ENGINEERING RESEARCH PAPERS

neural network IEEE PAPER 2016




Towards Human-like Performance Face Detection: A Convolutional Neural NetworkApproach
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ABSTRACT Face detection is a widely discussed topic in the field of computer vision. The application of face detection varies from convenient HMI-to government-scale surveillance applications. The face detectors perform well when a image is provided that is in a perfect

Speciation and Diversity Balance for Genetic Algorithms and Application to Structural Neural Network Learning
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Abstract:Following analyzing existing challenges in addressing the balance between exploration and exploitation encountered by evolutionary algorithms, this paper develops a Genetic Algorithm with speciation (GASP). It first incorporates a novel encoding scheme

Monitoring, Analysis and Modelling of Ambient Air Quality Status at Indoshell Mould Ltd., Sidco, Coimbatore using Artificial Neural Network
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ABSTRACT Nowadays, the advancement in technologies and the industrial revolution has led to generation of tremendous amount of wastes thereby polluting the environment. The Air quality changes due to various activities taking place in the environment, but the most

EFFECTS OF ARTIFICIAL NEURAL NETWORK PARAMETERS ON ROLLING ELEMENT BEARING FAULT DIAGNOSIS
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Abstract:To ensure asset reliability the industries have been focused on the condition based maintenance (CBM). Fault detection and diagnosis are two of three condition based maintenance mainstays. Rolling Element is one of the major and essential components of

Bi-directional recurrent neural network with ranking loss for spoken language understanding
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ABSTRACT This paper presents our latest investigation of recurrent neural networks for the slot filling task of spoken language understanding. We implement a bi-directional Elman- type recurrent neural network which takes the information not only from the past but also

Learning Step Size Controllers for Robust Neural Network Training
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Abstract This paper investigates algorithms to automatically adapt the learning rate of neural networks (NNs). Starting with stochastic gradient descent, a large variety of learning methods has been proposed for the NN setting. However, these methods are usually

Measuring Cortical Connectivity in Alzheimer's Disease as a Brain Neural NetworkPathology: Toward Clinical Applications
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Abstract Objectives: The objective was to review the literature on diffusion tensor imaging as well as resting-state functional magnetic resonance imaging and electroencephalography (EEG) to unveil neuroanatomical and neurophysiological substrates of Alzheimer's

CUED-RNNLM–An Open-Source Toolkit for Efficient Training and Evaluation of RecurrentNeural Network Language Models
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CUED-RNNLM – An Open-Source Toolkit for Efficient Training and Evaluation of Recurrent Neural Network Language Models Xie Chen, Xunying Liu, Yanmin Qian, Mark Gales and Phil Woodland April 1, 2016 Page 5. ICASSP 2016 Recurrent Neural Network LMs Input layer

A Neural Network Perspective on the Syntactic-Semantic Association between Mass and Count Nouns
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ABSTRACT Analysing aspects of how our brain processes language may provide, even before the language faculty is really understood, useful insights into higher order cognitive functions. We take a small exploratory step in this direction with an attempt to test the

Minimum Word Error Training of Long Short-Term Memory Recurrent Neural NetworkLanguage Models for Speech Recognition
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Abstract This paper describes minimum word error (MWE) training of recurrent neural network language models (RNNLMs) for speech recognition. RNNLMs are usually trained to minimize a cross entropy of estimated word probabilities against the correct word

Anti-synchronization of 3-cells cellular neural network attractors via integral sliding mode control
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Abstract: In this research work, we first discuss the properties of the 3-cells Cellular Neural Network (CNN attractor) discovered by Arena et al.(1998). Recent research has shown the importance of biological control in many biological systems appearing in nature. In

Linearly Augmented Deep Neural Network
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Page 1. Linearly Augmented Deep Neural Network Pegah Ghahremani, Johns Hopkins University, Jasha Droppo, Michael L. Seltzer, Microsoft Research Page 2. Typical DNN Architecture Layers are a composition of an affine and a non- linear function. = ( + )

Neural Network with Bee Colony Optimization for MRI Brain Cancer Image Classification.
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Abstract: Brain tumor is one of the foremost causes for the increase in mortality among children and adults. Computer visions are being used by doctors to analysis and diagnose the medical problems. Magnetic Resonance Imaging (MRI) is a medical imaging

Artificial Neural Network Model for Estimation of Wear and Temperature in Pin-disc Contact
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Abstract This work aims to investigate experimentally the parameters affecting on the wear debris and the temperature rise due to friction as well as developing the artificial neural network model (ANN) using MATLAB program for predicting the wear, and temperature of

Extension Neural Network Learning Algorithms and Models and their Applications in Fault Diagnosis of Rolling Bearing
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Abstract Extension neural network is a new type of neural network that combines extension theory and artificial neural network. Extension neural network has been applied to pattern recognition, fault diagnosis and clustering. According to fault characteristics of rolling

Determine of Energy indicators and Modeling of output energy for alfalfa production using artificial neural network in Lorestan province of Iran
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ABSTRACT: The aim of this study was to examine energy use pattern and predict the output yield for alfalfa production in Lorestan province of Iran. The data used in this study were collected from growers by using a face to face survey. The results revealed that diesel fuel

Transmission Line Fault Detection and Classification Using Discrete Wavelet Transform and Artificial Neural Network
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Abstract: Fault diagnosis is a major area of investigation for power system and intelligent system applications. When the fault occurs on the transmission lines, the current and voltage waveforms contain significant high frequency transient signals. This paper presents a

Event-driven deep neural network hardware system for sensor fusion
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Abstract:This paper presents a real-time multi-modal spiking Deep Neural Network (DNN) implemented on an FPGA platform. The hardware DNN system, called n-Minitaur, demonstrates a 4-fold improvement in computational speed over the previous DNN FPGA

The Combination Forecasting Model of Auto Sales Based on Seasonal Index and RBF Neural Network
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Abstract To effectively predict auto sales and improve the competitiveness of automotive enterprise, the characteristics of actual auto sales were analyzed, owing to the seasonal fluctuations and the nonlinearity of monthly sales, the combination forecasting model

A Study of the Weaving conditions for Conductive Fabric by using Taguchi Approach andNeural Network Algorithm
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Abstract In recent year, the demand of conductive fabric is growing up. The electrical properties in the textile have provided an additional value than traditional textile. Therefore, the control of electrical conductivity has become feasible for those applying into textile or

Image Compression and Reconstruction Using Artificial Neural Network
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Abstract: In this paper a neural network based image compression method is presented. Neural networks offer the potential for providing a novel solution to the problem of data compression by its ability to generate an internal data representation. This network, which The name of second author was inadvertently removed by Volume Editor from Chapter 73 and now he would like to include the name of second author 'Madhulika Bhatia' through this erratum. The online version of the original chapter can be found under 10.1007/978-81-

Artificial Neural Network Technique for Modeling of Groundwater Level in Langat Basin, Malaysia
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ABSTRACT Forecasting of groundwater level variations is a significantly needed in groundwater resource management. Precise water level prediction assists in practical and optimal usage of water resources. The main objective of using an artificial neural network (

Formulation and Comparison of Experimental Based Mathematical Model with Artificial Neural Network Simulation Model for Optimal Performance of Cotton Pre
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Abstract Cotton is a natural vegetable fiber produced in many countries of the world. Cotton is the backbone and basic foundation of the world's textile trade and industry. The cultivation and production of cotton and cloth had been the main agro based businesses in India.

