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نتیجه جستجو - RNN

تعداد مقالات یافته شده: 32
ردیف عنوان نوع
1 An integrated approach using CNN-RNN-LSTM for classification of fruit images
یک رویکرد یکپارچه با استفاده از CNN-RNN-LSTM برای طبقه بندی تصاویر میوه-2021
With the advancement in technology, Computer and machine vision system is getting involved in the agriculture sector for the last few years. Deep Learning is a recent advancement in the Artificial Intelligence field. In the present era, many researchers have used deep learning applications for the classification of images, and is found to be one of the emerging areas in computer vision. In the classification of fruit images, the main goal is to improve the accuracy of the classification system. The accuracy of the classifier depends on various factors like the nature of acquired images, the number of features, types of features, selection of optimal features from extracted features, and type of classifiers used. In the pro- posed article, integration of CNN, RNN, and LSTM for the classification of fruit images are defined. In this approach, CNN and RNN are employed for the development of discriminative characteristics and sequential-labels respectively. LSTM presents an explanation by integrating a memory cell to encode learning at each interval of classification. Key parameters: accuracy, F-measure, sensitivity, and specificity are applied to assess the achievement of the proposed scheme. From empirical results, it has been declared that the offered classification method provides efficient results.© 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 1st International Conference on Computations in Materials and Applied Engineering – 2021.
Keywords: CNN | RNN | LSTM | Integrated Approach | Fruit classification
مقاله انگلیسی
2 Modified deep learning and reinforcement learning for an incentive-based demand response model
یادگیری عمیق اصلاح شده و یادگیری تقویتی برای یک مدل پاسخ تقاضای مبتنی بر انگیزه-2020
Incentive-based demand response (DR) program can induce end users (EUs) to reduce electricity demand during peak period through rewards. In this study, an incentive-based DR program with modified deep learning and reinforcement learning is proposed. A modified deep learning model based on recurrent neural network (MDL-RNN) was first proposed to identify the future uncertainties of environment by forecasting day-ahead wholesale electricity price, photovoltaic (PV) power output, and power load. Then, reinforcement learning (RL) was utilized to explore the optimal incentive rates at each hour which can maximize the profits of both energy service providers (ESPs) and EUs. The results showed that the proposed modified deep learning model can achieve more accurate forecasting results compared with some other methods. It can support the development of incentive-based DR programs under uncertain environment. Meanwhile, the optimized incentive rate can increase the total profits of ESPs and EUs while reducing the peak electricity demand. A short-term DR program was developed for peak electricity demand period, and the experimental results show that peak electricity demand can be reduced by 17%. This contributes to mitigating the supply-demand imbalance and enhancing power system security.
Keywords: Demand response | Modified deep learning | Reinforcement learning | Smart grid
مقاله انگلیسی
3 Self-organization of action hierarchy and compositionality by reinforcement learning with recurrent neural networks
خود سازماندهی سلسله مراتبی و ترکیب پذیری عمل با یادگیری تقویتی و شبکه های عصبی بازگشتی -2020
Recurrent neural networks (RNNs) for reinforcement learning (RL) have shown distinct advantages, e.g., solving memory-dependent tasks and meta-learning. However, little effort has been spent on improving RNN architectures and on understanding the underlying neural mechanisms for performance gain. In this paper, we propose a novel, multiple-timescale, stochastic RNN for RL. Empirical results show that the network can autonomously learn to abstract sub-goals and can self-develop an action hierarchy using internal dynamics in a challenging continuous control task. Furthermore, we show that the self-developed compositionality of the network enhances faster re-learning when adapting to a new task that is a re-composition of previously learned sub-goals, than when starting from scratch. We also found that improved performance can be achieved when neural activities are subject to stochastic rather than deterministic dynamics.
