artificial neural network IEEE PAPER 2022


Artificial neural networks, usually simply called neural networks, are computing systems inspired by the biological neural networks that constitute animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain







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),

A Review Paper on Prediction of Construction Productivity Using Artificial Neural Network Model
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elements influencing productivity by way of using Artificial Neural Network (ANN). The effect of for developing productiveness model the use of Artificial Neural Network (ANN) approach.

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].

Characterization of Superalloys by Artificial Neural Network Method
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Abstract: In this study, the use of artificial neural networks in the classification of a superalloys whose Artificial neural networks (ANNs), usually simply called neural networks (NNs), are

Power quality improvement of distribution systems asymmetry caused by power disturbances based on particle swarm optimization- artificial neural network
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with using artificial neural network technique applied to the unified power flow quality conditioner (PSO-ANN UPQC). Design particle swarm optimization and artificial neural networkThe analysis consisted of descriptive statistics and artificial neural network [22]. The results An artificial neural network model was fit to the survey data with benefits of online education

Catalyst Optimization Design Based on Artificial Neural Network
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artificial neural network . The application principle, construction method and research progress of BP artificial neural network The development and innovation of artificial neural network

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

An Asymetric-key Cryptosystem based on Artificial Neural Network
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of an autoencoder neural network to randomize the training process and create a distinct ASCII code codification based on an initialization password (2) The neural weights that will

Modelling and Optimization of Biodiesel Production Process Parameters from Jansa Seed Oil (Cussonia bateri) Using Artificial Neural Network
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artificial neural network (ANN) for the prediction of biofuel yield and process parameters. The biodiesel was produced through transesterification reaction by reacting jansa seed oil (FFA

A Short-circuit Model Based on Artificial Neural Network and Artificial Bee Colony Algorithm for SiC MOSFETs
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neural network (ANN) and improved artificial bee colony (ABC) algorithm is proposed in this paper. In order to improve the search ability of the ABC, particle swarm optimization (PSO)

Using Water Quality Parameters To Predict Trihalomethane Ion Based On An Artificial Neural Network Model
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Background: Trihalomethanes (THMs) are the rst disinfectant by-products in the drinking water distribution network and classi ed as potential carcinogens. The presence of THMs in

Prediction of Physiological Characteristic Changes in Pears Subject to Dynamic Loading Using Artificial Neural Network (ANN)
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In the present study, multilayer perceptron (MLP) artificial neural network featuring a hidden layer and two activating functions (hyperbolic tangentsigmoid) and a total number of 5 and

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

Slope Stability Prediction of Road Embankment using Artificial Neural Network Combined with Genetic Algorithm
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The prediction of slope stability was performed using artificial neural networks (ANNs) in this work. The factor of safety determined by numerical analysis was used to develop ANN s

Artificial Neural Network Prediction Model of Dust Effect on Photovoltaic Performance for Residential Applications: Malaysia Case Study
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This paper investigates the dust-related degradation in the PV output performance based on the developed Artificial Neural Network (ANN) predictive model. For this purpose, two

Prediction of properties of reservoir using Artificial Neural Network and Monte Carlo Simulation
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For the Efe field in Nigeria and the Amukpe field, we apply Monte Carlo Simulation and Artificial Neural Network to review reservoir distribution and identify future development regions.

Logistics Distribution for Path Optimization Using Artificial Neural Network and Decision Support System
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network topology: The topology of the traffic network is mainly based on the official traffic network Application of artificial neural network for predicting crack growth direction in multiple

Utilization of laser-induced breakdown spectroscopy, with principal component analysis and artificial neural network in revealing cheating of similarly looking
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spectroscopy with arti cial neural network processing for material An arti cial neural network approach to laser-induced Convolutional neural network as a novel classi cation approach

Plant Disease Detection and Monitoring Using Artificial Neural Network
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and prediction using artificial neural network techniques are algorithms of an artificial neural network to improve the then implemented in the neural network toolbox in Matlab.

