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Journal articles on the topic 'Robust Long-Short Term Memory (RoLSTM)'

Author: Grafiati

Published: 28 September 2022

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1

Javid, Gelareh, Djaffar Ould Abdeslam, and Michel Basset. "Adaptive Online State of Charge Estimation of EVs Lithium-Ion Batteries with Deep Recurrent Neural Networks." Energies 14, no. 3 (2021): 758. http://dx.doi.org/10.3390/en14030758.

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The State of Charge (SOC) estimation is a significant issue for safe performance and the lifespan of Lithium-ion (Li-ion) batteries. In this paper, a Robust Adaptive Online Long Short-Term Memory (RoLSTM) method is proposed to extract SOC estimation for Li-ion Batteries in Electric Vehicles (EVs). This real-time, as its name suggests, method is based on a Recurrent Neural Network (RNN) containing Long Short-Term Memory (LSTM) units and using the Robust and Adaptive online gradient learning method (RoAdam) for optimization. In the proposed architecture, one sequential model is defined for each

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Fister, Dušan, Matjaž Perc, and Timotej Jagrič. "Two robust long short-term memory frameworks for trading stocks." Applied Intelligence 51, no. 10 (2021): 7177–95. http://dx.doi.org/10.1007/s10489-021-02249-x.

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Liu, Yong, Xin Hao, Biling Zhang, and Yuyan Zhang. "Simplified long short-term memory model for robust and fast prediction." Pattern Recognition Letters 136 (August 2020): 81–86. http://dx.doi.org/10.1016/j.patrec.2020.05.033.

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Yang, Haimin, Zhisong Pan, and Qing Tao. "Robust and Adaptive Online Time Series Prediction with Long Short-Term Memory." Computational Intelligence and Neuroscience 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/9478952.

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Online time series prediction is the mainstream method in a wide range of fields, ranging from speech analysis and noise cancelation to stock market analysis. However, the data often contains many outliers with the increasing length of time series in real world. These outliers can mislead the learned model if treated as normal points in the process of prediction. To address this issue, in this paper, we propose a robust and adaptive online gradient learning method, RoAdam (Robust Adam), for long short-term memory (LSTM) to predict time series with outliers. This method tunes the learning rate

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Son, Namrye, Seunghak Yang, and Jeongseung Na. "Hybrid Forecasting Model for Short-Term Wind Power Prediction Using Modified Long Short-Term Memory." Energies 12, no. 20 (2019): 3901. http://dx.doi.org/10.3390/en12203901.

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Renewable energy has recently gained considerable attention. In particular, the interest in wind energy is rapidly growing globally. However, the characteristics of instability and volatility in wind energy systems also affect power systems significantly. To address these issues, many studies have been carried out to predict wind speed and power. Methods of predicting wind energy are divided into four categories: physical methods, statistical methods, artificial intelligence methods, and hybrid methods. In this study, we proposed a hybrid model using modified LSTM (Long short-term Memory) to p

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Ngoc-Lan Huynh, Anh, Ravinesh C. Deo, Mumtaz Ali, Shahab Abdulla, and Nawin Raj. "Novel short-term solar radiation hybrid model: Long short-term memory network integrated with robust local mean decomposition." Applied Energy 298 (September 2021): 117193. http://dx.doi.org/10.1016/j.apenergy.2021.117193.

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Darling, Stephen, Richard J. Allen, and Jelena Havelka. "Visuospatial Bootstrapping." Current Directions in Psychological Science 26, no. 1 (2017): 3–9. http://dx.doi.org/10.1177/0963721416665342.

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Visuospatial bootstrapping is the name given to a phenomenon whereby performance on visually presented verbal serial-recall tasks is better when stimuli are presented in a spatial array rather than a single location. However, the display used has to be a familiar one. This phenomenon implies communication between cognitive systems involved in storing short-term memory for verbal and visual information, alongside connections to and from knowledge held in long-term memory. Bootstrapping is a robust, replicable phenomenon that should be incorporated in theories of working memory and its interacti

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Avci, Gunes, Steven P. Woods, Marizela Verduzco, et al. "Effect of Retrieval Practice on Short-Term and Long-Term Retention in HIV+ Individuals." Journal of the International Neuropsychological Society 23, no. 3 (2017): 214–22. http://dx.doi.org/10.1017/s1355617716001089.

