ENGINEERING RESEARCH PAPERS

neural network research papers 2015 IEEE PAPER




Downscaling medium-range ensemble forecasts using a neural network approach
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Abstract. In this study, we present an application of self-organizing maps (SOMs) to downscaling weekly ensemble forecasts for probabilistic prediction of local precipitation in Japan. SOM is simultaneously employed on four elemental variables derived from the

Recurrent Neural Network and LSTM Models for Lexical Utterance Classification
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Abstract Utterance classification is a critical pre-processing step for many speech understanding and dialog systems. In multi-user settings, one needs to first identify if an utterance is even directed at the system, followed by another level of classification to

A time delay neural network architecture for efficient modeling of long temporal contexts
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Abstract Recurrent neural network architectures have been shown to efficiently model long term temporal dependencies between acoustic events. However the training time of recurrent networks is higher than feedforward networks due to the sequential nature of the

Forecasting Beijing Transportation Hub Areas's Pedestrian Flow Using Modular Neural Network
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Along with the increasing proportion of urban public transportation trip, pedestrian flow in transportation hub areas increased. For effectively improving the emergency handling ability of related management apartments and preventing the incident of pedestrian congestion,

Application of artificial neural network into the freshwater fish caught in Turkey
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Abstract Artificial neural networks (ANNs) are computational intelligence techniques, which are used in many applications, such as forecast. The aim of this study was to evaluate artificial neural networks created for freshwater fish caught in Turkey between the years of

Of Short Term Load Forecasting Using Multilayer Feed Forward Neural Network With Back Propagation Learning And Radial Basis Functional Neural Network
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ABSTRACT The term load forecast refers to the projected load requirement using systematic process of defining load in sufficient quantitative detail so that important power system expansion decisions can be made. Load forecasting is necessary for economic

Semi-supervised maximum mutual information training of deep neural network acoustic models
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Abstract Maximum Mutual Information (MMI) is a popular discriminative criterion that has been used in supervised training of acoustic models for automatic speech recognition. However, standard discriminative training is very sensitive to the accuracy of the

3-cells cellular neural network (CNN) attractor and its adaptive biological control
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Abstract: Recent research has shown the importance of biological control in many biological systems appearing in nature. In computer science, machine learning and biology, cellular neural networks (CNN) are a parallel computing paradigm, similar to neural networks with

Synchronization of 3-cells cellular neural network (CNN) attractors via adaptive control method
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Abstract: In this research work, we first discuss the properties of the 3-cells 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 computer science, machine

Facial expression recognition classification using hybridization of ICA, GA, and Neural Network for Human-Computer Interaction
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Abstract Facial Expression classification is becoming one of the foremost challenges in the area of computer vision artificial intelligence that can be used for security, entertainment and human machine interface. The emotion recognition and classification depends upon

An Overview Of Driver Seat Comfort: Objective and Subjective Measures and Its Neural Network Modeling
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Abstract The purpose of this work is to investigate driver seat comfort and discuss some of the subjective and objective factors that impact it. Comfort describes the nature of the interaction between a human being and a specific environment and is characterized as a

A Linguistically Informed Convolutional Neural Network
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Abstract Sentiment lexicons and other linguistic knowledge proved to be beneficial in polarity classification. This paper introduces a linguistically informed Convolutional Neural Network (lingCNN), which incorporates this valuable kind of information into the model.

What Happens Next Event Prediction Using a Compositional Neural Network Model
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Abstract We address the problem of automatically acquiring knowledge of event sequences from text, with the aim of providing a predictive model for use in narrative generation systems. We present a neural network model that simultaneously learns embeddings for

Learning Deep Neural Network Policies with Continuous Memory States
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Abstract Policy learning for partially observed control tasks requires policies that can remember salient information from past observations. In this paper, we present a method for learning policies with internal memory for high-dimensional, continuous systems, such as

The use of probabilistic neural network and UV reflectance spectroscopy as an objective cultured pearl quality grading method
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Abstract Pearl quality and value are determined as a combination of different features, with mollusk species, nacre thickness, luster, surface, shape, color and pearl size, being the most important. A pearl grader has to quantify visual observations and to assign a grading level

CCG Supertagging with a Recurrent Neural Network
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Abstract Recent work on supertagging using a feedforward neural network achieved significant improvements for CCG supertagging and parsing (Lewis and Steedman, 2014). However, their architecture is limited to considering local contexts and does not naturally

Classification of Handwritten Digits Using an Artificial Neural Network
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Classification of Handwritten Digits Using an Artificial NeuralNetwork Michael Mason Colorado School of Mines April 20, 2015 Michael Mason A Tale of Ten Digits Page 2. 2/16

Document modeling with gated recurrent neural network for sentiment classification
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Abstract Document level sentiment classification remains a challenge: encoding the intrinsic relations between sentences in the semantic meaning of a document. To address this, we introduce a neural network model to learn vector-based document representation in a

