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Literatura académica sobre el tema "LSTM Temporel"

Autor: Grafiati

Publicado: 29 de marzo de 2025

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Artículos de revistas sobre el tema "LSTM Temporel"

Liu, Jun, Tong Zhang, Guangjie Han y Yu Gou. "TD-LSTM: Temporal Dependence-Based LSTM Networks for Marine Temperature Prediction". Sensors 18, n.º 11 (6 de noviembre de 2018): 3797. http://dx.doi.org/10.3390/s18113797.

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Changes in ocean temperature over time have important implications for marine ecosystems and global climate change. Marine temperature changes with time and has the features of closeness, period, and trend. This paper analyzes the temporal dependence of marine temperature variation at multiple depths and proposes a new ocean-temperature time-series prediction method based on the temporal dependence parameter matrix fusion of historical observation data. The Temporal Dependence-Based Long Short-Term Memory (LSTM) Networks for Marine Temperature Prediction (TD-LSTM) proves better than other methods while predicting sea-surface temperature (SST) by using Argo data. The performances were good at various depths and different regions.

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Baddar, Wissam J. y Yong Man Ro. "Mode Variational LSTM Robust to Unseen Modes of Variation: Application to Facial Expression Recognition". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 de julio de 2019): 3215–23. http://dx.doi.org/10.1609/aaai.v33i01.33013215.

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Spatio-temporal feature encoding is essential for encoding the dynamics in video sequences. Recurrent neural networks, particularly long short-term memory (LSTM) units, have been popular as an efficient tool for encoding spatio-temporal features in sequences. In this work, we investigate the effect of mode variations on the encoded spatio-temporal features using LSTMs. We show that the LSTM retains information related to the mode variation in the sequence, which is irrelevant to the task at hand (e.g. classification facial expressions). Actually, the LSTM forget mechanism is not robust enough to mode variations and preserves information that could negatively affect the encoded spatio-temporal features. We propose the mode variational LSTM to encode spatio-temporal features robust to unseen modes of variation. The mode variational LSTM modifies the original LSTM structure by adding an additional cell state that focuses on encoding the mode variation in the input sequence. To efficiently regulate what features should be stored in the additional cell state, additional gating functionality is also introduced. The effectiveness of the proposed mode variational LSTM is verified using the facial expression recognition task. Comparative experiments on publicly available datasets verified that the proposed mode variational LSTM outperforms existing methods. Moreover, a new dynamic facial expression dataset with different modes of variation, including various modes like pose and illumination variations, was collected to comprehensively evaluate the proposed mode variational LSTM. Experimental results verified that the proposed mode variational LSTM encodes spatio-temporal features robust to unseen modes of variation.

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D, Usha, Jesmalar L, Noorbasha Nagoor Meeravali, Mihirkumar B.Suthar, Rajeswari J, Pothumarthi Sridevi y Vengatesh T. "Enhanced Dengue Fever Prediction in India through Deep Learning with Spatially Attentive LSTMs". Cuestiones de Fisioterapia 54, n.º 2 (10 de enero de 2025): 3804–12. https://doi.org/10.48047/v3dm7y10.

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This research intends to forecast dengue fever occurrences in India using machine learningmethods. A dataset comprising weekly dengue occurrences at the state level in India from 2017 to2024 was sourced from the India Open Data website and contains factors such as climate, geography,and demographics. Six distinct long short-term memory (LSTM) models were created and assessedfor dengue forecasting in India: LSTM, stacked LSTM (S-LSTM), LSTM with temporal attention(TA-LSTM), S-LSTM with temporal attention (STA-LSTM), LSTM with spatial attention (SALSTM), and S-LSTM with spatial attention (SSA-LSTM). The models were trained and tested on adataset of monthly dengue occurrences in India from 2017 to 2024, aiming to predict the number ofdengue cases using various climate, topographic, demographic, and land-use factors.

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Tao, Hong, Yue Deng, Yunqiu Xiang y Long Liu. "Performance of long short-term memory networks in predicting athlete injury risk". Journal of Computational Methods in Sciences and Engineering 24, n.º 4-5 (14 de agosto de 2024): 3155–71. http://dx.doi.org/10.3233/jcm-247563.

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Conventional approaches to forecasting the risk of athlete injuries are constrained by their narrow scope in feature extraction, often failing to adequately account for temporal dependencies and the effects of long-term memory. This paper enhances the Long Short-Term Memory (LSTM) network, specifically tailoring it to harness temporal data pertaining to athletes. This advancement significantly boosts the accuracy and effectiveness of predicting the risk of injuries among athletes. The network structure of the LSTM model was improved, and the collected data was converted into the temporal data form of the LSTM input. Finally, historical data labeled with injury labels were used to train the improved LSTM model, and gradient descent iterative optimization was used to adjust the parameters of the improved LSTM model. The improved LSTM network model was compared with the traditional athlete injury risk prediction model in terms of performance. The incorporation of enhanced LSTM networks for the analysis of temporal athlete data holds significant research significance. This approach has the potential to substantially enhance the accuracy and effectiveness of athlete injury risk prediction, contributing to a deeper understanding of the temporal dynamics influencing injuries in sports.

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Majeed, Mokhalad A., Helmi Zulhaidi Mohd Shafri, Zed Zulkafli y Aimrun Wayayok. "A Deep Learning Approach for Dengue Fever Prediction in Malaysia Using LSTM with Spatial Attention". International Journal of Environmental Research and Public Health 20, n.º 5 (25 de febrero de 2023): 4130. http://dx.doi.org/10.3390/ijerph20054130.

