ENGINEERING RESEARCH PAPERS

neural-network IEEE PAPER 2017




JPEG-Phase-Aware Convolutional Neural Network for Steganalysis of JPEG Images
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ABSTRACT Detection of modern JPEG steganographic algorithms has traditionally relied on features aware of the JPEG phase. In this paper, we port JPEG-phase awareness into the architecture of a convolutional neural network to boost the detection accuracy of such

NoScope: Optimizing Neural Network Queries over Video at Scale
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ABSTRACT Recent advances in computer vision in the form of deep neural networks have made it possible to query increasing volumes of video data with high accuracy. However, neural network inference is computationally expensive at scale: applying a state-

Privacy-Preserving Classification on Deep Neural Network .
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Abstract Neural Networks (NN) are today increasingly used in Machine Learning where they have become deeper and deeper to accurately model or classify high-level abstractions of data. Their development however also gives rise to important data privacy risks. This

Markov Transitions between Attractor States in a Recurrent Neural Network
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Markov Transitions between Attractor States in a Recurrent Neural Network Jeremy Bernstein∗ Computation and Neural Systems CaliforniaThese values are within one standard

Deep Neural Network Regression as a Component of a Forecast Ensemble
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Abstract Marquette Universitys GasDay Project specializes in short-term load forecasting of natural gas demand. Traditionally, this forecasting is done using artificial neural networks and linear regression. This paper examines the viability of using DNNs as component

Multi-task Convolutional Neural Network for Patient Detection and Skin Segmentation in Continuous Non-contact Vital Sign Monitoring
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Abstract Patient detection and skin segmentation are important steps in non-contact vital sign monitoring as skin regions contain pulsatile information required for the estimation of vital signs such as heart rate, respiratory rate and peripheral oxygen saturation (SpO2).

Deep Neural Network Regression for Short-Term Load Forecasting of Natural Gas
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Abstract This paper proposes a short-term load forecasting method for natural gas using deep learning. Deep learning has proven to be a powerful tool for many classification problems seeing significant use in machine learning fields like image recognition and

A Hybrid Forecasting Method Based On Exponential Smoothing and Multiplicative Neuron Model Artificial Neural Network
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Abstract Holt exponential smoothing method is an effective method for forecasting of non seasonal time series. In Holt method, moving average operator with exponential decay weights is used. Multiplicative neuron model artificial neural network is a non-linear time

Comparison of Artificial Neural Network and Multiple Linear Regression Models for the Prediction of Body Mass Index
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Abstract Body Mass Index (BMI) is a simple measurement that uses a weight-to-height ratio and is used to classify adults who are underweight, overweight or obese. A higher BMI is determined to be frequently associated with the increased risk of cardiovascular hearth

An Artificial Neural Network approach to predicting electrostatic separation performance for food waste recovery
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Abstract This study presents the empirical exploration of food waste recovery throughout the electrostatic separation process. In addition, the paper discusses the potential of artificial neural network (ANN) in predicting the responses. A five-level three-factor Taguchi

Convolutional Neural Network Visualization for fMRI Brain Disease Classification Tasks
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Abstract Neurologists face an increasingly overwhelming amount of data that they must use to determine diagnoses for patients with potential brain diseases. Our project aims to supplement the upcoming technology of automated brain disease classification using deep

TRANSFORMER PROTECTION USING ARTIFICIAL NEURAL NETWORK
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Abstract This paper gives idea about use of artificial neural network to the protection of power transformer. The high pointed demand includes the requirements of dependability associated with no false tripping and operating speed with short fault detection and clearing

Improved Automatic Speech Recognition using Subband Temporal Envelope Features and Time-delay Neural Network Denoising Autoencoder
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Abstract This paper investigates the use of perceptually-motivated subband temporal envelope (STE) features and time-delay neural network (TDNN) denoising autoencoder (DAE) to improve deep neural network (DNN)-based automatic speech recognition (ASR).