Breakout during Continuous Casting Process in Manufacturing of Steel Slabs in Steel Plant Using Artificial Neural Network with Backpropagation Algorithms
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], and a convolutional neural network by using sticker pattern of a hybrid neural network and artificial intelligence to simulate This work has developed an artificial neural network with a

Prediction of cooking characteristics of moisture conditioned and infrared heated cowpea using Artificial neural network
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Arti cial neural network was used as a machine learning tool to study the effect of a created in this study utilizing an arti cial neural network (a type of machine learning) outperformed the Some of the most popular artificial neural network technologies are the back propagation neural network (BPNN), support vector machine (SVM), and self-organizing linear output (

Prediction of Sand-Casting Defects by Artificial Neural Network
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Artificial Neural Networks (ANN) is one of the famous tools of Artificial Intelligence that can parameters and defects by means of artificial neural network systems. The implementation of

Identification Of Fake Profiles in Online Social Networks Using Artificial Neural Network
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In this paper, we use machine learning, namely an artificial neural network to determine what are the chances that a friend request is authentic are or not. Each equation at each neuron (

SKIN DISEASE DETECTION AND CLASSIFICATION USING ARTIFICIAL NEURAL NETWORK
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Later, these color values were analyzed by Artificial Neural Network (ANN) classifier, and 90 single deep neural network . In: Proceedings of the Thirty-First AAAI Conference on Artificial

Development of rain attenuation prediction in south west Nigeria on terrestrial link using artificial neural network
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The predicted precipitation accuracy in light of the current global climate change is very important. The Back-Propagation Neural Network (BPNN) technique then utilized the Artificial

SHORT-TERM LOAD FORECASTING USING ARTIFICIAL NEURAL NETWORK
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Abstract In electrical engineering load forecasting have been tried out using traditional forecasting models and artificial neural network and have become one of the major research

WIND GENERATION PROGNOSTICATION USING ARTIFICIAL NEURAL NETWORK
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this paper proposes an improved neural system model for wind energy prognostication. To parameters to be fused into the artificial neural networks for demonstrating and examination

Soft Computing based Artificial Neural Network and Multiple Feature set Intelligence system for Image Retrieval
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This paper proposes an Image Retrieval model using Multiple Feature Sets and Arti cial Neural Network (IR-MFS-ANN), where the multiple features Histogram of oriented Gradient (HoG

Optimal Conditions of Paint Wastewater Coagulation With Gastropod Shell Conchiolin Using Response Surface Design and Artificial Neural NetworkGenetic
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308 Response prediction model was developed via back propagation neural network (BP) method in ANNMATLAB 12 environment. BP algorithm constitutes of input, hidden layer and

Mathematical Analysis of Supply Chain Optimization focusing on Artificial Neural Network : A case study in Apparel Manufacturing Organization
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In this study, implemented artificial nueral network (Ann) in apparel manufacturing organizations to optimize the supply chain converging on right supplier selection by analyzing their

Prediction of the scour depth of bridge pier using artificial neural network model and comparison with empirical equations
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The results of this study showed that the artificial neural network model able to predict scour the best results are related to the artificial neural network model and it decreased error of

Modelling of Performance Outputs of a Pneumatic Piston-Engine with Single Cylinder by means of Artificial Neural Network
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In this study, an air-cooled gasoline engine with 13 HP power has been transformed into a pneumatic engine/compressed air engine, and engine moment experiments have been

Artificial Neural Network Modeling and Control for Dynamical and Statistical Characteristics of Photonic Quantum Memristor
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A new statistical physics technique based on Artificial Neural Networks (ANNs) has been developed recently in . Such ANNs are well-trained with the data collected from the Ab Initio

Implementing The Multi Expression Programming, Nonlinear Regression, Artificial Neural Network and M5P-Tree Models to Predict The Long-Term of
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In the recent decade, supplementary cementing ingredients have become an essential part of various strength ranges of concrete and cement-mortar mix design. Examples are natural

Artificial neural network techniques to predict the moisture ratio content during hot air drying and vacuum drying of Radix isatidis extract
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Methods: Artificial neural networks were designed using the MATLAB neural network toolbox mackerel using artificial neural network based modelin. Food Res Dev. 2021;42(7):13 19. The performance of a neural network is determined by a variety of factors, and activation a neural network . The hidden layer s activation feature defines the extent to which the network

Early Blight Disease in Tomato Plant based on Visible/Near-Infrared Spectroscopy and Principal Components Analysis- Artificial Neural Network Prior to Visual
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(PCA) coupled with artificial neural network (ANN) was used. The analysis (PCA) assisted artificial neural network (ANN) of and artificial neural network . Food Chemistry 192: 134-141.