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This research aims to predict dengue fever cases in Malaysia using machine learning techniques. A dataset consisting of weekly dengue cases at the state level in Malaysia from 2010 to 2016 was obtained from the Malaysia Open Data website and includes variables such as climate, geography, and demographics. Six different long short-term memory (LSTM) models were developed and compared for dengue prediction in Malaysia: LSTM, stacked LSTM (S-LSTM), LSTM with temporal attention (TA-LSTM), S-LSTM with temporal attention (STA-LSTM), LSTM with spatial attention (SA-LSTM), and S-LSTM with spatial attention (SSA-LSTM). The models were trained and evaluated on a dataset of monthly dengue cases in Malaysia from 2010 to 2016, with the task of predicting the number of dengue cases based on various climate, topographic, demographic, and land-use variables. The SSA-LSTM model, which used both stacked LSTM layers and spatial attention, performed the best, with an average root mean squared error (RMSE) of 3.17 across all lookback periods. When compared to three benchmark models (SVM, DT, ANN), the SSA-LSTM model had a significantly lower average RMSE. The SSA-LSTM model also performed well in different states in Malaysia, with RMSE values ranging from 2.91 to 4.55. When comparing temporal and spatial attention models, the spatial models generally performed better at predicting dengue cases. The SSA-LSTM model was also found to perform well at different prediction horizons, with the lowest RMSE at 4- and 5-month lookback periods. Overall, the results suggest that the SSA-LSTM model is effective at predicting dengue cases in Malaysia.

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Lin, Fei, Yudi Xu, Yang Yang y Hong Ma. "A Spatial-Temporal Hybrid Model for Short-Term Traffic Prediction". Mathematical Problems in Engineering 2019 (14 de enero de 2019): 1–12. http://dx.doi.org/10.1155/2019/4858546.

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Accurate and timely short-term traffic prediction is important for Intelligent Transportation System (ITS) to solve the traffic problem. This paper presents a hybrid model called SpAE-LSTM. This model considers the temporal and spatial features of traffic flow and it consists of sparse autoencoder and long short-term memory (LSTM) network based on memory units. Sparse autoencoder extracts the spatial features within the spatial-temporal matrix via full connected layers. It cooperates with the LSTM network to capture the spatial-temporal features of traffic flow evolution and make prediction. To validate the performance of the SpAE-LSTM, we implement it on the real-world traffic data from Qingyang District of Chengdu city, China, and compare it with advanced traffic prediction models, such as models only based on LSTM or SAE. The results demonstrate that the proposed model reduces the mean absolute percent error by more than 15%. The robustness of the proposed model is also validated and the mean absolute percent error on more than 86% road segments is below 20%. This research provides strong evidence suggesting that the proposed SpAE-LSTM effectively captures the spatial-temporal features of the traffic flow and achieves promising results.

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Chen, Wantong, Hailong Wu y Shiyu Ren. "CM-LSTM Based Spectrum Sensing". Sensors 22, n.º 6 (16 de marzo de 2022): 2286. http://dx.doi.org/10.3390/s22062286.

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This paper presents spectrum sensing as a classification problem, and uses a spectrum-sensing algorithm based on a signal covariance matrix and long short-term memory network (CM-LSTM). We jointly exploited the spatial cross-correlation of multiple signals received by the antenna array and the temporal autocorrelation of single signals; we used the long short-term memory network (LSTM), which is good at extracting temporal correlation features, as the classification model; we then input the covariance matrix of the signals received by the array into the LSTM classification model to achieve the fusion learning of spatial correlation features and temporal correlation features of the signals, thus significantly improving the performance of spectrum sensing. Simulation analysis shows that the CM-LSTM-based spectrum-sensing algorithm shows better performance compared with support vector machine (SVM), gradient boosting machine (GBM), random forest (RF), and energy detection (ED) algorithm-based spectrum-sensing algorithms for different signal-to-noise ratios (SNRs) and different numbers of secondary users (SUs). Among them, SVM is a classical machine-learning algorithm, GBM and RF are two integrated learning methods with better generalization capability, and ED is a classical, traditional, and spectrum-sensing algorithm.

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Tang, Qicheng, Mengning Yang y Ying Yang. "ST-LSTM: A Deep Learning Approach Combined Spatio-Temporal Features for Short-Term Forecast in Rail Transit". Journal of Advanced Transportation 2019 (6 de febrero de 2019): 1–8. http://dx.doi.org/10.1155/2019/8392592.

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The short-term forecast of rail transit is one of the most essential issues in urban intelligent transportation system (ITS). Accurate forecast result can provide support for the forewarning of flow outburst and enables passengers to make an appropriate travel plan. Therefore, it is significant to develop a more accurate forecast model. Long short-term memory (LSTM) network has been proved to be effective on data with temporal features. However, it cannot process the correlation between time and space in rail transit. As a result, a novel forecast model combining spatio-temporal features based on LSTM network (ST-LSTM) is proposed. Different from other forecast methods, ST-LSTM network uses a new method to extract spatio-temporal features from the data and combines them together as the input. Compared with other conventional models, ST-LSTM network can achieve a better performance in experiments.

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Geng, Yue, Lingling Su, Yunhong Jia y Ce Han. "Seismic Events Prediction Using Deep Temporal Convolution Networks". Journal of Electrical and Computer Engineering 2019 (2 de abril de 2019): 1–14. http://dx.doi.org/10.1155/2019/7343784.

