Special Issue Editors
 |
 Role: Guest Editor Department of Electrical Engineering, Chongqing University, China |
Published Papers (6 papers)
This special issue presents interdisciplinary research contributions focused on computing technologies, engineering systems, and multidisciplinary scientific applications. The published papers investigate generative artificial intelligence, prompt engineering methodologies, explainable AI for photovoltaic fault detection, machine learning for student performance prediction, AI maturity models for digitalized shipbuilding systems, cooperative game theory for grid service pricing, and convolutional neural network based photography assessment systems. Additional studies address renewable energy optimization, intelligent analytics, smart infrastructure, digital transformation, predictive modeling, and sustainable engineering frameworks. The issue demonstrates how the integration of computational intelligence, engineering methodologies, and multidisciplinary scientific approaches supports the development of adaptive, scalable, intelligent, and sustainable technological solutions for modern industrial and societal challenges.
Download Complete Issue
Front Cover
Editorial Board
Editorial
Table of Contents
Generative Artificial Intelligence and Prompt Engineering: A Comprehensive Guide to Models, Methods, and Best Practices
This article enhances discussions on Generative Artificial Intelligence (GenAI) and prompt engineering by exploring critical pitfalls and industry-specific advantages. It begins with a foundational overview of AI evolution, emphasizing how generative models such as GANs, VAEs, and Transformers have revolutionized language processing, image generation, and drug discovery. Prompt engineering is highlighted as a key methodology…
Read More Machine Learning Methods for University Student Performance Prediction in Basic Skills based on Psychometric Profile
Ensuring the quality of higher education in Brazil presents a complex challenge, intensified by factors that directly affect students’ academic performance. The pervasive influence of social media and the overconsumption of superficial digital content undermine students’ ability to engage in deep comprehension, critical thinking, and the practical application of knowledge. Furthermore, inadequate preparation during the…
Read More The Impact of Digitalization on Shipbuilding as Measured by Artificial Intelligence (AI) Maturity Models: a Systematic Review
Artificial Intelligence (AI) is reshaping the global shipbuilding sector, yet existing maturity models fail to capture the domain-specific complexities of this capital-intensive industry. This study reviews over 50 AI maturity models and introduces a specialized framework tailored for shipbuilding. The proposed model outlines four progressive stages—Beginner, Innovation, Integration, and Expert—across eight key dimensions: culture, resilience,…
Read More Cooperative Game Theory for Grid Service Pricing: A Utility-Centric Approach
This study presents a novel alternative to traditional Net Energy Metering (NEM) by proposing a set of innovative pricing schemes for solar customers participating in utility-led grid service programs through the aggregation of Distributed Energy Resources (DERs). Grounded in cooperative game theory, the proposed framework facilitates equitable and efficient value allocation among key stakeholders, namely…
Read More AI-Based Photography Assessment System using Convolutional Neural Networks
Providing timely and meaningful feedback in photography education is challenging, particularly in large classes where manual assessment can delay skill development. This paper presents M-Stock, an AI-based automated photo evaluation system that uses Convolutional Neural Networks (CNNs) to assess student photography assignments on web browser. M-Stock evaluates both technical aspects (such as lighting, composition, and…
Read More Explainable AI and Active Learning for Photovoltaic System Fault Detection: A Bibliometric Study and Future Directions
Persistent anomalies in modern photovoltaic (PV) systems present a formidable challenge, impeding optimal power output and system resilience. Artificial Intelligence (AI) has surfaced as a game-changing solution, yet existing research has merely scratched the surface of solar panel prognosis, leaving a critical void in leveraging AI’s explainable nature and active learning capabilities. This pioneering study…
Read More