Deep Neural Networks 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