Relevant bibliographies by topics / RNN NETWORK

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'RNN NETWORK'

Author: Grafiati
Published: 11 September 2023

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Journal articles on the topic "RNN NETWORK"

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Yin, Qiwei, Ruixun Zhang, and XiuLi Shao. "CNN and RNN mixed model for image classification." MATEC Web of Conferences 277 (2019): 02001. http://dx.doi.org/10.1051/matecconf/201927702001.

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In this paper, we propose a CNN(Convolutional neural networks) and RNN(recurrent neural networks) mixed model for image classification, the proposed network, called CNN-RNN model. Image data can be viewed as two-dimensional wave data, and convolution calculation is a filtering process. It can filter non-critical band information in an image, leaving behind important features of image information. The CNN-RNN model can use the RNN to Calculate the Dependency and Continuity Features of the Intermediate Layer Output of the CNN Model, connect the characteristics of these middle tiers to the final full-connection network for classification prediction, which will result in better classification accuracy. At the same time, in order to satisfy the restriction of the length of the input sequence by the RNN model and prevent the gradient explosion or gradient disappearing in the network, this paper combines the wavelet transform (WT) method in the Fourier transform to filter the input data. We will test the proposed CNN-RNN model on a widely-used datasets CIFAR-10. The results prove the proposed method has a better classification effect than the original CNN network, and that further investigation is needed.
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Tridarma, Panggih, and Sukmawati Nur Endah. "Pengenalan Ucapan Bahasa Indonesia Menggunakan MFCC dan Recurrent Neural Network." JURNAL MASYARAKAT INFORMATIKA 11, no. 2 (November 17, 2020): 36–44. http://dx.doi.org/10.14710/jmasif.11.2.34874.

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Pengenalan ucapan (speech recognition) merupakan perkembangan teknologi dalam bidang suara. Pengenalan ucapan memungkinkan suatu perangkat lunak mengenali kata-kata yang diucapkan oleh manusia dan ditampilkan dalam bentuk tulisan. Namun masih terdapat masalah untuk mengenali kata-kata yang diucapkan, seperti karakteristik suara yang berbeda, usia, kesehatan, dan jenis kelamin. Penelitian ini membahas pengenalan ucapan bahasa Indonesia dengan menggunakan Mel-Frequency Cepstral Coefficient (MFCC) sebagai metode ekstraksi ciri dan Recurrent Neural Network (RNN) sebagai metode pengenalannya dengan membandingkan arsitektur Elman RNN dan arsitektur Jordan RNN. Pembagian data latih dan data uji dilakukan dengan menggunakan metode k-fold cross validation dengan nilai k=5. Hasil penelitian menunjukkan bahwa arsitektur Elman RNN pada parameter 900 hidden neuron, target error 0.0005, learning rate 0.01, dan maksimal epoch 10000 dengan koefisien MFCC 20 menghasilkan akurasi terbaik sebesar 72.65%. Sedangkan hasil penelitian untuk arsitektur Jordan RNN pada parameter 500 hidden neuron, target error 0.0005, learning rate 0.01, dan maksimal epoch 10000 dengan koefisien MFCC 12 menghasilkan akurasi terbaik sebesar 73.55%. Sehingga berdasarkan hasil penelitian yang didapat, arsitektur Jordan RNN memiliki kinerja yang lebih baik dibandingkan dengan arsitektur Elman RNN dalam mengenali ucapan Bahasa Indonesia berjenis continuous speech
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Ma, Qianli, Zhenxi Lin, Enhuan Chen, and Garrison Cottrell. "Temporal Pyramid Recurrent Neural Network." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 5061–68. http://dx.doi.org/10.1609/aaai.v34i04.5947.

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Learning long-term and multi-scale dependencies in sequential data is a challenging task for recurrent neural networks (RNNs). In this paper, a novel RNN structure called temporal pyramid RNN (TP-RNN) is proposed to achieve these two goals. TP-RNN is a pyramid-like structure and generally has multiple layers. In each layer of the network, there are several sub-pyramids connected by a shortcut path to the output, which can efficiently aggregate historical information from hidden states and provide many gradient feedback short-paths. This avoids back-propagating through many hidden states as in usual RNNs. In particular, in the multi-layer structure of TP-RNN, the input sequence of the higher layer is a large-scale aggregated state sequence produced by the sub-pyramids in the previous layer, instead of the usual sequence of hidden states. In this way, TP-RNN can explicitly learn multi-scale dependencies with multi-scale input sequences of different layers, and shorten the input sequence and gradient feedback paths of each layer. This avoids the vanishing gradient problem in deep RNNs and allows the network to efficiently learn long-term dependencies. We evaluate TP-RNN on several sequence modeling tasks, including the masked addition problem, pixel-by-pixel image classification, signal recognition and speaker identification. Experimental results demonstrate that TP-RNN consistently outperforms existing RNNs for learning long-term and multi-scale dependencies in sequential data.
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Mosavat, Majid, and Guido Montorsi. "Single-Frequency Network Terrestrial Broadcasting with 5GNR Numerology Using Recurrent Neural Network." Electronics 11, no. 19 (September 29, 2022): 3130. http://dx.doi.org/10.3390/electronics11193130.