In Vitro Restoration of an Amyloid-Beta Altered Network Circuitry in a'Mutated Biomimetic Acetylcholinesterase'Memristor/Memcapacitor Neural Prosthesis
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A normal neuralnetwork circuitry constantly fires HFO in 80-200 Hz producing synchronization for long-term memory storage during SWS, where pHFO fires randomly leading to seizures and epilepsy (200-600 Hz) [16-18]. Results

Enhancing the Levenberg-Marquardt Method in Neural Network training using the direct computation of the Error Cost Function Hessian
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ABSTRACT The Levenberg-Marquardt (LM) algorithm is a very popular training method in Neural Networks due to its accuracy and robustness. LM outperforms gradient based methods that use direct calculation of the first derivative of the error cost function through

CLASSIFICATION OF SATELLITE IMAGES USING CONTEXT FEATURES AND BASED ONNEURAL NETWORK METHODS
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ABSTRACT: One of the important techniques for interpretation of remote sensing images is classification, which is frequently used in evaluation of landscape changes. The key issue is how to precisely classify the satellite images at high spatial resolution. This study attempts

Anti-synchronization of 3-cells cellular neural network attractors via adaptive control method
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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

Recurrent neural network language model training with noise contrastive estimation for speech recognition
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ABSTRACT In recent years recurrent neural network language models (RNNLMs) have been successfully applied to a range of tasks including speech recognition. However, an important issue that limits the quantity of data used, and their possible application areas, is

Permeability Prediction of Tight Sandstone Reservoirs Using Improved BP Neural Network
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Abstract: By analyzing the permeability controlling factors of tight sandstone reservoir in Oil Field, the permeability is considered to be mainly controlled by porosity, clay content, irreducible water saturation and diagenetic coefficient. Because the

Neural network river forecasting with multi-objective fully informed particle swarm optimization
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ABSTRACT In this work, we suggest that the poorer results obtained with particle swarm optimization (PSO) in some previous studies should be attributed to the cross-validation scheme commonly employed to improve generalization of PSO-trained neural network

Random walks and neural network language models on knowledge bases
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Abstract Random walks over large knowledge bases like WordNet have been successfully used in word similarity, relatedness and disambiguation tasks. Unfortunately, those algorithms are relatively slow for large repositories, with significant memory footprints. In

A Comparison of Neural Network Feature Transforms for Speaker Diarization
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Abstract Speaker diarization finds contiguous speaker segments in an audio stream and clusters them by speaker identity, without using a-priori knowledge about the number of speakers or enrollment data. Diarization typically clusters speech segments based on

Improving the training and evaluation efficiency of recurrent neural network language models
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ABSTRACT Recurrent neural network language models (RNNLMs) are becoming increasingly popular for speech recognition. Previously, we have shown that RNNLMs with a full (non-classed) output layer (F-RNNLMs) can be trained efficiently using a GPU giving a

Modeling of glycolysis of flexible polyurethane foam wastes by artificial neural networkmethodology
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Abstract The glycolysis process as a useful approach to recycling flexible polyurethane foam wastes is modeled in this work. To obtain high quality recycled polyol, the effects of influential processing and material parameters, ie process time, process temperature,

Sentence-level control vectors for deep neural network speech synthesis
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Abstract This paper describes the use of a low-dimensional vector representation of sentence acoustics to control the output of a feed-forward deep neural network text-to- speech system on a sentence-by-sentence basis. Vector representations for sentences in

Experimental Performance and Neural Network Modeling of a Large-scale Greenhouse Solar Dryer for Drying Natural Rubber Sheets
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Abstract: This paper presents experimental performance and artificial neural network modeling of a large-scale greenhouse solar dryer for drying of natural rubber sheets. The dryer consists of a parabolic roof structure covered with polycarbonate sheets on a

Time delay deep neural network-based universal background models for speaker recognition
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ABSTRACT Recently, deep neural networks (DNN) have been incorporated into i-vector- based speaker recognition systems, where they have significantly improved state-of-the-art performance. In these systems, a DNN is used to collect sufficient statistics for i-vector

Network Intrusion Detection System Using Neural Network Classification of Attack Behavior
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Abstract:Intrusion Detection Systems (IDS) have become a necessity in computer security systems because of the increase in unauthorized accesses and attacks. Intrusion Detection is a major component in computer security systems that can be classified as Host-based

A comparison of neural network methods for unsupervised representation learning on the Zero Resource Speech Challenge
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Abstract The success of supervised deep neural networks (DNNs) in speech recognition cannot be transferred to zero-resource languages where the requisite transcriptions are unavailable. We investigate unsupervised neural network based methods for learning

Deep neural network context embeddings for model selection in rich-context HMM synthesis
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Abstract This paper introduces a novel form of parametric synthesis that uses context embeddings produced by the bottleneck layer of a deep neural network to guide the selection of models in a rich-context HMM-based synthesiser. Rich-context synthesis–in

