DEEP NEURAL NETWORK IEEE PAPERS AND PROJECTS-2020



A deep neural network (DNN) is an artificial neural network (ANN) with multiple layers between the input and output layers. The DNN finds the correct mathematical manipulation to turn the input into the output, whether it be a linear relationship or a non-linear relationship.



Deep neural network structures solving variational inequalities
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Motivated by structures that appear in deep neural networks, we investigate nonlinear composite models alternating proximity and affine operators defined on different spaces. We first show that a wide range of activation operators used in neural networks are actually Nowadays, a lot of people have the oral health problems due to continuous changes in the lifestyle such as the persons speech which can be affected by the malocclusion in teeth and the crooked teeth. The dental problems can cause cavity and bacterial infection. The dental

Adaptive Approximation and Generalization of Deep Neural Network with Intrinsic Dimensionality
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In this study, we prove that an intrinsic low dimensionality of covariates is the main factor that determines the performance of deep neural networks (DNNs). DNNs generally provide outstanding empirical performance. Hence, numerous studies have actively investigated the

Detection of Atrial Fibrillation from Single Lead ECG Signal Using Multirate Cosine Filter Bank and Deep Neural Network .
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Atrial fibrillation (AF) is a cardiac arrhythmia which is characterized based on the irregsular beating of atria, resulting in, the abnormal atrial patterns that are observed in the electrocardiogram (ECG) signal. The early detection of this pathology is very helpful forThis paper presents a novel approach for synthesizing facial affect; either in terms of the six basic expressions (ie, anger, disgust, fear, joy, sadness and surprise), or in terms of valence (ie, how positive or negative is an emotion) and arousal (ie, power of the emotion activation) As a specific application of analytical methods on marine radar big data, this paper introduces deep learning theory into the field of sea clutter parameters estimation. A reasonable deep neural network model is built to estimate the parameters of amplitude

A Comprehensive Analysis of Convolutional Neural Network Models
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neural networks. A type of feed forward network, Convolutional Neural Network (CNN), is one of the most widespread deep neural network models. This paper presents a brief survey of well-known and established CNN models. It The increment of new words and text categories requires more accurate and robust classification methods. In this paper, we propose a novel multi-label text classification method that combines dynamic semantic representation model and deep neural network

Hand Classification from Fingerprint image using Deep Neural Network
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Fingerprint security technology has attracted a great deal of attention in recent years because of its unique biometric information that does not change over an individuals lifetime and is a highly reliable and secure way to identify a certain individuals. AFIS (Automated Computational Psychiatry is an emerging field of science. It focuses on identifying the complex relationship between the brains neurobiology. Mental illness has recently become an important problem to be addressed as the number of people affected is increasing over Image data in healthcare is playing a vital role. Medical data records are increasing rapidly, which is beneficial and detrimental at the same time. Large Image dataset are difficult to handle, extracting information, and machine learning. The mammograms data used in thisRecommender systems have been an efficient strategy to deal with information overload by producing personalized predictions. Recommendation systems based on deep learning have accomplished magnificent results, but most of these systems are traditional Deep neural network (DNN) exhibits state-of-the-art performance in many fields including weld defect classification. However, there is still a large room for improving the classification performance over the generic DNN models. In this paper, a unified deep neural network with

A deep neural network for fast and accurate scatter estimation in quantitative SPECT/CT under challenging scatter conditions
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Purpose A major challenge for accurate quantitative SPECT imaging of some radionuclides is the inadequacy of simple energy window-based scatter estimation methods, widely available on clinic systems. A deep learning approach for SPECT/CT scatter estimation is

Deep neural network based Rider-Cuckoo Search Algorithm for plant disease detection
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Agriculture is the main source of wealth, and its contribution is essential to humans. However, several obstacles faced by the farmers are due to different kinds of plant diseases. The determination and anticipation of plant diseases are the major concerns and should be This work proposes to use deep neural networks (DNN) model for prediction of road accidents. DNN consists of two or more hidden layers with large number of nodes. Accident data of non-urban sections of eight highways were collected from official records, and Appearance-based gender classification is one of the key areas in pedestrian analysis, and it has many useful applications such as visual surveillance, predict demographics statistics, population prediction, and human computer interaction. For pedestrian gender Background subtraction is generally used for foreground segmentation (moving object detection) from video sequences. Several background subtraction methods have been proposed for visual surveillance applications. However, the existing methods fail in case of Deep neural network (DNN) has been applied in many fields and achieved great successes. However, DNN suffers from poor robustness for uncertainties because of its characteristic of the deterministic representation. To overcome this problem, a novel robust DNN (RDNN) is Social media platforms have simplified the sharing of information, which includes news as well, as compared to traditional ways. The ease of access and sharing the data with the revolution in mobile technology has led to the proliferation of fake news. Fake news has the