Artificial Neural Network Model Using Immune-infiltration Modules for Endometrial Receptivity Assessment of Implantation Failure
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Objectives: This study was anchored on the state of local immune-infiltration in the endometrium, 10 which acts as critical factors affecting embryonic implantation, and aimed at

An Optimized Neural Network Using Genetic Algorithm for Cardiovascular Disease Prediction
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Further, this paper employs artificial neural networks for predicting cardiovascular disease. However, despite the wide application of ANNs in various fields, selecting its optimal

Application of Artificial Neural Networks Technology for Handwritten Arabic Letters Recognition
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In this paper, we apply recognition artificial neural network ( of the network of neurons is used in Artificial neural network on artificially are connecting as an Artificial neural network . Artificial Neural Network (ANN) is the method used for this ANN has hundreds or thousands of artificial neurons called information, and the neural network attempts to learn about

Software development based on artificial neural networks for fitness club
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An artificial neural network is a system of simple processors ( artificial neurons) that are The scientific novelty in this study lies in the use of neural network technologies (information

A Hybrid Neural Network and Box-Jenkins Models for Time Series Forecasting
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Liu, A hybrid neural network model for sales forecasting based economy using dendritic neural network Computational based on the hybrid artificial neural network and GARCH-type A model proposed for estimating the combined flexure-torsion strengths of prestressed concrete beams using an artificial neural network algorithm. The findings of experimental tests on

Multi-step forecasting of the Philippine storm frequencies using Poisson Neural Network
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More specifically, the nonlinear kernel of the model is defined by the Artificial Neural Network ( In particular, we propose an Artificial Neural Network (ANN) model with one hidden layer

Liquid-liquid equilibria for aromatic extraction systems using artificial neural networks
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in the input layer, and hidden layers, the output layer gives the output of the neural network . in the multilayer perceptron neural network . Neural network with Levenberg-Marquardt

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

Experimental Implementation of a Neural Network Optical Channel Equalizer in Restricted Hardware Using Pruning and Quantization
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The deployment of artificial neural networks-based optical channel equalizers on edge- mainly due to the computational complexity of the artificial neural networks (NNs) required for the

Score Prediction of Sports Events Based on Parallel Self-Organizing Nonlinear Neural Network
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e artificial neural network data prediction, data transformation, data representation, and data We made a more in-depth study on the use of artificial neural network methods for clustering

Simulation and Prediction of Groundwater Quality of a Semi-Arid Region Using Fuzzy Inference System and Neural Network Techniques
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Artificial Neural Network (ANN) has been applied to simulate groundwater quality and Geographic Information System (GIS) has been applied to preprocessing and post-processing

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).

Survey of Cryptocurrency Volatility Prediction Literature Using Artificial Neural Networks
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literature has attempted to use artificial neural network methods to predict such volatility. and results used in artificial neural network models predicting cryptocurrency volatility. In

A YOLO and convolutional neural network for the detection and classification of leukocytes in leukemia
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The second step input data to deep learning algorithms such as convolutional neural network ( network (FCN) and U-Net used for segmentation while the artificial neural network (ANN)

PREDICTING ASPHALT PAVEMENT TEMPERATURE BY USING NEURAL NETWORK AND MULTIPLE LINEAR REGRESSION APPROACH IN THE
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This study compares the feasibility of using artificial neural networks (ANN) and multi-linear regression (MLR) for predicting an hourly temperature of the pavement, considering the

Performance of artificial neural networks in an inverse problem of laser beam diagnostics
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be achieved with the artificial neural network (ANN) approach investigated in the the neural network using synthetic data also allowed to estimate the potential of the proposed artificial

Deep Learning Based Convolutional Neural Network Structured New Image Classification Approach for Eye Disease Identification
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In this study, a deep learning-based convolutional artificial neural networks structured a new image classification method approach, which can be used to identify eye diseases, was

Performance and Energy Optimisation of Artificial Neural Networks on Hybrid Heterogeneous Platforms PhD Transfer Report
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Neural Networks: In short, we call them Fully Connected (FC) in this report. In this type of artificial neural Wilson, Implementing neural network models on parallel computers, The Com

ARTIFICIAL NEURAL NETWORKS BASED ON COMMITTEE MACHINE TO PREDICT THE AMOUNT OF SULFUR AND PHOSPHORUS IN THE HOT METAL OF
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In this context, the main objective of this work was to develop an artificial neural network for predicting the sulfur and phosphorus content in cast iron. A mathematical model was