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We explore the feasibility of Terrestrial Broadcasting in a Single-Frequency Network (SFN) with standard 5G New Radio (5GNR) numerology designed for uni-cast transmission. Instead of the classical OFDM symbol-by-symbol detector scheme or a more complex equalization technique, we designed a Recurrent-Neural-Network (RNN)-based detector that replaces the channel estimation and equalization blocks. The RNN is a bidirectional Long Short-Term Memory (bi-LSTM) that computes the log-likelihood ratios delivered to the LDPC decoder starting from the received symbols affected by strong intersymbol/intercarrier interference (ISI/ICI) on time-varying channels. To simplify the RNN receiver and reduce the system overhead, pilot and data signals in our proposed scheme are superimposed instead of interspersed. We describe the parameter optimization of the RNN and provide end-to-end simulation results, comparing them with those of a classical system, where the OFDM waveform is specifically designed for Terrestrial Broadcasting. We show that the system outperforms classical receivers, especially in challenging scenarios associated with large intersite distance and large mobility. We also provide evidence of the robustness of the designed RNN receiver, showing that an RNN receiver trained on a single signal-to-noise ratio and user velocity performs efficiently also in a large range of scenarios with different signal-to-noise ratios and velocities.
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Du, Xiuli, Xiaohui Ding, and Fan Tao. "Network Security Situation Prediction Based on Optimized Clock-Cycle Recurrent Neural Network for Sensor-Enabled Networks." Sensors 23, no. 13 (July 1, 2023): 6087. http://dx.doi.org/10.3390/s23136087.

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We propose an optimized Clockwork Recurrent Neural Network (CW-RNN) based approach to address temporal dynamics and nonlinearity in network security situations, improving prediction accuracy and real-time performance. By leveraging the clock-cycle RNN, we enable the model to capture both short-term and long-term temporal features of network security situations. Additionally, we utilize the Grey Wolf Optimization (GWO) algorithm to optimize the hyperparameters of the network, thus constructing an enhanced network security situation prediction model. The introduction of a clock-cycle for hidden units allows the model to learn short-term information from high-frequency update modules while retaining long-term memory from low-frequency update modules, thereby enhancing the model’s ability to capture data patterns. Experimental results demonstrate that the optimized clock-cycle RNN outperforms other network models in extracting the temporal and nonlinear features of network security situations, leading to improved prediction accuracy. Furthermore, our approach has low time complexity and excellent real-time performance, ideal for monitoring large-scale network traffic in sensor networks.
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Choi, Seongjin, Hwasoo Yeo, and Jiwon Kim. "Network-Wide Vehicle Trajectory Prediction in Urban Traffic Networks using Deep Learning." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 45 (September 7, 2018): 173–84. http://dx.doi.org/10.1177/0361198118794735.

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This paper proposes a deep learning approach to learning and predicting network-wide vehicle movement patterns in urban networks. Inspired by recent success in predicting sequence data using recurrent neural networks (RNN), specifically in language modeling that predicts the next words in a sentence given previous words, this research aims to apply RNN to predict the next locations in a vehicle’s trajectory, given previous locations, by viewing a vehicle trajectory as a sentence and a set of locations in a network as vocabulary in human language. To extract a finite set of “locations,” this study partitions the network into “cells,” which represent subregions, and expresses each vehicle trajectory as a sequence of cells. Using large amounts of Bluetooth vehicle trajectory data collected in Brisbane, Australia, this study trains an RNN model to predict cell sequences. It tests the model’s performance by computing the probability of correctly predicting the next [Formula: see text] consecutive cells. Compared with a base-case model that relies on a simple transition matrix, the proposed RNN model shows substantially better prediction results. Network-level aggregate measures such as total cell visit count and intercell flow are also tested, and the RNN model is observed to be capable of replicating real-world traffic patterns.
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Nowak, Mateusz P., and Piotr Pecka. "Routing Algorithms Simulation for Self-Aware SDN." Electronics 11, no. 1 (December 29, 2021): 104. http://dx.doi.org/10.3390/electronics11010104.

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This paper presents a self-aware network approach with cognitive packets, with a routing engine based on random neural networks. The simulation study, performed using a custom simulator extension of OmNeT++, compares RNN routing with other routing methods. The performance results of RNN-based routing, combined with the distributed nature of its operation inaccessible to other presented methods, demonstrate the advantages of introducing neural networks as a decision-making mechanism in selecting network paths. This work also confirms the usefulness of the simulator for SDN networks with cognitive packets and various routing algorithms, including RNN-based routing engines.
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Muhuri, Pramita Sree, Prosenjit Chatterjee, Xiaohong Yuan, Kaushik Roy, and Albert Esterline. "Using a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) to Classify Network Attacks." Information 11, no. 5 (May 1, 2020): 243. http://dx.doi.org/10.3390/info11050243.

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An intrusion detection system (IDS) identifies whether the network traffic behavior is normal or abnormal or identifies the attack types. Recently, deep learning has emerged as a successful approach in IDSs, having a high accuracy rate with its distinctive learning mechanism. In this research, we developed a new method for intrusion detection to classify the NSL-KDD dataset by combining a genetic algorithm (GA) for optimal feature selection and long short-term memory (LSTM) with a recurrent neural network (RNN). We found that using LSTM-RNN classifiers with the optimal feature set improves intrusion detection. The performance of the IDS was analyzed by calculating the accuracy, recall, precision, f-score, and confusion matrix. The NSL-KDD dataset was used to analyze the performances of the classifiers. An LSTM-RNN was used to classify the NSL-KDD datasets into binary (normal and abnormal) and multi-class (Normal, DoS, Probing, U2R, and R2L) sets. The results indicate that applying the GA increases the classification accuracy of LSTM-RNN in both binary and multi-class classification. The results of the LSTM-RNN classifier were also compared with the results using a support vector machine (SVM) and random forest (RF). For multi-class classification, the classification accuracy of LSTM-RNN with the GA model is much higher than SVM and RF. For binary classification, the classification accuracy of LSTM-RNN is similar to that of RF and higher than that of SVM.
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Paramasivan, Senthil Kumar. "Deep Learning Based Recurrent Neural Networks to Enhance the Performance of Wind Energy Forecasting: A Review." Revue d'Intelligence Artificielle 35, no. 1 (February 28, 2021): 1–10. http://dx.doi.org/10.18280/ria.350101.

