Relevant bibliographies by topics / Réseau Long Short-Term Memory (LSTM)

Academic literature on the topic 'Réseau Long Short-Term Memory (LSTM)'

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Published: 7 July 2024
Last updated: 7 July 2024

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Journal articles on the topic "Réseau Long Short-Term Memory (LSTM)":

1

HARINAIVO, A., H. HAUDUC, and I. TAKACS. "Anticiper l’impact de la météo sur l’influent des stations d’épuration grâce à l’intelligence artificielle." Techniques Sciences Méthodes 3 (March 20, 2023): 33–42. http://dx.doi.org/10.36904/202303033.

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Le changement climatique a pour conséquence l’apparition de forts événements pluvieux de plus en plus fréquents, occasionnant de fortes variations de débit et de concentrations à l’influent des stations d’épuration. La connaissance des risques d’orage et des débits potentiels plusieurs heures ou plusieurs jours en avance permettrait d’anticiper les adaptations opérationnelles pour préparer la station et protéger les différents ouvrages des risques de défaillance. Dans cette étude, les données météorologiques (pluies, température, vents, humidités, précipitations…) et l’historique des données d’influent de la station sont utilisés pour entraîner un algorithme d’intelligence artificielle, d’apprentissage automatique et d’apprentissage profond pour prédire les débits entrants sur la station jusqu’à une semaine en avance. Trois jeux de données journalières et horaires, de 1 à 3 ans, sont utilisés pour entraîner un modèle de Forêt aléatoire à 30 arbres, un modèle LSTM (long short-term memory) et un modèle GRU (gate recurrent unit) à trois couches de 100 neurones suivis chacun d’un dropout de 20 % et une couche de sortie entièrement connectée. Les données sont préalablement nettoyées pour supprimer les valeurs aberrantes et sont réparties à 80 % pour les données pour l’apprentissage et 20 % pour les données de test afin d’obtenir des modèles avec les meilleures prédictions. Les algorithmes utilisés dans cette étude sont simples et détectent bien les pics. La durée de l’entraînement sur les données de trois ans se fait en moins de deux minutes pour la Forêt aléatoire et en moins d’une demi-heure pour les réseaux de neurones LSTM et GRU. Les résultats montrent que les données horaires et la prise en compte de l’effet de l’historique par l’utilisation des réseaux de neurones récurrents LSTM et GRU permettent d’obtenir une meilleure prédiction des débits d’influent. Les séries de données plus longues permettent également un meilleur apprentissage des algorithmes et une meilleure prédiction du modèle.
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Hochreiter, Sepp, and Jürgen Schmidhuber. "Long Short-Term Memory." Neural Computation 9, no. 8 (November 1, 1997): 1735–80. http://dx.doi.org/10.1162/neco.1997.9.8.1735.

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Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O. 1. Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.
3

Septiadi, Jaka, Budi Warsito, and Adi Wibowo. "Human Activity Prediction using Long Short Term Memory." E3S Web of Conferences 202 (2020): 15008. http://dx.doi.org/10.1051/e3sconf/202020215008.

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Early symptoms of dementia is one of the causes decrease in quality of life. Human activity recognition (HAR) system is proposed to recognize the daily routines which has an important role in detecting early symptoms of dementia. Long Short Term Memory (LSTM) is very useful for sequence analysis that can find the pattern of activities that carried out in daily routines. However, the LSTM model is slow to achieving convergence and take a long time during training. In this paper, we investigated the sequence of actions recorded in smart home sensors data using LSTM model, then the model will be optimized using several optimization methods. The optimization methods were Stochastic Gradient Descent (SGD), Adagrad, Adadelta, RMSProp, and Adam. The results showed that using Adam to optimized LSTM is better than other optimization methods.
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Singh, Arjun, Shashi Kant Dargar, Amit Gupta, Ashish Kumar, Atul Kumar Srivastava, Mitali Srivastava, Pradeep Kumar Tiwari, and Mohammad Aman Ullah. "Evolving Long Short-Term Memory Network-Based Text Classification." Computational Intelligence and Neuroscience 2022 (February 21, 2022): 1–11. http://dx.doi.org/10.1155/2022/4725639.

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Recently, long short-term memory (LSTM) networks are extensively utilized for text classification. Compared to feed-forward neural networks, it has feedback connections, and thus, it has the ability to learn long-term dependencies. However, the LSTM networks suffer from the parameter tuning problem. Generally, initial and control parameters of LSTM are selected on a trial and error basis. Therefore, in this paper, an evolving LSTM (ELSTM) network is proposed. A multiobjective genetic algorithm (MOGA) is used to optimize the architecture and weights of LSTM. The proposed model is tested on a well-known factory reports dataset. Extensive analyses are performed to evaluate the performance of the proposed ELSTM network. From the comparative analysis, it is found that the LSTM network outperforms the competitive models.
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Bhalke, D. G., Daideep Bhingarde, Siddhi Deshmukh, and Digvijay Dhere. "Stock Price Prediction Using Long Short Term Memory." SAMRIDDHI : A Journal of Physical Sciences, Engineering and Technology 14, Spl-2 issu (June 30, 2022): 271–73. http://dx.doi.org/10.18090/samriddhi.v14spli02.12.

