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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 predominantly for pattern storage, and GA are further used to develop the weight matrix to explore the global optimal solution reflecting the correct invocation of the storage pattern. Here, Edge Detection (ED) and self-organizing map (SOM) methods are applied for the purpose of feature extraction. The modified BPR and GA have been employed to store patterns, and recalling respectively. The proposed hybrid RTNN performance is examined for the handwritten Greek symbols.
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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-ERNN-COA-SE-HA). Initially, the clean speech and noisy speech are amassed from MS-SNSD dataset. The input speech signals are encoded using vocoder analysis, and then the Sample RNN decode the bit stream into samples. The input speech signals are extracted using Ternary pattern and discrete wavelet transforms (TP-DWT) in the training phase. In the enhancement stage, R-ERNN forecasts the associated clean speech spectra from noisy speech spectra, then reconstructs a clean speech waveform. Chimp Optimization Algorithm (COA) is considered for optimizing the R-ERNN which enhances speech. The proposed method is implemented in MATLAB, and its efficiency is evaluated under some metrics. The R-ERNN-COA-SE-HA method provides 23.74%, 24.81%, and 19.33% higher PESQ compared with existing methods, such as RGRNN-SE-HA, PACDNN-SE-HA, ARN-SE-HA respectively.
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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 memory. We show experimentally that RNNs based on RUMs can solve basic sequential tasks such as memory copying and memory recall much better than LSTMs/GRUs. We further demonstrate that by replacing LSTM/GRU with RUM units we can apply neural networks to real-world problems such as language modeling and text summarization, yielding results comparable to the state of the art.
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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 accurately detecting stress. At first, the stress dataset is collected from the Kaggle website, which actually hold the records for the data generated using the nanoelectronic and optoelectronic devices. Afterward, the preprocessing method eliminates noise and improves input data by utilizing adaptive filter method. Next, the preprocessing output is fed to the Feature extraction section. The features are extracted based on discrete wavelet Transform (DWT). After that, the extracted data are updated to the classification process using a Recalling-Enhanced Recurrent Neural Network (RE-RNN) to accurately detect stress. Hence, the African Buffalo Optimization (ABO) is proposed to adjust RE-RNN, which precisely classifies stress detection. The performance of the proposed RE-RNN approach attains 99.89%, 98 98.76 and 98.07% high accuracy, and 0.1%, 0.2%, and 0.2% lower computation Time.
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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 analyzed. The experimental results show that this algorithm can better optimize the recurrent neural network structure and reduce the redundancy of network parameters.
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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 are: construction of a set of graphs of control flows for software applications in computer system; construction of the set of used APIs based on the set of graphs of control flows; construction of a set of frequencies of API on the basis of a set of graphs of control flows; construction of a set of API sequences based on a set of graphs of control flows; extraction of features from network DNS-traffic; construction of a test sample; processing a test sample using a deep neural network; processing a test sample using a recurrent neural network; combinations of malware detection results using a deep neural network and a recurrent neural network; malicious software removal. Experimental studies were carried out, the results of which showed that the use of a deep neural network makes it possible to obtain the reliability of malicious software detection at the level from 94.75 to 98.66%, the use of a recurrent neural network – from 96.63% to 99.17%. The combination of the results of the classification of deep and recurrent neural networks allows achieving the best results, in which the reliability of malicious software detection is at the level of 97.29 to 99.42%. The usage of the developed method allowed to increase the reliability of malware detection in computer systems.
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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 carried out: (1) architectures based on recurrent neural networks: recurrent neural network with long short-term memory, recurrent neural network with long short-term memory with fully connected layers, bidirectional recurrent neural network with long short-term memory, convolutional recurrent neural network with long short-term memory; (2) architectures based on convolutional neural networks with 1D convolutions: convolutional neural network, fully convolutional neural network, residual neural network. Trained long short-term memory recurrent neural network architectures showed worse results in accuracy in comparison with 1D convolutional neural network architectures. Residual neural network (model_Resnet) demonstrated the highest accuracy rates in three experimental conditions more than 88% in detecting age-related differences in brain activation during spatial-numerical association tasks considering the individual characteristics of the respondents’ signal.