Thresholding Neural Network (TNN) Based Noise Reduction with a New Improved Thresholding Function
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In this paper, a new thresholding function is introduced for image de-noising in wavelet domain. In this technique we combined the new thresholding function with discrete wavelet transform (DWT). This thresholding function is continuous and nonlinear, so it is applicable

Brain Cancer classification Based on Features and Artificial Neural Network
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Abstract: MRI (Magnetic resonance Imaging) brain tumor images Classification is a difficult task due to the variance and complexity of tumors. This paper proposed techniques to classify the MR human brain images. The proposed classification technique consists of three

Speaker diarization using deep neural network embeddings
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ABSTRACT Speaker diarization is an important front-end for many speech technologies in the presence of multiple speakers, but current methods that employ i-vector clustering for short segments of speech are potentially too cumbersome and costly for the front-end role. In

Representation Learning of Users and Items for Review Rating Prediction Using Attention-based Convolutional Neural Network
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Abstract It is common nowadays for e-commerce websites to encourage their users to rate shopping items and write review text. This review text information has been proven to be very useful in understanding user preferences and item properties, and thus enhances the

Wind Power Interval Prediction Based on Improved PSO and BP Neural Network
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Abstract As is known to all that the output of wind power generation has a character of randomness and volatility because of the influence of natural environment conditions. At present, the research of wind power prediction mainly focuses on point forecasting, which

Joint Optimisation of Tandem Systems using Gaussian Mixture Density Neural Network Discriminative Sequence Training
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Page 1. Joint Optimisation of Tandem Systems using Gaussian Mixture Density Neural Network Discriminative Sequence Training Chao Zhang and Phil Woodland March 8, 2017 Cambridge University Engineering Department Page 2. Introduction Tandem Systems as Mixture Density

GENERAL REGRESSION NEURAL NETWORK MODELING OF SOIL CHARACTERISTICS FROM FIELD TESTS
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ABSTRACT: The Standard Penetrating Test (SPT) can be considered as one of the most common in-situ popular and economic tests for subsurface investigation. Therefore, many empirical correlations have been developed between the SPT N-value, and other properties

Assessment of Total Dissolved Solid Concentration in Groundwater of Nadia District, West Bengal, India using Artificial Neural Network
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Abstract: An attempt was made to predict the total dissolved solid (TDS) concentration of groundwater for Nadia district, West Bengal using artificial neural network approach. The sole aim of the study was simulating TDS in groundwater as one of the major indicators of

Controlling Biochemical Oxygen Demand in the Multi-Soil-Layering using Neural Network tool
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Abstract The effect of a set of environmental parameters on the pollution indicators BOD5, in Multi-Soil-Layering (MSL) has been studied using neural networks (NNs) and multiple regression analysis (MRA). The obtained results show that, NNs may be used as a practical

Investigating bidirectional recurrent neural network language models for speech recognition
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ABSTRACT Recurrent neural network language models (RNNLMs) are powerful language modeling techniques. Significant performance improvements have been reported in a range of tasks including speech recognition compared to n-gram language models. Conventional

Prediction of Number of Passengers in Public Transportation by Using Artificial Neural Network Method
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Abstract In order to organize activities efficiently in the public transportation, there are some factors such as bus lines, stops, distances, traffic conditions etc. Before making the necessary planning and scheduling activities, it is important to estimate the demand for the

Use of Artificial Neural Network for the Analysis of Color Expression of Granulated Blast Furnace Slag Mortar Using Carbon Amino Silica Black
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This study was to investigate the effects of granulated blast furnace slag (GBFS) on the color expression of black colored mortar. For this purpose, color evaluation and was carried out on white Portland cement (WPC) mortar mixed with of carbon amino silica black (CASB) by

Deep neural network models of genomic sequence
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The state-of-the-art in many genomic prediction tasks is advancing quickly using deep neural networks applied to genomic sequences even though their history in this field is relatively short [1, 2]. However, there are several different neural network architectures in

Transformation of a Nomogram for Drug-induced QT-Prolongation to a Computerized Neural Network
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ABSTRACT Drug-induced prolongation of the QT interval of the electrocardiogram is well recognized as a risk for increased mortality. A nomogram relating QT-interval to heart rate has been previously developed to assess this risk. Since other risk factors may be operative,

Parallel Improved Pulse Coupled Neural Network Application for Edge Detection in Image Processing
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Abstract Edge detection is the base of most image processing applications. There are various classical methods for performing edge detection such as canny operator. The main flaw of these methods is that they are not flexible. Pulse coupled neural network (PCNN) is