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Stock market price prediction is difficult and complex task. Prediction in stock market is very complex and unstable Process. Stock Price are most of the time tend to follow patterns those are more or less regular in stock price curve. Machine Learning techniques use different predictive models and algorithms to predict and automate things to reduce human effort. This research paper focuses on the use of Long Short Term Memory (LSTM) to predict the future stock market company price of stock using each day closing price analysis. LSTM is very helpful in sequential data models. In this paper LSTM algorithm has been used to train and forecast the future stock prices.
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Zhou, Chenze. "Long Short-term Memory Applied on Amazon's Stock Prediction." Highlights in Science, Engineering and Technology 34 (February 28, 2023): 71–76. http://dx.doi.org/10.54097/hset.v34i.5380.

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More and more investors are paying attention to how to use data mining technology into stock investing decisions as a result of the introduction of big data and the quick expansion of financial markets. Machine learning can automatically apply complex mathematical calculations to big data repeatedly and faster. The machine model can analyze all the factors and indicators affecting stock price and achieve high efficiency. Based on the Amazon stock price published on Kaggle, this paper adopts the Long Short-term Memory (LSTM) method for model training. The Keras package in the Python program is used to normalize the data. The Sequence model in Keras establishes a two-layer LSTM network and a three-layer LSTM network to compare and analyze the fitting effect of the model on stock prices. By calculating RMSE and RMPE, the study found that the stock price prediction accuracy of two-layer LSTM is similar to that of three-layer LSTM. In terms of F-measure and Accuracy, the LSTM model of the three-layer network is significantly better than the LSTM model of the two-layer network layer. In general, the LSTM model can accurately predict stock price. Therefore, investors will know the upward or downward trend of stock prices in advance according to the prediction results of the model to make corresponding decisions.
7

Mukhlis, Mukhlis, Aziz Kustiyo, and Aries Suharso. "Peramalan Produksi Pertanian Menggunakan Model Long Short-Term Memory." BINA INSANI ICT JOURNAL 8, no. 1 (June 24, 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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Nguyen, Sang Thi Thanh, and Bao Duy Tran. "Long Short-Term Memory Based Movie Recommendation." Science & Technology Development Journal - Engineering and Technology 3, SI1 (September 19, 2020): SI1—SI9. http://dx.doi.org/10.32508/stdjet.v3isi1.540.

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Recommender systems (RS) have become a fundamental tool for helping users make decisions around millions of different choices nowadays – the era of Big Data. It brings a huge benefit for many business models around the world due to their effectiveness on the target customers. A lot of recommendation models and techniques have been proposed and many accomplished incredible outcomes. Collaborative filtering and content-based filtering methods are common, but these both have some disadvantages. A critical one is that they only focus on a user's long-term static preference while ignoring his or her short-term transactional patterns, which results in missing the user's preference shift through the time. In this case, the user's intent at a certain time point may be easily submerged by his or her historical decision behaviors, which leads to unreliable recommendations. To deal with this issue, a session of user interactions with the items can be considered as a solution. In this study, Long Short-Term Memory (LSTM) networks will be analyzed to be applied to user sessions in a recommender system. The MovieLens dataset is considered as a case study of movie recommender systems. This dataset is preprocessed to extract user-movie sessions for user behavior discovery and making movie recommendations to users. Several experiments have been carried out to evaluate the LSTM-based movie recommender system. In the experiments, the LSTM networks are compared with a similar deep learning method, which is Recurrent Neural Networks (RNN), and a baseline machine learning method, which is the collaborative filtering using item-based nearest neighbors (item-KNN). It has been found that the LSTM networks are able to be improved by optimizing their hyperparameters and outperform the other methods when predicting the next movies interested by users.
9

Rezza, Muhammad, M. Ismail Yusuf, and Redi Ratiandi Yacoub. "Prediksi Radiasi Surya Menggunakan Metode Long Short-Term Memory." ILKOMNIKA: Journal of Computer Science and Applied Informatics 6, no. 1 (April 30, 2024): 33–44. http://dx.doi.org/10.28926/ilkomnika.v6i1.571.

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Penelitian ini memfokuskan pada optimalisasi pemanfaatan energi matahari di Kalimantan Barat, sebuah wilayah yang kaya akan sumber daya matahari, dengan total pembangkit listrik tenaga surya (PLTS) mencapai 1.58 MW. Untuk memprediksi potensi energi matahari, penelitian menggunakan metode jaringan syaraf tiruan Long Short-Term Memory (LSTM) dengan data yang diperoleh dari data logger yang merekam tegangan, arus, dan daya keluaran panel surya selama 57 hari dengan interval 1-2 detik, menghasilkan 4.294.273 data. Dalam pengolahan data, 80% digunakan untuk pelatihan dan sisanya untuk pengujian model LSTM. Model LSTM yang digunakan terdiri dari 2 layer LSTM, masing-masing dengan 50 node LSTM. Penggunaan Google Colaboratory sebagai platform komputasi memungkinkan pelatihan model LSTM dalam dua skenario, yaitu dengan epoch sebesar 1 dan 10. Evaluasi model dilakukan menggunakan metrik Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), dan R-squared (R2). Hasil pengujian menunjukkan bahwa model dengan epoch 10 memiliki nilai evaluasi yang lebih baik, dengan MSE sebesar 0.04444, RMSE sebesar 0.00456, MAE sebesar 0.06753, dan R2 sebesar 0.99961, menunjukkan performa prediksi energi matahari yang sangat akurat dan efisien.
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Wimbassa, Muhamad Dwirizqy, Taswiyah Marsyah Noor, Salma Yasara, Vannesha Vannesha, Tubagus Muhammad Arsyah, and Abdiansah Abdiansah. "Emotional Text Detection dengan Long Short Term Memory (LSTM)." Format : Jurnal Ilmiah Teknik Informatika 12, no. 2 (July 5, 2023): 158. http://dx.doi.org/10.22441/format.2023.v12.i2.009.