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Seismic events prediction is a crucial task for preventing coal mine rock burst hazards. Currently, this task attracts increasing research enthusiasms from many mining experts. Considering the temporal characteristics of monitoring data, seismic events prediction can be abstracted as a time series prediction task. This paper contributes to address the problem of long-term historical dependence on seismic time series prediction with deep temporal convolution neural networks (CNN). We propose a dilated causal temporal convolution network (DCTCNN) and a CNN long short-term memory hybrid model (CNN-LSTM) to forecast seismic events. In particular, DCTCNN is designed with dilated CNN kernels, causal strategy, and residual connections; CNN-LSTM is established in a hybrid modeling way by utilizing advantage of CNN and LSTM. Based on these manners, both of DCTCNN and CNN-LSTM can extract long-term historical features from the monitoring seismic data. The proposed models are experimentally tested on two real-life coal mine seismic datasets. Furthermore, they are also compared with one traditional time series prediction method, two classic machine learning algorithms, and two standard deep learning networks. Results show that DCTCNN and CNN-LSTM are superior than the other five algorithms, and they successfully complete the seismic prediction task.

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Vaseekaran S, Pragadeeswaran S y Mrs S Janani. "Brain Tumour Prediction Using Temporal Memory". International Research Journal on Advanced Engineering Hub (IRJAEH) 3, n.º 02 (20 de febrero de 2025): 235–39. https://doi.org/10.47392/irjaeh.2025.0033.

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Brain tumor prediction plays a critical role in advancing early diagnosis and effective treatment planning, directly impacting patient survival rates. Traditional methods for detecting brain tumors involve extensive image processing and manual feature extraction, which can be time-consuming and prone to errors. Recent advancements in deep learning have introduced neural networks, specifically Long Short-Term Memory (LSTM) networks, as effective tools for handling the sequential nature of medical imaging data. This study presents an approach leveraging LSTM-based models for brain tumor prediction, focusing on capturing temporal dependencies in MRI scans. By utilizing a time-sequence approach to model variations in patient data, the LSTM model effectively identifies and classifies tumor presence with improved accuracy. Through extensive training on labeled MRI datasets, the proposed method demonstrates high predictive performance, reducing the need for manual feature engineering and setting a new standard in automated brain tumor detection.

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Tesis sobre el tema "LSTM Temporel"

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 autre aspect des parcours patients afin d'améliorer la performance prédictive de notre approche proposée. Notre recherche s'inscrit dans le cadre des travaux de CEGEDIM ASSURANCES, une business unit du groupe CEGEDIM qui fournit des logiciels et des services pour les secteurs de l'assurance maladie complémentaire et de la gestion des risques en France. En analysant le parcours de soins et en utilisant l'approche que nous proposons, nous pouvons extraire des informations précieuses et identifier des patterns dans les parcours médicaux des patients afin de prédire des événements médicaux potentiels ou la consommation médicale à venir. Cela permettra aux assureurs de prévoir les futures demandes de soins de santé et donc de négocier de meilleurs tarifs avec les prestataires de soins de santé, ce qui permettra une planification financière précise, des modèles de tarification équitables et une réduction des coûts. En outre, ça permettra aux assureurs privés de concevoir des plans de santé personnalisés qui répondent aux besoins spécifiques des patients, en veillant à ce qu'ils reçoivent les soins adéquats au bon moment afin de prévenir la progression de la maladie. Enfin, l'offre de programmes de soins préventifs et de produits et services de santé personnalisés renforce les relations avec les clients, améliore leur satisfaction et réduit l'attrition. Dans ce travail, nous visons à développer une approche permettant d'analyser les parcours patients et de prédire les événements médicaux ou les traitements à venir, sur la base d'un large portefeuille de remboursements. Pour atteindre cet objectif, nous proposons tout d'abord un nouveau modèle basé sur les LSTM qui tient compte de la notion temporelle et qui permet de réaliser de la classification binaire et multi-label. Le modèle proposé est ensuite étendu par un autre aspect des parcours de soins, à savoir des informations supplémentaires provenant d'un clustering flou du même portefeuille. Nous démontrons que l'approche proposée est plus performante que les méthodes traditionnelles et d'apprentissage profond dans la prédiction médicale binaire et multi-label. Par la suite, nous améliorons la performance prédictive de l'approche proposée en exploitant un aspect supplémentaire des parcours patients, qui consiste en une description textuelle détaillée des traitements médicaux consommés. Ceci est réalisé grâce à la conception de F-BERTMed, une nouvelle approche de vectorisation et de représentation sémantique de phrases pour le domaine médical français. Celle-ci présente des avantages significatifs par rapport aux méthodes de l'état de l'art du traitement automatique du langage naturel (TAL). F-BERTMed est basé sur FlauBERT, dont le pré-entraînement utilisant la tâche MLM (Modélisation Masqué du Langage) a été étendu sur des textes médicaux français avant d'être fine-tuné sur les tâches NLI (Inférence du Langage Naturel) et STS (Similarité Sémantique Textuelle). Nous démontrons enfin que l'utilisation de F-BERTMed pour générer une nouvelle représentation des parcours patients améliore les performances prédictives de notre modèle proposé pour les tâches de classification binaire et multi-label
This thesis work falls into the category of healthcare informatics research, specifically the analysis and prediction of patients’ care pathways, which are the sequences of medical services consumed by patients over time. Our aim is to propose an innovative approach for the exploitation of patient care trajectory data in order to achieve not only binary, but also multi-label classification. We also design a new sentence embedding framework exclusively for the french medical domain, which will harness another view of the patients’ care pathways in order to enhance the predictive performance of our proposed approach. Our research is part of the work of CEGEDIM ASSURANCES, a business unit of the CEGEDIM Group that provides software and services for the french supplementary healthcare insurance and risk management sectors. By analyzing the patient care pathway and leveraging our proposed approach, we can extract valuable insights and identify patterns within the patients’ medical journeys in order to predict potential medical events or upcoming medical consumption. This will allow insurers to forecast future healthcare claims and therefore negotiate better rates with healthcare providers, allowing for accurate financial planning, fair pricing models and cost reductions. Furthermore, it enables private healthcare insurers to design personalized health plans that meet the specific needs of the patients, ensuring they receive the right care at the right time to prevent disease progression. Ultimately, offering preventive care programs and customized health products and services enhances client relationship, improving their satisfaction and reducing churn. In this work, we aim to develop an approach to analyze patient care pathways and predict medical events or upcoming treatments, based on a large portfolio of reimbursed medical records. To achieve this goal, we first propose a new time-aware long-short term memory based framework that can achieve both binary and multi-label classification. The proposed framework is then extended with another aspect of the patient healthcare trajectories, namely additional information from a fuzzy clustering of the same portfolio. We show that our proposed approach outperforms traditional and deep learning methods in medical binary and multi-label prediction. Subsequently, we enhance the predictive performance of our proposed approach by exploiting a supplementary view of the patient care pathways that consists of a detailed textual description of the consumed medical treatments. This is achieved through the design of F-BERTMed, a new sentence embedding framework for the french medical domain that presents significant advantages over the natural language processing (NLP) state-of-the-art methods. F-BERTMed is based on FlauBERT, whose pre-training using MLM (Masked Language Modeling) was extended on french medical texts before being fine-tuned on NLI (Natural Language Inference) and STS (Semantic Textual Similarity) tasks. We finally show that using F-BERTMed to generate a new representation of the patient care pathways enhances the performance of our proposed medical predictive framework on both binary and multi-label classification tasks

