neural network 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 plexusâconvolutional neural network framework for fast remote sensing image superâresolution 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