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Emotional Text Detection is a technique in natural language processing that aims to identify the emotions contained in conversations or text messages. The LSTM (Long Short-Term Memory) method is one of the techniques used in natural language processing to model and predict sequential data. In this study, we propose the use of the LSTM method for emotion detection in conversation. The dataset used is a conversational dataset that contains positive, negative, and neutral emotions. We process datasets using data pre-processing techniques such as tokenization, data cleansing and one-hot encoding. Then, we train the LSTM model on the processed dataset and obtain evaluation results using accuracy metrics. The experimental results show that the LSTM model can be used to detect emotions in conversation with a good degree of accuracy. In addition, we also conducted an analysis on the prediction results of the model and showed that the LSTM model can correctly identify emotions. In conclusion, the LSTM method can be used to detect emotions in conversation with a good degree of accuracy. This method can be used to improve user experience in chat applications and increase the effectiveness of human and machine interactions.
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Journal articles Dissertations / Theses

Dissertations / Theses on the topic "Réseau Long Short-Term Memory (LSTM)":

1

Javid, Gelareh. "Contribution à l’estimation de charge et à la gestion optimisée d’une batterie Lithium-ion : application au véhicule électrique." Thesis, Mulhouse, 2021. https://www.learning-center.uha.fr/.

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L'estimation de l'état de charge (SOC) est un point crucial pour la sécurité des performances et la durée de vie des batteries lithium-ion (Li-ion) utilisées pour alimenter les VE.Dans cette thèse, la précision de l'estimation de l'état de charge est étudiée à l'aide d'algorithmes de réseaux neuronaux récurrents profonds (DRNN). Pour ce faire, pour une cellule d’une batterie Li-ion, trois nouvelles méthodes sont proposées : une mémoire bidirectionnelle à long et court terme (BiLSTM), une mémoire robuste à long et court terme (RoLSTM) et une technique d'unités récurrentes à grille (GRU).En utilisant ces techniques, on ne dépend pas de modèles précis de la batterie et on peut éviter les méthodes mathématiques complexes, en particulier dans un bloc de batterie. En outre, ces modèles sont capables d'estimer précisément le SOC à des températures variables. En outre, contrairement au réseau de neurones récursif traditionnel dont le contenu est réécrit à chaque fois, ces réseaux peuvent décider de préserver la mémoire actuelle grâce aux passerelles proposées. Dans ce cas, il peut facilement transférer l'information sur de longs chemins pour recevoir et maintenir des dépendances à long terme.La comparaison des résultats indique que le réseau BiLSTM a de meilleures performances que les deux autres méthodes. De plus, le modèle BiLSTM peut travailler avec des séquences plus longues provenant de deux directions, le passé et le futur, sans problème de disparition du gradient. Cette caractéristique permet de sélectionner une longueur de séquence équivalente à une période de décharge dans un cycle de conduite, et d'obtenir une plus grande précision dans l'estimation. En outre, ce modèle s'est bien comporté face à une valeur initiale incorrecte du SOC.Enfin, une nouvelle méthode BiLSTM a été introduite pour estimer le SOC d'un pack de batteries dans un EV. Le logiciel IPG Carmaker a été utilisé pour collecter les données et tester le modèle en simulation. Les résultats ont montré que l'algorithme proposé peut fournir une bonne estimation du SOC sans utilisation de filtre dans le système de gestion de la batterie (BMS)
The State Of Charge (SOC) estimation is a significant issue for safe performance and the lifespan of Lithium-ion (Li-ion) batteries, which is used to power the Electric Vehicles (EVs). In this thesis, the accuracy of SOC estimation is investigated using Deep Recurrent Neural Network (DRNN) algorithms. To do this, for a one cell Li-ion battery, three new SOC estimator based on different DRNN algorithms are proposed: a Bidirectional LSTM (BiLSTM) method, Robust Long-Short Term Memory (RoLSTM) algorithm, and a Gated Recurrent Units (GRUs) technique. Using these, one is not dependent on precise battery models and can avoid complicated mathematical methods especially in a battery pack. In addition, these models are able to precisely estimate the SOC at varying temperature. Also, unlike the traditional recursive neural network where content is re-written at each time, these networks can decide on preserving the current memory through the proposed gateways. In such case, it can easily transfer the information over long paths to receive and maintain long-term dependencies. Comparing the results indicates the BiLSTM network has a better performance than the other two. Moreover, the BiLSTM model can work with longer sequences from two direction, the past and the future, without gradient vanishing problem. This feature helps to select a sequence length as much as a discharge period in one drive cycle, and to have more accuracy in the estimation. Also, this model well behaved against the incorrect initial value of SOC. Finally, a new BiLSTM method introduced to estimate the SOC of a pack of batteries in an Ev. IPG Carmaker software was used to collect data and test the model in the simulation. The results showed that the suggested algorithm can provide a good SOC estimation without using any filter in the Battery Management System (BMS)
2

Cifonelli, Antonio. "Probabilistic exponential smoothing for explainable AI in the supply chain domain." Electronic Thesis or Diss., Normandie, 2023. http://www.theses.fr/2023NORMIR41.

