Results 141 to 150 of about 11,734,216 (296)

A comprehensive review of detection, optical character recognition, and structuring methods for herbarium label digitization

Applications in Plant Sciences, EarlyView.
Abstract Global herbarium digitization has produced over 50 million specimen images, many of which contain handwritten or printed labels with rich metadata such as species names, collection dates, localities, and ecological notes. Manual transcription of this information is labor intensive, error prone, and not scalable, which has motivated the ...
Hiba Abbad, Hanane Ariouat, Nicolas Turenne, Eric Chenin, Abderrazak Sebaa, Edi Prifti, Jean‐Daniel Zucker, Youcef Sklab   +7 more
wiley   +1 more source

Monitoring and deformation of deep excavation engineering based on DFOS technology and hybrid deep learning

Scientific Reports
With the increasing urbanization in China, monitoring and predicting the deformation of deep excavations has become increasingly critical. Concurrently, as neural network models find application and development in deep excavation displacement prediction,
Yanli Peng, Jie Zhao, Yijiang Fan, Cheng Fan   +3 more
doaj   +1 more source

Classification of Low Earth Orbit (LEO) Resident Space Objects' (RSO) Light Curves Using a Support Vector Machine (SVM) and Long Short-Term Memory (LSTM). [PDF]

Sensors (Basel), 2023
Qashoa R, Lee R.
europepmc   +1 more source

Enabling Bioprocess Upscaling Prediction Through Hybrid Modelling and Transfer Learning Under Small‐Data Scenarios

Biotechnology and Bioengineering, EarlyView.
ABSTRACT Accurate prediction of bioprocess scale‐up is critical for accelerating the deployment of novel and sustainable biomanufacturing systems. However, this remains challenging as multi‐scale data is expensive to generate and mechanistic understanding is often incomplete, leading upscaling decisions to rely heavily on empirical expertise. This work
Harvey Al‐Ramadhan, Fernando Vega‐Ramon, Keju Jing, Dongda Zhang   +3 more
wiley   +1 more source

Predicting machine's performance record using the stacked long short-term memory (LSTM) neural networks. [PDF]

J Appl Clin Med Phys, 2022
Ma M, Liu C, Wei R, Liang B, Dai J.
europepmc   +1 more source

Accelerating green heterocyclic synthesis: Convergence of AI, automation, and sustainable chemistry

Bulletin of the Korean Chemical Society, EarlyView.
Green heterocyclic synthesis and data‐driven tools such as AI, high‐throughput experimentation, continuous flow, and self‐driving laboratories have developed largely in parallel. This review connects them and asks how green metrics such as E‐factor and PMI might serve as explicit optimization targets rather than post‐hoc measures, toward sustainable ...
Noor Aisyah Ahmad Shah, Amirreza Mottafegh, Su‐Hyun Yoo, Jongwoon Kim, Minseon Byeon, Juhwan Noh, Yea‐Lee Lee, Jin Suk Myung, Gwang‐Noh Ahn   +8 more
wiley   +1 more source

Modelling Itasy Lake Water Quality by Long Short Term Memory (LSTM) using Landsat8 Data

Geoplanning: Journal of Geomatics and Planning
Modeling lake water quality is very important to preserve and protect this resource. Several algorithms can be used to model lake water quality using in-situ measurement data.
Randrianiaina Jerry Jean Christien Frederick, Rakotonirina Rija Itokiana, Jean Robertin Rasoloariniaina, Fils Lahatra Razafindramisa   +3 more
doaj   +1 more source

New onset delirium prediction using machine learning and long short-term memory (LSTM) in electronic health record. [PDF]

J Am Med Inform Assoc, 2022
Liu S, Schlesinger JJ, McCoy AB, Reese TJ, Steitz B, Russo E, Koh B, Wright A.   +7 more
europepmc   +1 more source

Artificial Intelligence–Driven and Digital Practices for Circular Business and Finance: Insights for Advancing Hubs for Circularity

Business Strategy and the Environment, EarlyView.
ABSTRACT The emerging concept of Hubs for Circularity (H4Cs) presents an opportunity to create collaborative, self‐sustaining regional industrial ecosystems that drive circular economy transitions at scale. However, the operationalisation of H4Cs faces financial, organisational and data‐driven challenges.
Aditya Tripathi, Marcos R. Machado, Laura Spierdijk, Devrim Murat Yazan   +3 more
wiley   +1 more source

Enhanced Forecasting Accuracy of a Grid-Connected Photovoltaic Power Plant: A Novel Approach Using Hybrid Variational Mode Decomposition and a CNN-LSTM Model

Energies
Renewable energies have become pivotal in the global energy landscape. Their adoption is crucial for phasing out fossil fuels and promoting environmentally friendly energy solutions. In recent years, the energy management system (EMS) concept has emerged
Lakhdar Nadjib Boucetta, Youssouf Amrane, Aissa Chouder, Saliha Arezki, Sofiane Kichou   +4 more
doaj   +1 more source