Version 2 of the IASI NH 3 neural network retrieval algorithm; near-real time and reanalysed datasets
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Abstract. Recently, Whitburn et al.(2016) presented a neural network -based algorithm for retrieving atmospheric ammonia (NH3) columns from IASI satellite observations. In the past year, several improvements have been introduced and the resulting new baseline version,

An Artificial Neural Network algorithm to solve third-order Emden-Fowler type problems
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Abstract In this article we suggest, the hybrid algorithm based on Bessel polynomials and Artificial Neural Network (BeNN) to solve non-linear Emden-Fowler type of differential equations. The problems with singular point at x= 0 which have the second order of initial

Shortcomings of Current Artificial Nodal Neural Network Models
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Published: February 15, 2017 The usefulness of small-networks to model large-networks is limited in biological systems and synaptic studies give little insight into conduction in more highly evolved brain-neural-networks where axon conduction is diverse and seemingly

Development of Cloud Action for Seamless Robot Using Backpropagation Neural Network
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Abstract This paper presents the cloud action model for a five DOF Seamless robotic arm using the inverse kinematics solution based on artificial neural network (ANN). Levenberg Marquardt method is used in training algorithm. The desired position and orientation of the

A Convolutional Neural Network Model for Predicting a Products Function, Given Its Form
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ABSTRACT Quantifying the ability of a digital design concept to perform a function currently requires the use of costly and intensive solutions such as Computational Fluid Dynamics. To mitigate these challenges, the authors of this work propose a deep learning approach based

Plant Leaf Disease Detection using Deep Learning and Convolutional Neural Network
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Abstract: When plants and crops are affected by pests it affects the agricultural production of the country. Usually farmers or experts observe the plants with naked eye for detection and identification of disease. But this method can be time processing, expens ive and inaccurate.

Neural Network Based Generalized Predictive Control for RFT-30 Cyclotron System
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Abstract Beamline tuning is time consuming and difficult work in accelerator system. In this work, we propose a neural generalized predictive control (NGPC) approach for the RFT-30 cyclotron beamline. The proposed approach performs system identification with the NN

On Sampling Strategies for Neural Network -based Collaborative Filtering
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Collaborative Filtering (CF) has been one of the most effective methods in recommender systems, and methods like matrix factorization [17, 18, 27] are widely adopted. However, one of its limitation is the dealing of cold-start problem, where there are few or no observed

Neural Network Based Prediction of 3D Protein Structure as a Function of Enzyme Family Type and Amino Acid Sequences
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Abstract Prediction of dihedral angles from amino acid sequences based on the neural network to predict protein structure is promising in the field of bioinformatics. The present proposed study presents a prediction tool for 3-Dimensional (3D) protein structure as a

Detecting Drive-by Download Attacks from Proxy Log Information using Convolutional Neural Network
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Abstract Many hosts are still infected by drive-by download attacks despite the efforts of many security researchers and venders. In the drive-by download attacks, the attackers maliciously change popular web sites. Then, the users are redirected via the redirect URLs

Hgo-cnn: Hybrid generic-organ convolutional neural network for multi-organ plant classification
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ABSTRACT Classification of plants based on a multi-organ approach is very challenging. Although additional data provides more information that might help to disambiguate between species, the variability in shape and appearance in plant organs also raises the

Lidar-based individual tree species classification using convolutional neural network
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ABSTRACT Terrestrial lidar is commonly used for detailed documentation in the field of forest inventory investigation. Recent improvements of point cloud processing techniques enabled efficient and precise computation of an individual tree shape parameters, such as

Long Short-Term Memory Projection Recurrent Neural Network Architectures for Pianos Continuous Note Recognition
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Abstract Long Short-Term Memory (LSTM) is a kind of Recurrent Neural Networks (RNN) relating to time series, which has achieved good performance in speech recognition, image recognition and pattern recognition. Long Short-Term Memory Projection (LSTMP) is a

Evolving fuzzy neural network equalization of channel impulse response in optical mode division multiplexing
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Optical mode division multiplexing (MDM) systems suffer from the inter-symbol interference (ISI) issues due to nonlinear channel impairments in multimode fiber from mode coupling and modal dispersion. Existing equalization algorithms in MDM systems such as least mean

