Machine learning for the detection and identification of Internet of Things devices: A survey

Y Liu, J Wang, J Li, S Niu… - IEEE Internet of Things …, 2021 - ieeexplore.ieee.org
The Internet of Things (IoT) is becoming an indispensable part of everyday life, enabling a
variety of emerging services and applications. However, the presence of rogue IoT devices …

Statistical analysis and flight route extraction from automatic dependent surveillance-broadcast data

I Ostroumov, N Kuzmenko - 2022 Integrated Communication …, 2022 - ieeexplore.ieee.org
Modern air traffic management considers wide integration of Automatic Dependent
Surveillance-Broadcast (ADS-B) technology in existing air navigation system. Each airspace …

Flight delay regression prediction model based on Att-Conv-LSTM

J Qu, M **ao, L Yang, W **e - Entropy, 2023 - mdpi.com
Accurate prediction results can provide an excellent reference value for the prevention of
large-scale flight delays. Most of the currently available regression prediction algorithms use …

Spatial–temporal graph data mining for IoT-enabled air mobility prediction

Y Jiang, S Niu, K Zhang, B Chen, C Xu… - IEEE Internet of …, 2021 - ieeexplore.ieee.org
Big data analytics and mining have the potential to enable real-time decision making and
control in a range of Internet of Things (IoT) application domains, such as the Internet of …

Smart short-term load forecasting through coordination of LSTM-based models and feature engineering methods during the COVID-19 pandemic

SM Shobeiry, S Azad, MT Ameli - Electric Power Components and …, 2023 - Taylor & Francis
Short-term load forecasting is essential for power companies because it is necessary to
ensure sufficient capacity. This article proposes a smart load forecasting scheme to forecast …

Forex market forecasting with two-layer stacked Long Short-Term Memory neural network (LSTM) and correlation analysis

M Ayitey Junior, P Appiahene, O Appiah - Journal of Electrical Systems …, 2022 - Springer
Since it is one of the world's most significant financial markets, the foreign exchange (Forex)
market has attracted a large number of investors. Accurately anticipating the forex trend has …

Phased flight trajectory prediction with deep learning

K Zhang, B Chen - arxiv preprint arxiv:2203.09033, 2022 - arxiv.org
The unprecedented increase of commercial airlines and private jets over the next ten years
presents a challenge for air traffic control. Precise flight trajectory prediction is of great …

Learning-to-dispatch: Reinforcement learning based flight planning under emergency

K Zhang, Y Yang, C Xu, D Liu… - 2021 IEEE International …, 2021 - ieeexplore.ieee.org
The effectiveness of resource allocation under emergencies especially hurricane disasters
is crucial. However, most researchers focus on emergency resource allocation in a ground …

Machine learning-enabled adaptive air traffic recommendation system for disaster evacuation

Y Yang, K Zhang, H Song, D Liu - 2021 IEEE/AIAA 40th Digital …, 2021 - ieeexplore.ieee.org
Extreme weather conditions, such as floods, hurricanes and wildfires, cause large-scale
human population movements and evacuations in the world. Taking flights to evacuate the …

Analysis of flight delay data using different machine learning algorithms

BTL SS, H Al Ali, AAAM Majid… - 2022 New Trends in …, 2022 - ieeexplore.ieee.org
Accurate prediction of flights arrival remains a challenge due to dynamic environments. On
predominantly challenging days, unforeseen peaks in flight volumes can stretch operational …