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In the modern era, deep learning is a powerful technique in the field of wind energy forecasting. The deep neural network effectively handles the seasonal variation and uncertainty characteristics of wind speed by proper structural design, objective function optimization, and feature learning. The present paper focuses on the critical analysis of wind energy forecasting using deep learning based Recurrent neural networks (RNN) models. It explores RNN and its variants, such as simple RNN, Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional RNN models. The recurrent neural network processes the input time series data sequentially and captures well the temporal dependencies exist in the successive input data. This review investigates the RNN models of wind energy forecasting, the data sources utilized, and the performance achieved in terms of the error measures. The overall review shows that the deep learning based RNN improves the performance of wind energy forecasting compared to the conventional techniques.
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Yan, Jiapeng, Huifang Kong, and Zhihong Man. "Recurrent Neural Network-Based Nonlinear Optimization for Braking Control of Electric Vehicles." Energies 15, no. 24 (December 14, 2022): 9486. http://dx.doi.org/10.3390/en15249486.

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In this paper, electro-hydraulic braking (EHB) force allocation for electric vehicles (EVs) is modeled as a constrained nonlinear optimization problem (NOP). Recurrent neural networks (RNNs) are advantageous in many folds for solving NOPs, yet existing RNNs’ convergence usually requires convexity with calculation of second-order partial derivatives. In this paper, a recurrent neural network-based NOP solver (RNN-NOPS) is developed. It is seen that the RNN-NOPS is designed to drive all state variables to asymptotically converge to the feasible region, with loose requirement on the NOP’s first-order partial derivative. In addition, the RNN-NOPS’s equilibria are proved to meet Karush–Kuhn–Tucker (KKT) conditions, and the RNN-NOPS behaves with a strong robustness against the violation of the constraints. The comparative studies are conducted to show RNN-NOPS’s advantages for solving the EHB force allocation problem, it is reported that the overall regenerative energy of RNN-NOPS is 15.39% more than that of the method for comparison under SC03 cycle.
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Dissertations / Theses on the topic "RNN NETWORK"

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Bäärnhielm, Arvid. "Multiple time-series forecasting on mobile network data using an RNN-RBM model." Thesis, Uppsala universitet, Datalogi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-315782.

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The purpose of this project is to evaluate the performance of a forecasting model based on a multivariate dataset consisting of time series of traffic characteristic performance data from a mobile network. The forecasting is made using machine learning with a deep neural network. The first part of the project involves the adaption of the model design to fit the dataset and is followed by a number of simulations where the aim is to tune the parameters of the model to give the best performance. The simulations show that with well tuned parameters, the neural network performes better than the baseline model, even when using only a univariate dataset. If a multivariate dataset is used, the neural network outperforms the baseline model even when the dataset is small.
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Vikström, Filip. "A recurrent neural network approach to quantification of risks surrounding the Swedish property market." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-126192.

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As the real estate market plays a central role in a countries financial situation, as a life insurer, a bank and a property developer, Skandia wants a method for better assessing the risks connected to the real estate market. The goal of this paper is to increase the understanding of property market risk and its covariate risks and to conduct an analysis of how a fall in real estate prices could affect Skandia’s exposed assets.This paper explores a recurrent neural network model with the aim of quantifying identified risk factors using exogenous data. The recurrent neural network model is compared to a vector autoregressive model with exogenous inputs that represent economic conditions.The results of this paper are inconclusive as to which method that produces the most accurate model under the specified settings. The recurrent neural network approach produces what seem to be better results in out-of-sample validation but both the recurrent neural network model and the vector autoregressive model fail to capture the hypothesized relationship between the exogenous and modeled variables. However producing results that does not fit previous assumptions, further research into artificial neural networks and tests with additional variables and longer sample series for calibration is suggested as the model preconditions are promising.
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Billingsley, Richard John. "Deep Learning for Semantic and Syntactic Structures." Thesis, The University of Sydney, 2014. http://hdl.handle.net/2123/12825.

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Deep machine learning has enjoyed recent success in vision and speech-to-text tasks, using deep multi-layered neural networks. They have obtained remarkable results particularly where the internal representation of the task is unclear. In parsing, where the structure of syntax is well studied and understood from linguistics, neural networks have so far not performed so well. State-of-the-art parsers use a tree-based graphical model that requires a large number of equivalent classes to represent each parse node and its phrase label. A recursive neural network (RNN) parser has been developed that works well on short sentences, but falls short of the state-of-the-art results on longer sentences. This thesis aims to investigate deep learning and improve parsing by examining how neural networks could perform state-of-the-art parsing by comparison with PCFG parsers. We hypothesize that a neural network could be configured to implement an algorithm parallel to PCFG parsers, and examine their suitability to this task from an analytic perspective. This highlights a missing term that the RNN parser is unable to model, and we identify the role of this missing term in parsing. We finally present two methods to improve the RNN parser by building upon the analysis in earlier chapters, one using an iterative process similar to belief propagation that yields a 0.38% improvement and another replacing the scoring method with a deeper neural model yielding a 0.83% improvement. By examining an RNN parser as an exemplar of a deep neural network, we gain insights to deep machine learning and some of the approximations it must make by comparing it with well studied non-neural parsers that achieve state-of-the-art results. In this way, our research provides a better understanding of deep machine learning and a step towards improvements in parsing that will lead to smarter algorithms that can learn more accurate representations of information and the syntax and semantics of text.
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Liu, Chang. "Data Analysis of Minimally-Structured Heterogeneous Logs : An experimental study of log template extraction and anomaly detection based on Recurrent Neural Network and Naive Bayes." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-191334.

