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.
Transform domain representation-driven convolutional neural networks for skin lesion segmentation
انتقال شبکه های عصبی کانولوشن نمایندگی محور دامنه برای تقسیم بندی ضایعه پوستی-2020
Automated diagnosis systems provide a huge improvement in early detection of skin cancer, and con- sequently, contribute to successful treatment. Recent research on convolutional neural network has achieved enormous success in segmentation and object detection tasks. However, these networks require large amount of data that is a big challenge in medical domain where often have insufficient data and even a pretrained model on medical images can be hardly found. Lesion segmentation as the initial step of skin cancer analysis remains a challenging issue since datasets are small and include a variety of im- ages in terms of light, color, scale, and marks which have led researchers to use extensive augmentation and preprocessing techniques or fine tuning the network with a pretrained model on irrelevant images. A segmentation model based on convolutional neural networks is proposed in this study for the tasks of skin lesion segmentation and dermoscopic feature segmentation. The network is trained from scratch and despite the small size of datasets neither excessive data augmentation nor any preprocessing to remove artifacts or enhance the images are applied. Alternatively, we investigated incorporating image represen- tations of the transform domain to the convolutional neural network and compared to a model with more convolutional layers that resulted in 6% higher Jaccard index and has shorter training time. The model improved by applying CIELAB color space and the performance of the final proposed architecture is evaluated on publicly available datasets from ISBI challenges in 2016 and 2017. The proposed model has resulted in an improvement of as much as 7% for the segmentation metrics and 17% for the fea- ture segmentation, which demonstrates the robustness of this unique hybrid framework and its future applications as well as further improvement.
Keywords: Convolutional neural network | Dermoscopic features | Melanoma | Skin lesion segmentation | Transform domain
TDP: Two-dimensional perceptron for image recognition
TDP: پرسپترون دو بعدی برای تشخیص تصویر-2020
Convolutional neural network (CNN) is widely applied to different areas due to good recognition performance. However, convolution operation is a complex computation and consumes the bulk of processing time for CNN. It is still a hot problem how to develop a novel model with good recognition performance for deep learning. Here, we propose a novel model, namely, two-dimensional perceptron (TDP), to get direct input of two-dimensional data for further processing. A TDP has a new network architecture and an innovative computation process of hidden neurons. In cases with the same number of hidden neurons, compared with multilayer perceptron (MLP), TDP achieves good recognition performance with 1×-36× speedup and a decrease of parameters by exceeding 97% on MNIST and COIL-20 datasets. Meanwhile, TDP obtains 1%–32% improvement of recognition accuracy in comparison to CNN on CIFAR-10 and SVHN datasets. Furthermore, on INFUSE dataset, TDP has an increase of F1 score by up to almost 11% in comparison with MLP and CNN. The results indicate that TDP is a promising and novel model with excellent recognition performance.
Keywords: Convolutional neural network | Multilayer perceptron | Two-dimensional perceptron | Recognition performance | F1 score
Optimal policy for structure maintenance: A deep reinforcement learning framework
سیاست بهینه برای نگهداری ساختار: یک چارچوب یادگیری تقویت عمیق-2020
The cost-effective management of aged infrastructure is an issue of worldwide concern. Markov decision process (MDP) models have been used in developing structural maintenance policies. Recent advances in the artificial intelligence (AI) community have shown that deep reinforcement learning (DRL) has the potential to solve large MDP optimization tasks. This paper proposes a novel automated DRL framework to obtain an optimized structural maintenance policy. The DRL framework contains a decision maker (AI agent) and the structure that needs to be maintained (AI task environment). The agent outputs maintenance policies and chooses maintenance actions, and the task environment determines the state transition of the structure and returns rewards to the agent under given maintenance actions. The advantages of the DRL framework include: (1) a deep neural network (DNN) is employed to learn the state-action Q value (defined as the predicted discounted expectation of the return for consequences under a given state-action pair), either based on simulations or historical data, and the policy is then obtained from the Q value; (2) optimization of the learning process is sample-based so that it can learn directly from real historical data collected from multiple bridges (i.e., big data from a large number of bridges); and (3) a general framework is used for different structure maintenance tasks with minimal changes to the neural network architecture. Case studies for a simple bridge deck with seven components and a long-span cable-stayed bridge with 263 components are performed to demonstrate the proposed procedure. The results show that the DRL is efficient at finding the optimal policy for maintenance tasks for both simple and complex structures.