Personal Attributes Extraction in Chinese Text Based on Recurrent Neural Network
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Abstract. Personal Attributes Extraction in Chinese Text Task is designed to extract person specific attributes, for example, spouse/husband, children, education, title etc. from unstructured Chinese texts. According to the characteristics of the personal attributes Abstract. An unsupervised method for convolutional neural network (CNN) architecture design is proposed. The method relies on a variable neighborhood search-based approach for finding CNN architectures and hyperparameter values that improve classification

Platoon Merging Distance Prediction using a Neural Network Vehicle Speed Model?
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Abstract: Heavy-duty vehicle platooning has been an important research topic in recent years. By driving closely together, the vehicles save fuel by reducing total air drag and utilize the road more efficiently. Often the heavy-duty vehicles will catch-up in order to platoon

Experiments with Convolutional Neural Network Models for Answer Selection
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ABSTRACT In recent years, neural networks have been applied to many text processing problems. One example is learning a similarity function between pairs of text, which has applications to paraphrase extraction, plagiarism detection, question answering, and ad hoc

A Multi-modal Deep Neural Network approach to Bird-song identification
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Abstract. We present a multi-modal Deep Neural Network (DNN) approach for bird song identification. The presented approach takes both audio samples and metadata as input. The audio is fed into a Convolutional Neural Network (CNN) using four convolutional layers.

Old School vs. New School: Comparing Transition-Based Parsers with and without Neural Network Enhancement.
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Abstract In this paper, we attempt a comparison between new school transitionbased parsers that use neural networks and their classical old school counterpart. We carry out experiments on treebanks from the Universal Dependencies project. To facilitate the

Identification of potentially undelivered packages with an artificial neural network method
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Abstract In this paper we propose an artificial neural network method to determine whether an internationally shipped package is at risk of ending up lost in post before reaching its intended recipient. The network uses several features of transactions with customers as

Vortex Shedding Frequency Estimation of a Circular Cylinder with Splitter Plate at Incidence Using Artificial Neural Network
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Abstract Bluff bodies such as a circular and square cylinder encountering in many engineering applications has significant disadvantages like vortex induced vibration that can be lead to resonance. Therefore, prediction of vortex shedding frequency has vital important

PREDICTION OF PUNCHING SHEAR CAPACITY OF RC FLAT SLABS USING ARTIFICIAL NEURAL NETWORK
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ABSTRACT Punching shear of flat slabs is a local, brittle failure that may occur before the more favourable ductile flexural failure. This study develops an artificial neural network (ANN) modelling for the prediction of punching shear strength of flat slabs using 281 test With the improvement of living standards, more and more people pay attention to their own health problems and performing an ECG test is the preferred selection for preventing cardiovascular disease. Although it is easy to sample ECG now, diagnostic conclusion

Aerodynamic Force Estimation of A NACA 0015 Airfoil with DBD Plasma Actuator Using Artificial Neural Network
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Abstract Flow control with dielectric barrier discharge (DBD) has significant importance because of its simple structure, rapid responds and light weight. In the laboratory research, DBD plasma actuator producing ozone and required high voltage pose a threat for the

Playing Othello by Deep Learning Neural Network
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Abstract Board game implementation has long been a way of showing the ability of AI. Among different board games, Othello is one of the popular subjects due to its simple rules and well-defined strategic concepts. Today, the majority of the Othello programmes at a

Remarks on Direct System Identification Using Hypercomplex Valued Neural Network with Application to Time Series Estimation
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Abstract This study discusses a design method for system identification using multilayer hypercomplex valued neural networks, such as complex, hyperbolic, bicomplex and quaternion neural networks, and investigates its characteristics. The direct transfer function

The OLCI Neural Network Swarm (ONNS): A Bio-geo-optical Algorithm for Open Ocean and Coastal Waters
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The processing scheme of a novel in-water algorithm for the retrieval of ocean color products from Sentinel-3 OLCI is introduced. The algorithm consists of several blended neural networks that are specialized for 13 different optical water classes. These comprise

Analyze EEG Signals with Convolutional Neural Network Based on Power Spectrum Feature Selection
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EEG (electroencephography) is a modern aided examination of the brain, which is used to record the brains weak bioelectrical magnification in the form of a graph to help diagnose the disease without any trauma to the subject. Brain Computer Interface refers to establish a

Improved the Prediction of Clinical Data Accuracy using RBF Neural Network Model
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ABSTRACT Now a days data mining technique used in the field of medical diagonise of critical desesis and clinical data. the prediction of mining technique is major issue. For the enhancement of mining technique used various approach such as fuzzy logic, feature