Keywords: Recurrent neural network | Reinforcement learning | Partially observable Markov decision | process | Multiple timescale | Compositionality
مقاله انگلیسی
4 Towards a real-time processing framework based on improved distributed recurrent neural network variants with fastText for social big data analytics
به سمت یک چارچوب پردازش در زمان واقعی بر اساس بهبود انواع شبکه عصبی مکرر توزیع شده با fastText برای تجزیه و تحلیل داده های بزرگ اجتماعی-2020
Big data generated by social media stands for a valuable source of information, which offers an excellent opportunity to mine valuable insights. Particularly, User-generated contents such as reviews, recommendations, and users’ behavior data are useful for supporting several marketing activities of many companies. Knowing what users are saying about the products they bought or the services they used through reviews in social media represents a key factor for making decisions. Sentiment analysis is one of the fundamental tasks in Natural Language Processing. Although deep learning for sentiment analysis has achieved great success and allowed several firms to analyze and extract relevant information from their textual data, but as the volume of data grows, a model that runs in a traditional environment cannot be effective, which implies the importance of efficient distributed deep learning models for social Big Data analytics. Besides, it is known that social media analysis is a complex process, which involves a set of complex tasks. Therefore, it is important to address the challenges and issues of social big data analytics and enhance the performance of deep learning techniques in terms of classification accuracy to obtain better decisions. In this paper, we propose an approach for sentiment analysis, which is devoted to adopting fastText with Recurrent neural network variants to represent textual data efficiently. Then, it employs the new representations to perform the classification task. Its main objective is to enhance the performance of well-known Recurrent Neural Network (RNN) variants in terms of classification accuracy and handle large scale data. In addition, we propose a distributed intelligent system for real-time social big data analytics. It is designed to ingest, store, process, index, and visualize the huge amount of information in real-time. The proposed system adopts distributed machine learning with our proposed method for enhancing decision-making processes. Extensive experiments conducted on two benchmark data sets demonstrate that our proposal for sentiment analysis outperforms well-known distributed recurrent neural network variants (i.e., Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), and Gated Recurrent Unit (GRU)). Specifically, we tested the efficiency of our approach using the three different deep learning models. The results show that our proposed approach is able to enhance the performance of the three models. The current work can provide several benefits for researchers and practitioners who want to collect, handle, analyze and visualize several sources of information in real-time. Also, it can contribute to a better understanding of public opinion and user behaviors using our proposed system with the improved variants of the most powerful distributed deep learning and machine learning algorithms. Furthermore, it is able to increase the classification accuracy of several existing works based on RNN models for sentiment analysis.
Keywords: Big data | FastText | Recurrent neural networks | LSTM | BiLSTM | GRU | Natural language processing | Sentiment analysis | Social big data analytics
مقاله انگلیسی
5 Solder joint reliability risk estimation by AI modeling
برآورد خطر قابلیت اطمینان اتصال لحیم کاری با مدل سازی هوش مصنوعی -2020
This paper studies AI modeling for the solder joint fatigue risk estimation under the thermal cycle loading of redistributed wafer level packaging. The artificial neural network (ANN), recurrent neural network (RNN) and vectorized-gate network long short-term memory (VNLSTM) architectures have been trained by the same dataset to investigate their performance for this task. The learning accuracy criterion, the implementation of all neural network architecture, the learning results and result analysis would be covered. Because the involvement of the time/temperaturedependent nonlinearity material characteristics, it is recommended that more than three hidden layers and a proper neural network architecture, which is capable of the sequential data processing, should be considered in order to guarantee the required accuracy and the satisfied convergence speed.
Keywords: Solder joint fatigue risk estimation | Time/temperature-dependent nonlinearity | ANN | RNN | LSTM | machine learning
مقاله انگلیسی
6 Structured pruning of recurrent neural networks through neuron selection
هرس ساختاری شبکه های عصبی مکرر از طریق انتخاب نورون-2020
Recurrent neural networks (RNNs) have recently achieved remarkable successes in a number of applications. However, the huge sizes and computational burden of these models make it difficult for their deployment on edge devices. A practically effective approach is to reduce the overall storage and computation costs of RNNs by network pruning techniques. Despite their successful applications, those pruning methods based on Lasso either produce irregular sparse patterns in weight matrices, which is not helpful in practical speedup. To address these issues, we propose a structured pruning method through neuron selection which can remove the independent neuron of RNNs. More specifically, we introduce two sets of binary random variables, which can be interpreted as gates or switches to the input neurons and the hidden neurons, respectively. We demonstrate that the corresponding optimization problem can be addressed by minimizing the L0 norm of the weight matrix. Finally, experimental results on language modeling and machine reading comprehension tasks have indicated the advantages of the proposed method in comparison with state-of-the-art pruning competitors. In particular, nearly 20× practical speedup during inference was achieved without losing performance for the language model on the Penn TreeBank dataset, indicating the promising performance of the proposed method.