ISAAC: A Convolutional Neural Network Accelerator with In-Situ Analog Arithmetic in Crossbars
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Abstract:A number of recent efforts have attempted to design accelerators for popular machine learning algorithms, such as those involving convolutional and deep neural networks (CNNs and DNNs). These algorithms typically involve a large number of multiply

Uncertainty quantification in natural frequency of composite plates: an artificial neural network based approach
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ABSTRACT This paper presents the stochastic natural frequency for laminated composite plates by using artificial neural network (ANN) model. The ANN model is employed as a surrogate and is trained by using Latin hypercube sampling. Subsequently the stochastic

Research on Ant Colony Algorithm Optimization Neural Network Weights Blind Equalization Algorithm
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Abstract The project of ant colony algorithm optimization neural network combining blind equalization algorithm is proposed. The better initial weights of neural networks are provided because of the randomness, ergodicity and positive feedback of the ant colony

Nonlinear activations for convolutional neural network acoustic models
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Abstract Following their triumphs in visual recognition tasks, convolutional neural networks (CNNs) have recently been used to learn the emission probabilities of hidden Markov models in speech recognition. The key distinction of CNNs over deep neural networks (

Privacy-cnh: A framework to detect photo privacy with convolutional neural network using hierarchical features
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Abstract Photo privacy is a very important problem in the digital age where photos are commonly shared on social networking sites and mobile devices. The main challenge in photo privacy detection is how to generate discriminant features to accurately detect

Feature Based Hybrid Neural Network or Hand Gesture Recognition
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Abstract. This paper presents using neural network as a method for classifying hand gesture effectiveness in home appliances or serious games. Neural learning from imbalanced data has some difficulties, but, we presents Feature based Hybrid Neural Network (FHNN) that

Context-Sensitive Twitter Sentiment Classification Using Neural Network
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Abstract Sentiment classification on Twitter has attracted increasing research in recent years. Most existing work focuses on feature engineering according to the tweet content itself. In this paper, we propose a contextbased neural network model for Twitter sentiment Abstract This work presents the neural network combined with the sliding mode control (NNSMC) to design a robust controller for the two-links robot system. Sliding mode control (SMC) is well known for its robustness and efficiency to deal with a wide range of control

Diabetes Prediction by using Bacterial Foraging Optimization Algorithm And Artificial Neural Network
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Abstract-Diagnosis of any diseases earlier is always preferable. diabetes is one such diseases. it reaches to the epidemic proportional in many developing countries and also reaching to industrialized nations. In this study, we investigate a programmable method to

Closed-loop, neural network controlled accelerometer design
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ABSTRACT In this paper, a closed-loop, smart transducer design is proposed, based on artificial neural network (ANN) techniques. The design aims to improve the performance of open-loop, off-the-shelf capacitive acceleration sensors and increase their robustness to

Offline signature cognition and verification using artificial neural network
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Abstract: The signature verification is the oldest security technique to verify the identification of person. Recently, signature recognition schemes are growing in the world of security technology. It offers two different types of schemes those are offline and online method.

Artificial Neural Network to Construct a Pattern Recognition System for Inspecting Fabric Fefects
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Abstract–In this paper, we evaluate the efficiency and accuracy of a method of detecting fabric defects that have been classified into different categories by a neural network. Four kinds of fabric defects most likely to be found during weaving were learned by the network.

Expression Recognition Based on Artificial Neural Network Using Error Backpropagation Learning Algorithm
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Abstract Humans communicate with others by expressing emotion and thoughts through facial expressions. Thus, this research was conducted with the idea that communication between man and machine could be more diversified if the machine can recognize the

Multi-Objective Optimization of Electrical Discharge Machining Processes Using ArtificialNeural Network
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Abstract The present study provides predictive models for the functional relationship amongst the input and output variables of Electrical Discharge Machine (EDM) environment. The parametric optimization of this process can be regarded as a multiobjective task. No The series Studies in Computational Intelligence(SCI) publishes new developments and advances in the various areas of computational intelligence quickly and with a high quality. The intent is to cover the theory, applications, and design methods of computational

Neural Network for Diagnosis of Ovarian Cancer based on Proteomic Patterns in Serum
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Abstract:Ovarian Cancer detection is an emerging research area as it is the most common and fifth most common cause of death in women. The diagnosis of early stage ovarian tumour would significantly decrease the morbidity and mortality rate from this disease. In

Classification and Detection of Oil Spills Using Artificial Neural Network (ANN)
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Abstract: Oil spills represented a greater threat to marine ecosystem and their health. From the recent incident in the Gulf of Mexico, the adverse effects of oil spills on the nature are imminent. Synthetic Aperture Radar (SAR), a high resolution remote sensing imagery, can

Contextual anomaly detection for cyber-physical security in Smart Grids based on an artificialneural network model
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AM Kosek, O Gehrke, Contextual anomaly detection for cyber-physical security in Smart Grids based on an artificial neural network model 2016 Joint Workshop on Cyber-Physical Security

Automatic Generation Control Using Artificial Neural Network Based Narma L-2 Controller
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Abstract: This study presents the design and implementation of intelligent controller, based on artificial neural networks (ANN) for automatic generation control (AGC) of a two area hydrothermal power system. The AGC performance with intelligent controller is compared

A Novel Vehicle Classification Model for Urban Traffic Surveillance Using the Deep Neural Network Model
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Abstract The vehicle detection is the backbone of the urban surveillance systems, which is used to obtain and identify the various statistics of the urban vehicular mobility. Also the urban surveillance systems are used for the vehicle tracking or vehicular object

Fault Detection System in Gas Metering Station Using Neural Network
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Abstract:This research study focuses on the discussion regarding the development of fault detection in gas metering station using an Artificial Neural Network (ANN). The proposed model of fault detection applies ANN approach in order to provide a good detection

Model for MANET using Recurrent Neural Network Extended Kalman Filter
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Abstract In recent years there has been a growing concern by researchers in developing methods, techniques and algorithms for predicting the behaviour of various routing protocols in Mobile ad hoc network environment. The proposed work addresses the problem of