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AbstractObjectives: Episodic memory deficits are both common and impactful among persons infected with HIV; however, we know little about how to improve such deficits in the laboratory or in real life. Retrieval practice, by which retrieval of newly learned material improves subsequent recall more than simple restudy, is a robust memory boosting strategy that is effective in both healthy and clinical populations. In this study, we investigated the benefits of retrieval practice in 52 people living with HIV and 21 seronegatives. Methods: In a within-subjects design, all participants studied 48

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Bukhari, Syed Basit Ali, Khawaja Khalid Mehmood, Abdul Wadood, and Herie Park. "Intelligent Islanding Detection of Microgrids Using Long Short-Term Memory Networks." Energies 14, no. 18 (2021): 5762. http://dx.doi.org/10.3390/en14185762.

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This paper presents a new intelligent islanding detection scheme (IIDS) based on empirical wavelet transform (EWT) and long short-term memory (LSTM) network to identify islanding events in microgrids. The concept of EWT is extended to extract features from three-phase signals. First, the three-phase voltage signals sampled at the terminal of targeted distributed energy resource (DER) or point of common coupling (PCC) are decomposed into empirical modes/frequency subbands using EWT. Then, instantaneous amplitudes and instantaneous frequencies of the three-phases at different frequency subbands

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Baddar, Wissam J., and Yong Man Ro. "Encoding features robust to unseen modes of variation with attentive long short-term memory." Pattern Recognition 100 (April 2020): 107159. http://dx.doi.org/10.1016/j.patcog.2019.107159.

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Lee, Byung-Jun, and Kee-Eung Kim. "Dialog History Construction with Long-Short Term Memory for Robust Generative Dialog State Tracking." Dialogue & Discourse 7, no. 3 (2016): 47–64. http://dx.doi.org/10.5087/dad.2016.302.

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 One of the crucial components of dialog system is the dialog state tracker, which infers user’s intention from preliminary speech processing. Since the overall performance of the dialog system is heavily affected by that of the dialog tracker, it has been one of the core areas of research on dialog systems. In this paper, we present a dialog state tracker that combines a generative probabilistic model of dialog state tracking with the recurrent neural network for encoding important aspects of the dialog history. We describe a two-step gradient descent algorithm that optimizes the tracke

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Zhou, Zhiyu, Ruoxi Zhang, and Zefei Zhu. "Robust Kalman filtering with long short-term memory for image-based visual servo control." Multimedia Tools and Applications 78, no. 18 (2019): 26341–71. http://dx.doi.org/10.1007/s11042-019-07773-0.

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Lees, Thomas, Steven Reece, Frederik Kratzert, et al. "Hydrological concept formation inside long short-term memory (LSTM) networks." Hydrology and Earth System Sciences 26, no. 12 (2022): 3079–101. http://dx.doi.org/10.5194/hess-26-3079-2022.

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Abstract. Neural networks have been shown to be extremely effective rainfall-runoff models, where the river discharge is predicted from meteorological inputs. However, the question remains: what have these models learned? Is it possible to extract information about the learned relationships that map inputs to outputs, and do these mappings represent known hydrological concepts? Small-scale experiments have demonstrated that the internal states of long short-term memory networks (LSTMs), a particular neural network architecture predisposed to hydrological modelling, can be interpreted. By extra

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Haynes, James T., Emily Frith, Eveleen Sng, and Paul D. Loprinzi. "Experimental Effects of Acute Exercise on Episodic Memory Function: Considerations for the Timing of Exercise." Psychological Reports 122, no. 5 (2018): 1744–54. http://dx.doi.org/10.1177/0033294118786688.

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Our previous work employing a between-subject randomized controlled trial design suggests that exercising prior to memory encoding is more advantageous in enhancing retrospective episodic memory function when compared to exercise occurring during or after memory encoding. The present experiment evaluates this potential temporal effect of acute exercise on memory function while employing a within-subject, counterbalanced design. In a counterbalanced order (via Latin squares), 24 participants completed four visits including (1) exercising (moderate-intensity walking) prior to memory encoding, (2

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Deng, Lu. "Short-Term Noise and the Robustness of Two Log-Periodogram Estimators in Long Memory Series." Applied Mechanics and Materials 313-314 (March 2013): 1235–38. http://dx.doi.org/10.4028/www.scientific.net/amm.313-314.1235.