Dictionary Learning for Sparse Representation of Neural Network Exemplars in Speech Recognition
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Abstract:Conventional speech recognition systems relying on exemplar-based sparse representation require huge size exemplars collection to represent the linguistic units. Recent work demonstrates that despite of consistent improvement in automatic speech

Investigations on phrase-based decoding with recurrent neural network language and translation models
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Abstract This work explores the application of recurrent neural network (RNN) language and translation models during phrasebased decoding. Due to their use of unbounded context, the decoder integration of RNNs is more challenging compared to the integration of

Recurrent Neural Network Language Model Adaptation for Multi-Genre Broadcast Speech Recognition
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Abstract Recurrent neural network language models (RNNLMs) have recently become increasingly popular for many applications including speech recognition. In previous research RNNLMs have normally been trained on well-matched in-domain data. The

Learning Control Law of Mode Switching for Hypersonic Morphing Aircraft Based on Type-2 TSK Fuzzy Neural Network
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Abstract:A novel learning method of control law of mode switching for hypersonic morphing aircraft, based on type-2 Takagi-Sugeno-Kang (TSK) fuzzy neural network, is proposed in this paper. The purpose of this method is to learn the control law of mode switching from a

User modeling with neural network for review rating prediction
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Abstract We present a neural network method for review rating prediction in this paper. Existing neural network methods for sentiment prediction typically only capture the semantics of texts, but ignore the user who expresses the sentiment. This is not desirable

Shallow Convolutional Neural Network for Implicit Discourse Relation Recognition
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Abstract Implicit discourse relation recognition remains a serious challenge due to the absence of discourse connectives. In this paper, we propose a Shallow Convolutional Neural Network (SCNN) for implicit discourse relation recognition, which contains only

Evaluation of Dump Slope Stability of a Coal Mine Using Artificial Neural Network. J Powder Metall Min 4: 128. doi: 10.4172/2168-9806.1000128 Page 2 of 5
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This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

A Comparison between Count and Neural Network Models Based on Joint Translation and Reordering Sequences
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Abstract We propose a conversion of bilingual sentence pairs and the corresponding word alignments into novel linear sequences. These are joint translation and reordering (JTR) uniquely defined sequences, combining interdepending lexical and alignment

ARTIFICIAL NEURAL NETWORK AS TOOL FOR QUALITY BY DESIGN IN FORMULATION DEVELOPMENT OF SOLID DISPERSION OF FENOFIBRATE
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Optimization techniques are abundant in pharmaceutical industry. In general, all the required information should be obtained from as few experiments as possible. Conventional techniques such as response surface models or simplex optimization are often used. With

Analysis of a low-dimensional bottleneck neural network representation of speech for modelling speech dynamics
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Abstract This paper presents an analysis of a low-dimensional representation of speech for modelling speech dynamics, extracted using bottleneck neural networks. The input to the neural network is a set of spectral feature vectors. We explore the effect of various designs

Neural Network PID Control based Scheduling Mechanism for Cloud Computing
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Abstract: With the progress of academia and industry, cloud computing is moving from theory to practice. Job scheduling is an important issue in the high performance cloud computing environment. An appropriate scheduling mechanism can efficiently reduce the response

Multi-Feature Modular Deep Neural Network Acoustic Models
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C. Plahl, R. Schlüter, and H. Ney,Improved acoustic feature combination for lvcsr by neural networks., INTERSPEECH, 2011 K. Kilgour, T. Seytzer, Q. Nguyen, and A. Waibel,Warped minimum variance distortionless response based bottle-neck features for LVCSR,

Rule Based Ensembles Using Pair Wise Neural Network Classifiers
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Abstract:In value estimation, the inexperienced people's estimation average is good approximation to true value, provided that the answer of these individual are independent. Classifier ensemble is the implementation of mentioned principle in classification tasks ABSTRACT Recent progress in acoustic modeling with deep neural network has significantly improved the performance of automatic speech recognition systems. However, it remains as an open problem how to rapidly adapt these networks with limited,

Lung Cancer Detection Using Artificial Neural Network Fuzzy Clustering
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Abstract: To evaluate the performance of Computer Aided Diagnosis (CAD) for Lung Cancer using artificial neural intelligence on CT scan images. Lung Cancer can be summarized by evaluating region of interest using maximum entropy and supervised learning. Lung

Shape Driven Kernel Adaptation in Convolutional Neural Network for Robust Facial Trait Recognition
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Abstract One key challenge of facial trait recognition is the large non-rigid appearance variations due to some irrelevant real world factors, such as viewpoint and expression changes. In this paper, we explore how the shape information, ie facial landmark positions

Development of a Content Based Recommender Using Dynamic Artificial Neural Network
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Abstract: In recent years, we've been facing a boost in advancement of Artificial Intelligence (AI). It's continuously setting benchmark of our thought level of which AI is possible. Although using Artificial Neural Network (ANN) to achieve AI is somewhat less explored area

Neural network Based approach for Image skeletonization on Gurumukhi Characters
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Abstract-Skeletonization is one of the most important pre-processing steps used in many digital image processing applications. It is the process of creating unit pixel width skeletons. It plays a very crucial role in many image processing applications such as character

CHARACTER RECOGNITION USING NEURAL NETWORK
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ABSTRACT In this paper, Character recognition (CR) system based on Artificial Neural Networks (ANNs) is discussed. Neural networks are mostly used to solve sample- recognition problems. One of these is character recognition. With the help of Matlab's

Mining User Consumption Intention from Social Media Using Domain Adaptive ConvolutionalNeural Network
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Abstract Social media platforms are often used by people to express their needs and desires. Such data offer great opportunities to identify users' consumption intention from user- generated contents, so that better tailored products or services can be recommended.