Bit Error Rate Improvement by using Active Constellation Extension and Neural Network in OFDM System
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Abstract–The demand for multimedia data services has grown up rapidly. One of the most promising multi-carrier system, Orthogonal Frequency Division Multiplexing (OFDM) form basis for all 4G wireless communication systems due to its large capacity to allow the

Mood Prediction in Consideration of Certainty Factor Using Multilayer Deep Neural Networkand Storage-Type Prediction Models
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Depression has become a social problem in Japan. To prevent depression, people need to recognize their mental health in daily life. Previous research supports mental health care by predicting tomorrow's mood with 73% accuracy using weather information and biological

COMPARISON OF ARTIFICIAL NEURAL NETWORK APPROACH AND DATA MINING TECHNIQUE FOR THE PREDICTION OF SURFACE ROUGHNESS IN
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ABSTRACT: This article presents a procedure for using machine vision data to predict surface roughness parameter of the end milled components. Stylus based surface roughness measurements were used and compared to vision based prediction of surface

A Novel Image Segmentation Combined Color Recognition Algorithm through Boundary Detection and Deep Neural Network
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Abstract With the fast development of industry and computer science technology, the image color recognition has been a hot topic. The prior research focus more on sensor and hardware based approaches which are not intelligent or convenient. In this paper, we

Power Quality Disturbance Classification Based on A Novel Fourier Neural Network and Hyperbolic S-transform
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Abstract Power quality (PQ) disturbances recognition is the foundation of power quality analysis and improvement. In order to improve the classification accuracy and efficiency, a new classification approach based on modified Fourier neural networks (FNN) and

Real-Time System based on a Neural Network and PID Flight Control
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Abstract: The modern flight control systems are complex since they have a non-linear nature. Also, modern aerospace vehicles are expected to have non-conventional flight envelopes and, in order to operate in uncertain environments, they must guarantee a high level of

Applying an artificial neural network to predict coagulation capacity of reactive dyeing wastewater by chitosan
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Abstract Chitosan derived from waste shrimp shells was experimentally evaluated to treat reactive dye (red 24) in aqueous solution. The research was conducted using one-factor-at- a-time experiment design (ie, pH, reaction time, agitation speeds, initial dye, and chitosan

Modelling the Restoration of Activity in a Biological Neural Network
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Abstract:Understanding the mechanisms of restoration of activity in biological neural systems following exposure to damage is key for design of future neuro-prosthetic devices and restorative treatments. The pyloric rhythm network within the crustacean

Modeling of Electrical Discharge Machining of 17-4 PH steel Using Regression Analysis and Artificial Neural Network
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Abstract The aim of the present work is to develop empirical models and prediction of machining quality for Electrical Discharge Machining of martensitic Precipitation Hardening (PH) stainless steel with copper tungsten electrode. The important process input

Face Recognition Using Neural Network: A Review
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Abstract Face recognition from the real data, capture images, sensor images and database images is challenging problem due to the wide variation of face appearances, illumination effect and the complexity of the image background. Face recognition is one of the most

Fuzzy Neural Network Model and its Application in Water Quality Evaluation
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ABSTRACT In view of the defect of traditional water quality evaluation model, based on fuzzy neural network theory, a new model of fuzzy neural network (FNN) comprehensive evaluation is developed to evaluate surface water quality in Suzhou. A fuzzy neural

PRIME: A Novel Processing-In-Memory Architecture for Neural Network Computation in ReRAM-based Main Memory
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Abstract:Processing-in-memory (PIM) is a promising solution to address the memory wall challenges for future computer systems. All proposed PIM architectures put additional computation logic in or near memory. The emerging metal-oxide resistive random access

Performance Identification of Different Heart Diseases Based On Neural NetworkClassification
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Abstract The heart diseases are the most widespread induce for human dying. Every year, 7.4 million deaths are attributed to heart diseases (cardiac arrhythmia) including 52% of deaths due to strokes and 47% deaths due to coronary heart diseases. Hence

Facial Image Recognition Algorithm Based on BP Neural Network
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Abstract The efficiency, quality and accuracy of facial image recognition are restricted by luminance, posture, image quality, massive data and method of image recognition, etc. In response to this, this thesis proposes a facial image recognition algorithm based on BP

Prediction of Forces during Drilling of Composite Laminates Using Artificial Neural Network: A New Approach
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Drilling of fiber-reinforced plastics (FRP's) is an inevitable machining operation, because it facilitates assembly of several components by means of mechanical fastening. But, drilling of FRP leads to delamination which results in reduced life and efficiency of the FRP part. The

A Combination Method of Differential Evolution Algorithm and Neural Network for Automatic Identification Oil Spill at Vietnam East Sea
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Abstract: This paper proposes an automatic detection of oil spills on SAR (synthetic aperture radar) images using DE (differential evolution), neutral network and BP (back propagation) algorithm. Here, DE and BP are combined to train a multilayer perceptron (MLP) network

A Web-based Analysis for Dengue Tracking and Prediction using Artificial Neural Network
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Abstract. Providing an accurate analysis of dengue epidemic seasons will allow sufficient time in taking necessary decisions and actions to safeguard the situation for local authorities. This research aims to develop a web-based dengue tracking system (DTS)

Control of Dynamic Systems Using LSTM supported Neural Network
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Abstract Model Predictive Control (MPC) is an effective control strategy that computes control action by solving an optimization objective. However, application of MPC can be computationally demanding, and requires estimating the hidden states of the system,

Using Artificial Neural Network Technique to Estimation the Concentration of Copper for Contaminated Soils
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Abstract The aim of this paper is to design artificial neural network (ANN) as an alternative accurate tool to estimate concentration of Copper (Cu) in contaminated soils. First, sixteen (4x4) soil samples were harvested from a phytoremediated contaminated site located in

A Parameters Optimization of Synergetic Neural Network Based on Differential Evolution Algorithm
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Abstract Synergetic neural network (SNN) is a top-down network to explain the phase transition and self-organization in non-equilibrium system. The network parameters have a crucial impact on the recognition performance of synergetic neural network. At present,

SYNTHESIS AND REDUCED LOGIC GATE REALIZATION OF MULTI-VALUED LOGIC FUNCTIONS USING NEURAL NETWORK DEPLOYMENT ALGORITHM
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Abstract In this paper an evolutionary technique for synthesizing Multi-Valued Logic (MVL) functions using Neural Network Deployment Algorithm (NNDA) is presented. The algorithm is combined with back-propagation learning capability and neural MVL operators. This

