Artykuły w czasopismach na temat „LSTM Neural networks”
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Bakir, Houda, Ghassen Chniti i Hédi Zaher. "E-Commerce Price Forecasting Using LSTM Neural Networks". International Journal of Machine Learning and Computing 8, nr 2 (kwiecień 2018): 169–74. http://dx.doi.org/10.18178/ijmlc.2018.8.2.682.
Pełny tekst źródłaYu, Yong, Xiaosheng Si, Changhua Hu i Jianxun Zhang. "A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures". Neural Computation 31, nr 7 (lipiec 2019): 1235–70. http://dx.doi.org/10.1162/neco_a_01199.
Pełny tekst źródłaKalinin, Maxim, Vasiliy Krundyshev i Evgeny Zubkov. "Estimation of applicability of modern neural network methods for preventing cyberthreats to self-organizing network infrastructures of digital economy platforms",. SHS Web of Conferences 44 (2018): 00044. http://dx.doi.org/10.1051/shsconf/20184400044.
Pełny tekst źródłaZhang, Chuanwei, Xusheng Xu, Yikun Li, Jing Huang, Chenxi Li i Weixin Sun. "Research on SOC Estimation Method for Lithium-Ion Batteries Based on Neural Network". World Electric Vehicle Journal 14, nr 10 (2.10.2023): 275. http://dx.doi.org/10.3390/wevj14100275.
Pełny tekst źródłaSridhar, C., i Aniruddha Kanhe. "Performance Comparison of Various Neural Networks for Speech Recognition". Journal of Physics: Conference Series 2466, nr 1 (1.03.2023): 012008. http://dx.doi.org/10.1088/1742-6596/2466/1/012008.
Pełny tekst źródłaWan, Yingliang, Hong Tao i Li Ma. "Forecasting Zhejiang Province's GDP Using a CNN-LSTM Model". Frontiers in Business, Economics and Management 13, nr 3 (5.03.2024): 233–35. http://dx.doi.org/10.54097/bmq2dy63.
Pełny tekst źródłaLiu, David, i An Wei. "Regulated LSTM Artificial Neural Networks for Option Risks". FinTech 1, nr 2 (2.06.2022): 180–90. http://dx.doi.org/10.3390/fintech1020014.
Pełny tekst źródłaPal, Subarno, Soumadip Ghosh i Amitava Nag. "Sentiment Analysis in the Light of LSTM Recurrent Neural Networks". International Journal of Synthetic Emotions 9, nr 1 (styczeń 2018): 33–39. http://dx.doi.org/10.4018/ijse.2018010103.
Pełny tekst źródłaKabildjanov, A. S., Ch Z. Okhunboboeva i S. Yo Ismailov. "Intelligent forecasting of growth and development of fruit trees by deep learning recurrent neural networks". IOP Conference Series: Earth and Environmental Science 1206, nr 1 (1.06.2023): 012015. http://dx.doi.org/10.1088/1755-1315/1206/1/012015.
Pełny tekst źródłaYu, Dian, i Shouqian Sun. "A Systematic Exploration of Deep Neural Networks for EDA-Based Emotion Recognition". Information 11, nr 4 (15.04.2020): 212. http://dx.doi.org/10.3390/info11040212.
Pełny tekst źródłaZhang, Chun-Xiang, Shu-Yang Pang, Xue-Yao Gao, Jia-Qi Lu i Bo Yu. "Attention Neural Network for Biomedical Word Sense Disambiguation". Discrete Dynamics in Nature and Society 2022 (10.01.2022): 1–14. http://dx.doi.org/10.1155/2022/6182058.
Pełny tekst źródłaMao, Congmin, i Sujing Liu. "A Study on Speech Recognition by a Neural Network Based on English Speech Feature Parameters". Journal of Advanced Computational Intelligence and Intelligent Informatics 28, nr 3 (20.05.2024): 679–84. http://dx.doi.org/10.20965/jaciii.2024.p0679.
Pełny tekst źródłaMountzouris, Konstantinos, Isidoros Perikos i Ioannis Hatzilygeroudis. "Speech Emotion Recognition Using Convolutional Neural Networks with Attention Mechanism". Electronics 12, nr 20 (23.10.2023): 4376. http://dx.doi.org/10.3390/electronics12204376.
