deep neural network IEEE PAPER 2022


A deep neural network is a neural network with a certain level of complexity, a neural network with more than two layers. Deep neural networks use sophisticated mathematical modeling to process data in complex ways.







Reducing Flipping Errors in Deep Neural Networks
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Deep neural networks (DNNs) have been widely applied in various domains in artificial intelligence including computer vision and natural language processing. A DNN is typically

Double JPEG compression detection using spatial-domain deep neural networks
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With the increasing interest in Joint Photographic Experts Group (JPEG) image compression, one of the most important issues in digital image manipulation is finding a proper method to

A Survey on Face Recognition Based on Deep Neural Networks
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of DNN and unsupervised neural networks for face recognition. In the next deep neural networks for face recognition in section 2. In section 3 we study unsupervised neural networks

UCSM-DNN: User and Card Style Modeling with Deep Neural Networks for Personalized Game AI
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This paper tries to resolve long waiting time to find a matching person in player versus player mode of online sports games, such as baseball, soccer and basketball. In player versus

Robust Fine-tuning of Deep Neural Networks with Hessian-based Generalization Guarantees
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of transfer learning from pretrained neural networks to solve a of fine-tuning on deep neural networks . Our main result is a We leverage a layerwise decomposition of a neural network

Multiterminal Pathfinding in Practical VLSI Systems with Deep Neural Networks
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Modern ML image processing solutions are reduced to convolution operations (eg, within a convolutional kernel or convolutional layers of deep neural networks ), that are decomposed

FILM-QNN: Efficient FPGA Acceleration of Deep Neural Networks with Intra-Layer, Mixed-Precision Quantization
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In Advances in neural information processing systems ( Binarized neural networks : Training deep neural networks architecture for accelerating deep neural networks . In Proceedings of

A Robust Learning Method for Deep Neural Networks Using Unsupervised Competitive Learning and Brain-Like Information Processing
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Modern deep neural networks (DNNs) have a wide applicability in many fields; however they are vulnerable mainly because of the use of supervised learning for their representation

ActiveGuard: Active Intellectual Property Protection for Deep Neural Networks via Adversarial Examples based User Fingerprinting
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The training of Deep Neural Networks (DNN) is costly, thus DNN can be considered as the intellectual properties (IP) of the model owners. To date, most of the existing works protect the

Gradient and Magnitude Based Pruning for Sparse Deep Neural Networks
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Deep Neural Networks have memory and computational demands that often render them vdnn: Virtualized deep neural networks for scalable, memory-efficient neural network design The objective of this work is to apply deep neural networks to the syntactic analysis of sentences. We present a study about the possibilities that some models of deep neural networks

Geometric deep learning: progress, applications and challenges
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the number of neural network layers in the deep learning field. The traditional deep neural networks can stack hundreds of layers to obtain better performance, because the deep neural

A Fault Identification Method Based on an Ensemble Deep Neural Network and a Correlation Coefficient
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Inspired by the work in [ 2 27], we propose a fault diagnosis method based on ensemble deep neural networks (EDNNs) and correlation coefficients. The method first uses an

NEW METHOD OF A LOW-COST NEURAL NETWORK: ECONOMIC AND MODELING ISSUES
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using deep neural network in modelling economic variables. For all these reasons the low-cost neural networks With the mass acceptance of deep neural networks (DNN) across a wide

TreeSp2Vec: a method for developing tree species embeddings using deep neural network
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In this study, an approach for learning abstract representations of tree species basing 258 on tree classification using deep neural networks was presented. The developed models 259

Ensemble of Deep Capsule Neural Networks : An Application to Pneumonia Prediction
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Observing the representation power CNNs, the constraints of sub-sampling layers of CNNs and capabilities of capsule networks we propose a deep neural network framework that

Transfer Learning via Test-time Neural Networks Aggregation
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As future work, we will generalise our approach exploring the training with Ni deep neural networks for i = ,n, with n being the number of datasets, in a federated learning scenario.