Artificial neural network IEEE PAPER 2021




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

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. Self

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

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

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

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

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 WSNsThe influence of the proximity of slope on the laterally loaded piles has been the subject of several researches. The main purpose of this study is developing a neural model able to predict the deflection of laterally loaded piles placed near a slope. To achieve this goal, we

properties of hydrated-lime activated rice-husk-ash (HARHA) modified soft soil for pavement construction purposes by artificial neural network (ANN) and fuzzy
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Artificial neural network and fuzzy logic based model soft-computing techniques were adapted in the research study for the evaluation of the expansive clay soil-HARHA mixtures consistency limit, compressibility and mechanical strength properties. The problematic clay In order to study the problems of inadequate maintenance measures, inappropriate maintenance time, and unreasonable use of funds in asphalt pavement maintenance of Highway in China, the maintenance of highway pavement is taken as the research object in Natural and artificial body marks like mole and tattoos are used to identify the victims, such as suspected, and unidentified bodies like in mass death in a plane crash and the tsunami it is a very complex situation to identify the body; in recent years, classification andPrecipitation prediction during flood season has been a key task of climate prediction for a long time. This type of prediction is linked with the national economy and peoples livelihood, and is also one of the difficult problems in climatology. At present, there are some

Predicting oil production rate using artificial neural network and decline curve analytical methods
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In oil fields where direct measurement of oil production is not feasible, it is always a challenge to accurately predict the rate of production. In such circumstances, oil production rate is estimated through Decline Curve Analytical Methods and Empirical Correlations. In

Consistency measurement using the artificial neural network of the results obtained with fuzzy topsis method for the diagnosis of prostate cancer
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In recent years great attention has been paid to studies on artificial intelligence since it can be applied easily to several areas like medical diagnosis, engineering and economics, among others. In this paper we present an example in medicine which aims to diagnose the

DESIGN AND ANALYSIS ON MEDICAL IMAGE CLASSIFICATION FOR DENGUE DETECTION USING ARTIFICIAL NEURAL NETWORK CLASSIFIER
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Dengue is regarded as a serious threats to humanity, globally and this is a vital disease with huge spreading of virus that affects the health of humans. The virus is spreading at a rapid rate through mosquitoes that even kill the one who is affected with dengue. In this

COMPARATIVE APPROACH OF ARTIFICIAL NEURAL NETWORK AND THIN LAYER MODELLING FOR DRYING KINETICS AND OPTIMIZATION OF
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Bael is a seasonal fruit and available in a particular period in year. To study the drying characteristics of bael pulp in the sun, hot-air, microwave, and freeze-drying process thin layer drying models as well as artificial neural network modeling were adopted. The

Artificial Neural Network Model for Decreased Rank Attack Detection in RPL Based on IoT Networks
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Abstract Internet of Things (IoT) cyber-attacks are growing day by day because of the constrained nature of the IoT devices and the lack of effective security countermeasures. These attacks have small variants in their behavior and properties, implying that the

Long-Term Forecasting Method in the Supply Chain Based on an Artificial Neural Network with Multi-Agent Metaheuristic Training.
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The problem of increasing the efficiency of long-term forecasting in the supply chain is examined. Neural network forecasting methods that are based on reservoir calculations, which increases the forecast accuracy, are proposed. Methods for identifying parameters of

Artificial Neural Network Intelligent System on the Early Warning System of Landslide.
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Landslide is a natural sloping ground movement disaster that can occur due to several factors such as high rainfall, soil moisture in the depth of the soil of an area, vibrations experienced in the region, and the slope of the ground structure. A system that can deliver Optimization of casting parameters is essential in terms of quality factors in foundries. Nowadays, to optimize process parameters, new approaches such as artificial neural networks method are being used. In this study, a neural network model has been developed Data mining is a tool for turning large amounts of data into managed information in the form of patterns, relationships among the largest massive data and generates a comprehensive structure for further decision making. These methods are used in analyzing and

MS/Ayad-Marwan Network: A new architecture of an Artificial Neural Network
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Due to its parallelism property in processing the data, the Artificial Neural Networks (ANN) has been gaining wide interest in recent years as a tool for processing data. Different ANN architectures have been defined for various applications. Yet, a number of difficulties existed

Prediction of Interest Rate Using Artificial Neural Network and Novel Meta-Heuristic Algorithms
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One of the most parameters and variables in every economics is the interest rate. Government officials and lawmakers change interest rates for various purposes: controlling liquidity, inflation, and prices, Economic growth and development, lending, etc. So, it is

Photodegradation of roxarsone in the aquatic environment: influencing factors, mechanisms and artificial neural network modeling
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Roxarsone (ROX) is an organoarsenic feed additive, and can be discharged into aquatic environment. ROX can photodegrade into more toxic inorganic arsenics, causing arsenic pollution. However, the photodegradation behavior of ROX in aquatic environment is stillCuneiform language is an old language that was invented by the people of Sumerian nation. It is an essential language for many archeologists. Especially who are interested in studying and investigating the old nations of Iraq. Dealing with this type of language usually requires

