neural network IEEE PAPER 2022


A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. In this sense, neural networks refer to systems of neurons, either organic or artificial in nature.







Two-level Graph Neural Network
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proposed the Graph Attention Network (GAT), which leverages masked self-attentional develop a simple neural architecture referred to as the Graph Isomorphism Network (GIN).

A Multiscale Recurrent Neural Network Model for Long-Term Prediction of Geothermal Energy Production
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Recurrent neural network (RNN) is a class of neural networks (2021) designed a dynamic neural network architecture for Neural networks have shown superior performance in many

Design of an Artificial Neural Network (BPNN) to Predict the Content of Silicon Oxide (SiO2) based on the Values of the Rock Main Oxides: Glass Factory Feed
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Abstract: Artificial neural network (ANN) is one of the practical methods for prediction in , according to the artificial neural network algorithm back propagation neural network (BPNN),

Semi-Deterministic Dynamic Millimeter-wave Channel Modeling Based on an Optimal Neural Network Approach
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In [29], the feed-forward neural network (NN) and RBF NN is In this work, we propose an optimal neural network (ONN) : A method to optimize neural network by searching the optimal

Score Prediction of Sports Events Based on Parallel Self-Organizing Nonlinear Neural Network
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used BP neural network and self-organizing neural network in neural network models. en of self-organizing, and a better neural network structure was found through comparison. A

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 Delve is a tool for looking at how a neural network represents data, and how these

PBDiff: Neural network based program-wide diffing method for binaries
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In this paper, we propose a program-wide binary diffing method based on neural network Neural network model is applied in the bottom up process to vectorize the semantic features

Pan-sharpening with Customized Transformer and Invertible Neural Network
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and effective invertible neural feature fusion neural network in this paper. To the best of our knowledge, this is the first attempt to introduce transformer and invertible neural network into

Detection of multi-type data anomaly for structural health monitoring using pattern recognition neural network
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The artificial neural network has a simpler structure and is And the artificial neural network is more computationally on feature extraction and pattern recognition neural network (PRNN).

BreaCNet: A high-accuracy breast thermogram classifier based on mobile convolutional neural network
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mobile neural network model breast cancer mobile network ( of the input dataset to the network can improve the classifier s data privacy and handle an unreliable network connection.

A neural network for a generalized vertical complementarity problem
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Abstract: In this paper, an efficient artificial neural network is proposed for solving a of the neural network are addressed in detail. In addition, it is also proved that if the neural network

A YOLO and convolutional neural network for the detection and classification of leukocytes in leukemia
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The terms CNN refer to a convolutional neural network which are case of deep learning neural networks that used mainly for classification [18]. The main different between ANN and

State solutions for distribution systems and switching event using a neural network : State Solution Using Neural Network
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neural network . The scaling function chosen for the ANN for the 28-bus and 123-bus systems was the well-known z-score function; this is specifically a standardisation scaling technique

Assessment of Power Plant Based on Unsafe Behavior of Workers through Backpropagation Neural Network Model
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output of a backpropagation (BP) neural network based unsafe behavior assessment model. An A threelayer feedforward BP neural network assessment model of 27-13-1 layers was

PRIMA: general and precise neural network certification via scalable convex hull approximations
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activation functions, realistic network architectures and relevant neural network for autonomous driving within a few minutes. the key challenge in neural network verification rests in

Automatic Measurement Algorithm for Brinell Indentations Based on Convolutional Neural Network
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neural network (CNN) is proposed. To eliminate the influence of factors such as scratches and collapses of the material surface on the measurement accuracy, the Brinell indentation

A Review Paper on Prediction of Construction Productivity Using Artificial Neural Network Model
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making use of radial foundation feature neural network . The version specializes in labor neural network radial basis function (RBF), and generalized` regression neural network . The In this specific case, neural network algorithms were utilized in order to conduct an intensive study to determine and analyze the prevailing driver behavior and driving styles. This

A multichannel optical neural network for advanced machine vision
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We compare our Monet with an electronic convolution 211 neural network with a similar network architecture4 46 (denoted as CNN, see Supplementary Fig. S3). The object-level

Use of convolutional neural network for sensor modeling and on-board diagnostic
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It is in this context that a neural network (NN) thermodynamic model of the engine coolant work, the size of the final hybrid convolutional neural network (HCNN) is not a problem and it

Enhancement of convolutional neural network for urban environment parking space classification
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Th aims of this study are to modified the architecture of Convolutional Neural Networks, part of deep learning to classify parking spaces. Modification of the Convolutional Neural Deep Neural Network : Deep neural network or DNN [27] is a basic neural network structure, which is used in our model building. This comprises basically three components: input layer,

Deep convolutional neural networkbased system for fish classification.
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Adam can update the weights of the neural network repeatedly based on the training data. Moreover, Adam s algorithm calculates the adaptive treatment learning rate based on the

Modeling and optimization of small-scale NF/RO seawater desalination using the artificial neural network (ANN)
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In this current work, the artificial neural network was used due to their flexibility to conform to any type of data set and due to their success with various engineering problems [40-45].

