دانلود و نمایش مقالات مرتبط با Deep learning techniques::صفحه 2
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نتیجه جستجو - Deep learning techniques

تعداد مقالات یافته شده: 29
ردیف عنوان نوع
11 Application of deep transfer learning for automated brain abnormality classification using MR images
کاربرد یادگیری انتقال عمیق برای طبقه بندی خودکار ناهنجاری مغزی با استفاده از تصاویر MR-2019
Magnetic resonance imaging (MRI) is the most common imaging technique used to detect abnormal brain tumors. Traditionally, MRI images are analyzed manually by radiologists to detect the abnormal conditions in the brain. Manual interpretation of huge volume of images is time consuming and difficult. Hence, computer-based detection helps in accurate and fast diagnosis. In this study, we proposed an approach that uses deep transfer learning to automatically classify normal and abnormal brain MR images. Convolutional neural network (CNN) based ResNet34 model is used as a deep learning model. We have used current deep learning techniques such as data augmentation, optimal learning rate finder and fine-tuning to train the model. The proposed model achieved 5-fold classification accuracy of 100% on 613 MR images. Our developed system is ready to test on huge database and can assist the radiologists in their daily screening of MR images.
Keywords: MRI classification | Abnormal brain images | Deep transfer learning | CNN
مقاله انگلیسی
12 DeepClas4Bio: Connecting bioimaging tools with deep learning frameworks for image classification
DeepClas4Bio: اتصال ابزارهای تصویربرداری با چارچوبهای یادگیری عمیق برای طبقه بندی تصویر-2019
Background and objective: Deep learning techniques have been successfully applied to tackle several image classification problems in bioimaging. However, the models created from deep learning frameworks cannot be easily accessed from bioimaging tools such as ImageJ or Icy; this means that life scientists are not able to take advantage of the results obtained with those models from their usual tools. In this paper, we aim to facilitate the interoperability of bioimaging tools with deep learning frameworks. Methods: In this project, called DeepClas4Bio, we have developed an extensible API that provides a common access point for classification models of several deep learning frameworks. In addition, this API might be employed to compare deep learning models, and to extend the functionality of bioimaging programs by creating plugins. Results: Using the DeepClas4Bio API, we have developed a metagenerator to easily create ImageJ plugins. In addition, we have implemented a Java application that allows users to compare several deep learning models in a simple way using the DeepClas4Bio API. Moreover, we present three examples where we show how to work with different models and frameworks included in the DeepClas4Bio API using several bioimaging tools — namely, ImageJ, Icy and ImagePy. Conclusions: This project brings to the table benefits from several perspectives. Developers of deep learning models can disseminate those models using well-known tools widely employed by life-scientists. Developers of bioimaging programs can easily create plugins that use models from deep learning frameworks. Finally, users of bioimaging tools have access to powerful tools in a known environment for them.
Keywords: Deep learning | Bioimaging | Image classification | Interoperability
مقاله انگلیسی
13 Optimization under uncertainty in the era of big data and deep learning: When machine learning meets mathematical programming
بهینه سازی تحت عدم اطمینان در عصر داده های بزرگ و یادگیری عمیق: وقتی یادگیری ماشین با برنامه نویسی ریاضی ملاقات می کند-2019
This paper reviews recent advances in the field of optimization under uncertainty via a modern data lens, highlights key research challenges and promise of data-driven optimization that organically integrates machine learning and mathematical programming for decision-making under uncertainty, and identifies potential research opportunities. A brief review of classical mathematical programming techniques for hedging against uncertainty is first presented, along with their wide spectrum of applications in Process Systems Engineering. A comprehensive review and classification of the relevant publications on data- driven distributionally robust optimization, data-driven chance constrained program, data-driven robust optimization, and data-driven scenario-based optimization is then presented. This paper also identifies fertile avenues for future research that focuses on a closed-loop data-driven optimization framework, which allows the feedback from mathematical programming to machine learning, as well as scenario- based optimization leveraging the power of deep learning techniques. Perspectives on online learning- based data-driven multistage optimization with a learning-while-optimizing scheme are presented.
Keywords: Data-driven optimization | Decision making under uncertainty | Big data | Machine learning | Deep learning
مقاله انگلیسی
14 DeepLink: Recovering issue-commit links based on deep learning
DeepLink: بازیابی پیوندهای issue-commit براساس یادگیری عمیق-2019
The links between issues in an issue-tracking system and commits resolving the issues in a version con- trol system are important for a variety of software engineering tasks (e.g., bug prediction, bug localization and feature location). However, only a small portion of such links are established by manually including issue identifiers in commit logs, leaving a large portion of them lost in the evolution history. To recover issue-commit links, heuristic-based and learning-based techniques leverage the metadata and text/code similarity in issues and commits; however, they fail to capture the embedded semantics in issues and commits and the hidden semantic correlations between issues and commits. As a result, this semantic gap inhibits the accuracy of link recovery. To bridge this gap, we propose a semantically-enhanced link recovery approach, named DeepLink , which is built on top of deep learning techniques. Specifically, we develop a neural network architecture, using word embedding and recurrent neural network, to learn the semantic representation of natural language descriptions and code in issues and commits as well as the semantic correlation between issues and commits. In experiments, to quantify the prevalence of missing issue-commit links, we analyzed 1078 highly-starred GitHub Java projects (i.e., 583,795 closed issues) and found that only 42.2% of issues were linked to corresponding commits. To evaluate the effectiveness of DeepLink , we compared DeepLink with a state-of-the-art link recovery approach FRLink using ten GitHub Java projects and demonstrated that DeepLink can outperform FRLink in terms of F -measure.
