[HTML][HTML] Nature-inspired algorithms for feed-forward neural network classifiers: A survey of one decade of research
AM Hemeida, SA Hassan, AAA Mohamed… - Ain Shams Engineering …, 2020 - Elsevier
Recently, an explosive growth in the potential use of natural metaphors in modelling and
solving large-scale non-linear optimization problems. Artificial neural network (ANN) is a …
solving large-scale non-linear optimization problems. Artificial neural network (ANN) is a …
Deep learning on computational‐resource‐limited platforms: A survey
C Chen, P Zhang, H Zhang, J Dai, Y Yi… - Mobile Information …, 2020 - Wiley Online Library
Nowadays, Internet of Things (IoT) gives rise to a huge amount of data. IoT nodes equipped
with smart sensors can immediately extract meaningful knowledge from the data through …
with smart sensors can immediately extract meaningful knowledge from the data through …
[HTML][HTML] Forecasting of COVID-19 cases using deep learning models: Is it reliable and practically significant?
J Devaraj, RM Elavarasan, R Pugazhendhi… - Results in Physics, 2021 - Elsevier
The ongoing outbreak of the COVID-19 pandemic prevails as an ultimatum to the global
economic growth and henceforth, all of society since neither a curing drug nor a preventing …
economic growth and henceforth, all of society since neither a curing drug nor a preventing …
A deep learning approach for intrusion detection in Internet of Things using focal loss function
Abstract Internet of Things (IoT) is likely to revolutionize healthcare, energy, education,
transportation, manufacturing, military, agriculture, and other industries. However, for the …
transportation, manufacturing, military, agriculture, and other industries. However, for the …
Online joint-prediction of multi-forward-step battery SOC using LSTM neural networks and multiple linear regression for real-world electric vehicles
Prediction of state of charge (SOC) is critical to the reliability and durability of battery systems
in electric vehicles. The existing techniques are mostly model-based SOC estimation using …
in electric vehicles. The existing techniques are mostly model-based SOC estimation using …
A multi-stage classification approach for iot intrusion detection based on clustering with oversampling
Intrusion detection of IoT-based data is a hot topic and has received a lot of interests from
researchers and practitioners since the security of IoT networks is crucial. Both supervised …
researchers and practitioners since the security of IoT networks is crucial. Both supervised …
Convergent newton method and neural network for the electric energy usage prediction
In the neural network adaptation, the Newton method could find a minimum with its second-
order partial derivatives, and convergent gradient steepest descent could assure its error …
order partial derivatives, and convergent gradient steepest descent could assure its error …
Effect of balancing data using synthetic data on the performance of machine learning classifiers for intrusion detection in computer networks
Attacks on computer networks have increased significantly in recent days, due in part to the
availability of sophisticated tools for launching such attacks as well as the thriving …
availability of sophisticated tools for launching such attacks as well as the thriving …
Optimisation of two-stage biomass gasification for hydrogen production via artificial neural network
A two-stage gasification has been proven as an effective and robust approach for converting
low-valued and/or highly heterogeneous materials ie waste, into hydrogen and/or syngas …
low-valued and/or highly heterogeneous materials ie waste, into hydrogen and/or syngas …
A multi-layer classification approach for intrusion detection in iot networks based on deep learning
The security of IoT networks is an important concern to researchers and business owners,
which is taken into careful consideration due to its direct impact on the availability of the …
which is taken into careful consideration due to its direct impact on the availability of the …