Pełny tekst źródłaWan, Huaiyu, Shengnan Guo, Kang Yin, Xiaohui Liang i Youfang Lin. "CTS-LSTM: LSTM-based neural networks for correlatedtime series prediction". Knowledge-Based Systems 191 (marzec 2020): 105239. http://dx.doi.org/10.1016/j.knosys.2019.105239.
Pełny tekst źródłaXu, Lingfeng, Xiang Chen, Shuai Cao, Xu Zhang i Xun Chen. "Feasibility Study of Advanced Neural Networks Applied to sEMG-Based Force Estimation". Sensors 18, nr 10 (25.09.2018): 3226. http://dx.doi.org/10.3390/s18103226.
Pełny tekst źródłaBlinov, I., V. Miroshnyk i V. Sychova. "Short-term forecasting of electricity imbalances using artificial neural networks". IOP Conference Series: Earth and Environmental Science 1254, nr 1 (1.10.2023): 012029. http://dx.doi.org/10.1088/1755-1315/1254/1/012029.
Pełny tekst źródłaPavlatos, Christos, Evangelos Makris, Georgios Fotis, Vasiliki Vita i Valeri Mladenov. "Enhancing Electrical Load Prediction Using a Bidirectional LSTM Neural Network". Electronics 12, nr 22 (15.11.2023): 4652. http://dx.doi.org/10.3390/electronics12224652.
Pełny tekst źródłaSong, Dazhi, i Dazhi Song. "Stock Price Prediction based on Time Series Model and Long Short-term Memory Method". Highlights in Business, Economics and Management 24 (22.01.2024): 1203–10. http://dx.doi.org/10.54097/e75xgk49.
Pełny tekst źródłaGers, Felix A., Jürgen Schmidhuber i Fred Cummins. "Learning to Forget: Continual Prediction with LSTM". Neural Computation 12, nr 10 (1.10.2000): 2451–71. http://dx.doi.org/10.1162/089976600300015015.
Pełny tekst źródłaWei, Jun, Fan Yang, Xiao-Chen Ren i Silin Zou. "A Short-Term Prediction Model of PM2.5 Concentration Based on Deep Learning and Mode Decomposition Methods". Applied Sciences 11, nr 15 (27.07.2021): 6915. http://dx.doi.org/10.3390/app11156915.
Pełny tekst źródłaBucci, Andrea. "Realized Volatility Forecasting with Neural Networks". Journal of Financial Econometrics 18, nr 3 (2020): 502–31. http://dx.doi.org/10.1093/jjfinec/nbaa008.
Pełny tekst źródłaDu, Shaohui, Zhenghan Chen, Haoyan Wu, Yihong Tang i YuanQing Li. "Image Recommendation Algorithm Combined with Deep Neural Network Designed for Social Networks". Complexity 2021 (2.07.2021): 1–9. http://dx.doi.org/10.1155/2021/5196190.
Pełny tekst źródłaSingh, Arjun, Shashi Kant Dargar, Amit Gupta, Ashish Kumar, Atul Kumar Srivastava, Mitali Srivastava, Pradeep Kumar Tiwari i Mohammad Aman Ullah. "Evolving Long Short-Term Memory Network-Based Text Classification". Computational Intelligence and Neuroscience 2022 (21.02.2022): 1–11. http://dx.doi.org/10.1155/2022/4725639.
Pełny tekst źródłaZhang, Cheng, Luying Li, Yanmei Liu, Xuejiao Luo, Shangguan Song i 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.
Pełny tekst źródłaMero, Kevin, Nelson Salgado, Jaime Meza, Janeth Pacheco-Delgado i Sebastián Ventura. "Unemployment Rate Prediction Using a Hybrid Model of Recurrent Neural Networks and Genetic Algorithms". Applied Sciences 14, nr 8 (10.04.2024): 3174. http://dx.doi.org/10.3390/app14083174.
Pełny tekst źródłaChuang, Chia-Chun, Chien-Ching Lee, Chia-Hong Yeng, Edmund-Cheung So i Yeou-Jiunn Chen. "Attention Mechanism-Based Convolutional Long Short-Term Memory Neural Networks to Electrocardiogram-Based Blood Pressure Estimation". Applied Sciences 11, nr 24 (17.12.2021): 12019. http://dx.doi.org/10.3390/app112412019.