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This paper focuses on the robustness of estimates and its mechanism with presence of short-term noise. Simulation results show that although AG estimator derives lower bias and better robustness than the GPH in most situations, the modification effects are evident only when the short noise has small negative roots. The problem of over-modification on larger negative roots and the under-modification on the positive roots are still lack of advanced study. The standard deviation it is not sensitive to short-term noise but the mean square errors increase sharply with short-term noise. Besides, the

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Tan, Truong-Ngoc, Ali Khenchaf, Fabrice Comblet, Pierre Franck, Jean-Marc Champeyroux, and Olivier Reichert. "Robust-Extended Kalman Filter and Long Short-Term Memory Combination to Enhance the Quality of Single Point Positioning." Applied Sciences 10, no. 12 (2020): 4335. http://dx.doi.org/10.3390/app10124335.

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In the recent years, multi-constellation and multi-frequency have improved the positioning precision in GNSS applications and significantly expanded the range of applications to new areas and services. However, the use of multiple signals presents advantages as well as disadvantages, since they may contain poor quality signals that negatively impact the position precision. The objective of this study is to improve the Single Point Positioning (SPP) accuracy using multi-GNSS data fusion. We propose the use of robust-Extended Kalman Filter (referred to as robust-EKF hereafter) to eliminate outli

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Chherawala, Youssouf, Partha Pratim Roy, and Mohamed Cheriet. "Combination of context-dependent bidirectional long short-term memory classifiers for robust offline handwriting recognition." Pattern Recognition Letters 90 (April 2017): 58–64. http://dx.doi.org/10.1016/j.patrec.2017.03.012.

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Wöllmer, Martin, Felix Weninger, Jürgen Geiger, Björn Schuller, and Gerhard Rigoll. "Noise robust ASR in reverberated multisource environments applying convolutive NMF and Long Short-Term Memory." Computer Speech & Language 27, no. 3 (2013): 780–97. http://dx.doi.org/10.1016/j.csl.2012.05.002.

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Jadid Abdulkadir, Said, Hitham Alhussian, Muhammad Nazmi, and Asim A Elsheikh. "Long Short Term Memory Recurrent Network for Standard and Poor’s 500 Index Modelling." International Journal of Engineering & Technology 7, no. 4.15 (2018): 25. http://dx.doi.org/10.14419/ijet.v7i4.15.21365.

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Forecasting time-series data are imperative especially when planning is required through modelling using uncertain knowledge of future events. Recurrent neural network models have been applied in the industry and outperform standard artificial neural networks in forecasting, but fail in long term time-series forecasting due to the vanishing gradient problem. This study offers a robust solution that can be implemented for long-term forecasting using a special architecture of recurrent neural network known as Long Short Term Memory (LSTM) model to overcome the vanishing gradient problem. LSTM is

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Bing, Qichun, Fuxin Shen, Xiufeng Chen, Weijian Zhang, Yanran Hu, and Dayi Qu. "A Hybrid Short-Term Traffic Flow Multistep Prediction Method Based on Variational Mode Decomposition and Long Short-Term Memory Model." Discrete Dynamics in Nature and Society 2021 (October 12, 2021): 1–13. http://dx.doi.org/10.1155/2021/4097149.

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Timely and accurate traffic prediction information is essential for advanced traffic management system (ATMS) and advanced traveler information system (ATIS). Because of the characteristics of nonlinearity, nonstationarity, and randomness, short-term traffic flow prediction could be still a challenging task. In this study, a hybrid short-term traffic flow multistep prediction method is proposed by combining the variational mode decomposition (VMD) algorithm and long short-term memory (LSTM) model. Firstly, the VMD algorithm is employed to decompose the original traffic flow data into a series

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Valada, Abhinav, and Wolfram Burgard. "Deep spatiotemporal models for robust proprioceptive terrain classification." International Journal of Robotics Research 36, no. 13-14 (2017): 1521–39. http://dx.doi.org/10.1177/0278364917727062.

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Terrain classification is a critical component of any autonomous mobile robot system operating in unknown real-world environments. Over the years, several proprioceptive terrain classification techniques have been introduced to increase robustness or act as a fallback for traditional vision based approaches. However, they lack widespread adaptation due to various factors that include inadequate accuracy, robustness and slow run-times. In this paper, we use vehicle-terrain interaction sounds as a proprioceptive modality and propose a deep long-short term memory based recurrent model that captur

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Wu, Jianqing, Qiang Wu, Jun Shen, and Chen Cai. "Towards Attention-Based Convolutional Long Short-Term Memory for Travel Time Prediction of Bus Journeys." Sensors 20, no. 12 (2020): 3354. http://dx.doi.org/10.3390/s20123354.