Application of Data Fusion Technology Based on Weight Improved Particle Swarm Optimization Neural Network Algorithm in Wireless Sensor Networks
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Abstract With the development of sensor technology, network technology, embedded control technology and wireless communication technology, the application of wireless sensor networks (WSN) has become more and more widely. Wireless sensor networks have been

Vehicle Detection in Satellite Images by Incorporating Objectness and Convolutional Neural Network
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Abstract:Automatic vehicle detection from high-resolution remote sensing images plays a fundamental role in a wide range of applications. Various approaches have been proposed to address this issue in decades, however a fast and robust approach has not yet been

Combining Artificial Neural Network with Image Data Partitioning for Personal Iris Recognition System
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Abstract With fast development of iris image acquisition technology, iris recognition is expected to become a fundamental component of modern society, with wide application areas in national ID card (AADHAR), banking, e-commerce, welfare distribution, biometric

Artificial Neural Network Optimization of a Carbon Paste Electrode for the Detection of Zinc Ions
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Abstract The present work focuses on the fabrication of a potentiometric sensor for the determination of zinc ions using 3, 7, 12, 17-Tetramethyl-8, 13-divinyl-2, 18- porphinedipropionic acid disodium salt (protoporphyrin IX disodium) as the ionophore.

DISEASE IDENTIFICATION IN PACHAIKODI VARIETY OF BETEL VINE PLANTS USING HISTOGRAM AND NEURAL NETWORK BASED DIGITAL IMAGING
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Abstract-The betel vine cultivation is very much affected by diseases and outcome of the farmer is big loss for betel vine cultivation. The aim of this paper is to detection of Powdery mildew disease in the betel vine plants using digital image processing techniques. The

Neural Network Modeling for Simulation of Error Optimized QCA Adder Circuit
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Abstract-Artificial intelligence based Hopfield Neural Network model (PNN) has been proposed for the designing and simulation of error optimized QCA adder circuit. The proposed PNN model has been introduced to improve the skill of QCA adder circuit. The

Time Series Prediction Based on Multiple Artificial Neural Network
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Abstract: Time series prediction is a challenging research area with broad application prospects. Accurate time series prediction can provide important information for the relevant decision-makers. Many works extended different architecture of artificial neural networks The present study is an attempt to find the impact of climate change on water-based power plants. The investigation use decision-making method: Analytical Hierarchy Process (AHP) and a new variant of artificial neural networks; Group Method of Data Handling (GMDH) in

HAND GESTURE RECOGNISATION FOR HCI BASED ON ARTIFICIAL NEURAL NETWORK
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Abstract: Human computer interaction (HCI) is growing widely day by day with the improvement in its approaches and in its algorithms. A human can easily interacted with computer by utilizing different ways of natural communications and skills. One of such

Performance Variation of Support Vector Machine and Probabilistic Neural Network in Classification of Cancer Datasets
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Abstract Scientific diagnosis and analysis is the primary concern for the optimal treatment of cancer patients. Better analysis of the data helps the radiologist in providing effective treatment and preventive measures. In this paper, algorithm on support vector machine (

Novel Approach of Fault Diagnosis in Wireless Sensor Networks Node Based On Rough Set and Neural Network Model
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Abstract Nodes of wireless sensor network (WSN) will appear various faults, because the influence of many unavoidable factors and environment is very complex and harsh. Rough set can deal with incomplete information, especially in the data reduction, and it is easy to

ARTIFICIAL NEURAL NETWORK BASED INTELLIGENT FAULT IDENTIFICATION OF ROTATING MACHINERY
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ABSTRACT In general intelligent diagnosis is carried out when known inputs are fed into black boxes which subsequently produce outputs in accordance with machine faults. Neural networks (NN) are suitable for these tasks and have been widely researched as an

Principal Component Regression with Artificial Neural Network to Improve Prediction of Electricity Demand
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Abstract: Planning for electricity demand is a key factor for the success in the development of any countries. Such success can only be achieved if the demand for electricity is predicted correctly and accurately. This study introduces a new hybrid approach that combines

A neural network and IoT based scheme for performance assessment in Internet of Robotic Things
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Abstract:Internet of Robotic Things (IoRT) is a new concept introduced for the first time by ABI Research. Unlike the Internet of Things (IoT), IoRT provides an active sensorization and is considered as the new evolution of IoT. This new concept will bring new opportunities

Artificial Neural Network Model for Rainfall-Runoff-A Case Study
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Abstract Soft computing models like Artificial Neural Network (ANN) have been widely used to model complex hydrological processes, such as rainfall-runoff and have been reported to be one of the promising tools in hydrology. In this paper, the influences of back

Architecture Optimization Model of Probabilistic Neural Network
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Abstract Random Probabilistic neural networks are more approximate to humans than determinist neural network. Therefore, it is trivial in our study to use random criterion. There exist several random tools, but the most popular is the Probabilistic Self Organizing Maps. This monograph is the continuation and completion of the author's earlier 2011 Intelligent Systems Reference Library, Volume 19. In this monograph we present the

Neural Network and Genetic Algorithm Based Finite Element Model for Optimal Die Shape Design in Al-1100 Cold Forward Extrusion
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Abstract This paper employs rigid-plastic finite element DEFORMTM 3D software to estimate the plastic deformation behavior of an aluminum billet during its axisymmetric extrusion through a conical die. The die and container are assumed to be rigid bodies and the

Climate Change Prediction Using Artificial Neural Network
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Abstract Climate forecasting is scientifically and technologically challenging problem around the world which requires observing and processing large amounts of climate data. Predicting the climate is important for the best and worst of climate. The aim of this paper

Additional Antidepressant Pharmacotherapies According to a Neural Network
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Abstract Major depression, a frequent psychiatric disease, is associated with neurotransmitter alterations in the midbrain, hypothalamus and hippocampus. Deficiency of postsynaptic excitatory neurotransmitters such as dopamine, noradrenaline and serotonin

Adaptive neural network cascade control system with entropy-based design
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Abstract: A neural network (NN) based cascade control system is developed, in which the primary PID controller is constructed by NN. A new entropy-based measure, named the centred error entropy (CEE) index, which is a weighted combination of the error cross

Simulation Study on Optimizing Neural Network in Short-Term Electric Load Prediction
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Abstract It researches the short-term electric load prediction and short-term electric load has the characteristics of time-varying, uncertainty, nonlinearity, etc., so the traditional linear prediction method cannot correctly describe the changing rule of the short-term electric

Design of Brushless DC Motor Controller Based on Adaptive RBF Neural Network
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Abstract Brushless DC motor (BLDCM) has been widely used in industries because of its advantages. BLDCM control system is a nonlinear, multi-variable, strong-coupling system. It is difficult to get satisfied control performance with conventional PID controller. By using

