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
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
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
A Deep Learning System for Synthetic Knee Magnetic Resonance Imaging
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Objectives: The aim of this study was to determine the feasibility and performance of a deep learning system used to create synthetic artificial intelligence‐based fat-suppressed magnetic resonance imaging (AFSMRI) scans of the knee. Materials and Methods: 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 distinguishTraffic-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
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
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
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
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
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
Intelligent breast cancer prediction empowered with fusion and deep learning
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Breast cancer is the most frequently detected tumor that eventually could result in a significant increase in female mortality globally. According to clinical statistics, one woman out of eight is under the threat of breast cancer. Lifestyle and inheritance patterns be a
Uncertainty-aware deep learning for safe landing site selection
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Hazard detection is critical for enabling autonomous landing on planetary surfaces. Current state-of-the-art methods leverage traditional computer vision approaches to automate identification of safe terrain from input digital elevation models (DEMs). However The difficulty of criminal psychological recognition is that it is difficult to classify emotions, and the accuracy of traditional recognition methods is insufficient. Therefore, it is necessary to improve the accuracy rate in combination with modern computer technology. This study
Fail-safe execution of deep learning based systems through uncertainty monitoring
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Modern software systems rely on Deep Neural Networks (DNN) when processing complex, unstructured inputs, such as images, videos, natural language texts or audio signals. Provided the intractably large size of such input spaces, the intrinsic limitations of learning
Deep Learning Approach in Brain Tumor Classification
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Various Computer-Aided Diagnosis (CAD) systems have been recently used in medical imaging to assist radiologist about their patients. Generally, various image technique such as Computer Tomography (CT), Magnetic Resonance Imaging (MRI) and ultrasound image
Application of Deep Learning for Credit Card Approval: A Comparison with Two Machine Learning Techniques
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The increased credit card defaulters have forced the companies to think carefully before the approval of credit applications. Credit card companies usually use their judgment to determine whether a credit card should be issued to the customer satisfying certain criteria Many important problem classes are governed by anisotropic features, which typically appear as singularities concentrated on lower-dimensional embedded manifolds. Examples include edges in images or shock fronts in solutions of transport-dominated equations Growth in consumption of Oryza sativa (rice) has led the farmers across Asian countries to cultivate Oryza sativa, with an impact of 2.5 percent increase in the cultivation of the crop every year. Along with the growth in Oryza sativa cultivation, there are new challenges that
A deep learning framework to detect Covid-19 disease via chest X-ray and CT scan images.
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COVID-19 disease has rapidly spread all over the world at the beginning of this year. The hospitals reports have told that low sensitivity of RT-PCR tests in the infection early stage. At which point, a rapid and accurate diagnostic technique, is needed to detect the Covid-19. CT
A novel approach for code smells detection based on deep learning
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Compared to software bugs, code smells are more significant in software engineering research. It is not easy to detect code smells through traditional methods. In this work, we propose a novel code smells detection approach based on deep learning . The experiments