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Morris, Richard G. M. "Long-term potentiation and memory." Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 358, no. 1432 (2003): 643–47. http://dx.doi.org/10.1098/rstb.2002.1230.
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The discovery of long-term potentiation (LTP) transformed research on the neurobiology of learning and memory. This did not happen overnight, but the discovery of an experimentally demonstrable phenomenon reflecting activity-driven neuronal and synaptic plasticity changed discussions about what might underlie learning from speculation into something much more concrete. Equally, however, the relationship between the discovery of LTP and research on the neurobiology of learning and memory has been reciprocal; for it is also true that studies of the psychological, anatomical and neurochemical basis of memory provided a developing and critical intellectual context for the physiological discovery. The emerging concept of multiple memory systems, from 1970 onwards, paved the way for the development of new behavioural and cognitive tasks, including the watermaze described in this paper. The use of this task in turn provided key evidence that pharmacological interference with an LTP induction mechanism would also interfere with learning, a finding that was by no means a foregone conclusion. This reciprocal relationship between studies of LTP and the neurobiology of memory helped the physiological phenomenon to be recognized as a major discovery.
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Barajas-Azpeleta, Raquel, Jianping Wu, Jason Gill, et al. "Antimicrobial peptides modulate long-term memory." PLOS Genetics 14, no. 10 (2018): e1007440. http://dx.doi.org/10.1371/journal.pgen.1007440.
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Czachesz, István. "Long-Term, Explicit Memory in Rituals." Journal of Cognition and Culture 10, no. 3-4 (2010): 327–39. http://dx.doi.org/10.1163/156853710x531212.
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AbstractThis article reconsiders the problem of memorization in rituals in light of recent empirical work in memory research. Four hypotheses are put forward in particular: (1) Emotionally laden details will enhance the formation of memories about any detail of the ritual; (2) harsh sensory stimuli will function as attention-magnets, resulting in increased memorization of the stimuli at the cost of remembering other elements of the ritual; (3) the self-relatedness of a ritual will enhance the formation of memories about the ritual, although the positive effect might be limited to details that are self-related; and (4) stress can be understood to function as a “zoom,” limiting the range of details remembered. The effects of stress will be modulated by gender differences and the timing of the ritual within the circadian cycle. The consequences of the four hypotheses are compared with the predictions of the Modes Theory and the Ritual Form Theory.
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Skodzik, Timo, Heinz Holling, and Anya Pedersen. "Long-Term Memory Performance in Adult ADHD." Journal of Attention Disorders 21, no. 4 (2016): 267–83. http://dx.doi.org/10.1177/1087054713510561.
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Objective: Memory problems are a frequently reported symptom in adult ADHD, and it is well-documented that adults with ADHD perform poorly on long-term memory tests. However, the cause of this effect is still controversial. The present meta-analysis examined underlying mechanisms that may lead to long-term memory impairments in adult ADHD. Method: We performed separate meta-analyses of measures of memory acquisition and long-term memory using both verbal and visual memory tests. In addition, the influence of potential moderator variables was examined. Results: Adults with ADHD performed significantly worse than controls on verbal but not on visual long-term memory and memory acquisition subtests. The long-term memory deficit was strongly statistically related to the memory acquisition deficit. In contrast, no retrieval problems were observable. Conclusion: Our results suggest that memory deficits in adult ADHD reflect a learning deficit induced at the stage of encoding. Implications for clinical and research settings are presented.
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Lynch, G. "Long-term potentiation in the Eocene." Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 358, no. 1432 (2003): 625–28. http://dx.doi.org/10.1098/rstb.2002.1253.
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The first ten years of long-term potentiation (LTP) research are reviewed. Surprisingly, given the intensity of current interest, the discovery paper did not trigger a wave of follow-on experiments. Despite this, the initial work laid out what ultimately became standard questions and paradigms. The application of the then still novel hippocampal slice technique oriented LTP towards basic neuroscience, perhaps somewhat at the cost of lesser attention to its functional significance. The use of slices led to the discovery of the events that trigger the formation of LTP and provided some first clues about its extraordinary persistence. Signs of the intense controversy over the nature of LTP expression (release vs receptors) emerged towards the end of the first decade of work. What appears to be lacking in the literature of that time is a widespread concern about LTP and memory. This may reflect a somewhat different attitude that neurobiologists then had towards memory research and a perceived need to integrate the new potentiation phenomenon into the web of established science before advancing extended arguments about its contributions to behaviour.
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Salander, P�r, Thomas Karlsson, Tommy Bergenheim, and Roger Henriksson. "Long-term memory deficits in patients with malignant gliomas." Journal of Neuro-Oncology 25, no. 3 (1995): 227–38. http://dx.doi.org/10.1007/bf01053156.
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Lee, Justin Q., Erin L. Zelinski, Robert J. McDonald, and Robert J. Sutherland. "Heterarchic reinstatement of long-term memory: A concept on hippocampal amnesia in rodent memory research." Neuroscience & Biobehavioral Reviews 71 (December 2016): 154–66. http://dx.doi.org/10.1016/j.neubiorev.2016.08.034.
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Xie, Qi, Gengguo Cheng, Xu Xu, and Zixuan Zhao. "Research Based on Stock Predicting Model of Neural Networks Ensemble Learning." MATEC Web of Conferences 232 (2018): 02029. http://dx.doi.org/10.1051/matecconf/201823202029.
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Financial time series is always one of the focus of financial market analysis and research. In recent years, with the rapid development of artificial intelligence, machine learning and financial market are more and more closely linked. Artificial neural network is usually used to analyze and predict financial time series. Based on deep learning, six layer long short-term memory neural networks were constructed. Eight long short-term memory neural networks were combined with Bagging method in ensemble learning and predicting model of neural networks ensemble learning was used in Chinese Stock Market. The experiment tested Shanghai Composite Index, Shenzhen Composite Index, Shanghai Stock Exchange 50 Index, Shanghai-Shenzhen 300 Index, Medium and Small Plate Index and Gem Index during the period from January 4, 2012 to December 29, 2017. For long short-term memory neural network ensemble learning model, its accuracy is 58.5%, precision is 58.33%, recall is 73.5%, F1 value is 64.5%, and AUC value is 57.67%, which are better than those of multilayer long short-term memory neural network model and reflect a good prediction outcome.
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Huynh, Viet Quoc, Quynh Nguyen-Thi-Nhu, Minh Duc Tran, Anh Ngoc Le, Phuoc Thanh Nguyen, and Tuan Van Huynh. "Application of long short term memory algorithm in classification electroencephalogram." Science and Technology Development Journal - Natural Sciences 5, no. 2 (2021): first. http://dx.doi.org/10.32508/stdjns.v5i2.1006.
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Human emotion plays an important role in communication without language, and it also supports research on human behavior. In addition, electroencephalogram signals have been highly confirmed by researchers for reliability as well as ease of storage and recognition. So, the use of electroencephalogram to identify emotion signals are currently a relatively new field. Many researchers are targeting the key ideas in this research field such as signal preprocessing, feature extraction and algorithm optimization. In this paper, we aim to recognize emotion signals using Long Short Term Memory (LSTM) algorithms. Emotional signals dataset was taken from DEAP database of koelstra authors and associates to serve this research. The research will focus on accuracy and training time, and it will test different architectural types as well as the initials of LSTM. The obtained results show the 3-dimensional cubes's structure has better performance than the 2-dimensional cubes's structure. In addition, our research is also compared with other authors' studies to prove the effectiveness of the classification algorithm.
