Results 1 to 10 of about 19,414 (84)

Efficient reporting delay calibration in spatial metapopulation models for reconstructing cross-regional epidemic dynamics [PDF]

Biosafety and Health
Reconstructing the early spatiotemporal dynamics of emerging infectious diseases (EIDs) is essential for effective public health response but remains difficult due to reporting delays, heterogeneous surveillance systems, and cryptic transmission chains ...
Huichun Li, Yue Teng, Zhenghu Zu, Jingning Chen   +3 more
doaj   +2 more sources

Performance of Machine Learning-Based Multi-Model Voting Ensemble Methods for Network Threat Detection in Agriculture 4.0

Sensors, 2021
The upcoming agricultural revolution, known as Agriculture 4.0, integrates cutting-edge Information and Communication Technologies in existing operations.
Nikolaos Peppes, Emmanouil Daskalakis, Theodoros Alexakis, Evgenia Adamopoulou, Konstantinos Demestichas   +4 more
doaj   +1 more source

Phalaenopsis growth phase classification using convolutional neural network

Smart Agricultural Technology, 2022
During phalaenopsis production and sales, a large number of manual classification operations must be done because phalaenopsis in different growth phases have different sales grades and prices.
Kehui Xiao, Lei Zhou, Hong Yang, Lei Yang   +3 more
doaj   +1 more source

Innovating knowledge and information for a firm-level automobile demand forecast system: A machine learning perspective

Journal of Innovation & Knowledge, 2023
Accurate demand forecasting is important for automotive manufacturing and sales planning because it allows firms to minimize costs and improve their effectiveness.
Sehoon Kim
doaj   +1 more source

Utilizing the Switching Stochasticity of HfO2/TiOx-Based ReRAM Devices and the Concept of Multiple Device Synapses for the Classification of Overlapping and Noisy Patterns

Frontiers in Neuroscience, 2021
With the arrival of the Internet of Things (IoT) and the challenges arising from Big Data, neuromorphic chip concepts are seen as key solutions for coping with the massive amount of unstructured data streams by moving the computation closer to the ...
Christopher Bengel, Felix Cüppers, Melika Payvand, Regina Dittmann, Rainer Waser, Rainer Waser, Susanne Hoffmann-Eifert, Stephan Menzel   +7 more
doaj   +1 more source

Optimal Power Flow in Distribution Network: A Review on Problem Formulation and Optimization Methods

Energies, 2023
Distributed generators (DGs) have a high penetration rate in distribution networks (DNs). Understanding their impact on a DN is essential for achieving optimal power flow (OPF).
Cheng Yang, Yupeng Sun, Yujie Zou, Fei Zheng, Shuangyu Liu, Bochao Zhao, Ming Wu, Haoyang Cui   +7 more
doaj   +1 more source

Hybrid neuromorphic circuits exploiting non-conventional properties of RRAM for massively parallel local plasticity mechanisms

APL Materials, 2019
Recurrent neural networks are currently subject to intensive research efforts to solve temporal computing problems. Neuromorphic processors (NPs), composed of networked neuron and synapse circuit models, natively compute in time and offer an ultralow ...
Thomas Dalgaty, Melika Payvand, Filippo Moro, Denys R. B. Ly, Florian Pebay-Peyroula, Jerome Casas, Giacomo Indiveri, Elisa Vianello   +7 more
doaj   +1 more source

Review of the analysis and methods of natural gas pipeline network operation optimization

Natural Gas Industry B
The expansion of natural gas consumption and pipeline construction makes integrating artificial intelligence into pipeline network operations increasingly essential.
Deming Zhao, Wuchang Wang, Yuxing Li
doaj   +1 more source

Crafting efficient blockchain adoption strategies under risk and uncertain environments

Alexandria Engineering Journal
Risk and uncertainty are crucial factors in decision-making processes, especially when integrating emerging technologies into essential systems like supply chains.
Ardavan Babaei, Erfan Babaee Tirkolaee, Alireza Amjadian   +2 more
doaj   +1 more source

Inverse Procedure to Initial Parameter Estimation for Air-Dropped Packages Using Neural Networks

Applied Sciences
This paper presents a neural network–driven framework for solving the inverse problem of initial parameter estimation in air-dropped package missions.
Beata Potrzeszcz-Sut, Marta Grzyb
doaj   +1 more source