Results 101 to 110 of about 11,734,216 (296)

SELF-OPTIMIZED LONG SHORT-TERM MEMORY FOR PREDICTING REMAINING USEFUL LIFE OF MACHINES

, 2023
Remaining Useful Life (RUL) is the period from the current time to the time when a machine fails to operate. Unexpected machine failure brings critical damages to the industry, such as loss of investment in assets and high unplanned maintenance costs ...
RASHID, NOOR ADILAH
core  

Automating AI Discovery for Biomedicine Through Knowledge Graphs and Large Language Models Agents

Advanced Intelligent Discovery, EarlyView.
This work proposes a novel framework that automates biomedical discovery by integrating knowledge graphs with multiagent large language models. A biologically aligned graph exploration strategy identifies hidden pathways between biomedical entities, and specialized agents use this pathway to iteratively design AI predictors and wet‐lab validation ...
Naafey Aamer, Muhammad Nabeel Asim, Shan Munir, Andreas Dengel   +3 more
wiley   +1 more source

Ship motion identification model based on enhanced Bi-LSTM

Zhongguo Jianchuan Yanjiu
ObjectiveAiming at the low prediction precision and poor adaptability of ship models based on the data-driven modeling strategy, an enhanced bi-directional long short-term memory (Bi-LSTM) model is proposed for the high-precision non-parametric modeling ...
Haozhe ZHANG, Zhibo YANG, Xuguo JIAO, Chengxing LÜ, Peng LEI   +4 more
doaj   +1 more source

Prediksi Indeks Harga Konsumen Menggunakan Metode Long Short Term Memory (LSTM) Berbasis Cloud Computing

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 2019
Long Short Term Memory (LSTM) is known as optimized Recurrent Neural Network (RNN) architectures that overcome RNN’s lact about maintaining long period of memories.
Soffa Zahara, Sugianto, M. Bahril Ilmiddafiq   +2 more
doaj   +1 more source

Comparative Study of Long Short-Term Memory (LSTM) and Quantum Long Short-Term Memory (QLSTM): Prediction of Stock Market Movement

CoRR
In recent years, financial analysts have been trying to develop models to predict the movement of a stock price index. The task becomes challenging in vague economic, social, and political situations like in Pakistan. In this study, we employed efficient models of machine learning such as long short-term memory (LSTM) and quantum long short-term memory
Tariq Mahmood, Ibtasam Ahmad, Malik Muhammad Zeeshan Ansar, Jumanah Ahmed Darwish, Rehan Ahmad Khan Sherwani   +4 more
openaire   +3 more sources

A Multimodal Intelligent System for Human Digital Twin Simulation with Continuous Kinematic Data Tracking, Biometric Prognosis, and Cognitive State Feedback in Industrial Environments

Advanced Intelligent Discovery, EarlyView.
This article implements a unified human digital twin framework that integrates cutting edge actuation, sensing, simulation, and bidirectional feedback capability. The approach includes integrating multimodal sensing, AI, and biomechanical simulation into one compact system.
Tajbeed Ahmed Chowdhury, Sebastian Gratz‐Kelly, Eric Wagner, Paul Motzki, Martina Lehser   +4 more
wiley   +1 more source

Distant and Recent Historical Data Fusion for Improving Short- and Medium-Term Traffic Forecasting

Applied Sciences
Traffic became a major issue in large and crowded metropolitan cities and might cause people to waste in the order of days within a year. It is notable that traffic speed estimation problems were addressed in three main horizons: short term, medium term,
Metin Usta, H. Irem Turkmen, M. Amac Guvensan   +2 more
doaj   +1 more source

Preservation of long-term memory and synaptic plasticity despite short-term impairments in the Tc1 mouse model of Down syndrome [PDF]

, 2008
Down syndrome (DS) is a genetic disorder arising from the presence of a third copy of the human chromosome 21 (Hsa21). Recently, O’Doherty and colleagues in an earlier study generated a new genetic mouse model of DS (Tc1) that carries an almost complete ...
Morice, E., Cooke, S.F., Bliss, T.V.P., Vanes, L., Andreae, L.C., Tybulewicz, V.L.J., Fisher, E.M.C.   +6 more
core  

Analysis of Long Short Term Memory (LSTM) Networks in the stateful and stateless mode for COVID-19 Impact Prediction

, 2020
This is the Python code for the Book Chapter Title "Analysis of Long Short Term Memory (LSTM) Networks in the stateful and stateless mode for COVID-19 Impact Prediction" This includes the Python Code from Google Colab as well as the dataset used for ...
Vinayak Ashok Bharadi
core   +1 more source

DeepMapper: Attention‐Based AutoEncoder for System Identification in Wound Healing and Stage Prediction

Advanced Intelligent Discovery, EarlyView.
The authors develop a deep learning model for real‐time tracking of wound progression. The deep learning framework maps the nonlinear evolution of a time series of images to a latent space, where they learn a linear representation of the dynamics. The linear model is interpretable and suitable for applications in feedback control.
Fan Lu, Ksenia Zlobina, Michael Briden, Oscar Avalos, Héctor Carrión, Sebastian Osorio, Hsin‐ya Yang, Alexandra Nava, Michelle D. Bagood, Marco Rolandi, Roslyn Rivkah Isseroff, Marcella Gomez   +11 more
wiley   +1 more source