Artificial neural network 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