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Labusov, M. V. "SHORT-TERM FINANCIAL TIME SERIES ANALYSIS WITH LONG SHORT-TERM MEMORY NEURAL NETWORKS." EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA 3, no. 4 (2021): 165–77. http://dx.doi.org/10.36871/ek.up.p.r.2021.04.03.023.
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The process of creating a long short-term memory neural network for high-frequency financial time series analyzing and forecasting is considered in the article. The research base is compiled in the beginning. Further the estimation of long short-term memory neural network parameters is carried out on the learning subsamples. The forecast of future returns signs is made for the horizon of 90 minutes with the estimated neural network. In conclusion the trading strategy is formulated.
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Lan, Feng, and Bao Hua Chen. "Research on the Long-Term Memory of Commodity Housing Price Volatility Based on the FIGARCH Model." Advanced Materials Research 1079-1080 (December 2014): 1194–98. http://dx.doi.org/10.4028/www.scientific.net/amr.1079-1080.1194.
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The purpose of this paper is to test whether there exists a long-term memory volatility characteristics of housing price. The paper based on the data ranging of Zhengzhou from January 2004 to May 2014, by adopting the FIGARCH model, empirically studies and analysis this characteristics. The research results indicate that the price fluctuation of Zhengzhou commodity homes exist effect of cluster and long-term memory characteristic. FIGARCH model can capture the long memory well, and can predict the future price of commodity residential house for a period of time .Therefore, FIGARCH model can well catch long-term memory and forecast the commodity housing price in the future period of time, which illustrates that external shocks have long-standing impact on the volatility of commodity housing price as well, reaching the conclusion that long-effect Mechanism of regulation and control should be set and developed during the macro-control of the government.
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Salman, Afan Galih, Yaya Heryadi, Edi Abdurahman, and Wayan Suparta. "Weather Forecasting Using Merged Long Short-term Memory Model." Bulletin of Electrical Engineering and Informatics 7, no. 3 (2018): 377–85. http://dx.doi.org/10.11591/eei.v7i3.1181.
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Over decades, weather forecasting has attracted researchers from worldwide communities due to itssignificant effect to global human life ranging from agriculture, air trafic control to public security. Although formal study on weather forecasting has been started since 19th century, research attention to weather forecasting tasks increased significantly after weather big data are widely available. This paper proposed merged-Long Short-term Memory for forecasting ground visibility at the airpot using timeseries of predictor variable combined with another variable as moderating variable. The proposed models were tested using weather timeseries data at Hang Nadim Airport, Batam. The experiment results showedthe best average accuracy for forecasting visibility using merged Long Short-term Memory model and temperature and dew point as a moderating variable was (88.6%); whilst, using basic Long Short-term Memory without moderating variablewasonly (83.8%) respectively (increased by 4.8%).
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Chaitip, Prasert, Péter Balogh, Sándor Kovács, and Chukiat Chaiboonsri. "On tests for long-term dependence: India’s international tourism market." Applied Studies in Agribusiness and Commerce 4, no. 5-6 (2010): 77–81. http://dx.doi.org/10.19041/apstract/2010/5-6/13.
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There have been growing interest in studying behavior of long memory process in tourism market. In this research examine the behavior of India’s international tourism market based on long-memory analysis. The international tourism market of India combined with nine countries: USA, UK, Canada, Germany, France, Japan, Malaysia, Australia and Sri Lanka. Moreover, three statistical tests for longmemory process such as R/S test, Modified R/S test and GPH-test are employed to test in these market. The empirical findings in general provide more support for no long memory process or no long-term dependence in international tourism market of India.
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MA, CHAOQUN, HONGQUAN LI, LIN ZOU, and ZHIJIAN WU. "LONG-TERM MEMORY IN EMERGING MARKETS: EVIDENCE FROM THE CHINESE STOCK MARKET." International Journal of Information Technology & Decision Making 05, no. 03 (2006): 495–501. http://dx.doi.org/10.1142/s0219622006002088.
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The notion of long-term memory has received considerable attention in empirical finance. This paper makes two main contributions. First one is, the paper provides evidence of long-term memory dynamics in the equity market of China. An analysis of market patterns in the Chinese market (a typical emerging market) instead of US market (a developed market) will be meaningful because little research on the behaviors of emerging markets has been carried out previously. Second one is, we present a comprehensive research on the long-term memory characteristics in the Chinese stock market returns as well as volatilities. While many empirical results have been obtained on the detection of long-term memory in returns series, very few investigations are focused on the market volatility, though the long-term dependence in volatility may lead to some types of volatility persistence as observed in financial markets and affect volatility forecasts and derivative pricing formulas. By means of using modified rescaled range analysis and Autoregressive Fractally Integrated Moving Average model testing, this study examines the long-term dependence in Chinese stock market returns and volatility. The results show that although the returns themselves contain little serial correlation, the variability of returns has significant long-term dependence. It would be beneficial to encompass long-term memory structure to assess the behavior of stock prices and to research on financial market theory.
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Todd, Travis P., and David J. Bucci. "Retrosplenial Cortex and Long-Term Memory: Molecules to Behavior." Neural Plasticity 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/414173.
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The retrosplenial cortex (RSC) is reciprocally connected with the hippocampus and various parahippocampal cortical regions, suggesting that RSC is well-positioned to contribute to hippocampal-dependent memory. Consistent with this, substantial behavioral evidence indicates that RSC is essential for consolidating and/or retrieving contextual and spatial memories. In addition, there is growing evidence that RSC neurons undergo activity-dependent plastic changes during memory formation and retrieval. In this paper we review both the behavioral and cellular/molecular data and posit that the RSC has a particularly important role in the storage and retrieval of spatial and contextual memories perhaps due its involvement in binding together multiple cues in the environment. We identify remaining questions and avenues for future research that take advantage of emerging methods to selectively manipulate RSC neurons both spatially and temporally and to image the RSC in awake, behaving animals.
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Lewis, Amy, Dorthe Berntsen, and Josep Call. "Long-Term Memory of Past Events in Great Apes." Current Directions in Psychological Science 28, no. 2 (2019): 117–23. http://dx.doi.org/10.1177/0963721418812781.
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It has been claimed that the ability to recall personal past events is uniquely human. We review recent evidence that great apes can remember specific events for long periods of time, spanning months and even years, and that such memories can be enhanced by distinctiveness (irrespective of reinforcement) and follow a forgetting curve similar to that in humans. Moreover, recall is enhanced when apes are presented with features that are diagnostic of the event, consistent with notions of encoding specificity and cue overload in human memory. These findings are also consistent with the involuntary retrieval of past events in humans, a mode of remembering that is thought to be less cognitively demanding than voluntary retrieval. Taken together, these findings reveal further similarities between the way humans and animals remember past events and open new avenues of research on long-term memory in nonhuman animals.
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Mukhlis, Mukhlis, Aziz Kustiyo, and Aries Suharso. "Peramalan Produksi Pertanian Menggunakan Model Long Short-Term Memory." BINA INSANI ICT JOURNAL 8, no. 1 (2021): 22. http://dx.doi.org/10.51211/biict.v8i1.1492.