AI based on frequency slicing deep neural network for underwater visible light communication
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In this paper, we propose a low-complexity frequency slicing deep neural network (FSDNN) for wide-band signal post-equalization in a 1.2 m underwater visible light communication system. FSDNN and deep neural network (DNN) outperform the least mean square Abstract Currently, significant amounts of IoT data are being collected as big data for deep neural network learning algorithms that are used to extract meaningful information from big data and applied to various areas

Image-based fluid data assimilation with deep neural network
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Estimating unknown parameters or conditions based on observation in a numerical model is a problem considered in data assimilation. In this study, we investigate a data assimilation approach using an image-processing deep neural network (DNN) as a likelihood function Heart disease is ranked second in the top ten death causes in Taiwan in 201 while the amount of heart disease fatalities is increasing by about 700 individuals each year. Therefore, it is urgent to improve heart disease diagnostic accuracy. This research combines The aim of this work is to develop efficient named entity recognition from the given text that in turn improves the performance of the systems that use natural language processing (NLP). The performance of IoT-based devices such as Alexa and Cortana significantly depends Abstract The advent of Industrial Internet of Things (IIoT) has determined the proliferation of smart devices connected to the Internet and injected a vast amount of data into it, which undergo many computational stages at several clusters. On the one hand, the benefits Computer Vision (CV) domain is widely used in the current era of automation and visual surveillance for the detection and classification of different objects in a diverse environment. The automatic machine inspection of different objects in the scenes is based on internal and Deep neural networks are susceptible to tiny crafted adversarial perturbations which are always added to all the pixels of the image to craft an adversarial example. Most of the existing adversarial attacks can reduce the L 2 distance between the adversarial image and In developed countries, colorectal cancer is the second cause of cancer-related mortality. Chemotherapy is considered a standard treatment for colorectal liver metastases (CLM). Among patients who develop CLM, the assessment of patient response to chemotherapy isPurpose Gliomas are the most common and aggressive type of brain tumors due to their infiltrative nature and rapid progression. The process of distinguishing tumor boundaries from healthy cells is still a challenging task in the clinical routine. Fluid-attenuated inversion Email Spam is a variety of automated spam where unbidden messages, used for business purpose, sent extensively to multiple mailing lists, individuals or newsgroups. To build a fruitful system for spam detection, we introduced Random Forest integrated with Deep In this study, a novel transfer path analysis formulation using an emerging deep neural network model is presented and numerically validated for a multi-structural system. In the proposed formulation, only the operational responses of structures are utilized to identify the With the development of technology, the importance of the research on speech emotion recognition and semantic analysis has increased. The research is primarily applied in companion robot, technology products and medical purpose. In this research, a

HyperNews: Simultaneous News Recommendation and Active-Time Prediction via a Double-Task Deep Neural Network
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Personalized news recommendation can help users stay on top of the current affairs without being overwhelmed by the endless torrents of online news. However, the freshness or timeliness of news has been largely ignored by current news recommendation systems. In

Evaluation of Deep Neural Network in Directional Prediction of Forex Market
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Abstract: In this paper we apply deep neural network techniques in order to forecast future trend of Forex market. We will utilize deep neural network to predict whether there will be uptrend or downtrend or neutral. The result can be used by traders as a baseline to make further decision An advanced traffic sign recognition (ATSR) system using novel pre-processing techniques and optimization techniques has been proposed. During the pre-processing of input road images, color contrasts are enhanced and edges are made clearer, for easier detection ofPurpose To develop an automated framework for sleep stage scoring from PSG via a deep neural network . Methods An automated deep neural network was proposed by using a multi- model integration strategy with multiple signal channels as input. All of the data were

MADNN: A Multi-scale Attention Deep Neural Network for Arrythmia Classification
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The morphological features for arrhythmia diagnosis are normally identified in different sizes. Attention-based deep neural networks have been proven to boost meaningful features on different scales, while suppressing the weak features. To boost capability of extracting the Study on deep neural networks and big data is merging now by several aspects to enhance the capabilities of intrusion detection system (IDS). Many IDS models has been introduced to provide security over big data. This study focuses on the intrusion detection in computer Aiming at the abnormal data behavior such as huge amount of data and easy to be stolen or lost in the process of distributed cloud computing in cloud storage environment, an abnormal data mining and detection algorithm of MapReduce based on Hadoop distributed file system fungal imaging. The present study uses principal component analysis based deep neural network model using Grey Wolf Optimization (GWO) algorithm to classify the extracted features of diabetic retinopathy dataset. The use