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Singh, Akash. "Anomaly Detection for Temporal Data using Long Short-Term Memory (LSTM)". Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-215723.

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We explore the use of Long short-term memory (LSTM) for anomaly detection in temporal data. Due to the challenges in obtaining labeled anomaly datasets, an unsupervised approach is employed. We train recurrent neural networks (RNNs) with LSTM units to learn the normal time series patterns and predict future values. The resulting prediction errors are modeled to give anomaly scores. We investigate different ways of maintaining LSTM state, and the effect of using a fixed number of time steps on LSTM prediction and detection performance. LSTMs are also compared to feed-forward neural networks with fixed size time windows over inputs. Our experiments, with three real-world datasets, show that while LSTM RNNs are suitable for general purpose time series modeling and anomaly detection, maintaining LSTM state is crucial for getting desired results. Moreover, LSTMs may not be required at all for simple time series.
Vi undersöker Long short-term memory (LSTM) för avvikelsedetektion i tidsseriedata. På grund av svårigheterna i att hitta data med etiketter så har ett oövervakat an-greppssätt använts. Vi tränar rekursiva neuronnät (RNN) med LSTM-noder för att lära modellen det normala tidsseriemönstret och prediktera framtida värden. Vi undersö-ker olika sätt av att behålla LSTM-tillståndet och effekter av att använda ett konstant antal tidssteg på LSTM-prediktionen och avvikelsedetektionsprestandan. LSTM är också jämförda med vanliga neuronnät med fasta tidsfönster över indata. Våra experiment med tre verkliga datasetvisar att även om LSTM RNN är tillämpbara för generell tidsseriemodellering och avvikelsedetektion så är det avgörande att behålla LSTM-tillståndet för att få de önskaderesultaten. Dessutom är det inte nödvändigt att använda LSTM för enkla tidsserier.

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Holm, Noah y Emil Plynning. "Spatio-temporal prediction of residential burglaries using convolutional LSTM neural networks". Thesis, KTH, Geoinformatik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229952.

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The low amount solved residential burglary crimes calls for new and innovative methods in the prevention and investigation of the cases. There were 22 600 reported residential burglaries in Sweden 2017 but only four to five percent of these will ever be solved. There are many initiatives in both Sweden and abroad for decreasing the amount of occurring residential burglaries and one of the areas that are being tested is the use of prediction methods for more efficient preventive actions. This thesis is an investigation of a potential method of prediction by using neural networks to identify areas that have a higher risk of burglaries on a daily basis. The model use reported burglaries to learn patterns in both space and time. The rationale for the existence of patterns is based on near repeat theories in criminology which states that after a burglary both the burgled victim and an area around that victim has an increased risk of additional burglaries. The work has been conducted in cooperation with the Swedish Police authority. The machine learning is implemented with convolutional long short-term memory (LSTM) neural networks with max pooling in three dimensions that learn from ten years of residential burglary data (2007-2016) in a study area in Stockholm, Sweden. The model's accuracy is measured by performing predictions of burglaries during 2017 on a daily basis. It classifies cells in a 36x36 grid with 600 meter square grid cells as areas with elevated risk or not. By classifying 4% of all grid cells during the year as risk areas, 43% of all burglaries are correctly predicted. The performance of the model could potentially be improved by further configuration of the parameters of the neural network, along with a use of more data with factors that are correlated to burglaries, for instance weather. Consequently, further work in these areas could increase the accuracy. The conclusion is that neural networks or machine learning in general could be a powerful and innovative tool for the Swedish Police authority to predict and moreover prevent certain crime. This thesis serves as a first prototype of how such a system could be implemented and used.