GaN Power Amplifiers Design Using Genetic Neural Network Model
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Abstract:A genetic neural network based large-signal model for GaN HEMT transistor suitable for designing power amplifiers (PAs) is presented. The model has been demonstrated by designing linear power efficient amplifier. Two different approaches have

A SURVEY ON NEURAL NETWORK MODELS FOR DIAGNOSTIC PROBLEMS
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Abstract:Neural network plays an important role in Health care. It really helps to predict the disease based on collated data, Diagnosis in the medical field is a complicated task that should be performed with accuracy and efficiency. A diagnosis performed by a physician

Travel Time Prediction under Egypt Heterogeneous Traffic Conditions using Neural Networkand Data Fusion
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Abstract Cairo is experiencing traffic congestion that places it among the worst in the world. Obviously, it is difficult if not impossible to solve the transportation problem because it is multi-dimensional problem but it's good to reduce this waste of money and the associated

On Efficient Training of Word Classes and Their Application to Recurrent Neural NetworkLanguage Models
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Abstract In this paper, we investigated various word clustering methods, by studying two clustering algorithms: Brown clustering and exchange algorithm, and three objective functions derived from different class-based language models (CBLM): two-sided,

Efficient Harmful Email Identification Using Neural Network
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ABSTRACT Phishing is a form of online fraud that aims to steal a user's sensitive information such as online banking passwords or credit card numbers. In this paper, we present a technique to quickly detect suspected email using Neural Network Pruning approach. The

INVESTIGATION OF NEURAL NETWORK MODELLING FOR THE PREDICTION OF CO2 CORROSION IN THE OIL AND GAS INDUSTRY
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Carbon dioxide corrosion is responsible for a third of all corrosion related failures in the oil and gas industry. Due to the fact that there are too many environmental and metallurgical factors with complex interactions, it has been difficult to effectively predict the rate of

Integration of Speech Enhancement and Recognition using Long-Short Term Memory Recurrent Neural Network
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Abstract Long Short-Term Memory (LSTM) recurrent neural network has proven effective in modeling speech and has achieved outstanding performance in both speech enhancement (SE) and automatic speech recognition (ASR). To further improve the performance of

of a Computer-Aided Application for Analyzing ECG Signals and Detection of Cardiac Arrhythmia Using Back Propagation Neural Network-Part I: Model
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ABSTRACT Electrocardiogram (ECG) is a graphic recording of the electrical activity produced by the heart. The accuracy of any electrocardiogram waveform extraction plays a vital role in helping a better diagnosis of any heart related illnesses. We present a

Application of the Fractional Order Pidµ Controller With Neural Network In The Inverter Power Supply
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Abstract:The mathematical model of three-phase bridge inverter power supply is established. The fractional order PIDµ controller is introduced into the control system of the three-phase bridge inverter power supply. The 5 parameters of the controller are self-

Tea Leaf Diseases Recognition using Neural Network Ensemble
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ABSTRACT This paper proposes a tea leaf diseases recognizer (TLDR), an initiative to recognize diseases of the tea leaf. In TLDR, at first the image of the tea leaf is cropped, resized and converted to its threshold value in the image processing. Then feature

Paraphrastic recurrent neural network language models
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ABSTRACT Recurrent neural network language models (RNNLM) have become an increasingly popular choice for state-of-the-art speech recognition systems. Linguistic factors influencing the realization of surface word sequences, for example, expressive richness,

An effective neural network model for graph-based dependency parsing
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Abstract Most existing graph-based parsing models rely on millions of hand-crafted features, which limits their generalization ability and slows down the parsing speed. In this paper, we propose a general and effective Neural Network model for graph-based dependency

How to Avoid Unwanted Pregnancies: Domain Adaptation using Neural Network Models
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Abstract We present novel models for domain adaptation based on the neural network joint model (NNJM). Our models maximize the cross entropy by regularizing the loss function with respect to in-domain model. Domain adaptation is carried out by assigning higher weight

Early Forming a Hummingbird-like Hovering Neural Network Circuitry Pattern with Reentrant Spatiotemporal Energy-Sensory Orientation Privileged to Avoid
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Abstract: The hummingbird's significant asymmetry hovering flight with energy conservation pattern is remarkable among all vertebrates. However, little is known to human's neuronal network circuitry current flow pattern for whether or not has this privilege during slow wave

