Lei Wang^1,2, Menghan Wei², Ziwei Huangfu³, Shunjie Zhu², Xuejian Ge^1,*, Zhengquan Li⁴

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-32, 2026, DOI:10.32604/cmc.2025.071295 - 09 December 2025

Abstract Iterative Learning Control (ILC) provides an effective framework for optimizing repetitive tasks, making it particularly suitable for high-precision applications in both precision manufacturing and intelligent transportation systems (ITS). This paper presents a systematic review of ILC’s developmental progress, current methodologies, and practical implementations across these two critical domains. The review first analyzes the key technical challenges encountered when integrating ILC into precision manufacturing workflows. Through case studies, it evaluates demonstrated improvements in positioning accuracy, surface finish quality, and production throughput. Furthermore, the study examines ILC’s applications in ITS, with particular focus on vehicular motion control More >

Qun Song¹, Chao Gao¹, Han Wu¹, Zhiheng Rao¹, Huafeng Qin^1,*, Simon Fong^1,2,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-49, 2026, DOI:10.32604/cmc.2025.070918 - 09 December 2025

Abstract Metaheuristic algorithms, renowned for strong global search capabilities, are effective tools for solving complex optimization problems and show substantial potential in e-Health applications. This review provides a systematic overview of recent advancements in metaheuristic algorithms and highlights their applications in e-Health. We selected representative algorithms published between 2019 and 2024, and quantified their influence using an entropy-weighted method based on journal impact factors and citation counts. CThe Harris Hawks Optimizer (HHO) demonstrated the highest early citation impact. The study also examined applications in disease prediction models, clinical decision support, and intelligent health monitoring. Notably, the More >

Zhijie Lin¹, Chao Yang^1,2,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-33, 2026, DOI:10.32604/cmc.2025.070735 - 09 December 2025

Abstract Sustainable aluminum alloys, renowned for their lower energy consumption and carbon emissions, present a critical path towards a circular materials economy. However, their design is fraught with challenges, including complex performance variability due to impurity elements and the time-consuming, cost-prohibitive nature of traditional trial-and-error methods. The high-dimensional parameter space in processing optimization and the reliance on human expertise for quality control further complicate their development. This paper provides a comprehensive review of Artificial Intelligence (AI) techniques applied to sustainable aluminum alloy design, analyzing their methodologies and identifying key challenges and optimization strategies. We review how… More >

Ahmed Abdel-Wahab¹, Mohammad Alkhatib^2,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-34, 2026, DOI:10.32604/cmc.2025.070010 - 09 December 2025

Abstract Deepfake is a sort of fake media made by advanced AI methods like Generative Adversarial Networks (GANs). Deepfake technology has many useful uses in education and entertainment, but it also raises a lot of ethical, social, and security issues, such as identity theft, the dissemination of false information, and privacy violations. This study seeks to provide a comprehensive analysis of several methods for identifying and circumventing Deepfakes, with a particular focus on image-based Deepfakes. There are three main types of detection methods: classical, machine learning (ML) and deep learning (DL)-based, and hybrid methods. There are… More >

Ameer Hamza, Robertas Damaševičius^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-41, 2026, DOI:10.32604/cmc.2025.069721 - 10 November 2025

Abstract This systematic review aims to comprehensively examine and compare deep learning methods for brain tumor segmentation and classification using MRI and other imaging modalities, focusing on recent trends from 2022 to 2025. The primary objective is to evaluate methodological advancements, model performance, dataset usage, and existing challenges in developing clinically robust AI systems. We included peer-reviewed journal articles and high-impact conference papers published between 2022 and 2025, written in English, that proposed or evaluated deep learning methods for brain tumor segmentation and/or classification. Excluded were non-open-access publications, books, and non-English articles. A structured search was… More >

Maryan Rizinski^1,2,*, Dimitar Trajanov^1,2

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-34, 2026, DOI:10.32604/cmc.2025.069678 - 10 November 2025