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Nowadays, the ideas of continuous integration and continuous delivery are under heavy usage in order to achieve rapid software development speed and quick product delivery to the customers with good quality. During the process ofmodern software development, the testing stage has always been with great significance so that the delivered software is meeting all the requirements and with high quality, maintainability, sustainability, scalability, etc. The key assignment of software testing is to find bugs from every test and solve them. The developers and test engineers at Ericsson, who are working on a large scale software architecture, are mainly relying on the logs generated during the testing, which contains important information regarding the system behavior and software status, to debug the software. However, the volume of the data is too big and the variety is too complex and unpredictable, therefore, it is very time consuming and with great efforts for them to manually locate and resolve the bugs from such vast amount of log data. The objective of this thesis project is to explore a way to conduct log analysis efficiently and effectively by applying relevant machine learning algorithms in order to help people quickly detect the test failure and its possible causalities. In this project, a method of preprocessing and clusering original logs is designed and implemented in order to obtain useful data which can be fed to machine learning algorithms. The comparable log analysis, based on two machine learning algorithms - Recurrent Neural Network and Naive Bayes, is conducted for detecting the place of system failures and anomalies. Finally, relevant experimental results are provided and analyzed.
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Li, Edwin. "LSTM Neural Network Models for Market Movement Prediction." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231627.

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Interpreting time varying phenomena is a key challenge in the capital markets. Time series analysis using autoregressive methods has been carried out over the last couple of decades, often with reassuring results. However, such methods sometimes fail to explain trends and cyclical fluctuations, which may be characterized by long-range dependencies or even dependencies between the input features. The purpose of this thesis is to investigate whether recurrent neural networks with LSTM-cells can be used to capture these dependencies, and ultimately be used as a complement for index trading decisions. Experiments are made on different setups of the S&P-500 stock index, and two distinct models are built, each one being an improvement of the previous model. The first model is a multivariate regression model, and the second model is a multivariate binary classifier. The output of each model is used to reason about the future behavior of the index. The experiment shows for the configuration provided that LSTM RNNs are unsuitable for predicting exact values of daily returns, but gives satisfactory results when used to predict the direction of the movement.
Att förstå och kunna förutsäga hur index varierar med tiden och andra parametrar är ett viktigt problem inom kapitalmarknader. Tidsserieanalys med autoregressiva metoder har funnits sedan årtionden tillbaka, och har oftast gett goda resultat. Dessa metoder saknar dock möjligheten att förklara trender och cykliska variationer i tidsserien, något som kan karaktäriseras av tidsvarierande samband, men även samband mellan parametrar som indexet beror utav. Syftet med denna studie är att undersöka om recurrent neural networks (RNN) med long short-term memory-celler (LSTM) kan användas för att fånga dessa samband, för att slutligen användas som en modell för att komplettera indexhandel. Experimenten är gjorda mot en modifierad S&P-500 datamängd, och två distinkta modeller har tagits fram. Den ena är en multivariat regressionsmodell för att förutspå exakta värden, och den andra modellen är en multivariat klassifierare som förutspår riktningen på nästa dags indexrörelse. Experimenten visar för den konfiguration som presenteras i rapporten att LSTM RNN inte passar för att förutspå exakta värden för indexet, men ger tillfredsställande resultat när modellen ska förutsäga indexets framtida riktning.
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Ďuriš, Denis. "Detekce ohně a kouře z obrazového signálu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2020. http://www.nusl.cz/ntk/nusl-412968.

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This diploma thesis deals with the detection of fire and smoke from the image signal. The approach of this work uses a combination of convolutional and recurrent neural network. Machine learning models created in this work contain inception modules and blocks of long short-term memory. The research part describes selected models of machine learning used in solving the problem of fire detection in static and dynamic image data. As part of the solution, a data set containing videos and still images used to train the designed neural networks was created. The results of this approach are evaluated in conclusion.
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Racette, Olsén Michael. "Electrocardiographic deviation detection : Using long short-term memory recurrent neural networks to detect deviations within electrocardiographic records." Thesis, Linnéuniversitetet, Institutionen för datavetenskap (DV), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-76411.

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Artificial neural networks have been gaining attention in recent years due to theirimpressive ability to map out complex nonlinear relations within data. In this report,an attempt is made to use a Long short-term memory neural network for detectinganomalies within electrocardiographic records. The hypothesis is that if a neuralnetwork is trained on records of normal ECGs to predict future ECG sequences, it isexpected to have trouble predicting abnormalities not previously seen in the trainingdata. Three different LSTM model configurations were trained using records fromthe MIT-BIH Arrhythmia database. Afterwards the models were evaluated for theirability to predict previously unseen normal and anomalous sections. This was doneby measuring the mean squared error of each prediction and the uncertainty of over-lapping predictions. The preliminary results of this study demonstrate that recurrentneural networks with the use of LSTM units are capable of detecting anomalies.
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Carman, Benjamin Andrew. "Translating LaTeX to Coq: A Recurrent Neural Network Approach to Formalizing Natural Language Proofs." Ohio University Honors Tutorial College / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ouhonors161919616626269.

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Ljungehed, Jesper. "Predicting Customer Churn Using Recurrent Neural Networks." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-210670.

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Churn prediction is used to identify customers that are becoming less loyal and is an important tool for companies that want to stay competitive in a rapidly growing market. In retail, a dynamic definition of churn is needed to identify churners correctly. Customer Lifetime Value (CLV) is the monetary value of a customer relationship. No change in CLV for a given customer indicates a decrease in loyalty. This thesis proposes a novel approach to churn prediction. The proposed model uses a Recurrent Neural Network to identify churners based on Customer Lifetime Value time series regression. The results show that the model performs better than random. This thesis also investigated the use of the K-means algorithm as a replacement to a rule-extraction algorithm. The K-means algorithm contributed to a more comprehensive analytical context regarding the churn prediction of the proposed model.
Illojalitet prediktering används för att identifiera kunder som är påväg att bli mindre lojala och är ett hjälpsamt verktyg för att ett företag ska kunna driva en konkurrenskraftig verksamhet. I detaljhandel behöves en dynamisk definition av illojalitet för att korrekt kunna identifera illojala kunder. Kundens livstidsvärde är ett mått på monetärt värde av en kundrelation. En avstannad förändring av detta värde indikerar en minskning av kundens lojalitet. Denna rapport föreslår en ny metod för att utföra illojalitet prediktering. Den föreslagna metoden består av ett återkommande neuralt nätverk som används för att identifiera illojalitet hos kunder genom att prediktera kunders livstidsvärde. Resultaten visar att den föreslagna modellen presterar bättre jämfört med slumpmässig metod. Rapporten undersöker också användningen av en k-medelvärdesalgoritm som ett substitut för en regelextraktionsalgoritm. K-medelsalgoritm bidrog till en mer omfattande analys av illojalitet predikteringen.
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Смішний, Денис Миколайович. "Система прогнозування економічних показників." Master's thesis, КПІ ім. Ігоря Сікорського, 2019. https://ela.kpi.ua/handle/123456789/30950.

