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Journal articles on the topic 'Recalling-based recurrent neural network'

Author: Grafiati

Published: 2 June 2025

Last updated: 19 July 2025

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1

Goel, Raj Kumar, Ganesh Kumar Dixit, Saurabh Shrivastava, Manu Pratap Singh, and Shweta Vishnoi. "Implementing RNN with Non-Randomized GA for the Storage of Static Image Patterns." International Journal on Electrical Engineering and Informatics 12, no. 4 (2020): 966–78. http://dx.doi.org/10.15676/ijeei.2020.12.4.16.

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The hybridization of evolutionary technology has been extensively used to enhance the performance of recurrent type neural networks (RTNN) for storing patterns and their recalling. Several experiments have been done to link evolutionary processes such as genetic algorithm (GA) with RTNN regarding the connection weight among the processing elements. This integration strengthens the efficiency of the Recurrent neural network (RNN) to effectively recall the increased capacity and patterns of sample storage to reduce the flaw of local minima. Bipolar product rule (BPR) has been applied predominant

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Rai, Rahul R., and M. Mathivanan. "Recalling-Enhanced Recurrent Neural Network optimized with Chimp Optimization Algorithm based speech enhancement for hearing aids." Intelligent Decision Technologies 18, no. 1 (2024): 123–34. http://dx.doi.org/10.3233/idt-230211.

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Background noise often distorts the speech signals obtained in a real-world environment. This deterioration occurs in certain applications, like speech recognition, hearing aids. The aim of Speech enhancement (SE) is to suppress the unnecessary background noise in the obtained speech signal. The existing approaches for speech enhancement (SE) face more challenges like low Source-distortion ratio and memory requirements. In this manuscript, Recalling-Enhanced Recurrent Neural Network (R-ERNN) optimized with Chimp Optimization Algorithm based speech enhancement is proposed for hearing aids (R-ER

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Dangovski, Rumen, Li Jing, Preslav Nakov, Mićo Tatalović, and Marin Soljačić. "Rotational Unit of Memory: A Novel Representation Unit for RNNs with Scalable Applications." Transactions of the Association for Computational Linguistics 7 (November 2019): 121–38. http://dx.doi.org/10.1162/tacl_a_00258.

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Stacking long short-term memory (LSTM) cells or gated recurrent units (GRUs) as part of a recurrent neural network (RNN) has become a standard approach to solving a number of tasks ranging from language modeling to text summarization. Although LSTMs and GRUs were designed to model long-range dependencies more accurately than conventional RNNs, they nevertheless have problems copying or recalling information from the long distant past. Here, we derive a phase-coded representation of the memory state, Rotational Unit of Memory (RUM), that unifies the concepts of unitary learning and associative

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Irshad, Reyazur Rashid, Hamad Ali Abosaq, Mohammed Al Yami, et al. "Effective Stress Detection and Classification System Using African Buffalo Optimization and Recalling-Enhanced Recurrent Neural Network for Nano-Electronic Typed Data." Journal of Nanoelectronics and Optoelectronics 19, no. 7 (2024): 773–81. http://dx.doi.org/10.1166/jno.2024.3623.

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A body’s altered emotional reactions to a variety of conditions, including despair, anxiety, rage, grief, guilt, low self-worth, etc., can lead to stress. Stress hurts a person’s performance and is the underlying cause of many mental health issues, including dementia and depression. Numerous prevailing approaches to stress detection are exploited with deep learning, but it needs to categorize the stress precisely, and it takes high computation time. To engulf these complications, an African buffalo optimization and the Recalling-Enhanced Recurrent Neural Network (RE-RNN) are newly proposed for

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Zhang, Cheng, Luying Li, Yanmei Liu, Xuejiao Luo, Shangguan Song, and Dingchun Xia. "Research on recurrent neural network model based on weight activity evaluation." ITM Web of Conferences 47 (2022): 02046. http://dx.doi.org/10.1051/itmconf/20224702046.

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Given the complex structure and parameter redundancy of recurrent neural networks such as LSTM, related research and analysis on the structure of recurrent neural networks have been done. To improve the structural rationality of the recurrent neural network and reduce the amount of calculation of network parameters, a weight activity evaluation algorithm is proposed that evaluates the activity of the basic unit of the network. Through experiments and tests on arrhythmia data, the differences in the weight activity of the LSTM network and the change characteristics of weights and gradients are

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Gao, Tao, Xiaoling Gong, Kai Zhang, et al. "A recalling-enhanced recurrent neural network: Conjugate gradient learning algorithm and its convergence analysis." Information Sciences 519 (May 2020): 273–88. http://dx.doi.org/10.1016/j.ins.2020.01.045.

