[HTML][HTML] Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review
The recent advancements made in the realms of Artificial Intelligence (AI) and Artificial
Intelligence of Things (AIoT) have unveiled transformative prospects and opportunities to …
Intelligence of Things (AIoT) have unveiled transformative prospects and opportunities to …
Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda
Artificial intelligence (AI) will transform business practices and industries and has the
potential to address major societal problems, including sustainability. Degradation of the …
potential to address major societal problems, including sustainability. Degradation of the …
Machine learning for resource management in cellular and IoT networks: Potentials, current solutions, and open challenges
Internet-of-Things (IoT) refers to a massively heterogeneous network formed through smart
devices connected to the Internet. In the wake of disruptive IoT with a huge amount and …
devices connected to the Internet. In the wake of disruptive IoT with a huge amount and …
Attention meets long short-term memory: A deep learning network for traffic flow forecasting
W Fang, W Zhuo, J Yan, Y Song, D Jiang… - Physica A: Statistical …, 2022 - Elsevier
Accurate forecasting of future traffic flow has a wide range of applications, which is a
fundamental component of intelligent transportation systems. However, timely and accurate …
fundamental component of intelligent transportation systems. However, timely and accurate …
A temporal-aware lstm enhanced by loss-switch mechanism for traffic flow forecasting
Short-term traffic flow forecasting at isolated points is a fundamental yet challenging task in
many intelligent transportation systems. We present a novel long short-term memory (LSTM) …
many intelligent transportation systems. We present a novel long short-term memory (LSTM) …
PSO-ELM: A hybrid learning model for short-term traffic flow forecasting
Accurate and reliable traffic flow forecasting is of importance for urban planning and
mitigation of traffic congestion, and it is also the basis for the deployment of intelligent traffic …
mitigation of traffic congestion, and it is also the basis for the deployment of intelligent traffic …
A noise-immune Kalman filter for short-term traffic flow forecasting
L Cai, Z Zhang, J Yang, Y Yu, T Zhou, J Qin - Physica A: Statistical …, 2019 - Elsevier
This paper formulates the traffic flow forecasting task by introducing a maximum correntropy
deduced Kalman filter. The traditional Kalman filter is based on minimum mean square error …
deduced Kalman filter. The traditional Kalman filter is based on minimum mean square error …
Δfree-LSTM: An error distribution free deep learning for short-term traffic flow forecasting
Timely and accurate traffic flow forecasting is open challenging. Canonical long short-term
memory (LSTM) network is considered qualified to capture the long-term temporal …
memory (LSTM) network is considered qualified to capture the long-term temporal …
St-trafficnet: A spatial-temporal deep learning network for traffic forecasting
This paper presents a spatial-temporal deep learning network, termed ST-TrafficNet, for
traffic flow forecasting. Recent deep learning methods highly relate accurate predetermined …
traffic flow forecasting. Recent deep learning methods highly relate accurate predetermined …
Short-term traffic flow prediction based on improved wavelet neural network
Q Chen, Y Song, J Zhao - Neural Computing and Applications, 2021 - Springer
Due to the characteristics of time-varying traffic and nonlinearity, the short-term traffic flow
data are difficult to predict accurately. The purpose of this paper is to improve the short-term …
data are difficult to predict accurately. The purpose of this paper is to improve the short-term …