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Hudgins, Hayden. "Human Path Prediction using Auto Encoder LSTMs and Single Temporal Encoders". DigitalCommons@CalPoly, 2020. https://digitalcommons.calpoly.edu/theses/2119.

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Due to automation, the world is changing at a rapid pace. Autonomous agents have become more common over the last several years and, as a result, have created a need for improved software to back them up. The most important aspect of this greater software is path prediction, as robots need to be able to decide where to move in the future. In order to accomplish this, a robot must know how to avoid humans, putting frame prediction at the core of many modern day solutions. A popular way to solve this complex problem of frame prediction is Auto Encoder LSTMs. Though there are many implementations of this, at its core, it is a neural network comprised of a series of time sensitive processing blocks that shrink and then grow the data’s dimensions to make a prediction. The idea of using Auto Encoder styled networks to do frame prediction has also been adapted by others to make Temporal Encoders. These neural networks work much like traditional Auto Encoders, in which the data is reduced then expanded back up. These networks attempt to tease out a series of frames, including a predictive frame of the future. The problem with many of these networks is that they take an immense amount of computation power, and time to get them performing at an acceptable level. This thesis presents possible ways of pre-processing input frames to these networks in order to gain performance, in the best case seeing a 360x improvement in accuracy compared to the original models. This thesis also extends the work done with Temporal Encoders to create more precise prediction models, which showed consistent improvements of at least 50% for some metrics. All of the generated models were compared using a simulated data set collected from recordings of ground level viewpoints from Cities: Skylines. These predicted frames were then analyzed using a common perceptual distance metric, that is, Minkowski distance, as well as a custom metric that tracked distinct areas in frames. All of the following was run on a constrained system in order to see the effects of the changes as they pertain to systems with limited hardware access.

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Lindström, Per. "Deep Imitation Learning on Spatio-Temporal Data with Multiple Adversarial Agents Applied on Soccer". Thesis, Linköpings universitet, Databas och informationsteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158076.

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Recently, the availability of high quality and high resolution spatio-temporal data has increased for many sports. This enabled deep analysis of player behaviour and game strategy. This thesis investigates the assumption that game strategy is latent information in tracking data from soccer games and the possibility of modelling player behaviour with deep imitation learning. A possible application would be to perform counterfactual analysis, and switch an observed player in a real sequence, with a simulated player to asses alternative scenarios. An imitation learning application is implemented using recurrent neural networks. It is shown that the application is able to learn individual player behaviour and perform rollouts on previously unseen sequences.

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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 à la prise de décision clinique.LSTM sont efficaces pour traiter les données séquentielles comme les DSE, mais ils présentent deux limitations majeures : l'incapacité à interpréter les résultats des prédictions et à prendre en compte des intervalles de temps irréguliers entre les événements consécutifs. Pour surmonter ces limitations, nous introduisons de nouveaux réseaux neuronaux à mémoire dynamique profonde appelés Multi-Way Adaptive et Adaptive Multi-Way Interpretable Time-Aware LSTM (MWTA-LSTM etAMITA), conçus pour les données séquentielles collectées de manière irrégulière.L'objectif principal des deux modèles est de tirer parti des dossiers médicaux pour mémoriser les trajectoires de maladie et les processus de soins, estimer les états de maladie actuels et prédire les risques futurs, offrant ainsi un haut niveau de précision et de pouvoir prédictif
In personalized predictive medicine, accurately modeling a patient's illness and care processes is crucial due to the inherent long-term temporal dependencies. However, Electronic Health Records (EHRs) often consist of episodic and irregularly timed data, stemming from sporadic hospital admissions, which create unique patterns for each hospital stay. Consequently, constructing a personalized predictive model necessitates careful consideration of these factors to accurately capture the patient's health journey and assist in clinical decision-making. LSTM networks are effective for handling sequential data like EHRs, but they face two significant limitations: the inability to interpret prediction results and to take into account irregular time intervals between consecutive events. To address limitations, we introduce novel deep dynamic memory neural networks called Multi-Way Adaptive and Adaptive Multi-Way Interpretable Time-Aware LSTM (MWTA-LSTM and AMITA) designed for irregularly collected sequential data. The primary objective of both models is to leverage medical records to memorize illness trajectories and care processes, estimate current illness states, and predict future risks, thereby providing a high level of precision and predictive power

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Mukhedkar, Dhananjay. "Polyphonic Music Instrument Detection on Weakly Labelled Data using Sequence Learning Models". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279060.