Deep convolutional neural network textual features and multiple kernel learning for utterance-level multimodal sentiment analysis
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Abstract We present a novel way of extracting features from short texts, based on the activation values of an inner layer of a deep convolutional neural network. We use the extracted features in multimodal sentiment analysis of short video clips representing one

Convolutional Neural Network for Modeling Sentences and Sentiment Analysis
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Abstract Language understanding is the central problem in natural language processing. Critical to this understanding is accurate representation of sentences. We use a novel architecture for neural networks dubbed the Dynamic Convolutional Neural Network (

Predicting the Poisson Ratio of Lightweight Concretes using Artificial Neural Network
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Lightweight aggregate concretes (LWAC) are known as very attracting concretes because of their good sound and heat insulation properties, reducing the dead load of a structure, well fire resistance and having high freezethawing resistance [1]. The use of LWAC due to

Bag-of-Words Input for Long History Representation in Neural Network-Based Language Models for Speech Recognition
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Abstract In most of previous works on neural network based language models (NNLMs), the words are represented as 1-of-N encoded feature vectors. In this paper we investigate an alternative encoding of the word history, known as bag-of-words (BOW) representation of How to answer this question We first assume that the oscillation is a limit cycle–a trajectory through state space that is repeated reliably every cycle. The phase of a limit cycle, defined over an interval of 0–1 or 0–2p, describes the current state of the oscillator. A common

Automatic Number Plate Recognition Using Artificial Neural Network
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Abstract-Automatic Number Plate Recognition (ANPR) became a very important tool in our daily life because of the unlimited increase of cars and transportation systems, which make it impossible to be fully managed and monitored by humans. Examples are so many, like

The Fixed-Size Ordinally-Forgetting Encoding Method for Neural Network Language Models
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Abstract In this paper, we propose the new fixedsize ordinally-forgetting encoding (FOFE) method, which can almost uniquely encode any variable-length sequence of words into a fixed-size representation. FOFE can model the word order in a sequence using a simple

Audio Chord Recognition with a Hybrid Recurrent Neural Network
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ABSTRACT In this paper, we present a novel architecture for audio chord estimation using a hybrid recurrent neural network. The architecture replaces hidden Markov models (HMMs) with recurrent neural network (RNN) based language models for modelling temporal

Back propagation neural network (BPNN) to detect surface crack on dates using RGB images
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Abstract:Surface crack is a type of defect which depreciates dates quality. An automated system to detect and sort cracked dates is needed in the factories in order to ensure quality. The objective of this study was to determine the efficiency of a computer vision system with

Hierarchical Recurrent Neural Network for Document Modeling
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Abstract This paper proposes a novel hierarchical recurrent neural network language model (HRNNLM) for document modeling. After establishing a RNN to capture the coherence between sentences in a document, HRNNLM integrates it as the sentence history

Neural Network-Based Health Personnel Monitoring System
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ABSTRACT Problems of the conventional health personnel management system include poor monitoring and evaluation. The system also suffers inadequate record-keeping, delayed personnel data aggregation and poor talent and file management. In this

Research on Risk Early-Warning Model in Airport Flight Area based on Information Entropy Attribute Reduction and BP Neural Network
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Abstract According to the fussy of evaluating index and the uncertainty of evaluating information in flight area, systems thinking tool of complex science management theoryexplore graph, is used to construct the fight area risk evaluation index system. And a

Discourse Relation Recognition by Comparing Various Units of Sentence Expression with Recursive Neural Network
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Abstract We propose a method for implicit discourse relation recognition using a recursive neural network (RNN). Many previous studies have used the word-pair feature to compare the meaning of two sentences for implicit discourse relation recognition. Our proposed

Neural network modeling and optimization of process parameters for production of chhana cake using genetic algorithm.
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Abstract Chhana cake, locally termed chhana podo, is a baked traditional dairy product of India. The present study was undertaken for optimization of process parameters pertaining to production of chhana podo. Independent variables, namely, moisture content of feed-

A deep neural network based approach to Mandarin consonant/vowel separation
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Abstract:In this paper, we study the problem of Mandarin consonant/vowel separation which is an integral part of many Mandarin speech applications. We propose a deep neural network (DNN) based approach and compare its performance with the support vector

Neural network modeling and geochemical water analyses to understand and forecast karst and non-karst part of flash floods (case study on the Lez river,
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Abstract. Flash floods forecasting in the Mediterranean area is a major economic and societal issue. Specifically, considering karst basins, heterogeneous structure and nonlinear behaviour make the flash flood forecasting very difficult. In this context, this work proposes

Traffic Accidents Forecasting using Singular Value Decomposition and an AutoregressiveNeural Network Based on PSO
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Abstract In this paper, we propose a strategy to improve the forecasting of traffic accidents in Concepción, Chile. The forecasting strategy consists of four stages: embedding, decomposition, estimation and recomposition. At the irst stage, the Hankel matrix is used