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Магістерська дисертація: 88 с., 20 рис., 27 табл., 1 додаток, 33 джерел. Актуальність проблеми. Глобалізація та збільшення числа населення сприяють розвитку глобальної економіки, а отже — появі нових видів госпо-дарської діяльності та нових гравців на ринку праці. При реалізації власного підприємства важливо правильно оцінити ризики ринку, проаналізувавши та спробувавши спрогнозувати рух котирувань на найближчий час задля мінімальних фінансових втрат. Зв’язок роботи з науковими програмами, планами, темами. Наразі, не має конкретних зв’язків з науковими програмами чи планами. Мета і задачі дослідження. Завданням цієї роботи є дослідження мож-ливості прогнозування економічних параметрів підприємств на прикладі цін на акції компаній на фондовій біржі. Метою є розроблення системи, побудо-ваної на базі нейронної мережі, здатної проаналізувати задані економічні по-казники та, на основі отриманих даних спрогнозувати їхню динаміку. Об’єкт дослідження. Процес прогнозування економічних показників з використанням елементів нейронної мережі. Предмет дослідження. Методи аналізу та обробки економічних даних за певний період. Новизна. Отримання програмного продукту, що здатний прогнозувати коливання економічних показників. Дослідження можливості реалізації мо-делі на основі нейронної мережі для виконання поставленої мети та завдань.
Master's Thesis: 88 pp., 20 figs., 27 tables, 1 appendix, 33 sources. The urgency of the problem. Globalization and population growth are con-tributing to the development of the global economy and, consequently, to the emergence of new types of economic activity and new players in the labor market. When implementing your own business it is important to properly evaluate the risks of the market, analyzing and trying to predict the movement of quotations in the near future for minimal financial losses. Relationship with working with scientific programs, plans, topics. Cur-rently, it has no specific links to scientific programs or plans. The purpose and objectives of the study. The purpose of this work is re-search possibility of forecasting the economic parameters of enterprises on the ex-ample of stock prices of companies on the stock exchange. The purpose is to de-velop a system based on a neural network, capable of analyzing specified economic indicators and, based on the data obtained, to predict their dynamics. Object of study. The process of forecasting economic performance using neural network elements. Subject of study. Methods of analysis and processing of economic data for a certain period. Novelty. Obtaining a software product capable of predicting economic fluc-tuations. Investigation of the possibility of creating a universal model based on a neural network, which would not require specialization and would be able to work effectively with any set of input data without further training.
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Books on the topic "RNN NETWORK"

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Collins, Lesley J. RNA infrastructure and networks. New York, N.Y: Springer Science+Business Media, 2011.

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National Education Association of the United States. Professional and Organizational Development. and National Education Association of the United States. Research Division., eds. Research computer network: Operators handbook. Washington, D.C: The Association, 1989.

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Collins, Lesley J., ed. RNA Infrastructure and Networks. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-0332-6.

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Maselnikova, Alice. The AIM Network: Artists' Initiatives Meetings. Stockholm: AIM Network/Supermarket, 2017.

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Yawen, Xiao, ed. Chao you xiao lian shu ji ke shu: Bu hua qian jiu neng ju ji liu lan ren chao de jing ren fang fa. Taibei Shi: Tian xia za zhi gu fen you xian gong si, 2012.

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Peng, Mugen, Zhongyuan Zhao, and Yaohua Sun. Fog Radio Access Networks (F-RAN). Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50735-0.

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Zhongguo ren mai. Beijing Shi: Shi jie zhi shi chu ban she, 2010.

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Wang: Zhangguo shi ren mai = The network : relationship in China. Wuhan: Wuhan da xue chu ban she, 2006.

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Shanghai zheng da yan jiu suo, ed. Xin Shanghai ren. Beijing: Dong fang chu ban she, 2002.

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Ren gong shen jing wang luo ji chu. Ha'erbin: Ha'erbin gong cheng da xue chu ban she, 2008.

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Book chapters on the topic "RNN NETWORK"

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Das, Susmita, Amara Tariq, Thiago Santos, Sai Sandeep Kantareddy, and Imon Banerjee. "Recurrent Neural Networks (RNNs): Architectures, Training Tricks, and Introduction to Influential Research." In Machine Learning for Brain Disorders, 117–38. New York, NY: Springer US, 2012. http://dx.doi.org/10.1007/978-1-0716-3195-9_4.

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AbstractRecurrent neural networks (RNNs) are neural network architectures with hidden state and which use feedback loops to process a sequence of data that ultimately informs the final output. Therefore, RNN models can recognize sequential characteristics in the data and help to predict the next likely data point in the data sequence. Leveraging the power of sequential data processing, RNN use cases tend to be connected to either language models or time-series data analysis. However, multiple popular RNN architectures have been introduced in the field, starting from SimpleRNN and LSTM to deep RNN, and applied in different experimental settings. In this chapter, we will present six distinct RNN architectures and will highlight the pros and cons of each model. Afterward, we will discuss real-life tips and tricks for training the RNN models. Finally, we will present four popular language modeling applications of the RNN models –text classification, summarization, machine translation, and image-to-text translation– thereby demonstrating influential research in the field.
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Yellin, Daniel M., and Gail Weiss. "Synthesizing Context-free Grammars from Recurrent Neural Networks." In Tools and Algorithms for the Construction and Analysis of Systems, 351–69. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72016-2_19.

