deep learning IEEE PAPER 2022


Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost.







An Overview of Organs-on-Chips Based on Deep Learning
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of deep learning and OoCs (Figure 1). Following the introduction of the basic concepts of OoCs and deep learning, we review deep learning as a of deep learning in the field of OoCs.

An Integrated Deep Learning Framework for Fruits Diseases Classification
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In this study, we focused on both classical and deep learning features, ie, LBP, color, and deep features extracted through the ResNet50 CNN pretrained model. A mathematical

Geometric deep learning : progress, applications and challenges
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and realized an effective combination of deep learning technology and non-Euclidean data, Euclidean data with deep learning . We will introduce geometric deep learning from several

Stock prediction based on technical indicators using deep learning model
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Deep Learning architectures and STIs. (2) Identification of STIs that are positively correlated to close price. (3) Propose and build a Deep Learning proposed deep learning architecture

A Practical Overview of Safety Concerns and Mitigation Methods for Visual Deep Learning Algorithms
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of faults in a visual deep learning algorithm to provide a list of cause for different visual deep learning algorithm components. related to a visual deep learning algorithm and provides

Temporary Traffic Control Device Detection for Road Construction Projects using Deep Learning Application
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Deep learning applications can support these inspection processes. The first step of the inspection using deep learning is recognizing traffic control devices in the work zone. This study

Deep learning based intelligent industrial fault diagnosis model
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Recently, with the considerable development of Machine Learning (ML) and Deep Learning (DL) methodologies, fault diagnosis approaches rely on them is now being current research.

Human Gait Recognition Using Deep Learning and Improved Ant Colony Optimization
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Deep learning is a new machine learning technique that is gaining popularity. Many techniques for HGR based on deep learning are We proposed a fully automated deep learning and

Deep learning in plant science: A mini-review
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deep learning plays in modern life is undeniably essential. It is also certain that deep learning data to a more profound level, deep learning techniques are pushing the frontiers of plant

Trusted Multi-View Deep Learning with Opinion Aggregation
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deep learning results, we revisit the multi-view deep learning from the perspective of opinion aggregation and thereby implement a trusted multi-view deep learning view deep learning

Human Gait Recognition: A Deep Learning and Best Feature Selection Framework
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A hybrid method is proposed or efficient HGR using deep learning and selection of best features. Four major steps are involved in this work-preprocessing of the video frames,

Deep learning based automated detection of diseases from apple leaf images
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This research work aims in detecting the two apple diseases, Apple Scab and Marsonina coronaria through machine learning and deep learning . Leaf affected with Marsonina

A Large-Scale Dataset and Deep Learning Model for Detecting and Counting Olive Trees in Satellite Imagery
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farms is a major impediment to deep learning techniques being used in this field. erefore, we begin by creating a large-scale olive dataset for deep learning research and applications. e

Deep LearningBased Skin Lesion Diagnosis Model Using Dermoscopic Images
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This study develops intelligent multilevel thresholding with deep learning (IMLT-DL) based skin lesion segmentation and classification model using dermoscopic images to address and deep learning learning artificial intelligence, and deep learning have guided this research to a new dimension . Convolutional neural networks are products of deep learning that in the examination and proposes a deep learning model to detect those unusual activities. -frames and then applies advanced deep learning Convolutional Neural Network algorithm

Hybrid Approach for Taxonomic Classification Based on Deep Learning
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Networks (RNNs) are supreme models in deep learning that can be used for DNA signal Since deep learning models can only work with numerical values, we need to transform the The second half of this chapter focuses on deep neural networks, which, over the last decade, have become the method of choice for most computer vision recognition and lower-level

Deep learning in multi-step forecasting of chaotic dynamics
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For this reason, it became the standard technique implemented in all the deep learning Fabio Dercole that supervised this research and offered deep insights into this study.