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BOBROVNIKOVA, K., and D. DENYSIUK. "METHOD FOR MALWARE DETECTION BASED ON THE NETWORK TRAFFIC ANALYSIS AND SOFTWARE BEHAVIOR IN COMPUTER SYSTEMS." Herald of Khmelnytskyi National University. Technical sciences 287, no. 4 (2020): 7–11. https://doi.org/10.31891/2307-5732-2020-287-4-7-11.

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The paper presents a method for malware detection by analyzing network traffic and software behavior in computer systems. The method is based on the classification of API call sets extracted from the constructed control flow graphs for software applications, and based on the analysis of DNS traffic of the computer network. As a classifier a combination of deep neural network and recurrent neural network is used. The proposed method consists of two stages: the deep neural network and the recurrent neural network learning stage and the malware detecting stage. The steps of the malware detecting

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Asadullaev, R. G., and M. A. Sitnikova. "INTELLIGENT MODEL FOR CLASSIFYING HEMODYNAMIC PATTERNS OF BRAIN ACTIVATION TO IDENTIFY NEUROCOGNITIVE MECHANISMS OF SPATIAL-NUMERICAL ASSOCIATIONS." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 235 (January 2024): 38–45. http://dx.doi.org/10.14489/vkit.2024.01.pp.038-045.

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The study presents the results of the development and testing of deep learning neural network architectures, which demonstrate high accuracy rates in classifying neurophysiological data, in particular hemodynamic brain activation patterns obtained by functional near-infrared spectroscopy, during solving mathematical problems on spatial-numerical associations. The analyzed signal represents a multidimensional time series of oxyhemoglobin and deoxyhemoglobin dynamics. Taking the specificity of the fNIRS signal into account, a comparative analysis of 2 types of neural network architectures was ca

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Kambar, Ashwini, V. M. Chougala, and Shettar Rajashekar. "Recurrent neural network based image compression." Invertis Journal of Science & Technology 13, no. 3 (2020): 129. http://dx.doi.org/10.5958/2454-762x.2020.00013.x.

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Park, Dong-Chul. "Multiresolution-based bilinear recurrent neural network." Knowledge and Information Systems 19, no. 2 (2008): 235–48. http://dx.doi.org/10.1007/s10115-008-0155-1.

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Xiao, Yao, Yashu Zhang, Xiangguang Dai, and Dongfang Yan. "Clustering Based on Continuous Hopfield Network." Mathematics 10, no. 6 (2022): 944. http://dx.doi.org/10.3390/math10060944.

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Clustering aims to group n data samples into k clusters. In this paper, we reformulate the clustering problem into an integer optimization problem and propose a recurrent neural network with n×k neurons to solve it. We prove the stability and convergence of the proposed recurrent neural network theoretically. Moreover, clustering experiments demonstrate that the proposed clustering algorithm based on the recurrent neural network can achieve the better clustering performance than existing clustering algorithms.

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Wang, Xiaohui. "Design of English Translation Model Based on Recurrent Neural Network." Mathematical Problems in Engineering 2022 (August 25, 2022): 1–7. http://dx.doi.org/10.1155/2022/5177069.

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In order to improve the accuracy and stability of English translation, this paper proposes an English translation model based on recurrent neural network. Based on the end-to-end encoder-decoder architecture, a recursive neural network (RNN) English machine translation model is designed to promote machine autonomous learning features, transform the distributed corpus data into word vectors, and directly map the source language and target language through the recurrent neural network. Selecting semantic errors to construct the objective function during training can well balance the influence of

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Xing, Yan, Jieqing Tan, Peilin Hong, Yeyuan He, and Min Hu. "Mesh Denoising Based on Recurrent Neural Networks." Symmetry 14, no. 6 (2022): 1233. http://dx.doi.org/10.3390/sym14061233.