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Travel time prediction is critical for advanced traveler information systems (ATISs), which provides valuable information for enhancing the efficiency and effectiveness of the urban transportation systems. However, in the area of bus trips, existing studies have focused on directly using the structured data to predict travel time for a single bus trip. For state-of-the-art public transportation information systems, a bus journey generally has multiple bus trips. Additionally, due to the lack of study on data fusion, it is even inadequate for the development of underlying intelligent transporta

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Deva Hema, D., J. Tharun, G. Arun Dev, and N. Sateesh. "A Robust False Spam Review Detection Using Deep Long Short-Term Memory (LSTM) Based Recurrent Neural Network." Journal of Computational and Theoretical Nanoscience 17, no. 8 (2020): 3421–26. http://dx.doi.org/10.1166/jctn.2020.9198.

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Our day-to-day activity is highly influenced by development of Internet. One of the rapid growing area in Internet is E-commerce. People are eager to buy products from online sites like Amazon, embay, Flipkart etc. Customers can write reviews about the products purchased online. The purchasing of good through online has been increasing exponentially since last few years. As there is no physical contact with goods before purchasing through online, people totally rely on reviews about the product before purchasing it. Hence review plays an important role in deciding the quality of the product. T

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Hattinger, E., S. Scheiblhofer, E. Roesler, T. Thalhamer, J. Thalhamer, and R. Weiss. "Prophylactic mRNA Vaccination against Allergy Confers Long-Term Memory Responses and Persistent Protection in Mice." Journal of Immunology Research 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/797421.

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Recently, mRNA vaccines have been introduced as a safety-optimized alternative to plasmid DNA-based vaccines for protection against allergy. However, it remained unclear whether the short persistence of this vaccine type would limit memory responses and whether the protective immune response type would be maintained during recurrent exposure to allergen. We tested the duration of protective memory responses in mice vaccinated with mRNA encoding the grass pollen allergen Phl p 5 by challenging them with recombinant allergen, 3.5, 6, and 9 months after vaccination. In a second experiment, vaccin

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Shen, Jing, Chao Tao, Ji Qi, and Hao Wang. "Semi-Supervised Convolutional Long Short-Term Memory Neural Networks for Time Series Land Cover Classification." Remote Sensing 13, no. 17 (2021): 3504. http://dx.doi.org/10.3390/rs13173504.

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Time series images with temporal features are beneficial to improve the classification accuracy. For abstract temporal and spatial contextual information, deep neural networks have become an effective method. However, there is usually a lack of sufficient samples in network training: one is the loss of images or the discontinuous distribution of time series data because of the inevitable cloud cover, and the other is the lack of known labeled data. In this paper, we proposed a Semi-supervised convolutional Long Short-Term Memory neural network (SemiLSTM) for time series remote sensing images,

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Loprinzi, Paul D., Sierra Day, Rebecca Hendry, et al. "The effects of acute exercise on short- and long-term memory: Considerations for the timing of exercise and phases of memory." Europe’s Journal of Psychology 17, no. 1 (2021): 85–103. http://dx.doi.org/10.5964/ejop.2955.

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The specific questions addressed from this research include: (1) Does high-intensity acute exercise improve memory?, (2) If so, do the mechanisms occur via encoding, consolidation, or retrieval? and (3) If acute exercise occurs in multiple phases of memory (e.g., before encoding and during consolidation), does this have an additive effect on memory? Three experimental, within-subject, counterbalanced studies were conducted among young adults. High-intensity exercise involved a 20-minutes bout of exercise at 75% of heart rate reserve. Memory was evaluated from a word-list task, including multip

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Betsch, Tilmann, Nancy Quittenbaum, and Manfred Lüders. "On the Robustness of the Quizzing Effect under Real Teaching Conditions." Zeitschrift für Pädagogische Psychologie 29, no. 2 (2015): 109–14. http://dx.doi.org/10.1024/1010-0652/a000149.

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Lab experiments and field studies showed that studying improves short-term memory, whereas active retrieval improves long-term memory – a phenomenon known as the quizzing (or testing) effect. In a quasi-experimental field study with four elementary school classes (18 < n < 22) and a pretest – posttest (5 min after the intervention) – posttest (6 weeks later) design, we tested the robustness of the quizzing effect under real conditions involving verbal teacher-student interactions in geometry lessons on symmetry in elementary schoolers. Results showed that re-studying compared to active r

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Lee, Jungshin, and Hyochoong Bang. "A Robust Terrain Aided Navigation Using the Rao-Blackwellized Particle Filter Trained by Long Short-Term Memory Networks." Sensors 18, no. 9 (2018): 2886. http://dx.doi.org/10.3390/s18092886.

