The primary focus of the “Computer Science & Engineering” Section of Electronics (ISSN: 2079-9292) is the field of advanced computer science and engineering. It presents high-quality papers that address state-of-the-art technology, including deep tech, edge computing, fog computing, artificial intelligence, machine learning, deep learning, emotional systems, fintech, blockchain, IoT, industry 4.0, smart cities, smart grids, intelligent textiles and distributed computing, as well as other chief technologies in this field.

This Section is open to reviews of the state of the art and latest advances, theoretical studies, and applications in a wide range of related fields, including predictive maintenance systems, industry 4.0 and internet of things, bioinformatics, smart cities, social computing and blockchain, etc.

You have free and unlimited access to the full texts of all of the open access articles published in our journal. We invite you to read our most highly cited papers published in 2025, listed below:

1. “Integrating Artificial Intelligence Agents with the Internet of Things for Enhanced Environmental Monitoring: Applications in Water Quality and Climate Data”
by Tymoteusz Miller, Irmina Durlik, Ewelina Kostecka, Polina Kozlovska, Adrianna Łobodzińska, Sylwia Sokołowska and Agnieszka Nowy
Electronics 2025, 14(4), 696; https://doi.org/10.3390/electronics14040696
Available online: https://www.mdpi.com/2079-9292/14/4/696

2. “A Comprehensive Review of AI-Based Digital Twin Applications in Manufacturing: Integration Across Operator, Product, and Process Dimensions”
by David Alfaro-Viquez, Mauricio Zamora-Hernandez, Michael Fernandez-Vega, Jose Garcia-Rodriguez and Jorge Azorin-Lopez
Electronics 2025, 14(4), 646; https://doi.org/10.3390/electronics14040646
Available online: https://www.mdpi.com/2079-9292/14/4/646

3. “A Performance Analysis of You Only Look Once Models for Deployment on Constrained Computational Edge Devices in Drone Applications”
by Lucas Rey, Ana M. Bernardos, Andrzej D. Dobrzycki, David Carramiñana, Luca Bergesio, Juan A. Besada and José Ramón Casar
Electronics 2025, 14(3), 638; https://doi.org/10.3390/electronics14030638
Available online: https://www.mdpi.com/2079-9292/14/3/638

4. “Autonomous Medical Robot Trajectory Planning with Local Planner Time Elastic Band Algorithm”
by Arjon Turnip, Muhamad Arsyad Faridhan, Bambang Mukti Wibawa and Nursanti Anggriani
Electronics 2025, 14(1), 183; https://doi.org/10.3390/electronics14010183
Available online: https://www.mdpi.com/2079-9292/14/1/183

5. “IoT–Cloud Integration Security: A Survey of Challenges, Solutions, and Directions”
by Mohammed Almutairi and Frederick T. Sheldon
Electronics 2025, 14(7), 1394; https://doi.org/10.3390/electronics14071394
Available online: https://www.mdpi.com/2079-9292/14/7/1394

6. “State of the Art in Parallel and Distributed Systems: Emerging Trends and Challenges”
by Fei Dai, Md Akbar Hossain and Yi Wang
Electronics 2025, 14(4), 677; https://doi.org/10.3390/electronics14040677
Available online: https://www.mdpi.com/2079-9292/14/4/677

7. “Comparison of LSTM- and GRU-Type RNN Networks for Attention and Meditation Prediction on Raw EEG Data from Low-Cost Headsets”
by Fernando Rivas, Jesús Enrique Sierra-Garcia and Jose María Camara
Electronics 2025, 14(4), 707; https://doi.org/10.3390/electronics14040707
Available online: https://www.mdpi.com/2079-9292/14/4/707

8. “Edge Intelligence: A Review of Deep Neural Network Inference in Resource-Limited Environments”
by Dat Ngo, Hyun-Cheol Park and Bongsoon Kang
Electronics 2025, 14(12), 2495; https://doi.org/10.3390/electronics14122495
Available online: https://www.mdpi.com/2079-9292/14/12/2495

9. “A Malware-Detection Method Using Deep Learning to Fully Extract API Sequence Features”
by Shuhui Zhang, Mingyu Gao, Lianhai Wang, Shujiang Xu, Wei Shao and Ruixue Kuang
Electronics 2025, 14(1), 167; https://doi.org/10.3390/electronics14010167
Available online: https://www.mdpi.com/2079-9292/14/1/167

10. “Big Data-Driven Distributed Machine Learning for Scalable Credit Card Fraud Detection Using PySpark, XGBoost, and CatBoost”
by Leonidas Theodorakopoulos, Alexandra Theodoropoulou, Anastasios Tsimakis and Constantinos Halkiopoulos
Electronics 2025, 14(9), 1754; https://doi.org/10.3390/electronics14091754
Available online: https://www.mdpi.com/2079-9292/14/9/1754