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Mesh denoising is a classical task in mesh processing. Many state-of-the-art methods are still unable to quickly and robustly denoise multifarious noisy 3D meshes, especially in the case of high noise. Recently, neural network-based models have played a leading role in natural language, audio, image, video, and 3D model processing. Inspired by these works, we propose a data-driven mesh denoising method based on recurrent neural networks, which learns the relationship between the feature descriptors and the ground-truth normals. The recurrent neural network has a feedback loop before entering t

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Mohammed, Ahmed Salahuddin, Amin Salih Mohammed, and Shahab Wahhab Kareem. "Deep Learning and Neural Network-Based Wind Speed Prediction Model." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 30, no. 03 (2022): 403–25. http://dx.doi.org/10.1142/s021848852240013x.

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This paper aims to develop a wind speed prediction model by utilizing deep learning and neural networks. The analysis of weather data using a neural network architecture has been completed. The Long Short-Term Memory (LSTM) architecture is a type of artificial Recurrent Neural Network (RNN) used in deep learning is the first method plots the predicting Wind Speed based on the dataset and predicts the future spread. A dataset from a real-time weather station is used in the implementation model. The dataset consists of information from the weather station implements of the recurrent neural netwo

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Bartsev, S. I., P. M. Baturina, and G. M. Markova. "Neural Network-Based Decoding Input Stimulus Data Based on Recurrent Neural Network Neural Activity Pattern." Doklady Biological Sciences 502, no. 1 (2022): 1–5. http://dx.doi.org/10.1134/s001249662201001x.

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Abstract The paper reports the assessment of the possibility to recover information obtained using an artificial neural network via inspecting neural activity patterns. A simple recurrent neural network forms dynamic excitation patterns for storing data on input stimulus in the course of the advanced delayed match to sample test with varying duration of pause between the received stimuli. Information stored in these patterns can be used by the neural network at any moment within the specified interval (three to six clock cycles), whereby it appears possible to detect invariant representation o

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Bartsev, S. I., and G. M. Markova. "Decoding of stimuli time series by neural activity patterns of recurrent neural network." Journal of Physics: Conference Series 2388, no. 1 (2022): 012052. http://dx.doi.org/10.1088/1742-6596/2388/1/012052.

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Abstract The study is concerned with question whether it is possible to identify the specific sequence of input stimuli received by artificial neural network using its neural activity pattern. We used neural activity of simple recurrent neural network in course of “Even-Odd” game simulation. For identification of input sequences we applied the method of neural network-based decoding. Multilayer decoding neural network is required for this task. The accuracy of decoding appears up to 80%. Based on the results: 1) residual excitation levels of recurrent network’s neurons are important for stimul

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Levin, Maxim, Anastasia Sevostyanova, Stanislav Nagornov, Irina Kovalenko, and Ekaterina Levina. "METHOD OF CONSTRUCTING A NEURAL NETWORK BASED ON BIOMATERIALS." SCIENCE IN THE CENTRAL RUSSIA, no. 6 (December 27, 2024): 105–13. https://doi.org/10.35887/2305-2538-2024-6-105-113.

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The modern approach to building neural networks evolves and continues to develop in improving the mathematical model of neuron functioning, which leads to new differences from real biological analogues, since a highly simplified model of the basic element (neuron) is used to model modern neural networks. The purpose of this work is to calculate the information capacity of a neural network built on a biological neuron, to provide evidence of the prospects for studying methods for building a neural network using biological neurons. A mathematical description of the main structural elements of a

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Hindarto, Djarot. "Comparison of RNN Architectures and Non-RNN Architectures in Sentiment Analysis." sinkron 8, no. 4 (2023): 2537–46. http://dx.doi.org/10.33395/sinkron.v8i4.13048.

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This study compares the sentiment analysis performance of multiple Recurrent Neural Network architectures and One-Dimensional Convolutional Neural Networks. THE METHODS EVALUATED ARE simple Recurrent Neural Network, Long Short-Term Memory, Gated Recurrent Unit, Bidirectional Recurrent Neural Network, and 1D ConvNets. A dataset comprising text reviews with positive or negative sentiment labels was evaluated. All evaluated models demonstrated an extremely high accuracy, ranging from 99.81% to 99.99%. Apart from that, the loss generated by these models is also low, ranging from 0.0043 to 0.0021.

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Hu, Wenjin, Jiawei Xiong, Ning Wang, Feng Liu, Yao Kong, and Chaozhong Yang. "Integrated Model Text Classification Based on Multineural Networks." Electronics 13, no. 2 (2024): 453. http://dx.doi.org/10.3390/electronics13020453.