DAMAGE PREDICTION OF THE STEEL ARCH BRIDGE MODEL BASED ON ARTIFICIAL NEURAL NETWORK METHOD
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Failure in the advance prediction of bridge structure collapse requires an enormous cost of rehabilitation. In most cases, the projection of decreases or damage to the structure due to difficulty in the testing condition. Therefore, this study analyses the damage and

Activated Gas Tungsten Arc Welding Process Optimization Using Artificial Neural Network and Heuristic Algorithms
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Apart from different merits of using conventional gas tungsten arc welding (C-GTAW) process, shallow penetration has been considered as the most important drawback of the process. Recently, in order to cope with the low penetration, using a paste like coating of Prior conclusion of malignancy cell development prompts spare bunches of valuable human lives. It is important to build up some mechanized instrument, so as to distinguish dangerous state toward the starting stage itself. Numerous calculations had been proposed before by

A Single Hidden Layer Artificial Neural Network Model that Employs Algebraic Neurons: Algebraic Learning Machine
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Artificial neural networks (ANN) have been employed successfully because of their high modeling capability. Many versions of the ANN have been proposed to increase the modeling ability. Since ANN based on the biological neural network system, the only

Modeling and Designing of a Compact Single Band PIFA Antenna for Wireless Application using Artificial Neural Network
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In this paper, we are interested to design a compact single band PIFA antenna using the artificial neural networks (ANN) based on the multilayer perceptrons (MLP). The designed antenna will operate at the frequency 2.45 GHz for ISM (Industrial, Scientific and Medical)

Comparison of Forecasting Models for Banking Stock: Multiple Linear Regression and Artificial Neural Network
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In this paper, two machine learning algorithms including multiple linear regression and artificial neural network are employed as forecasting models for the banking stock of Bangkok Bank Public Company Limited, Thailand. Five predictors including the SET50

An Innovative Approach to Determination of Double-Porosity Fractured Aquifers Hydraulic Parameters Using Artificial Neural Network
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Accurate determination of hydraulic parameter values is the first step to the sustainable 13 development of an aquifer. Since Theis (1935), type curve matching technique (TCMT) has been 14 used to estimate the aquifer parameters from pumping test data. The TCMT is

Tuning weight values by resolving imbalances at nodes in an augmented artificial neural network
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Tuning the weight values in an artificial neural network for a computational function is essential for artificial intelligence. This letter proposes tuning based on a mathematical model, which conceptually extends the artificial neural network by allowing an imbalanceEvaporation is an integral part of water cycle. The measurement of evaporation plays a significant role in water management planning, irrigation requirement and to know the water availability in storage system. Considering the complexity in estimation of evaporation by

Statistical Downscaling of Rainfall Under Climate Change in Krishna River Sub-basin of Andhra Pradesh, India Using Artificial Neural Network (ANN).
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Due to the very coarse spatial resolution of the different global circulation model (GCM), we cannot use them in their natural form to study the various impacts of climate change. For matching this spatial inequality between the GCMs output (predictor) and historical

Liquefaction Resistance Evaluation of Soils using Artificial Neural Network for Dhaka City, Bangladesh
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Soil liquefaction resistance evaluation is an important site investigation for seismically active areas. To minimize the 24 loss of life and property, liquefaction hazard analysis is a prerequisite for seismic risk management and development 25 of an area. Liquefaction

Ten-year estimation of Oriental beech (Fagus orientalis Lipsky) volume increment in natural forests: A comparison of an artificial neural networks model, multiple
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and disadvantages. One of these methods involves the artificial neural network techniques, which can be effective in natural resource management due to its flexibility and potentially high accuracy in prediction. This research

ARTIFICIAL NEURAL NETWORK APPROACH FOR RESERVOIR STAGE PREDICTION
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Feed forward multilayer neural networks are widely used as predictors in several fields of water resources applications. The present study demonstrates the application of neural networks to real time prediction of daily reservoir stage. Prediction of reservoir stage helps in

DEVELOPMENT OF A SOLAR ENERGY TRACKING MECHANISM WITH ARTIFICIAL NEURAL NETWORK ENHANCEMENT
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ABSTRACT A solar tracker is a generic term used to describe a device that orients solar panel towards the sun. Solar trackers are employed to maximise the quantity of energy generated from a fixed amount of installed photovoltaic (PV) cells. This paper presents a

Assessment of Drug Proarrhythmicity Using Artificial Neural Network with in Silico Deterministic Model Outputs
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Methodologies for predicting the occurrence of torsade de pointes by drugs via computer simulations have been developed and veri ed recently, as part of the Comprehensive in vitro Proarrhythmia Assay initiative. However, the predictive performance still requires




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