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Le rôle clé que l’IA pourrait jouer dans l’amélioration des activités commerciales est connu depuis longtemps, mais le processus de pénétration de cette nouvelle technologie a rencontré certains freins au sein des entreprises, en particulier, les coûts de mise œuvre. En moyenne, 2.8 ans sont nécessaires depuis la sélection du fournisseur jusqu’au déploiement complet d’une nouvelle solution. Trois points fondamentaux doivent être pris en compte lors du développement d’un nouveau modèle. Le désalignement des attentes, le besoin de compréhension et d’explications et les problèmes de performance et de fiabilité. Dans le cas de modèles traitant des données de la supply chain, cinq questions spécifiques viennent s’ajouter aux précédentes : - La gestion des incertitudes. Les décideurs cherchent un moyen de minimiser le risque associé à chaque décision qu’ils doivent prendre en présence d’incertitude. Obtenir une prévision exacte est un rêve ; obtenir une prévision assez précise et en calculer les limites est réaliste et judicieux. - Le traitement des données entières et positives. La plupart des articles ne peuvent pas être vendus en sous-unités. Cet aspect simple de la vente se traduit par une contrainte qui doit être satisfaite : le résultat doit être un entier positif. - L’observabilité. La demande du client ne peut pas être mesurée directement, seules les ventes peuvent être enregistrées et servir de proxy. - La rareté et la parcimonie. Les ventes sont une quantité discontinue. En enregistrant les ventes par jour, une année entière est condensée en seulement 365 points. De plus, une grande partie d’entre elles sera à zéro. - L’optimisation juste-à-temps. La prévision est une fonction clé, mais elle n’est qu’un élément d’une chaîne de traitements soutenant la prise de décision. Le temps est une ressource précieuse qui ne peut pas être consacrée entièrement à une seule fonction. Le processus de décision et les adaptations associées doivent donc être effectuées dans un temps limité et d’une manière suffisamment flexible pour pouvoir être interrompu et relancé en cas de besoin afin d’incorporer des événements inattendus ou des ajustements nécessaires. Cette thèse s’insère dans ce contexte et est le résultat du travail effectué au cœur de Lokad. La recherche doctorale a été financée par Lokad en collaboration avec l’ANRT dans le cadre d’un contrat CIFRE. Le travail proposé a l’ambition d’être un bon compromis entre les nouvelles technologies et les attentes des entreprises, en abordant les divers aspects précédemment présentés. Nous avons commencé à effectuer des prévisions en utilisant la famille des lissages exponentiels, qui sont faciles à mettre en œuvre et extrêmement rapides à exécuter. Largement utilisés dans l’industrie, elles ont déjà gagné la confiance des utilisateurs. De plus, elles sont faciles à comprendre et à expliquer à un public non averti. En exploitant des techniques plus avancées relevant du domaine de l’IA, certaines des limites des modèles utilisés peuvent être surmontées. L’apprentissage par transfert s’est avéré être une approche pertinente pour extrapoler des informations utiles dans le cas où le nombre de données disponibles était très limité. Nous avons proposé d’utiliser un modèle associé à une loi de Poisson, une binomiale négative qui correspond mieux à la nature des phénomènes que nous cherchons à modéliser et à prévoir. Nous avons aussi proposé de traiter l’incertitude par des simulations de Monte Carlo. Un certain nombre de scénarios sont générés, échantillonnés et modélisés par dans une distribution. À partir de cette dernière, des intervalles de confiance de taille différentes et adaptés peuvent être déduits. Sur des données réelles de l’entreprise, nous avons comparé notre approche avec les méthodes de l’état de l’art comme DeepAR, DeepSSMs et N-Beats. Nous en avons déduit un nouveau modèle conçu à partir de la méthode Holt-Winter [...]
The key role that AI could play in improving business operations has been known for a long time, but the penetration process of this new technology has encountered certain obstacles within companies, in particular, implementation costs. On average, it takes 2.8 years from supplier selection to full deployment of a new solution. There are three fundamental points to consider when developing a new model. Misalignment of expectations, the need for understanding and explanation, and performance and reliability issues. In the case of models dealing with supply chain data, there are five additionally specific issues: - Managing uncertainty. Precision is not everything. Decision-makers are looking for a way to minimise the risk associated with each decision they have to make in the presence of uncertainty. Obtaining an exact forecast is a advantageous; obtaining a fairly accurate forecast and calculating its limits is realistic and appropriate. - Handling integer and positive data. Most items sold in retail cannot be sold in subunits. This simple aspect of selling, results in a constraint that must be satisfied by the result of any given method or model: the result must be a positive integer. - Observability. Customer demand cannot be measured directly, only sales can be recorded and used as a proxy. - Scarcity and parsimony. Sales are a discontinuous quantity. By recording sales by day, an entire year is condensed into just 365 points. What’s more, a large proportion of them will be zero. - Just-in-time optimisation. Forecasting is a key function, but it is only one element in a chain of processes supporting decision-making. Time is a precious resource that cannot be devoted entirely to a single function. The decision-making process and associated adaptations must therefore be carried out within a limited time frame, and in a sufficiently flexible manner to be able to be interrupted and restarted if necessary in order to incorporate unexpected events or necessary adjustments. This thesis fits into this context and is the result of the work carried out at the heart of Lokad, a Paris-based software company aiming to bridge the gap between technology and the supply chain. The doctoral research was funded by Lokad in collaborationwith the ANRT under a CIFRE contract. The proposed work aims to be a good compromise between new technologies and business expectations, addressing the various aspects presented above. We have started forecasting using the exponential smoothing family which are easy to implement and extremely fast to run. As they are widely used in the industry, they have already won the confidence of users. What’s more, they are easy to understand and explain to an unlettered audience. By exploiting more advanced AI techniques, some of the limitations of the models used can be overcome. Cross-learning proved to be a relevant approach for extrapolating useful information when the number of available data was very limited. Since the common Gaussian assumption is not suitable for discrete sales data, we proposed using a model associatedwith either a Poisson distribution or a Negative Binomial one, which better corresponds to the nature of the phenomena we are seeking to model and predict. We also proposed using Monte Carlo simulations to deal with uncertainty. A number of scenarios are generated, sampled and modelled using a distribution. From this distribution, confidence intervals of different and adapted sizes can be deduced. Using real company data, we compared our approach with state-of-the-art methods such as DeepAR model, DeepSSMs and N-Beats. We deduced a new model based on the Holt-Winter method. These models were implemented in Lokad’s work flow
3