Keywords: Feature selection | Recurrent neural networks | Learning sparse models | Model compression
مقاله انگلیسی
7 Medi-Care AI: Predicting medications from billing codes via robust recurrent neural networks
Medi-Care AI: پیش بینی داروها از کدهای صورتحساب از طریق شبکه های عصبی تکراری شتاب دار-2020
In this paper, we present an effective deep prediction framework based on robust recurrent neural networks (RNNs) to predict the likely therapeutic classes of medications a patient is taking, given a sequence of diagnostic billing codes in their record. Accurately capturing the list of medications currently taken by a given patient is extremely challenging due to undefined errors and omissions. We present a general robust framework that explicitly models the possible contamination through overtime decay mechanism on the input billing codes and noise injection into the recurrent hidden states, respectively. By doing this, billing codes are reformulated into its temporal patterns with decay rates on each medical variable, and the hidden states of RNNs are regularized by random noises which serve as dropout to improved RNNs robustness towards data variability in terms of missing values and multiple errors. The proposed method is extensively evaluated on real health care data to demonstrate its effectiveness in suggesting medication orders from contaminated values.
Keywords: Billing codes | Robust recurrent neural networks | Health care data | Medication prediction
مقاله انگلیسی
8 Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach
پیش بینی پیش بینی پایگاه داده های سری زمانی با استفاده از شبکه های عصبی مکرر در گروه های مشابه سری: یک روش خوشه بندی-2020
With the advent of Big Data, nowadays in many applications databases containing large quantities of sim- ilar time series are available. Forecasting time series in these domains with traditional univariate fore- casting procedures leaves great potentials for producing accurate forecasts untapped. Recurrent neural networks (RNNs), and in particular Long Short Term Memory (LSTM) networks, have proven recently that they are able to outperform state-of-the-art univariate time series forecasting methods in this context, when trained across all available time series. However, if the time series database is heterogeneous, ac- curacy may degenerate, so that on the way towards fully automatic forecasting methods in this space, a notion of similarity between the time series needs to be built into the methods. To this end, we present a prediction model that can be used with different types of RNN models on subgroups of similar time series, which are identified by time series clustering techniques. We assess our proposed methodology using LSTM networks, a widely popular RNN variant, together with various clustering algorithms, such as kMeans, DBScan, Partition Around Medoids (PAM), and Snob. Our method achieves competitive results on benchmarking datasets under competition evaluation procedures. In particular, in terms of mean sMAPE accuracy it consistently outperforms the baseline LSTM model, and outperforms all other methods on the CIF2016 forecasting competition dataset.
Keywords: Big data forecasting | RNN | LSTM | Time series clustering | Neural networks
مقاله انگلیسی
9 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
مقاله انگلیسی
10 The role of AI in capital structure to enhance corporate funding strategies
نقش هوش مصنوعی در ساختار سرمایه برای تقویت استراتژی های بودجه شرکت ها-2020
The purpose of this study was to assess if Artificial Intelligence (AI) could be used in the Capital Asset Pricing Model (CAPM) and whether the use of AI could bring a more accurate estimation of expected returns. Cost of capital defines the minimum return expected from any investment made by a firm. Hence for managers to maximise the value of the corporation, it is essential to have an accurate estimation of the cost of capital. For the purpose of analysing securities, the adjusted closing stock prices of 10 high-tech public companies were studied from January 2013 to January 2019. This research assumed that there is a need to predict returns for the next year. Hence one year of historical data was used to calculate traditional CAPM value and also train the Recurrent Neural Networks (RNN) to predict stock prices of the upcoming year. A generic deep learning network architecture was developed with the use of Long Short Term Memory (LSTM) and dropout layers. After calculating the returns using traditional and AI approaches, two methods for calculation of CAPM were proposed and compared. Following the analysis, it was found that the use of AI improved the accuracy of cost of equity estimations by over 60%. The strong ability of the selected deep learning neural network to predict stock prices, increased the accuracy of estimating returns by at least 18%. This study concluded that AI has significant potentials to replace traditional asset pricing models in the near future.
Keywords: Artificial intelligence| Capital structure | Investment | CAPM (expected returns) | Neural network | Cost of capital
مقاله انگلیسی
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