Comparing and enhancing deeplearning models with temporal logics
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achieved through the use of deep neural networks . Several previously For instance, it has been argued that deep neural networks Moreover, deep neural networks are complex and it is

The long journey to the training of a deep neural network for segmenting pores and fibers
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of data before it is used as input to deep neural networks is often only described as a side note. deep learning network architecture, and a quantitative comparison between networks deep convolutional neural networks (CNNs) model, which includes an AlexNet-based deep Schmidhuber, Multi-column deep neural networks for image classification, 201 arXiv:

Collocation approximation by deep neural ReLU networks for parametric and stochastic PDEs with lognormal inputs
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D Dung 2022 viasm.edu.vn In this section, we present some auxiliary knowledge on deep ReLU neural networks which will be used as a tool of approximation. We will consider deep feed-forward neural networks

UNDERSTUDIESPERFORMANCE PREDICTION USING DEEP NEURAL NETWORK
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Context-dependent pre-trained deep neural networks forlarge-vocabulary speech A unifiedarchitecture for natural language processing: Deep neuralnetworks with multitask learning. In deep neural networks which further connect to other branches, eg, random matrix theory [52], etc. In this book, we will provide our personal selections of statistical mechanical tech In the output layer of neural networks we sometimes use sigmoid function, which has its own importance. That is in order to get the sum of all outputs as 1; this function transforms the

A Semantically Enhanced Deep Neural Network Framework for Reputation System in Web Mining for Covid-19 Twitter Dataset
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interesting and unique is its application of deep neural networks where the machine learns itself with least human intervention. Not only does it use deep learning, but also merges it

A DEEP LEARNING TECHNIQUE FOR OPTICAL WORD RECOGNITION FOR INDIC SCRIPT
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In this project giving a step towards convolutional neural networks for scene text recognition deep neural networks and several pre- processing steps are applied to use a deep neural

Exact solving scheduling problems accelerated by graph neural networks
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Lately, using deep neural approximators over combinatorial Moreover, the additional potential is demonstrated in using deep The main characteristic of deep neural approaches is the

METHOD FOR ESTIMATING THE TIME OF USEFUL USE OF EQUIPMENT BASED ON NEURAL NETWORKS
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An ensemble based on neural networks with random by deep neural networks // Journal of applied informatics. 2019. V. based on deep convolutional neural networks and continuous

2.2 Loss Functions for Neural Networks
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Recently, deep neural networks (DNNs) which extract embedding vectors have shown superior performance than conventional systems in both ASV and anti-spoofing tasks. In this work

Deep Convolutional Neural Networks for generating grain size logs from core photos
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The successful blind test of the ML process allows moving forward with deployment through 244 simply retraining the deep neural networks using grain size data from all previously

Automatic No-reference Quality Assessment in Chest Radiographs Based on Deep Convolutional Neural Networks
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The purpose of this study is to develop and evaluate the performance of deep learning- Conclusions: The developed deep learning model could rapidly and automatically evaluate the

Decomposition and composition of deep convolutional neural networks and training acceleration via sub-network transfer learning
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Fully connected neural networks . For 1D input data, fully connected neural networks or 1D For fully connected neural networks the fully connected layer can be similarly regarded as

Research Title Improving Multi-task deep neural network architecture for natural language understanding usage
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less noise than conventional window-based neural networks . The result showed that RCNN outperformed convolutional neural networks (CNN) and RecursiveNN utilizing four different

A Multilevel Deep Feature Selection Framework for Diabetic Retinopathy Image Classification
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Recently, Computer Vision (CV) with deep neural networks The proposed deep learning framework utilizes the strength The deep learning models are fit for measuring the highlights

PREDICTION OF HEART DISEASE RISK BASED ON DEEP LEARNING
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common notation used for representing deep neural networks , and approach of deep learning for neural networks has been neural networks has led to an increase in performance of

Computer-Assisted Detection Of Intracranial Aneurysms Using A Transformer Deep Neural Network In 3D MR Angiography
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in Transformer Deep Neural Network models Neural Networks are especially suitable for long sequence tasks because they incorporate self-attention mechanisms, including neural

