Big Data Deep Learning: Challenges and Perspectives

Chen, Xue-Wen; Lin, Xiaotong

doi:10.1109/access.2014.2325029

Cited by 1,022 publications

(234 citation statements)

References 76 publications

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“…Using the angiographic images a three-dimensional triangulated surface is obtained using an automatic segmentation method based on geodesic active regions in combination with an image standardization technique (Hernandez and Frangi 2007). Employing this model, different features are extracted.…”

Section: Feature Extraction Methods Employed With Aneurysm Datamentioning

confidence: 99%

“…Currently Deep Learning Techniques have attracted the attention of the scientific comunity (Chen and Lin 2014). In this project we are going to explore both classical and novel machine learning techniques as a data scientist to find the best suitable techniques in a data mining process.…”

Section: Machine Learning and Feature Extractionmentioning

confidence: 99%

“…Several machine learning techniques have been used in medicine in the past to diagnose patients (Hu et al 2013), to provide support to the radiologist in the detection of aneurysms (Suniaga et al 2012) …”

Section: Machine Learning and Feature Extractionmentioning

confidence: 99%

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Applying machine learning and image feature extraction techniques to the problem of cerebral aneurysm rupture

Chabert¹,

Mardones²,

Riveros³

et al. 2017

RIO

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Cerebral aneurysm is a cerebrovascular disorder characterized by a bulging in a weak area in the wall of an artery that supplies blood to the brain. It is relevant to understand the mechanisms leading to the apparition of aneurysms, their growth and, more important, leading to their rupture. The purpose of this study is to study the impact on aneurysm rupture of the combination of different parameters, instead of focusing on only one factor at a time as is frequently found in the literature, using machine learning and feature extraction techniques. This discussion takes relevance in the context of the complex decision that the physicians have to take to decide which therapy to apply, as each intervention bares its own risks, and implies to use a complex ensemble of resources (human resources, OR, etc.) in hospitals always under very high work load.This project has been raised in our actual working team, composed of interventional neuroradiologist, radiologic technologist, informatics engineers and biomedical engineers, "interdisciplinary platform for innovation in health", as part of a bigger project leaded by Universidad de Valparaiso (PMI UVA1402). It is relevant to emphasize that this project is made feasible by the existence of this network between physicians and engineers, and by the existence of data already registered in an orderly manner, structured and recorded in digital format.The present proposal arises from the description in nowadays literature that the actual indicators, whether based on morphological description of the aneurysm, or based on characterization of biomechanical factor or others, these indicators were shown not to provide sufficient information in order to predict by themselves the risk of rupture. Therefore, our hypothesis is that the risk of rupture lies on the combination of multiple actors. These actors together would play different roles that could be: weakening of the artery wall, increasing biomechanical stresses on the wall induced by blood flow, in addition to personal sensitivity due to family history, or personal history of comorbidity, or even seasonal variations that could gate different inflammation mechanisms.The main goal of this project is to identify relevant variables that may help in the process of predicting the risk of intracranial aneurysm rupture using machine learning and image processing techniques based on structured and non-structured data from multiple sources. We believe that the identification and the combined use of relevant variables extracted from clinical, demographical, environmental and medical imaging data sources will improve the estimation of the aneurysm rupture risk, with respect to the actual practiced method based essentially on the aneurysm size.The methodology of this work consist of four phases: (1) Data collection and storage, (2) feature extraction from multiple sources in particular from angiographic images, (3) development of the model that could describe the risk of aneurysm rupture based on the fusion and combination of the ...

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Section: Feature Extraction Methods Employed With Aneurysm Datamentioning

confidence: 99%

Section: Machine Learning and Feature Extractionmentioning

confidence: 99%

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Applying machine learning and image feature extraction techniques to the problem of cerebral aneurysm rupture

Chabert¹,

Mardones²,

Riveros³

et al. 2017

RIO

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“…To address this issue, an effective training algorithm, which learns one layer at a time and each pair of layers is seen as one RBM model, is proposed and introduced in Refs. [41,42]. As DBN is formed by units of RBM, the basic unit of DBN, i.e., RBM, is introduced first.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

A Deep Learning Approach for Fault Diagnosis of Induction Motors in Manufacturing

Shao

Sun

Yan

et al. 2017

Chin. J. Mech. Eng.

134

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Extracting features from original signals is a key procedure for traditional fault diagnosis of induction motors, as it directly influences the performance of fault recognition. However, high quality features need expert knowledge and human intervention. In this paper, a deep learning approach based on deep belief networks (DBN) is developed to learn features from frequency distribution of vibration signals with the purpose of characterizing working status of induction motors. It combines feature extraction procedure with classification task together to achieve automated and intelligent fault diagnosis. The DBN model is built by stacking multiple-units of restricted Boltzmann machine (RBM), and is trained using layer-bylayer pre-training algorithm. Compared with traditional diagnostic approaches where feature extraction is needed, the presented approach has the ability of learning hierarchical representations, which are suitable for fault classification, directly from frequency distribution of the measurement data. The structure of the DBN model is investigated as the scale and depth of the DBN architecture directly affect its classification performance. Experimental study conducted on a machine fault simulator verifies the effectiveness of the deep learning approach for fault diagnosis of induction motors. This research proposes an intelligent diagnosis method for induction motor which utilizes deep learning model to automatically learn features from sensor data and realize working status recognition.

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“…The current trend for improving AI to tackle increasingly complex problems is therefore a brute force solution to scale up the infrastructure: more data, more computing power, more neurons in deep learning algorithms, see for instance [2]. Tuning the very large number of latent parameters controlling the resulting deep architectures is a di cult optimisation problem where over-tting (or learning the noise) is one of the major issues.…”

Section: Introductionmentioning

confidence: 99%

Bonseyes

Llewellynn¹,

Fernández-Carrobles

Deniz

et al. 2017

Proceedings of the Computing Frontiers Conference

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The Bonseyes EU H2020 collaborative project aims to develop a platform consisting of a Data Marketplace, a Deep Learning Toolbox, and Developer Reference Platforms for organizations wanting to adopt Arti cial Intelligence. The project will be focused on using arti cial intelligence in low power Internet of Things (IoT) devices ("edge computing"), embedded computing systems, and data center servers ("cloud computing"). It will bring about orders of magnitude improvements in e ciency, performance, reliability, security, and productivity in the design and programming of systems of arti cial intelligence that incorporate Smart Cyber-Physical Systems (CPS). In addition, it will solve a causality problem for organizations who lack access to Data and Models. Its open software architecture will facilitate adoption of the whole concept on a wider scale. To evaluate the e ectiveness, technical feasibility, and to quantify the real-world improvements in e ciency, security, performance, e ort and cost of adding AI to products and services using the Bonseyes platform, Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for pro t or commercial advantage and that copies bear this notice and the full citation on the rst page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).

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Big Data Deep Learning: Challenges and Perspectives

Cited by 1,022 publications

References 76 publications

Applying machine learning and image feature extraction techniques to the problem of cerebral aneurysm rupture

Applying machine learning and image feature extraction techniques to the problem of cerebral aneurysm rupture

A Deep Learning Approach for Fault Diagnosis of Induction Motors in Manufacturing

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