Keywords: Issue-commit links | Deep learning | Semantic understanding
مقاله انگلیسی
15 Nemesyst: A hybrid parallelism deep learning-based framework applied for internet of things enabled food retailing refrigeration systems
Nemesyst: یک چارچوب مبتنی بر یادگیری عمیق موازی ترکیبی برای سیستم های تبرید خرده فروشی مواد غذایی توانا شده با اینترنت اشیا-2019
Deep learning has attracted considerable attention across multiple application domains, including com-puter vision, signal processing and natural language processing. Although quite a few single node deeplearning frameworks exist, such as tensorflow, pytorch and keras, we still lack a complete processingstructure that can accommodate large scale data processing, version control, and deployment, all whilestaying agnostic of any specific single node framework. To bridge this gap, this paper proposes a new,higher level framework, i.e. Nemesyst, which uses databases along with model sequentialisation to allowprocesses to be fed unique and transformed data at the point of need. This facilitates near real-time appli-cation and makes models available for further training or use at any node that has access to the databasesimultaneously. Nemesyst is well suited as an application framework for internet of things aggregatedcontrol systems, deploying deep learning techniques to optimise individual machines in massive net-works. To demonstrate this framework, we adopted a case study in a novel domain; deploying deeplearning to optimise the high speed control of electrical power consumed by a massive internet of thingsnetwork of retail refrigeration systems in proportion to load available on the UK National Grid (a demandside response). The case study demonstrated for the first time in such a setting how deep learning models,such as Recurrent Neural Networks (vanilla and Long-Short-Term Memory) and Generative AdversarialNetworks paired with Nemesyst, achieve compelling performance, whilst still being malleable to futureadjustments as both the data and requirements inevitably change over time.
Keywords:Deep learning | Databases | Distributed computing | Parallel computing | Demand side | responseRefrigeration | Internet of things
مقاله انگلیسی
16 Deep Learning for EEG motor imagery classification based on multi-layer CNNs feature fusion
یادگیری عمیق برای طبقه بندی تصاویر موتور EEG بر اساس همجوشی ویژگی های CNNs چند لایه-2019
Electroencephalography (EEG) motor imagery (MI) signals have recently gained a lot of attention as these signals encode a person’s intent of performing an action. Researchers have used MI signals to help disabled persons, control devices such as wheelchairs and even for autonomous driving. Hence decoding these signals accurately is important for a Brain–Computer interface (BCI) system. But EEG decoding is a challenging task because of its complexity, dynamic nature and low signal to noise ratio. Convolution neural network (CNN) has shown that it can extract spatial and temporal features from EEG, but in order to learn the dynamic correlations present in MI signals, we need improved CNN models. CNN can extract good features with both shallow and deep models pointing to the fact that, at different levels relevant features can be extracted. Fusion of multiple CNN models has not been experimented for EEG data. In this work, we propose a multi-layer CNNs method for fusing CNNs with different characteristics and architectures to improve EEG MI classification accuracy. Our method utilizes different convolutional features to capture spatial and temporal features from raw EEG data. We demonstrate that our novel MCNN and CCNN fusion methods outperforms all the state-of-the-art machine learning and deep learning techniques for EEG classification. We have performed various experiments to evaluate the performance of the proposed CNN fusion method on public datasets. The proposed MCNN method achieves 75.7% and 95.4% on the BCI Competition IV-2a dataset and the High Gamma Dataset respectively. The proposed CCNN method based on autoencoder cross-encoding achieves more than 10% improvement for cross-subject EEG classification.
Keywords: EEG motor imagery classification | Deep learning | Convolution neural network | Multi-layer CNNs feature fusion
مقاله انگلیسی
17 An adaptive deep learning model to differentiate syndromes of infectious fever in smart medicine
یک مدل یادگیری عمیق سازگار برای تمایز سندرمهای تب عفونی در پزشکی هوشمند-2019
Recently, smart medicine has been considered as a promising technique to treat the intractable diseases by combining deep learning techniques with medical Internet of Things. As an important component in the integration of traditional and western medicine, smart medicine is particularly effective to treat infectious fever. Before the cause of infectious fever diseases is ascertained, the Chinese medicine intervention is able to alleviating symptoms and strive for time for the causes detection. However, accurate syndrome differentiation, a difficult issue in infectious fever, is the premise of the Chinese medicine intervention. This work presents a possible adaptive deep learning model by integrating an adaptive dropout function into the stacked auto-encoder for computeraided syndrome differentiation in infective fever. Moreover, we summarize the main syndromes and prescriptions in infectious fever. This work is expected to further the development of smart medicine, especially smart Chinese medicine. More importantly, it points out a novel research direction and medical technique in the treatment of infectious fever in clinic.