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Based on the original deep network architecture, this paper replaces the deep integrated network by integrating shallow FastText, a bidirectional gated recurrent unit (GRU) network and the convolutional neural networks (CNNs). In FastText, word embedding, 2-grams and 3-grams are combined to extract text features. In recurrent neural networks (RNNs), a bidirectional GRU network is used to lessen information loss during the process of transmission. In CNNs, text features are extracted using various convolutional kernel sizes. Additionally, three optimization algorithms are utilized to improve th

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D., Geraldine Bessie Amali, and M. Dinakaran. "A Review of Heuristic Global Optimization Based Artificial Neural Network Training Approahes." IAES International Journal of Artificial Intelligence (IJ-AI) 6, no. 1 (2017): 26–32. https://doi.org/10.5281/zenodo.4108225.

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Artificial Neural Networks have earned popularity in recent years because of their ability to approximate nonlinear functions. Training a neural network involves minimizing the mean square error between the target and network output. The error surface is nonconvex and highly multimodal. Finding the minimum of a multimodal function is a NP complete problem and cannot be solved completely. Thus application of heuristic global optimization algorithms that computes a good global minimum to neural network training is of interest. This paper reviews the various heuristic global optimization algorith

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Deageon, Kim. "A New Method of Text Classification Based on Recurrent Neural Network." International Journal of Applied Engineering & Technology 5, no. 1 (2023): 13–23. https://doi.org/10.5281/zenodo.7601982.

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<strong>With the development of modern information science and technology, the number of Internet users continues to increase substantially, and the processing of massive data is now a hot spot in data research. Artificial Neural Network (ANN) plays a crucial role in the screening and processing of big data. Artificial neural network has successfully solved many practical problems that have puzzled people for many years in the fields of computer vision, machine translation, automatic driving, etc. Therefore, artificial neural network has been increasingly applied to text classification in Natu

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Al Seyab, R. K., and Yi Cao. "Differential recurrent neural network based predictive control." Computers & Chemical Engineering 32, no. 7 (2008): 1533–45. http://dx.doi.org/10.1016/j.compchemeng.2007.07.007.

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Chen, Dongming, Mingshuo Nie, Qianqian Gan, and Dongqi Wang. "Evolving network representation learning based on recurrent neural network." International Journal of Sensor Networks 46, no. 2 (2024): 114–22. http://dx.doi.org/10.1504/ijsnet.2024.141767.

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Liu, Qingshan, Jinde Cao, and Guanrong Chen. "A Novel Recurrent Neural Network with Finite-Time Convergence for Linear Programming." Neural Computation 22, no. 11 (2010): 2962–78. http://dx.doi.org/10.1162/neco_a_00029.

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In this letter, a novel recurrent neural network based on the gradient method is proposed for solving linear programming problems. Finite-time convergence of the proposed neural network is proved by using the Lyapunov method. Compared with the existing neural networks for linear programming, the proposed neural network is globally convergent to exact optimal solutions in finite time, which is remarkable and rare in the literature of neural networks for optimization. Some numerical examples are given to show the effectiveness and excellent performance of the new recurrent neural network.

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Paul, M. Robin Raj, and Dr K. Santhi Sree. "Ensemble Based Detection of Phishing URLs Using Hybrid, Deep Learning and Machine Learning Models." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 6402–15. https://doi.org/10.22214/ijraset.2025.71708.

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Abstract: Phishing attacks pose a serious cybersecurity threat, requiring advanced detection mechanisms. This study proposes an ensemble-based phishing Uniform Resource Locator(URL) detection framework integrating both machine learning and deep learning models. The first phase employs Adaboost, Naïve Bayes(NB), Random Forest(RF), Logistic Regression(LR), Support Vector Machine(SVM), Artificial Neural Network(ANN), Convolutional Neural Network(CNN), Recurrent Neural Network(RNN), Long Short TermMemory(LSTM) and Stacked Gated Recurrent Unit(Stacked GRU), combined using voting ensemble. The secon

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Shapalin, Vitaliy Gennadiyevich, and Denis Vladimirovich Nikolayenko. "Comparison of the structure, efficiency, and speed of operation of feedforward, convolutional, and recurrent neural networks." Research Result. Information technologies 9, no. 4 (2024): 21–35. https://doi.org/10.18413/2518-1092-2024-9-4-0-3.