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AbstractWe present an algorithm for extracting a subclass of the context free grammars (CFGs) from a trained recurrent neural network (RNN). We develop a new framework, pattern rule sets (PRSs), which describe sequences of deterministic finite automata (DFAs) that approximate a non-regular language. We present an algorithm for recovering the PRS behind a sequence of such automata, and apply it to the sequences of automata extracted from trained RNNs using the $$L^{*}$$ L ∗ algorithm. We then show how the PRS may converted into a CFG, enabling a familiar and useful presentation of the learned language.Extracting the learned language of an RNN is important to facilitate understanding of the RNN and to verify its correctness. Furthermore, the extracted CFG can augment the RNN in classifying correct sentences, as the RNN’s predictive accuracy decreases when the recursion depth and distance between matching delimiters of its input sequences increases.
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Kobayashi, Naoki, and Minchao Wu. "Neural Network-Guided Synthesis of Recursive List Functions." In Tools and Algorithms for the Construction and Analysis of Systems, 227–45. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-30823-9_12.

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AbstractKobayashi et al. have recently proposed NeuGuS, a framework of neural-network-guided synthesis of logical formulas or simple program fragments, where a neural network is first trained based on data, and then a logical formula over integers is constructed by using the weights and biases of the trained network as hints. The previous method was, however, restricted the class of formulas of quantifier-free linear integer arithmetic. In this paper, we propose a NeuGuS method for the synthesis of recursive predicates over lists definable by using the left fold function. To this end, we design and train a special-purpose recurrent neural network (RNN), and use the weights of the trained RNN to synthesize a recursive predicate. We have implemented the proposed method and conducted preliminary experiments to confirm the effectiveness of the method.
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Kanumuri, Saketh, Vinay Teja Kantipudi, A. Viji Amutha Mary, and Mercy Paul Selvan. "Detection of Ransomware Based on Recurrent Neural Network (RNN)." In Lecture Notes in Electrical Engineering, 569–75. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-7511-2_57.

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Jayashree, D., O. Pandithurai, S. Shreevathsav, and P. Shyamala. "Generation of Handwriting Applying RNN with Mixture Density Network." In Advances in Automation, Signal Processing, Instrumentation, and Control, 2593–601. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8221-9_241.

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Al Mamun, S. M. Abdullah, and Mehmet Beyaz. "LSTM Recurrent Neural Network (RNN) for Anomaly Detection in Cellular Mobile Networks." In Machine Learning for Networking, 222–37. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19945-6_15.

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Ji, Jinbao, Zongxiang Hu, Weiqi Zhang, and Sen Yang. "Development of Deep Learning Algorithms, Frameworks and Hardwares." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications, 696–710. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_71.

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AbstractAs the core algorithm of artificial intelligence, deep learning has brought new breakthroughs and opportunities to all walks of life. This paper summarizes the principles of deep learning algorithms such as Autoencoder (AE), Boltzmann Machine (BM), Deep Belief Network (DBM), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Recursive Neural Network (RNN). The characteristics and differences of deep learning frameworks such as Tensorflow, Caffe, Theano and PyTorch are compared and analyzed. Finally, the application and performance of hardware platforms such as CPU and GPU in deep learning acceleration are introduced. In this paper, the development and application of deep learning algorithm, framework and hardware technology can provide reference and basis for the selection of deep learning technology.
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Jin, Jialong, Wei Zhou, and Baichen Jiang. "Maritime Target Trajectory Prediction Model Based on the RNN Network." In Lecture Notes in Electrical Engineering, 334–42. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0187-6_39.

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Liu, Enhan, Yan Chu, Lan Luan, Guang Li, and Zhengkui Wang. "Mixing-RNN: A Recommendation Algorithm Based on Recurrent Neural Network." In Knowledge Science, Engineering and Management, 109–17. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29551-6_10.

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Wang, Qi. "RNN Neural Network for Recovery Characteristic System of Resistant Polymer." In 2021 International Conference on Applications and Techniques in Cyber Intelligence, 725–29. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79200-8_108.

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Conference papers on the topic "RNN NETWORK"

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Ghodsi, Mohammadreza, Xiaofeng Liu, James Apfel, Rodrigo Cabrera, and Eugene Weinstein. "Rnn-Transducer with Stateless Prediction Network." In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9054419.

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Tseng, Yung-Chiao, Chao-Chun Chen, Chiang Lee, and Yuan-Ko Huang. "Incremental In-Network RNN Search in Wireless Sensor Networks." In 2007 International Conference on Parallel Processing Workshops (ICPPW 2007). IEEE, 2007. http://dx.doi.org/10.1109/icppw.2007.47.

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Park, Shin Hyuk, Hyun Jae Park, and Young-June Choi. "RNN-based Prediction for Network Intrusion Detection." In 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC). IEEE, 2020. http://dx.doi.org/10.1109/icaiic48513.2020.9065249.

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Venturini, M. "Simulation of Compressor Transient Behavior Through Recurrent Neural Network Models." In ASME Turbo Expo 2005: Power for Land, Sea, and Air. ASMEDC, 2005. http://dx.doi.org/10.1115/gt2005-68030.

