Bibliografías temáticas / Time-Aware LSTM

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Literatura académica sobre el tema "Time-Aware LSTM"

Autor: Grafiati
Publicado: 29 de marzo de 2025

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Artículos de revistas sobre el tema "Time-Aware LSTM"

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Cheng, Lin, Yuliang Shi, Kun Zhang, Xinjun Wang, and Zhiyong Chen. "GGATB-LSTM: Grouping and Global Attention-based Time-aware Bidirectional LSTM Medical Treatment Behavior Prediction." ACM Transactions on Knowledge Discovery from Data 15, no. 3 (2021): 1–16. http://dx.doi.org/10.1145/3441454.

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In China, with the continuous development of national health insurance policies, more and more people have joined the health insurance. How to accurately predict patients future medical treatment behavior becomes a hotspot issue. The biggest challenge in this issue is how to improve the prediction performance by modeling health insurance data with high-dimensional time characteristics. At present, most of the research is to solve this issue by using Recurrent Neural Networks (RNNs) to construct an overall prediction model for the medical visit sequences. However, RNNs can not effectively solve
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Wiessner, Paul, Grigor Bezirganyan, Sana Sellami, Richard Chbeir, and Hans-Joachim Bungartz. "Uncertainty-Aware Time Series Anomaly Detection." Future Internet 16, no. 11 (2024): 403. http://dx.doi.org/10.3390/fi16110403.

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Traditional anomaly detection methods in time series data often struggle with inherent uncertainties like noise and missing values. Indeed, current approaches mostly focus on quantifying epistemic uncertainty and ignore data-dependent uncertainty. However, consideration of noise in data is important as it may have the potential to lead to more robust detection of anomalies and a better capability of distinguishing between real anomalies and anomalous patterns provoked by noise. In this paper, we propose LSTMAE-UQ (Long Short-Term Memory Autoencoder with Aleatoric and Epistemic Uncertainty Quan
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Yadulla, Akhila Reddy, Mounica Yenugula, Vinay Kumar Kasula, Bhargavi Konda, Santosh Reddy Addula, and Sarath Babu Rakki. "A time-aware LSTM model for detecting criminal activities in blockchain transactions." International Journal of Communication and Information Technology 4, no. 2 (2023): 33–39. https://doi.org/10.33545/2707661x.2023.v4.i2a.108.

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Yang, Xuan, and James A. Esquivel. "Time-Aware LSTM Neural Networks for Dynamic Personalized Recommendation on Business Intelligence." Tsinghua Science and Technology 29, no. 1 (2024): 185–96. http://dx.doi.org/10.26599/tst.2023.9010025.

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Chen, Long, Zhiyao Tian, Shunhua Zhou, Quanmei Gong, and Honggui Di. "Attitude deviation prediction of shield tunneling machine using Time-Aware LSTM networks." Transportation Geotechnics 45 (March 2024): 101195. http://dx.doi.org/10.1016/j.trgeo.2024.101195.

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Chen, Jie, Chang Liu, Jiawu Xie, Jie An, and Nan Huang. "Time–Frequency Mask-Aware Bidirectional LSTM: A Deep Learning Approach for Underwater Acoustic Signal Separation." Sensors 22, no. 15 (2022): 5598. http://dx.doi.org/10.3390/s22155598.

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Underwater acoustic signal separation is a key technique for underwater communications. The existing methods are mostly model-based, and cannot accurately characterize the practical underwater acoustic communication environment. They are only suitable for binary signal separation and cannot handle multivariate signal separation. However, recurrent neural networks (RNNs) show a powerful ability to extract the features of temporal sequences. Inspired by this, in this paper, we present a data-driven approach for underwater acoustic signal separation using deep learning technology. We use a bidire
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Zhang, Jinkai, Wenming Ma, En Zhang, and Xuchen Xia. "Time-Aware Dual LSTM Neural Network with Similarity Graph Learning for Remote Sensing Service Recommendation." Sensors 24, no. 4 (2024): 1185. http://dx.doi.org/10.3390/s24041185.

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Technological progress has led to significant advancements in Earth observation and satellite systems. However, some services associated with remote sensing face issues related to timeliness and relevance, which affect the application of remote sensing resources in various fields and disciplines. The challenge now is to help end-users make precise decisions and recommendations for relevant resources that meet the demands of their specific domains from the vast array of remote sensing resources available. In this study, we propose a remote sensing resource service recommendation model that inco
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Zheng, Ruixuan, Yanping Bao, Lihua Zhao, and Lidong Xing. "Prediction of steelmaking process variables using K-medoids and a time-aware LSTM network." Heliyon 10, no. 12 (2024): e32901. http://dx.doi.org/10.1016/j.heliyon.2024.e32901.