Deep neural learning based protein function prediction
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Atalay, DEEPred: automated protein function prediction with multi-task feed-forward deep(2) For the emotion classification challenge, we present a deep neural network called Bi-LSTM (Bi-LSTM), which learns [17] investigated deep convolutional neural networks for facial

Delve: Neural Network Feature Variance Analysis
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Designing neural networks is a complex task. Deep neural networks are often referred to as black box models little insight in the function they approximate is gained from looking at

Automated Detection Of Enlarged Extraocular Muscle In Graves Ophthalmopathy With Computed Tomography And Deep Neural Network
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Supervised machine learning systems, known as neural networks have been applied to medical research. Many studies on the diagnostic and classi cation performance of deep

Wavelet Transform for the Analysis of Convolutional Neural Networks in Texture Recognition
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toy models and empirically on realistic deep neural networks does not occur by chance and be a natural candidate for the same analysis on neural networks . Nevertheless, a simple

Training High-performance Spiking Neural Networks Through Reducing Quantization Error
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Spiking neural networks (SNNs) as a kind of efficient model by mimicking the spiking nature of brain neurons has have attracted more and more attention. It transmits binary spike

BREGMAN NEURAL NETWORKS
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A key aspect of deep neural networks is their ability to learn a neural networks and gives rise to novel multilayer networks . neural networks and their asymptotic properties. Feedforward

Deep adaptation control for acoustic echo cancellation
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We propose a general framework for adaptation control using deep neural networks (NNs) Then, a deep NN is trained to learn the relation between the input data and the optimal step-

A Real-Time Polyp Detection System with Clinical Application in Colonoscopy Using Deep Convolutional Neural Networks
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recognition was particularly shaped by various deep learning methods from the beginning for the object recognition capabilities of Convolutional Neural Networks . For example, in 2015

Graph neural networks for biochemistry that incorporate substructure
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We plan to develop an AI-based Deep learning architecture to automate tracking of sub- Furthermore, we will curate a comprehensive particle tracking dataset for deep neural network

Intensity and phase imaging through scattering media via deep despeckle complex neural networks
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we developed a deep despeckle method to simultaneously predict the intensity and phase information of biological samples through the scattering media via a complex neural network ( This paper proposes usage of images to classify music according to genre. Musical characteristics are extracted from these images for music genre recognition and categorization.

Network anomaly detection using deep learning techniques
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In the work done in [15], deep neural networks were proposed towards the task of anomaly detection. The result shows that DL methods are good for detecting anomalies in the software

Structure preservation for the Deep Neural Network Multigrid Solver
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[35] use convolutional neural networks and techniques from of partial differential equations using neural networks there have the benefits of neural networks and classical methods and

Peaches Detection Using a Deep Learning Technique A Contribution to Yield Estimation, Resources Management, and Circular Economy. Climate 202 10
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Many state-of-the-art methods for fruit detection use convolutional neural networks (CNNs). Precision agriculture applications, such as deep neural networks (DNNs), as proposed in

Defect Detection of Mechanical Design Products Using Deep Neural Network
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The workpiece surface defect detection algorithm with deep learning uses convolutional neural networks to automatically extract effective image information, and has strong featureWe have also analyzed the performance of Convolutional Neural Networks (CNN) on the dataset using performance measures like accuracy, precision, Recall, and area of the ROCThe CNN architecture is based on a deep neural network structure. It consists of several layers, including convolution, ReLU, pooling, flattening, and fully connected. in the end, a

Optimized Deep Convolutional Neural Networks Based State of Charge Estimation for Lithium-Ion Battery
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methods based on recurrent neural networks such as: recurrent neural network (RNN), a SOC estimation method based on an optimized deep convolutional neural networks (CNN) [46]

Detecting Runtime Exceptions by Deep Code Representation Learning with Attention-Based Graph Neural Networks
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Inspired by the recent successes in applying deep learning to bug detection, we propose a deep learning-based technique, named DREX, to identify not only the types of runtime

