با سلام خدمت کاربران عزیز، به اطلاع می رساند ترجمه مقالاتی که سال انتشار آن ها زیر 2008 می باشد رایگان بوده و میتوانید با وارد شدن در صفحه جزییات مقاله به رایگان ترجمه را دانلود نمایید.
Statistical investigations of transfer learning-based methodology for shortterm building energy predictions
تحقیقات آماری از روش مبتنی بر یادگیری انتقال برای پیش بینی انرژی کوتاه مدت ساختنمان -2020
The wide availability of massive building operational data has motivated the development of advanced datadriven methods for building energy predictions. Existing data-driven prediction methods are typically customized for individual buildings and their performance are highly influenced by the training data amount and quality. In practice, buildings may only possess limited measurements due to the lack of advanced monitoring systems or data accumulation time. As a result, existing data-driven approaches may not present sufficient values for practical applications. A novel solution can be developed based on transfer learning, which utilizes the knowledge learnt from well-measured buildings to facilitate prediction tasks in other buildings. However, the potentials of transfer learning-based methods for building energy predictions have not been systematically examined. To address this research gap, a transfer learning-based methodology is proposed for 24-h ahead building energy demand predictions. Experiments have been designed to investigate the potentials of transfer learning in different scenarios with different implementation strategies. Statistical assessments have been performed to validate the value of transfer learning in short-term building energy predictions. Compared with standalone models, the transfer learning-based methodology could reduce approximately 15% to 78% of prediction errors. The research outcomes are useful for developing advanced transfer learning-based methods for typical tasks in building energy management. The insights obtained can help the building industry to fully realize the value of existing building data resources and advanced data analytics.
Keywords: Building energy predictions | Transfer learning | Deep learning | Data-driven models | Smart building energy management
Leveraging Google Earth Engine (GEE) and machine learning algorithms to incorporate in situ measurement from different times for rangelands monitoring
اهرم موتور زمین گوگل و الگوریتم های یادگیری ماشین برای ترکیب در اندازه گیری درجا از زمان های مختلف برای نظارت بر مراتع-2020
Mapping and monitoring of indicators of soil cover, vegetation structure, and various native and non-native species is a critical aspect of rangeland management. With the advancement in satellite imagery as well as cloud storage and computing, the capability now exists to conduct planetary-scale analysis, including mapping of rangeland indicators. Combined with recent investments in the collection of large amounts of in situ data in the western U.S., new approaches using machine learning can enable prediction of surface conditions at times and places when no in situ data are available. However, little analysis has yet been done on how the temporal relevancy of training data influences model performance. Here, we have leveraged the Google Earth Engine (GEE) platform and a machine learning algorithm (Random Forest, after comparison with other candidates) to identify the potential impact of different sampling times (across months and years) on estimation of rangeland indicators from the Bureau of Land Managements (BLM) Assessment, Inventory, and Monitoring (AIM) and Landscape Monitoring Framework (LMF) programs. Our results indicate that temporally relevant training data improves predictions, though the training data need not be from the exact same month and year for a prediction to be temporally relevant. Moreover, inclusion of training data from the time when predictions are desired leads to lower prediction error but the addition of training data from other times does not contribute to overall model error. Using all of the available training data can lead to biases, toward the mean, for times when indicator values are especially high or low. However, for mapping purposes, limiting training data to just the time when predictions are desired can lead to poor predictions of values outside the spatial range of the training data for that period. We conclude that the best Random Forest prediction maps will use training data from all possible times with the understanding that estimates at the extremes will be biased.