Abstract Artificial intelligence (AI) is reshaping financial systems and services, as intelligent AI agents increasingly form the foundation of autonomous, goal-driven systems capable of reasoning, learning, and action. This review synthesizes recent research and developments in the application of AI agents across core financial domains. Specifically, it covers the deployment of agent-based AI in algorithmic trading, fraud detection, credit risk assessment, robo-advisory, and regulatory compliance (RegTech). The review focuses on advanced agent-based methodologies, including reinforcement learning, multi-agent systems, and autonomous decision-making frameworks, particularly those leveraging large language models (LLMs), contrasting these with traditional AI or purely… More >

Ravita Chahar, Ashutosh Kumar Dubey^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-65, 2026, DOI:10.32604/cmc.2025.066990 - 10 November 2025

Abstract Objective: The increasing global prevalence of mental health disorders highlights the urgent need for the development of innovative diagnostic methods. Conditions such as anxiety, depression, stress, bipolar disorder (BD), and autism spectrum disorder (ASD) frequently arise from the complex interplay of demographic, biological, and socioeconomic factors, resulting in aggravated symptoms. This review investigates machine intelligence approaches for the early detection and prediction of mental health conditions. Methods: The preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework was employed to conduct a systematic review and analysis covering the period 2018 to 2025. The potential… More >

Kyung-Hyun Suh^1,*, Sung-Jin Chung¹, Goo-Churl Jeong¹, Kunho Lee¹, Ji-Hyun Ryu²

International Journal of Mental Health Promotion, Vol.27, No.12, pp. 1829-1845, 2025, DOI:10.32604/ijmhp.2025.071879 - 31 December 2025

Abstract Background: Adolescents increasingly engage with social media for connection, self-expression, and identity exploration. This growing digital engagement has raised concerns about its potential risks and mental health implications. Methods: This narrative review examines literature on adolescent social media use by exploring underlying motivations, risk and protective factors across personal, environmental, and digital domains, with a focus on mental health outcomes. Results: Individual vulnerabilities—such as low self-esteem, impulsivity, and poor sleep—interact with contextual factors like peer pressure and family conflict to elevate risks. Digital environments shaped by algorithmic feeds, feedback mechanisms, and curated content promote social comparison and More >

Mohammed Majid Abed^1,2,*, Murat Aydin¹, Esma Yiğider¹, Melek Ekinci³, Ertan Yildirim³

Phyton-International Journal of Experimental Botany, Vol.94, No.12, pp. 3845-3860, 2025, DOI:10.32604/phyton.2025.073163 - 29 December 2025

Abstract Plants are continuously exposed to abiotic and biotic stresses that threaten their growth, reproduction, and survival. Adaptation to these stresses requires complex regulatory networks that coordinate physiological, molecular, and ecological responses. However, such adaptation often incurs significant costs, including reduced growth, yield penalties, and altered ecological interactions. This review systematically synthesizes recent advances published between 2018 and 2025, following PRISMA criteria, on plant responses to abiotic and biotic stressors, with an emphasis on the trade-offs between adaptation and productivity. It also highlights major discrepancies in the literature and discusses strategies for enhancing plant stress tolerance More >

John Hordines¹, Shirley Ge², Dima Raskolnikov¹, Alexander C. Small¹, Kara L. Watts^1,*

Canadian Journal of Urology, Vol.32, No.6, pp. 551-560, 2025, DOI:10.32604/cju.2025.065988 - 30 December 2025

Abstract Background: The healthcare industry contributes nearly 5% of worldwide carbon emissions. In an effort to mitigate this impact, urology practices can take steps to reduce their carbon footprints. We conducted a systematic review which aimed to summarise the current literature on the environmental impact of urologic-related care. Methods: A systematic literature review evaluating the impact of urologic procedures, telehealth and conferences/interviews was conducted on PubMed and Cochrane databases using a Boolean search strategy and the following search terms: urology, planetary health, environmental impact, carbon emissions, carbon footprint, and waste. Full-text articles published in English were… More >