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This article examines the efficiency of fully connected, recurrent, and convolutional neural networks in the context of developing a simple model for weather forecasting. The architectures and working principles of fully connected neural networks, the structure of one-dimensional and two-dimensional convolutional neural networks, as well as the architecture, features, advantages, and disadvantages of recurrent neural networks—specifically, simple recurrent neural networks, LSTM, and GRU, along with their bidirectional variants for each of the three aforementioned types—are discussed. Based on

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Zhang, Zao, and Yuan Dong. "Temperature Forecasting via Convolutional Recurrent Neural Networks Based on Time-Series Data." Complexity 2020 (March 20, 2020): 1–8. http://dx.doi.org/10.1155/2020/3536572.

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Today, artificial intelligence and deep neural networks have been successfully used in many applications that have fundamentally changed people’s lives in many areas. However, very limited research has been done in the meteorology area, where meteorological forecasts still rely on simulations via extensive computing resources. In this paper, we propose an approach to using the neural network to forecast the future temperature according to the past temperature values. Specifically, we design a convolutional recurrent neural network (CRNN) model that is composed of convolution neural network (CN

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Andriyanov, Nikita A., David A. Petrosov, and Andrey V. Polyakov. "SELECTING AN ARTIFICIAL NEURAL NETWORK ARCHITECTURE FOR ASSESSING THE STATE OF A GENETIC ALGORITHM POPULATION IN THE PROBLEM OF STRUCTURAL-PARAMETRIC SYNTHESIS OF SIMULATION MODELS OF BUSINESS PROCESSES." SOFT MEASUREMENTS AND COMPUTING 12, no. 73 (2023): 70–81. http://dx.doi.org/10.36871/2618-9976.2023.12.007.

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This article proposes a study aimed at determining the architecture of artificial neural networks to solve the problem of determining the population state of a genetic algorithm adapted to solve the problem of structuralparametric synthesis of simulation models of business processes. As the initial data for training the artificial neural network, we used the results of computational experiments obtained when operating a genetic algorithm model based on mathematical nested Petri nets, which solves the problem of synthesizing business process models (Petri net models) based on a given behavior.

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P, Suma, and Senthil Kumar R. "Automatic Classification of Normal and Infected Blood Cells for Leukemia Through Color Based Segmentation Technique Over Innovative CNN Algorithm and Comparing the Error Rate with RNN." ECS Transactions 107, no. 1 (2022): 14123–34. http://dx.doi.org/10.1149/10701.14123ecst.

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To classify the normal infected blood cells through color-based segmentation for leukemia by comparing the error rate for the innovative Convolutional Neural Network and Recurrent Neural Network algorithm. Materials and Methods: Convolutional Neural Network algorithm, which has been taken as an input image and differentiating according to the properties of the image. Here the white blood cells acted as the major parameter for detecting the disease. Result: Data collection was carried out and the analysis could have been done by using blood cell sample images to detect the result and error rate

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Xu, Jing, and Xiu Li Wang. "A Structural Identification Method Based on Recurrent Neural Network and Auto-Regressive and Moving Average Model." Applied Mechanics and Materials 256-259 (December 2012): 2261–65. http://dx.doi.org/10.4028/www.scientific.net/amm.256-259.2261.

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The work presented a structural identification method based on recurrent neural network and auto-regressive and moving average model. The proposed approach involves two steps. The first step is to build a recurrent neural network to map the complex nonlinear relation between the excitations and responses of the structure-unknown system by on-line learning . The second step is to propose a procedure to determine the modal parameters of the structure from the trained neural networks. The dynamic characteristics of the structure are directly evaluated from the weighting matrices of the trained re

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Feng, Kai, Xitian Pi, Hongying Liu, and Kai Sun. "Myocardial Infarction Classification Based on Convolutional Neural Network and Recurrent Neural Network." Applied Sciences 9, no. 9 (2019): 1879. http://dx.doi.org/10.3390/app9091879.

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Myocardial infarction is one of the most threatening cardiovascular diseases for human beings. With the rapid development of wearable devices and portable electrocardiogram (ECG) medical devices, it is possible and conceivable to detect and monitor myocardial infarction ECG signals in time. This paper proposed a multi-channel automatic classification algorithm combining a 16-layer convolutional neural network (CNN) and long-short term memory network (LSTM) for I-lead myocardial infarction ECG. The algorithm preprocessed the raw data to first extract the heartbeat segments; then it was trained

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Kihas, Dejan, Zeljko Djurovic, and Branko Kovacevic. "Adaptive filtering based on recurrent neural networks." Journal of Automatic Control 13, no. 1 (2003): 13–24. http://dx.doi.org/10.2298/jac0301013k.

