The era of artificial intelligence in the healthcare revolution - Elashmawi, W.H.; Sheta, A.; Surani, S. (Innovations in Healthcare in the 21st Century, 2025)
Artificial intelligence (AI) is a distinct area of computer science that enables machines to handle and interpret complex data effectively. In recent years, there has been a dramatic uptick in studies devoted to AI, with many focusing on healthcare and medical research. This article delves deep into the potential of AI in several areas of healthcare, including the diagnosis and treatment of diseases. In recent years, Machine learning (ML) and deep learning (DL) have emerged as the most widely used artificial intelligence technologies in the healthcare industry. Moreover, this research demonstrates the crucial significance of progressing AI technologies, namely generative AI and large language models (LLMs), highlighting their revolutionary influence on healthcare. Finally, we highlight upcoming innovations and offer profound insights into the significant ethical, medical, and technological challenges associated with AI in healthcare. © 2025 Nova Science Publishers, Inc. All rights reserved.
Keywords: Artificial intelligence; Generative AI; Healthcare; Large language models; Machine learning; dept.csc

Optimizing MPPT Control of DC-DC Boost Converters Using Metaheuristic Search Algorithms and Nonlinear PID (N-PID) Controllers - Khellaf, L.; Djellal, A.; Mayache, H.; Sheta, A. (Smart Computing and Control Renewable Energy Systems, 2025)
Photovoltaic systems have proven to be one of the most widely used renewable energies and the best replacement for conventional energy. Yet, their non-linear nature remains a challenge when it comes to extracting maximum power from photovoltaic modules. Therefore, in this work, a nonlinear PID controller has been used to meet the requirements of the photovoltaic system. In addition, to improve system performance and response, metaheuristic search algorithms were introduced into the tuning process of both the NPID controller and conventional PID controller parameters in order to compare them. The use of Artificial Intelligence to fine-tune the controller parameters will enable the optimum values of proportional, integral, derivative and nonlinear gains to be determined as system condition change. Finally, a comparison between the algorithms applied is conducted in terms of efficiency, rise time, settling time and overshoot as well as the overall system stability.
Keywords: Artificial intelligence; Computer science; Control; Control (management); Control engineering; Control theory (sociology); Converters; Electrical engineering; Engineering; Evolutionary Algorithm; Machine Learning; Nonlinear system; PID controller; Physics; Swarm Intelligence Algorithm; Temperature control; dept.csc