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Polyphonic or multiple music instrument detection is a difficult problem compared to detecting single or solo instruments in an audio recording. As music is time series data it be can modelled using sequence learning methods within deep learning. Recently, temporal convolutional networks (TCN) have shown to outperform conventional recurrent neural networks (RNN) on various sequence modelling tasks. Though there have been significant improvements in deep learning methods, data scarcity becomes a problem in training large scale models. Weakly labelled data is an alternative where a clip is annotated for presence or absence of instruments without specifying the times at which an instrument is sounding. This study investigates how TCN model compares to a Long Short-Term Memory (LSTM) model while trained on weakly labelled dataset. The results showed successful training of both models along with generalisation on a separate dataset. The comparison showed that TCN performed better than LSTM, but only marginally. Therefore, from the experiments carried out it could not be explicitly concluded if TCN is convincingly a better choice over LSTM in the context of instrument detection, but definitely a strong alternative.
Polyfonisk eller multipel musikinstrumentdetektering är ett svårt problem jämfört med att detektera enstaka eller soloinstrument i en ljudinspelning. Eftersom musik är tidsseriedata kan den modelleras med hjälp av sekvensinlärningsmetoder inom djup inlärning. Nyligen har ’Temporal Convolutional Network’ (TCN) visat sig överträffa konventionella ’Recurrent Neural Network’ (RNN) på flertalet sekvensmodelleringsuppgifter. Även om det har skett betydande förbättringar i metoder för djup inlärning, blir dataknapphet ett problem vid utbildning av storskaliga modeller. Svagt märkta data är ett alternativ där ett klipp kommenteras för närvaro av frånvaro av instrument utan att ange de tidpunkter då ett instrument låter. Denna studie undersöker hur TCN-modellen jämförs med en ’Long Short-Term Memory’ (LSTM) -modell medan den tränas i svagt märkta datasätt. Resultaten visade framgångsrik utbildning av båda modellerna tillsammans med generalisering i en separat datasats. Jämförelsen visade att TCN presterade bättre än LSTM, men endast marginellt. Därför kan man från de genomförda experimenten inte uttryckligen dra slutsatsen om TCN övertygande är ett bättre val jämfört med LSTM i samband med instrumentdetektering, men definitivt ett starkt alternativ.

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Jain, Monika. "Regularized ensemble correlation filter tracking". Thesis, Queensland University of Technology, 2022. https://eprints.qut.edu.au/229266/1/Monika_Jain_Thesis.pdf.

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Visual Object Tracking is the task of tracking an object within a video. Broadly, most tracking algorithms can be classified into neural network based, correlation filter based, and hybrid. This thesis investigates various methods to improve tracking using correlation filters. The thesis contributes four novel trackers. The first tracker uses an appearance model pool to avoid faulty filter updates. Next, the appearance feature channel weights are learned using the graph-based similarity followed by modelling sparse spatio-temporal variations. At last, non-linearity of the appearance features is captured. The thesis also presents extensive evaluation of the proposed trackers on standard datasets.

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Raminella, Marco. "Predizione real-time da dati di sensori impiantistici e ambientali". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18643/.

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L'utilizzo dell'Intelligenza Artificiale in ambito industriale sta prendendo piede negli ultimi anni e il caso studiato in questa tesi ne è la prova. Lo sviluppo della tecnologia ha reso disponibile sempre più potenza computazionale a minor prezzo, rendendo possibile l'utilizzo delle Reti Neurali Profonde, studiate fin dagli anni ottanta, in un modo che fino a non molti anni fa era economicamente insostenibile. Si andrà a vedere il caso concreto della realizzazione di un sistema che esegue previsioni in tempo reale su telemetrie di un impianto per la gestione delle acque, con lo scopo di assistere gli operatori nelle decisioni critiche da prendere in situazioni che potrebbero portare a un'emergenza. Sono state utilizzate tecniche allo stato dell'arte del Deep Learning per la realizzazione della rete previsionale, soluzioni di Big Data e Cloud Computing per la raffinazione dei dati grezzi e rendere possibile il training della rete neurale. Sono state studiate le basi teoriche richieste per realizzare un sistema in streaming, è stata poi progettata e realizzata una architettura apposita dedicata alla trasformazione in tempo reale dei dati per poter realizzare previsioni aggiornate.

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

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This thesis examines two different approaches to parsing event-based vehicular signal data to produce input to a neural network prediction model: event parsing, where the data is kept unevenly spaced over the temporal domain, and slice parsing, where the data is made to be evenly spaced over the temporal domain instead. The dataset used as a basis for these experiments consists of a number of vehicular signal logs taken at Scania AB. Comparisons between the parsing methods have been made by first training long short-term memory (LSTM) recurrent neural networks (RNN) on each of the parsed datasets and then measuring the output error and resource costs of each such model after having validated them on a number of shared validation sets. The results from these tests clearly show that slice parsing compares favourably to event parsing.
Denna avhandling jämför två olika tillvägagångssätt vad gäller parsningen av händelsebaserad signaldata från fordon för att producera indata till en förutsägelsemodell i form av ett neuronnät, nämligen händelseparsning, där datan förblir ojämnt fördelad över tidsdomänen, och skivparsning, där datan är omgjord till att istället vara jämnt fördelad över tidsdomänen. Det dataset som används för dessa experiment är ett antal signalloggar från fordon som kommer från Scania. Jämförelser mellan parsningsmetoderna gjordes genom att först träna ett lång korttidsminne (LSTM) återkommande neuronnät (RNN) på vardera av de skapade dataseten för att sedan mäta utmatningsfelet och resurskostnader för varje modell efter att de validerats på en delad uppsättning av valideringsdata. Resultaten från dessa tester visar tydligt på att skivparsning står sig väl mot händelseparsning.

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Capítulos de libros sobre el tema "LSTM Temporel"

Zheng, Lin, Chaowei Qi y Shibo Zhao. "Multivariate Passenger Flow Forecast Based on ACLB Model". En Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications, 104–13. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_12.