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Kalman filter is an optimal filtering solution in certain cases, however, it is more often than not, regarded as a non-robust filter. The slight mismatch in noise statistics or process model may lead to large performance deterioration and the loss of optimality. This research paper proposes an alternative method for robust adaptive filtering concerning lack of information of noise statistics. The method is based on the application of recurrent neural networks trained by a dynamic identity observer. The method is explained in details and tested in the case analysis of object tracking model. Per

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Pascual, Santiago, Joan Serrà, and Antonio Bonafonte. "Exploring Efficient Neural Architectures for Linguistic–Acoustic Mapping in Text-To-Speech." Applied Sciences 9, no. 16 (2019): 3391. http://dx.doi.org/10.3390/app9163391.

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Conversion from text to speech relies on the accurate mapping from linguistic to acoustic symbol sequences, for which current practice employs recurrent statistical models such as recurrent neural networks. Despite the good performance of such models (in terms of low distortion in the generated speech), their recursive structure with intermediate affine transformations tends to make them slow to train and to sample from. In this work, we explore two different mechanisms that enhance the operational efficiency of recurrent neural networks, and study their performance–speed trade-off. The first

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Zhao, Shijie, Yan Cui, Linwei Huang, et al. "Supervised Brain Network Learning Based on Deep Recurrent Neural Networks." IEEE Access 8 (2020): 69967–78. http://dx.doi.org/10.1109/access.2020.2984948.

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Lu, Ruochen, and Muchao Lu. "Stock Trend Prediction Algorithm Based on Deep Recurrent Neural Network." Wireless Communications and Mobile Computing 2021 (September 14, 2021): 1–10. http://dx.doi.org/10.1155/2021/5694975.

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With the return of deep learning methods to the public eye, more and more scholars and industry researchers have tried to start exploring the possibility of neural networks to solve the problem, and some progress has been made. However, although neural networks have powerful function fitting ability, they are often criticized for their lack of explanatory power. Due to the large number of parameters and complex structure of neural network models, academics are unable to explain the predictive logic of most neural networks, test the significance of model parameters, and summarize the laws that

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Semyonov, E. D., M. Ya Braginsky, D. V. Tarakanov, and I. L. Nazarova. "NEURAL NETWORK FORECASTING OF INPUT PARAMETERS IN OIL DEVELOPMENT." PROCEEDINGS IN CYBERNETICS 22, no. 4 (2023): 42–51. http://dx.doi.org/10.35266/1999-7604-2023-4-6.

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The article examines use of artificial neural networks for forecasting of technological pa-rameters of oil development. Artificial neural networks based on the long short-term memory architecture and gated recurrent units are used to solve the problem. The findings of neural network forecasting prove the effi-cacy of recurrent neural networks, especially the long short-term memory one, for forecasting of time series.

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Cheng, Pengzhou, Kai Xu, Simin Li, and Mu Han. "TCAN-IDS: Intrusion Detection System for Internet of Vehicle Using Temporal Convolutional Attention Network." Symmetry 14, no. 2 (2022): 310. http://dx.doi.org/10.3390/sym14020310.

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Intrusion detection systems based on recurrent neural network (RNN) have been considered as one of the effective methods to detect time-series data of in-vehicle networks. However, building a model for each arbitration bit is not only complex in structure but also has high computational overhead. Convolutional neural network (CNN) has always performed excellently in processing images, but they have recently shown great performance in learning features of normal and attack traffic by constructing message matrices in such a manner as to achieve real-time monitoring but suffer from the problem of

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P., Vijay Babu, and Senthil Kumar R. "Performance Evaluation of Brain Tumor Identification and Examination Using MRI Images with Innovative Convolution Neural Networks and Comparing the Accuracy with RNN Algorithm." ECS Transactions 107, no. 1 (2022): 12405–14. http://dx.doi.org/10.1149/10701.12405ecst.

