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Relevant bibliographies by topics / Recalling-based recurrent neural network

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Academic literature on the topic 'Recalling-based recurrent neural network'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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

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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 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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Dissertations / Theses on the topic "Recalling-based recurrent neural network"

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He, Jian. "Adaptive power system stabilizer based on recurrent neural network." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0008/NQ38471.pdf.

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Moradi, Mahdi. "TIME SERIES FORECASTING USING DUAL-STAGE ATTENTION-BASED RECURRENT NEURAL NETWORK." OpenSIUC, 2020. https://opensiuc.lib.siu.edu/theses/2701.

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AN ABSTRACT OF THE RESEARCH PAPER OFMahdi Moradi, for the Master of Science degree in Computer Science, presented on April 1, 2020, at Southern Illinois University Carbondale.TITLE: TIME SERIES FORECASTING USING DUAL-STAGE ATTENTION-BASED RECURRENT NEURAL NETWORKMAJOR PROFESSOR: Dr. Banafsheh Rekabdar

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Wang, Yuchen. "Detection of Opioid Addicts via Attention-based bidirectional Recurrent Neural Network." Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1592255095863388.

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Wang, Xutao. "Chinese Text Classification Based On Deep Learning." Thesis, Mittuniversitetet, Avdelningen för informationssystem och -teknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-35322.

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Text classification has always been a concern in area of natural language processing, especially nowadays the data are getting massive due to the development of internet. Recurrent neural network (RNN) is one of the most popular method for natural language processing due to its recurrent architecture which give it ability to process serialized information. In the meanwhile, Convolutional neural network (CNN) has shown its ability to extract features from visual imagery. This paper combine the advantages of RNN and CNN and proposed a model called BLSTM-C for Chinese text classification. BLSTM-C

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Taylor, Adrian. "Anomaly-Based Detection of Malicious Activity in In-Vehicle Networks." Thesis, Université d'Ottawa / University of Ottawa, 2017. http://hdl.handle.net/10393/36120.

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Modern automobiles have been proven vulnerable to hacking by security researchers. By exploiting vulnerabilities in the car's external interfaces, attackers can access a car's controller area network (CAN) bus and cause malicious effects. We seek to detect these attacks on the bus as a last line of defence against automotive cyber attacks. The CAN bus standard defines a low-level message structure, upon which manufacturers layer their own proprietary command protocols; attacks must similarly be tailored for their target. This variability makes intrusion detection methods difficult to apply to

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Zheng, Yilin. "Text-Based Speech Video Synthesis from a Single Face Image." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1572168353691788.

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Max, Lindblad. "The impact of parsing methods on recurrent neural networks applied to event-based vehicular signal data." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-223966.

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This thesis examines two different approaches to parsing event-based vehicular signal data to produce input to a neural network prediction model: event parsing, where the data is kept unevenly spaced over the temporal domain, and slice parsing, where the data is made to be evenly spaced over the temporal domain instead. The dataset used as a basis for these experiments consists of a number of vehicular signal logs taken at Scania AB. Comparisons between the parsing methods have been made by first training long short-term memory (LSTM) recurrent neural networks (RNN) on each of the parsed datas

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Liu, Chang. "Data Analysis of Minimally-Structured Heterogeneous Logs : An experimental study of log template extraction and anomaly detection based on Recurrent Neural Network and Naive Bayes." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-191334.

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Nowadays, the ideas of continuous integration and continuous delivery are under heavy usage in order to achieve rapid software development speed and quick product delivery to the customers with good quality. During the process ofmodern software development, the testing stage has always been with great significance so that the delivered software is meeting all the requirements and with high quality, maintainability, sustainability, scalability, etc. The key assignment of software testing is to find bugs from every test and solve them. The developers and test engineers at Ericsson, who are worki

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Keisala, Simon. "Using a Character-Based Language Model for Caption Generation." Thesis, Linköpings universitet, Interaktiva och kognitiva system, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-163001.

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Using AI to automatically describe images is a challenging task. The aim of this study has been to compare the use of character-based language models with one of the current state-of-the-art token-based language models, im2txt, to generate image captions, with focus on morphological correctness. Previous work has shown that character-based language models are able to outperform token-based language models in morphologically rich languages. Other studies show that simple multi-layered LSTM-blocks are able to learn to replicate the syntax of its training data. To study the usability of character

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He, Fan. "Real-time Process Modelling Based on Big Data Stream Learning." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-35823.

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Most control systems now are assumed to be unchangeable, but this is an ideal situation. In real applications, they are often accompanied with many changes. Some of changes are from environment changes, and some are system requirements. So, the goal of thesis is to model a dynamic adaptive real-time control system process with big data stream. In this way, control system model can adjust itself using example measurements acquired during the operation and give suggestion to next arrival input, which also indicates the accuracy of states under control highly depends on quality of the process mod

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Book chapters on the topic "Recalling-based recurrent neural network"

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Saheed, Yakub Kayode. "Data Analytics for Intrusion Detection System Based on Recurrent Neural Network and Supervised Machine Learning Methods." In Recurrent Neural Networks. CRC Press, 2022. http://dx.doi.org/10.1201/9781003307822-12.

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Zhao, Haitao, Zhihui Lai, Henry Leung, and Xianyi Zhang. "Neural-Network-Based Feature Learning: Recurrent Neural Network." In Information Fusion and Data Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-40794-0_12.

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Zhang, Yufei, and Jiaju Wu. "Speech Enhancement Based on Deep Neural Network and Recurrent Neural Network." In Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70665-4_15.

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Rokui, Jun. "Autoassociative Signature Authentication Based on Recurrent Neural Network." In Artificial Intelligence and Soft Computing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91253-0_9.

