deep learning technology IEEE PAPERS-2020



Deep Learning refers to the circumstance that we derive the output for our input by passing it through multiple layers / a hierarchy of transformations (i.e., we go through some depth of transforms), instead of having a single, linear formula that tells us the output value directly.

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.



GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing.
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Abstract We present GluonCV and GluonNLP, the deep learning toolkits for computer vision and natural language processing based on Apache MXNet (incubating). These toolkits provide state-of-the-art pre-trained models, training scripts, and training logs, to facilitate

Design and implementation of accurate frequency estimator depend on deep learning
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An Accurate, efficient, and stable system to estimate the unknown input frequency of a sinusoidal signal is presented. Thezproposed designzsolves the mainzdrawback of the existing phase-based estimator which called a derivative estimator depend on deep

Potential COVID-2019 3C-like protease inhibitors designed using generative deep learning approaches
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Coronaviruses (CoVs) are a large family of viruses belonging to the family Coronaviridae. The limited number of coronaviruses known to be circulating in humans cause mild infections and they were regarded as relatively harmless respiratory human pathogens 1

Deep learning based drug screening for novel coronavirus 2019-nCov
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A novel coronavirus, called 2019-nCoV, was recently found in Wuhan, Hubei Province of China, and now is spreading across China and other parts of the world. Although there are some drugs to treat 2019-nCoV, there is no proper scientific evidence about its activity on

A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19)
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Background: To control the spread of Corona Virus Disease (COVID-19), screening large numbers of suspected cases for appropriate quarantine and treatment measures is a priority. Pathogenic laboratory testing is the diagnostic gold standard but it is time consuming with

Deep learning techniques for music generation
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Creativity has become the motto of the modern world: everyone, every institution, and every company is exhorted to create, to innovate, to think out of the box. This calls for the design of a new class of technology, aimed at assisting humans in tasks that are deemed creative. Developing a machineUsing detailed simulations of calorimeter showers as training data, we investigate the use of deep learning algorithms for the simulation and reconstruction of single isolated particles produced in high-energy physics collisions. We train neural networks on single-particle

Advances in deep learning
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This book discusses the state-of-the-art deep learning models used by researchers recently. Various deep architectures and their components are discussed in detail. Algorithms that are used to train deep architectures with fast convergence rate are illustrated with applications

Hardware-Assisted Intellectual Property Protection of Deep Learning Models
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The protection of intellectual property (IP) rights of welltrained deep learning (DL) models has become a matter of major concern, especially with the growing trend of deployment of Machine Learning as a Service (MLaaS). In this work, we demonstrate the utilization of a

Deep learningbased detection for COVID-19 from chest CT using weak label
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Accurate and rapid diagnosis of COVID-19 suspected cases plays a crucial role in timely quarantine and medical treatment. Developing a deep learningbased model for automatic COVID-19 detection on chest CT is helpful to counter the outbreak of SARS-CoV-2. A weakly Cervical cancer is one of the fastest growing global health problems and leading cause of mortality among women of developing countries. Automated Pap smear cell recognition and classification in early stage of cell development is crucial for effective disease diagnosis andAdditive manufacturing of metal components with laser-powder bed fusion is a very complex process, since powder has to be melted and cooled in each layer to produce a part. Many parameters influence the printing process; however, defects resulting from suboptimal

Communication-efficient distributed deep learning with merged gradient sparsification on gpus
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Distributed synchronous stochastic gradient descent (SGD) algorithms are widely used in large-scale deep learning applications, while it is known that the communication bottleneck limits the scalability of the distributed system. Gradient sparsification is a promising

Quality analysis in metal additive manufacturing with deep learning
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As a promising modern technology, additive manufacturing (AM) has been receiving increasing research and industrial attention in the recent years. With its rapid development, the importance of quality monitoring in AM process has been recognized, which significantly

Deep learning models for electrocardiograms are susceptible to adversarial attack
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Electrocardiogram (ECG) acquisition is increasingly widespread in medical and commercial devices, necessitating the development of automated interpretation strategies. Recently, deep neural networks have been used to automatically analyze ECG tracings and

Data Curation with Deep Learning .
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Data curation the process of discovering, integrating, and cleaning data is one of the oldest, hardest, yet inevitable data management problems. Despite decades of efforts from both researchers and practitioners, it is still one of the most time consuming and least

Conservative set valued fields, automatic differentiation, stochastic gradient methods and deep learning
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Modern problems in AI or in numerical analysis require nonsmooth approaches with a flexible calculus. We introduce generalized derivatives called conservative fields for which we develop a calculus and provide representation formulas. Functions having a