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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 each part of the semantics and fully consider the alignment information, providing a strong guidance for the training of deep recurrent neural networks. The experimental results show that the English translation model based on recurrent neural network has high effectiveness and stability. Compared with the baseline system, it has improved about 1.51–1.86 BLEU scores. Conclusion. The model improves the performance and quality of English machine translation model, and the translation effect is better.
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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 the output layer. By means of the self-feedback of neurons, the output of a recurrent neural network is related not only to the current input but also to the output of the previous moments. To deal with meshes with various geometric features, we use k-means to cluster the faces of the mesh according to geometric similarity and train neural networks for each category individually in the offline learning stage. Each network model, acting similar to a normal regression function, will map the geometric feature descriptor of each facet extracted from the mesh to the denoised facet normal. Then, the denoised normals are used to calculate the new feature descriptors, which become the input of the next similar regression model. In this system, three normal regression modules are cascaded to generate the last facet normals. Lastly, the model’s vertex positions are updated according to the denoised normals. A large number of visual and numerical results have demonstrated that the proposed model outperforms the state-of-the-art methods in most cases.
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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 network model that plots the past spread and predicts the future stretch of the weather. The performance of the recurrent neural network model is presented and compared with Adaline neural network, Autoregressive Neural Network (NAR), and Group Method of Data Handling (GMDH). The NAR used three hidden layers. The performance of the model is analyzed by presenting the Wind Speeds of Erbil city. The dataset consists of the Wind Speed of (1992-2020) years, and each year consist of twelve months (from January to December).
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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 of received stimulus. To identify these representations, the neural network-based decoding method that shows 100% efficiency of received stimuli recognition has been suggested. This method allows for identification the minimum subset of neurons, the excitation pattern of which contains comprehensive information about the stimulus received by the neural network.
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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 stimuli time series processing, 2) trajectories of neural activity of recurrent networks while receiving a specific input stimuli sequence are complex cycles, we claim the presence of neural activity attractors even in extremely simple neural networks. This result suggests the fundamental role of attractor dynamics in reflexive processes.
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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 biological neural network is given: neurons, axons and dendrites and neurotransmitters, as key parameters for data exchange between neurons. It is shown that biological neural networks, unlike artificial ones, require up to 6000 times less energy for computing work and lower production costs. Artificial neural networks are an expensive technology due to the high costs of training and development, despite the fact that many manufacturers mass-produce integrated circuits. It was found that using a biochip it is possible to reduce these costs many times, because living cells learn much faster, are distinguished by high neuroplasticity. But in creating a neural network based on a biological neuron, problems were noted: maintaining the vital activity of a biological neural network; synthesis of cells for the system; as well as integration of biomaterials and a processor. In the work, the information capacity of a biological neural network was calculated and compared with different types of artificial ones (unidirectional sigmoid neural network, radical neural network, recurrent neural networks, recurrent networks based on a perceptron, neural networks with self-organization based on competition, fuzzy neural networks). The results of experimental calculations showed that a neural network built on biomaterials is 92% higher in information capacity compared to a computer model, which indicates the achievement of the goals set in the work.
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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. However, there are minor performance differences between the evaluated architectures. The Long Short-Term Memory and Gated Recurrent Unit models mainly perform marginally better than the Simple Recurrent Neural Network, albeit with slightly lower accuracy and loss. In the meantime, the Bidirectional Recurrent Neural Network model demonstrates competitive performance, as it can effectively manage text context from both directions. Additionally, One-Dimensional Convolutional Neural Networks provide satisfactory results, indicating that convolution-based approaches are also effective in sentiment analysis. The findings of this study provide practitioners with essential insights for selecting an appropriate architecture for sentiment analysis tasks. While all models yield excellent performance, the choice of architecture can impact computational efficiency and training time. Therefore, a comprehensive comprehension of the respective characteristics of Recurrent Neural Network architectures and One-Dimensional Convolutional Neural Networks is essential for making more informed decisions when constructing sentiment analysis models.