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AbstractWith the rapid increase in urban population, urban traffic problems are becoming severe. Passenger flow forecasting is critical to improving the ability of urban buses to meet the travel needs of urban residents and alleviating urban traffic pressure. However, the factors affecting passenger flow have complex non-linear characteristics, which creates a bottleneck in passenger flow prediction. Deep learning models CNN, LSTM, BISTM and the gradually emerging attention mechanism are the key points to solve the above problems. Based on summarizing the characteristics of various models, this paper proposes a multivariate prediction model ACLB to extract the nonlinear spatio-temporal characteristics of passenger flow data. We compare the performance of ACLB model with CNN, LSTM, BILSTM, CNN-LSTM, FCN-ALSTM through experiments. ACLB performance is better than other models.

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Bakalos, Nikolaos, Athanasios Voulodimos, Nikolaos Doulamis, Anastasios Doulamis, Kassiani Papasotiriou y Matthaios Bimpas. "Fusing RGB and Thermal Imagery with Channel State Information for Abnormal Activity Detection Using Multimodal Bidirectional LSTM". En Cyber-Physical Security for Critical Infrastructures Protection, 77–86. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69781-5_6.

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AbstractIn this paper, we present a multimodal deep model for detection of abnormal activity, based on bidirectional Long Short-Term Memory neural networks (LSTM). The proposed model exploits three different input modalities: RGB imagery, thermographic imagery and Channel State Information from Wi-Fi signal reflectance to estimate human intrusion and suspicious activity. The fused multimodal information is used as input in a Bidirectional LSTM, which has the benefit of being able to capture temporal interdependencies in both past and future time instances, a significant aspect in the discussed unusual activity detection scenario. We also present a Bayesian optimization framework that fine-tunes the Bidirectional LSTM parameters in an optimal manner. The proposed framework is evaluated on real-world data from a critical water infrastructure protection and monitoring scenario and the results indicate a superior performance compared to other unimodal and multimodal approaches and classification models.

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Wang, Huimu, Zhen Liu, Zhiqiang Pu y Jianqiang Yi. "STGA-LSTM: A Spatial-Temporal Graph Attentional LSTM Scheme for Multi-agent Cooperation". En Neural Information Processing, 663–75. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63833-7_56.

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Li, Hongsheng, Guangming Zhu, Liang Zhang, Juan Song y Peiyi Shen. "Graph-Temporal LSTM Networks for Skeleton-Based Action Recognition". En Pattern Recognition and Computer Vision, 480–91. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60639-8_40.

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Singh, Vikram y Sohan Kumar. "Temporal Intelligence: Recognizing User Activities with Stacked LSTM Networks". En Smart Innovation, Systems and Technologies, 309–19. Singapore: Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-97-6222-4_25.

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Silva, Rafael, Lourenço Abrunhosa Rodrigues, André Lourenço y Hugo Plácido da Silva. "Temporal Dynamics of Drowsiness Detection Using LSTM-Based Models". En Advances in Computational Intelligence, 211–20. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43085-5_17.

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Cissoko, Mamadou Ben Hamidou, Vincent Castelain y Nicolas Lachiche. "Modeling Temporal Dynamics in Irregular ICU Data Using MWTA-LSTM". En Lecture Notes in Computer Science, 26–37. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-73500-4_3.

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Liu, Jun, Amir Shahroudy, Dong Xu y Gang Wang. "Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition". En Computer Vision – ECCV 2016, 816–33. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46487-9_50.

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Yao, Li y Ying Qian. "DT-3DResNet-LSTM: An Architecture for Temporal Activity Recognition in Videos". En Advances in Multimedia Information Processing – PCM 2018, 622–32. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00776-8_57.

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Huang, Rui, Wanyue Zhang, Abhijit Kundu, Caroline Pantofaru, David A. Ross, Thomas Funkhouser y Alireza Fathi. "An LSTM Approach to Temporal 3D Object Detection in LiDAR Point Clouds". En Computer Vision – ECCV 2020, 266–82. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58523-5_16.

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Actas de conferencias sobre el tema "LSTM Temporel"

Wang, Peicheng. "Multi-Feature Temporal Prediction Based on Hybrid LSTM Models". En 2024 IEEE 7th International Conference on Automation, Electronics and Electrical Engineering (AUTEEE), 207–10. IEEE, 2024. https://doi.org/10.1109/auteee62881.2024.10869794.

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Li, Ming, Furui Zhang, Yuqing Wang, Jing Ren y Qiang Zhou. "Multidimensional Temporal Photovoltaic Power Prediction Based on VMD-SSA-LSTM". En 2024 6th International Conference on Energy Systems and Electrical Power (ICESEP), 192–97. IEEE, 2024. http://dx.doi.org/10.1109/icesep62218.2024.10651709.

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Medeiros, Thiago y Alfredo Weitzenfeld. "A Place Cell Model for Spatio-Temporal Navigation Learning with LSTM". En 2024 International Joint Conference on Neural Networks (IJCNN), 1–8. IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650241.

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Albino, Adrian Joseph, Julian Ernest Curativo y Christine F. Peña. "Spatio-Temporal Crime Prediction Using Dynamic Mode Decomposition and CNN-LSTM". En 2024 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT), 384–90. IEEE, 2024. https://doi.org/10.1109/comnetsat63286.2024.10862638.

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Roy, Pritha Singha y Vinay Kukreja. "Temporal Evolution of Color Variations in Land Lotus Using CNN-LSTM Method". En 2024 Global Conference on Communications and Information Technologies (GCCIT), 1–6. IEEE, 2024. https://doi.org/10.1109/gccit63234.2024.10862966.

