However, thus far, most camera gauges do not provide the robustness of accurate measurements due to the varying appearance of water in the stream throughout
Gemmini: Enabling systematic deep learning architecture evaluation via full-stack integration
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DNN accelerators are often developed and evaluated in isolation without considering the cross-stack, system-level effects in real-world environments. This makes it difficult to appreciate the impact of Systemon-Chip (SoC) resource contention, OS overheads, and Abstract Purpose Liver Imaging Reporting and Data System (LI-RADS) uses multiphasic contrast-enhanced imaging for hepatocellular carcinoma (HCC) diagnosis. The goal of this feasibility study was to establish a proof-of-principle concept towards automating the
Recognition of corona virus disease (COVID-19) using deep learning network
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Corona virus disease (COVID19) has an incredible influence in the last few months. It causes thousands of deaths in round the world. This make a rapid research movement to deal with this new virus. As a computer science, many technical researches have been done Recognizing human activity from highly sparse body sensor data is becoming an important problem in Internet of Medical Things (IoMT) industry. As IoMT currently uses batteryless or passive wearable body sensors for activity recognition, the data signals of these sensors
Fluid Temperature Detection Based on its Sound with a Deep Learning Approach [J]
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The present study, the main idea of which was based on one of the questions of IPT competition, aimed to develop a high-precision relationship between the fluid temperature and the sound produced when colliding with different surfaces, by creating a data collection
Toward a deep learningbased intrusion detection system for IoT against botnet attacks
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The massive network traffic data between connected devices in the internet of things have taken a big challenge to many traditional intrusion detection systems (IDS) to find probable security breaches. However, security attacks lean towards unpredictability. There are This book was drafted based on my recent lectures, talks, and seminars for our postgraduate students at the Auckland University of Technology (AUT), New Zealand. We integrate the materials of deep learning and machine learning as well as artificial neural networks
Detection of citrus leaf diseases using a deep learning technique
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The food security major threats are the diseases affected in plants such as citrus so that the identification in an earlier time is very important. Convenient malady recognition can assist the client with responding immediately and sketch for some guarded activities. This
A novel perceptual two layer image fusion using deep learning for imbalanced COVID-19 dataset.
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Background and Purpose: COVID-19 is a new strain of viruses that causes life stoppage worldwide. At this time, the new coronavirus COVID-19 is spreading rapidly across the world and poses a threat to peoples health. Experimental medical tests and analysis have shown
matter: Introducing gated convolutional neural networks (GCNN) and key-value-predict attention networks (KVP) for next event prediction with deep learning .
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Predicting next events in predictive process monitoring enables companies to manage and control processes at an early stage and reduce their action distance. In recent years, approaches have steadily moved from classical statistical methods towards the application
Global placement with deep learningenabled explicit routability optimization
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Placement and routing (PnR) is the most time-consuming part of the physical design flow. Recognizing the routing performance ahead of time can assist designers and design tools to optimize placement results in advance. In this paper, we propose a fully convolutional
Gesture Recognition for UAV-based Rescue Operation based on Deep Learning .
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UAVs play an important role in different application fields, especially in rescue. To achieve good communication between the onboard UAV and humans, an approach to accurately recognize various body gestures in the wild environment by using deep learning algorithms
Anatomical modeling of brain vasculature in two-photon microscopy by generalizable deep learning
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Objective and Impact Statement. Segmentation of blood vessels from two-photon microscopy (2PM) angiograms of brains has important applications in hemodynamic analysis and disease diagnosis. Here, we develop a generalizable deep learning technique
Self-supervised Deep Learning Approaches to Speaker Recognition: A Ph. D. Thesis Overview
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Abstract Recent advances in Deep Learning (DL) for speaker recognition have improved the performance but are constrained to the need of labels for the background data, which is difficult in practice. In i-vector based speaker recognition, cosine (unsupervised) and PLDA
Future Directions and Challenges of Deep Learning
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Deep learning is certainly not a limited finding tactic, however, it follows several procedures and also topographies which could be associated with a huge speculum of complex problems. The strategy knows the illustratory in addition to differential attributes in a stratified