Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs

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Advanced Digital Signal Processing And Noise Reduction

Applications of deep learning in fundus images: A review.

Biometrics is the branch of science that deals with the identification and verification of an individual based on the physiological and behavioral traits. These traits or identifiers are permanent, unique and can separate one individual from another. Biometric recognition systems integrate complex definitional, technological and operational selection under various contexts. The systems are not going to replace the authentication tools and technologies, but the combination of biometric approaches and authentication methods to help in improving the security aspects of the applications where user cooperation can be inferred. Biometric based recognition methods and tools have become popular for the development of many useful, challenging and widely accepted applications such as security issues, surveillance, forensic investigations, fraudulent technologies, identity access management and access control. These systems also help to identify an individual in group of industrial networks, home/office building and control system. For the successful implementation of the biometric systems, deep artificial neural networks are in great demand. These systems can be built up either on the single modality or multiple modalities. This article explicates the comprehensive and deep survey that compactly and systematically summarizes the literature work done on unimodal and multimodal biometric systems and analyzes the feature extraction techniques, classifiers, datasets, results, efficiency and reliability of the system with high and multi-dimensional perspectives. This article also justifies in detail the classical methods, influential methods and taxonomy based on the biometric attributes. The goal is to aware the researchers of this area regarding various dimensions for the development of biometric systems to enhance the security aspects. The article begins with the fundamentals, types, need of system, challenges, uncertainties, motivations and then to the survey work. The tabular representation prepares for each biometric trait shows the author, year, major findings and results achieved with the synthesis analysis and the evaluation. The article finally ends up with the 3D biometric, a future perspective and concluding remarks.

A comprehensive survey on the biometric recognition systems based on physiological and behavioral modalities

Deep learning approach to detect seizure using reconstructed phase space images

Speech recorded from a throat microphone is robust to the surrounding noise, but sounds unnatural unlike the speech recorded from a close-speaking microphone. This paper addresses the issue of improving the perceptual quality of the throat microphone speech by mapping the speech spectra from the throat microphone to the close-speaking microphone. A neural network model is used to capture the speaker-dependent functional relationship between the feature vectors (cepstral coefficients) of the two speech signals. A method is proposed to ensure the stability of the all-pole synthesis filter. Objective evaluations indicate the effectiveness of the proposed mapping scheme. The advantage of this method is that the model gives a smooth estimate of the spectra of the close-speaking microphone speech. No distortions are perceived in the reconstructed speech. This mapping technique is also used for bandwidth extension of telephone speech.

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Mapping speech spectra from throat microphone to close-speaking microphone: a neural network approach

Glaucoma is one of the leading causes of visual impairment in the world. It deteriorates the optic nerve fibers over time, and cannot be cured once it reaches the later stages. Hence early detection is of utmost importance for the aging society. In this paper, we propose a novel deep learning multi-model network termed G-EyeNet for glaucoma detection from retinal fundus images. G-EyeNet consists of a deep convolutional autoencoder and a traditional convolutional neural network (CNN) classifier sharing the encoder framework. The multi - model network is jointly optimized for minimizing both image reconstruction error and the classification error based on a multi-task learning procedure. Extensive training experiments are performed on publicly available datasets HRF, DRISHTI-GS, RIM ONE v.3. and evaluated on the publicly available DRIONS-DB dataset. Unsupervised training of the encoder framework helps in learning a good distribution of the input, which helps in classification. This architecture is especially effective when the training dataset is small, which is usually the case in medical imaging. Experimental results show that an area under the curve (AUC) of the receiver operator characteristic (ROC) curve of 0.923 is achieved, which is better compared to the state-of-the-art deep learning algorithms.

G-Eyenet: A Convolutional Autoencoding Classifier Framework for the Detection of Glaucoma from Retinal Fundus Images

Automatic identification of a language in a noisy environment is a challenging task. Performance of a language identification system depends on the quality of the input speech signal. In the presence of high levels of background noise, speech signals recorded using a close-speaking microphone are degraded. In contrast, a throat microphone picks up high quality speech unaffected by the surrounding noise. This paper explores the possibility of using throat microphone speech signals for text-independent language identification in noisy conditions. Languages are modelled using autoassociative neural networks based on the vocal tract system features and excitation source features derived from the throat speech signal. The results of this study show that the throat microphone speech-based language identification system performs well in noisy environments.

http://ieeexplore.ieee.org/iel5/10237/32656/01529485.pdf

Language identification in noisy environments using throat microphone signals

The objective of this paper is to improve the performance of the speaker recognition system by combining speaker specific evidences present in the spectral characteristics of the standard microphone speech and the throat microphone speech. Certain vocal tract spectral features extracted from these two speech signals are distinct and could be complimentary to one another. These features could also be speech specific as well as speaker specific. These distinguishing and complimentary nature of the spectral features are due to the difference in the placement of the two microphones. Auto associative neural networks are used to model the speaker characteristics based on the system features represented by weighted linear prediction cepstral coefficients. The speaker recognition system based on Throat Microphone (TM) spectral features is comparable (though slightly less accurate) to that based on standard (or Normal) Microphone (NM) features. By combining the evidence from both the NM and TM based systems using late integration, an improvement in performance is observed from about 91% (obtained using NM features alone) to 94% (NM and TM combined). This shows the potential of combining various other speaker specific characteristics of the NM and two speech signals for further improvement in performance.

A. Shahina

Papers

Deep learning approach to detect seizure using reconstructed phase space images

Mapping speech spectra from throat microphone to close-speaking microphone: a neural network approach

G-Eyenet: A Convolutional Autoencoding Classifier Framework for the Detection of Glaucoma from Retinal Fundus Images

Language identification in noisy environments using throat microphone signals

Combining spectral features of standard and Throat Microphones for speaker identification