Automatic Age and Gender Recognition in Human Face Image Dataset using ConvolutionalNeural Network System
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Abstract: Age and gender classification has become applicable to an extending measure of applications, particularly resulting to the ascent of social platforms and social media. Regardless, execution of existing strategies on real-world images is still fundamentally

ANALYSIS OF ELECTRICAL LOAD FORECASTING BY USING MATLAB TOOL BOX THROUGH ARTIFICIAL NEURAL NETWORK
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Abstract Load forecasting is a central integral process in the planning and operation of electric utilities. Load forecasting has become in recent years one of the major areas of research in electrical engineering. The main problem for the planning is the determination

Investigation of single and dual step shot peening effects on mechanical and metallurgical properties of 18CrNiMo7-6 steel using artificial neural network
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Abstract:Shot peening is a process of cold working a part that increase its resistance to metal fatigue and some forms of stress corrosion. Shot peening causes plastic deformation in the surface of the peened part and leads some changes in mechanical and

Bogdanov-Takens singularity of a neural network model with delay
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Abstract. In this article, we study Bogdanov-Takens (BT) singularity of a tree-neuron model with time delay. By using the frameworks of Campbell-Yuan [2] and Faria-Magalhaes [4, 5], the normal form on the center manifold is derived for this singularity and hence the

Efficient Spam Detection using Single Hidden Layer Feed Forward Neural Network
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Abstract-Recent development in social media, which is one of the crucial communication tools owning to wide spreading of internet technologies. On social network sites, spammers frequently cover themselves by creating fake accounts and hijacking normal user account

The Performance of Hybrid Artificial Neural Network Models for Option Pricing during Financial Crises.
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Abstract: this paper provides a novel research on the pricing ability of the hybrid ANNs based upon the Hang Seng Index Options spanning a period of from Nov, 2005 to Oct, 2011, during which time the 2007-20008 financial crisis had developed. We study the

Performance Evaluation By Artificial Neural Network Using WEKA
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Abstract-Data mining is the process of extracting hidden patterns and useful information from large set of data is now becoming part of current inventions. Data mining now can be

Extending Ionospheric Correction Coverage Area By Using A Neural Network Method
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Abstract The coverage area of a GNSS regional ionospheric delay model is mainly determined by the distribution of GNSS ground monitoring stations. Extrapolation of the ionospheric model data can extend the coverage area. An extrapolation algorithm, which

Study on Regional Scientific and Technological Innovation Platform Innovations Evaluation Based on BP Neural Network
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Abstract Based on the needs of the Regional Scientific and Technological Innovation Platform, innovations evaluation system was established by AHP. In order to simulate the experts' experiences and thinking, we used the improved BP neural network model. After

Solving the Weighted Constraint Satisfaction Problems Via the Neural Network Approach
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Abstract:A wide variety of real world optimization problems can be modelled as Weighted Constraint Satisfaction Problems (WCSPs). In this paper, we model this problem in terms of in original 0-1 quadratic programming subject to leaner constraints. View it performance,

Hand-Drawn Annotation and Underline Detection and Removal in Scanned Documents Using Artificial Neural Network Fuzzy C-Means Clustering
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ABSTRACT The OCR system is computerized scanning system that enables user to scan a text document into an electronic computer file that can be edited, usually the OCR system's performance gets badly affected due to the presence of hand drawn underlines (straight,

Correlation of Breakdown Strength Parameters of Solid Insulation using Artificial Neural Network (ANN)
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ABSTRACT The objective of this work is to determine if a relationship could be found between dielectric strength and other basic properties of electrical insulating materials such as volume resistivity, dielectric constant or relative permittivity and dissipation factor or

Elman Neural Network Mortality Predictor for Prediction of Mortality Due to Pollution
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Abstract Air pollution is a significant risk factor for a number of health conditions including respiratory infections, heart disease like stroke and lung cancer. Further, air pollution exposure is a risk factor correlating with increased total mortality from cardiovascular

Neural Network-Based Analysis of Precipitation and Remotely Sensed Data
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Abstract:In analyzing the relationship between precipitation and remotely sensed data, firstly, we investigate the correlation among Global Vegetation Index (GVI) from NOAA satellite with precipitation from ground-based rainfall measurements. Observation GVI

On Demand Ciphering Engine Using Artificial Neural Network
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Abstract: In this paper, a new light weight highly secured ciphering engine creation methodology we called On Demand Ciphering Engine (ODCE) was suggested. The main feature of this method is that both, the ciphering engine and the secret key, are kept

Hybrid ELMAN Neural Network Approach for Reliability and Fault Analysis of Quantum Dot Cellular Automata Circuits
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Abstract: In this stduy, fault analysis of Quantum dot Cellular Automata (QCA) circuits is carried out employing a proposed hybrid version of ELMAN neural network approach. The QCA are basically designed structures with locally interconnected cellular automata like

Mouse Bechaviour Primarily Based Signature Authentication Victimisation Neural Network
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Abstract: Several all-embracing password leakages apparent users to accessory amount consummate accident of adumbration and corruption of their info. the blemish of password- based affidavit mechanisms is axis into a cogent affair for the accomplished advice society

Neural Network based Linear Regression Model for Modeling Productivity Construction in Saudi Arabia
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Abstract: The competitive nature of Construction engineering and its heuristic problem- solving needs paved a way for the development of some advanced decision-making tools for it. Research in artificial intelligence (AI) has provided more suitable tools for the

Power System Contingency Analysis Using Artificial Neural Network
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Abstract: Contingency analysis is the essential characteristic of the Power system security. The security evaluation is a crucial venture because it offers the statistics regarding the system state within the occasion of a contingency. Contingency analysis technique is

Cascade Stage Artificial Neural Network for Identifying Volcano Hotspots using Satellite Images
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Abstract Volcanoes are natural hazard which cannot be avoided but causes can be reduced by identifying in advance using remote sensed satellite images. The use of remote sensing within the domain of natural hazards and disasters has become increasingly common,

= Health impact on Iran's economy by an artificial neural network dynamic ordinary least squares approaches
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"l V Abstract Background and Objectives: Economic growth is one of the most important macroeconomic variables which reaching to it depends on the correct understanding of potential routes and the factors affecting it. The aim of this study is the evaluation of the health effect on Iran's

A COMPUTATIONAL HYBRID MODEL WITH TWO LEVEL CLASSIFICATION USING SVM ANDNEURAL NETWORK FOR PREDICTING THE DIABETES
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ABSTRACT Data Mining is a collection of number of computational approaches. These approaches are used to develop Knowledge inference systems by identifying the hidden and convincing patterns from the input data. The aim of this study is to propose a

