Neural-fuzzy with representative sets for prediction of student performance
In this paper, a new method for handling the Multi-Input Multi-Output Student Academic
Performance Prediction (MIMO SAPP) problem is proposed. The MIMO SAPP aims to predict …
Performance Prediction (MIMO SAPP) problem is proposed. The MIMO SAPP aims to predict …
Data quality-aware task offloading in mobile edge computing: An optimal stopping theory approach
An important use case of the Mobile Edge Computing (MEC) paradigm is task and data
offloading. Computational offloading is beneficial for a wide variety of mobile applications on …
offloading. Computational offloading is beneficial for a wide variety of mobile applications on …
Systematic Analysis of Artificial Intelligence‐Based Platforms for Identifying Governance and Access Control
Artificial intelligence (AI) has become omnipotent with its variety of applications and
advantages. Considering the other side of the coin, the eruption of technology has created …
advantages. Considering the other side of the coin, the eruption of technology has created …
StaResGRU-CNN with CMedLMs: A stacked residual GRU-CNN with pre-trained biomedical language models for predictive intelligence
As a task requiring strong professional experience as supports, predictive biomedical
intelligence cannot be separated from the support of a large amount of external domain …
intelligence cannot be separated from the support of a large amount of external domain …
Cluster-based industrial KPIs forecasting considering the periodicity and holiday effect using LSTM network and MSVR
Z Wang, C Zhou, Y Liu, K Huang, C Yang - Advanced Engineering …, 2023 - Elsevier
Key performance indicators (KPIs) are key indicators to measure the status and performance
of various facilities, time, and resources in the process of production and life. Forecasting …
of various facilities, time, and resources in the process of production and life. Forecasting …
Multi-task offloading based on optimal stopping theory in edge computing empowered internet of vehicles
L Mu, B Ge, C Xia, C Wu - Entropy, 2022 - mdpi.com
Vehicular edge computing is a new computing paradigm. By introducing edge computing
into the Internet of Vehicles (IoV), service providers are able to serve users with low-latency …
into the Internet of Vehicles (IoV), service providers are able to serve users with low-latency …
Improving awareness in early stages of security analysis: A zone partition method based on GrC
We present a method based on granular computing to support decision makers in analysing
and protecting large-scale infrastructures or urban areas from external attacks by identifying …
and protecting large-scale infrastructures or urban areas from external attacks by identifying …
Federated learning for distributed reasoning on edge computing
The development of the Internet of Things over the last decade has led to large amounts of
data being generated at the network edge. This highlights the importance of local data …
data being generated at the network edge. This highlights the importance of local data …
On the optimality of task offloading in mobile edge computing environments
I Alghamdi, C Anagnostopoulos… - 2019 IEEE Global …, 2019 - ieeexplore.ieee.org
Mobile Edge Computing (MEC) has emerged as new computing paradigm to improve the
QoS of users' applications. A challenge in MEC is computation (task/data) offloading, whose …
QoS of users' applications. A challenge in MEC is computation (task/data) offloading, whose …
Time-optimized task offloading decision making in mobile edge computing
I Alghamdi, C Anagnostopoulos… - 2019 Wireless Days …, 2019 - ieeexplore.ieee.org
Mobile Edge Computing application domains such as vehicular networks, unmanned aerial
vehicles, data analytics tasks at the edge and augmented reality have recently emerged …
vehicles, data analytics tasks at the edge and augmented reality have recently emerged …