Intelligent Mobile Plant Disease Diagnostic System Using NASNet-Mobile Deep Learning .
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2 shows the differences between the traditional machine learning approach and deep learning approach. Deep learning approach is built on artificial neural networks (ANN). Neural

Deep Learningbased Artificial Intelligence Improves Accuracy of Error-prone Lung Nodules
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The AI program is based on a deep learning framework. Cui et al. built on a 50-layer deep neural network and trained a large multi-center database; its deep learning algorithm showed learning and deep learning for securing an IoT system. The derivation of deep learning and the taxonomy of classifying deep learning models are also discussed in this chapter. for the Deep Learning approach [15]. When talking about the advantage of Deep Learning and for a specific assignment, since some deep learning models are more appropriate than

Intelligent aquaculture system for pisciculture simulation using deep learning algorithm
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Since the recurrent neural network was already proven effective in time series analysis , [18], it was chosen in this project as the deep learning algorithm to use in knowledge

Generalisable Framework for Atrial Volume Estimation for Cardiac CT Images using Deep Learning with Quality Control Assessment
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Our objective is to apply deep learning (DL) techniques to these models further using transfer learning approach (ie by In other words, our deep learning models are oblivious to the

A Decision-Making Method for Air Combat Maneuver Based on Hybrid Deep Learning Network
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Abstract In this paper, a hybrid deep learning network-based model is proposed and implemented for maneuver decision-making in an air combat environment. The model consists of of the deep learningbased models. The deep learning based emotion recognition approaches shows much better performance [ 2 2 29] than the machine learningbased

A TWO-STEP FEATURE EXTRACTION ALGORITHM: APPLICATION TO DEEP LEARNING FOR POINT CLOUD CLASSIFICATION
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Most deep learning (DL) methods that are not end-to-end use several multi-scale and multi-type hand-crafted features that make the network challenging, more computationally

Financial sentiment analysis of tweets based on deep learning approach
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deep learnings hierarchical learning process. On the other side, when dealing with large amounts of data, these deep learning the most powerful deep learning models, convolutional

Classification of Citrus Plant Diseases Using Deep Transfer Learning
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Abstract: In recent years, the field of deep learning has played an deep learning based technique is proposed for citrus disease classification. Two different pre-trained deep learning

Fake News Detection Related to the COVID-19 in Slovak Language Using Deep Learning Methods
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During the recent years, deep learning models proved to be focuses on using the deep learning models for the automatic and evaluate the various deep learning models. Thanks to the

A hybrid deep learning and handcrafted feature approach for the prediction of protein structural class.
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using a hybrid deep learning method and handcrafted features to a feed-forward deep neural network. The latter will out that using deep learning methods affords satisfactory

The research of recognition of peep door open state of ethylene cracking furnace based on deep learning
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We propose a CNN-based deep learning model to recognize the open and closed state of the peep door of the ethylene cracking furnace. The test samples are first subject to a series of deep learning method begins to be applied to anomaly detection tasks for multi-dimensional time series due to its wide coverage and strong learning and then the deep learningbased Learning and Deep Learning Methods to Power System Problems aims to evaluate the application of machine learning / deep learning and applying machine learning to power system

IMAGE COMPRESSION USING AI: BRIEF INSIGHTS INTO DEEP LEARNING TECHNIQUES AND AI FRAMEWORKS
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This paper gives brief insights into some Deep Learning techniques, some AI platforms and learning and deep learning . TensorFlow allows you to custom-build and train deep learning

A combination of feature extraction methods and deep learning for brain tumour classification
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any form of deep learning model. ANNs can be either shallow or deep . They are called They are called deep when hidden layers are more than one. This is where the expression

Multimodal Detection of Hateful Messages Using Visual-Linguistic Pre-Trained Deep Learning Models
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Another wave of studies took place since 201 after the popularization of deep learning . Badjatiya Though their works had largely surpassed traditional machine learning methods, they

DeepGamma: A deep learning model for activity coefficient prediction
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Herein, we introduce DeepGamma, a deep learning method for predicting activity coefficients of binary mixtures directly from the molecular structures of their components. DeepGamma

Deep Learning Based Data Fusion Methods for Multimodal Emotion Recognition
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of deep learning ( learning . 2) We systematically compare the performance of these models on three multimodal datasets. Our experimental results demonstrate that multi-task learning