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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 the classification capabilities of each network architecture. The experimental findings using the social network news dataset demonstrate that the integrated model is effective in improving the accuracy of text classification.
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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 Natural Language Processing, NLP), which is a hot and difficult point in NLP at present. Using artificial neural network can not only process massive data quickly and efficiently, but also improve the accuracy of data processing to a certain extent. However, there are many differences between English and Chinese in character level and word level. Compared with English, the number of Chinese in character level and word level is larger than that of English. At present, the Chinese text classification technology still has some problems in processing speed, accuracy and word segmentation. In this paper, some contents of THUC News data set compiled from Sina news data are extracted for text classification. Firstly, a word embedding matrix based on character level is proposed, and the vector dimension of each word is only 13 dimensions. Through experimental comparison, it is found that the word vector based on character level proposed in this paper has better classification effect than the word vector trained by word2vec. A tower-shaped three-layer bidirectional network structure based on LSTM(Long Short-Term Memory) is also designed, which is connected to the full connection layer composed of three layers of DNN(Deep Neural Networks) networks. Through experimental comparison, it is found that the network model designed in this paper achieves better effect than Text CNN network. In the aspect of convolutional neural network, this paper puts forward a weight compensation scheme to solve the problem that convolutional kernel ignores edge information and extends it to higher dimensions, and puts forward an optimized pooling structure to solve the problem of erroneous information extraction caused by traditional maximum pooling and average pooling. Through experiments, the optimized pooling designed in this paper is superior to the maximum pooling in training convergence speed and classification effect. In addition, a convolution neural network with five parallel connections based on one-dimensional convolution is designed. Through experimental verification and analysis, the parallel convolution neural network designed in this paper achieves better results than the Text CNN network structure. Finally, a CRNN (Convective Recurrent Neural Networks) network which combines CNN (Convective Neural Networks) and RNN( Recurrent Neural Network) is designed. Through experimental verification and analysis, the text classification effect is also better than that of Text CNN network structure.</strong>
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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 algorithms used for training feedforward neural networks and recurrent neural networks. The training algorithms are compared in terms of the learning rate, convergence speed and accuracy of the output produced by the neural network. The paper concludes by suggesting directions for novel ANN training algorithms based on recent advances in global optimization.
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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 second phase includes detection with hybrid deep learning models, including Neural Network -Long Short Term Memory(NN_LSTM), StackedGated Recurrent Unit-Convolutional Neural Network-Long Short Term Memory(StackedGRU_CNN_LSTM), Deep Belief Network -StackedGated Recurrent UnitTransformer(DBN_StackedGRU_Transformer), Autoencoder+Convolutional Neural Network-Long Short Term Memory+BiGated Recurrent Unit(AutoencoderCNNLSTMBiGRU), and Multi LayerPerceptron-Bi-Long Short Term MemoryConvolutional Neural Network-Gated Recurrent Unit(MLP_BiLSTM_CNN_GRU), utilizing stacking and a host of other ensemble methods like Voting,Weighted Averaging, Confidence-Based Stacking, Gated Mixture of Experts, Neural Greedy Selector, Stacked with Featuresfor improved classification. Performance evaluation using accuracy, precision, recall, and F1- score shows that ensemble learning significantly enhances phishing detection accuracy, making it a robust cybersecurity solution.