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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Shojaee, Ali B. S. "Bacteria Growth Modeling using Long-Short-Term-Memory Networks." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1617105038908441.

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Gustafsson, Anton, and Julian Sjödal. "Energy Predictions of Multiple Buildings using Bi-directional Long short-term Memory." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-43552.

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The process of energy consumption and monitoring of a buildingis time-consuming. Therefore, an feasible approach for using trans-fer learning is presented to decrease the necessary time to extract re-quired large dataset. The technique applies a bidirectional long shortterm memory recurrent neural network using sequence to sequenceprediction. The idea involves a training phase that extracts informa-tion and patterns of a building that is presented with a reasonablysized dataset. The validation phase uses a dataset that is not sufficientin size. This dataset was acquired through a related paper, the resultscan therefore be validated accordingly. The conducted experimentsinclude four cases that involve different strategies in training and val-idation phases and percentages of fine-tuning. Our proposed modelgenerated better scores in terms of prediction performance comparedto the related paper.
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Valluru, Aravind-Deshikh. "Realization of LSTM Based Cognitive Radio Network." Thesis, University of North Texas, 2019. https://digital.library.unt.edu/ark:/67531/metadc1538697/.

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This thesis presents the realization of an intelligent cognitive radio network that uses long short term memory (LSTM) neural network for sensing and predicting the spectrum activity at each instant of time. The simulation is done using Python and GNU Radio. The implementation is done using GNU Radio and Universal Software Radio Peripherals (USRP). Simulation results show that the confidence factor of opportunistic users not causing interference to licensed users of the spectrum is 98.75%. The implementation results demonstrate high reliability of the LSTM based cognitive radio network.
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Corni, Gabriele. "A study on the applicability of Long Short-Term Memory networks to industrial OCR." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018.

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This thesis summarises the research-oriented study of applicability of Long Short-Term Memory Recurrent Neural Networks (LSTMs) to industrial Optical Character Recognition (OCR) problems. Traditionally solved through Convolutional Neural Network-based approaches (CNNs), the reported work aims to detect the OCR aspects that could be improved by exploiting recurrent patterns among pixel intensities, and speed up the overall character detection process. Accuracy, speed and complexity act as the main key performance indicators. After studying the core Deep Learning foundations, the best training technique to fit this problem first, and the best parametrisation next, have been selected. A set of tests eventually validated the preciseness of this solution. The final results highlight how difficult is to perform better than CNNs for what OCR tasks are concerned. Nonetheless, with favourable background conditions, the proposed LSTM-based approach is capable of reaching a comparable accuracy rate in (potentially) less time.
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van, der Westhuizen Jos. "Biological applications, visualizations, and extensions of the long short-term memory network." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/287476.

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Sequences are ubiquitous in the domain of biology. One of the current best machine learning techniques for analysing sequences is the long short-term memory (LSTM) network. Owing to significant barriers to adoption in biology, focussed efforts are required to realize the use of LSTMs in practice. Thus, the aim of this work is to improve the state of LSTMs for biology, and we focus on biological tasks pertaining to physiological signals, peripheral neural signals, and molecules. This goal drives the three subplots in this thesis: biological applications, visualizations, and extensions. We start by demonstrating the utility of LSTMs for biological applications. On two new physiological-signal datasets, LSTMs were found to outperform hidden Markov models. LSTM-based models, implemented by other researchers, also constituted the majority of the best performing approaches on publicly available medical datasets. However, even if these models achieve the best performance on such datasets, their adoption will be limited if they fail to indicate when they are likely mistaken. Thus, we demonstrate on medical data that it is straightforward to use LSTMs in a Bayesian framework via dropout, providing model predictions with corresponding uncertainty estimates. Another dataset used to show the utility of LSTMs is a novel collection of peripheral neural signals. Manual labelling of this dataset is prohibitively expensive, and as a remedy, we propose a sequence-to-sequence model regularized by Wasserstein adversarial networks. The results indicate that the proposed model is able to infer which actions a subject performed based on its peripheral neural signals with reasonable accuracy. As these LSTMs achieve state-of-the-art performance on many biological datasets, one of the main concerns for their practical adoption is their interpretability. We explore various visualization techniques for LSTMs applied to continuous-valued medical time series and find that learning a mask to optimally delete information in the input provides useful interpretations. Furthermore, we find that the input features looked for by the LSTM align well with medical theory. For many applications, extensions of the LSTM can provide enhanced suitability. One such application is drug discovery -- another important aspect of biology. Deep learning can aid drug discovery by means of generative models, but they often produce invalid molecules due to their complex discrete structures. As a solution, we propose a version of active learning that leverages the sequential nature of the LSTM along with its Bayesian capabilities. This approach enables efficient learning of the grammar that governs the generation of discrete-valued sequences such as molecules. Efficiency is achieved by reducing the search space from one over sequences to one over the set of possible elements at each time step -- a much smaller space. Having demonstrated the suitability of LSTMs for biological applications, we seek a hardware efficient implementation. Given the success of the gated recurrent unit (GRU), which has two gates, a natural question is whether any of the LSTM gates are redundant. Research has shown that the forget gate is one of the most important gates in the LSTM. Hence, we propose a forget-gate-only version of the LSTM -- the JANET -- which outperforms both the LSTM and some of the best contemporary models on benchmark datasets, while also reducing computational cost.
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Nawaz, Sabeen. "Analysis of Transactional Data with Long Short-Term Memory Recurrent Neural Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281282.

