با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
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ResTS: Residual Deep interpretable architecture for plant disease detection
ResTS: Residual Deep interpretable architecture for plant disease detection-2021 Recently many methods have been induced for plant disease detection by the influence of Deep Neural Networks in Computer Vision. However, the dearth of transparency in these types of research makes their acquisition in the real-world scenario less approving. We pro- pose an architecture named ResTS (Residual Teacher/Student) that can be used as visualization and a classification technique for diagnosis of the plant disease. ResTS is a tertiary adaptation of formerly suggested Teacher/Student architecture. ResTS is grounded on a Convolutional Neural Network (CNN) structure that comprises two classifiers (ResTeacher and ResStudent) and a decoder. This architecture trains both the classifiers in a reciprocal mode and the conveyed representation between ResTeacher and ResStudent is used as a proxy to envision the dominant areas in the image for categorization. The experiments have shown that the proposed structure ResTS (F1 score: 0.991) has surpassed the Tea- cher/Student architecture (F1 score: 0.972) and can yield finer visualizations of symptoms of the disease. Novel ResTS architecture incorporates the residual connections in all the constituents and it executes batch normalization after each convolution operation which is dissimilar to the formerly proposed Teacher/Student architecture for plant disease diag- nosis. Residual connections in ResTS help in preserving the gradients and circumvent the problem of vanishing or exploding gradients. In addition, batch normalization after each convolution operation aids in swift convergence and increased reliability. All test results are attained on the PlantVillage dataset comprising 54 306 images of 14 crop species.© 2021 China Agricultural University. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/). Keywords: Autoencoders | Xception | Deep Learning | Computer Vision | Agriculture |
مقاله انگلیسی |
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Intelligent fault diagnosis of cooling radiator based on deep learning analysis of infrared thermal images
تشخیص خطای هوشمند رادیاتور خنک کننده بر اساس تجزیه و تحلیل یادگیری عمیق از تصاویر حرارتی مادون قرمز-2019 Detection of faults and intelligent monitoring of equipment operations are essential for modern industries.
Cooling radiator condition is one of the factors that affects engine performance. This paper proposes a novel and
accurate radiator condition monitoring and intelligent fault detection based on thermal images and using a deep
convolutional neural network (CNN) which has a specific configuration to combine the feature extraction and
classification steps. The CNN model is constructed from VGG-16 structure that is followed by batch normalization
layer, dropout layer, and dense layer. The suggested CNN model directly uses infrared thermal images as
input to classify six conditions of the radiator: normal, tubes blockage, coolant leakage, cap failure, loose
connections between fins & tubes and fins blockage. Evaluation of the model demonstrates that leads to results
better than traditional computational intelligence methods, such as an artificial neural network, and can be
employed with high performance and accuracy for fault diagnosis and condition monitoring of the cooling
radiator under various working circumstances. Keywords: Cooling radiator | Fault detection | Thermal image analysis | Deep learning | Convolutional neural network |
مقاله انگلیسی |
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Predicting Inpatient Payments Prior to Lower Extremity Arthroplasty Using Deep Learning: Which Model Architecture Is Best?
پیش بینی پرداخت های بستری قبل از آرتروپلاستی با اندام تحتانی با استفاده از آموزش عمیق: کدام مدل معماری بهترین است؟-2019 Background: Recent advances in machine learning have given rise to deep learning, which uses hierarchical
layers to build models, offering the ability to advance value-based healthcare by better predicting patient
outcomes and costs of a given treatment. The purpose of this study is to compare the performance of 2
common deep learning models, traditional multilayer perceptron (MLP), and the newer dense neural
network (DenseNet), in predicting outcomes for primary total hip arthroplasty (THA) and total knee
arthroplasty (TKA) as a foundation for future musculoskeletal studies seeking to utilize machine learning.
Methods: Using 295,605 patients undergoing primary THA and TKA from a New York State inpatient
administrative database from 2009 to 2016, 2 neural network designs (MLP vs DenseNet) with different
model regularization techniques (dropout, batch normalization, and DeCovLoss) were applied to
compare model performance on predicting inpatient procedural cost using the area under the receiver
operating characteristic curve (AUC). Models were implemented to identify high-cost surgical cases.
Results: DenseNet performed similarly to or better than MLP across the different regularization techniques
in predicting procedural costs of THA and TKA. Applying regularization to DenseNet resulted in a
significantly higher AUC as compared to DenseNet alone (0.813 vs 0.792, P ¼ .011). When regularization
methods were applied to MLP, the AUC was significantly lower than without regularization (0.621 vs
0.791, P ¼ 1.1 1015). When the optimal MLP and DenseNet models were compared in a head-to-head
fashion, they performed similarly at cost prediction (P > .999).
Conclusion: This study establishes that in predicting costs of lower extremity arthroplasty, DenseNet
models improve in performance with regularization, whereas simple neural network models perform
significantly worse without regularization. In light of the resource-intensive nature of creating and
testing deep learning models for orthopedic surgery, particularly for value-centric procedures such as
arthroplasty, this study establishes a set of key technical features that resulted in better prediction of
inpatient surgical costs. We demonstrated that regularization is critically important for neural networks
in arthroplasty cost prediction and that future studies should utilize these deep learning techniques to
predict arthroplasty costs.
