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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 |