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Abstrak: Masalah yang timbul dalam peramalan hasil produksi pertanian antara lain adalah sulit untuk mendapatkan data yang lengkap dari variabel-variabel yang mempengaruhi hasil pertanian dalam jangka panjang. Kondisi ini akan semakin sulit ketika peramalan mencakup wilayah yang cukup luas. Akibatnya, variabel-variabel tersebut harus diinterpolasi sehingga akan menyebabkan bias terhadap hasil peramalan. (1) Mengetahui gambaran meta analisis penelitian peramalan produk pertanian menggunakan Long Short Term Memory (LSTM), (2) Mengetahui penelitian meta analisis cakupan wilayah, komoditi dan periode data terkait produk pertanian terutama gandum, kedelai jagung dan pisang, (3) Mengetahui praproses data antara lain menghilangkan data yang tidak sesuai, menangani data yang kosong, serta memilih variabel tertentu. Sebagai solusi dari masalah tersebut, peramalan hasil produksi pertanian dilakukan berdasarkan data historis hasil produksi pertanian. Salah model peramalan yang saat ini banyak dikembangkan adalah model jaringan syaraf LSTM yang merupakan pengembangan dari model jaringan syaraf recurrent (RNN). Tulisan ini merupakan hasil kajian literatur pengembangan model-model LSTM untuk peramalan hasil produksi pertanian meliputi gandum, kedelai, jagung dan pisang. Perbaikan kinerja model LSTM dilakukan mulai dari praproses, tuning hyperparameter, sampai dengan penggabungan dengan metode lain. Berdasarkan kajian tersebut, model-model LSTM memiliki kinerja yang lebih baik dibandingkan dengan model benchmark.
 
 Kata kunci: jaringan syaraf, LSTM, peramalan, produksi pertanian, RNN.
 
 Abstract: Problems that arise in forecasting agricultural products include the difficulty of obtaining complete data on the variables that affect agricultural yields in the long term. This condition will be more difficult when the forecast covers a large area. As a result, these variables must be interpolated so that it will cause a bias towards the forecasting results. (1) Knowing the description of research maps for forecasting agricultural products using Long short term memory (LSTM), (2) Knowing Research Coverage areas, commodities, and data periods related to agricultural products, especially Wheat, Soybeans, corn, and bananas, (3) Knowing Preprocessing data between others remove inappropriate data, handle blank data, and select certain variables. This paper is the result of a literature review on the development of LSTM models for crop yields forecasting including wheat, soybeans, corn, and bananas. The Performance Improvements of the LSTM models were carried out by preprocessing data, hyperparameter tuning, and combining LSTM with other methods. Based on this study, LSTM models have better performance compared to the benchmark model. 
 
 Keywords: neural network, LSTM, forecasting, crop yield, RNN.
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Moore, D. C., S. Ryu, and P. D. Loprinzi. "Experimental effects of acute exercise on forgetting." Physiology International 107, no. 3 (2020): 359–75. http://dx.doi.org/10.1556/2060.2020.00033.
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AbstractObjectivePrior research has evaluated the effects of acute exercise on episodic memory function. These studies have, on occasion, demonstrated that acute exercise may enhance both short- and long-term memory. It is uncertain as to whether the acute exercise improvements in long-term memory are a result of acute exercise attenuating declines in long-term memory, or rather, are driven by the enhancement effects of acute exercise on short-term memory. The present empirical study evaluates whether the decline from short- to long-term is influenced by acute exercise. This relationship is plausible as exercise has been shown to activate neurophysiological pathways (e.g., RAC1) that are involved in the mechanisms of forgetting.MethodsTo evaluate the effects of acute exercise on forgetting, we used data from 12 of our laboratory's prior experiments (N = 538). Across these 12 experiments, acute exercise ranged from 10 to 15 mins in duration (moderate-to-vigorous intensity). Episodic memory was assessed from word-list or paragraph-based assessments. Short-term memory was assessed immediately after encoding, with long-term memory assessed approximately 20-min later. Forgetting was calculated as the difference in short- and long-term memory performance.ResultsAcute exercise (vs. seated control) was not associated with an attenuated forgetting effect (d = 0.10; 95% CI: −0.04, 0.25, P = 0.17). We observed no evidence of a significant moderation effect (Q = 6.16, df = 17, P = 0.17, I2 = 0.00) for any of the evaluated parameters, including study design, exercise intensity and delay period.ConclusionAcross our 12 experimental studies, acute exercise was not associated with an attenuated forgetting effect. We discuss these implications for future research that evaluates the effects of acute exercise on long-term memory function.
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Zhou, Dandan, Jie Luo, Zizhen Yi, et al. "The hand-lateralization of spatial associations in working memory and long-term memory." Quarterly Journal of Experimental Psychology 73, no. 8 (2020): 1150–61. http://dx.doi.org/10.1177/1747021819899533.
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Spatial-numerical and spatial-positional associations have been well documented in the domains of numerical cognition and working memory, respectively. However, such associations are typically calculated by directly comparing (e.g., subtracting) left- versus right-hand responses; it remains an open question whether such associations reside in each hand individually, or are exclusively localised in one of the two hands. We conducted six experiments to investigate the hand-lateralization of both spatial-numerical and spatial-positional associations. All experiments revealed that the spatial associations stemmed from left-hand responses, irrespective of the handedness of the subjects, but with the exception of the magnitude comparison task (Experiments 5 and 6). We propose that the hemispheric lateralization of the tasks in combination with the task-relevance of spatial associations can explain this pattern. More generally, we suggest that the contributions of left and right hands require more attention in spatial-numerical and spatial-positional research.
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Liang, Xuheng. "Research of semantic role labeling based on long short-term memory neural networks." Journal of Physics: Conference Series 1966, no. 1 (2021): 012005. http://dx.doi.org/10.1088/1742-6596/1966/1/012005.
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Xu, Heng, Binbin Tao, Yaqing Shu, and Yafei Wang. "Long-term memory law and empirical research on dry bulks shipping market fluctuations." Ocean & Coastal Management 213 (November 2021): 105838. http://dx.doi.org/10.1016/j.ocecoaman.2021.105838.
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Chaitip, Prasert, Péter Balogh, Sándor Kovács, and Chukiat Chaiboonsri. "On tests for long-term dependence: India ’s international tourism market." Applied Studies in Agribusiness and Commerce 5, no. 1-2 (2011): 109–13. http://dx.doi.org/10.19041/apstract/2011/1-2/13.
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There have been growing interest in studying behavior of long memory process in tourism market. In this research examine the behavior of India’s international tourism market based on long-memory analysis. The international tourism market of India combined with nine countries: USA, UK, Canada, Germany, France, Japan, Malaysia, Australia and Sri Lanka. Moreover, three statistical tests for longmemory process such as R/S test, Modified R/S test and GPH-test are employed to test in these market. The empirical findings in general provide more support for no long memory process or no long-term dependence in international tourism market of India.
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Meade, Nigel, and Margaret R. Maier. "Evidence of long memory in short-term interest rates." Journal of Forecasting 22, no. 8 (2003): 553–68. http://dx.doi.org/10.1002/for.873.