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Bougteb, Yahya, Bouchra Frikh, Brahim Ouhbi, and El Moukhtar Zemmouri. "Attention-Based Recurrent Neural Network for Multicriteria Recommendations." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-47724-9_18.

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Li, Bofang, Tao Liu, Zhe Zhao, and Xiaoyong Du. "Attention-Based Recurrent Neural Network for Sequence Labeling." In Web and Big Data. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96890-2_28.

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Aliev, Rafik, Bijan Fazlollahi, Rashad Aliev, and Babek Guirimov. "Fuzzy Time Series Prediction Method Based on Fuzzy Recurrent Neural Network." In Neural Information Processing. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11893257_95.

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Qiao, Junfei, Xiaoqi Huang, and Honggui Han. "Recurrent Neural Network-Based Control for Wastewater Treatment Process." In Advances in Neural Networks – ISNN 2012. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31362-2_55.

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Lotrič, Uroš, and Andrej Dobnikar. "Recurrent neural network with integrated wavelet based denoising unit." In Artificial Neural Nets and Genetic Algorithms. Springer Vienna, 2003. http://dx.doi.org/10.1007/978-3-7091-0646-4_8.

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Maravall, Darlo, Javier de Lope, and Miguel Ángel Patricio. "A Recurrent Neural Network for Robotic Sensory-based Search." In Artificial Neural Nets Problem Solving Methods. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44869-1_20.

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Conference papers on the topic "Recalling-based recurrent neural network"

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Leal, Sergio, and Luis Lago. "Recurrent Neural Network based Counter Automata." In ESANN 2024. Ciaco - i6doc.com, 2024. http://dx.doi.org/10.14428/esann/2024.es2024-211.

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Parto, Midya, Gordon H. Y. Li, Ryoto Sekine, et al. "An Optical Neural Network Based on Nanophotonic Optical Parametric Oscillators." In CLEO: Science and Innovations. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/cleo_si.2024.stu3p.7.

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We experimentally demonstrate a recurrent optical neural network based on a nanophotonic optical parametric oscillator fabricated on thin-film lithium niobate. Our demonstration paves the way for realizing optical neural networks exhibiting ultra-low la-tencies.

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Di Capaci, Riccardo Bacci, Gabriele Pannocchia, Marco Vaccari, and Arianna Nocente. "Recurrent Neural Network-Based NMPC for Nonlinear Processes." In 2025 25th International Conference on Process Control (PC). IEEE, 2025. https://doi.org/10.1109/pc65047.2025.11047442.

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Joad, Faaiz, Rachad Atat, Hayat Mbayed, and Abdulrahman Takiddin. "Recurrent Graph Neural Network-Based Identification of Replay Attacks in Power Networks." In 2024 4th International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME). IEEE, 2024. https://doi.org/10.1109/iceccme62383.2024.10796488.

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Shang, Fengmei. "Chinese-English Neural Machine Translation Model Based on Improved Interval Value Based Recurrent Neural Network." In 2024 International Conference on Data Science and Network Security (ICDSNS). IEEE, 2024. http://dx.doi.org/10.1109/icdsns62112.2024.10691311.

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Gan, Chengyu, and Kourosh Danai. "Fault Diagnosis With a Model-Based Recurrent Neural Network." In ASME 2000 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/imece2000-2327.

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Abstract The utility of a model-based recurrent neural network (MBRNN) is demonstrated in fault diagnosis. The MBRNN can be formatted according to a state-space model. Therefore, it can use model-based fault detection and isolation (FDI) solutions as a starting point, and improve them via training by adapting them to plant nonlinearities. In this paper, the application of MBRNN to the IFAC Benchmark Problem is explored and its performance is compared with ‘black box’ neural network solutions. For this problem, the MBRNN is formulated according to the Eigen-Structure Assignment (ESA) residual g

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Hu, Haojin, Mengfan Liao, Chao Zhang, and Yanmei Jing. "Text classification based recurrent neural network." In 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC). IEEE, 2020. http://dx.doi.org/10.1109/itoec49072.2020.9141747.

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Xu, Zhao, Qing Song, and Danwei Wang. "Recurrent neural network based tracking control." In Vision (ICARCV 2010). IEEE, 2010. http://dx.doi.org/10.1109/icarcv.2010.5707971.

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Montajabi, Zahra, Vahid Khorasani Ghassab, and Nizar Bouguila. "Recurrent Neural Network-Based Video Compression." In 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2022. http://dx.doi.org/10.1109/icmla55696.2022.00154.

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Jun, Wang, Cao Jun-xing, and You Jia-chun. "Log reconstruction based on gated recurrent unit recurrent neural network." In SEG 2019 Workshop: Mathematical Geophysics: Traditional vs Learning, Beijing, China, 5-7 November 2019. Society of Exploration Geophysicists, 2020. http://dx.doi.org/10.1190/iwmg2019_22.1.

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Reports on the topic "Recalling-based recurrent neural network"

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Shao, Lu. Automatic Seizure Detection based on a Convolutional Neural Network-Recurrent Neural Network Model. Iowa State University, 2022. http://dx.doi.org/10.31274/cc-20240624-269.

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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detecti

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Pasupuleti, Murali Krishna. Neural Computation and Learning Theory: Expressivity, Dynamics, and Biologically Inspired AI. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv425.

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Abstract: Neural computation and learning theory provide the foundational principles for understanding how artificial and biological neural networks encode, process, and learn from data. This research explores expressivity, computational dynamics, and biologically inspired AI, focusing on theoretical expressivity limits, infinite-width neural networks, recurrent and spiking neural networks, attractor models, and synaptic plasticity. The study investigates mathematical models of function approximation, kernel methods, dynamical systems, and stability properties to assess the generalization capa

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