AN IMPROVED CLASSIFICATION OF MR IMAGES FOR CERVICAL CANCER USING CONVOLUTIONAL NEURAL NETWORKS
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This study aims to construct a deep Convolutional Neural Network (CNN) to automatically detect cervical cancer from colposcopy images. This strategy eliminates the need for

Multi-lane Modelling using Convolutional Neural Networks and Conditional Random Fields
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Atrous convolution: Atrous convolution increases the spatial resolution of feature maps while using deep convolution neural networks . In Atrous convolution, dilation rate defines a

Fundamentals of deep Q-Learning
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Recent advances on Deep Learning have led to implement deep neural networks to approximate the mapping function, referred as deep Q-Learning. In this brief article, we review the

Fashion Recommendations with Operation-aware Neural Networks
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By participating in this challenge, we are interested in to what extent those state-of-the-art deep Autoint: Automatic feature interaction learning via self-attentive neural networks . In: Pro

Deep Neural Networks IEEE PAPER 2021




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 withIn this paper we investigate the relationships between a multipreferential semantics for defeasible reasoning in knowledge representation and a deep neural network model. Weighted knowledge bases for description logics are considered under a concept-wise

Computation Offloading Scheduling for Deep Neural Network Inference in Mobile Computing
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The quality of service (QoS) of intelligent applications on mobile devices heavily depends on the inference speed of Deep Neural Network (DNN) models. Cooperative DNN inference has become an efficient way to reduce inference latency. In cooperative inference, a mobile

Enhancing Direct-Path Relative Transfer Function Using Deep Neural Network for Robust Sound Source Localization
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This paper proposes a deep neural network (DNN) based direct-path relative transfer function (DP-RTF) enhancement method for robust direction of arrival (DOA) estimation in noisy and reverberant environments. The DP-RTF refers to the ratio between the direct-path

Overview of Configuring Adaptive Activation Functions for Deep Neural Networks-A Comparative Study
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filters, training and testing datasets. Deep neural network is providing many solutions for nonlinear partial differential equations (PDE) architectures deployed for efficient networks. Designing an efficient deep neural network is based on depth-wise convolution layers [16]

A Stacking-Based Deep Neural Network Approach for Effective Network Anomaly Detection
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An anomaly-based intrusion detection system (A-IDS) provides a critical aspect in a modern computing infrastructure since new types of attacks can be discovered. It prevalently utilizes several machine learning algorithms (ML) for detecting and classifying network traffic. To

The Deep Neural Network based Photometry Framework for Wide Field Small Aperture Telescopes
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Wide field small aperture telescopes (WFSATs) are mainly used to obtain scientific information of point like and streak like celestial objects. However, qualities of images obtained by WFSATs are seriously affected by the background noise and variable point

Design of Autonomous Production Using Deep Neural Network for Complex Job‑Shops
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ABSTRACT Deep Neural Network (DNN) in recent era offers new opportunities to manage the production systems with increasing complexity. In this paper, DNN is trained with discrete event simulation and a process based association to make the job shop an

Speech Enhancement from Fused Features Based on Deep Neural Network and Gated Recurrent Unit Network
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Speech is easily interfered by the external environment in reality, which will lose the important features. Deep learning method has become the mainstream method of speech enhancement because of its superior potential in complex nonlinear mapping problems

Deep Neural Network With the Hmm To Judge the Observation Probabilities
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Method of query oriented summarization on the single document by using unsupervised auto-encoder for extracting the features and did not require the query at any stage of training with small local vocabulary and reduced training and deployment computational costs. For

Italian sign language alphabet recognition from surface EMG and IMU sensors with a deep neural network
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The use of surface electromyography (EMG) and Inertial Measurement Unit (IMU) data emerged as a possible alternative to computer vision-based gesture recognition. As a consequence, the convenience of using such data in the automatic recognition of sign