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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 the available theoretical materials, simple neural networks were developed to compare the efficiency of each architecture, with training time and error magnitude serving as criteria, and temperature, wind speed, and atmospheric pressure as training data. The training speed, minimum and average error values for the fully connected neural network, convolutional neural network, simple recurrent network, LSTM, and GRU, as well as for bidirectional recurrent neural networks, were examined. Based on the results obtained, an analysis was conducted to explore the possible reasons for the effectiveness of each architecture. Graphs were plotted to show the relationship between processing speed and error magnitude for the three datasets examined: temperature, wind speed, and atmospheric pressure. Conclusions were drawn about the efficiency of specific models in the context of forecasting time series of meteorological data.
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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 (CNN) portion and recurrent neural network (RNN) portion. The model can learn the time correlation and space correlation of temperature changes from historical data through neural networks. To evaluate the proposed CRNN model, we use the daily temperature data of mainland China from 1952 to 2018 as training data. The results show that our model can predict future temperature with an error around 0.907°C.
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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. As examples of artificial neural network architectures for managing the process of finding solutions based on an evolutionary procedure, the following are considered: fully connected artificial neural network (FCNN), simple recurrent artificial neural network (Simple RNN), long shortterm memory recurrent network (LSTN), closed recurrent recurrent network block (GRU) and bidirectional LSTM (Bidirectional LSTM). The deep learning algorithms used were: Support Vector Classifier, Decision Tree Classifier and Random Forest Classifier. The article discusses the presented architectures of artificial neural networks and various training methods. Based on the computational experiments carried out and the analysis of the results obtained, conclusions were drawn about the feasibility of using artificial neural networks with RNN architecture to solve the problem of recognizing the state of the population and controlling the process of synthesis of solutions.
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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 of a particular algorithm. Here in this proposed work, the error rate was reduced in innovative Convolutional Neural Networks compared to Recurrent Neural Networks. Conclusion: The data was collected from various resources for the usage of disease detection. The reduced error rate for the Convolutional Neural Network (87.02%) was used as an algorithm for the whole disease detection process for reduced error rate results compared to the Recurrent Neural Network (89.42%).
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Ziemke, Tom. "Radar Image Segmentation Using Self-Adapting Recurrent Networks." International Journal of Neural Systems 08, no. 01 (1997): 47–54. http://dx.doi.org/10.1142/s0129065797000070.
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This paper presents a novel approach to the segmentation and integration of (radar) images using a second-order recurrent artificial neural network architecture consisting of two sub-networks: a function network that classifies radar measurements into four different categories of objects in sea environments (water, oil spills, land and boats), and a context network that dynamically computes the function network's input weights. It is shown that in experiments (using simulated radar images) this mechanism outperforms conventional artificial neural networks since it allows the network to learn to solve the task through a dynamic adaptation of its classification function based on its internal state closely reflecting the current context.
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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 recurrent neural network. Furthermore, a illustrative example demonstrates the feasibility of using the proposed method to identify modal parameters of structure-unknown systems.
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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 in the multi-channel CNN and LSTM to automatically learn the acquired features and complete the myocardial infarction ECG classification. We utilized the Physikalisch-Technische Bundesanstalt (PTB) database for algorithm verification, and obtained an accuracy rate of 95.4%, a sensitivity of 98.2%, a specificity of 86.5%, and an F1 score of 96.8%, indicating that the model can achieve good classification performance without complex handcrafted features.
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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. Performance evaluation is made for cases of the standard Kalman filter, a noise-adaptive Kalman filter, the adaptive filter with a recurrent neural network trained by a static identity observer, and the adaptive filter with recurrent neural network trained by a dynamic identity observer. The results for different noise statistics as well as noise statistics mismatches are compared and presented. It is shown that in cases with a lack of knowledge of the noise statistics it is beneficial to use the filtering method proposed in this research work.
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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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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 mechanism is based on the quasi-recurrent neural network, where expensive affine transformations are removed from temporal connections and placed only on feed-forward computational directions. The second mechanism includes a module based on the transformer decoder network, designed without recurrent connections but emulating them with attention and positioning codes. Our results show that the proposed decoder networks are competitive in terms of distortion when compared to a recurrent baseline, whilst being significantly faster in terms of CPU and GPU inference time. The best performing model is the one based on the quasi-recurrent mechanism, reaching the same level of naturalness as the recurrent neural network based model with a speedup of 11.2 on CPU and 3.3 on GPU.