Exploring deep features: deeper fully convolutional neural network for image segmentation
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Classi?cation of images has been a widely regarded challenge for the past decade, but a new type of object recognition problem which deals with pixellevel segmentation is posing a more complex task for both computer vision enthusiasts and researcher alike. The

An Unsupervised Feed Forward Neural Network Method for Efficient Clustering
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Abstract: This paper presents a Real Unsupervised Feed Forward Neural Network (RUFFNN) clustering method with one epoch training and data dimensionality reduction ability to overcome some critical problems such as low training speed, low accuracy as well

Electrical Characterization of a Photovoltaic Module Through Artificial Neural Network : A Review
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Abstract: The aim of this paper is to present a review of IV characteristics of photovoltaic module using artificial neural network (ANN). The ANN approach has found to be the efficient tool over complex non-linear mathematical equations and complicated models for

BP NEURAL NETWORK -BASED SMOG ENVIRONMENT AND THE RISK MODEL OF MOOD DRIVING
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Abstract. In order to evaluate and study the influence of smog environment on driving safety, this paper utilizes the measurability of mood states, adopts back propagation (BP) artificial neural network instrument to establish a smog-risky mood network model. Six pictures in

Code supporting: A Neural Network Multi-Task Learning Approach to Biomedical Named Entity Recognition
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Example usage: python multi-output_MT. pypath/to/data-filesdataset-1, , datasetnpath/to/ vectorfile multi-output_MT-var-dataset. py: The model used in the multi-task experiments which investigated the effect of multi-task learning on datasets of various sizes. Specify the

Sequence Prediction Using Neural Network Classiers
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Abstract Being able to guess the next element of a sequence is an important question in many fields. In this paper we present our approaches used in the Sequence Prediction ChallengE (SPiCe), whose goal is to compare the different approaches to that problem on

A Novel Hybrid Chemical Reaction Optimization Algorithm with Adaptive Differential Evolution Mutation Strategies for Higher Order Neural Network Training.
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Abstract: In this paper, an application of a hybrid Chemical Reaction Optimization (CRO) algorithm with adaptive Differential Evolution (DE) mutation strategies for training Higher Order Neural Networks (HONNs), especially the Pi-Sigma Network (PSN) is presented.

End-to-End Convolutional Neural Network -based Voice Presentation Attack Detection
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Abstract Development of countermeasures to detect attacks performed on speaker verification systems through presentation of forged or altered speech samples is a challenging and open research problem. Typically, this problem is approached by extracting

Hybrid Approach to Optimize the Centers of Radial Basis Function Neural Network Using Particle Swarm Optimization.
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Abstract: Function approximation is an important type of supervised machine learning techniques, which aims to create a model for an unknown function to find a relationship between input and output data. The aim of the proposed approach is to develop and

Artificial Neural Network modelling for pressure drop estimation of oil-water flow for various pipe diameters
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Abstract The flow of two immiscible liquids in pipeline occurs many times in chemical industries. Oil-water mixture is dispersion and estimation of pressure gradient for flow through pipeline using empirical equation is tedious and less accurate. The present work is

Application of Gegenbaer Neural Network to solve the MHD Falkner Skan flow
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Abstract In this examination, a new approach based on single layer Gegenbauer Artificial neural network to solve the MHD Falkner Skan equation on the semi-infinite domain has been proposed. Here, the feed forward neural network model of the unsupervised type has

Pedestrian detection in video surveillance using fully convolutional YOLO neural network
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ABSTRACT More than 80% of video surveillance systems are used for monitoring people. Old detection algorithms, based on background and foreground models, couldnt even deal with a group of people, to say nothing of a crowd. Recent robust and highly effective

A Grey Neural Network Model Optimized by Fruit Fly Optimization Algorithm for Short-term Traffic Forecasting.
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Abstract Accurate short-term traffic forecasting can relieve traffic congestion and improve the mobility of transportation, which is very important for management modernization of transportation systems. However, it is quite difficult to predict effectively and accurately short-

Deep Convolutional Neural Network based Approach for Aspect-based Sentiment Analysis
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Abstract. Sentiment analysis is an important task in natural language processing and has a wide range of applications. This paper describes our deep learning approach to multilingual aspect-based sentiment analysis. Our model use a deep convolutional neural network for