Keywords: Bridge maintenance policy | Deep reinforcement learning (DRL) | Markov decision process (MDP) | Deep Q-network (DQN) | Convolutional neural network (CNN)
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
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
A deep feature mining method of electronic nose sensor data for identifying beer olfactory information
یک روش استخراج عمیق از داده های حسگر بینی الکترونیکی برای شناسایی اطلاعات بویایی آبجو-2019
In this work, a deep feature mining method for electronic nose (E-nose) sensor data based on the convolutional neural network (CNN) was proposed in combination with a support vector machine (SVM) to identify beer olfactory information. According to the characteristics of E-nose sensor data, the structure and parameters of the CNN was designed. By means of convolution and pooling operations, the beer olfaction features were extracted automatically. Meanwhile, the SVM replaced the full connection layer of the CNN to enhance the generalization ability of the model, and two important parameters affecting the classification performance of the SVM were optimized based on an improved particle swarm optimization (PSO). The results indicated that the CNN-SVM model achieved deep feature automatic extraction of beer olfactory information, and a good classification performance of 96.67% was obtained in the testing set. This study shows that the CNN-SVM can be used as an effective tool for high precision intelligent identification of beer olfactory information
Keywords: Electronic nose | Feature mining | Convolutional neural network | Support vector machine | Beer
A deep learning framework for automatic diagnosis of unipolar depression
یک چارچوب یادگیری عمیق برای تشخیص خودکار افسردگی تک قطبی-2019
Background and purpose: In recent years, the development of machine learning (ML) frameworks for automatic diagnosis of unipolar depression has escalated to a next level of deep learning frameworks. However, this idea needs further validation. Therefore, this paper has proposed an electroencephalographic (EEG)-based deep learning framework that automatically discriminated depressed and healthy controls and provided the diagnosis. Basic procedures: In this paper, two different deep learning architectures were proposed that utilized one dimensional convolutional neural network (1DCNN) and 1DCNN with long short-term memory (LSTM) architecture. The proposed deep learning architectures automatically learn patterns in the EEG data that were useful for classifying the depressed and healthy controls. In addition, the proposed models were validated with restingstate EEG data obtained from 33 depressed patients and 30 healthy controls. Main findings: As results, significant differences were observed between the two groups. The classification results involving the CNN model were accuracy=98.32%, precision=99.78%, recall=98.34%, and f-score= 97.65%. In addition, the study has reported LSTM with 1DCNN classification accuracy=95.97%, precision= 99.23%, recall=93.67%, and f-score=95.14%. Conclusions: Deep learning frameworks could revolutionize the clinical applications for EEG-based diagnosis for depression. Based on the results, it may be concluded that the deep learning framework could be used as an automatic method for diagnosing the depression.
Keywords: EEG-based deep learning for depression | EEG-based diagnosis of unipolar depression | Convolutional neural network for depression | Long short-term memory classifiers for depression | EEG-based machine learning methods for depression
Deep convolutional learning for general early design stage prediction models
یادگیری همگرای عمیق برای مدل های پیش گویی مرحله اولیه طراحی-2019
Designers rely on performance predictions to direct the design toward appropriate requirements. Machine learning (ML) models exhibit the potential for rapid and accurate predictions. Developing conventional ML models that can be generalized well in unseen design cases requires an effective feature engineering and selection. Identifying generalizable features calls for good domain knowledge by the ML model developer. Therefore, developing ML models for all design performance parameters with conventional ML will be a timeconsuming and expensive process. Automation in terms of feature engineering and selection will accelerate the use of ML models in design. Deep learning models extract features from data, which aid in model generalization. In this study, we (1) evaluate the deep learning model’s capability to predict the heating and cooling demand on unseen design cases and (2) obtain an understanding of extracted features. Results indicate that deep learning model generalization is similar to or better than that of a simple neural network with appropriate features. The reason for the satisfactory generalization using the deep learning model is its ability to identify similar design options within the data distribution. The results also indicate that deep learning models can filter out irrelevant features, reducing the need for feature selection.
Keywords: Convolutional neural network | Energy predictions | Machine learning | Feature learning
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
Deep learning for vibrational spectral analysis: Recent progress and a practical guide
یادگیری عمیق برای تجزیه و تحلیل طیفی ارتعاش: پیشرفت های اخیر و یک راهنمای عملی-2019
The development of chemometrics aims to provide an effective analysis approach for data generated by advanced analytical instruments. The success of existing analytical approaches in spectral analysis still relies on preprocessing and feature selection techniques to remove signal artifacts based on prior experiences. Data-driven deep learning analysis has been developed and successfully applied in many domains in the last few years. How to integrate deep learning with spectral analysis received increased attention for chemometrics. Approximately 20 recently published studies demonstrate that deep neural networks can learn critical patterns from raw spectra, which significantly reduces the demand for feature engineering. The composition of multiple processing layers improves the fitting and feature extraction capability and makes them applicable to various analytical tasks. This advance offers a new solution for chemometrics toward resolving challenges related to spectral data with rapidly increased sample numbers from various sources. We further provide a practical guide to the development of a deep convolutional neural network-based analytical workflow. The design of the network structure, tuning the hyperparameters in the training process, and repeatability of results is mainly discussed. Future studies are needed on interpretability and repeatability of the deep learning approach in spectral analysis.
Keywords: Chemometrics | Artificial intelligence | Deep learning | Spectroscopy | Convolutional neural network | Analysis