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Subapriya Vijayakumar and Rajaprakash Singaravelu. "Time Aware Long Short-Term Memory and Kronecker Gated Intelligent Transportation for Smart Car Parking." Journal of Advanced Research in Applied Sciences and Engineering Technology 44, no. 1 (2024): 134–50. http://dx.doi.org/10.37934/araset.44.1.134150.

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Technology desires to improve quality of life and impart citizen’s health as well as happiness. The concept of Internet of Things (IoT) refers to smart world where prevailing objects are said to be embedded and hence interact with each other (i.e., between objects and human beings) to achieve an objective. In the period of IoT as well as smart city, there is requirement for Intelligent Transport System-based (ITS) ingenious smart parking or car parking space prediction (CPSP) for more feasible cities. With the increase in population and mushroom growth in vehicles are bringing about several di
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Gui, Zhipeng, Yunzeng Sun, Le Yang, et al. "LSI-LSTM: An attention-aware LSTM for real-time driving destination prediction by considering location semantics and location importance of trajectory points." Neurocomputing 440 (June 2021): 72–88. http://dx.doi.org/10.1016/j.neucom.2021.01.067.

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Tesis sobre el tema "Time-Aware LSTM"

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Cissoko, Mamadou Ben Hamidou. "Adaptive time-aware LSTM for predicting and interpreting ICU patient trajectories from irregular data." Electronic Thesis or Diss., Strasbourg, 2024. http://www.theses.fr/2024STRAD012.

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En médecine prédictive personnalisée, modéliser avec précision la maladie et les processus de soins d'un patient est crucial en raison des dépendances temporelles à long terme inhérentes. Cependant, les dossiers de santé électroniques (DSE) se composent souvent de données épisodiques et irrégulières, issues des admissions hospitalières sporadiques, créant des schémas uniques pour chaque séjour hospitalier.Par conséquent, la construction d'un modèle prédictif personnalisé nécessite une considération attentive de ces facteurs pour capturer avec précision le parcours de santé du patient et aider
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Gaddari, Abdelhamid. "Analysis and Prediction of Patient Pathways in the Context of Supplemental Health Insurance." Electronic Thesis or Diss., Lyon 1, 2024. http://www.theses.fr/2024LYO10299.

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Ce travail de thèse s'inscrit dans la catégorie de la recherche en informatique de santé, en particulier l'analyse et la prédiction des parcours patients, qui sont les séquences des actes médicaux consommés par les patients au fil du temps. Notre objectif est de proposer une approche innovante pour l'exploitation des données de parcours de soins afin de réaliser non seulement une classification binaire, mais aussi multi-label. Nous concevons également une nouvelle approche de vectorisation et représentation sémantique exclusivement pour le domaine médical français, qui permettra d'exploiter un
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Capítulos de libros sobre el tema "Time-Aware LSTM"

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Lee, Jeong Min, and Milos Hauskrecht. "Recent Context-Aware LSTM for Clinical Event Time-Series Prediction." In Artificial Intelligence in Medicine. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21642-9_3.

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Sahu, Parth, S. Raghavan, K. Chandrasekaran, and Divakarla Usha. "Time-Aware Online QoS Prediction Using LSTM and Non-negative Matrix Factorization." In Algorithms for Intelligent Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2248-9_35.

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Nguyen, An, Srijeet Chatterjee, Sven Weinzierl, Leo Schwinn, Martin Matzner, and Bjoern Eskofier. "Time Matters: Time-Aware LSTMs for Predictive Business Process Monitoring." In Lecture Notes in Business Information Processing. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72693-5_9.

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Mishra, Abhinav. "Public Opinion Regarding COVID-19 Analyzed for Emotion Using Deep Learning Techniques." In Demystifying Emerging Trends in Machine Learning. BENTHAM SCIENCE PUBLISHERS, 2025. https://doi.org/10.2174/9789815305395125020034.