NEURAL NETWORK BASED INDIVIDUAL CLASSIFICATION SYSTEM
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Abstract: Neural Networks one of the rejuvenating concepts in the modern computer world has come across many applications concerned with recognition. Recently it been extended to the concept Classification. This paper aims at designing and developing a Neural

A novel approach for automatic blood vessel extraction in retinal images: complex ripplet-I transform and complex valued artificial neural network
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Turkish Journal of Electrical Engineering Computer Sciences http://journals.tubitak.gov.tr/elektrik/ Research Article A novel approach for automatic blood vessel extraction in retinal images: complex ripplet-I transform and complex valued artificial neuralnetwork

Man-Machine Function Allocation for Cockpit Based on Genetic BP Neural Network
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Abstract The key to improve the automation level of the aircraft cockpit system is getting on man-machine function allocation scientifically and reasonably. In order to improve the reliability of man-machine function allocation for cockpit, based on the study of genetic

DESIGN OF ARTIFICIAL NEURAL NETWORK BASED CONTROL SCHEME FOR THE MANAGEMENT OF THE UNIFIED POWER FLOW CONTROLLER IN A
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DESIGN OF ARTIFICIAL NEURAL NETWORK BASED CONTROL SCHEME FOR THE MANAGEMENT OF THE UNIFIED POWER FLOW CONTROLLER IN A SINGLE MACHINE INFINITE BUS BAR SYSTEM Dr. K. Pounraj 1 and Dr. S. Selvaperumal 2 Address for Correspondence

Implement of Reservoir Productivity Prediction Based on BP Neural Network in Huanjiang Chang 6
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Abstract Back Propagation Neural Network is a multilayer feed-forward network technology based on the error back propagation, which can be a better way to solve the problem of nonlinear mapping and data generalization, and allows some errors of the sample. The

The Dimensional Reduction to Improve the Speed and Accuracy of Neural Network in Identyfying the Senior High Scool Students' Major
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Abstract The appropriateness of choosing the major for the Senior High School students is considered as the most important part of the academic process to be considered in order to determine the right major for their further study in the highest level. The presence of back

Gain And Excess Noise Analysis of Avalanche Photodetector Using Multilayer PerceptronNeural Network
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Abstract–In this paper, we predict gain and excess noise factor for different avalanche photodetectors using multilayer perceptron neural network. We assume the most important factors such as bias voltage, length, type and doping concentration of absorption and

Enhanced Data Security Using Neural Network in Cloud Environment
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Abstract-Cloud is an on-demand computing service which provides a dynamic environment for end users to assure Quality of Service (QoS) on data for its confidentiality in cloud data centers. A set of information from a cloud database with a high security level accessing

SOLAR PHOTOVOLTAIC OUTPUT POWER FORECASTING USING BACK PROPAGATIONNEURAL NETWORK.
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Abstract Solar Energy is an important renewable and unlimited source of energy. Solar photovoltaic power forecasting, is an estimation of the expected power production, that help the grid operators to better manage the electric balance between power demand and

VND-NN: Neural Network Based Vampire Node Detection in Wireless Sensor Networks
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ABSTRACT Wireless sensor networks are widely used for environment monitoring. The sensor nodes are deployed in the areas of interest to obtain the physical world data. These nodes are resource constrained in power, memory and computation. Boarder security is

Delay Modeling of Un-signalized Roundabouts Using Neural Network and Regression
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Delay is one of the primary variables of efficiency of intersections and roundabouts. There are not extensive experiences about delay in roundabouts in comparison with delay at intersections, and there haven't been many studies in Iran particularly about un-signalized

Survey on Artificial Neural Network Learning Technique Algorithms
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Abstract-Automation is now ubiquitous, and research for developing new ones is constantly growing. It plays an important role in decision-making, prediction, classification to provide a fast and accurate result. Due to their adaptive learning and nonlinear mapping properties,

Data Processing of H-Type Electromagnetic Eddy Current Sensor for Thickness Measurement Base on Neural Network
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Abstract The BP neural network algorithm was used to create a connection between the collected differential induced voltage signal and the thickness of tested aluminum foil with

Modeling of Wear Performance of Si3N4-hBN Composite Using Artificial Neural Network(ANN)
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Abstract---Wear particles generated due to rolling/sliding motion between artificial joint leads to joint failure, which need to be minimised to extend the joint life. Silicon nitride (Si3N4) is nonoxide ceramic suggested as a new alternative for hip/knee joint replacement.

Credit Scoring Models for a Tunisian Microfinance Institution: Comparison between ArtificialNeural Network and Logistic Regression
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Abstract: This paper compares, for a microfinance institution, the performance of two individual classification models: Logistic Regression (Logit) and Multi-Layer Perceptron Neural Network (MLP), to evaluate the credit risk problem and discriminate good creditors

P2P Personal Credit Risk Simulation Model Based on BP Neural Network
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Abstract In recent years, P2P lending platforms are in a stage of rapid development in our country. By August 2015, its turnover has reached 97.463 billion Yuan, but the credit risk starts to highlight. There are many platforms that continue to collapse. Whether we can

An Algorithm of Setting the Weights of a Neural Network Controller
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Abstract:The paper considers a novel method of setting a neural networks controller that takes part in the control of a dynamic plant with unknown parameters. The uncertainties are usually overcome by using sliding mode for controller with a switching input signal.

A Review of Relation Classification with Convolutional Neural Network
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ABSTRACT Relation classification is one of the important research issues in the field of Natural Language Processing (NLP). It is a crucial intermediate step in complex knowledge intensive applications like automatic knowledgebase construction, question answering,

STUDY OF THE SPECTRUM SENSOR ARCHITECTURE BASED ON DISCRETE WAVELET TRANSFORM AND FEED-FORWARD NEURAL NETWORK
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Abstract. In this paper, we present an experimental study of a new spectrum sensor architecture based on application of discrete wavelet transform for preprocessing and feed forward neural network for classification. For the experimental study, we select three

Hybrid Neural Network Model for Metocean Data Analysis
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Abstract Metocean time-series data is generally classified as highly chaotic thus making the analysis process tedious. The main aim of forecasting Metocean data is to obtain an effective solution for offshore engineering projects, such analysis of environmental

Performance of Artificial Neural Network for Traffic Flow Prediction
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Abstract:-The prediction of traffic flow plays an important part in Intelligent Transportation System (ITS). It plays a vital role in functioning of various ITS components such as in-vehicle route guidance systems (RGS), advanced traveller information systems (ATIS) and

Self-Organizing Schedulers in LTE System for Optimized Pixel Throughput Using Neural Network.
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Evolution (LTE) is to minimize the costs and effort of network planning, optimization and configuration to the lowest possible level, while keeping a very good acceptable performance level which can be achieved by using self-organizing networks (SON)