Level of Evidence: III. Keywords: machine learning | deep learning | neural networks | big data | total knee arthroplasty | total hip arthroplasty |
مقاله انگلیسی |
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Enhancing batch normalized convolutional networks using displaced rectifier linear units: A systematic comparative study
افزایش شبکه های نرم افزاری بصورت جمع شده با استفاده از واحدهای خطی یکسو کننده جابجا شده: یک مطالعه مقایسه ای سیستماتیک-2019 A substantial number of expert and intelligent systems rely on deep learning methods to solve problems in areas such as economics, physics, and medicine. Improving the accuracy of the activation functions used by such methods can directly and positively impact the overall performance and quality of the mentioned systems at no cost whatsoever. In this sense, enhancing the design of such theoretical fun- damental blocks is of great significance as it immediately impacts a broad range of current and future real-world deep learning based applications. Therefore, in this paper, we turn our attention to the inter- working between the activation functions and the batch normalization, which is practically a mandatory technique to train deep networks currently. We propose the activation function Displaced Rectifier Linear Unit (DReLU) by conjecturing that extending the identity function of ReLU to the third quadrant enhances compatibility with batch normalization. Moreover, we used statistical tests to compare the impact of us- ing distinct activation functions (ReLU, LReLU, PReLU, ELU, and DReLU) on the learning speed and test accuracy performance of standardized VGG and Residual Networks state-of-the-art models. These Convo- lutional Neural Networks were trained on CIFAR-100 and CIFAR-10, the most commonly used deep learn- ing computer vision datasets. The results showed DReLU speeded up learning in all models and datasets. Besides, statistical significant performance assessments ( p < 0.05) showed DReLU enhanced the test accu- racy presented by ReLU in all scenarios. Furthermore, DReLU showed better test accuracy than any other tested activation function in all experiments with one exception, in which case it presented the second best performance. Therefore, this work demonstrates that it is possible to increase performance replacing ReLU by an enhanced activation function. Keywords: DReLU | Activation function | Batch normalization | Comparative study | Convolutional Neural Networks | Deep learning |
مقاله انگلیسی |
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Enhancing metabolomics research through data mining
افزایش تحقیقات متابولومیک از طریق داده کاوی-2015 Available online 7 February 2015 Metabolomics research, like other disciplines utilizing high-throughput technologies, generatesa large amount of data for every sample. Although handling this data is a challenge and oneKeywords:MetabolomicsInter-batch normalization Statistical assumptions AgingMANOVALinear regressionof the biggest bottlenecks of the metabolomics workflow, it is also the clue to accomplish valuable results. This work has been designed to supply methodological data mining guidelines, describing systematically the steps to be followed in metabolomics data exploration. Instrumental raw data refinement in the pre-processing step and assessment of the statistical assumptions in pre-treatment directly affect the results of subsequent univariate and multivariate analyses. A study of aging in a healthy population was selected to represent this data mining process. Multivariate analysis of variance and linear regression methods were used to analyze the metabolic changes underlying aging. Selection of both multivariate methods aims to illustrate the treatment of age from two rather different perspectives, as a categorical variable and a continuous variable.Biological significanceMetabolomics is a discipline involving the analysis of a large amount of data to gather relevant information. Researchers in this field have to overcome the challenges of complex data processing and statistical analysis issues. A wide range of tasks has to be executed, from the minimization of batch-to-batch/systematic variations in pre-processing, to the application of common data analysis techniques relying on statistical assumptions. In this work, a real-data metabolic profiling research on aging was used to illustrate the proposed workflow and suggest a set of guidelines for analyzing metabolomics data.This article is part of a Special Issue entitled: HUPO 2014.© 2015 Elsevier B.V. All rights reserved.Abbreviations: UPLC, ultra performance liquid chromatography; MS, mass spectrometry; MANOVA, multivariate analysis of variance; ANOVA, analysis of variance; GGT, gamma glutamyl transferase; ALT, alanine transaminase; QC, quality control; RSD, relative standard deviation; Q–Q plot, quantile–quantile plot; VIF, variable inflation factor.☆ This article is part of a Special Issue entitled: HUPO 2014.☆☆ Financial Support: Supported by Spanish Plan Nacional I1D SAF 2011-29851 (J.M.M.), ETORTEK-2010 Gobierno Vasco (J.M.M.),Educación Gobierno Vasco 2012 (J.M.M.), BBVA Foundation (J.M.M.), MINECO-ISCiii PIE14/00031 (J.M.M.), Ministerio Economía y Competitividad IPT-010000-2010-013 (I.M.-A., R.M., I.M. and C.A.), Gobierno Vasco, Dpto. Industria, Innovación, Comercio y Turismo IG-2012/0000346 (I.M.-A., I.M. and C.A.). CIBERehd is funded by ISCiii.⁎ Corresponding author at: CIC bioGUNE, Parque Tecnológico de Bizkaia, 48160 Derio, Bizkaia, Spain. Tel.: +34 944 061300; fax: +34 944 0611301. E-mail address: director@cicbiogune.es (J.M. Mato).http://dx.doi.org/10.1016/j.jprot.2015.01.0191874-3919/© 2015 Elsevier B.V. All rights reserved.
Metabolomics | Inter-batch normalization | Statistical assumptions | Aging | MANOVA | Linear regression |
مقاله انگلیسی |