Relevant bibliographies by topics / Memory (LSTM)

Contents

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

Academic literature on the topic 'Memory (LSTM)'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Memory (LSTM)"

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Hochreiter, Sepp, and Jürgen Schmidhuber. "Long Short-Term Memory." Neural Computation 9, no. 8 (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
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Halim, Kevin Yudhaprawira, Dodon Turianto Nugrahadi, Mohammad Reza Faisal, Rudy Herteno, and Irwan Budiman. "Gender Classification Based on Electrocardiogram Signals Using Long Short Term Memory and Bidirectional Long Short Term Memory." Jurnal Ilmiah Teknik Elektro Komputer dan Informatika 9, no. 3 (2023): 606–18. https://doi.org/10.26555/jiteki.v9i3.26354.

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Gender classification by computer is essential for applications in many domains, such as human-computer interaction or biometric system applications. Generally, gender classification by computer can be done by using a face photo, fingerprint, or voice. However, researchers have demonstrated the potential of the electrocardiogram (ECG) as a biometric recognition and gender classification. In facilitating the process of gender classification based on ECG signals, a method is needed, namely Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (Bi-LSTM). Researchers use these two
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Ding, Xianghua, Jingnan Wang, Yiqi Liu, and Uk Jung. "Multivariate Time Series Anomaly Detection Using Working Memory Connections in Bi-Directional Long Short-Term Memory Autoencoder Network." Applied Sciences 15, no. 5 (2025): 2861. https://doi.org/10.3390/app15052861.

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“Normal” events are characterized as data patterns or behaviors that align with expected operational conditions, while “anomalies” are defined as deviations from these patterns, potentially signaling faults, errors, or unexpected system behaviors. The timely and accurate detection of anomalies plays a critical role in domains such as industrial manufacturing, financial transactions, and other related domains. In the context of Industry 4.0, the proliferation of sensors has resulted in a massive influx of time series data, making the anomaly detection of such multivariate time series data a pop
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Simanihuruk, Laurensia, and Hari Suparwito. "Long Short-Term Memory and Bidirectional Long Short-Term Memory Algorithms for Sentiment Analysis of Skintific Product Reviews." ITM Web of Conferences 71 (2025): 01016. https://doi.org/10.1051/itmconf/20257101016.

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In the era of ever-evolving digital technology, conducting customer sentiment analysis through product reviews has become crucial for businesses to improve their offerings and increase customer satisfaction. This research aims to analyze the sentiment of SKINTIFIC skincare products on the Shopee online store platform using advanced deep learning models: Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (Bi-LSTM). These models were evaluated using learning rate, number of units, and dropout rate. The dataset consists of 9,184 product reviews extracted through the Shopee API
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Karyadi, Yadi. "Prediksi Kualitas Udara Dengan Metoda LSTM, Bidirectional LSTM, dan GRU." JATISI (Jurnal Teknik Informatika dan Sistem Informasi) 9, no. 1 (2022): 671–84. http://dx.doi.org/10.35957/jatisi.v9i1.1588.

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Kualitas udara menjadi salah satu masalah utama di kota besar. Salah satu cara pengendalian kualitas udara adalah dengan cara memprediksi beberapa parameter utama dengan menggunakan algoritma deep learning. Penelitian ini menggunakan metoda deep learning yang merupakan bagian dari Recurrent Neural network yaitu Long Short Term Memory, Bidirectional Long Short Term Memory, dan Gated Recurrent Unit yang diterapkan pada permasalahan memprediksi data time series kualitas udara dengan parameter suhu, kelembaban, particular matter PM10, dan Indeks Standar Pencemar Udara (ISPU). Dari hasil pengujian
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Zoremsanga, Chawngthu, and Jamal Hussain. "An Evaluation of Bidirectional Long Short-Term Memory Model for Estimating Monthly Rainfall in India." Indian Journal Of Science And Technology 17, no. 18 (2024): 1828–37. http://dx.doi.org/10.17485/ijst/v17i18.2505.

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Objectives: Predicting the amount of rainfall is difficult due to its complexity and non-linearity. The objective of this study is to predict the average rainfall one month ahead using the all-India monthly average rainfall dataset from 1871 to 2016. Methods: This study proposed a Bidirectional Long Short-Term Memory (LSTM) model to predict the average monthly rainfall in India. The parameters of the models are determined using the grid search method. This study utilized the average monthly rainfall as an input, and the dataset consists of 1752 months of rainfall data prepared from thirty (30)
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Singh, Arjun, Shashi Kant Dargar, Amit Gupta, et al. "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 we
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Fu, Kun, Yang Li, Wenkai Zhang, Hongfeng Yu, and Xian Sun. "Boosting Memory with a Persistent Memory Mechanism for Remote Sensing Image Captioning." Remote Sensing 12, no. 11 (2020): 1874. http://dx.doi.org/10.3390/rs12111874.