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In the paper, self-adapting models capable of reproducing time-dependent data with high computational speed are investigated. The considered models are recurrent feed-forward neural networks (RNNs) with one feedback loop in a recursive computational structure, trained by using a back-propagation learning algorithm. The data used for both training and testing the RNNs have been generated by means of a non-linear physics-based model for compressor dynamic simulation, which was calibrated on a multi-stage axial-centrifugal small size compressor. The first step of the analysis is the selection of the compressor maneuver to be used for optimizing RNN training. The subsequent step consists in evaluating the most appropriate RNN structure (optimal number of neurons in the hidden layer and number of outputs) and RNN proper delay time. Then, the robustness of the model response towards measurement uncertainty is ascertained, by comparing the performance of RNNs trained on data uncorrupted or corrupted with measurement errors with respect to the simulation of data both uncorrupted and corrupted with measurement errors. Finally, the best RNN model is tested on field data taken on the axial-centrifugal compressor on which the physics-based model was calibrated, by comparing physics-based model and RNN predictions against measured data. The comparison between RNN predictions and measured data shows that the agreement can be considered acceptable for inlet pressure, outlet pressure and outlet temperature, while errors are significant for inlet mass flow rate.
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Xia, Rui, Mengran Zhang, and Zixiang Ding. "RTHN: A RNN-Transformer Hierarchical Network for Emotion Cause Extraction." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/734.

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The emotion cause extraction (ECE) task aims at discovering the potential causes behind a certain emotion expression in a document. Techniques including rule-based methods, traditional machine learning methods and deep neural networks have been proposed to solve this task. However, most of the previous work considered ECE as a set of independent clause classification problems and ignored the relations between multiple clauses in a document. In this work, we propose a joint emotion cause extraction framework, named RNN-Transformer Hierarchical Network (RTHN), to encode and classify multiple clauses synchronously. RTHN is composed of a lower word-level encoder based on RNNs to encode multiple words in each clause, and an upper clause-level encoder based on Transformer to learn the correlation between multiple clauses in a document. We furthermore propose ways to encode the relative position and global predication information into Transformer that can capture the causality between clauses and make RTHN more efficient. We finally achieve the best performance among 12 compared systems and improve the F1 score of the state-of-the-art from 72.69% to 76.77%.
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Li, Xuelong, Bin Zhao, and Xiaoqiang Lu. "MAM-RNN: Multi-level Attention Model Based RNN for Video Captioning." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/307.

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Visual information is quite important for the task of video captioning. However, in the video, there are a lot of uncorrelated content, which may cause interference to generate a correct caption. Based on this point, we attempt to exploit the visual features which are most correlated to the caption. In this paper, a Multi-level Attention Model based Recurrent Neural Network (MAM-RNN) is proposed, where MAM is utilized to encode the visual feature and RNN works as the decoder to generate the video caption. During generation, the proposed approach is able to adaptively attend to the salient regions in the frame and the frames correlated to the caption. Practically, the experimental results on two benchmark datasets, i.e., MSVD and Charades, have shown the excellent performance of the proposed approach.
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Lyu, Muyang, Ruixuan Liu, and Junyi Wang. "Solving Raven's Progressive Matrices Using RNN Reasoning Network." In 2022 7th International Conference on Computational Intelligence and Applications (ICCIA). IEEE, 2022. http://dx.doi.org/10.1109/iccia55271.2022.9828445.

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Li, Dailun, Zeying Tian, and Yining Duan. "Self-attention on RNN-based text classification." In International Conference on Computer Network Security and Software Engineering (CNSSE 2022), edited by Wenshun Sheng and Yongquan Yan. SPIE, 2022. http://dx.doi.org/10.1117/12.2641031.

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Nam, Sukhyun, Jiyoon Lim, Jae-Hyoung Yoo, and James Won-Ki Hong. "Network Anomaly Detection Based on In-band Network Telemetry with RNN." In 2020 IEEE International Conference on Consumer Electronics - Asia (ICCE-Asia). IEEE, 2020. http://dx.doi.org/10.1109/icce-asia49877.2020.9276768.

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Luo, Donghao, Bingbing Ni, Yichao Yan, and Xiaokang Yang. "Image Matching via Loopy RNN." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/335.

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Most existing matching algorithms are one-off algorithms, i.e., they usually measure the distance between the two image feature representation vectors for only one time. In contrast, human's vision system achieves this task, i.e., image matching, by recursively looking at specific/related parts of both images and then making the final judgement. Towards this end, we propose a novel loopy recurrent neural network (Loopy RNN), which is capable of aggregating relationship information of two input images in a progressive/iterative manner and outputting the consolidated matching score in the final iteration. A Loopy RNN features two uniqueness. First, built on conventional long short-term memory (LSTM) nodes, it links the output gate of the tail node to the input gate of the head node, thus it brings up symmetry property required for matching. Second, a monotonous loss designed for the proposed network guarantees increasing confidence during the recursive matching process. Extensive experiments on several image matching benchmarks demonstrate the great potential of the proposed method.
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Reports on the topic "RNN NETWORK"

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Brooks, Richard. Reactive Sensor Networks (RSN). Fort Belvoir, VA: Defense Technical Information Center, October 2003. http://dx.doi.org/10.21236/ada419219.

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Allende López, Marcos, Diego López, Sergio Cerón, Antonio Leal, Adrián Pareja, Marcelo Da Silva, Alejandro Pardo, et al. Quantum-Resistance in Blockchain Networks. Inter-American Development Bank, June 2021. http://dx.doi.org/10.18235/0003313.

