A multidirectional LSTM model for predicting the stability of a smart grid

M Alazab, S Khan, SSR Krishnan, QV Pham… - IEEE …, 2020 - ieeexplore.ieee.org
The grid denotes the electric grid which consists of communication lines, control stations,
transformers, and distributors that aids in supplying power from the electrical plant to the …

[PDF][PDF] Training multi-layer perceptron with enhanced brain storm optimization metaheuristics

N Bacanin, K Alhazmi, M Zivkovic… - … , Materials & Continua, 2022 - researchgate.net
In the domain of artificial neural networks, the learning process represents one of the most
challenging tasks. Since the classification accuracy highly depends on the weights and …

Deep learning for intelligent IoT: Opportunities, challenges and solutions

YB Zikria, MK Afzal, SW Kim, A Marin… - Computer …, 2020 - Elsevier
Next-generation wireless networks have to be robust and self-sustained. Internet of things
(IoT) is reshaping the technological adaptation in the daily life of human beings. IoT …

Accurate performance prediction of IoT communication systems for smart cities: An efficient deep learning based solution

O Said, A Tolba - Sustainable Cities and Society, 2021 - Elsevier
Abstract The Internet of Things (IoT), owing to its ability to support sustainability in various
fields, has recently been considered one of the most important components of the …

Intelligent intrusion detection system in smart grid using computational intelligence and machine learning

S Khan, K Kifayat, A Kashif Bashir… - Transactions on …, 2021 - Wiley Online Library
Smart grid systems enhanced the capability of traditional power networks while being
vulnerable to different types of cyber‐attacks. These vulnerabilities could cause attackers to …

Efficient artificial neural network for smart grid stability prediction

S Mohsen, M Bajaj, H Kotb… - … on Electrical Energy …, 2023 - Wiley Online Library
According to the stability process of smart grids, which starts by gathering information of
consumers, and then evaluating this information based on specifications of a power supply …

Securing IoT based maritime transportation system through entropy-based dual-stack machine learning framework

F Ali, S Sarwar, QM Shafi, M Iqbal… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
Internet of Things (IoTs) is envisaged to widely capture the realm of logistics and
transportation services in future. The applications of ubiquitous IoTs have been extended to …

Development of mobile IoT solutions: approaches, architectures, and methodologies

N Magaia, P Gomes, L Silva, B Sousa… - IEEE Internet of …, 2020 - ieeexplore.ieee.org
Modern Living, as we know it, has been impacted meaningfully by the Internet of Things
(IoT). IoT consists of a network of things that collect data from machines (eg, mobile devices) …

Impact of IoT in Biomedical Applications Using Machine and Deep Learning

RA Rayan, I Zafar, H Rajab, MAM Zubair… - … Algorithms for Signal …, 2022 - Wiley Online Library
Machine learning (ML) is a strong means to bring perspectives concealed on the internet of
things (IoT) data. Such composite fields act cleverly to improve the decision‐making …

Data Analytics and Modeling in IoT-Fog Environment for Resourceconstrained IoT-Applications: A Review

O Farooq, P Singh - Recent Advances in Computer Science …, 2022 - ingentaconnect.com
Objective: The emergence of the concepts like Big Data, Data Science, Machine Learning
(ML), and the Internet of Things (IoT) in recent years has added the potential of research in …