Measuring of Software Maintainability Using Adaptive Fuzzy Neural Network.
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Abstract:Software maintenance mainly refers to the process of correcting software after delivery. Maintenance process is usually a high percentage of Organizational effort to the whole process of software programs. As a result, the effectiveness of the entire production

Measuring Quality of Service of VoIP Based on Artificial Neural Network Approach
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Abstract---Voice over Internet Protocol (VoIP) is one of the fastest growing applications over Internet. The quality of service of VoIP can be measured by ITU E-model which is a subjective measure. In this paper, we discuss a novel method using Artificial Neural

Face Detection and Recognition Technology for HCI based on RBF Neural Network
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Abstract In this paper, feature extraction and facial recognition are studied in order to resolve problems like high-dimension problem, small size samples and no-linear separable problem that exist in facial recognition technology. In the part of feature extraction we use a

Deep neural network based supervised speech segregation generalizes to novel noises through large-scale training
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Abstract Deep neural network (DNN) based supervised speech segregation has been successful in improving human speech intelligibility in noise, especially when DNN is trained and tested on the same noise type. A simple and effective way for improving

Large-scale Dictionary Construction via Pivot-based Statistical Machine Translation with Significance Pruning and Neural Network Features
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Abstract We present our ongoing work on large-scale Japanese-Chinese bilingual dictionary construction via pivot-based statistical machine translation. We utilize statistical significance pruning to control noisy translation pairs that are induced by pivoting. We

Convolutional neural network for modelling sentences and its uses in sentiment analysis
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The ability to accurately represent sentences is central to language understanding. Semantic modelling is at the core of many tasks involving a degree of natural language comprehension. These tasks include sentiment analysis, paraphrase detection, entailment

Exploiting Hidden Layer Modular Redundancy for Fault-Tolerance in Neural NetworkAccelerators
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Leveraging CMOS Scaling for Improved Performance is Becoming Increasingly Hard Contributing factors making it difficult include: Fixed power budgets An eventual slowdown of Moore's Law

A Study of the Recurrent Neural Network Encoder-Decoder for Large Vocabulary Speech Recognition
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Abstract Deep neural networks have advanced the state-of-the-art in automatic speech recognition, when combined with hidden Markov models (HMMs). Recently there has been interest in using systems based on recurrent neural networks (RNNs) to perform sequence

Comparative Approach of Artificial Neural Network and ARIMA Models on Births per Month in Gaza Strip using R
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1.1 Background In recent years there has been an increase in both applied and theoretical research in time series modelling and forecasting. Forecasting future events based on the basis of historical and present data, captured people's attention and specialists

Selection of Optimum Features for Neural Network Using Genetic Algorithm in Classification of Brain Computer Interface Data
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Abstract: This paper aims to classify three mental task which includes left hand movement imagination, right hand movement imagination and word generation. The classification of three or more tasks is a crucial process. It depends upon how the features are extracted

Automatic Fire Classification and Extinguish System Using Embedded Based Neural Network
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Abstract:his paper proposed a monitoring and control system in Fire control process using microcontroller based embedded system. The monitoring process is implemented by sensing six types of gases along with temperature and determines the source of fire and

Feedforward Neural Network for Force Coding of an MRI-Compatible Tactile Sensor Array Based on Fiber Bragg Grating
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This work shows the development and characterization of a fiber optic tactile sensor based on Fiber Bragg Grating (FBG) technology. The sensor is a 3× 3 array of FBGs encapsulated in a PDMS compliant polymer. The strain experienced by each FBG is transduced into a

A Convolutional Neural Network Cascade for Face Detection
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Abstract In real-world face detection, large visual variations, such as those due to pose, expression, and lighting, demand an advanced discriminative model to accurately differentiate faces from the backgrounds. Consequently, effective models for the problem

Research on Modification and Optimization of Network Topology and Internet Security based on Neural Network Structure and Mathematical Optimization
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Abstract With the rapid and bursting development of computer science and related technology, the design and optimization of network topology has been a most important issue for thee researchers to solve. However, the increasing needs of the current control

The Forest Convolutional Network: Compositional Distributional Semantics with a Neural Chart and without Binarization
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Current re- cursive neuralnetwork (RNN) approaches for computing sentence meaning therefore run into a number of practical difficulties, including the need to carefully select a parser appropriate for the task, deciding how and to what extent syntactic context modifies the

Fault Detection and Fault Classification of Double Circuit Transmission Line Using ArtificialNeural Network
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Abstract-Fault detection and classification plays a critical role in the protection. Accurate and fast fault detection, fault classification is important from the service restoration and reliability point of view. However in case of double circuit transmission line due to mutual coupling

Hybrid chaos synchronization of 3-cells cellular neural network attractors via adaptive control method
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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

Generalized Learning of Neural Network based Semantic Similarity Models and its Application in Movie Search
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ABSTRACT Modeling text semantic similarity via neural network approaches has significantly improved performance on a set of information retrieval tasks in recent studies. However these neural network based latent semantic models are mostly trained by using