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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 interaction with long-term memory. This article provides an overview of bootstrapping, contextualizes it within research on links between long-term knowledge and short-term memory, and addresses how it can help inform current working memory theory.
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Jacobs, Linda A., Abigail N. Blauch, Donna A. Pucci, and Steven C. Palmer. "Long-term and late effects (LLEs), anxiety, and depression among long-term survivors of pediatric, adolescent, and young adult (P-AYA) cancer." Journal of Clinical Oncology 37, no. 31_suppl (2019): 143. http://dx.doi.org/10.1200/jco.2019.37.31_suppl.143.
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143 Background: Adult P-AYA cancer survivors report numerous LLEs, as well as potential decrements in psychological well-being. The relationship between LLEs and psychological well-being, however, has not been well described in this population. We examined this relationship and predictors of LLEs and well-being in a sample of adult survivors of P-AYA cancer. Methods: Survivors of P-AYA cancer > 2 years from end of treatment completed the Hospital Anxiety and Depression Scale (HADS) and a measure of presence and severity of 11 common LLEs following a survivorship visit. LLEs included fatigue, pain, insomnia, numbness, joint or muscle pain, difficulties with concentration or memory, body image concerns, decreased sexual interest, and weight. Results: Participants (N = 237) were predominately white (89%), college educated (76%), single (63%), and had an annual income of > $60,000 (69%). A plurality had diagnoses of leukemia (30%) or Hodgkin's lymphoma (29%) treated 17 years previously. Treatment included surgery (35%), chemotherapy (91%), XRT (59%), and BMT (17%). Anxiety (M = 6.02; SD = 3.07) and depression (M = 2.54; SD = 2.85) scores were generally low and below the cutpoint of 8 (all t < -7.8; all p < .001 ), although 21% and 9% screened positively for anxiety or depression, respectively. 91% of participants reported at least 1 LLE (M = 4.8, SD = 3.1), most commonly fatigue (73%), concentration (57%) and memory difficulties (53%), and body image problems (48%). Total number LLEs was associated with elevations in both anxiety and depression, as was severity for each individual LLE (all p < 0.001). Only one LLE, difficulty with body image, produced large effects for both anxiety and depression. Low income status were associated with both LLEs and elevations in anxiety and depression (all p < 0.01). Conclusions: Most P-AYA survivors report LLEs. Although anxiety and depression are modest, elevations occur in a substantial number of survivors. Presence of LLEs is associated with worse psychosocial outcomes, particularly difficulties with body image. Lower income individuals and those with body image concerns may be at particular risk of poorer psychosocial outcomes.
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Nurrohmat, Muh Amin, and Azhari SN. "Sentiment Analysis of Novel Review Using Long Short-Term Memory Method." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 13, no. 3 (2019): 209. http://dx.doi.org/10.22146/ijccs.41236.
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The rapid development of the internet and social media and a large amount of text data has become an important research subject in obtaining information from the text data. In recent years, there has been an increase in research on sentiment analysis in the review text to determine the polarity of opinion on social media. However, there are still few studies that apply the deep learning method, namely Long Short-Term Memory for sentiment analysis in Indonesian texts.This study aims to classify Indonesian novel novels based on positive, neutral and negative sentiments using the Long Short-Term Memory (LSTM) method. The dataset used is a review of Indonesian language novels taken from the goodreads.com site. In the testing process, the LSTM method will be compared with the Naïve Bayes method based on the calculation of the values of accuracy, precision, recall, f-measure.Based on the test results show that the Long Short-Term Memory method has better accuracy results than the Naïve Bayes method with an accuracy value of 72.85%, 73% precision, 72% recall, and 72% f-measure compared to the results of the Naïve Bayes method accuracy with accuracy value of 67.88%, precision 69%, recall 68%, and f-measure 68%.
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Apriliyanto, Andi, and Retno Kusumaningrum. "HOAX DETECTION IN INDONESIA LANGUAGE USING LONG SHORT-TERM MEMORY MODEL." SINERGI 24, no. 3 (2020): 189. http://dx.doi.org/10.22441/sinergi.2020.3.003.
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Nowadays, the internet and social media grow fast. This condition has positive and negative effects on society. They become media to communicate and share information without limitation. However, many people use that easiness to broadcast news or information which do not accurate with the facts and gather people's opinions to get benefits or we called a hoax. Therefore, we need to develop a system that can detect hoax. This research uses the neural network method with Long Short-Term Memory (LSTM) model. The process of the LSTM model to identify hoax has several steps, including dataset collection, pre-processing data, word embedding using pre-trained Word2Vec, built the LSTM model. Detection model performance measurement using precision, recall, and f1-measure matrix. This research results the highest average score of precision is 0.819, recall is 0.809, and f1-measure is 0.807. These results obtained from the combination of the following parameters, i.e., Skip-gram Word2Vec Model Architecture, Hierarchical Softmax, 100 as vector dimension, max pooling, 0.5 as dropout value, and 0.001 of learning rate.
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Lee, Chung Won, and Jin Ho Kim. "The Influence of LED Lighting on Attention and Long-Term Memory." International Journal of Optics 2020 (March 23, 2020): 1–6. http://dx.doi.org/10.1155/2020/8652108.
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The fact that the illuminance of LED lights affects human attention and long-term memory has been verified through various studies, but there are no consistent research results about what level of illuminance is effective. The aims of this study were to systematically verify the effects of LED lighting on attention and long-term memory. The experiment was designed with four illuminance levels—300 lx, 400 lx, 500 lx, and 1,000 lx—as experimental conditions to determine the effects of LED lights on attention and long-term memory. Participants in the experiment were 18 college students. The attention task was performed using a handmade attention measuring instrument. Long-term memory was measured by the word fragment completion (hereinafter, referred to as “WFC”) task on the memory retention volume of the learning task that was learned exactly 24 hours before. Of the total 20 tasks, the ratio of correctly retrieval tasks was used as a dependent variable. As a result, attention showed the highest performance with a mean performance of 19.39 (SD = 3.78) at 1,000 lx. A statistically significant difference was also found between the 1,000 lx and 300 lx conditions (p=0.01). On the contrary, long-term memory showed the highest retrieval rate at an average of 58.06% (SD = 22.57) at 400 lx, and long-term memory performance was better in the order of 500 lx (mean = 48.89, SD = 20.33), 1,000 lx (mean = 45.83, SD = 23.53), and 300 lx (Mean = 43.33, SD = 19.10). Statistically, there was a significant difference between 300 lx and 400 lx (p=0.01), 400 lx and 1,000 lx (p=0.01). Through this study, it was verified that the effects of attention and long-term memory are different according to the illuminance of LED lighting, and these results can be important data to clarify the influence of light on human memory in the future.
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Fredriksen, Lauren, Renee’ Zucchero, Brock Partlow, Ruth Infante, Janie Taylor, and Haley Washburn. "The Impact of Memory Stereotype Threat on Memory and Memory Self-Efficacy in Older Adults." Innovation in Aging 4, Supplement_1 (2020): 326–27. http://dx.doi.org/10.1093/geroni/igaa057.1047.