Convolutional and Recurrent Neural Network for Gomoku
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Abstract This paper proposes to use neural networks to solve a simplified version of Gomoku. More specifically, convolutional neural network and multi-dimensional recurrent network are trained separately using high-level human games. While the convolutional

System Identification for Quad-rotor Parameters Using Neural Network
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This paper presents a new technique to identify the system parameters without using the system governing equations. This technique is the time series prediction using neural network. The theoretical model was applied using simulations, after that the experiments

Neural network based prediction models for evaporation
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ABSTRACT. From statistical perspective, artificial neural networks (ANNs) are interesting because of their potential use in prediction. In this study, ANNs based approach has been used to assess the prediction of evaporation with meteorological variables, viz., maximum

Direct Inverse Neural Network Control of a Double Propeller Boat Model using a Backpropagation Neural Networks
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ABSTRACT This paper presents the utilization of backpropagation neural network as a direct inverse control system for a double propeller boat model. Fine tuning method is then conducted by re-learning of the neural controller through the error between the inversed-

Chaotic Time Series Prediction Based on RBF Neural Network
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Abstract This paper conducts research on the forecasting method of chaotic time series based on RBF (Radial Basis Function in abbreviation) neural network. Firstly, it provides an analysis of the influence of the parameters of the chaotic dynamic system on their system.

ARTIFICIAL NEURAL NETWORK FOR DIAGNOSIS OF PANCREATIC CANCER
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ABSTRACT Cancer is malignant growth or tumour which forms due to an uncontrolled division of cells in a part of body which may even lead to death. These are of different types depending upon the part of body affected. If it is Pancreas then the disease is termed as

A Framework for Event Classification from Video Sequences using Bayesian Neural Network
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ABSTRACT Due to immense growth happened in multimedia field, research with videos and images have been received significant attention among the researchers. The automatic detection of the events presented in the video content may provide more useful

Neural Network based Intelligent Sensor Fault Detection in a Three Tanks Interacting Level Process
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Abstract:This paper presents sensor fault detection in a three interacting level system and fault detection and control under sensor failure conditions. Trained Back Propagation neural networks estimate the process states. The fault detection and isolation is done by decision

targetS USING NEURAL NETWORK
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Abstract:targets has significance in navigation and surveillance applications. In such case,target is appearing as a point when imaging is done from very far distance.targets detection

Hybridization of Improved K-Means and Artificial Neural Network for Heart Disease Prediction
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ABSTRACT The healthcare field is composed of a variety of data, but the task of getting efficient knowledge and hidden information from those data is very difficult and time- consuming by the traditional method. Data mining technique is supportive for extracting

UPDRS tracking using linear regression and neural network for Parkinson's disease prediction
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Abstract The Unified Parkinson's Disease Rating Scale (UPDRS) is often used to track Parkinson's disease (PD) but it requires costly and logistically inconvenient for patient and clinical staff. In this work we present clinically useful accuracy replication of UPDRS, so we

Neural Network Based Splice Site Prediction Methods
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Abstract:Splice site is an important signal in genomic sequences that signifies the presence of coding region and plays an important role in gene structure prediction. In recent past, neural network based splice site prediction methods have shown remarkable

Time Series Artificial Neural Network Approach for Prediction of Optical Lens Properties
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Electron optical systems are widely used in many scientific instruments, such as electron microscopes, time of flight spectrometers, and electron guns [1, 2]. Among these optical tools, three-element electrostatic cylindrical lens systems are perfectly suited to use in

Neural Network for Kidney Stone Detection
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Abstract: Kidney stone detector proves to be a major challenge for detecting the kidney stone disease. In this paper, two neural network algorithms viz Radial basis function and Learning vector quantization are used for diagnosis purpose Also a comparison is made

Automatically Detection of Skin Cancer by Classification of Neural Network
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Abstract:Skin cancers are the most common form of cancers found in humans. The persistent raise of this cancer in the worldwide, the high medical cost and death rate have prioritized the early diagnosis of this cancer. The different components in an automated

Design of Neural Network Based Fuzzy Inference System for Speed Control of Heavy Duty Vehicles with Electronic Throttle Control System
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Abstract:-The objective of this study is to apply various control approaches to control the speed of a heavy duty vehicle (HDV) using an electronic throttle control system. However, the dc servo motor is used for controlling the angular position of electronic throotle valve.

OF ENGINEERING SCIENCES RESEARCH TECHNOLOGY FAULT ANALYSIS ON TRANSMISSION LINES USING ARTIFICIAL NEURAL NETWORK
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ABSTRACT In power distribution technique it is essential to minimize transients, line voltage dips and spikes which are present due to the occurrence of fault detection. As a fault occurs in the power system, the necessary steps are taken to remove the fault in transmission line

Analysis of Surface Roughness in Turning Process Using Neural Network
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Abstract: Machine parts are significantly influenced by surface roughness quality. For efficient use of machine tools, optimum cutting parameters are required. Therefore it is necessary to find a suitable optimization method which can find optimum values of cutting

Emotion Recognition Using Neural Network Approaches: A Review
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Abstract:It is very interesting to recognize the human gesture for general life applications. For example, observing the gesture of a driver when he/she is driving and alerting him/her when in sleepy mood will be quite useful. Human gestures can be identified by observing

Application of Wavelet Neural Network PID for Maximum Power Point Tracking in Wind Power System
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Abstract A maximum power point tracking (MPPT) controller based on wavelet neural network PID for variable speed wind energy conversion system (WECS) is proposed. The proposed method, without requiring the knowledge of wind speed, air density or turbine

Data Analysis to Generate Models Based on Neural Network and Regression for Solar Power Generation Forecasting
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Abstract:This paper presents methods for forecasting solar power generation by a solar plant. Solar power generation depends primarily on relative position of sun and some extrinsic as well as intrinsic factors. Extrinsic factors such as cloud cover, temperature,

Modelling of Algae Biodiesel Blended CI Engine for the Prediction of Performance and Emission Parameters using Artificial Neural Network
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ABSTRACT The fast depletion of the petroleum reserves and the pollution related problems with the diesel engine it becomes necessary to find a substitute for the diesel fuel. Biofuels such as biodiesel is now gaining popularity as an alternative fuel in the CI engine. This

Control of Autonomous Underwater Vehicles using Neural Network Based Robust Control System
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Abstract:-A neural network based robust control system design for the yaw angle of autonomous underwater vehicle (AUV) is presented in this paper. Two types of control structure were used to control prescribed trajectories of an AUV. The results of the

Artificial Neural Network based Lesion Segmentation of Brain MRI
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ABSTRACT An Artificial Neural Network based segmentation method for lesion in brain is proposed. First, Magnetic Resonance Images (MRI) are denoised and intensity inhomogeneities are corrected in the preprocessing steps. Artificial neural network is used