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As a result of the COVID-19 epidemic, many individuals are experiencing extreme worry, dread, and other difficult emotions. Since coronavirus immunizations were first introduced, people's reactions have gotten more nuanced and varied. In this study, we will use deep learning methods to decode their emotions. Twitter provides a glimpse into what is popular and what is on people's minds at any given time, and social media is presently the finest means to convey sentiments and emotions. Our goal while conducting this study was to have a better grasp of how different groups of individuals feel about vaccinations. The research period for this research's tweet was from December 21st to July 21st. Of the most talked-about vaccines that have recently been available in various regions of the world were the subject of several tweets. The term Valence Aware Sentiment Dictionary An NLP program called Believed (VADER) was used to examine people's sentiments on certain vaccines. We were better able to see the big picture after categorizing the collected attitudes into positive (33.96 percent), negative (17.55 percent), and neutral (48.49 percent) camps. We also included into our study an examination of the tweets' chronology, given that attitudes changed over time. The performance of the forecasting models was evaluated using an RNN-oriented design that included bidirectional LSTM (Bi-LSTM) as well as long short-term memory (LSTM); LSTM attained an accuracy of 90.59% as well as BiLSTM of 90.83%. Additional performance metrics, such as Precision, F1-score, as well as a matrix of confusion, were used to confirm our hypotheses as well as outcomes. The findings of this research provide credence to efforts to eradicate coronavirus across the globe by expanding our knowledge of public opinion on COVID-19 vaccines.
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Actas de conferencias sobre el tema "Time-Aware LSTM"

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Baytas, Inci M., Cao Xiao, Xi Zhang, Fei Wang, Anil K. Jain, and Jiayu Zhou. "Patient Subtyping via Time-Aware LSTM Networks." In KDD '17: The 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2017. http://dx.doi.org/10.1145/3097983.3097997.

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Zhang, Yuan, Xi Yang, Julie Ivy, and Min Chi. "ATTAIN: Attention-based Time-Aware LSTM Networks for Disease Progression Modeling." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/607.

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Modeling patient disease progression using Electronic Health Records (EHRs) is critical to assist clinical decision making. Long-Short Term Memory (LSTM) is an effective model to handle sequential data, such as EHRs, but it encounters two major limitations when applied to EHRs: it is unable to interpret the prediction results and it ignores the irregular time intervals between consecutive events. To tackle these limitations, we propose an attention-based time-aware LSTM Networks (ATTAIN), to improve the interpretability of LSTM and to identify the critical previous events for current diagnosis
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Liu, Lucas Jing, Victor Ortiz-Soriano, Javier A. Neyra, and Jin Chen. "KIT-LSTM: Knowledge-guided Time-aware LSTM for Continuous Clinical Risk Prediction." In 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2022. http://dx.doi.org/10.1109/bibm55620.2022.9994931.

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Chen, Zhiqi, Yao Wang, Gadi Wollstein, Maria de los Angeles Ramos-Cadena, Joel Schuman, and Hiroshi Ishikawa. "Macular GCIPL Thickness Map Prediction via Time-Aware Convolutional LSTM." In 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). IEEE, 2020. http://dx.doi.org/10.1109/isbi45749.2020.9098614.

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Navarin, Nicolo, Beatrice Vincenzi, Mirko Polato, and Alessandro Sperduti. "LSTM networks for data-aware remaining time prediction of business process instances." In 2017 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2017. http://dx.doi.org/10.1109/ssci.2017.8285184.

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Yin, Changchang, Sayoko E. Moroi, and Ping Zhang. "Predicting Age-Related Macular Degeneration Progression with Contrastive Attention and Time-Aware LSTM." In KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 2022. http://dx.doi.org/10.1145/3534678.3539163.

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Yamamura, Tatsuya, Ismail Arai, Masatoshi Kakiuchi, Arata Endo, and Kazutoshi Fujikawa. "Bus Ridership Prediction with Time Section, Weather, and Ridership Trend Aware Multiple LSTM." In 2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops). IEEE, 2023. http://dx.doi.org/10.1109/percomworkshops56833.2023.10150218.

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Chen, Dehua, Liping Zhang, Ming Zuo, and Qiao Pan. "Risk Assessment Model for Diabetic Cardiovascular Disease Via Personality and Time-Aware LSTM Network." In International Conference on Biotechnology and Biomedicine. SCITEPRESS - Science and Technology Publications, 2022. http://dx.doi.org/10.5220/0012032600003633.

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Abdelhamid, Gaddari, Elghazel Haytham, Jaziri Rakia, Hacid Mohand-Saïd, and Comble Pierre-Henri. "A New Time-Aware LSTM based Framework for Multi-label Classification on Healthcare Data." In 2023 20th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA). IEEE, 2023. http://dx.doi.org/10.1109/aiccsa59173.2023.10479260.

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Perera, Dilruk, and Roger Zimmermann. "LSTM Networks for Online Cross-Network Recommendations." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/532.

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Cross-network recommender systems use auxiliary information from multiple source networks to create holistic user profiles and improve recommendations in a target network. However, we find two major limitations in existing cross-network solutions that reduce overall recommender performance. Existing models (1) fail to capture complex non-linear relationships in user interactions, and (2) are designed for offline settings hence, not updated online with incoming interactions to capture the dynamics in the recommender environment. We propose a novel multi-layered Long Short-Term Memory (LSTM) net
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