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Abstract This study examined the impact of memory stereotype threat on memory duration (e.g., short-term and long-term) and modality (e.g., verbal and non-verbal), and memory self-efficacy in older adults who live independently (Mage = 77 years). Participants (N= 66) were randomly assigned to a group that received either neutral instructions or memory stereotype threat inducing instructions. All participants completed the California Verbal Memory Test-Second Edition (CVLT-2), the Rey Complex Figure Test (RCFT), a memory self-efficacy measure, and a demographics survey. An independent samples t-test indicated participants in the stereotype threat group reported significantly lower memory self-efficacy than participants in the neutral group. The main effect of the within-subjects factor of a 2x2 mixed analysis of variance (ANOVA) indicated that participants performed significantly better on short-term non-verbal memory than long-term non-verbal memory. There was no significant difference between the neutral and stereotype threat groups in memory modality or duration. These results may indicate that the instructions used to induce memory stereotype threat were not phrased strongly enough to elicit poorer performance on the CVLT-2 and RCFT in the memory stereotype threat group. Additionally, participants reported having a high level of education (i.e., a master’s degree was the modal educational level), which may have served as a buffer for memory stereotype threat. The findings call for future research to explore the impact of level of education on memory self-efficacy in older adults. Also, future research may focus on the impact of the strength of memory stereotype threat on memory performance.
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Fandakova, Yana, and Silvia A. Bunge. "What Connections Can We Draw Between Research on Long-Term Memory and Student Learning?" Mind, Brain, and Education 10, no. 3 (2016): 135–41. http://dx.doi.org/10.1111/mbe.12123.
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Zhang, Jianbo, Ting Peng, Linhai Mo, Xu Hu, and Xiaoling Wang. "Research on Highway Slope Monitoring Data Prediction Based on Long Short-term Memory Network." IOP Conference Series: Earth and Environmental Science 571 (November 26, 2020): 012087. http://dx.doi.org/10.1088/1755-1315/571/1/012087.
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Yang, Wei, Xiang Zhang, Qian Lei, Dengye Shen, Ping Xiao, and Yu Huang. "Lane Position Detection Based on Long Short-Term Memory (LSTM)." Sensors 20, no. 11 (2020): 3115. http://dx.doi.org/10.3390/s20113115.
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Accurate detection of lane lines is of great significance for improving vehicle driving safety. In our previous research, by improving the horizontal and vertical density of the detection grid in the YOLO v3 (You Only Look Once, the 3th version) model, the obtained lane line (LL) algorithm, YOLO v3 (S × 2S), has high accuracy. However, like the traditional LL detection algorithms, they do not use spatial information and have low detection accuracy under occlusion, deformation, worn, poor lighting, and other non-ideal environmental conditions. After studying the spatial information between LLs and learning the distribution law of LLs, an LL prediction model based on long short-term memory (LSTM) and recursive neural network (RcNN) was established; the method can predict the future LL position by using historical LL position information. Moreover, by combining the LL information predicted with YOLO v3 (S × 2S) detection results using Dempster Shafer (D-S) evidence theory, the LL detection accuracy can be improved effectively, and the uncertainty of this system be reduced correspondingly. The results show that the accuracy of LL detection can be significantly improved in rainy, snowy weather, and obstacle scenes.
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Mameniškienė, Rūta, Kristijonas Puteikis, Arminas Jasionis, and Dalius Jatužis. "A Review of Accelerated Long-Term Forgetting in Epilepsy." Brain Sciences 10, no. 12 (2020): 945. http://dx.doi.org/10.3390/brainsci10120945.
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Accelerated long-term forgetting (ALF) is a memory disorder that manifests by a distinct pattern of normal memory for up to an hour after learning, but an increased rate of forgetting during the subsequent hours and days. The topic of ALF has gained much attention in group studies with epilepsy patients and the phenomenon has been shown to have contradictory associations with seizures, epileptiform activity, imaging data, sleep, and antiepileptic medication. The aim of this review was to explore how clinical and imaging data could help determine the topographic and physiological substrate of ALF, and what is the possible use of this information in the clinical setting. We have reviewed 51 group studies in English to provide a synthesis of the existing findings concerning ALF in epilepsy. Analysis of recently reported data among patients with temporal lobe epilepsy, transient epileptic amnesia, and generalized and extratemporal epilepsies provided further indication that ALF is likely a disorder of late memory consolidation. The spatial substrate of ALF might be located along the parts of the hippocampal–neocortical network and novel studies reveal the increasingly possible importance of damage in extrahippocampal sites. Further research is needed to explore the mechanisms of cellular impairment in ALF and to develop effective methods of care for patients with the disorder.
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Buchsbaum, Bradley R., Aarthi Padmanabhan, and Karen Faith Berman. "The Neural Substrates of Recognition Memory for Verbal Information: Spanning the Divide between Short- and Long-term Memory." Journal of Cognitive Neuroscience 23, no. 4 (2011): 978–91. http://dx.doi.org/10.1162/jocn.2010.21496.
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One of the classic categorical divisions in the history of memory research is that between short-term and long-term memory. Indeed, because memory for the immediate past (a few seconds) and memory for the relatively more remote past (several seconds and beyond) are assumed to rely on distinct neural systems, more often than not, memory research has focused either on short- (or “working memory”) or on long-term memory. Using an auditory–verbal continuous recognition paradigm designed for fMRI, we examined how the neural signatures of recognition memory change across an interval of time (from 2.5 to 30 sec) that spans this hypothetical division between short- and long-term memory. The results revealed that activity during successful auditory–verbal item recognition in inferior parietal cortex and the posterior superior temporal lobe was maximal for early lags, whereas, conversely, activity in the left inferior frontal gyrus increased as a function of lag. Taken together, the results reveal that as the interval between item repetitions increases, there is a shift in the distribution of memory-related activity that moves from posterior temporo-parietal cortex (lags 1–4) to inferior frontal regions (lags 5–10), indicating that as time advances, the burden of recognition memory is increasingly placed on top–down retrieval mechanisms that are mediated by structures in inferior frontal cortex.
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Moutsopoulou, Karolina, Christina Pfeuffer, Andrea Kiesel, Qing Yang, and Florian Waszak. "How long is long-term priming? Classification and action priming in the scale of days." Quarterly Journal of Experimental Psychology 72, no. 5 (2018): 1183–99. http://dx.doi.org/10.1177/1747021818784261.
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Previous research has shown that stimulus–response associations comprise associations between the stimulus and the task (a classification task in particular) and the stimulus and the action performed as a response. These associations, contributing to the phenomenon of priming, affect behaviour after a delay of hundreds of trials and they are resistant against overwriting. Here, we investigate their longevity, testing their effects in short-term (seconds after priming) and long-term (24 hr and 1 week after priming) memory. Three experiments demonstrated that both stimulus–classification (S-C) and stimulus–action (S-A) associations show long-term memory effects. The results also show that retrieval of these associations can be modulated by the amount of engagement on the same task between encoding and retrieval, that is, how often participants performed this task between prime and probe sessions. Finally, results show that differences in processing time during encoding are linked to the amount of conflict caused during retrieval of S-C, but not S-A associations. These findings add new information to the existing model of priming as a memory system and pose questions about the interactions of priming and top-down control processes.
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Phat, Huu Nguyen, and Nguyen Thi Minh Anh. "Vietnamese Text Classification Algorithm using Long Short Term Memory and Word2Vec." Informatics and Automation 19, no. 6 (2020): 1255–79. http://dx.doi.org/10.15622/ia.2020.19.6.5.