Pełny tekst źródłaTra, Nguyen Ngoc, Ho Phuoc Tien, Nguyen Thanh Dat i Nguyen Ngoc Vu. "VN-INDEX TREND PREDICTION USING LONG-SHORT TERM MEMORY NEURAL NETWORKS". Journal of Science and Technology: Issue on Information and Communications Technology 17, nr 12.2 (9.12.2019): 61. http://dx.doi.org/10.31130/ict-ud.2019.94.
Pełny tekst źródłaNguyen, Viet-Hung, Minh-Tuan Nguyen, Jeongsik Choi i Yong-Hwa Kim. "NLOS Identification in WLANs Using Deep LSTM with CNN Features". Sensors 18, nr 11 (20.11.2018): 4057. http://dx.doi.org/10.3390/s18114057.
Pełny tekst źródłaNogueira Filho, Francisco José Matos, Francisco de Assis Souza Filho, Victor Costa Porto, Renan Vieira Rocha, Ályson Brayner Sousa Estácio i Eduardo Sávio Passos Rodrigues Martins. "Deep Learning for Streamflow Regionalization for Ungauged Basins: Application of Long-Short-Term-Memory Cells in Semiarid Regions". Water 14, nr 9 (19.04.2022): 1318. http://dx.doi.org/10.3390/w14091318.
Pełny tekst źródłaLiu, Lunhaojie, Wen Fu, Xingao Bian i Juntao Fei. "Adaptive Intelligent Sliding Mode Control of a Dynamic System with a Long Short-Term Memory Structure". Mathematics 10, nr 7 (6.04.2022): 1197. http://dx.doi.org/10.3390/math10071197.
Pełny tekst źródłaBecerra Muriel, Cristian. "Forecasting the Future Value of a Colombian Investment Fund with LSTM Recurrent Neural Networks (LSTM)". System Analysis & Mathematical Modeling 6, nr 1 (30.03.2024): 78–88. http://dx.doi.org/10.17150/2713-1734.2024.6(1).78-88.
Pełny tekst źródłaZhang, Feizhou, Ke Shang, Lei Yan, Haijing Nan i Zicong Miao. "Prediction of Parking Space Availability Using Improved MAT-LSTM Network". ISPRS International Journal of Geo-Information 13, nr 5 (1.05.2024): 151. http://dx.doi.org/10.3390/ijgi13050151.
Pełny tekst źródłaAlaameri, Zahra Hasan Oleiwi, i Mustafa Abdulsahib Faihan. "Forecasting the Accounting Profits of the Banks Listed in Iraq Stock Exchange Using Artificial Neural Networks". Webology 19, nr 1 (20.01.2022): 2669–82. http://dx.doi.org/10.14704/web/v19i1/web19177.
Pełny tekst źródłaMoskalenko, Valentyna, Anastasija Santalova i Nataliia Fonta. "STUDY OF NEURAL NETWORKS FOR FORECASTING THE VALUE OF COMPANY SHARES IN AN UNSTABLE ECONOMY". Bulletin of National Technical University "KhPI". Series: System Analysis, Control and Information Technologies, nr 2 (8) (23.12.2022): 16–23. http://dx.doi.org/10.20998/2079-0023.2022.02.03.
Pełny tekst źródłaLiu, Chen. "Prediction and Analysis of Artwork Price Based on Deep Neural Network". Scientific Programming 2022 (10.03.2022): 1–10. http://dx.doi.org/10.1155/2022/7133910.
Pełny tekst źródłaLee, Jaekyung, Hyunwoo Kim i Hyungkyoo Kim. "Commercial Vacancy Prediction Using LSTM Neural Networks". Sustainability 13, nr 10 (12.05.2021): 5400. http://dx.doi.org/10.3390/su13105400.
Pełny tekst źródłaKhalil, Kasem, Omar Eldash, Ashok Kumar i Magdy Bayoumi. "Economic LSTM Approach for Recurrent Neural Networks". IEEE Transactions on Circuits and Systems II: Express Briefs 66, nr 11 (listopad 2019): 1885–89. http://dx.doi.org/10.1109/tcsii.2019.2924663.
Pełny tekst źródłaErgen, Tolga, i Suleyman Serdar Kozat. "Unsupervised Anomaly Detection With LSTM Neural Networks". IEEE Transactions on Neural Networks and Learning Systems 31, nr 8 (sierpień 2020): 3127–41. http://dx.doi.org/10.1109/tnnls.2019.2935975.