DEEP LEARNING AND TIME SERIES ANALYSIS APPLICATION ON TRAFFIC FLOW FORECASTING
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on deep architecture models. Recently, deep learning which is a type of machine learning method, Deep learning algorithms use multi-layer or deep architectures to extract the inherentIn this descriptive review, we will review the imaging analysis workflow and examine how hand-crafted features also referred to as traditional Machine Learning (ML), Deep Learning (DL

A hybrid deep learning network for tight and shale reservoir characterization using pressure and rate transient data
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In this study, a hybrid deep learning (DL) model is developed 11 to offer an alternative approach to characterize the reservoir (ie, reservoir shape, flow boundary 12 type, and porosity

DLBT: Deep LearningBased Transformer to Generate Pseudo-Code from Source Code
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In this paper, a novel deep learningbased transformer (DLBT) model is proposed for automatic Pseudo-code generation from the source code. The proposed model uses deep learning

Implications of Additivity and Nonadditivity for Machine Learning and Deep Learning Models in Drug Design
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several machine learning and deep learning algorithms to ) to deep learning algorithm (single and multitask deep neural Our aim is to evaluate the capability of machine learningWe planned to work with a fast deep learning algorithm that allows us to identify weeds in real-time in a cultivated area and implemented an smart weeding system. In our

A Deep Learning and Genetic Algorithm Based Feature Selection Processes on Leukemia Data
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To this aim, we adopt Deep Learning models (eg, feature selection techniques and classifiers methods). The analysis has been performed on a dataset consisting of samples from learning and deep learning models (primarily including artificial neural networks). Some important developments propelled by the use of these techniques and its applications in the

Short-and Very Short-Term Firm-Level Load Forecasting for Warehouses: A Comparison of Machine Learning and Deep Learning Models. Energies 202 15
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) and three machine learning models Support Vector Regression ( of the deep learning and machine learning models for using machine learning and deep learning for warehouse

Application of Deep Learning Technology in Glioma for Healthcare Applications
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based on deep learning and improving the structure of related deep learning models, we propose an automatic glioma segmentation method based on deep learning to effectively

Unsupervised Deep Learning to Detect Agitation from Videos in People with Dementia
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] was proposed that used deep spatio-temporal convolutional an ensemble learning method based on rotation forests. of-concept of an unsupervised deep learning approach, in which

CellsDeepNet: A Novel Deep LearningBased Web Application for the Automated Morphometric Analysis of Corneal Endothelial Cells. Mathematics 202 10
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Recently, several machine learning and deep learning Finally, a key concern of deep learning segmentation segmentation of CECs using a deep learning approach based on an

The Importance of Deep Learning on Constructivism Approach
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on students commitment to learning represented by the learning approach they practice. Therefore, this study aims to review the characteristics of the deep learning approach and its

New Algorithms for Programmatic Deep Learning with Applications to Behavior Modeling
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deep learning that tackle some of the new challenges encountered in behavior modeling. Our work in programmatic deep learning via neurosymbolic program learning . Augmenting

Comparison of Deep Learning Product Recommendation Engines in Different Settings
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: We show how two deep learning collaborative filtering based of the two deep learning approaches and highlights the literature, where the deep learning algorithms clearly outperform

Deep Learning in Life Science: Generative Models Deep Learning in Life Science: Generative Models
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general--as methods for learning the data hyperplane. We discuss Learning Methods in diagnostic and therapeutic applications. In the practical work we apply Representation Learning

A Survey on Deep Learning Algorithms in Facial Emotion Detection and Recognition
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Research has far advanced for this cause, and Deep learning is at its heart. This paper systematically discusses state-of-the-art deep learning architectures and algorithms for facialthe learning rate and learning rate begins with 0.001 and is divided by 10. Their core models the convolutional neural networks of deep learning and transfers learning to deep learning .

Image Analysis using Deep Learning for Electrical Overhead Line Tower Management
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The focus of this thesis is not the development of new deep learning methods, per se. Rather, it is interested in the development of novel techniques, combining existing methods to

A deep learningbased approach for defect detection in powder bed fusion additive manufacturing using transfer learning
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In this study, the deep learning method is a novel application in powder bed fusion additive manufacturing. It is concluded that the learning transfer method can be used in the detection

deep learning IEEE PAPER 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 intelligenceā€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




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