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An issue authorities and banks face is fraud related to payments and transactions where huge monetary losses occur to a party or where money laundering schemes are carried out. Previous work in the field of machine learning for fraud detection has addressed the issue as a supervised learning problem. In this thesis, we propose a model which can be used in a fraud detection system with transactions and payments that are unlabeled. The proposed modelis a Long Short-term Memory in an auto-encoder decoder network (LSTMAED)which is trained and tested on transformed data. The data is transformed by reducing it to Principal Components and clustering it with K-means. The model is trained to reconstruct the sequence with high accuracy. Our results indicate that the LSTM-AED performs better than a random sequence generating process in learning and reconstructing a sequence of payments. We also found that huge a loss of information occurs in the pre-processing stages.
Obehöriga transaktioner och bedrägerier i betalningar kan leda till stora ekonomiska förluster för banker och myndigheter. Inom maskininlärning har detta problem tidigare hanterats med hjälp av klassifierare via supervised learning. I detta examensarbete föreslår vi en modell som kan användas i ett system för att upptäcka bedrägerier. Modellen appliceras på omärkt data med många olika variabler. Modellen som används är en Long Short-term memory i en auto-encoder decoder nätverk. Datan transformeras med PCA och klustras med K-means. Modellen tränas till att rekonstruera en sekvens av betalningar med hög noggrannhet. Vår resultat visar att LSTM-AED presterar bättre än en modell som endast gissar nästa punkt i sekvensen. Resultatet visar också att mycket information i datan går förlorad när den förbehandlas och transformeras.
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Paschou, Michail. "ASIC implementation of LSTM neural network algorithm." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254290.

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LSTM neural networks have been used for speech recognition, image recognition and other artificial intelligence applications for many years. Most applications perform the LSTM algorithm and the required calculations on cloud computers. Off-line solutions include the use of FPGAs and GPUs but the most promising solutions include ASIC accelerators designed for this purpose only. This report presents an ASIC design capable of performing the multiple iterations of the LSTM algorithm on a unidirectional and without peepholes neural network architecture. The proposed design provides arithmetic level parallelism options as blocks are instantiated based on parameters. The internal structure of the design implements pipelined, parallel or serial solutions depending on which is optimal in every case. The implications concerning these decisions are discussed in detail in the report. The design process is described in detail and the evaluation of the design is also presented to measure accuracy and error of the design output.This thesis work resulted in a complete synthesizable ASIC design implementing an LSTM layer, a Fully Connected layer and a Softmax layer which can perform classification of data based on trained weight matrices and bias vectors. The design primarily uses 16-bit fixed point format with 5 integer and 11 fractional bits but increased precision representations are used in some blocks to reduce error output. Additionally, a verification environment has also been designed and is capable of performing simulations, evaluating the design output by comparing it with results produced from performing the same operations with 64-bit floating point precision on a SystemVerilog testbench and measuring the encountered error. The results concerning the accuracy and the design output error margin are presented in this thesis report. The design went through Logic and Physical synthesis and successfully resulted in a functional netlist for every tested configuration. Timing, area and power measurements on the generated netlists of various configurations of the design show consistency and are reported in this report.
LSTM neurala nätverk har använts för taligenkänning, bildigenkänning och andra artificiella intelligensapplikationer i många år. De flesta applikationer utför LSTM-algoritmen och de nödvändiga beräkningarna i digitala moln. Offline lösningar inkluderar användningen av FPGA och GPU men de mest lovande lösningarna inkluderar ASIC-acceleratorer utformade för endast dettaändamål. Denna rapport presenterar en ASIC-design som kan utföra multipla iterationer av LSTM-algoritmen på en enkelriktad neural nätverksarkitetur utan peepholes. Den föreslagna designed ger aritmetrisk nivå-parallellismalternativ som block som är instansierat baserat på parametrar. Designens inre konstruktion implementerar pipelinerade, parallella, eller seriella lösningar beroende på vilket anternativ som är optimalt till alla fall. Konsekvenserna för dessa beslut diskuteras i detalj i rapporten. Designprocessen beskrivs i detalj och utvärderingen av designen presenteras också för att mäta noggrannheten och felmarginal i designutgången. Resultatet av arbetet från denna rapport är en fullständig syntetiserbar ASIC design som har implementerat ett LSTM-lager, ett fullständigt anslutet lager och ett Softmax-lager som kan utföra klassificering av data baserat på tränade viktmatriser och biasvektorer. Designen använder huvudsakligen 16bitars fast flytpunktsformat med 5 heltal och 11 fraktions bitar men ökade precisionsrepresentationer används i vissa block för att minska felmarginal. Till detta har även en verifieringsmiljö utformats som kan utföra simuleringar, utvärdera designresultatet genom att jämföra det med resultatet som produceras från att utföra samma operationer med 64-bitars flytpunktsprecision på en SystemVerilog testbänk och mäta uppstådda felmarginal. Resultaten avseende noggrannheten och designutgångens felmarginal presenteras i denna rapport.Designen gick genom Logisk och Fysisk syntes och framgångsrikt resulterade i en funktionell nätlista för varje testad konfiguration. Timing, area och effektmätningar på den genererade nätlistorna av olika konfigurationer av designen visar konsistens och rapporteras i denna rapport.
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Book chapters on the topic "Réseau Long Short-Term Memory (LSTM)":