Performance of Convolution Neural Network on the Recognition of Speech Emotion and Images
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In this paper, the performance of Convolution Neural Network (CNN) in image recognition and emotion recognition in speech will be presented. Feature extraction and selection in pattern recognition is an important issue and have been frequently discussed. Moreover,

Boundedness and Convergence of Batch Gradient Method for Training Pi-Sigma Neural Network with Inner-Penalty and Momentum
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Abstract:In the process industries convergence of a batch gradient method with inner- penalty and adaptive momentum is inspection for training pi-sigma neural networks. The role of the usual penalty is considered, which is a term proportional to the norm of the

Concurrent Algorithm for Learning of Neural Networks in Searching of the Best LearnedNeural Network.
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There is a trend to use parallel processing for different problems in Mathematics and Informatics. In the current article an algorithm for concurrent processing on multi-layer networks is presented. Also the advantages of neural networks concurrent learning are

Artificial Neural Network Approach to Dissolved Gas Analysis for Interpretation of Fault in Power Transformer
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Abstract:Power transformer is most crucial equipment in the power system. Monitoring the behavior of transformer is necessary to avoid catastrophic failures, costly outages. Dissolved Gas Analysis (DGA) is a widely used technique to estimate the condition of oilimmersed

A Dynamic Channel Allocation Algorithm Based on Back-Propagation Neural Network for Vertical Handover in HetNets
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Abstract:The vertical handover decision algorithm plays an important role to guarantee the seamless mobility in a single mobile terminal for heterogeneous wireless networks in the next generation. Hence, the efficient management of available radio resource utilization

The Analysis of Influencing Factors of Steel Bar's Property by Neural Network
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Abstract:This paper presents an analysis of influencing factors of rolled steel bar's property by a novel neural network (NN) computational method. Through the learning process of NN, the nonlinear and complicated relationships among bar's mechanical properties, billet

Different Pattern Classifier Techniques Based on Fuzzy Min-Max Neural Network
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Abstract: For classification of patterns, various neural networks related to Fuzzy Min-Max (FMM) have been studied. An Enhanced Fuzzy Min-Max (EFMM) neural network is most recent. EFMM Neural Network classifier that utilizes fuzzy sets as pattern classes has

Speech Recognition Using Artificial Neural Network–A Review
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Abstract--Speech is the most efficient mode of communication between peoples. This, being the best way of communication, could also be a useful interface to communicate with machines. Therefore the popularity of automatic speech recognition system has been

Power Transformer Fault Diagnosis Based on the SOM Network Combined with the BPNeural Network
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Abstract By analyzing the bottleneck facing the current power transformer fault diagnosis model, this paper puts forward a power transformer fault diagnosis model. The SOM model is introduced to optimize the training samples collected and to eliminate interference

Optimization of Electrochemical Performance of a Solid Oxide Fuel Cell using Artificial Neural Network
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Abstract:A neural network model is developed for prediction of solid oxide fuel cell performance. The back propagation algorithm is used for the cell voltage and power prediction. As the model is developed, the neural network model's prediction is presented

Hand Gesture Recognition on Indian Sign Language using Neural Network
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Abstract-This paper presents a novel technique for recognize the hand gesture during human–computer interaction. Frequently Hand gesture recognition system is designed such that no require any special hardware other than webcam. Human-Computer Interaction (

Automatic Atlas-Based Segmentation of NISSL Stained Mouse Brain Sections Using Convolutional Neural Network
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Abstract In this paper, we focus on segmenting NISSL stained mouse brain images into main brain regions-grey (cerebrum, brainstem, and cerebellum), fiber tracts, and ventricular systems. We feed mouse brain experimental slices acquired by the biology department

Neural Network Optimization
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Abstract In this report we want to investigate different methods of Artificial Neural Network optimization. Different local and global methods can be used. Backpropagation is the most common method for optimization. Other methods like genetic algorithm, Tabu search, and

Optimization of Feed-Forward Neural Network with Evolutionary Computation
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Abstract Neural network and evolutionary computation are important components of computational intelligence. This paper researches the architecture optimization of BP neural network of adaptive genetic algorithm (AGA). Combining AGA and BP neural network

Accurate Fault Classification in Series Compensated Multi-Terminal Extra High Voltage Transmission Line using Probabilistic Neural Network
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Abstract:This paper presents a novel method proposed for protection of multi-bus EHV transmission system which is employed with a series capacitor bank. It has been shown that for a wide range of operating conditions and fault scenarios arising in a series

COMPARISON OF CALL ADMISSION CONTROL USING NEURAL NETWORK AND SVM
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ABSTRACT This paper presents a survey on different studies done in the field of Call Admission Control. Necessity of Call Admission Control is to provide better QoS. This paper provides an overview of different two techniques used for CAC. In recent years,

Development of Artificial Neural Network based Speed and Torque Prediction Models of a DC Shunt Motor
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Abstract:DC motor models are widely used to predict speed and torque and also in various speed control applications. The statistical modeling approach is based on curve fitting using regression and exhibits poor accuracy when the range of control factors is too large or the

Review on Language Translator Using Quantum Neural Network (QNN)
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Abstract: This paper presents machine translation based on machine learning, which learns the semantically correct corpus. The machine learning process based on Quantum Neural Network (QNN) is used to recognizing the corpus pattern in realistic way. It translates on

Large Scale Recurrent Neural Network Based Modelling of Gene Regulatory NetworkUsing Cuckoo Search-Flower Pollination Algorithm
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Abstract: Accurate prediction of genetic networks using computational tools is one of the greatest challenges in the post-genomic era, for biologists and computer science researchers. Recurrent Neural Network is one of the most popular but simple approaches

A Comparative Study on Time-delay Neural Network and GARCH Models for Forecasting Agricultural Commodity Price Volatility
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SUMMARY In this paper, forecasting performance of time-delay neural network and GARCH models for predicting the volatility using monthly price series of edible oils in domestic and international markets is evaluated. An attempt has also been made to investigate whether

Providing a better life for Amyotrophic Lateral Sclerosis Patient or Spinal Cord Injured Patient by Artificial Neural Network or BrainGate
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Abstract:BrainGate is a brain implant system built and previously owned by Cyber kinetics, currently under development and in clinical trials, designed to help those who have lost control of their limbs, or other bodily functions, such as patients with amyotrophic lateral

A Rigid Map Neural Network for Anatomical Joint Constraint Modelling
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Abstract–Accurate individual anatomical joint models are becoming increasingly important for both realistic animation and diagnostic medical applications. A number of recent approaches have exploited unit quaternions to eliminate singularities when modelling