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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 humans can understand and use. Inspired by the technical analysis theory in the field of stock investment, this paper selects neural network models with different characteristics and extracts effective feature combinations from short-term stock price fluctuation data. In addition, on the basis of ensuring that the prediction effect of the model is not lower than that of the mainstream models, this paper uses the attention mechanism to further explore the predictive K -line patterns, which summarizes usable judgment experience for human researchers on the one hand and explains the prediction logic of the hybrid neural network on the other. Experiments show that the classification effect is better using this model, and the investor sentiment is obtained more accurately, and the accuracy rate can reach 85%, which lays the foundation for the establishment of the whole stock trend prediction model. In terms of explaining the prediction logic of the model, it is experimentally demonstrated that the K -line patterns mined using the attention mechanism have more significant predictive power than the general K -line patterns, and this result explains the prediction basis of the hybrid neural network.
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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 temporal relationships in context and inadequate feature representation in key regions. Therefore, this paper proposes a temporal convolutional network with global attention to construct an in-vehicle network intrusion detection model, called TCAN-IDS. Specifically, the TCAN-IDS model continuously encodes 19-bit features consisting of an arbitration bit and data field of the original message into a message matrix, which is symmetric to messages recalling a historical moment. Thereafter, the feature extraction model extracts its spatial-temporal detail features. Notably, global attention enables global critical region attention based on channel and spatial feature coefficients, thus ignoring unimportant byte changes. Finally, anomalous traffic is monitored by a two-class classification component. Experiments show that TCAN-IDS demonstrates high detection performance on publicly known attack datasets and is able to accomplish real-time monitoring. In particular, it is anticipated to provide a high level of symmetry between information security and illegal intrusion.
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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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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 the users by using a pattern matching module. Accuracy calculation was done by using neural network algorithms. Results: The accuracy of Innovative Convolutional Neural Network in brain tumor detection is more significantly improved which is more than 95% (approx.) than the Recurrent Neural Networks. Conclusion: Based on Independent T-test analysis using SPSS statistical software, the innovative Convolutional Neural Network algorithm is significant and has more accuracy compared to Recurrent Neural Networks.
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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 very advanced security solution. Thus, creating an effective and smart ID system is a massive research problem. Various ID datasets are publicly available for ID research. Due to the complex nature of malicious attacks with a constantly changing attack detection mechanism, publicly existing ID datasets must be modified systematically on a regular basis. So, in this paper, a convolutional recurrent neural network (CRNN) is used to create a DL-based hybrid ID framework that predicts and classifies malicious cyberattacks in the network. In the HCRNNIDS, the convolutional neural network (CNN) performs convolution to capture local features, and the recurrent neural network (RNN) captures temporal features to improve the ID system’s performance and prediction. To assess the efficacy of the hybrid convolutional recurrent neural network intrusion detection system (HCRNNIDS), experiments were done on publicly available ID data, specifically the modern and realistic CSE-CIC-DS2018 data. The simulation outcomes prove that the proposed HCRNNIDS substantially outperforms current ID methodologies, attaining a high malicious attack detection rate accuracy of up to 97.75% for CSE-CIC-IDS2018 data with 10-fold cross-validation.
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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 regression of the maneuvering models. When embedding the neural network prediction model, this paper proposes a method for extracting Sigma points using the UT transformation by ‘unfolding’ multi-sequence vectors and explores design techniques for the time sliding window length of recurrent neural networks. Ultimately, an intelligent tracking algorithm based on unscented filtering, called TCN-LSTM-UKF, was developed, effectively addressing the difficulties of constructing models and transition delays under high-maneuvering conditions and significantly improving the tracking performance of highly maneuvering targets.