Keywords: Google earth engine | Big data | Machine learning | Domain adaptation | Transfer learning | Feature selection | Rangeland monitoring
Object Memorability Prediction using Deep Learning: Location and Size Bias
پیش بینی قابلیت یادآوری شی با استفاده از یادگیری عمیق: محل و اندازه-2019
Object memorability prediction is a task of estimating the probability that a human recognises the recurrence of an object after a single view. Initial research on object memorability showed that it is possible to predict the object memorability scores from the intrinsic features of an object. Though the existing works proposed some of the features for object memorability prediction task, the influence of Spatial-location and Spatial-size of an object to its memorability have not been explored yet. In this work, the importance of these two characteristics in determining object memorability prediction is investigated and the same is demonstrated by building a baseline model. Further, a deep learning model is devised for automatic feature learning on these two object characteristics. Experimental results highlight that the Spatial-location and Spatial-size of an object play a significant role in object memorability prediction and the proposed models outperformed the existing methods
Keywords: Object Memorability | Deep Learning | Transfer Learning
Research on image steganography analysis based on deep learning
تحقیق در مورد تجزیه و تحلیل استگانوگرافی تصویر بر اساس یادگیری عمیق-2019
Although steganalysis has developed rapidly in recent years, it still faces many difficulties and challenges. Based on the theory of in-depth learning method and image-based general steganalysis, this paper makes a deep study of the hot and difficult problem of steganalysis feature expression, and tries to establish a new steganalysis paradigm from the idea of feature learning. The main contributions of this paper are as follows: 1. An innovative steganalysis paradigm based on in-depth learning is proposed. Based on the representative deep learning method CNN, the model is designed and adjusted according to the characteristics of steganalysis, which makes the proposed model more effective in capturing the statistical characteristics such as neighborhood correlation. 2. A steganalysis feature learning method based on global information constraints is proposed. Based on the previous research of steganalysis method based on CNN, this work focuses on the importance of global information in steganalysis feature expression. 3. A feature learning method for low embedding rate steganalysis is proposed. 4. A general steganalysis method for multi-class steganography is proposed. The ultimate goal of general steganalysis is to construct steganalysis detectors without distinguishing specific types of steganalysis algorithms
Keywords: Steganalysis | Steganography | Feature learning | Deep learning | Convolutional neural network | Transfer learning | Multitask learning
Improving air quality prediction accuracy at larger temporal resolutions using deep learning and transfer learning techniques
بهبود دقت پیش بینی کیفیت هوا در وضوح زمانی بزرگتر با استفاده از تکنیک های یادگیری عمیق و انتقال یادگیری-2019
As air pollution becomes more and more severe, air quality prediction has become an important approach for air pollution management and prevention. In recent years, a number of methods have been proposed to predict air quality, such as deterministic methods, statistical methods as well as machine learning methods. However, these methods have some limitations. Deterministic methods require expensive computations and specific knowledge for parameter identification, while the forecasting performance of statistical methods is limited due to the linear assumption and the multicollinearity problem. Most of the machine learning methods, on the other hand, cannot capture the time series patterns or learn from the long-term dependencies of air pollutant concentrations. Furthermore, there is a lack of methods that could generate high prediction accuracy for air quality forecasting at larger temporal resolutions, such as daily and weekly or even monthly. This paper, therefore, proposes a deep learning-based method namely transferred bi-directional long short-term memory (TL-BLSTM) model for air quality prediction. The methodology framework utilizes the bi-directional LSTM model to learn from the longterm dependencies of PM2.5, and applies transfer learning to transfer the knowledge learned from smaller temporal resolutions to larger temporal resolutions. A case study is conducted in Guangdong, China to test the proposed methodology framework. The performance of the framework is compared with other commonly seen machine learning algorithms, and the results show that the proposed TL-BLSTM model has smaller errors, especially for larger temporal resolutions
Keywords: Air quality prediction | Large temporal resolution | Deep learning | Long short-term memory | Transfer learning
TOP-GAN: Stain-free cancer cell classification using deep learning with a small training set
TOP-GAN: طبقه بندی سلول های سرطانی بدون لکه با استفاده از یادگیری عمیق با یک مجموعه آموزشی کوچک-2019
We propose a new deep learning approach for medical imaging that copes with the problem of a small training set, the main bottleneck of deep learning, and apply it for classification of healthy and cancer cell lines acquired by quantitative phase imaging. The proposed method, called transferring of pre-trained generative adversarial network (TOP-GAN), is hybridization between transfer learning and generative ad- versarial networks (GANs). Healthy cells and cancer cells of different metastatic potential have been im- aged by low-coherence off-axis holography. After the acquisition, the optical path delay maps of the cells are extracted and directly used as inputs to the networks. In order to cope with the small number of clas- sified images, we use GANs to train a large number of unclassified images from another cell type (sperm cells). After this preliminary training, we change the last layers of the network and design automatic classifiers for the correct cell type (healthy/primary cancer/metastatic cancer) with 90–99% accuracies, although small training sets of down to several images are used. These results are better in comparison to other classic methods that aim at coping with the same problem of a small training set. We believe that our approach makes the combination of holographic microscopy and deep learning networks more accessible to the medical field by enabling a rapid, automatic and accurate classification in stain-free imaging flow cytometry. Furthermore, our approach is expected to be applicable to many other medical image classification tasks, suffering from a small training set.