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In the context of the ongoing forth industrial revolution and fast computer science development the amount of textual information becomes huge. So, prior to applying the seemingly appropriate methodologies and techniques to the above data processing their nature and characteristics should be thoroughly analyzed and understood. At that, automatic text processing incorporated in the existing systems may facilitate many procedures. So far, text classification is one of the basic applications to natural language processing accounting for such factors as emotions’ analysis, subject labeling etc. In particular, the existing advancements in deep learning networks demonstrate that the proposed methods may fit the documents’ classifying, since they possess certain extra efficiency; for instance, they appeared to be effective for classifying texts in English. The thorough study revealed that practically no research effort was put into an expertise of the documents in Vietnamese language. In the scope of our study, there is not much research for documents in Vietnamese. The development of deep learning models for document classification has demonstrated certain improvements for texts in Vietnamese. Therefore, the use of long short term memory network with Word2vec is proposed to classify text that improves both performance and accuracy. The here developed approach when compared with other traditional methods demonstrated somewhat better results at classifying texts in Vietnamese language. The evaluation made over datasets in Vietnamese shows an accuracy of over 90%; also the proposed approach looks quite promising for real applications.
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Pap, Judit M. "Long-term solar irradiance variability." Symposium - International Astronomical Union 181 (1997): 235–50. http://dx.doi.org/10.1017/s0074180900061180.
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Measurements of the solar energy throughout the solar spectrum and understanding its variability provide important information about the physical processes and structural changes in the solar interior and in the solar atmosphere. Solar irradiance measurements (both bolometric and at various wavelengths) over the last two decades have demonstrated that the solar radiative output changes with time as an effect of the waxing and waning solar activity. Although the overall pattern of the long-term variations is similar in the entire spectrum and at various wavelengths, being higher during high solar activity conditions, remarkable differences exist between the magnitude and shape of the observed changes. These differences arise from the different physical conditions in the solar atmosphere where the irradiances are emitted. The aim of this paper is to discuss the solar-cycle-related long-term changes in solar total and UV irradiances. The space-borne irradiance observations are compared to ground-based indices of solar magnetic activity, such as the Photometric Sunspot Index, full disk magnetic flux, and the Mt. Wilson Magnetic Plage Strength Index. Considerable part of the research described in this paper was stimulated by the discussions with the late Philippe Delache, who will always remain in the heart and memory of the author of this paper.
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BALOGLU, ULAS BARAN, and ÖZAL YILDIRIM. "CONVOLUTIONAL LONG-SHORT TERM MEMORY NETWORKS MODEL FOR LONG DURATION EEG SIGNAL CLASSIFICATION." Journal of Mechanics in Medicine and Biology 19, no. 01 (2019): 1940005. http://dx.doi.org/10.1142/s0219519419400050.
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Background and objective: Deep learning structures have recently achieved remarkable success in the field of machine learning. Convolutional neural networks (CNN) in image processing and long-short term memory (LSTM) in the time-series analysis are commonly used deep learning algorithms. Healthcare applications of deep learning algorithms provide important contributions for computer-aided diagnosis research. In this study, convolutional long-short term memory (CLSTM) network was used for automatic classification of EEG signals and automatic seizure detection. Methods: A new nine-layer deep network model consisting of convolutional and LSTM layers was designed. The signals processed in the convolutional layers were given as an input to the LSTM network whose outputs were processed in densely connected neural network layers. The EEG data is appropriate for a model having 1-D convolution layers. A bidirectional model was employed in the LSTM layer. Results: Bonn University EEG database with five different datasets was used for experimental studies. In this database, each dataset contains 23.6[Formula: see text]s duration 100 single channel EEG segments which consist of 4097 dimensional samples (173.61[Formula: see text]Hz). Eight two-class and three three-class clinical scenarios were examined. When the experimental results were evaluated, it was seen that the proposed model had high accuracy on both binary and ternary classification tasks. Conclusions: The proposed end-to-end learning structure showed a good performance without using any hand-crafted feature extraction or shallow classifiers to detect the seizures. The model does not require filtering, and also automatically learns to filter the input as well. As a result, the proposed model can process long duration EEG signals without applying segmentation, and can detect epileptic seizures automatically by using the correlation of ictal and interictal signals of raw data.
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Bruck, Jason N. "Decades-long social memory in bottlenose dolphins." Proceedings of the Royal Society B: Biological Sciences 280, no. 1768 (2013): 20131726. http://dx.doi.org/10.1098/rspb.2013.1726.
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Long-term social memory is important, because it is an ecologically relevant test of cognitive capacity, it helps us understand which social relationships are remembered and it relates two seemingly disparate disciplines: cognition and sociality. For dolphins, long-term memory for conspecifics could help assess social threats as well as potential social or hunting alliances in a very fluid and complex fission–fusion social system, yet we have no idea how long dolphins can remember each other. Through a playback study conducted within a multi-institution dolphin breeding consortium (where animals are moved between different facilities), recognition of unfamiliar versus familiar signature whistles of former tank mates was assessed. This research shows that dolphins have the potential for lifelong memory for each other regardless of relatedness, sex or duration of association. This is, to my knowledge, the first study to show that social recognition can last for at least 20 years in a non-human species and the first large-scale study to address long-term memory in a cetacean. These results, paired with evidence from elephants and humans, provide suggestive evidence that sociality and cognition could be related, as a good memory is necessary in a fluid social system.
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Rasch, Björn, and Jan Born. "About Sleep's Role in Memory." Physiological Reviews 93, no. 2 (2013): 681–766. http://dx.doi.org/10.1152/physrev.00032.2012.
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Over more than a century of research has established the fact that sleep benefits the retention of memory. In this review we aim to comprehensively cover the field of “sleep and memory” research by providing a historical perspective on concepts and a discussion of more recent key findings. Whereas initial theories posed a passive role for sleep enhancing memories by protecting them from interfering stimuli, current theories highlight an active role for sleep in which memories undergo a process of system consolidation during sleep. Whereas older research concentrated on the role of rapid-eye-movement (REM) sleep, recent work has revealed the importance of slow-wave sleep (SWS) for memory consolidation and also enlightened some of the underlying electrophysiological, neurochemical, and genetic mechanisms, as well as developmental aspects in these processes. Specifically, newer findings characterize sleep as a brain state optimizing memory consolidation, in opposition to the waking brain being optimized for encoding of memories. Consolidation originates from reactivation of recently encoded neuronal memory representations, which occur during SWS and transform respective representations for integration into long-term memory. Ensuing REM sleep may stabilize transformed memories. While elaborated with respect to hippocampus-dependent memories, the concept of an active redistribution of memory representations from networks serving as temporary store into long-term stores might hold also for non-hippocampus-dependent memory, and even for nonneuronal, i.e., immunological memories, giving rise to the idea that the offline consolidation of memory during sleep represents a principle of long-term memory formation established in quite different physiological systems.
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Shors, Tracey J., and Louis D. Matzel. "Long-term potentiation: What's learning got to do with it?" Behavioral and Brain Sciences 20, no. 4 (1997): 597–614. http://dx.doi.org/10.1017/s0140525x97001593.