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The encoder–decoder framework has been widely used in the remote sensing image captioning task. When we need to extract remote sensing images containing specific characteristics from the described sentences for research, rich sentences can improve the final extraction results. However, the Long Short-Term Memory (LSTM) network used in decoders still loses some information in the picture over time when the generated caption is long. In this paper, we present a new model component named the Persistent Memory Mechanism (PMM), which can expand the information storage capacity of LSTM with an exter
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Faulina, Ria, Nuramaliyah Nuramaliyah, and Emeylia Safitri. "Air Temperature Prediction System Using Long Short-Term Memory Algorithm." Rekayasa 17, no. 3 (2024): 463–73. https://doi.org/10.21107/rekayasa.v17i3.28229.

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Air temperature is a highly essential parameter in weather forecasting methods and a critical variable for predicting future weather patterns. An accurate temperature prediction system can assist individuals and organizations in preparing for activities heavily influenced by weather conditions. Therefore, developing a precise temperature prediction model requires a reliable and effective algorithm. In this study, the Long Short-Term Memory (LSTM) algorithm, a type of artificial neural network (Recurrent Neural Network - RNN), is implemented with time series data decomposition for variable inpu
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Putera Khano, Muhammad Nazhif Abda, Dewi Retno Sari Saputro, Sutanto Sutanto, and Antoni Wibowo. "SENTIMENT ANALYSIS WITH LONG-SHORT TERM MEMORY (LSTM) AND GATED RECURRENT UNIT (GRU) ALGORITHMS." BAREKENG: Jurnal Ilmu Matematika dan Terapan 17, no. 4 (2023): 2235–42. http://dx.doi.org/10.30598/barekengvol17iss4pp2235-2242.

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Sentiment analysis is a form of machine learning that functions to obtain emotional polarity values or data tendencies from data in the form of text. Sentiment analysis is needed to analyze opinions, sentiments, reviews, and criticisms from someone for a product, service, organization, topic, etc. Recurrent Neural Network (RNN) is one of the Natural Language Processing (NLP) algorithms that is used in sentiment analysis. RNN is a neural network that can use internal memory to process input. RNN itself has a weakness in Long-Term Memory (LTM). Therefore, this article examines the combination of
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Dissertations / Theses on the topic "Memory (LSTM)"

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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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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 wit
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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
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Verner, Alexander. "LSTM Networks for Detection and Classification of Anomalies in Raw Sensor Data." Diss., NSUWorks, 2019. https://nsuworks.nova.edu/gscis_etd/1074.

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In order to ensure the validity of sensor data, it must be thoroughly analyzed for various types of anomalies. Traditional machine learning methods of anomaly detections in sensor data are based on domain-specific feature engineering. A typical approach is to use domain knowledge to analyze sensor data and manually create statistics-based features, which are then used to train the machine learning models to detect and classify the anomalies. Although this methodology is used in practice, it has a significant drawback due to the fact that feature extraction is usually labor intensive and requir
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Larsson, Joel. "Optimizing text-independent speaker recognition using an LSTM neural network." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-26312.

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In this paper a novel speaker recognition system is introduced. Automated speaker recognition has become increasingly popular to aid in crime investigations and authorization processes with the advances in computer science. Here, a recurrent neural network approach is used to learn to identify ten speakers within a set of 21 audio books. Audio signals are processed via spectral analysis into Mel Frequency Cepstral Coefficients that serve as speaker specific features, which are input to the neural network. The Long Short-Term Memory algorithm is examined for the first time within this area, wit
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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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Holm, Noah, and 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 are
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Pavai, Arumugam Thendramil. "SENSOR-BASED HUMAN ACTIVITY RECOGNITION USING BIDIRECTIONAL LSTM FOR CLOSELY RELATED ACTIVITIES." CSUSB ScholarWorks, 2018. https://scholarworks.lib.csusb.edu/etd/776.