Superensembling of Artificial Neural Network Models for Investigating The Effect of Polar Sea Ice on Sea Surface Temperature in Indian Ocean Region
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Abstract: There are two broad sources of errors when prediction of any atmospheric parameter is made by dynamical models-one due to errors in model initializations and two due to limited understanding of the physical phenomenon at hand. The error introduced by

Artificial Neural Network Modeling of NixMnxOx based Thermistor for Predicative Synthesis and Characterization
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As foremost sensors of ambient conditions, temperature sensors are regarded as the most vital ones in wide-ranging applications touching the societal life. Amongst the temperature sensors, NTC thermistors have captured their unique place due to the favorable metrics

A neural network -based algorithm for predicting stone-free status after ESWL therapy _
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Objective: The prototype artificial neural network (ANN) model was developed using data from patients with renal stone, in order to predict stone-free status and to help in planning treatment with Extracorporeal Shock Wave Lithotripsy (ESWL) for kidney stones. Materials

Improving music source separation based on deep neural networks through data augmentation and network blending
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problem. We de- scribe two different deep neural network architectures for this task, a feed-forward and a recurrent one, and show that each of them yields themselves state-of-the art results on the SiSEC DSD100 dataset. For

Application of Taguchi OA array and Artificial Neural Network for Optimizing and Modeling of Drilling Cutting Parameters
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Abstract This paper consists of two phases, in first phase experiments have been conducted with the use of Taguchi FFE (Fractional Factorial experimentation) to find optimal cutting process parameters spindle speed, feed rate and drill diameter with the objective of

Coin Recognition System using Artificial Neural Network on Static Image Dataset
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Abstract: Coins are frequently used in everyday life at various places like in banks, grocery stores, supermarkets, automated weighing machines, vending machines etc. So, there is a basic need to automate the counting and sorting of coins. However, currently available

Exploiting the PANORAMA Representation for Convolutional Neural Network Classification and Retrieval
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Abstract A novel 3D model classification and retrieval method, based on the PANORAMA representation and Convolutional Neural Networks, is presented. Initially, the 3D models are pose normalized using the SYMPAN method and consecutively the PANORAMA

RECOGNITION OF SILVERLEAF WHITEFLY AND WESTERN FLOWER THRIPS VIA IMAGE PROCESSING AND ARTIFICIAL NEURAL NETWORK
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Abstract-IPM (Integrated Pest Management) is used to minimize or reduce the use of chemicals in greenhouse agriculture. IPM is basically depends upon early detection and continuous monitoring of pest populations which is very critical or time consuming task as it

Artificial Neural Network Based Trend Analysis and Forecasting Model for Course Selection
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Abstract Selection of the proper higher educational courses is absolutely necessary for the prospective students. Selecting appropriate courses are really cumbersome job for the students who are having less information about present trend of education relating to get

Development of multimodal biometric system for person identification using neural network approach
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ABSTRACT Security is the most significant problem in recent development in the communication technology systems. The automatic identification of an individual is based on the biometric system which provides the unique features of an individual. Biometrics can be

HCTI at SemEval-2017 Task 1: Use convolutional neural network to evaluate semantic textual similarity
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Abstract This paper describes our convolutional neural network (CNN) system for the Semantic Textual Similarity (STS) task. We calculated semantic similarity score between two sentences by comparing their semantic vectors. We generated a semantic vector by max

An Improved Method of Power System Short Term Load Forecasting Based on Neural Network
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Abstract Load forecasting is an important content of planning and operating power system. It is the prerequisite to ensure the reliable power supply and economic operation. In this paper, an improved method of short-term load forecasting for load data of two different

Human Activity Recognition using Deep Neural Network with Contextual Information.
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Page 1. Human Activity Recognition using Deep Neural Network with Contextual Information University of Houston, Computer Science Department, Quantitative Imaging Laboratory Li Wei and Shishir K. Shah Introduction Propose group context feature that encodes the group interaction

Water Quality Evaluation Using Back Propagation Artificial Neural Network Based on Self-Adaptive Particle Swarm Optimization Algorithm and Chaos Theory
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Abstract To overcome the shortcomings of the traditional methods of water quality evaluation, in this paper, a novel model combines particle swarm optimization (PSO), chaos theory, self-adaptive strategy and back propagation artificial neural network (BP ANN) that