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Long-term potentiation (LTP) is operationally defined as a long-lasting increase in synaptic efficacy following high-frequency stimulation of afferent fibers. Since the first full description of the phenomenon in 1973, exploration of the mechanisms underlying LTP induction has been one of the most active areas of research in neuroscience. Of principal interest to those who study LTP, particularly in the mammalian hippocampus, is its presumed role in the establishment of stable memories, a role consistent with “Hebbian” descriptions of memory formation. Other characteristics of LTP, including its rapid induction, persistence, and correlation with natural brain rhythms, provide circumstantial support for this connection to memory storage. Nonetheless, there is little empirical evidence that directly links LTP to the storage of memories. In this target article we review a range of cellular and behavioral characteristics of LTP and evaluate whether they are consistent with the purported role of hippocampal LTP in memory formation. We suggest that much of the present focus on LTP reflects a preconception that LTP is a learning mechanism, although the empirical evidence often suggests that LTP is unsuitable for such a role. As an alternative to serving as a memory storage device, we propose that LTP may serve as a neural equivalent to an arousal or attention device in the brain. Accordingly, LTP may increase in a nonspecific way the effective salience of discrete external stimuli and may thereby facilitate the induction of memories at distant synapses. Other hypotheses regarding the functional utility of this intensely studied mechanism are conceivable; the intent of this target article is not to promote a single hypothesis but rather to stimulate discussion about the neural mechanisms underlying memory storage and to appraise whether LTP can be considered a viable candidate for such a mechanism.
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Rußwurm, M., and M. Körner. "MULTI-TEMPORAL LAND COVER CLASSIFICATION WITH LONG SHORT-TERM MEMORY NEURAL NETWORKS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1/W1 (May 31, 2017): 551–58. http://dx.doi.org/10.5194/isprs-archives-xlii-1-w1-551-2017.
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<i>Land cover classification (LCC)</i> is a central and wide field of research in earth observation and has already put forth a variety of classification techniques. Many approaches are based on classification techniques considering observation at certain points in time. However, some land cover classes, such as crops, change their spectral characteristics due to environmental influences and can thus not be monitored effectively with classical mono-temporal approaches. Nevertheless, these temporal observations should be utilized to benefit the classification process. After extensive research has been conducted on modeling temporal dynamics by spectro-temporal profiles using vegetation indices, we propose a deep learning approach to utilize these temporal characteristics for classification tasks. In this work, we show how <i>long short-term memory</i> (LSTM) neural networks can be employed for crop identification purposes with SENTINEL 2A observations from large study areas and label information provided by local authorities. We compare these temporal neural network models, <i>i.e.</i>, LSTM and <i>recurrent neural network</i> (RNN), with a classical non-temporal <i>convolutional neural network</i> (CNN) model and an additional <i>support vector machine</i> (SVM) baseline. With our rather straightforward LSTM variant, we exceeded state-of-the-art classification performance, thus opening promising potential for further research.
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Patel, Hamish, та Reza Zamani. "The role of PKMζ in the maintenance of long-term memory: a review". Reviews in the Neurosciences 32, № 5 (2021): 481–94. http://dx.doi.org/10.1515/revneuro-2020-0105.
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Abstract Long-term memories are thought to be stored in neurones and synapses that undergo physical changes, such as long-term potentiation (LTP), and these changes can be maintained for long periods of time. A candidate enzyme for the maintenance of LTP is protein kinase M zeta (PKMζ), a constitutively active protein kinase C isoform that is elevated during LTP and long-term memory maintenance. This paper reviews the evidence and controversies surrounding the role of PKMζ in the maintenance of long-term memory. PKMζ maintains synaptic potentiation by preventing AMPA receptor endocytosis and promoting stabilisation of dendritic spine growth. Inhibition of PKMζ, with zeta-inhibitory peptide (ZIP), can reverse LTP and impair established long-term memories. However, a deficit of memory retrieval cannot be ruled out. Furthermore, ZIP, and in high enough doses the control peptide scrambled ZIP, was recently shown to be neurotoxic, which may explain some of the effects of ZIP on memory impairment. PKMζ knockout mice show normal learning and memory. However, this is likely due to compensation by protein-kinase C iota/lambda (PKCι/λ), which is normally responsible for induction of LTP. It is not clear how, or if, this compensatory mechanism is activated under normal conditions. Future research should utilise inducible PKMζ knockdown in adult rodents to investigate whether PKMζ maintains memory in specific parts of the brain, or if it represents a global memory maintenance molecule. These insights may inform future therapeutic targets for disorders of memory loss.
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Ahles, Tim A., Andrew J. Saykin, Charlotte T. Furstenberg, et al. "Neuropsychologic Impact of Standard-Dose Systemic Chemotherapy in Long-Term Survivors of Breast Cancer and Lymphoma." Journal of Clinical Oncology 20, no. 2 (2002): 485–93. http://dx.doi.org/10.1200/jco.2002.20.2.485.
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PURPOSE: The primary purpose of this study was to compare the neuropsychologic functioning of long-term survivors of breast cancer and lymphoma who had been treated with standard-dose systemic chemotherapy or local therapy only. PATIENTS AND METHODS: Long-term survivors (5 years postdiagnosis, not presently receiving cancer treatment, and disease-free) of breast cancer or lymphoma who had been treated with systemic chemotherapy (breast cancer: n = 35, age, 59.1 ± 10.7 years; lymphoma: n = 36, age, 55.9 ± 12.1 years) or local therapy only (breast cancer: n = 35, age, 60.6 ± 10.5 years; lymphoma: n = 22, age, 48.7 ± 11.7 years) completed a battery of neuropsychologic and psychologic tests (Center for Epidemiological Study–Depression, Spielberger State-Trait Anxiety Inventory, and Fatigue Symptom Inventory). RESULTS: Multivariate analysis of variance, controlling for age and education, revealed that survivors who had been treated with systemic chemotherapy scored significantly lower on the battery of neuropsychologic tests compared with those treated with local therapy only (P < .04), particularly in the domains of verbal memory (P < .01) and psychomotor functioning (P < .03). Survivors treated with systemic chemotherapy were also more likely to score in the lower quartile on the Neuropsychological Performance Index (39% v 14%, P < .01) and to self-report greater problems with working memory on the Squire Memory Self-Rating Questionnaire (P < .02). CONCLUSION: Data from this study support the hypothesis that systemic chemotherapy can have a negative impact on cognitive functioning as measured by standardized neuropsychologic tests and self-report of memory changes. However, analysis of the Neuropsychological Performance Index suggests that only a subgroup of survivors may experience long-term cognitive deficits associated with systemic chemotherapy.
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Luo, Siyin, Youjian Gu, Xingxing Yao, and Wei Fan. "Research on Text Sentiment Analysis Based on Neural Network and Ensemble Learning." Revue d'Intelligence Artificielle 35, no. 1 (2021): 63–70. http://dx.doi.org/10.18280/ria.350107.
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In view of the fact that a single sentiment classification model may be unstable in classification, this paper attempts to propose a joint neural network and ensemble learning sentiment analysis method. After data preprocessing such as word segmentation on the text, combined with document vectorization method for feature extraction, we then use four basic classifiers including long short-term memory network, convolutional neural network, a serial model combining convolutional neural network and long short-term memory network, and support vector machine to train model, respectively. Finally, the integration is carried out by stacking ensemble learning. The experimental results show that the integrated model significantly improves the accuracy of text sentiment analysis and it can effectively predict the sentiment polarity of the text.