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The main aim of the paper is to find the accuracy for brain tumor detection using the Innovative CNN and RNN algorithms. The paper addresses the design and implementation of brain tumor detection with an accurate prediction. Materials and Methods: Innovative Convolutional Neural Networks and Recurrent Neural Networks are used for finding the accuracy of brain tumor detection. Data models were trained with the neural network algorithms where the brain tumor model adopts the data models and gives responses by adopting those effectively. The model checks patterns for providing the responses to th

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Khan, Muhammad Ashfaq. "HCRNNIDS: Hybrid Convolutional Recurrent Neural Network-Based Network Intrusion Detection System." Processes 9, no. 5 (2021): 834. http://dx.doi.org/10.3390/pr9050834.

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Nowadays, network attacks are the most crucial problem of modern society. All networks, from small to large, are vulnerable to network threats. An intrusion detection (ID) system is critical for mitigating and identifying malicious threats in networks. Currently, deep learning (DL) and machine learning (ML) are being applied in different domains, especially information security, for developing effective ID systems. These ID systems are capable of detecting malicious threats automatically and on time. However, malicious threats are occurring and changing continuously, so the network requires a

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Dong, Yunlong, Weiqi Li, Dongxue Li, Chao Liu, and Wei Xue. "Intelligent Tracking Method for Aerial Maneuvering Target Based on Unscented Kalman Filter." Remote Sensing 16, no. 17 (2024): 3301. http://dx.doi.org/10.3390/rs16173301.

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This paper constructs a nonlinear iterative filtering framework based on a neural network prediction model. It uses recurrent neural networks (RNNs) to achieve accurate regression of complex maneuvering target dynamic models and integrates them into the nonlinear iterative filtering system via Unscented Transformation (UT). In constructing the neural network prediction model, the Temporal Convolutional Network (TCN) modules that capture long-term dependencies and the Long Short-Term Memory (LSTM) modules that selectively forget non-essential information were utilized to achieve accurate regres

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Lin, Tsung-Chih, Yi-Ming Chang, and Tun-Yuan Lee. "System Identification Based on Dynamical Training for Recurrent Interval Type-2 Fuzzy Neural Network." International Journal of Fuzzy System Applications 1, no. 3 (2011): 66–85. http://dx.doi.org/10.4018/ijfsa.2011070105.

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This paper proposes a novel fuzzy modeling approach for identification of dynamic systems. A fuzzy model, recurrent interval type-2 fuzzy neural network (RIT2FNN), is constructed by using a recurrent neural network which recurrent weights, mean and standard deviation of the membership functions are updated. The complete back propagation (BP) algorithm tuning equations used to tune the antecedent and consequent parameters for the interval type-2 fuzzy neural networks (IT2FNNs) are developed to handle the training data corrupted by noise or rule uncertainties for nonlinear system identification

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Li, Wang, Zhang, Xin, and Liu. "Recurrent Neural Networks Based Photovoltaic Power Forecasting Approach." Energies 12, no. 13 (2019): 2538. http://dx.doi.org/10.3390/en12132538.

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The intermittency of solar energy resources has brought a big challenge for the optimization and planning of a future smart grid. To reduce the intermittency, an accurate prediction of photovoltaic (PV) power generation is very important. Therefore, this paper proposes a new forecasting method based on the recurrent neural network (RNN). At first, the entire solar power time series data is divided into inter-day data and intra-day data. Then, we apply RNN to discover the nonlinear features and invariant structures exhibited in the adjacent days and intra-day data. After that, a new point predi

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Zhang, Jianpeng, and Xueli Wang. "ICN intrusion detection method based on GA-CNN." PLOS One 20, no. 6 (2025): e0325367. https://doi.org/10.1371/journal.pone.0325367.

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The current industrial control system network is susceptible to data theft attacks such as SQL injection in practical applications, resulting in data loss or leakage of enterprise secrets. To solve the network intrusion problem faced by industrial control systems in the current global communication security environment, a network intrusion detection method based on genetic algorithm and improved convolutional neural network is proposed. Genetic algorithm is utilized to solve and optimize the data, one-dimensional multi-scale convolutional neural network is combined with gated recurrent unit to

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Bandyopadhyay, Samir Kuma. "Detection of Fraud Transactions Using Recurrent Neural Network during COVID-19." Journal of Advanced Research in Medical Science & Technology 07, no. 03 (2020): 16–21. http://dx.doi.org/10.24321/2394.6539.202012.

