Probabilistic Load Forecasting using an Improved Wavelet Neural Network Trained by Generalized Extreme Learning Machine
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ABSTRACT Competitive transactions resulting from recent restructuring of the electricity market, have made achieving a precise and reliable load forecasting, especially probabilistic load forecasting, an important topic. Hence, this paper presents a novel hybrid

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ABSTRACT In this paper we describe an extension of the Kaldi software toolkit to support neuralbased language modeling, intended for use in automatic speech recognition (ASR) and related tasks. We combine the use of subword features (letter ngrams) and one-hot

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Current rates, extents and intensities of land-use and land-cover change (LULCC) are driving important changes in ecosystems and environmental processes at local, regional and global scales. These changes encompass some of the greatest environmental concerns

Improving Heikin-Ashi Transformation Data Learning in Neural Network Using Volume Weight in Stock Market Data
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ABSTRACT This paper provides a further analysis and improvement of Heikin Ashi Transformation for Neural Network Learning. It has been demonstrated that Heikin Ashi Transformation can improve the learning effect of Neural Network . This paper introduces

Automatic Classification of Periodic Heart Sounds Using Convolutional Neural Network
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ABSTRACT This paper presents an automatic normal and abnormal heart sound classification model developed based on deep learning algorithm. MITHSDB heart sounds datasets obtained from the 2016 PhysioNet/Computing in Cardiology Challenge database were used

Neural Network Quantum States
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A Lecture for the Machine Learning and Many-Body Physics workshop 1Institute for Theoretical Physics, ETH Zurich, Wolfgang-Pauli-Str. 27, 8093 Zurich, Switzerland Every machine learning approach has two fundamental ingredients 1. The machine: typically an artificial

SECLAF: A Webserver and Deep Neural Network Design Tool for Hierarchical Biological Sequence Classification.
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ABSTRACT Summary: Artificial intelligence tools are gaining more and more ground each year in bioinformatics. Learning algorithms can be taught for specific tasks by using the existing enormous biological databases, and the resulting models can be used for the high-quality ABSTRACT Statistical language models (LMs) have been applied in several software engineering applications. However, they have issues in dealing with ambiguities in the names of program and API elements (classes and method calls). In this paper, inspired by

Groundwater Level Prediction Using Artificial Neural Network Model
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Groundwater is a highly valuable resource. Measurement and analysis of groundwater level is needed for maintaining groundwater availability. The accurate prediction of groundwater levels is essential for sustainable utilization and management of vital groundwater

Distinct contributions of functional and deep neural network features to representational similarity of scenes in human brain and behavior
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ABSTRACT Inherent correlations between visual and semantic features in real-world scenes make it difficult to determine how different scene properties contribute to neural representations. Here, we assessed the contributions of multiple properties to scene ABSTRACT This paper proposes a novel artificial neural network based method for real-time gait analysis with minimal number of Inertial Measurement Units (IMUs). Accurate lower limb attitude estimation has great potential for clinical gait diagnosis for orthopaedic patients and

Modeling minimum miscibility pressure during pure and impure CO2 flooding using hybrid of radial basis function neural network and evolutionary techniques
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ABSTRACT Minimum miscibility pressure (MMP) is the most significant parameter monitoring the efficiency of CO2 flooding by establishing the miscibility condition in the oil reservoirs resulting in a higher ultimate oil recovery factor. To date, considerable

Memory Architectures in Recurrent Neural Network Language Models
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ABSTRACT We compare and analyze sequential, random access, and stack memory architectures for recurrent neural network language models. Our experiments on the Penn Treebank and Wikitext-2 datasets show that stack-based memory architectures consistently

Simultaneous Object Classification and Viewpoint Estimation using Deep Multi-task Convolutional Neural Network
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ABSTRACT : Convolutional Neural Networks (CNNs) have shown an impressive performance in many computer vision tasks. Most of the CNN architectures were proposed to solve a single task. This paper proposes a CNN model to tackle the problem of object classification and