Keywords: Holography | Quantitative phase imaging | Deep learning | Machine learning algorithms | Image classification | Biological cells
Deep learning for waveform identification of resting needle electromyography signals
یادگیری عمیق برای شناسایی شکل موج سیگنالهای الکترومیوگرافی سوزن ساکن-2019
Objective: Given the recent advent in machine learning and artificial intelligence on medical data analysis, we hypothesized that the deep learning algorithm can classify resting needle electromyography (n- EMG) discharges. Methods: Six clinically observed resting n-EMG signals were used as a dataset. The data were converted to Mel-spectrogram. Data augmentation was then applied to the training data. Deep learning algorithms were applied to assess the accuracies of correct classification, with or without the use of pre-trained weights for deep-learning networks. Results: While the original data yielded the accuracy up to 0.86 on the test dataset, data-augmentation up to 200,000 training images showed significant increase in the accuracy to 1.0. The use of pre-trained weights (fine tuning) showed greater accuracy than ‘‘training from scratch”. Conclusions: Resting n-EMG signals were successfully classified by deep-learning algorithm, especially with the use of data augmentation and transfer learning techniques. Significance: Computer-aided signal identification of clinical n-EMG testing might be possible by deeplearning algorithms.
Keywords: Needle electromyography | Deep learning | Artificial neural network | Data augmentation | Resting discharge
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
Automated diagnosis of ear disease using ensemble deep learning with a big otoendoscopy image database
تشخیص خودکار بیماری گوش با استفاده از یادگیری عمیق گروه با یک پایگاه داده بزرگ تصویر otoendoscopy-2019
Background: Ear and mastoid disease can easily be treated by early detection and appropriate medical care. However, short of specialists and relatively lowdiagnostic accuracy calls for a newway of diagnostic strategy, inwhich deep learning may play a significant role. The current study presents a machine learning model to automatically diagnose ear disease using a large database of otoendoscopic images acquired in the clinical environment. Methods: Total 10,544 otoendoscopic images were used to train nine public convolution-based deep neural networks to classify eardrum and external auditory canal features into six categories of ear diseases, covering most ear diseases (Normal, Attic retraction, Tympanic perforation, Otitis externa±myringitis, Tumor). After evaluating several optimization schemes, two best-performingmodelswere selected to compose an ensemble classifier, by combining classification scores of each classifier. Findings: According to accuracy and training time, transfer learning models based on Inception-V3 and ResNet101 were chosen and the ensemble classifier using the two models yielded a significant improvement over each model, the accuracy of which is in average 93·67% for the 5-folds cross-validation. Considering substantial data-size dependency of classifier performance in the transfer learning, evaluated in this study, the high accuracy in the current model is attributable to the large database. Interpretation: The current study is unprecedented in terms of both disease diversity and diagnostic accuracy, which is compatible or even better than an average otolaryngologist. The classifier was trainedwith data in a various acquisition condition,which is suitable for the practical environment. This study shows the usefulness of utilizing a deep learning model in the early detection and treatment of ear disease in the clinical situation. Fund: This research was supported by Brain Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT(NRF-2017M3C7A1049051).
Keywords: Convolutional neural network | Deep learning | Otoendoscopy | Tympanic membrane | Ear disease | Ensemble learning
A deep learning framework for Hybrid Heterogeneous Transfer Learning
یک چارچوب یادگیری عمیق برای یادگیری انتقال ناهمگن ترکیبی-2019
Most previous methods in heterogeneous transfer learning learn a cross-domain feature mapping between different domains based on some cross-domain instance-correspon-dences. Such instance-correspondences are assumed to be representative in the source domain and the target domain, respectively. However, in many real-world scenarios, this assumption may not hold. As a result, the constructed feature mapping may not be pre-cise, and thus the transformed source-domain labeled data using the feature mapping are not useful to build an accurate classifier for the target domain. In this paper, we offer a new heterogeneous transfer learning framework named Hybrid Heterogeneous Transfer Learning (HHTL), which allows the selection of corresponding instances across domains to be biased to the source or target domain. Our basic idea is that though the correspond-ing instances are biased in the original feature space, there may exist other feature spaces, projected onto which, the corresponding instances may become unbiased or representa-tive to the source domain and the target domain, respectively. With such a representation, a more precise feature mapping across heterogeneous feature spaces can be learned for knowledge transfer. We design several deep-learning-based architectures and algorithms that enable learning aligned representations. Extensive experiments on two multilingual classification datasets verify the effectiveness of our proposed HHTL framework and algo-rithms compared with some state-of-the-art methods.
Keywords: Heterogeneous transfer learning | Deep learning | Multilingual text classification