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Hvitfeldt, Emil, and Julia Silge. "Long short-term memory (LSTM) networks." In Supervised Machine Learning for Text Analysis in R, 273–302. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003093459-14.

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Nandam, Srinivasa Rao, Adouthu Vamshi, and Inapanuri Sucharitha. "CAN Intrusion Detection Using Long Short-Term Memory (LSTM)." In Lecture Notes in Networks and Systems, 295–302. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1976-3_36.

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Salem, Fathi M. "Gated RNN: The Long Short-Term Memory (LSTM) RNN." In Recurrent Neural Networks, 71–82. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89929-5_4.

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Pal, Debasmita, and Partha Pratim Deb. "Stock Index Forecasting Using Stacked Long Short-Term Memory (LSTM)." In Synergistic Interaction of Big Data with Cloud Computing for Industry 4.0, 97–112. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003279044-7.

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Barone, Ben, David Coar, Ashley Shafer, Jinhong K. Guo, Brad Galego, and James Allen. "Interpreting Pilot Behavior Using Long Short-Term Memory (LSTM) Models." In Lecture Notes in Networks and Systems, 60–66. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80624-8_8.

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Ghosh, Sreyan, Kunj Pahuja, Joshua Mammen Jiji, Antony Puthussery, Samiksha Shukla, and Aynur Unal. "Classifying Bipolar Personality Disorder (BPD) Using Long Short-Term Memory (LSTM)." In Data Science and Security, 169–76. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5309-7_17.

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Abimannan, Satheesh, Deepak Jayaswal, Yue-Shan Chang, and K. Thirunavukkarasu. "Evolution of Long Short-Term Memory (LSTM) in Air Pollution Forecasting." In Handbook of Research on Machine Learning, 367–97. New York: Apple Academic Press, 2022. http://dx.doi.org/10.1201/9781003277330-18.

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Mukhtar, Harun, Muhammad Akmal Remli, Khairul Nizar Syazwan Wan Salihin Wong, and Yoze Rizki. "Long-Short Term Memory (LSTM) Based Architecture for Forecasting Tourist Arrivals." In Studies in Systems, Decision and Control, 585–600. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-49544-1_52.

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Sai Charan, P. V., T. Gireesh Kumar, and P. Mohan Anand. "Advance Persistent Threat Detection Using Long Short Term Memory (LSTM) Neural Networks." In Emerging Technologies in Computer Engineering: Microservices in Big Data Analytics, 45–54. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8300-7_5.

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Myakal, Sabhapathy, Rajarshi Pal, and Nekuri Naveen. "A Novel Pixel Value Predictor Using Long Short Term Memory (LSTM) Network." In Lecture Notes in Computer Science, 324–35. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-36402-0_30.

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Conference papers on the topic "Réseau Long Short-Term Memory (LSTM)":

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Lin, Yanbin, Dongliang Duan, Xueming Hong, Xiang Cheng, Liuqing Yang, and Shuguang Cui. "Very-Short-Term Solar Forecasting with Long Short-Term Memory (LSTM) Network." In 2020 Asia Energy and Electrical Engineering Symposium (AEEES). IEEE, 2020. http://dx.doi.org/10.1109/aeees48850.2020.9121512.

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Viswambaran, Ramya Anasseriyil, Gang Chen, Bing Xue, and Mohammad Nekooei. "Evolutionary Design of Long Short Term Memory (LSTM) Ensemble." In 2020 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2020. http://dx.doi.org/10.1109/ssci47803.2020.9308393.

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Nitesh, Koduru, Yanamala Abhiram, Rayapudi Krishna Teja, and S. Kavitha. "Weather Prediction using Long Short Term Memory (LSTM) model." In 2023 5th International Conference on Smart Systems and Inventive Technology (ICSSIT). IEEE, 2023. http://dx.doi.org/10.1109/icssit55814.2023.10061039.

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Singh, Shubhendu Kumar, Ruoyu Yang, Amir Behjat, Rahul Rai, Souma Chowdhury, and Ion Matei. "PI-LSTM: Physics-Infused Long Short-Term Memory Network." In 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA). IEEE, 2019. http://dx.doi.org/10.1109/icmla.2019.00015.

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LIAO, WEIHONG, ZHAOKAI YIN, RUOJIA WANG, and XIAOHUI LEI. "Rainfall-Runoff Modelling Based on Long Short-Term Memory (LSTM)." In 38th IAHR World Congress. The International Association for Hydro-Environment Engineering and Research (IAHR), 2019. http://dx.doi.org/10.3850/38wc092019-1488.