Pattern Classification of Fabric Defects Using a Probabilistic Neural Network and Its Hardware Implementation using the Field Programmable Gate Array
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Abstract This study proposes a fabric defect classification system using a Probabilistic Neural Network (PNN) and its hardware implementation using a Field Programmable Gate Arrays (FPGA) based system. The PNN classifier achieves an accuracy of 98±2% for the test

Assessment of the validity of the diagnosis of damage of tissues by multispectral method using neural network
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Abstract. In this paper is the expert system designed and analysed for diagnostic decision support solutions in the study of surface damage of tissues by using multispectral method and neural network to process the results. We have developed specialized software for

Contextual Bidirectional Long Short-Term Memory Recurrent Neural Network Language Models: A Generative Approach to Sentiment Analysis
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Abstract Traditional learning-based approaches to sentiment analysis of written text use the concept of bag-of-words or bag-of-ngrams, where a document is viewed as a set of terms or short combinations of terms disregarding grammar rules or word order. Novel approaches

Hand Written Digit Recognition using Convolutional Neural Network (CNN)
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ABSTRACT The field of machine learning is a rapidly developing one. Recent developments in the field of image processing and machine learning has led to efficient extraction of features from images of peoples faces. Recognizing handwritten digits from images isnt

QSAR studies of breast carcinoma using Artificial neural network , Bayesian classifier and Multiple linear regression
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Abstract-Breast cancer is a disease that affects millions. It starts as a tumor but end up spreading all over the body. It uses nutrition of body for its own growth and there is no regulatory mechanism for its growth. Breast cancer has no cure once it progresses to an

Topic Information Based Neural Network Model for Fine-grained Entity Type Classification
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Abstract. Entity recognition is an important part of natural language processing, but nowadays most entity recognition systems are restricted to a limited set of entity classes (eg, person, location, organization or miscellaneous). Therefore, fine-grained entity type

Residential Community Open-Up Strategy Based on Prims Algorithm and Neural Network Algorithm
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Abstract Open community has aroused widespread concern and research. This paper focuses on the system analysis research of the problem that based on statistics including the regression equation fitting function and mathematical theory, combined with the actual effect

Prediction of standard penetration test (SPT) value in Izmir, Tur-key using radial basis neural network
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Abstract Site exploration, characterization and prediction of soil properties by in-situ test are key parts of a geotechnical preliminary process. In-situ testing is progressively essential in geotechnical engineering to recognize soil characteristics alongside. In this study, radial

A Neural Network Based Soft Sensor For Air Fuel Ratio Dynamics In SI Engines
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Abstract Soft sensors have been widely used in control algorithms of engineering application to enhance the control performance and system robustness. This paper proposes a neural network (NN) based soft sensor scheme for air/fuel ratio sensor in spark-

Forecasting Currency Exchange Rates via Feedforward Backpropagation Neural Network
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Abstract The latest globalization trends resulted in increasingly interdependent economies of nations and multinational firms. This may leave companies operating internationally at the mercy of the volatility in currency exchange rates. Forecasting these exchange rates became

Automatic Classification of Wikipedia Articles by Using Convolutional Neural Network
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Abstract: Wikipedia has emerged as an important source of information for university students. It has been reported that the students tend to start their search with Google that leads to Wikipedia articles even in university libraries. Recent research findings indicate that

Predicting strength of SCC using artificial neural network and multivariable regression analysis
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Abstract. In the present study an Artificial Neural Network (ANN) was used to predict the compressive strength of selfcompacting concrete. The data developed experimentally for self-compacting concrete and the data sets of a total of 99 concrete samples were used in

Envision: A 0.26-to-10 TOPS/W Subword-Parallel Dynamic-Voltage-Accuracy-Frequency-Scalable Convolutional Neural Network Processor in 28nm FDSOI
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ConvNets, or Convolutional Neural Networks (CNN), are state-of-the-art classification algorithms, achieving near-human performance in visual recognition [1]. New trends such as augmented reality demand always-on visual processing in wearable devices. Yet, advanced

Speech reconstruction using a deep partially supervised neural network
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Statistical speech reconstruction for larynx-related dysphonia has achieved good performance using Gaussian mixture models and, more recently, restricted Boltzmann machine arrays, however deep neural network -based systems have been hampered by the

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