Pełny tekst źródłaWei, Xiaolu, Binbin Lei, Hongbing Ouyang i Qiufeng Wu. "Stock Index Prices Prediction via Temporal Pattern Attention and Long-Short-Term Memory". Advances in Multimedia 2020 (10.12.2020): 1–7. http://dx.doi.org/10.1155/2020/8831893.
Pełny tekst źródłaWei, Chih-Chiang. "Comparison of River Basin Water Level Forecasting Methods: Sequential Neural Networks and Multiple-Input Functional Neural Networks". Remote Sensing 12, nr 24 (20.12.2020): 4172. http://dx.doi.org/10.3390/rs12244172.
Pełny tekst źródłaHan, Shipeng, Zhen Meng, Xingcheng Zhang i Yuepeng Yan. "Hybrid Deep Recurrent Neural Networks for Noise Reduction of MEMS-IMU with Static and Dynamic Conditions". Micromachines 12, nr 2 (20.02.2021): 214. http://dx.doi.org/10.3390/mi12020214.
Pełny tekst źródłaWang, Qinghua, Yuexiao Yu, Hosameldin O. A. Ahmed, Mohamed Darwish i Asoke K. Nandi. "Open-Circuit Fault Detection and Classification of Modular Multilevel Converters in High Voltage Direct Current Systems (MMC-HVDC) with Long Short-Term Memory (LSTM) Method". Sensors 21, nr 12 (17.06.2021): 4159. http://dx.doi.org/10.3390/s21124159.
Pełny tekst źródłaVictor, Nancy, i Daphne Lopez. "sl-LSTM". International Journal of Grid and High Performance Computing 12, nr 3 (lipiec 2020): 1–16. http://dx.doi.org/10.4018/ijghpc.2020070101.
Pełny tekst źródłaKłosowski, Grzegorz, Anna Hoła, Tomasz Rymarczyk, Mariusz Mazurek, Konrad Niderla i Magdalena Rzemieniak. "Using Machine Learning in Electrical Tomography for Building Energy Efficiency through Moisture Detection". Energies 16, nr 4 (11.02.2023): 1818. http://dx.doi.org/10.3390/en16041818.
Pełny tekst źródłaAyyildiz, Ertugrul, i Melike Erdoğan. "Forecasting of daily dam occupancy rates using LSTM networks". World Journal of Environmental Research 12, nr 1 (31.05.2022): 33–42. http://dx.doi.org/10.18844/wjer.v12i1.7732.
Pełny tekst źródłaYou, Yue, Woo-Hyoung Kim i Yong-Seok Cho. "Stock Market Prediction Based on LSTM Neural Networks". Korea International Trade Research Institute 19, nr 2 (30.04.2023): 391–407. http://dx.doi.org/10.16980/jitc.19.2.202304.391.
Pełny tekst źródłaZhou, Lixia, Xia Chen, Runsha Dong i Shan Yang. "Hotspots Prediction Based on LSTM Neural Network for Cellular Networks". Journal of Physics: Conference Series 1624 (październik 2020): 052016. http://dx.doi.org/10.1088/1742-6596/1624/5/052016.
Pełny tekst źródłaWang, Geng, Xuemin Yao, Jianjun Cui, Yonggang Yan, Jun Dai i Wu Zhao. "A novel piezoelectric hysteresis modeling method combining LSTM and NARX neural networks". Modern Physics Letters B 34, nr 28 (16.06.2020): 2050306. http://dx.doi.org/10.1142/s0217984920503066.
Pełny tekst źródłaShewalkar, Apeksha, Deepika Nyavanandi i Simone A. Ludwig. "Performance Evaluation of Deep Neural Networks Applied to Speech Recognition: RNN, LSTM and GRU". Journal of Artificial Intelligence and Soft Computing Research 9, nr 4 (1.10.2019): 235–45. http://dx.doi.org/10.2478/jaiscr-2019-0006.
Pełny tekst źródłaYadav, Omprakash, Rachael Dsouza, Rhea Dsouza i Janice Jose. "Soccer Action video Classification using Deep Learning". International Journal for Research in Applied Science and Engineering Technology 10, nr 6 (30.06.2022): 1060–63. http://dx.doi.org/10.22214/ijraset.2022.43929.
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