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Terrain-aided navigation (TAN) is a technology that estimates the position of the vehicle by comparing the altitude measured by an altimeter and height from the digital elevation model (DEM). The particle filter (PF)-based TAN has been commonly used to obtain stable real-time navigation solutions in cases where the unmanned aerial vehicle (UAV) operates at a high altitude. Even though TAN performs well on rough and unique terrains, its performance degrades in flat and repetitive terrains. In particular, in the case of PF-based TAN, there has been no verified technique for deciding its terrain

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Jing, S., and T. Chao. "TIME SERIES LAND COVER CLASSIFICATION BASED ON SEMI-SUPERVISED CONVOLUTIONAL LONG SHORT-TERM MEMORY NEURAL NETWORKS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2020 (August 14, 2020): 1521–28. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2020-1521-2020.

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Abstract. Time series imagery containing high-dimensional temporal features are conducive to improving classification accuracy. With the plenty accumulation of historical images, the inclusion of time series data becomes available to utilize, but it is difficult to avoid missing values caused by cloud cover. Meanwhile, seeking a large amount of training labels for long time series also makes data collection troublesome. In this study, we proposed a semi-supervised convolutional long short-term memory neural network (Semi-LSTM) in long time series which achieves an accurate and automated land c

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Aldhyani, Theyazn H. H., and Hasan Alkahtani. "A Bidirectional Long Short-Term Memory Model Algorithm for Predicting COVID-19 in Gulf Countries." Life 11, no. 11 (2021): 1118. http://dx.doi.org/10.3390/life11111118.

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Accurate prediction models have become the first goal for aiding pandemic-related decisions. Modeling and predicting the number of new active cases and deaths are important steps for anticipating and controlling COVID-19 outbreaks. The aim of this research was to develop an accurate prediction system for the COVID-19 pandemic that can predict the numbers of active cases and deaths in the Gulf countries of Saudi Arabia, Oman, the United Arab Emirates (UAE), Kuwait, Bahrain, and Qatar. The novelty of the proposed approach is that it uses an advanced prediction model—the bidirectional long short-

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Asch, Anna, Ethan J. Brady, Hugo Gallardo, John Hood, Bryan Chu, and Mohammad Farazmand. "Model-assisted deep learning of rare extreme events from partial observations." Chaos: An Interdisciplinary Journal of Nonlinear Science 32, no. 4 (2022): 043112. http://dx.doi.org/10.1063/5.0077646.

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To predict rare extreme events using deep neural networks, one encounters the so-called small data problem because even long-term observations often contain few extreme events. Here, we investigate a model-assisted framework where the training data are obtained from numerical simulations, as opposed to observations, with adequate samples from extreme events. However, to ensure the trained networks are applicable in practice, the training is not performed on the full simulation data; instead, we only use a small subset of observable quantities, which can be measured in practice. We investigate

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Zhang, Xinxin, Ying Zhang, Xiaoyan Lu, et al. "Estimation of Lower-Stratosphere-to-Troposphere Ozone Profile Using Long Short-Term Memory (LSTM)." Remote Sensing 13, no. 7 (2021): 1374. http://dx.doi.org/10.3390/rs13071374.

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Climate change and air pollution are emerging topics due to their possible enormous implications for health and social perspectives. In recent years, tropospheric ozone has been recognized as an important greenhouse gas and pollutant that is detrimental to human health, agriculture, and natural ecosystems, and has shown a trend of increasing interest. Machine-learning-based approaches have been widely applied to the estimation of tropospheric ozone concentrations, but few studies have included tropospheric ozone profiles. This study aimed to predict the Northern Hemisphere distribution of Lowe

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Kim, Byoungjun, and Joonwhoan Lee. "A Bayesian Network-Based Information Fusion Combined with DNNs for Robust Video Fire Detection." Applied Sciences 11, no. 16 (2021): 7624. http://dx.doi.org/10.3390/app11167624.