Optimized Convolutional Neural Network Models for Skin Lesion Classification
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The first kind of deep learning model used for skin lesion image processing was the convolutional neural network (CNN). This architecture was demonstrated to exceed a dermatologist

An Optimized Neural Network Using Genetic Algorithm for Cardiovascular Disease Prediction
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for a neural network [22]. the neural network [24]. To improve the accuracy of ANN in cardiovascular disease prediction, this paper employs an optimization strategy in the neural network

An Improved Convolutional Neural Network Model for DNA Classification
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A modified convolutional neural network (CNN) is employed in this paper for building an integrated model for deoxyribonucleic acid (DNA) classification. In any CNN model,

A Hybrid Neural Network and Box-Jenkins Models for Time Series Forecasting
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A back propagation neural network (BPNN) is a neural network architecture that is widely to find a composite model of typical ARIMA neural networks using different styles. Currently, Prabhu and SV Bhandary, Improving the performance of convolutional neural network for the segmentation of optic disc in fundus images using attention gates and conditional random

Characterization of Superalloys by Artificial Neural Network Method
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inspired by the biological neural networks that constitute animal The appropriate artificial neural network model was was used to create the artificial neural network model. The high

The IoT and registration of MRI brain diagnosis based on genetic algorithm and convolutional neural network
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Neural networks are playing a significant part in medical diagnosis and neural network methods were implemented to relay the neural architecture of the image segmentation network

State of Charge estimation of lithium-ion power battery based on online parameter identification method and BP neural network
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of the wavelet transform of neural network and then uses the trained neural network to optimize the The neural network has strong learning ability. For the BP neural network the weight

SUPPNet: Neural network for stellar spectrum normalisation
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The core of the proposed method uses the novel, fully convolutional deep neural network (SUPP Network ) that was trained to predict a pseudo-continuum. The post-processing step

Esophageal Cancer Detection Using Feed-Forward Neural Network
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The proposed system detects esophageal cancer in its early stages using the Convolutional Neural Network (CNN) algorithm based on the features extracted by implementing the

A lightweight double-channel depthwise separable convolutional neural network for multimodal fusion gait recognition
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a lightweight doublechannel depthwise separable convolutional neural network (DC-DSCNN) a three-layer depthwise separable convolutional neural network (DSCNN) module. Next,

MARnet: multi-scale adaptive residual neural network for chest X-ray images recognition of lung diseases
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3) In order to solve the problem that the identification of neural network is difficult to be understood by human beings, we use Grad-CAM technology to visualize the disease area in the

An Invertible Graph Diffusion Neural Network for Source Localization
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However, they are applicable to a specific type of neural network and are poorly generalizable. A recent line of research works use Recurrent Neural Networks (RNN) to predict the

Effect of dual-convolutional neural network model fusion for Aluminum profile surface defects classification and recognition
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A traditional convolutional neural network framework includes a neural network model and a convolutional neural network . In contrast, the multi-convolutional neural network model

PREDICTION OF RAINFALL OVER KERAL USING DEEP NEURAL NETWORK
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The objective of our study is to build a deep neural network model which can accurately predict the rainfall a few days ahead accumulating over the Kerala region during the Indian

Using Water Quality Parameters To Predict Trihalomethane Ion Based On An Artificial Neural Network Model
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by-products in the drinking water distribution network and classi ed as potential carcinogens. parameters and modeled by an arti cial neural network (ANN) approach through ve water

An Instantaneous Corrosion Monitoring Technique based on Combining Modified Electrochemical Noise and Artificial Neural Network for Determination of
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wavelet transform and artificial neural network . Reliability of EES by EES, artificial neural network was introduced to build the demonstrated that artificial neural network is an excellent

Classification of Registered Human Posture using Supervised Neural Network
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This work aims to design a supervised neural network to classify body postures, taking into account recorded human movements and using modern ZED2 RGB-D vision sensors. Two

