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.
From chemical structure to quantitative polymer properties prediction through convolutional neural networks
از ساختار شیمیایی گرفته تا پیش بینی کمی از خواص پلیمر از طریق شبکه های عصبی در هم تنیده -2020
In this work convolutional-fully connected neural networks were designed and trained to predict the glass transition temperature of polymers based only on their chemical structure. This approach has shown to successfully predict the Tg of unknown polymers with average relative errors as low as 6%. Several networks with different architecture or hiperparameters were successfully trained using a previously studied glass transition temperatures dataset for validation, and then the same method was employed for an extended dataset, with larger Tg dispersion and polymer’s structure variability. This approach has shown to be accurate and reliable, and does not require any time consuming or expensive measurements and calculations as inputs. Furthermore, it is expected that this method can be easily extended to predict other properties. The possibility of predicting the properties of polymers not even synthesized will save time and resources for industrial development as well as accelerate the scientific understanding of structure-properties relationships in polymer science.
Keywords: QSPR | Properties prediction | Deep learning | Neural network | Smart design
Learning in the machine: To share or not to share?
یادگیری در دستگاه: برای به اشتراک گذاشتن یا عدم اشتراک گذاری؟-2020
Weight-sharing is one of the pillars behind Convolutional Neural Networks and their successes. However, in physical neural systems such as the brain, weight-sharing is implausible. This discrepancy raises the fundamental question of whether weight-sharing is necessary. If so, to which degree of precision? If not, what are the alternatives? The goal of this study is to investigate these questions, primarily through simulations where the weight-sharing assumption is relaxed. Taking inspiration from neural circuitry, we explore the use of Free Convolutional Networks and neurons with variable connection patterns. Using Free Convolutional Networks, we show that while weight-sharing is a pragmatic optimization approach, it is not a necessity in computer vision applications. Furthermore, Free Convolutional Networks match the performance observed in standard architectures when trained using properly translated data (akin to video). Under the assumption of translationally augmented data, Free Convolutional Networks learn translationally invariant representations that yield an approximate form of weight-sharing.
Keywords: Deep learning | Convolutional neural networks | Weight-sharing | Biologically plausible architectures
Deep learning model for end-to-end approximation of COSMIC functional size based on use-case names
مدل یادگیری عمیق برای تخمین پایان به پایان اندازه کاربردی COSMIC بر اساس نامهای مورد استفاده-2020
Context: COSMIC is a widely used functional size measurement (FSM) method that supports software development effort estimation. The FSM methods measure functional product size based on functional requirements. Unfortu- nately, when the description of the product’s functionality is often abstract or incomplete, the size of the product can only be approximated since the object to be measured is not yet fully described. Also, the measurement performed by human-experts can be time-consuming, therefore, it is worth considering automating it. Objective: Our objective is to design a new prediction model capable of approximating COSMIC-size of use cases based only on their names that is easier to train and more accurate than existing techniques. Method: Several neural-network architectures are investigated to build a COSMIC size approximation model. The accuracy of models is evaluated in a simulation study on the dataset of 437 use cases from 27 software develop- ment projects in the Management Information Systems (MIS) domain. The accuracy of the models is compared with the Average Use-Case approximation (AUC), and two recently proposed two-step models —Average Use-Case Goal-aware Approximation (AUCG) and Bayesian Network Use-Case Goal AproxImatioN (BN-UCGAIN). Results: The best prediction accuracy was obtained for a convolutional neural network using a word-embedding model trained on Wikipedia + Gigaworld. The accuracy of the model outperformed the baseline AUC model by ca. 20%, and the two-step models by ca. 5–7%. In the worst case, the improvement in the prediction accuracy is visible after estimating 10 use cases. Conclusions: The proposed deep learning model can be used to automatically approximate COSMIC size of software applications for which the requirements are documented in the form of use cases (or at least in the form of use- case names). The advantage of the model is that it does not require collecting historical data other than COSMIC size and names of use cases.
Keywords: Functional size approximation | Approximate software sizing methods | COSMIC | Deep learning | Word embeddings | Use cases
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
Improved sequence generation model for multi-label classification via CNN and initialized fully connection
بهبود مدل تولید توالی برای طبقه بندی چند برچسب از طریق CNN و اتصال کاملاً اولیه-2020
In multi-label text classification, considering the correlation between labels is an important yet challeng- ing task due to the combination possibility in the label space increasing exponentially. In recent years, neural network models have been widely applied and gradually achieved satisfactory performance in this field. However, existing methods either not model the fully internal correlations among labels or not capture the local and global semantic information of text simultaneously, which somewhat affects the classification results finally. In this paper, we implement a novel model for multi-label classification based on sequence-to-sequence learning, in which two different neural network modules are employed, named encoder and decoder respectively. The encoder uses the convolutional neural network to extract the high-level local sequential semantic, which is combined with the word vector to generate the final text representation through the recurrent neuron network and attention mechanism. The decoder, be- sides using a recurrent neural network to capture the global label correlation, employs an initialized fully connection layer to capture the correlation between any two different labels. When trained on RCV1- v2, AAPD and Ren-CECps datasets, the proposed model outperforms previous work in main evaluation metrics of hamming loss and micro-F1 score.