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Fire is an abnormal event that can cause significant damage to lives and property. Deep learning approach has made large progress in vision-based fire detection. However, there is still the problem of false detections due to the objects which have similar fire-like visual properties such as colors or textures. In the previous video-based approach, Faster Region-based Convolutional Neural Network (R-CNN) is used to detect the suspected regions of fire (SRoFs), and long short-term memory (LSTM) accumulates the local features within the bounding boxes to decide a fire in a short-term period. Then

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Dhabhar, Firdaus S., and Kavitha Viswanathan. "Short-term stress experienced at time of immunization induces a long-lasting increase in immunologic memory." American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 289, no. 3 (2005): R738—R744. http://dx.doi.org/10.1152/ajpregu.00145.2005.

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It would be extremely beneficial if one could harness natural, endogenous, health-promoting defense mechanisms to fight disease and restore health. The psychophysiological stress response is the most underappreciated of nature's survival mechanisms. We show that acute stress experienced before primary immunization induces a long-lasting increase in immunity. Compared with controls, mice restrained for 2.5 h before primary immunization with keyhole limpet hemocyanin (KLH) show a significantly enhanced immune response when reexposed to KLH 9 mo later. This immunoenhancement is mediated by an inc

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McBride, Dawn M., Jennifer H. Coane, Shuofeng Xu, Yi Feng, and Zhichun Yu. "Short-term false memories vary as a function of list type." Quarterly Journal of Experimental Psychology 72, no. 12 (2019): 2726–41. http://dx.doi.org/10.1177/1747021819859880.

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False memories have primarily been investigated at long-term delays in the Deese–Roediger–McDermott (DRM) procedure, but a few studies have reported meaning-based false memories at delays as short as 1–4 s. The current study further investigated the processes that contribute to short-term false memories with semantic and phonological lists (Experiment 1) and hybrid lists containing items of each type (Experiment 2). In Experiment 1, more false memories were found for phonological than for semantic lists. In Experiment 2, an asymmetrical hyper-additive effect was found such that including one o

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Kong, Longteng, Mengxiao Zhu, Nan Ran, Qingjie Liu, and Rui He. "Online Multiple Athlete Tracking with Pose-Based Long-Term Temporal Dependencies." Sensors 21, no. 1 (2020): 197. http://dx.doi.org/10.3390/s21010197.

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This paper addresses the Multi-Athlete Tracking (MAT) problem, which plays a crucial role in sports video analysis. There exist specific challenges in MAT, e.g., athletes share a high similarity in appearance and frequently occlude with each other, making existing approaches not applicable for this task. To address this problem, we propose a novel online multiple athlete tracking approach which make use of long-term temporal pose dynamics for better distinguishing different athletes. Firstly, we design a Pose-based Triple Stream Network (PTSN) based on Long Short-Term Memory (LSTM) networks, c

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Shao, Xiaorui, Chang-Soo Kim, and Palash Sontakke. "Accurate Deep Model for Electricity Consumption Forecasting Using Multi-channel and Multi-Scale Feature Fusion CNN–LSTM." Energies 13, no. 8 (2020): 1881. http://dx.doi.org/10.3390/en13081881.

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Electricity consumption forecasting is a vital task for smart grid building regarding the supply and demand of electric power. Many pieces of research focused on the factors of weather, holidays, and temperatures for electricity forecasting that requires to collect those data by using kinds of sensors, which raises the cost of time and resources. Besides, most of the existing methods only focused on one or two types of forecasts, which cannot satisfy the actual needs of decision-making. This paper proposes a novel hybrid deep model for multiple forecasts by combining Convolutional Neural Netwo

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Jamil, Ramish, Imran Ashraf, Furqan Rustam, Eysha Saad, Arif Mehmood, and Gyu Sang Choi. "Detecting sarcasm in multi-domain datasets using convolutional neural networks and long short term memory network model." PeerJ Computer Science 7 (August 25, 2021): e645. http://dx.doi.org/10.7717/peerj-cs.645.

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Sarcasm emerges as a common phenomenon across social networking sites because people express their negative thoughts, hatred and opinions using positive vocabulary which makes it a challenging task to detect sarcasm. Although various studies have investigated the sarcasm detection on baseline datasets, this work is the first to detect sarcasm from a multi-domain dataset that is constructed by combining Twitter and News Headlines datasets. This study proposes a hybrid approach where the convolutional neural networks (CNN) are used for feature extraction while the long short-term memory (LSTM) i

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Ma, Liang, Meng Liu, Na Wang, Lu Wang, Yang Yang, and Hongjun Wang. "Room-Level Fall Detection Based on Ultra-Wideband (UWB) Monostatic Radar and Convolutional Long Short-Term Memory (LSTM)." Sensors 20, no. 4 (2020): 1105. http://dx.doi.org/10.3390/s20041105.