Convolution Neural Network (CNN) for Video Processing: A Survey
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The past decade focused on Image Processing. Recently it is being found that many have shown their keen interest in Video Processing. Convolution Neural Network (CNN) showedThis paper presents a hybrid convolutional neural network based and long short-term memory network (CNN-LSTM) approach to a predictive maintenance problem. The proposed CNN-

TreeSp2Vec: a method for developing tree species embeddings using deep neural network
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species numeric representations using deep 12 neural networks and data from a National Among 15 the tested neural network architectures, a multi-layer perceptron with two hidden

Real-Time Exercise Feedback through a Convolutional Neural Network : A Machine Learning-Based Motion-Detecting Mobile Exercise Coaching Application
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Purpose: Mobile applications are widely used in the healthcare market. This study aimed to determine whether exercise using a machine learning-based motion-detecting mobile

AUC-oriented Graph Neural Network for Fraud Detection
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Benefited by the capability of mining profound dependence and correlations between entities, Graph Neural Networks (GNNs)[1 33] have been broadly implemented in fraud detection

Artificial Neural Network Potentials for Mechanics and Fracture Dynamics of Materials
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Then, we explore how the data selection affects the accuracy of the developed arti cial neural network potential (NNP), indicating that only the errors in total energies and atomic forces

UNDERSTUDIESPERFORMANCE PREDICTION USING DEEP NEURAL NETWORK
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In this paper, a neural organization called Deep Neural Network (DNN)model is suggested that shows The proposedprofound neural organization model accomplished up to 84.3%

ACCURACY COMPARISON AMONG WORD2VEC, GLOVE, AND FASTTEXT TOWARDS CONVOLUTION NEURAL NETWORK (CNN) TEXT CLASSIFICATION
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Word2vec is a feedforward neural network model, so it is often referred to as neural word embeddings, while GloVe is a log-bilinear model or often referred to as a count-based model. It Artificial Neural Network (ANN) is the method used for this project. ANN is known neural network attempts to learn about the information presented to produce one output. The network

A neural network based approach to classify VLF signals as rock rupture precursors
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In this paper, we show how such correlation can be used to train a neural network to classify Such characteristic spectral patterns have been correctly classi ed by our neural network

Defect Detection of Mechanical Design Products Using Deep Neural Network
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based on deep convolutional neural network focusing on solving the ResNet fully convolutional neural network . While realizing of the fully convolutional neural network to improve

NEW METHOD OF A LOW-COST NEURAL NETWORK : ECONOMIC AND MODELING ISSUES
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Even though numerous research studies worked on different neural network neural designs. This paper offers a deep neural network induction accelerator generator called neural

Vehicle Speed Prediction Using a Combined Neural Network of Convolution and Gated Recurrent Unit With Attention
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To generate temporal attention mask with a size of N, we adopt two layers of convolutional neural network followed by a Sigmoid function and Softmax function. The definition of TA

Automatic Classification of Real-Time Diseased Cotton Leaves and Plants Using a Deep-Convolutional Neural Network
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The automated detection and classi cation of plant diseases based on images of leaves is a signi cant milestone in agriculture. In this paper, the concept of deep learning was used to

Production Capacity Prediction of Thyristor Based on Fuzzy Neural Network
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of fuzzy c-means radial basis function neural network model, the Genetic algorithm neural network ) neural network is established of back propagation neural network and mathematical

Prediction of Physiological Characteristic Changes in Pears Subject to Dynamic Loading Using Artificial Neural Network (ANN)
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(MLP) artificial neural network featuring a hidden layer and two R2 for dynamic loading in a network with 5 neurons in the for the network the ability of the neural network to predict

EEG Motor Imagery Classification using Fusion Convolutional Neural Network
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It is used to recognize MI-EEG tasks via a deep learning techniques such as Convolutional Neural Network (CNN), which poses a potential problem in maintaining the integrity of

Advanced Method of Land Cover Classification Based on High Spatial Resolution Data and Convolutional Neural Network
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of this paper improved the neural network methodology for latest architectures of convolutional neural networks. The process and a convolutional neural network architecture to classify

neural network IEEE PAPER 2021




5G Network Simulation in Smart Cities using Neural Network Algorithm
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The speed of internet has increased dramatically with the introduction of 4G and 5G promises an even greater transmission rate with coverage outdoors and indoors in smart cities. This indicates that the introduction of 5G might result in replacing the Wi-Fi that is