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Online transactions are becoming more popular in present situation where the globe is facing an unknown disease COVID-19. Now authorities of several countries have requested people to use cashless transaction as far as possible. Practically, it is not always possible to use it in all transactions. Since number of such cashless transactions has been increasing during lockdown period due to COVID-19, fraudulent transactions are also increasing in a rapid way. Fraud can be analysed by viewing a series of customer transactions data that was done in his/ her previous transactions. Normally banks or

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Krupa, T. V. "New approach to computer-aided learning based on digital library user behavior." Scientific and Technical Libraries, no. 4 (April 26, 2022): 126–36. http://dx.doi.org/10.33186/1027-3689-2022-4-126-136.

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The author introduces the mathematical model of recurrent neural network with external memory. It is intended for predicting efficient education trajectory in digital information environments, e. g. digital libraries. The goal of computer-aided learning based on neural networks is to personalize user trajectories. In the study, user behavior is modeled for the more precise personalization in various aspects using recurrent neural networks. The method is designed for two types of recurrent neural networks, i. e. the classic one with sigmoidal activation function and that with LSTM (Long Short-T

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Krupa, T. V. "New approach to computer-aided learning based on digital library user behavior." Scientific and Technical Libraries, no. 4 (April 26, 2022): 126–36. http://dx.doi.org/10.33186/1027-3689-2022-4-126-136.

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The author introduces the mathematical model of recurrent neural network with external memory. It is intended for predicting efficient education trajectory in digital information environments, e. g. digital libraries. The goal of computer-aided learning based on neural networks is to personalize user trajectories. In the study, user behavior is modeled for the more precise personalization in various aspects using recurrent neural networks. The method is designed for two types of recurrent neural networks, i. e. the classic one with sigmoidal activation function and that with LSTM (Long Short-T

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Будыльский, Дмитрий, Dmitriy Budylskiy, Александр Подвесовский, and Aleksandr Podvesovskiy. "Application of deep learning models for aspect based sentiment analysis." Bulletin of Bryansk state technical university 2015, no. 3 (2015): 117–26. http://dx.doi.org/10.12737/22917.

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This paper describes actual problem of sentiment based aspect analysis and four deep learning models: convolutional neural network, recurrent neural network, GRU and LSTM networks. We evaluated these models on Russian text dataset from SentiRuEval-2015. Results show good efficiency and high potential for further natural language processing applications.

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Mittal, Nikita, and Akash Saxena. "Layer Recurrent Neural Network based Power System Load Forecasting." TELKOMNIKA Indonesian Journal of Electrical Engineering 16, no. 3 (2015): 423. http://dx.doi.org/10.11591/tijee.v16i3.1632.

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This paper presents a straight forward application of Layer Recurrent Neural Network (LRNN) to predict the load of a large distribution network. Short term load forecasting provides important information about the system’s load pattern, which is a premier requirement in planning periodical operations and facility expansion. Approximation of data patterns for forecasting is not an easy task to perform. In past, various approaches have been applied for forecasting. In this work application of LRNN is explored. The results of proposed architecture are compared with other conventional topologies o

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Akintunde, Michael E., Andreea Kevorchian, Alessio Lomuscio, and Edoardo Pirovano. "Verification of RNN-Based Neural Agent-Environment Systems." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 6006–13. http://dx.doi.org/10.1609/aaai.v33i01.33016006.

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We introduce agent-environment systems where the agent is stateful and executing a ReLU recurrent neural network. We define and study their verification problem by providing equivalences of recurrent and feed-forward neural networks on bounded execution traces. We give a sound and complete procedure for their verification against properties specified in a simplified version of LTL on bounded executions. We present an implementation and discuss the experimental results obtained.

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Cheng, Yepeng, Zuren Liu, and Yasuhiko Morimoto. "Attention-Based SeriesNet: An Attention-Based Hybrid Neural Network Model for Conditional Time Series Forecasting." Information 11, no. 6 (2020): 305. http://dx.doi.org/10.3390/info11060305.

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Traditional time series forecasting techniques can not extract good enough sequence data features, and their accuracies are limited. The deep learning structure SeriesNet is an advanced method, which adopts hybrid neural networks, including dilated causal convolutional neural network (DC-CNN) and Long-short term memory recurrent neural network (LSTM-RNN), to learn multi-range and multi-level features from multi-conditional time series with higher accuracy. However, they didn’t consider the attention mechanisms to learn temporal features. Besides, the conditioning method for CNN and RNN is not

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