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Timely calls for help can really make a difference for elders who suffer from falls, particularly in private locations. Considering privacy protection and convenience for the users, in this paper, we approach the problem by using impulse–radio ultra-wideband (IR-UWB) monostatic radar and propose a learning model that combines convolutional layers and convolutional long short term memory (ConvLSTM) to extract robust spatiotemporal features for fall detection. The performance of the proposed scheme was evaluated in terms of accuracy, sensitivity, and specificity. The results show that the propos

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Grobelny, Piotr, and Adam Narbudowicz. "MM-Wave Radar-Based Recognition of Multiple Hand Gestures Using Long Short-Term Memory (LSTM) Neural Network." Electronics 11, no. 5 (2022): 787. http://dx.doi.org/10.3390/electronics11050787.

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The paper proposes a simple machine learning solution for hand-gesture classification, based on processed MM-wave radar signal. It investigates the classification up to 12 different intuitive and ergonomic gestures, which are intended to serve as a contactless user interface. The system is based on AWR1642 boost Frequency-Modulated Continuous-Wave (FMCW) radar, which allows capturing standardized data to support the scalability of the proposed solution. More than 4000 samples were collected from 4 different people, with all signatures extracted from the radar hardware available in open-access

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Colettis, Natalia Claudia, Martín Habif, María Victoria Oberholzer, Federico Filippin, and Diana Alicia Jerusalinsky. "Differences in learning and memory between middle-aged female and male rats." Learning & Memory 29, no. 5 (2022): 120–25. http://dx.doi.org/10.1101/lm.053578.122.

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We observed differences in cognitive functions between middle-aged female and male Wistar rats. Both (like youngsters) discriminated new versus familiar objects, showing similar short- and long-term memory (STM and LTM, respectively). Only females show robust LTM for new location of an object. Both successfully form LTM of inhibitory avoidance, though males appeared to be amnesic for memory persistence. Habituation, locomotion, horizontal exploration, “stereotypies,” fear, and anxiety-like behavior were similar for both, while vertical exploration was significantly higher in middle-aged and yo

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Elliott, Terry. "Dynamic Integrative Synaptic Plasticity Explains the Spacing Effect in the Transition from Short- to Long-Term Memory." Neural Computation 31, no. 11 (2019): 2212–51. http://dx.doi.org/10.1162/neco_a_01227.

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Repeated stimuli that are spaced apart in time promote the transition from short- to long-term memory, while massing repetitions together does not. Previously, we showed that a model of integrative synaptic plasticity, in which plasticity induction signals are integrated by a low-pass filter before plasticity is expressed, gives rise to a natural timescale at which to repeat stimuli, hinting at a partial account of this spacing effect. The account was only partial because the important role of neuromodulation was not considered. We now show that by extending the model to allow dynamic integrat

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Qiu, Yibin, Qi Li, Yuru Pan, et al. "Modeling the Adaptive Uncertainty Sets of Robust Optimization Based on Long Short-Term Memory Network and Modified Fuzzy Information Granulation." IEEE Access 8 (2020): 56072–80. http://dx.doi.org/10.1109/access.2020.2964652.

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Mwaura, Anselim M., and Yong-Kuo Liu. "Prediction of the Loss of Feed Water Fault Signatures Using Machine Learning Techniques." Science and Technology of Nuclear Installations 2021 (August 9, 2021): 1–13. http://dx.doi.org/10.1155/2021/5511735.

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Fault diagnosis occurrence and its precise prediction in nuclear power plants are extremely important in avoiding disastrous consequences. The inherent limitations of the current fault diagnosis methods make machine learning techniques and their hybrid methodologies possible solutions to remedy this challenge. This study sought to develop, examine, compare, and contrast three robust machine learning methodologies of adaptive neurofuzzy inference system, long short-term memory, and radial basis function network by modeling the loss of feed water event using RELAP5. The performance indices of re

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Nguyen, Lam Van, Hoese Michel Tornyeviadzi, Dieu Tien Bui, and Razak Seidu. "Predicting Discharges in Sewer Pipes Using an Integrated Long Short-Term Memory and Entropy A-TOPSIS Modeling Framework." Water 14, no. 3 (2022): 300. http://dx.doi.org/10.3390/w14030300.