Keywords: Smart medicine | Medical Internet of Things | Deep learning | Infectious fever
مقاله انگلیسی
18 Integrating word embeddings and document topics with deep learning in a video classification framework
تجمیع جاسازی کلمات و موضوعات مستند با یادگیری عمیق در یک چارچوب طبقه بندی ویدیو-2019
The advent of MOOC platforms brought an abundance of video educational content that made the selec- tion of best fitting content for a specific topic a lengthy process. To tackle this challenge in this paper we report our research efforts of using deep learning techniques for managing and classifying educational content for various search and retrieval applications in order to provide a more personalized learning experience. In this regard, we propose a framework which takes advantages of feature representations and deep learning for classifying video lectures in a MOOC setting to aid effective search and retrieval. The framework consists of three main modules. The first module called pre-processing concerns with video-to-text conversion. The second module is transcript representation which represents text in lecture transcripts into vector space by exploiting different representation techniques including bag-of-words, embeddings, transfer learning, and topic modeling. The final module covers classifiers whose aim is to la- bel video lectures into the appropriate categories. Two deep learning models, namely feed-forward deep neural network (DNN) and convolutional neural network (CNN) are examined as part of the classifier module. Multiple simulations are carried out on a large-scale real dataset using various feature represen- tations and classification techniques to test and validate the proposed framework.
Keywords: Deep learning | Video classification | Embedding | Document topics | CNN | DNN
مقاله انگلیسی
19 Perceptions of built environment and health outcomes for older Chinese in Beijing: A big data approach with street view images and deep learning technique
برداشت از محیط ساخته شده و پیامدهای بهداشتی برای افراد مسن در پکن: یک رویکرد داده های بزرگ با تصاویر نمای خیابان و تکنیک یادگیری عمیق-2019
Built environment attributes have been demonstrated to be associated with various health outcomes. However, most empirical studies have typically focused on objective built environmental measures. Still, perceptions of the built environment also play an important role in health and may complement studies with objective measures. Some built environment attributes, such as liveliness or beauty, are difficult to measure objectively. Traditional methods to assess perceptions of the built environment, such as questionnaires and focus groups, are timeconsuming and prone to recall bias. The recent development in machine deep learning techniques and big data of street view images, makes it possible to assess perceptions of the built environment with street view images for a large-scale study area. By using online free Tencent Street View (TSV) images, this study assessed six perceptual attributes of the built environment: wealth, safety, liveliness, depression, bore and beauty. These attributes were associated with both the physical and the mental health outcomes of 1231 older adults in 48 neighborhoods in the Haidian District, Beijing, China. Results show that perceived safety was significantly associated with both the physical and mental health outcomes. Perceived depression and beauty were significant related to older adults mental health, while perceived wealth, bore and liveliness were significantly related to their physical health. The findings carry important policy implications and hence contribute to the development of healthy cities. It is urgent to improve residents positive perceptions and decrease their negative perceptions of the built environment, especially in neighborhoods that are highly populated by older adults.
Keywords: Tencent street view (TSV) | Perceived built environment attributes | Deep learning | Health outcomes | Older adults
مقاله انگلیسی
20 Day-ahead building-level load forecasts using deep learning vs: traditional time-series techniques
پیش بینی سطح بار ساختمان در سطح روز با استفاده از یادگیری عمیق در مقابل تکنیک های سری زمانی سنتی-2019
Load forecasting problems have traditionally been addressed using various statistical methods, among which autoregressive integrated moving average with exogenous inputs (ARIMAX) has gained the most attention as a classical time-series modeling method. Recently, the booming development of deep learning techniques make them promising alternatives to conventional data-driven approaches. While deep learning offers exceptional capability in handling complex non-linear relationships, model complexity and computation efficiency are of concern. A few papers have explored the possibility of applying deep neural networks to forecast time-series load data but only limited to system-level or single-step building-level forecasting. This study, however, aims at filling in the knowledge gap of deep learning-based techniques for day-ahead multi-step load forecasting in commercial buildings. Two classical deep neural network models, namely recurrent neural network (RNN) and convolutional neural network (CNN), have been proposed and formulated under both recursive and direct multi-step manners. Their performances are compared with the Seasonal ARIMAX model with regard to accuracy, computational efficiency, generalizability and robustness. Among all of the investigated deep learning techniques, the gated 24- h CNN model, performed in a direct multi-step manner, proves itself to have the best performance, improving the forecasting accuracy by 22.6% compared to that of the seasonal ARIMAX.
Keywords: Time-series building-level load forecasts | Deep learning | Gating mechanism | Seasonal ARIMAX
مقاله انگلیسی
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