Epilepsy diagnosis using artificial neural network learned by PSO
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Abstract: Electroencephalogram (EEG) is used routinely for diagnosis of diseases occurring in the brain. It is a very useful clinical tool in the classification of epileptic seizures and the diagnosis of epilepsy. In this study, epilepsy diagnosis has been investigated using EEG

Telephone Traffic Forecasting Based on Grey Neural Network Optimized by Improved Particle Swarm Optimization Algorithm
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Abstract To solve the problem that the parameters in grey neural network (GNN) are difficult to determine, the improved Particle Swarm Optimization (IPSO) algorithm is employed to search the optimums by the introduction of a threshold of velocity. When the particle

Global chaos control of 3-cells cellular neural network attractor via integral sliding mode control
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Abstract: In this research work, we first discuss the properties of the 3-cells 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 computer science, machine

Gear Remaining Useful Life Prediction Based on Grey Neural Network
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Abstract: The condition monitoring data of gears is asymmetric distributed, moreover, sampling is usually conducted discontinuously in practice. Thus makes it difficult to predict gear remaining useful life accurately considering the two reasons above. In this paper, a

Silent speech recognition from articulatory movements using deep neural network
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ABSTRACT Laryngectomee patients lose their ability to produce speech sounds and suffer in their daily communication. There are currently limited communication options for these patients. Silent speech interfaces (SSIs), which recognize speech from articulatory

Global chaos 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

Evolutionary Neural Network Modeling for Energy Prediction of Cloud Data Centers
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Traditionally, the training of neural networks (NNs) uses a method called backpropagation, which is based on gradient guidance to optimize the network weights with a fixed architecture to produce the lowest modeling error. Albeit the efficiency of this approach,

An Analysis of Effect of Proportionality among Hidden Layers in Standard Back PropagationNeural Network.
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Abstract--In this paper, an analysis is performed on the importance of proportionality among multiple hidden layers of BPNN. In case of any discrepancies in the network, maximum two layers are enough to train the whole neural network to get the desired result. But in some

Use of an Artificial Neural Network to Construct a Model of Predicting Deep Fungal Infection in Lung Cancer Patients.
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Abstract Background: The statistical methods to analyze and predict the related dangerous factors of deep fungal infection in lung cancer patients were several, such as logic regression analysis, meta-analysis, multivariate Cox proportional hazards model analysis,

Optimizing Number of Hidden Nodes for Artificial Neural Network using Competitive Learning Approach
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Abstract Artificial Neural Network is appropriate and most effective for pattern recognition, signal processing, and classification problems. For getting the proper result, ANN requires accurate data preprocessing, architecture selection, network training. The major problem

Rotating Your Face Using Multi-task Deep Neural Network
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Abstract Face recognition under viewpoint and illumination changes is a difficult problem, so many researchers have tried to solve this problem by producing the pose-and illumination- invariant feature. Zhu et al.[26] changed all arbitrary pose and illumination images to the

Application of Artificial Neural Network for Groundwater Level Simulation in Amritsar and Gurdaspur Districts of Punjab, India
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Abstract In this paper, the most stable and efficient neural network configuration for predicting groundwater level in Amritsar and Gurdaspur districts of Punjab, India is identified. For predicting the model efficiency and accuracy, different types of network

Large-scale atmospheric forcing and topographic modification of precipitation rates over High Asia-a neural-network-based approach
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Abstract. The heterogeneity of precipitation rates in high-mountain regions is not sufficiently captured by stateof-the-art climate reanalysis products due to their limited spatial resolution. Thus there exists a large gap between the available data sets and the demands of climate

Use of Artificial Neural Network in Predicting Permeability of Dispersive Clay Treated With Lime and Pozzolan
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Abstract. The treatment of a dispersive core soil can be achieved by mixing with lime and pozzolan, separately or simultaneously. On a dispersive soil treated with lime and pozzolan, experimental measurements of permeability were carried out with varying curing times

Navigation Model for a Lego Robot Using a Backpropagation Neural Network
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Abstract: The paper introduces a novel approach for the autonomous navigation to an unmanned vehicle based on neural networks. A robot from the Lego family is used to represent the vehicle used with the proposal. The main objective of this robot is to

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

Neural Network Control-Based Drive Design of Servomotor and Its Application to Automatic Guided Vehicle
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An automatic guided vehicle (AGV) is extensively used for productions in a flexible manufacture system with high efficiency and high flexibility. A servomotor-based AGV is designed and implemented in this paper. In order to steer the AGV to go along a

of Process Parameters for Turning Operation on CNC Lathe for ASTM A242 Type-2 Alloy Steel by Artificial Neural Network and Regression Analysis–A Review
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Abstract The purpose of this project is focused on the modelling of cutting conditions to get lowest surface roughness in turning ASTM A242 TYPE-2 ALLOYS STEEL by Artificial neural network and Regression Analysis method on the CNC lathe. The process of metal Cutting