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Huang, Ting, Gehui Shen, and Zhi-Hong Deng. "Leap-LSTM: Enhancing Long Short-Term Memory for Text Categorization." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/697.

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Recurrent Neural Networks (RNNs) are widely used in the field of natural language processing (NLP), ranging from text categorization to question answering and machine translation. However, RNNs generally read the whole text from beginning to end or vice versa sometimes, which makes it inefficient to process long texts. When reading a long document for a categorization task, such as topic categorization, large quantities of words are irrelevant and can be skipped. To this end, we propose Leap-LSTM, an LSTM-enhanced model which dynamically leaps between words while reading texts. At each step, we utilize several feature encoders to extract messages from preceding texts, following texts and the current word, and then determine whether to skip the current word. We evaluate Leap-LSTM on several text categorization tasks: sentiment analysis, news categorization, ontology classification and topic classification, with five benchmark data sets. The experimental results show that our model reads faster and predicts better than standard LSTM. Compared to previous models which can also skip words, our model achieves better trade-offs between performance and efficiency.
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Fauzan, Achmad, Maria SusanAnggreainy, Nicholas Nathaniel, and Afdhal Kurniawan. "Predicting Stock Market Movements Using Long Short-Term Memory (LSTM)." In 2023 4th International Conference on Artificial Intelligence and Data Sciences (AiDAS). IEEE, 2023. http://dx.doi.org/10.1109/aidas60501.2023.10284713.

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Gaurav, Akshat, Varsha Arya, Kwok Tai Chui, Brij B. Gupta, Chang Choi, and O.-Joun Lee. "Long Short-Term Memory Network (LSTM) based Stock Price Prediction." In RACS '23: International Conference on Research in Adaptive and Convergent Systems. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3599957.3606240.

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Ariwibowo, Suminar, Abba Suganda Girsang, and Diana. "Hate Speech Text Classification Using Long Short-Term Memory (LSTM)." In 2022 IEEE International Conference of Computer Science and Information Technology (ICOSNIKOM). IEEE, 2022. http://dx.doi.org/10.1109/icosnikom56551.2022.10034908.

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Dhabliya, Dharmesh, Hassan M. Al–Jawahry, Viklap Sharma, R. Jayadurga, and M. Jasmin. "Long Short-Term Memory (LSTM) Networks for Stock Market Prediction." In 2023 4th International Conference on Computation, Automation and Knowledge Management (ICCAKM). IEEE, 2023. http://dx.doi.org/10.1109/iccakm58659.2023.10449613.

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Reports on the topic "Réseau Long Short-Term Memory (LSTM)":

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Cárdenas-Cárdenas, Julián Alonso, Deicy J. Cristiano-Botia, and Nicolás Martínez-Cortés. Colombian inflation forecast using Long Short-Term Memory approach. Banco de la República, June 2023. http://dx.doi.org/10.32468/be.1241.

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We use Long Short Term Memory (LSTM) neural networks, a deep learning technique, to forecast Colombian headline inflation one year ahead through two approaches. The first one uses only information from the target variable, while the second one incorporates additional information from some relevant variables. We employ sample rolling to the traditional neuronal network construction process, selecting the hyperparameters with criteria for minimizing the forecast error. Our results show a better forecasting capacity of the network with information from additional variables, surpassing both the other LSTM application and ARIMA models optimized for forecasting (with and without explanatory variables). This improvement in forecasting accuracy is most pronounced over longer time horizons, specifically from the seventh month onwards.
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Ankel, Victoria, Stella Pantopoulou, Matthew Weathered, Darius Lisowski, Anthonie Cilliers, and Alexander Heifetz. One-Step Ahead Prediction of Thermal Mixing Tee Sensors with Long Short Term Memory (LSTM) Neural Networks. Office of Scientific and Technical Information (OSTI), December 2020. http://dx.doi.org/10.2172/1760289.

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Kumar, Kaushal, and Yupeng Wei. Attention-Based Data Analytic Models for Traffic Flow Predictions. Mineta Transportation Institute, March 2023. http://dx.doi.org/10.31979/mti.2023.2211.

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Traffic congestion causes Americans to lose millions of hours and dollars each year. In fact, 1.9 billion gallons of fuel are wasted each year due to traffic congestion, and each hour stuck in traffic costs about $21 in wasted time and fuel. The traffic congestion can be caused by various factors, such as bottlenecks, traffic incidents, bad weather, work zones, poor traffic signal timing, and special events. One key step to addressing traffic congestion and identifying its root cause is an accurate prediction of traffic flow. Accurate traffic flow prediction is also important for the successful deployment of smart transportation systems. It can help road users make better travel decisions to avoid traffic congestion areas so that passenger and freight movements can be optimized to improve the mobility of people and goods. Moreover, it can also help reduce carbon emissions and the risks of traffic incidents. Although numerous methods have been developed for traffic flow predictions, current methods have limitations in utilizing the most relevant part of traffic flow data and considering the correlation among the collected high-dimensional features. To address this issue, this project developed attention-based methodologies for traffic flow predictions. We propose the use of an attention-based deep learning model that incorporates the attention mechanism with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks. This attention mechanism can calculate the importance level of traffic flow data and enable the model to consider the most relevant part of the data while making predictions, thus improving accuracy and reducing prediction duration.

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