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Recognizing human activities using deep learning methods has significance in many fields such as sports, motion tracking, surveillance, healthcare and robotics. Inertial sensors comprising of accelerometers and gyroscopes are commonly used for sensor based HAR. In this study, a Bidirectional Long Short-Term Memory (BLSTM) approach is explored for human activity recognition and classification for closely related activities on a body worn inertial sensor data that is provided by the UTD-MHAD dataset. The BLSTM model of this study could achieve an overall accuracy of 98.05% for 15 different activ
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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 train
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Stark, Love. "Outlier detection with ensembled LSTM auto-encoders on PCA transformed financial data." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-296161.

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Financial institutions today generate a large amount of data, data that can contain interesting information to investigate to further the economic growth of said institution. There exists an interest in analyzing these points of information, especially if they are anomalous from the normal day-to-day work. However, to find these outliers is not an easy task and not possible to do manually due to the massive amounts of data being generated daily. Previous work to solve this has explored the usage of machine learning to find outliers in these financial datasets. Previous studies have shown that
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Books on the topic "Memory (LSTM)"

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Sangeetha, V., and S. Kevin Andrews. Introduction to Artificial Intelligence and Neural Networks. Magestic Technology Solutions (P) Ltd, Chennai, Tamil Nadu, India, 2023. http://dx.doi.org/10.47716/mts/978-93-92090-24-0.

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Artificial Intelligence (AI) has emerged as a defining force in the current era, shaping the contours of technology and deeply permeating our everyday lives. From autonomous vehicles to predictive analytics and personalized recommendations, AI continues to revolutionize various facets of human existence, progressively becoming the invisible hand guiding our decisions. Simultaneously, its growing influence necessitates the need for a nuanced understanding of AI, thereby providing the impetus for this book, “Introduction to Artificial Intelligence and Neural Networks.” This book aims to equip it
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Book chapters on the topic "Memory (LSTM)"

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Grósz, Tamás, and Mikko Kurimo. "LSTM-XL: Attention Enhanced Long-Term Memory for LSTM Cells." In Text, Speech, and Dialogue. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-83527-9_32.

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

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An, Weijie, Qin Chen, Yan Yang, and Liang He. "Knowledge Memory Based LSTM Model for Answer Selection." In Neural Information Processing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70096-0_4.

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Li, Yinhe, Yijun Yan, Jinchang Ren, Qiaoyuan Liu, and Haijiang Sun. "MLM-LSTM: Multi-layer Memory Learning Framework Based on LSTM for Hyperspectral Change Detection." In Advances in Brain Inspired Cognitive Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1417-9_5.

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Zhao, Mengliu, and Ghassan Hamarneh. "Tree-LSTM: Using LSTM to Encode Memory in Anatomical Tree Prediction from 3D Images." In Machine Learning in Medical Imaging. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32692-0_73.

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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. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1976-3_36.

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Rohit, G., Ekta Gautam Dharamshi, and Natarajan Subramanyam. "Approaches to Question Answering Using LSTM and Memory Networks." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1592-3_15.

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

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Adenuga, Olukorede Tijani, Khumbulani Mpofu, and Ragosebo Kgaugelo Modise. "Application of ARIMA-LSTM for Manufacturing Decarbonization Using 4IR Concepts." In Lecture Notes in Mechanical Engineering. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18326-3_12.

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AbstractIncreasing climate change concerns call for the manufacturing sector to decarbonize its process by introducing a mitigation strategy. Energy efficiency concepts within the manufacturing process value chain are proportional to the emission reductions, prompting decision makers to require predictive tools to execute decarbonization solutions. Accurate forecasting requires techniques with a strong capability for predicting automotive component manufacturing energy consumption and carbon emission data. In this paper we introduce a hybrid autoregressive moving average (ARIMA)-long short-ter
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Bakalos, Nikolaos, Athanasios Voulodimos, Nikolaos Doulamis, Anastasios Doulamis, Kassiani Papasotiriou, and Matthaios Bimpas. "Fusing RGB and Thermal Imagery with Channel State Information for Abnormal Activity Detection Using Multimodal Bidirectional LSTM." In Cyber-Physical Security for Critical Infrastructures Protection. 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
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Conference papers on the topic "Memory (LSTM)"

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Salman, Ali, Gilbert El Mir, Lea Diab, Elias El Haber, Gaby Abou Haidar, and Roger Achkar. "Earthquake Analysis Using Long-Short Term Memory - LSTM." In 2024 International Conference on Computer and Applications (ICCA). IEEE, 2024. https://doi.org/10.1109/icca62237.2024.10927849.

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Gade, Rachana, Aditya Bornare, Saurabh Jog, Amit Joshi, and Suraj Sawant. "Enhancing Processor Performance using Bidirectional LSTM Memory Prefetching." In 2024 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT). IEEE, 2024. https://doi.org/10.1109/3ict64318.2024.10824426.