Predicting Student Performance Using Artificial Neural Network: in the Faculty of Engineering and Information Technology
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Abstract In this paper an Artificial Neural Network (ANN) model, for predicting the performance of a sophomore student enrolled in engineering majors in the Faculty of Engineering and Information Technology in Al-Azhar University of Gaza was developed

Zero-watermarking Algorithm for Medical Volume Data Based on Legendre Chaotic Neural Network and Perceptual Hashing
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Abstract Medical information digitization makes the medical information storage and extraction more convenient. Medical image information security and also gradually being taken seriously, and some medical image watermarking has been

Unidirectional long short-term memory recurrent neural network with recurrent output layer for low-latency speech synthesis
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ABSTRACT Long short-term memory recurrent neural networks (LSTM-RNNs) have been applied to various speech applications including acoustic modeling for statistical parametric speech synthesis. One of the concerns for applying them to text-to-speech applications is

NEURAL NETWORK MODELING OF GRINDING PARAMETERS OF DUCTILE CAST IRON USING MINIMUM QUANTITY LUBRICATION.
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ABSTRACT This paper presents the optimization of the grinding parameters of ductile cast iron in wet conditions and with the minimum quantity lubrication (MQL) technique. The objective of this project is to investigate the performance of ductile cast iron during the

BP Neural Network Model-based Urbanization Process Traditional Sports Cultural Development Exploration
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Abstract: World economy is in the trend of globalization while Chinese urbanization construction has also constantly developed. Nation has paid more and more attention to spiritual cultural construction and sports cultural construction. Nowadays, people sports

Performance Evaluation of Generalized Regression Neural Network Path loss Prediction Model in Macrocellular Environment
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Abstract:A path loss prediction model for macrocellular environment is presented. The model consists of generalized regression neural network (GRNN). Compared with some conventional path loss prediction models like the Free Space, Hata and Egli models, the

Rainfall Runoff Modeling by Artificial Neural Network-A Case Study of Chotki Bharghi Watershed in Damodar Barakar Basin, Jharkhand
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Abstract Simulation of hydrological processes at the watershed outlet through modelling approach is essential for proper planning and implementation of appropriate soil conservation measures in Damodar Barakar catchment, Hazaribagh, India where soil

A fuzzy neural network for E. coli metabolism
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Abstract. In this paper we discuss the relevance of logic gates to capture the continuous nature of the variables involved in modeling the metabolic processes of E. coli bacteria. Tamsir et al. suggested in [1] the possibility of creating logic circuits using colonies of E.

Incremental Training of Neural Network with Knowledge Distillation
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Abstract Training a neural network on a huge dataset is challenging because it requires massive computational resources as well as long period of training and test time. On the other hand, simply training a neural network multiple times using different partitions of the

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

Predicting the Water Level Fluctuation in an Alpine Lake Using Physically Based, ArtificialNeural Network, and Time Series Forecasting Models
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Accurate prediction of water level fluctuation is important in lake management due to its significant impacts in various aspects. This study utilizes four model approaches to predict water levels in the Yuan-Yang Lake (YYL) in Taiwan: a three-dimensional hydrodynamic

OPTIMAL FEATURE SELECTION BY GENETIC ALGORITHM FOR CLASSIFICATION USINGNEURAL NETWORK
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Abstract-The present study considers three mental tasks which includes left hand movement imagination, right hand movement imagination and generation of words beginning with the same random letter. These tasks are classified by using neural network (NN). For good

Study of Ship Heading Control using RBF Neural Network
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Abstract Along with the development of shipping business, ships are becoming bigger, faster and more intelligent. Thus better performance of maneuver is demanded. To research for better control strategies, it is necessary to adopt new control theories and techniques. The

A Neural Network based Approach to Predict Machine Status for Big Data using R
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Abstract Big data studies recognize some of the major issues growing up in current market, like engaging new customers is quite difficult and not affordable than having legacy of old customers. Machine status (eg ON/OFF) prediction model to effectively manage the

Prediction of CNC Machine Tool Cutting Energy Consumption by BP Neural Network
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Abstract: The international machine tool producer is toward green production. The high- efficiency, low pollution, low energy consumption, energy recovery and reuse of resources are developed and machine tools are expected to be included in the energy consumption

Convolutional Neural Network for Computer Vision and Natural Language Processing
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Abstract Machine learning techniques are widely used in the domain of Natural Language Processing (NLP) and Computer Vision (CV), In order to capture complex and non-linear features deeper machine learning architectures become more and more popular. A lot of

Adaptive mutation particle swarm optimized BP neural network in state-of-charge estimation of Li-ion battery for electric vehicles
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The state of charge (SOC) of Li-ion battery on electric vehicle (EV) is highly nonlinear. The randomly selected initial parameters of BP neural network can cause significant inaccuracy and long training time. In the study presented in this paper, an optimized BP neural

Deep Neural Network Applications on Automatic Speech Recognition
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Abstract: This essay reviews the histories of automatic speech recognition and deep neural networks, introduces deep neural network applications in the three main areas of automatic speech recognition, analyzes current trends in automatic speech recognition and



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