Deep learning applied to glacier evolution modelling.
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We present a novel approach to simulate and reconstruct annual glacier-wide surface mass balance (SMB) series based on a deep artificial neural network (ANN; ie deep learning ). This method has been included as the SMB component of an open-source regional glacier In drug development, late stage toxicity issues of a compound are the main cause of failure in clinical trials. In silico methods are therefore of high importance to guide the early design process to reduce time, costs and animal testing. Technical advances and the ever growingDeep learning can be used to forecast emerging technologies based on patent data. However, it requires a large amount of labeled patent data as a training set, which is difficult to obtain due to various constraints. This study proposes a novel approach that integrates At present, how to make use of massive medical information resources to provide scientific decision-making for the diagnosis and treatment of diseases, summarize the curative effect of various treatment schemes, and better serve the decision-making management, medical

Identification and Quantification of cardiovascular structures from CCTA: an end-to-end, rapid, pixel-wise, deep learning method
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Objectives This study designed and evaluated an end-to-end deep learning solution for cardiac segmentation and quantification. Background Segmentation of cardiac structures from coronary computed tomography angiography (CCTA) images is laborious. WeBackground Recent advances in kernel-based Deep Learning models have introduced a new era in medical research. Originally designed for pattern recognition and image processing, Deep Learning models are now applied to survival prognosis of cancer patients

Mellowness Detection of Dragon Fruit Using Deep Learning Strategy
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The agriculture being a main source of income in many developing countries such as India, Indonesia, etc. The economic development of these countries depends on the GDP (Gross Domestic Progress) rate of the agricultural products. However due to miscalculations in the Predicting traffic disturbances is a challenging problem in urban cities. Emergency vehicles (EV) is one of the biggest disturbances that affect traffic fluidity. The goal of this paper is to provide a machine learning application to deal with emergency cases in traffic networks Current anti-malware technologies in last years demonstrated their evident weaknesses due to the signature-based approach adoption. Many alternative solutions were provided by the current state of art literature, but in general they suffer of a high false positive ratio and are

Extracting possibly representative COVID-19 Biomarkers from X-Ray images with Deep Learning approach and image data related to Pulmonary Diseases
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Purpose While the spread of COVID-19 is increased, new, automatic, and reliable methods for accurate detection are essential to reduce the exposure of the medical experts to the outbreak. X-ray imaging, although limited to specific visualizations, be helpful for the The paradigm of deep learning has achieved a wealth of successes, came with a plethora of concepts, methodologies, and ensuing algorithms and applications. Today, we are witnessing visible progress in this dynamically growing area. The growing interest is present

Unsupervised Deep Learning via Affinity Diffusion.
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Convolutional neural networks (CNNs) have achieved unprecedented success in a variety of computer vision tasks. However, they usually rely on supervised model learning with the need for massive labelled training data, limiting dramatically their usability and deployabilityBackground Protein succinylation has recently emerged as an important and common post- translation modification (PTM) that occurs on lysine residues. Succinylation is notable both in its size (eg, at 100 Da, it is one of the larger chemical PTMs) and in its ability to modify the The past years, medical imaging informatics research has increasingly been dominated by Artificial Intelligence (AI) as the primary topic and then especially deep learning (DL). DL applications for detection, segmentation, diagnosis and prediction are an ever-growing part

COVID-19 prediction and detection using deep learning
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(COVID-19) is one of the main challenges in the world, given the rapid spread of the disease. Recent statistics indicate that the number of people diagnosed with COVID-19 is increasing exponentially, with more than 1.6 million confirmed cases; the disease is Purpose Cerebrovascular aneurysms are being observed with rapidly increasing incidence. Therefore, tools are needed for accurate and efficient detection of aneurysms. We used deep learning techniques with CT angiography acquired from multiple medical centers and Nowadays, wireless sensor networks based applications is drastically increasing. One of the emerging and public safety applications is surveillance monitoring. This paper focused on monitoring a forest environment, is considered as the sample surveillance application. The

A review on deep learning in medical image reconstruction
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Medical imaging is crucial in modern clinics to provide guidance to the diagnosis and treatment of diseases. Medical image reconstruction is one of the most fundamental and important components of medical imaging, whose major objective is to acquire high-quality Objectives To assess the impact on image quality and dose reduction of a new deep learning image reconstruction (DLIR) algorithm compared with a hybrid iterative reconstruction (IR) algorithm. Methods Data acquisitions were performed at seven dose

Automating Cyberdeception Evaluation with Deep Learning .
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A machine learningbased methodology is proposed and implemented for conducting evaluations of cyberdeceptive defenses with minimal human involvement. This avoids impediments associated with deceptive research on humans, maximizing the efficacy ofTraffic Classification (TC), consisting in how to infer applications generating network traffic, is currently the enabler for valuable profiling information, other than being the workhorse for service differentiation/blocking. Further, TC is fostered by the blooming of mobile (mostly