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Nafiyanto, Mohamad Irfan, and Erwin Budi Setiawan. "Parameter Optimization for Long Short-Term Memory (LSTM) and Bi-LSTM in Netflix Recommendation System." In 2024 International Conference on Intelligent Cybernetics Technology & Applications (ICICyTA). IEEE, 2024. https://doi.org/10.1109/icicyta64807.2024.10913010.

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Kajita, Yukihide, Ryo Morishige, Jeong Moon Kyeong, and Taiji Mazda. "Displacement Prediction by Acceleration of Bridge Pier Using Long Short-Term Memory." In IABSE Symposium, Tokyo 2025: Environmentally Friendly Technologies and Structures: Focusing on Sustainable Approaches. International Association for Bridge and Structural Engineering (IABSE), 2025. https://doi.org/10.2749/tokyo.2025.2360.

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<p>After a large earthquake, prompt assessment of damage to seismic isolation bearings, especially in bridges, is critical. Although accelerometers are commonly used in structural health monitoring, displacement response calculations based on time integration of acceleration data are often inaccurate due to numerical errors. In this paper, using dynamic analysis results on a bridge model with seismic isolation bearings, a long short-term memory (LSTM) model was employed to predict displacement more accurately. Results showed that the LSTM model reduced the maximum response displacement e
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Gupta, Anish, Lalit Kumar Tyagi, and Vibhash Singh Sisodia. "Long Short-Term Memory (LSTM) Networks For Stock Market Prediction." In 2025 International Conference on Pervasive Computational Technologies (ICPCT). IEEE, 2025. https://doi.org/10.1109/icpct64145.2025.10939100.

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Damanik, Agung Althaaf Emha, Hilal H. Nuha, Niken Dwi Wahyu Cahyani, Setyorini, and Mohd Arfian Bin Ismail. "Email Spam Detection using Long Short-Term Memory (LSTM) Network Method." In 2024 International Conference on Decision Aid Sciences and Applications (DASA). IEEE, 2024. https://doi.org/10.1109/dasa63652.2024.10836586.

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Rahman, Md Mijanur, Israt Jahan, S. M. Nizamuddin Shuvo, and Md Rabbul Hossain Taj. "Parallel Hybrid LSTM with Longitudinal Memory: To Handle Longer Sequential Data." In 2025 International Conference on Electrical, Computer and Communication Engineering (ECCE). IEEE, 2025. https://doi.org/10.1109/ecce64574.2025.11013934.

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Maylawati, Dian Sa'adillah, Karima Marwazia Shalih, Muhammad Ali Ramdhani, Cepy Slamet, and Diena Rauda Ramdania. "Indonesian Abstractive Text Summarization with Bidirectional Long Short-Term Memory (Bi-LSTM)." In 2024 12th International Conference on Cyber and IT Service Management (CITSM). IEEE, 2024. https://doi.org/10.1109/citsm64103.2024.10775593.

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Parchekani, Bahram, Samira Nazari, Mohammad Hasan Ahmadilivani, et al. "Zero-Memory-Overhead Clipping-Based Fault Tolerance for LSTM Deep Neural Networks." In 2024 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT). IEEE, 2024. http://dx.doi.org/10.1109/dft63277.2024.10753533.

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Wang, Yan, and Weidi Guo. "Long Short-Term Memory Network (LSTM) is used to Model Action Sequences." In 2025 International Conference on Intelligent Systems and Computational Networks (ICISCN). IEEE, 2025. https://doi.org/10.1109/iciscn64258.2025.10934377.

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Reports on the topic "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, 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 ot
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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), 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, 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 successfu
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Kashefi, Mehrdad, and Thomas Krause. PR652-203801-R01 Large Standoff Magnetometry (LSM) Technology Literature Review. Pipeline Research Council International, Inc. (PRCI), 2021. http://dx.doi.org/10.55274/r0012021.

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With improving accuracy and sensitivity of magnetic sensors, an attractive branch of the magnetic memory method (MMM) technique has been developed. Large Standoff Magnetometry (LSM) is an emerging non-destructive, remote, passive, non-contact and magnetic test method based on Villari effect, which could be applied to detect anomalies related to elevated stresses. The robust technology searches for Stress Concentration Zones (SCZs) in steel pipelines and ferromagnetic structures. These hot spots are mainly associated with corrosion, crack and mechanical damages such as dent and bucket, or lands
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