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Predicting discharges in sewage systems play an essential role in reducing sewer overflows and impacts on the environment and public health. Choosing a suitable model to predict discharges in these systems is essential to realizing these aforementioned goals. Long Short-Term Memory (LSTM) has been proposed as a robust technique for predicting discharges in wastewater networks. This study explored the potential application of an LSTM model to predict discharges using 3-month data set in a sewer network in Ålesund city, Norway. Different sequence-to-sequence LSTMs were investigated using various

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Huang, Lin, Jianjun Yan, Shiyu Cai, Rui Guo, Haixia Yan, and Yiqin Wang. "Automated Segmentation of the Systolic and Diastolic Phases in Wrist Pulse Signal Using Long Short-Term Memory Network." BioMed Research International 2022 (August 21, 2022): 1–9. http://dx.doi.org/10.1155/2022/2766321.

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Purpose. Single-period segmentation is one of the important steps in time-domain analysis of pulse signals, which is the basis of time-domain feature extraction. The existing single-period segmentation methods have the disadvantages of generalization, reliability, and robustness. Method. This paper proposed a period segmentation method of pulse signals based on long short-term memory (LSTM) network. The preprocessing was performed to remove noises and baseline drift of pulse signals. Thus, LabelMe was used to label each period of the pulse signals into two parts according to the location of th

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Khadka, Shauharda, Jen Jen Chung, and Kagan Tumer. "Neuroevolution of a Modular Memory-Augmented Neural Network for Deep Memory Problems." Evolutionary Computation 27, no. 4 (2019): 639–64. http://dx.doi.org/10.1162/evco_a_00239.

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We present Modular Memory Units (MMUs), a new class of memory-augmented neural network. MMU builds on the gated neural architecture of Gated Recurrent Units (GRUs) and Long Short Term Memory (LSTMs), to incorporate an external memory block, similar to a Neural Turing Machine (NTM). MMU interacts with the memory block using independent read and write gates that serve to decouple the memory from the central feedforward operation. This allows for regimented memory access and update, giving our network the ability to choose when to read from memory, update it, or simply ignore it. This capacity to

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Huynh, Anh Ngoc-Lan, Ravinesh C. Deo, Duc-Anh An-Vo, Mumtaz Ali, Nawin Raj, and Shahab Abdulla. "Near Real-Time Global Solar Radiation Forecasting at Multiple Time-Step Horizons Using the Long Short-Term Memory Network." Energies 13, no. 14 (2020): 3517. http://dx.doi.org/10.3390/en13143517.

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This paper aims to develop the long short-term memory (LSTM) network modelling strategy based on deep learning principles, tailored for the very short-term, near-real-time global solar radiation (GSR) forecasting. To build the prescribed LSTM model, the partial autocorrelation function is applied to the high resolution, 1 min scaled solar radiation dataset that generates statistically significant lagged predictor variables describing the antecedent behaviour of GSR. The LSTM algorithm is adopted to capture the short- and the long-term dependencies within the GSR data series patterns to accurat

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Ramaraj, P. "A Neural Network in Convolution with Constant Error Carousel Based Long Short Term Memory for Better Face Recognition." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (2021): 2042–52. http://dx.doi.org/10.17762/turcomat.v12i2.1808.

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Unconstrained face identification, facial periocular recognition, facial land marking and pose prediction, facial expression recognition, 3D facial model design, and other facial-related problems require robust face detection in the wild. Despite the fact that the face recognition issue has been researched intensively for decades with different commercial implementations, it nevertheless faces problems in certain real-world scenarios due to multiple obstacles, such as severe facial occlusions, incredibly low resolutions, intense lighting, exceptionally pose inconsistencies, picture or video co

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Tran, Trung Duc, Vinh Ngoc Tran, and Jongho Kim. "Improving the Accuracy of Dam Inflow Predictions Using a Long Short-Term Memory Network Coupled with Wavelet Transform and Predictor Selection." Mathematics 9, no. 5 (2021): 551. http://dx.doi.org/10.3390/math9050551.

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Accurate and reliable dam inflow prediction models are essential for effective reservoir operation and management. This study presents a data-driven model that couples a long short-term memory (LSTM) network with robust input predictor selection, input reconstruction by wavelet transformation, and efficient hyper-parameter optimization by K-fold cross-validation and the random search. First, a robust analysis using a “correlation threshold” for partial autocorrelation and cross-correlation functions is proposed, and only variables greater than this threshold are selected as input predictors an

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