deep learning approach projects


Unsupervised and transfer learning challenge: a deep learning approach
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Learning good representations from a large set of unlabeled data is a particularly challenging task. Recent work (see Bengio (2009) for a review) shows that training deep architectures is a good way to extract such representations, by extracting and disentangling

A deep learning approach to universal skin disease classification
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Skin diseases are very common in peoples daily life. Each year, millions of people in American are affected by all kinds of skin disorders. Diagnosis of skin diseases sometimes requires a high-level of expertise due to the variety of their visual aspects. As human

Accurate and robust face recognition from RGB-D images with a deep learning approach .
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Face recognition from RGB-D images utilizes 2 complementary types of image data, ie colour and depth images, to achieve more accurate recognition. In this paper, we propose a face recognition system based on deep learning , which can be used to verify and identify a

A Hybrid Gaussian-HMM- Deep Learning Approach for Automatic Chord Estimation with Very Large Vocabulary.
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We propose a hybrid Gaussian-HMM- Deep – Learning approach for automatic chord estimation with very large chord vocabulary. The Gaussian-HMM part is similar to Chordino, which is used as a segmentation engine to divide input audio into note spectrogram

A deep learning approach to detect SNP interactions.
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The susceptibility of complex diseases are characterised by numerous genetic, lifestyle, and environmental causes individually or due to their interaction effects. The recent explosion in detecting genetic interacting factors is increasingly revealing the underlying biological

A Deep Learning Approach to Persian Plagiarism Detection.
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Plagiarism detection is defined as automatic identification of reused text materials. General availability of the internet and easy access to textual information enhances the need for automated plagiarism detection. In this regard, different algorithms have been proposed to

A deep learning approach to unsupervised ensemble learning
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We show how deep learning methods can be applied in the context of crowdsourcing and unsupervised ensemble learning . First, we prove that the popular model of Dawid and Skene, which assumes that all classifiers are conditionally independent, is equivalent to a

Hybrid Acoustic-Lexical Deep Learning Approach for Deception Detection.
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Automatic deception detection is an important problem with far-reaching implications for many disciplines. We present a series of experiments aimed at automatically detecting deception from speech. We use the Columbia X-Cultural Deception (CXD) Corpus, a large

Deep Language: a comprehensive deep learning approach to end-to-end language recognition.
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This work explores the use of various Deep Neural Network (DNN) architectures for an end- to-end language identification (LID) task. The approach has been proven to significantly improve the state-of-art in many domains include speech recognition, computer vision and

Multi-modal Deep Learning Approach for Flood Detection.
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In this paper we propose a multi-modal deep learning approach to detect floods in social media posts. Social media posts normally contain some metadata and/or visual information, therefore in order to detect the floods we use this information. The model is based on a

A deep learning approach for subject independent emotion recognition from facial expressions
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This paper proposes Deep Learning (DL) models for emotion recognition from facial expressions. We have focused on two deep neural models: Convolutional Neural Networks (CNN) and Deep Belief Networks (DBN). For each of these DL neural models, we

A deep learning approach to rhythm modeling with applications
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This paper presents a deep – learning architecture which is capable of modelling signatures that represent the rhythm of music recordings. The proposed architecture consists of a stack of Restricted Boltzmann Machines on top of which lies an associative memory. In the current

Financial time series forecasting a deep learning approach
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This paper is intended as a follow up to a previous study of ours-Financial Time Series Forecasting-A Machine Learning Approach . The aforementioned study evaluates traditional machine learning techniques for the task of financial time series forecasting. In this paper

A deep learning approach for tumor tissue image classification
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The performance of automatic tumor tissue classification using traditional machine learning methods greatly depends on the choice of meaningful descriptive features derived from the tissue images. Identification of such features requires domain-specific expert knowledge and

Open-Domain Audio-Visual Speech Recognition: A Deep Learning Approach .
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Automatic speech recognition (ASR) on video data naturally has access to two modalities: audio and video. In previous work, audio-visual ASR, which leverages visual features to help ASR, has been explored on restricted domains of videos. This paper aims to extend this

SATELLITE IMAGE CLASSIFICATION OF BUILDING DAMAGES USING AIRBORNE AND SATELLITE IMAGE SAMPLES IN A DEEP LEARNING APPROACH .
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The localization and detailed assessment of damaged buildings after a disastrous event is of utmost importance to guide response operations, recovery tasks or for insurance purposes. Several remote sensing platforms and sensors are currently used for the manual detection of

New York University 2016 System for KBP Event Nugget: A Deep Learning Approach .
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This is the first time New York University (NYU) participates in the event nugget (EN) evaluation of the Text Analysis Conference (TAC). We developed EN systems for both subtasks of event nugget, ie, EN Task 1: Event Nugget Detection and EN Task 2: Event

Attack classification analysis of IoT network via deep learning approach
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A variety of attacks in the transportation layer of IoT network seeks for a detection and prevention mechanism such as intrusion detection systems (IDSs). Anomaly detection is one of the most demanding task in IDSs. It requires a robust classifier model which is able to

A deep learning approach to modeling empathy in addiction counseling
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Motivational interviewing is a goal-oriented psychotherapy, employed in cases such as addiction, that aims to help clients explore and resolve their ambivalence about their problem. In motivational interviewing, it is desirable for the counselor to communicate

Diabetes detection using deep learning approach
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Deep learning is branch of Artificial Intelligence. It has same architecture as neural network but has extra hidden layers. Deep learning had shown more data processing power or capability than the shallow architecture. It had shown more accuracy in results. The Deep


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