https://publications.eai.eu/index.php/dtip <p>Digital transformation has given rise to a series of new innovative production processes, integration of supply chains for decision making, performance evaluation, optimization and adaptation, as well as greater complexity in the development of innovative products, simulation of complex behaviors and development of sophisticated equipment, on using advanced techniques and tools, as machine learning, artificial intelligence, Big Data And Analytics, Autonomous Robots, Industrial Internet Of Things (IIoT), Design of Cyber-Physical Production Systems (CPPS), Simulation/ Digital Twin, e-Maintenance, Augmented Reality, Additive Manufacturing and Systems Interoperability. All the papers developed on the mentioned above topics are welcome to this journal.</p> European Alliance for Innovation (EAI) en-US EAI Endorsed Transactions on Digital Transformation of Industrial Processes <p>This is an open access article distributed under the terms of the <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a>, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.</p> https://publications.eai.eu/index.php/dtip/article/view/8741 <p>INTRODUCTION: Population-based algorithms are popular stochastic algorithms used for solving optimization problems. Grey Wolf Optimizer (GWO) proposed in 2014 is one of the most studied algorithms in the past decade. Population-based two-phase trigonometric AB (TP-AB) is a recently proposed algorithm for handling optimization problems.</p><p>OBJECTIVES: The objective of this work is to propose one new hybrid algorithm combining the strengths of two better performing algorithms in two different phases. The performance is analysed using popular benchmarks and the results are compared with a few popular algorithms available in the literature.</p><p>METHODS: One new two-phase hybrid algorithm is designed by taking GWO in its first phase and the second phase of the TP-AB algorithm in the second phase. In the second phase, the Levy Strategy is introduced which was not in the original TP-AB algorithm.</p><p>RESULTS: The performance of the new hybrid GWO:TP-AB algorithm is analysed using 23 classic mathematical functions, 10 numbers of the CEC2019 dataset and 18 real-world engineering problems In addition, to demonstrate its capability to handle higher dimension problems, 13 scalable problems are solved. These include unimodal and multimodal instances with dimensions 30, 100, 500 and 1000.</p><p>CONCLUSION: The results demonstrate the better performance of the GWO:TP-AB algorithm when compared to several optimization algorithms of recent times.</p> Baskar A Anthony Xavior Michael Copyright (c) 2025 Baskar A, Anthony Xavior Michael https://creativecommons.org/licenses/by-nc-sa/4.0 2025-05-20 2025-05-20 1 2 https://publications.eai.eu/index.php/dtip/article/view/9052 <p>In the last 10–15 years, significant investments have been made in the integration of modern technologies into the educational process. These investments have been driven by the rapid development of the internet and information technologies, as well as global challenges such as the COVID-19 pandemic, which necessitated a shift toward online learning. The primary objective of these efforts is to improve the efficiencntialy of the educational process by enabling students to develop essential skills, such as problem-solving, teamwork, and analytical thinking, all supported by technology.</p><p>When discussing modern technologies, special attention must be given to Artificial Intelligence (AI) and its subfield, Generative Artificial Intelligence (GAI), which are increasingly integrated into the educational environment. In this context, Generative Artificial Intelligence has the potential to significantly reshape teaching and learning by offering personalized learning experiences, dynamic instructional content, and tools that foster creative thinking. For educators, GAI offers new possibilities for interactive teaching, tracking student achievements, and analyzing academic performance.</p><p>These tools, designed for both students and teachers, are easily accessible, intuitive to use, and constantly evolving. This paper presents a theoretical review of the implementation of GAI in education and examines how its application influences the work of students and teachers. Furthermore, the paper will explain the potential of GAI to enhance digital literacy, analytical thinking, and adaptive learning strategies necessary for competitiveness in the 21st-century labor market.</p> Vladan Čolić Enes Sukić Copyright (c) 2025 Vladan Čolić, Enes Sukić https://creativecommons.org/licenses/by-nc-sa/4.0 2025-05-20 2025-05-20 1 2