Dynamic Precision Training of Deep Neural Network Models on the Edge
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Recent developments in deep learning (DL) are putting a lot of pressure and pushing the demand for intelligent edge devices capable of on-site learning. The realization of such systems is, however, a massive challenge due to the limited resources available in an

Towards an Optimal Deep Neural Network for SOC Estimation of Electric-Vehicle Lithium-Ion Battery Cells
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This paper has identified a minimal configuration of a DNN architecture and hyperparameter settings to effectively estimate SOC of EV battery cells. The results from the experimental work has shown that a minimal configuration of hidden layers and neurons can reduce the

2-D Convolutional Deep Neural Network for the Multivariate Prediction of Photovoltaic Time Series. Energies 202 1 2392
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Here, we propose a new deep learning scheme to solve the energy time series prediction problem. The model implementation is based on the use of Long Short-Term Memory networks and Convolutional Neural Networks. These techniques are combined in such a

Onion Crop Monitoring with Multispectral Imagery using Deep Neural Network
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The worlds growing population leads the government of Pakistan to increase the supply of food for the coming years in a well-organized manner. Feasible agriculture plays a vital role for sustain food production and preserves the environment from any unnecessary chemicalsThis project is focusing more on educational purposes. The number of vehicles keeps increasing on the road since almost all people have a personal vehicle. However, tally with the increment of the vehicles on the road, the case of accidents on the road also increases 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

Exploring Factors Associated with Subjective Health of Older-Old using ReLU Deep Neural Network and Machine Learning
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Resolving the health issues of the elderly has emerged as an important task in the current society. This study developed models that could predict the subjective health of the older-old based on gradient boosting machine (GBM), naive Bayes model, classification and

FACIAL EXPRESSION RECOGNITION BASED ON DEEP NEURAL NETWORK STRUCTURE
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The face is an important entity which plays a crucial role in our daily social interaction, like conveying individuals identity. Face recognition is a biometric technology that extracts the facial features mathematically and then stores those features as a face print to identify the This research aims at recognizing emotions for a text-independent and speaker- independent emotion recognition system based on a novel classifier which is a hybrid of a cascaded Gaussian Mixture Model and Deep Neural Network (GMM-DNN). This hybrid

A Study on Multi-Exit Deep Neural Network for Real-time Processing
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The deep neural network has been wildly used in various fields in recent years, since it has excellent performance and is easy to use in classification and prediction tasks. It is also applied in real-time systems, such as self-driving and object tracking systems, etc. However

Novel Radar-based Gesture Recognition System using Optimized CNN-LSTM Deep Neural Network for Low-power Microcomputer Platform.
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The goal of the embedded hand gesture recognition based on a radar sensor is to improve a human-machine interface, while taking into consideration privacy issues of camera sensors. In addition, the system has to be deployable on a low-power microcomputer for the

A Deep Neural Network Based Approach to Building Budget-Constrained Models for Big Data Analysis
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Deep learning approaches require collection of data on many different input features or variables for accurate model training and prediction. Since data collection on input features could be costly, it is crucial to reduce the cost by selecting a subset of features and

Human-Animal Affective Robot Touch Classification Using Deep Neural Network .
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Touch gesture recognition is an important aspect in human robot interaction, as it makes such interaction effective and realistic. The novelty of this study is the development of a system that recognizes human animal affective robot touch (HAART) using a deep learning

Kannada to English Machine Translation Using Deep Neural Network .
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Accepted: 15 November In this paper, we focus on the unidirectional translation of Kannada text to English text using Neural Machine Translation (NMT). From studies, we found that using Recurrent Neural Network (RNN) has been the most efficient way to

Domain Adaptation Multi-task Deep Neural Network for Mitigating Unintended Bias in Toxic Language Detection.
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As online communities have grown, so has the ability to exchange ideas, which includes an increase in the spread of toxic language, including racism, sexual harassment, and other negative behaviors that are not tolerated in polite society. Hence, toxic language detection