Graph neural networkbased anomaly detection in multivariate time series
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Given high-dimensional time series data (eg, sensor data), how can we detect anomalous events, such as system faults and attacks More challengingly, how can we do this in a way that captures complex inter-sensor relationships, and detects and explains anomalies which The development of information technology and process technology have been enhanced the rapid changes in high-tech products and smart manufacturing, specifications become more sophisticated. Large amount of sensors are installed to record equipment condition Medical image segmentation is a crucial but challenging task for computer-aided diagnosis. In recent years, fully convolutional network-based methods have been widely applied to medical image segmentation. U-shape-based approaches are one of the most successful Stock data have a long memory, that is, changes in stock prices are closely related to historical transaction data. Also, Recurrent Neural Networks have good time series feature extraction capabilities. The paper proposed prediction models based on RNN/LSTM/GRUDear editor, With the extensive implementation of highspeed railway acceleration, higher requirements for safety and stability are put forward. In order to guarantee the safety of the high-speed train (HST), it is critical to detect the abnormal state in operation. Bogie that plays The medical model that uses artificial intelligence technology to assist diagnosis and treatment is called smart medicine. It can learn the medical knowledge of experts, and can simulate the thinking and reasoning of doctors to give patients a reliable diagnosis andInduction machines have extensive demand in industries as they are used for large-scale production and, therefore, vulnerable to both electrical and mechanical faults. Automated continuous condition monitoring of industrial machines to identify these faults has becomeDeep 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 systematic review on sequence-to-sequence learning with neural network and its models.
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We develop a precise writing survey on sequence-to-sequence learning with neural network and its models. The primary aim of this report is to enhance the knowledge of the sequence- to-sequence neural network and to locate the best way to deal with executing it. ThreeTodays 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 Based on the BP neural network model of machine learning method, the corresponding random input parameters are generated by Monte Carlo method, and the prediction of TBM driving speed is studied. In this study, the machine learning method is applied to the DICOM images which helps in diagnosis and prognosis would be critical component in health care systems. Speedy recovery of past historic DICOM images based on the given query image is becoming a critical requirement for the Laboratories and Doctors for quick

Classification and Prediction of Gastric Cancer from Saliva Diagnosis using Artificial Neural Network .
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In medical research, non-invasive diagnostic tools have become an emerging technique for the diagnosis of fatal disease in the last few years. Saliva analysis for the detection of Gastric cancer (GC) also belongs to this powerful new research field. According to the WHO, cancer

Knowledge-aware Coupled Graph Neural Network for Social Recommendation
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Social recommendation task aims to predict users preferences over items with the incorporation of social connections among users, so as to alleviate the sparse issue of collaborative filtering. While many recent efforts show the effectiveness of neural networkIn this work, we propose a class of numerical schemes for solving semilinear Hamilton Jacobi Bellman Isaacs (HJBI) boundary value problems which arise naturally from exit time problems of diffusion processes with controlled drift. We exploit policy iteration to reduce the

Drone Detection by Neural Network Using GLCM and SURF
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This paper presents a vision-based drone detection method. There are a number of researches on object detection which includes different feature extraction methods all of those are used distinctly for the experiments. But in the proposed model, a hybrid feature In this numerical study, a class of nonlinear singular boundary value problem is solved by implementation of a novel meta-heuristic computing tool based on the artificial neural networks (ANNs) modeling of system and the optimization of decision variable of ANNs

A plexus‐convolutional neural network framework for fast remote sensing image super‐resolution in wavelet domain
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Satellite image processing has been widely used in recent years in a number of applications such as land classification, Identification transfer, resource exploration, super-resolution image, etc. Due to the orbital location, revision time, quick view angle limitations, and

A Clustering-Based Approach for Features Extraction in Spectro-Temporal Domain Using Artificial Neural Network
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In this paper, a new feature extraction method is presented based on spectro-temporal representation of speech signal for phoneme classification. In the proposed method, an artificial neural network approach is used to cluster spectro-temporal domain. SelfIn recent years, the growing popularity of artificial neural networks has urged more and more researchers to try introduce these methods to the machining field, with some of them actually producing good results. The acquisition of cutting data often means higher cost and timeIn 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

Shapenet: A shapelet- neural network approach for multivariate time series classification
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Time series shapelets are short discriminative subsequences that recently have been found not only to be accurate but also interpretable for the classification problem of univariate time series (UTS). However, existing work on shapelets selection cannot be applied to