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Azizah, Nur, Eko Mulyanto Yuniarno y Mauridhi Hery Purnomo. "Lip Reading Using Spatio Temporal Convolutions (STCNN) And Long Short Term Memory (LSTM)". En 2024 International Seminar on Intelligent Technology and Its Applications (ISITIA), 734–39. IEEE, 2024. http://dx.doi.org/10.1109/isitia63062.2024.10667885.

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Song, Jingkuan, Lianli Gao, Zhao Guo, Wu Liu, Dongxiang Zhang y Heng Tao Shen. "Hierarchical LSTM with Adjusted Temporal Attention for Video Captioning". En Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/381.

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Recent progress has been made in using attention based encoder-decoder framework for video captioning. However, most existing decoders apply the attention mechanism to every generated words including both visual words (e.g., “gun” and "shooting“) and non-visual words (e.g. "the“, "a”).However, these non-visual words can be easily predicted using natural language model without considering visual signals or attention.Imposing attention mechanism on non-visual words could mislead and decrease the overall performance of video captioning.To address this issue, we propose a hierarchical LSTM with adjusted temporal attention (hLSTMat) approach for video captioning. Specifically, the proposed framework utilizes the temporal attention for selecting specific frames to predict related words, while the adjusted temporal attention is for deciding whether to depend on the visual information or the language context information. Also, a hierarchical LSTMs is designed to simultaneously consider both low-level visual information and deep semantic information to support the video caption generation. To demonstrate the effectiveness of our proposed framework, we test our method on two prevalent datasets: MSVD and MSR-VTT, and experimental results show that our approach outperforms the state-of-the-art methods on both two datasets.

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Almeida, Anderson, Marcos Amaris y Bruno Merlin. "Predição temporal e espaço-temporal dos parâmetros da qualidade da água". En Escola Regional de Alto Desempenho Norte 2. Sociedade Brasileira de Computação, 2021. http://dx.doi.org/10.5753/erad-no2.2021.18676.

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A qualidade da água está diretamente relacionada com o seu nível de poluição causada pelas ações antrópicas e industriais. Por isso, são realizados os monitoramentos limnológicos dos parâmetros básicos da qualidade da água, como forma de obtenção de dados que norteiam as tomadas de decisão dos órgãos gestores de recursos hídricos. Neste contexto, o presente estudo tem o objetivo de analisar o conjunto de dados e o desempenho dos algoritmos regressão linear, random forest, redes neurais MLP e LSTM na predição temporal e espaço-temporal. Os modelos são avaliados através das métricas MAPE e RMSE. Portanto, na predição temporal a técnica LSTM apresenta o menor MAPE médio, 4.66% e o MLP o menor RMSE médio, 2.47. Porém, na predição espaço-temporal, o MLP tem o menor resultado médio de MAPE e RMSE, respectivamente, 5.94% e 1.34.

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Kong, Dejiang y Fei Wu. "HST-LSTM: A Hierarchical Spatial-Temporal Long-Short Term Memory Network for Location Prediction". En Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/324.

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The widely use of positioning technology has made mining the movements of people feasible and plenty of trajectory data have been accumulated. How to efficiently leverage these data for location prediction has become an increasingly popular research topic as it is fundamental to location-based services (LBS). The existing methods often focus either on long time (days or months) visit prediction (i.e., the recommendation of point of interest) or on real time location prediction (i.e., trajectory prediction). In this paper, we are interested in the location prediction problem in a weak real time condition and aim to predict users' movement in next minutes or hours. We propose a Spatial-Temporal Long-Short Term Memory (ST-LSTM) model which naturally combines spatial-temporal influence into LSTM to mitigate the problem of data sparsity. Further, we employ a hierarchical extension of the proposed ST-LSTM (HST-LSTM) in an encoder-decoder manner which models the contextual historic visit information in order to boost the prediction performance. The proposed HST-LSTM is evaluated on a real world trajectory data set and the experimental results demonstrate the effectiveness of the proposed model.

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Jiang, P., I. Bychkov, J. Liu y A. Hmelnov. "Predicting of air pollutant concentrations based on spatio-temporal attention convolutional LSTM networks". En 1st International Workshop on Advanced Information and Computation Technologies and Systems 2020. Crossref, 2021. http://dx.doi.org/10.47350/aicts.2020.09.

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Forecasting of air pollutant concentration, which is influenced by air pollution accumulation, traffic flow and industrial emissions, has attracted extensive attention for decades. In this paper, we propose a spatio-temporal attention convolutional long short term memory neural networks (Attention-CNN-LSTM) for air pollutant concentration forecasting. Firstly, we analyze the Granger causalities between different stations and establish a hyperparametric Gaussian vector weight function to determine spatial autocorrelation variables, which is used as part of the input feature. Secondly, convolutional neural networks (CNN) is employed to extract the temporal dependence and spatial correlation of the input, while feature maps and channels are weighted by attention mechanism, so as to improve the effectiveness of the features. Finally, a depth long short term memory (LSTM) based time series predictor is established for learning the long-term and short-term dependence of pollutant concentration. In order to reduce the effect of diverse complex factors on LSTM, inherent features are extracted from historical air pollutant concentration data meteorological data and timestamp information are incorporated into the proposed model. Extensive experiments were performed using the Attention-CNNLSTM, autoregressive integrated moving average (ARIMA), support vector regression (SVR), traditional LSTM and CNN, respectively. The results demonstrated that the feasibility and practicability of Attention-CNN-LSTM on estimating CO and NO concentration.

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