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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 prediction model is proposed, only by taking the previous PV power data as input without weather information. The forecasting horizons are set from 15 to 90 minutes. The proposed forecasting method is tested by using real solar power in Flanders, Belgium. The classical persistence method (Persistence), back propagation neural network (BPNN), radial basis function (RBF) neural network and support vector machine (SVM), and long short-term memory (LSTM) networks are adopted as benchmarks. Extensive results show that the proposed forecasting method exhibits a good forecasting quality on very short-term forecasting, which demonstrates the feasibility and effectiveness of the proposed forecasting model.
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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 involving external disturbances. Only by using the current inputs and most recent outputs of the input layers, the system can be completely identified based on RIT2FNNs. In order to show that the interval IT2FNNs can handle the measurement uncertainties, training data are corrupted by white Gaussian noise with signal-to-noise ratio (SNR) 20 dB. Simulation results are obtained for the identification of nonlinear system, which yield more improved performance than those using recurrent type-1 fuzzy neural networks (RT1FNNs).
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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 other transaction authorities warn their customers about the transaction, if they notice any deviation from available patterns; the authorities consider it as a possibly fraudulent transaction. For detection of fraud during COVID-19, banks and credit card companies are applying various methods such as data mining, decision tree, rule based mining, neural network, fuzzy clustering approach and machine learning methods. The approach tries to find out normal usage pattern of customers based on their former activities. The objective of this paper is to propose a method to detect such fraud transactions during such unmanageable situation of the pandemic. Digital payment schemes are often threatened by fraudulent activities. Detecting fraud transactions during money transfer may save customers from financial loss. Mobile-based money transactions are focused in this paper for fraud detection. A Deep Learning (DL) framework is suggested in the paper that monitors and detects fraudulent activities. Implementing and applying Recurrent Neural Network on PaySim generated synthetic financial dataset, deceptive transactions are identified. The proposed method is capable to detect deceptive transactions with an accuracy of 99.87%, F1-Score of 0.99 and MSE of 0.01.
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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 improve the network intrusion detection model, and finally the detection and defense of industrial control network intrusion is completed. GA is used to optimize the feature selection process to identify the key feature subsets that have the greatest impact on model performance. One-dimensional multi-scale convolutional neural network captures multi-scale features in network traffic data through multi-scale convolutional kernels, compensating for key features that traditional convolutional neural networks may overlook. The introduction of gated recurrent unit addresses the dependency of time series data and effectively solves the problem of gradient vanishing or exploding in traditional recurrent neural networks when processing long sequence data. The results showed that the proposed model only took about 8 seconds to complete training and testing, while all other models required about 10 seconds. The running time of the proposed method was less than that of other methods. In addition, the detection rate, packet loss rate, and false alarm rate of the proposed method for industrial control systems were 96.97%, 1.256%, and 0.0947% respectively, and the defense success rate of intrusion was higher than 90%. The results above show that the proposed method has very superior intrusion detection performance and good generalization ability and can meet the needs of industrial control systems for network intrusion detection.
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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-Term Memory). The experiments demonstrated serious advantages of recurrent neural networks over analogous methods in predicting education trajectory. Thus, the proposed model is the more efficient in predictive accuracy (by 15–20% higher than analogous methods). Its prime application area is prediction of optimum user education trajectory in the digital information environment, and digital library, in particul
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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.
Pełny tekst źródłaStreszczenie:
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-Term Memory). The experiments demonstrated serious advantages of recurrent neural networks over analogous methods in predicting education trajectory. Thus, the proposed model is the more efficient in predictive accuracy (by 15–20% higher than analogous methods). Its prime application area is prediction of optimum user education trajectory in the digital information environment, and digital library, in particul
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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 of neural networks on the basis of Root Mean Square of Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE). It is observed that the results obtained from LRNN are comparatively more significant.
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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.