Beyond synthetic noise: Deep learning on controlled noisy labels
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Performing controlled experiments on noisy data is essential in understanding deep learning across noise levels. Due to the lack of suitable datasets, previous research has only examined deep learning on controlled synthetic label noise, and realworld label noise has As a new financing model, crowdfunding has been developed rapidly in recent years and has attracted the attention of investors and small-and medium-sized enterprises and entrepreneurs. However, many projects fail to be funded; thus, crowdfunding projectAbstract Machine learning methods have the potential to significantly accelerate drug discovery. However, the increasing rate of new methodological approaches being published in the literature raises the fundamental question of how models should be benchmarked and The application of cutting-edge technologies such as AI, smart sensors, and IoT in factories is revolutionizing the manufacturing industry. This emerging trend, so called smart manufacturing, is a collection of various technologies that support decision-making in real In this paper, we investigate the literature around deep learning to identify its usefulness in different application domains. Our paper identifies that the effectiveness of deep learning is highly visible in the medical imaging area. Other application domains are yet to make any

Time-series forecasting of mortality rates using deep learning
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Page 1. Time-Series Forecasting of Mortality Rates using Deep Learning Ronald Richman FIA FASSA CERA ronald.richman@qedact.com Associate Director QED Actuaries Consultants 9 September Page 2. Paper Perla, Francesca and Richman, Ronald and ScognamiglioWith the increasing amounts of existing unorganized images on the internet today and the necessity to use them efficiently in various types of applications. There is a critical need to discover rigid models that can classify and predict images successfully and instantaneouslyBackground Drug label, or packaging insert play a significant role in all the operations from production through drug distribution channels to the end consumer. Image of the label also called Display Panel or label could be used to identify illegal, illicit, unapproved and Question classification is a primary essential study for automatic question answering implementations. Linguistic features take a significant role to develop an accurate question classifier. Recently, deep learning systems have achieved remarkable success in various

Automated detection of defects with low semantic information in X-ray images based on deep learning
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Nondestructive testing using X-ray imaging has been widely adopted in the defect detection of casting parts for quality management. Deep learning has been proved to be an effective way to detect defects in X-ray images. In this work, Feature Pyramid Network (FPN) which Purpose Attenuation correction using CT transmission scanning increases the accuracy of single-photon emission computed tomography (SPECT) and enables quantitative analysis. Current existing SPECT-only systems normally do not support transmission scanning and Recognizing indoor scene and objects and estimating their poses present a wide range of applications in robotic field. This task becomes more challenging especially in cluttered environments like the indoor scenery. Scaling up convnets presents a key component in Objectives To take advantage of the deep learning algorithms to detect and calculate clot burden of acute pulmonary embolism (APE) on computed tomographic pulmonary angiography (CTPA). Materials and methods The training set in this retrospective study

Analysis and forecasting of the primary energy consumption in Poland using deep learning
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Consumption of fossil energy resources were increased dramatically, due to the economic and population growth. In turn, the consumption of fossil resources causes depletion of resources and contributes to environmental pollution. The European Unions climate

Deep learningbased cryptocurrency sentiment construction
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We study investor sentiment on a non-classical asset such as cryptocurrency using machine learning methods. We account for context-specific information and word similarity using efficient language modeling tools such as construction of featurized word representations In online advertising, an important quality control step is to audit advertising images ( creatives ) before they appear on publishers Web pages. This ensures that advertisements only appear on Web pages where the ad is appropriate. If a creative with sensitive content

A deep learning approach to the inversion of borehole resistivity measurements
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Borehole resistivity measurements are routinely employed to measure the electrical properties of rocks penetrated by a well and to quantify the hydrocarbon pore volume of a reservoir. Depending on the degree of geometrical complexity, inversion techniques are With the widespread use of GPUs for performing deep learning applications, the issue of efficient execution of multiple deep learning jobs in a GPU cluster has attracted great attention. It becomes more difficult to achieve efficient workloads parallelization since

Deep LearningDriven Simultaneous Layout Decomposition and Mask Optimization
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Combining multiple pattern lithography (MPL) and optical proximity correlation (OPC) pushes the limit of 193nm wavelength lithography to go further. Considering that layout decomposition generate plenty of solutions with diverse printabilities, relying on Accurate and timely investigation to concentrate grade and recovery is a premise of realizing automation control in a froth flotation process. This study seeks to use deep learning technologies modeling a manufacturing flotation process, forecasting the concentrate