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ABSTRACT Recent studies show that recurrent neural network provided promising results for character recognition. We have extracted number of features using sliding window approach from normalized Urdu Nastaliq text line image. The text line is scanned from right

An Adaptive Controller with An Orthogonal Neural Network and A Third Order Sliding Mode Observer for Robot Manipulators
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ABSTRACT In this paper, An adaptive controller with an orthogonal neural network (ONN) and a third order sliding mode (TOSM) observer for robot manipulators is proposed. Firstly, the TOSM observer is designed to observe joint velocities. Then, the ONN is designed to

On using deep Convolutional Neural Network architectures for automated object detection and classification within X-ray baggage security imagery.
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ABSTRACT We consider the use of deep Convolutional Neural Networks (CNN) with transfer learning for the image classification and detection problems posed within the context of X- ray baggage security imagery. The use of the CNN approach requires large amounts of data

Three Structures of a Multilayer Artificial Neural Network for Predicting the Solar Radiation of Baghdad City-Iraq
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ABSTRACT In this study, a multilayer neural network with three structures (4-4-4-1),(4-8-8-1) and (4-9-9-1) is investigated to predict the average daily solar radiation in the capital city of Iraq. A MATLAB algorithm is used to implement these structures with totally dataset of 2604

Finite Element Crushing Analysis, Neural Network Modelling and Multi-Objective Optimization of the Honeycomb Energy Absorbers.
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ABSTRACT : The thin-walled honeycomb structures are one of the most common energy absorber types. These structures are of particular use in different industries due to their high energy absorption capability. In this article, the finite element simulation of honeycomb

The Emergence of Polychronization and Feature Binding in a Spiking Neural Network Model of the Primate Ventral Visual System
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ABSTRACT We present a hierarchical neural network model, in which subpopulations of neurons develop fixed and regularly repeating temporal chains of spikes (polychronization), which respond specifically to randomised Poisson spike trains representing the input

Inferring Emotion from Conversational Voice Data: A Semi-supervised Multi-path Generative Neural Network Approach
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ABSTRACT To give a more humanized response in Voice Dialogue Applications (VDAs), inferring emotion states from users queries may play an important role. However, in VDAs, we have tremendous amount of VDA users and massive scale of unlabeled data with high

Application of Artificial Neural Network (ANN) for the Extraction of a Non-Steroidal Anti-Inflammatory Drug through Emulsion Liquid Membrane
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ABSTRACT In the present work, a non-steroidal anti-inflammatory drug (NSAID), Diclofenac (DCF) is removed with the help of emulsion liquid membrane and an artificial neural network is applied to predict the percentage extraction of Diclofenac at different levels of various ABSTRACT This paper presents a new learning algorithm developed for a three layered spiking neural network for pattern classification problems. The learning algorithm maximizes the interclass margin and is referred to as the two stage margin maximization spiking neural

Semantic Video Segmentation with Using Ensemble of Particular Classifiers and a Deep Neural Network for Systems of Detecting Abnormal Situations
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ABSTRACT A new approach based on the use of a deep neural network and an ensemble of particular classifiers is proposed. This approach is based on use of the novel block of fuzzy generalization for combines classes of objects into semantic groups, each of which

Deep Neural Network for Human Face Recognition
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ABSTRACT Face recognition (FR), the process of identifying people through facial images, has numerous practical applications in the area of biometrics, information security, access control, law enforcement, smart cards and surveillance system. Convolutional Neural

Artificial Neural Network (ANN) based novel Performance evaluation technique
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ABSTRACT : In this study we have highlighted different performance evaluation techniques in order to carry out adequate performance based appraisal of employees working in different organisations. The paper briefly describes and compares both traditional as well as several

Development and Analysis of Artificial Neural Network Models for Rainfall Prediction by Using Time-Series Data
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ABSTRACT Time Series data is large in volume, highly dimensional and continuous updating. Time series data analysis for forecasting, is one of the most important aspects of the practical usage. Accurate rainfall forecasting with the help of time series data analysis will help in

