The “Artificial Intelligence” Section of the journal Electronics (ISSN: 2079-9292) 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 all of the articles published in our journal are in an open access format, you have free and unlimited access to the full texts. We welcome you to read our most highly cited papers published in 2023 listed below:

1. “Deep Learning-Based Attack Detection and Classification in Android Devices”
by Alfonso Gómez and Antonio Muñoz
Electronics 2023, 12(15), 3253; https://doi.org/10.3390/electronics12153253
Available online: https://www.mdpi.com/2079-9292/12/15/3253

2. “An Empirical Survey on Explainable AI Technologies: Recent Trends, Use-Cases, and Categories from Technical and Application Perspectives”
by Mohammad Nagahisarchoghaei, Nasheen Nur, Logan Cummins, Nashtarin Nur, Mirhossein Mousavi Karimi, Shreya Nandanwar, Siddhartha Bhattacharyya and Shahram Rahimi
Electronics 2023, 12(5), 1092; https://doi.org/10.3390/electronics12051092
Available online: https://www.mdpi.com/2079-9292/12/5/1092

3. “Robust and Lightweight Deep Learning Model for Industrial Fault Diagnosis in Low-Quality and Noisy Data”
by Jaegwang Shin and Suan Lee
Electronics 2023, 12(2), 409; https://doi.org/10.3390/electronics12020409
Available online: https://www.mdpi.com/2079-9292/12/2/409

4. “Rotor Fault Diagnosis Method Using CNN-Based Transfer Learning with 2D Sound Spectrogram Analysis”
by Haiyoung Jung, Sugi Choi and Bohee Lee
Electronics 2023, 12(3), 480; https://doi.org/10.3390/electronics12030480
Available online: https://www.mdpi.com/2079-9292/12/3/480

5. “Experimental Machine Learning Approach for Optical Turbulence and FSO Outage Performance Modeling”
by Antonios Lionis, Antonios Sklavounos, Argyris Stassinakis, Keith Cohn, Andreas Tsigopoulos, Kostas Peppas, Konstantinos Aidinis and Hector Nistazakis
Electronics 2023, 12(3), 506; https://doi.org/10.3390/electronics12030506
Available online: https://www.mdpi.com/2079-9292/12/3/506

6. “An Efficient Adaptive Noise Removal Filter on Range Images for LiDAR Point Clouds”
by Minh-Hai Le, Ching-Hwa Cheng and Don-Gey Liu
Electronics 2023, 12(9), 2150; https://doi.org/10.3390/electronics12092150
Available online: https://www.mdpi.com/2079-9292/12/9/2150

7. “Fuzzy Rough Nearest Neighbour Methods for Aspect-Based Sentiment Analysis”
by Olha Kaminska, Chris Cornelis and Veronique Hoste
Electronics 2023, 12(5), 1088; https://doi.org/10.3390/electronics12051088
Available online: https://www.mdpi.com/2079-9292/12/5/1088

8.  “Towards Deploying DNN Models on Edge for Predictive Maintenance Applications”
by Rick Pandey, Sebastian Uziel, Tino Hutschenreuther and Silvia Krug
Electronics 2023, 12(3), 639; https://doi.org/10.3390/electronics12030639
Available online: https://www.mdpi.com/2079-9292/12/3/639

9. “LLM-Informed Multi-Armed Bandit Strategies for Non-Stationary Environments”
by J. De Curtò, I. de Zarzà de Curtò, Gemma Roig, Juan Carlos Cano, Pietro Manzoni and Carlos T. Calafate
Electronics 2023, 12(13), 2814; https://doi.org/10.3390/electronics12132814
Available online: https://www.mdpi.com/2079-9292/12/13/2814

10. “Intelligent Decision Support for Energy Management: A Methodology for Tailored Explainability of Artificial Intelligence Analytics”
by Dimitrios P. Panagoulias, Elissaios Sarmas, Vangelis Marinakis, Maria Virvou, George A. Tsihrintzis and Haris Doukas
Electronics 2023, 12(21), 4430; https://doi.org/10.3390/electronics12214430
Available online: https://www.mdpi.com/2079-9292/12/21/4430