Bounding Perception Neural Network Uncertainty for Safe Control of Autonomous Systems
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Future autonomous systems will rely on advanced sensors and deep neural networks for perceiving the environment, and then utilize the perceived information for system planning, control, adaptation, and general decision making. However, due to the inherent

Graph Neural Network to Dilute Outliers for Refactoring Monolith Application
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Microservices are becoming the defacto design choice for software architecture. It involves partitioning the software components into finer modules such that the development can happen independently. It also provides natural benefits when deployed on the cloud since

Adaptive comprehensive particle swarm optimisation-based functional-link neural network filtre model for denoising ultrasound images
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Multiplicative speckle is a dominant type of noise that spoils the inherent features of the medical ultrasound (US) images. Apart from the speckle, impulse and Gaussian noises also appear in the US image due to the error encountered during the data transmission andNeural Networks are designed and simulated using Matlab and performance of the networks are computed by Matlab simulation software. In this research work, 11 number of neural networks with different number of hidden layer (from 0 to 10) with two neurons in each

Secured node detection technique based on artificial neural network for wireless sensor network
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The wireless sensor network is becoming the most popular network in the last recent years as it can measure the environmental conditions and send them to process purposes. Many vital challenges face the deployment of WSNs such as energy consumption and security

Subseasonal Forecasts of Opportunity Identified by an Explainable Neural Network
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Page 1. Subseasonal Forecasts of Opportunity Identified by an Explainable Neural Network Kirsten J. Mayer, PhD Candidate, Dept. of Atmospheric Science, CSU Elizabeth A. Barnes, Associate Professor, Dept. of Atmospheric Science, CSU ISENES3/ESiWACE2 Virtual Workshop: Views Hoisting machinery as a material handling equipment, widely used in the national economy departments, in the national safe, efficient, green and harmonious under the application requirements, to improve the intrinsically safe hoisting machinery, a complex system, in this

Artificial neural network approach: An application to harmonic load flow for radial systems
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Radial Distribution Systems (RDS) require special load flow methods to solve power flow equations owing to their high R/X ratio. Increasing use of power electronic devices and effect of magnetic saturation cause harmonics in RDS. This paper reports a multi-layer feed

Constructive deep ReLU neural network approximation
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We propose an efficient, deterministic algorithm for constructing exponentially convergent deep neural network (DNN) approximations of multivariate, analytic maps f:[− 1] K→ R. We address in particular networks with the rectified linear unit (ReLU) activation function

Prediction of the Charpy V-notch impact energy of low carbon steel using a shallow neural network and deep learning
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The impact energy prediction model of low carbon steel was investigated based on industrial data. A three-layer neural network extreme learning machine, and deep neural network were compared with different activation functions, structure parameters, and training

Bangla language textual image description by hybrid neural network model
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Automatic image captioning task in different language is a challenging task which has not been well investigated yet due to the lack of dataset and effective models. It also requires good understanding of scene and contextual embedding for robust semantic interpretation

A conceptual artificial neural network model in warehouse receiving management
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Artificial Neural Network (ANN) method applying to warehouse receiving management. A conceptual ANN model is proposed to perform identification and counting of components. The proposed model consists of a standard image library, an ANN system to present objects

Artificial Neural Network Approach using Mobile Agent for Localization in Wireless Sensor Networks
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Wireless sensor networks (WSNs) are having large demands in enormous applications for the decade. The main issue in WSNs is estimating the exact location of unknown nodes. All applications are dependent on the location information of unknown nodes in WSNs

Off-line Handwritten Signature Verification System: Artificial Neural Network Approach
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Nowadays, it is evident that signature is commonly used for personal verification, this justifies the necessity for an Automatic Verification System (AVS). Based on the application, verification could either be achieved Offline or Online. An online system uses the signatures In social aware network (SAN) paradigm, the fundamental activities concentrate on exploring the behavior and attributes of the users. This investigation of user characteristic aids in the design of highly efficient and suitable protocols. In particular, the shilling attack

Affine symmetries and neural network identifiability
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We address the following question of neural network identifiability: Suppose we are given a function f: Rm→ Rn and a nonlinearity ρ. Can we specify the architecture, weights, and biases of all feed-forward neural networks with respect to ρ giving rise to f Existing literature

Multilevel Neural Network DTC with Balancing Strategy of Sensorless DSSM Using Extended Kalman Filter
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This paper presents direct torque control based on artificial neural networks of a double star synchronous machine without mechanical speed and stator flux linkage sensors. The estimation is performed using the extended Kalman filter, which is known for its ability to




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