Prediction of the Charpy V-notch impact energy of low carbon steel using a shallow neural network and deep learning
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A three-layer neural network, ex- treme learning machine, and deep neural network were compared with different activation functions, structure parameters, and training func- tions. Bayesian optimization was used to determine

Vibration Anomaly Detection using Deep Neural Network and Convolutional Neural Network
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Identifying the health state of the equipment is the domain of condition monitoring. The paper proposes a study of two models: DNN ( Deep Neural Network ) and CNN (Convolutional Neural Network) over an existent dataset provided by Case Western Reserve

Swarm-LSTM: Condition Monitoring of Gearbox Fault Diagnosis Based on Hybrid LSTM Deep Neural Network Optimized by Swarm Intelligence Algorithms
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Nowadays, renewable energy has been emerging as the major source of energy and is driven by its aggressive expansion and falling costs. Most of the renewable energy sources involve turbines and their operation and maintenance are vital and a difficult task. ConditionIn the era of the Big-Datawe hear a lot about machine learning for working on this big data. Machine learning helps us to predict and analyze data with better accuracy and least human intervention. Machine learning is autonomous but susceptible to errors. This is due to biased

Rice Plant Infection Recognition using Deep Neural Network Systems
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In this work, identification of diseases present in the plant of rice is carried out using methods of Deep Neural Network . So as to achieve image accession, a dataset having 2212 leaf images with different diseases is used. In this work, the entire dataset is divided into two

IMPLEMENTATION OF DEEP NEURAL NETWORK MODELS FOR STOCK MARKET PREDICTION
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ABSTRACT Stock Market Data is very dynamic and generated enormously on account of every single day. Every single second it changes which makes it Challenging and Complex. Investors and sellers will makes huge profit sometime as well as huge loss. In this work, an In this paper we presented a method to nuclei image segmentation and detection on public nuclei cancer images which we used from Kaggle Data Science Bowl data sets. For segmentation the deep neural network with semantic approaches are used. For accuracy

Hybrid Deep Neural Network for Malicious DNS Infiltration Detection System
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The domain name system (DNS) is arguably the most important infrastructure of the Internet. Due to its prominant role in communication, DNS has come under heavy security attacks which are most times catastrophic. Encapsulating DNS inside of HyperText Transfer Protocol

Single-Cell Conventional Pap Smear Image Classification Using Pre-Trained Deep Neural Network Architectures
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Background: Automating Pap smear based cervical cancer screening could alleviate the 14 challenge related to the shortage of skilled manpower, mainly pathologists in developing 15 countries. The astonishing accuracy and reproducibility of deep neural networks in 16

Detection of COVID-19 using X-Ray Images with Deep Neural Network
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In recent years, deep learning has multiplied and has played an important role in image recognition, including medical imagery. CNNs have been well used in many diseases, including heart disease, malarial disease, Alzheimers disease, various dental disorders

Hardening Deep Neural Networks in Condition Monitoring Systems against Adversarial Example Attacks
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systems. However, deep neural networks are vulnerable to attacks with adversarial examples. Adversarial examples are manipulated inputs, eg sensor signals, are able to mislead a deep neural network into misclas- sification. A

Opponent Hand Estimation in Gin Rummy Using Deep Neural Networks and Heuristic Strategies
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We do this by using heuristic utility functions to create an agent that approx- imates how a rational player would play, and then using the resulting game data to train a Deep Neural Network mapping known information to predictions about the opponents hand

Robust encoder-decoder learning framework for offline handwritten mathematical expression recognition based on a multi-scale deep neural network
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Citation Shan GC, Wang HY, Liang W, et al. Robust encoder decoder learning framework for offline handwritten mathematical expression recognition based on a multi-scale deep neural network . Sci China Inf Sci, 202 64 (3): 13910 https://doi. org/10.1007/s11432-018

Interpretation of Deep Neural Networks in Speech Classification
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This paper aims to bring more insight into how deep neural network models decide when predicting a persons gen- der from speech recording processed as a spectrogram, following previous work on this topic . Convolution neural network with AlexNet archi- tecture was




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