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Mircea, M. M. "Employing Long Short-Term Memory Networks in Trigger Detection for Emetophobia." Studia Universitatis Babeș-Bolyai Informatica 65, no. 2 (2020): 17. http://dx.doi.org/10.24193/subbi.2020.2.02.
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Research focused on mental health-related issues is vital to the modern person’s life. Specific phobias are part of the anxiety disorder umbrella and they are distressing afflictions. Emetophobia is the rarely known, yet fairly common and highly disruptive specific phobia of vomiting. Unlike other phobias, emetophobia is triggered not only by the object of the specific fear, but also by verbal and written mentions of said object. This paper proposes and compares ten neural network-based architectures that discern between triggering and non-triggering groups of written words. An interface is created, where the best models can be used in emetophobia-related applications. This interface is then integrated into an application that can be used by emetophobes to censor online content such that the exposure to triggers is controlled, patient-centered, and patient-paced.
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Wang, Lipeng. "An Improved Long Short-Term Memory Neural Network for Macroeconomic Forecast." Revue d'Intelligence Artificielle 34, no. 5 (2020): 577–84. http://dx.doi.org/10.18280/ria.340507.
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The statistics and cyclical swings of macroeconomics are necessary for exploring the internal laws and features of the market economy. To realize intelligent and efficient macroeconomic forecast, this paper puts forward a macroeconomic forecast model based on improved long short-term memory (LSTM) neural network. Firstly, a scientific evaluation index system (EIS) was constructed for macroeconomy. The correlation between indices was measured by Spearman correlation coefficient, and the index data were preprocessed by interpolating the missing items and converting low-frequency series into high-frequency series. Next, the corresponding mixed frequency dataset was constructed, followed by the derivation of the state space equation. Then, the LSTM neutral network was optimized by the Kalman filter or macroeconomic forecast. The effectiveness of the proposed forecast method was verified through experiments. The research results lay a theoretical basis for the application of LSTM in financial forecasts.
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Sneidere, Kristine, Jelena Harlamova, Zane Ulmane, Voldemars Arnis, Andra Vanaga, and Ainars Stepens. "RELATIONSHIP BETWEEN INVOLVEMENT IN LONG- TERM REGULAR PHYSICAL ACTIVITY AND MEMORY: PRELIMINARY RESULTS." Baltic Journal of Sport and Health Sciences 4, no. 107 (2017): 23–27. http://dx.doi.org/10.33607/bjshs.v4i107.36.
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Background. Ageing of the Western Society has become both – economic and social concern. Ageing has both – biological and psychological consequences, and, with changes in the brain due to ageing (e.g. decline in the brain volume in frontal, parietal and temporal areas, as well as hippocampus (Colcombe et al., 2003; Erickson, Voss, Shaurya, Basak, & Szabo, 2011)), there are changes in cognitive functioning. For the past years, research has indicated a relationship between aerobic activity interventions and increase in episodic memory (Ruscheweyh et al., 2011), face recognition associative memory (Hayes et al., 2015) and working memory (Erickson et al., 2011). Methods. The aim of the study was to examine the relationship between involvement in aerobic physical activities and memory; thus 43 seniors aged from 65 to 85 (M = 71.86, SD = 5.09, 23% male) were included in the study. Based on their physical activity experience, participants were divided into three groups – seniors with long- term aerobic physical activity experience (n = 16), seniors that have recently taken up aerobic physical activities (n = 19) and seniors not involved in physical activities (n = 8). Results. The preliminary data indicated relationship between long-term involvement in physical activities and working memory, as well as negative relationship between sedentary lifestyle and overall cognitive abilities. Conclusions. As this is still a work in progress, one of the limitations being the small sample, these results can be considered only as a tendency. Another limitation is the unequal gender distribution. This study was funded by the Latvian National Research Programme BIOMEDICINE 2014–2017.
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Duppati, Geeta, Anoop S. Kumar, Frank Scrimgeour, and Leon Li. "Long memory volatility in Asian stock markets." Pacific Accounting Review 29, no. 3 (2017): 423–42. http://dx.doi.org/10.1108/par-02-2016-0009.
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Purpose The purpose of this paper is to assess to what extent intraday data can explain and predict long-term memory. Design/methodology/approach This article analysed the presence of long-memory volatility in five Asian equity indices, namely, SENSEX, CNIA, NIKKEI225, KO11 and FTSTI, using five-min intraday return series from 05 January 2015 to 06 August 2015 using two approaches, i.e. conditional volatility and realized volatility, for forecasting long-term memory. It employs conditional-generalized autoregressive conditional heteroscedasticity (GARCH), i.e. autoregressive fractionally integrated moving average (ARFIMA)-FIGARCH model and ARFIMA-asymmetric power autoregressive conditional heteroscedasticity (APARCH) models, and unconditional volatility realized volatility using autoregressive integrated moving average (ARIMA) and ARFIMA in-sample forecasting models to estimate the persistence of the long-term memory. Findings Given the GARCH framework, the ARFIMA-APARCH long-memory model gave the better forecast results signifying the importance of accounting for asymmetric information when modelling volatility in a financial market. Using the unconditional realized volatility results from the Singapore and Indian markets, the ARIMA model outperforms the ARFIMA model in terms of forecast performance and provides reasonable forecasts. Practical implications The issue of long memory has important implications for the theory and practice of finance. It is well-known that accurate volatility forecasts are important in a variety of settings including option and other derivatives pricing, portfolio and risk management. Social implications It could be said that using long-memory augmented models would give better results to investors so that they could analyse the market trends in returns and volatility in a more accurate manner and reach at an informed decision. This is useful to minimize the risks. Originality/value This research enhances the literature by estimating the influence of intraday variables on daily volatility. This is one of very few studies that uses conditional GARCH framework models and unconditional realized volatility estimates for forecasting long-term memory. The authors find that the methods complement each other.
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Fu, Kun, Yang Li, Wenkai Zhang, Hongfeng Yu, and Xian Sun. "Boosting Memory with a Persistent Memory Mechanism for Remote Sensing Image Captioning." Remote Sensing 12, no. 11 (2020): 1874. http://dx.doi.org/10.3390/rs12111874.
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The encoder–decoder framework has been widely used in the remote sensing image captioning task. When we need to extract remote sensing images containing specific characteristics from the described sentences for research, rich sentences can improve the final extraction results. However, the Long Short-Term Memory (LSTM) network used in decoders still loses some information in the picture over time when the generated caption is long. In this paper, we present a new model component named the Persistent Memory Mechanism (PMM), which can expand the information storage capacity of LSTM with an external memory. The external memory is a memory matrix with a predetermined size. It can store all the hidden layer vectors of LSTM before the current time step. Thus, our method can effectively solve the above problem. At each time step, the PMM searches previous information related to the input information at the current time from the external memory. Then the PMM will process the captured long-term information and predict the next word with the current information. In addition, it updates its memory with the input information. This method can pick up the long-term information missed from the LSTM but useful to the caption generation. By applying this method to image captioning, our CIDEr scores on datasets UCM-Captions, Sydney-Captions, and RSICD increased by 3%, 5%, and 7%, respectively.