Classifying Milk Yield Using Deep Neural Network
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This study aim to describe the impact of the number of lactation, lactation days, age at first calving and breeding, and number of insemination (ratio) on cattle milk yield (last seven days in average). For this purpose, the milk yields of 156 Holstein Friesian cattle were

Heart Diseases Prediction using Deep Learning Neural Network Model
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Heart Disease is on of key area where Deep Neural Network can be used so we can improve the overall quality of the classification of heart disease In this model, we deploy the deep neural network model using Talos optimization

A Deep Neural Network for Gait Classification Based on Inertial Sensors in Post-Stroke Patients
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Background: Stroke survivors usually experience partial disability, due to abnormal gaits, which vary widely and require tailored rehabilitation programs. However, most gait classifications are based mainly on clinical assessments, which can be influenced by the

Digital Watermarking Using Deep Neural Network
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Intelligence, Machine learning and Deep Neural Network (DNN) driven us to the robust applications. Such as Image processing, speech recognition, and natural language processing, DNN Algorithms has succeeded in many drawbacks; especially the trained DNN Determination of the variety and quantity of Spares is the primary step to guarantee the spares supply. Firstly, this paper analyzes the factors affecting the reserve scheme of Spares. Then by analyzing the inherent five attributes of Spares, two methods are proposed

Survey on Neural Network Architectures with Deep Learning
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system. Deep neural network (DNN) is a prominent hybrid architecture [44] where the generative architecture of deep belief network is modified using discriminative architecture in training process deep neural network is evolved

Multi-Stream Deep Neural Network For 12-Lead ECG Classification
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Advances in artificial intelligence and computer science have allowed for powerful assistive tools in a wide range of fields. Decision support systems could help health professionals to provide patients with quick and cost-efficient diagnostic analysis. The CinC ChallengeVideo analysis for human action recognition is one of the most important research areas in pattern recognition and computer vision due to its wide applications. Deep learningbased approaches have been proven more effective than conventional feature engineering-based

Condition Monitoring of Elevator Systems using Deep Neural Network .
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In this research, we propose a generic deep autoencoder model for automatic calculation of highly informative deep features from the elevator data. Random forest algorithm is used for fault detection based on extracted deep features. Maintenance actions recorded are used to

Deep Neural Network Analysis of Clinical Variables Predicts Escalated Care in COVID-19 Patients
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This study sought to identify the most important clinical variables that can be used to determine which COVID-19 patients will need escalated care early on using deep-learning neural networks. Analysis was performed on hospitalized COVID-19 patients between Abstract Short-term load forecasting (STLF) is essential for power system operation. STLF based on deep neural network using LSTM layer is proposed. In order to apply the forecasting method to STLF, the input features are separated into historical and prediction data The protocol reverse engineering technique can be used to extract the specification of an unknown protocol. However, there is no standardized method, and in most cases, the extracting process is executed manually or semiautomatically. Since only frequently seenOver the past decades, Internet and information technologies have elevated security issues due to huge use of networks. Because of this advance information and communication and sharing information the threats of cyber security has been increasing daily. IntrusionSingle-channel speech separation (SCSS) plays an important role in speech processing. It is an underdetermined problem since several signals need to be recovered from one channel, which is more difficult to solve. To achieve SCSS more effectively, we propose a

Hardware-Assisted Intellectual Property Protection of Deep Learning Models
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We propose an obfuscation framework called Hardware Protected Neural Network (HPNN) in which a deep neural network is trained as a function of a secret key and then, the obfuscated DL model is hosted on a public model sharing platform

Pathway-Specific Protein Domains (PSPD) Discrimination by Using a Hybrid Feature Space Based on Deep Neural Network (DNN)
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The Pathway-specific protein domains (PSPDs) are important tools in examining drug growth as they provide a fast, reliable, and inexpensive way of estimating complex new molecular targets in specific diseases. The protein architecture prevents the formation of a

Electronic Nose for Detecting Multilevel Diabetes using Optimized Deep Neural Network .
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The application of electronic nose in the diagnosis of diabetes mellitus is the technology with early diagnosis feature, non-invasive and convenient. Hence, it has been favored by doctors and patients. Diabetes complication can cause acidosis, which is directly related to the

Explainable Deep Neural Network for Identifying Cardiac Abnormalities Using Class Activation Map
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In this study, we present a deep convolutional neural network (CNN) approach, called CNN- GAP, for classifying 12-lead ECGs with multilabel cardiac abnormalities. Additionally, Class Activation Mapping (CAM) is employed for further understanding the decision-making

PassEye: Sniffing Your Password from HTTP Sessions by Deep Neural Network .
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Passwords are the most widely used method for user authentication in HTTP websites. Password sniffing attacks are considered a common way to steal password. However, most existing methods have many deficiencies in versatility and automation, such as manual