Evaluation of Industrial Based Object Detection Method Using Artificial Neural Network
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Revised: 19/February/2017, Accepted: 23/February/2017, Published: 27/February/2017 ABSTRACT :-The essence of the study is to analyse an algorithm which will provide a robust and computationally light method, which might be suitable to implement in the real-time industrial

Detecting Hate Speech on Twitter Using a Convolution-GRU Based Deep Neural Network
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ABSTRACT . In recent years, the increasing propagation of hate speech on social media and the urgent need for effective counter-measures have drawn significant investment from governments, companies, and empirical research. Despite a large number of emerging

Estimation of Functions Representing Data Using Convolution Neural Network
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ABSTRACT Symbolic regression can be useful for finding suitable formula, with reasonable form and behavior, from a given data for the already known physical systems. However, it can also be time consuming with inaccurate estimation results when dealing with

An Improved Elman Neural Network Classifier for classification of Medical Data for Diagnosis of Diabetes
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ABSTRACT : The computer aided medical diagnosis became popular in all the important areas of medical sciences and studies. The classification plays a vital role in medical diagnosis and became an important tool for diagnosis and treatment planning of various diseases. The

Predicting User Reported Drug Side Effects Using a Gated Neural Network
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ABSTRACT The detection of Adverse Drug Events (ADE) or side effects of different drugs are necessary to minimize potential health risks of patients. Given the prevalence of user reported contents on Twitter and various health forums, recent research has focused on the

Evaluation of Feature Extraction Techniques using Neural Network as a Classifier: A Comparative Review for face Recognition
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ABSTRACT Face recognition has a wide range of possible applications from person identification and surveillance to electronic marketing and advertising for selected customers. In facial recognition, there are different steps such as preprocessing, feature

A Multiclass Deep Convolutional Neural Network Classifier for Detection of Common Rice Plant Anomalies
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ABSTRACT This study examines the use of deep convolutional neural network in the classification of rice plants according to health status based on images of its leaves. A three- class classifier was implemented representing normal, unhealthy, and snailinfested plants

Recognition of Future Air Quality Index Using Artificial Neural Network
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ABSTRACT -Due to a major increase in pollution day by day so it is required to predict pollution of the next date, next months, next year. Using some previous air related data. Air pollution is rapidly increasing due to various human activities, and it is the introduction into the

Panchromatic and Multispectral Remote Sensing Image Fusion Using Particle Swarm Optimization of Convolutional Neural Network for Effective Comparison of
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ABSTRACT . With the advance of remote sensing in observation engineering, high multispectral and spatial resolution of remote sensing imagery such as Landsat Thematic Mapper, Spot, Ikonos, Worldview, Seastar and Geoeye metaphors were attained by distinct types of

Recent improvements to neural network based acoustic modeling in the eml real-time transcription platform
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ABSTRACT : In this paper, we report some recent improvements to DNN/HMM hybrid acoustic modeling for the EML real-time large vocabulary speech recognition system, including the introduction of speaker adaptive long short term memory units (LSTMs) and efficient online

Convolutional Neural Network with a DAG Architecture for Control of a Robotic Arm by Means of Hand Gestures
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ABSTRACT This paper presents the implementation of a simulation of a robotic arm whose task is to collect different objects in a virtual environment. To develop this task, the control of the robotic arm is done through 10 different hand gestures, which are recognized by a CNN with

A Survey on Road Traffic Sign Recognition System using Convolution Neural Network
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ABSTRACT Road Traffic accidents is one of the major reason for deaths taking place in India. These accidents not only result into serious injuries but may also lead to deaths. Image recognition technology is one of the widely used techniques used in various fields in ABSTRACT Continuous respiratory monitoring is an important tool for clinical monitoring. The most widely used flow measure device is nasal cannulae connected to a pressure transducer. However, most of these devices are not easy to carry and continue working in