Electronics | Highly Cited Papers in 2021 in the Section “Artificial Intelligence”
The Section “Artificial Intelligence” mainly covers topics of interest within unique hardware-based deep learning AI and algorithmic deep learning AI using machine learning. The purpose of this Section is to bring together researchers and engineers, from both academia and industry, to present novel ideas and solid research on the hardware and algorithmic aspects of advanced applications of deep-learning-based AI.
As they are published in an open access format, you have free and unlimited access to the full text of all of the articles published in our journal. We welcome you to read our most highly cited papers published in 2021 below:
1. “Biometric User Identification Based on Human Activity Recognition Using Wearable Sensors: An Experiment Using Deep Learning Models”
by Sakorn Mekruksavanich and Anuchit Jitpattanakul
Electronics 2021, 10(3), 308; https://doi.org/10.3390/electronics10030308
Available online: http://www.mdpi.com/2079-9292/10/3/308
2. “Novel Control Strategy for Enhancing Microgrid Operation Connected to Photovoltaic Generation and Energy Storage Systems”
by Dina Emara et al.
Electronics 2021, 10(11), 1261; https://doi.org/10.3390/electronics10111261
Available online: http://www.mdpi.com/2079-9292/10/11/1261
3. “Deep Convolutional Neural Network with RNNs for Complex Activity Recognition Using Wrist-Worn Wearable Sensor Data”
by Sakorn Mekruksavanich and Anuchit Jitpattanakul
Electronics 2021, 10(14), 1685; https://doi.org/10.3390/electronics10141685
Available online: http://www.mdpi.com/2079-9292/10/14/1685
4. “Artificial Neural Networks Based Optimization Techniques: A Review”
by Maher G. M. Abdolrasol et al.
Electronics 2021, 10(21), 2689; https://doi.org/10.3390/electronics10212689
Available online: http://www.mdpi.com/2079-9292/10/21/2689
5. “An Advanced CNN-LSTM Model for Cryptocurrency Forecasting”
by Ioannis E. Livieris et al.
Electronics 2021, 10(3), 287; https://doi.org/10.3390/electronics10030287
Available online: https://www.mdpi.com/2079-9292/10/3/287
6. “A Fuzzy Logic Model for Hourly Electrical Power Demand Modeling”
by Marco Antonio Islas et al.
Electronics 2021, 10(4), 448; https://doi.org/10.3390/electronics10040448
Available online: http://www.mdpi.com/2079-9292/10/4/448
7. “A Survey on Machine Learning-Based Performance Improvement of Wireless Networks: PHY, MAC and Network Layer”
by Merima Kulin et al.
Electronics 2021, 10(3), 318; https://doi.org/10.3390/electronics10030318
Available online: http://www.mdpi.com/2079-9292/10/3/318
8. “Improved YOLOv3 Network for Insulator Detection in Aerial Images with Diverse Background Interference”
by Chuanyang Liu et al.
Electronics 2021, 10(7), 771; https://doi.org/10.3390/electronics10070771
Available online: http://www.mdpi.com/2079-9292/10/7/771
9. “Concrete Cracks Detection and Monitoring Using Deep Learning-Based Multiresolution Analysis”
by Ahcene Arbaoui et al.
Electronics 2021, 10(15), 1772; https://doi.org/10.3390/electronics10151772
Available online: http://www.mdpi.com/2079-9292/10/15/1772
10. “EWOA-OPF: Effective Whale Optimization Algorithm to Solve Optimal Power Flow Problem”
by Mohammad H. Nadimi-Shahraki et al.
Electronics 2021, 10(23), 2975; https://doi.org/10.3390/electronics10232975
Available online: http://www.mdpi.com/2079-9292/10/23/2975