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This paper describes the work carried out by the Inter-American Development Bank, the IDB Lab, LACChain, Cambridge Quantum Computing (CQC), and Tecnológico de Monterrey to identify and eliminate quantum threats in blockchain networks. The advent of quantum computing threatens internet protocols and blockchain networks because they utilize non-quantum resistant cryptographic algorithms. When quantum computers become robust enough to run Shor's algorithm on a large scale, the most used asymmetric algorithms, utilized for digital signatures and message encryption, such as RSA, (EC)DSA, and (EC)DH, will be no longer secure. Quantum computers will be able to break them within a short period of time. Similarly, Grover's algorithm concedes a quadratic advantage for mining blocks in certain consensus protocols such as proof of work. Today, there are hundreds of billions of dollars denominated in cryptocurrencies that rely on blockchain ledgers as well as the thousands of blockchain-based applications storing value in blockchain networks. Cryptocurrencies and blockchain-based applications require solutions that guarantee quantum resistance in order to preserve the integrity of data and assets in their public and immutable ledgers. We have designed and developed a layer-two solution to secure the exchange of information between blockchain nodes over the internet and introduced a second signature in transactions using post-quantum keys. Our versatile solution can be applied to any blockchain network. In our implementation, quantum entropy was provided via the IronBridge Platform from CQC and we used LACChain Besu as the blockchain network.
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Yeates, Jessica. The Foundations of Network Dynamics in an RNA Recombinase System. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.2915.

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Robert, J., and Michael Forte. Field evaluation of GNSS/GPS based RTK, RTN, and RTX correction systems. Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/11681/41864.

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This Coastal and Hydraulic Engineering Technical Note (CHETN) details an evaluation of three Global Navigation Satellite System (GNSS)/Global Positioning System (GPS) real-time correction methods capable of providing centimeter-level positioning. Internet and satellite-delivered correction systems, Real Time Network (RTN) and Real Time eXtended (RTX), respectively, are compared to a traditional ground-based two-way radio transmission correction system, generally referred to as Local RTK, or simply RTK. Results from this study will provide prospective users background information on each of these positioning systems and comparisons of their respective accuracies during in field operations.
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Ciobanu, Catalin Irinel. A Neural networks search for single top quark production in CDF Run I data. Office of Scientific and Technical Information (OSTI), August 2002. http://dx.doi.org/10.2172/1420934.

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Hotsur, Oksana. SOCIAL NETWORKS AND BLOGS AS TOOLS PR-CAMPAIGN IMPLEMENTATIONS. Ivan Franko National University of Lviv, March 2021. http://dx.doi.org/10.30970/vjo.2021.50.11110.

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The article deals with the ways in which social networks and the blogosphere influence the formation and implementation of a PR campaign. Examples from the political sphere (election campaigns, initiatives), business (TV brands, traditional and online media) have revealed the opportunities that Facebook, Telegram, Twitter, YouTube and blogs promote in promoting advertising, ideas, campaigns, thoughts, or products. Author blogs created on special websites or online media may not be as much of a tool in PR as an additional tool on social media. It is noted that choosing a blog as the main tool of PR campaign has both positive and negative points. Social networks intervene in the sphere of human life, become a means of communication, promotion, branding. The effectiveness of social networks has been evidenced by such historically significant events as Brexit, the Arab Spring, and the Revolution of Dignity. Special attention was paid to the 2019 presidential election. Based on the analysis of individual PR campaigns, the reasons for successful and unsuccessful campaigns from the point of view of network communication, which provide unlimited multimedia and interactive tools for PR, are highlighted. In fact, these concepts significantly affect the effectiveness of the implementation of PR-campaign, its final effectiveness, which is determined by the achievement of goals. Attention is drawn to the culture of communication during the PR campaign, as well as the concepts of “trolls”, “trolling”, “bots”, “botoin industry”. The social communication component of these concepts is unconditional. Choosing a blog as the main tool of a marketing campaign has both positive and negative aspects. Only a person with great creative potential can run and create a blog. In addition, it takes a long time. In fact, these two points are losing compared to other internet marketing tools. Further research is interesting in two respects. First, a comparison of the dynamics of the effectiveness of PR-campaign tools in Ukraine in 2020 and in the past, in particular, at the dawn of state independence. Secondly, to investigate how/or the concept of PR-campaigns in social networks and blogs is constantly changing.
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Farhi, Edward, and Hartmut Neven. Classification with Quantum Neural Networks on Near Term Processors. Web of Open Science, December 2020. http://dx.doi.org/10.37686/qrl.v1i2.80.

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We introduce a quantum neural network, QNN, that can represent labeled data, classical or quantum, and be trained by supervised learning. The quantum circuit consists of a sequence of parameter dependent unitary transformations which acts on an input quantum state. For binary classification a single Pauli operator is measured on a designated readout qubit. The measured output is the quantum neural network’s predictor of the binary label of the input state. We show through classical simulation that parameters can be found that allow the QNN to learn to correctly distinguish the two data sets. We then discuss presenting the data as quantum superpositions of computational basis states corresponding to different label values. Here we show through simulation that learning is possible. We consider using our QNN to learn the label of a general quantum state. By example we show that this can be done. Our work is exploratory and relies on the classical simulation of small quantum systems. The QNN proposed here was designed with near-term quantum processors in mind. Therefore it will be possible to run this QNN on a near term gate model quantum computer where its power can be explored beyond what can be explored with simulation.
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ELECTRONIC SYSTEMS CENTER HANSCOM AFB MA. Environmental Assessment Ground Wave Emergency Network for Northwestern Indiana Relay Node Site NO. RN 8C902IN. Fort Belvoir, VA: Defense Technical Information Center, March 1993. http://dx.doi.org/10.21236/ada267849.

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ELECTRONIC SYSTEMS CENTER HANSCOM AFB MA. Ground Wave Emergency Network Final Operational Capability: Environmental Assessment for Central Utah Relay Node, Site Number RN 8C920UT. Fort Belvoir, VA: Defense Technical Information Center, April 1993. http://dx.doi.org/10.21236/ada267628.

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ELECTRONIC SYSTEMS CENTER HANSCOM AFB MA. Ground Wave Emergency Network Final Operational Capability: Environmental Assessment for Northwestern Nebraska Relay Node, Site Number RN 8C930NE. Fort Belvoir, VA: Defense Technical Information Center, February 1993. http://dx.doi.org/10.21236/ada267629.

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