Keywords: Multi-label classification | CNN | LSTM | Text classification
Identification of animal individuals using deep learning: A case study of giant panda
شناسایی فردی حیوانی با استفاده از یادگیری عمیق: یک مطالعه موردی از پاندا غول پیکر-2020
Giant panda (Ailuropoda melanoleuca) is an iconic species of conservation. However, long-term monitoring of wild giant pandas has been a challenge, largely due to the lack of appropriate method for the identification of target panda individuals. Although there are some traditional methods, such as distance-bamboo stem fragments methods, molecular biological method, and manual visual identification, they all have some limitations that can restrict their application. Therefore, it is urgent to explore a reliable and efficient approach to identify giant panda individuals. Here, we applied the deep learning technology and developed a novel face-identification model based on convolutional neural network to identify giant panda individuals. The model was able to identify 95% of giant panda individuals in the validation dataset. In all simulated field situations where the quality of photo data was degraded, the model still accurately identified more than 90% of panda individuals. The identification accuracy of our model is robust to brightness, small rotation, and cleanness of photos, although large rotation angle (> 20°) of photos has significant influence on the identification accuracy of the model (P < 0.01). Our model can be applied in future studies of giant panda such as long-term monitoring, big data analysis for behavior and be adapted for individual identification of other wildlife species.
Keywords: Deep learning | convolutional neural network | Individual identification | Giant panda
Sparse low rank factorization for deep neural network compression
فاکتورسازی رتبه پراکنده برای فشرده سازی شبکه عصبی عمیق-2020
Storing and processing millions of parameters in deep neural networks is highly challenging during the deployment of model in real-time application on resource constrained devices. Popular low-rank approx- imation approach singular value decomposition (SVD) is generally applied to the weights of fully con- nected layers where compact storage is achieved by keeping only the most prominent components of the decomposed matrices. Years of research on pruning-based neural network model compression re- vealed that the relative importance or contribution of each neuron in a layer highly vary among each other. Recently, synapses pruning has also demonstrated that having sparse matrices in network archi- tecture achieve lower space and faster computation during inference time. We extend these arguments by proposing that the low-rank decomposition of weight matrices should also consider significance of both input as well as output neurons of a layer. Combining the ideas of sparsity and existence of un- equal contributions of neurons towards achieving the target, we propose sparse low rank (SLR) method which sparsifies SVD matrices to achieve better compression rate by keeping lower rank for unimportant neurons. We demonstrate the effectiveness of our method in compressing famous convolutional neural networks based image recognition frameworks which are trained on popular datasets. Experimental re- sults show that the proposed approach SLR outperforms vanilla truncated SVD and a pruning baseline, achieving better compression rates with minimal or no loss in the accuracy. Code of the proposed ap- proach is avaialble at https://github.com/sridarah/slr .
Keywords: Low-rank approximation | Singular value decomposition | Sparse matrix | Deep neural networks | Convolutional neural networks
An empirical case study on Indian consumers sentiment towards electric vehicles: A big data analytics approach
یک مطالعه موردی تجربی در مورد احساسات مصرف کنندگان هندی نسبت به وسایل نقلیه برقی: یک رویکرد تحلیل داده های بزرگ-2020
Today, climate change due to global warming is a significant concern to all of us. Indias rate of greenhouse gas emissions is increasing day by day, placing India in the top ten emitters in the world. Air pollution is one of the significant contributors to the greenhouse effect. Transportation contributes about 10% of the air pollution in India. The Indian government is taking steps to reduce air pollution by encouraging the use of electric vehicles. But, success depends on consumers sentiment, perception and understanding towards Electric Vehicles (EV). This case study tried to capture the feeling, attitude, and emotions of Indian consumers towards electric vehicles. The main objective of this study was to extract opinions valuable to prospective buyers (to know what is best for them), marketers (for determining what features should be advertised) and manufacturers (for deciding what features should be improved) using Deep Learning techniques (e.g Doc2Vec Algorithm, Recurrent Neural Network (RNN), Convolutional Neural Network (CNN)). Due to the very nature of social media data, big data platform was chosen to analyze the sentiment towards EV. Deep Learning based techniques were preferred over traditional machine learning algorithms (Support Vector Machine, Logistic regression and Decision tree, etc.) due to its superior text mining capabilities. Two years data (2016 to 2018) were collected from different social media platform for this case study. The results showed the efficiency of deep learning algorithms and found CNN yield better results in-compare to others. The proposed optimal model will help consumers, designers and manufacturers in their decision-making capabilities to choose, design and manufacture EV.
Keywords: Electric vehicles | Deep learning | Big data | Sentiment analysis | India
A hybrid deep learning model for efficient intrusion detection in big data environment
یک مدل یادگیری عمیق ترکیبی برای تشخیص نفوذ موثر در محیط داده های بزرگ-2020
The volume of network and Internet traffic is expanding daily, with data being created at the zettabyte to petabyte scale at an exceptionally high rate. These can be character- ized as big data, because they are large in volume, variety, velocity, and veracity. Security threats to networks, the Internet, websites, and organizations are growing alongside this growth in usage. Detecting intrusions in such a big data environment is difficult. Various intrusion-detection systems (IDSs) using artificial intelligence or machine learning have been proposed for different types of network attacks, but most of these systems either cannot recognize unknown attacks or cannot respond to such attacks in real time. Deep learning models, recently applied to large-scale big data analysis, have shown remarkable performance in general but have not been examined for detection of intrusions in a big data environment. This paper proposes a hybrid deep learning model to efficiently detect network intrusions based on a convolutional neural network (CNN) and a weight-dropped, long short-term memory (WDLSTM) network. We use the deep CNN to extract mean- ingful features from IDS big data and WDLSTM to retain long-term dependencies among extracted features to prevent overfitting on recurrent connections. The proposed hybrid method was compared with traditional approaches in terms of performance on a publicly available dataset, demonstrating its satisfactory performance.
Keywords: Big data | Intrusion | detection Deep learning | Convolution neural network | Weight-dropped long short-term memory | network
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