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 >

Lucija Galić, Zdenko Lončarić, Miroslav Lisjak^*

Phyton-International Journal of Experimental Botany, Vol.94, No.11, pp. 3297-3318, 2025, DOI:10.32604/phyton.2025.072504 - 01 December 2025

Abstract Phenolic compounds represent a broad and structurally diverse class of plant secondary metabolites with importance for both plant biology and human health. This review provides a comprehensive overview of their biosynthesis, chemical diversity, multifaceted functions in plants, roles in the wider ecosystem, and significance in human nutrition and biotechnology. Primarily synthesized via the phenylpropanoid pathway, these compounds encompass major classes such as lignin, flavonoids, and tannins. Within the plant, they perform critical functions including providing structural support (lignin), defending against biotic stresses (e.g., pathogens, herbivores), mediating ecological interactions (pollination, symbiosis, allelopathy), and protecting against abiotic… More >

Ruikang Ma^1,2, Ziying Huang¹, Zexiu Zhang³, Ruohui Lu⁴, Menghan Li¹, Zhiyi Luo³, Mengni Li⁵, Pengyue Zhang³, Xiaohong Lin³, Guozhuang Zhang^1,*, Linlin Dong^1,*

Phyton-International Journal of Experimental Botany, Vol.94, No.11, pp. 3721-3748, 2025, DOI:10.32604/phyton.2025.072062 - 01 December 2025

Abstract Dendrobium officinale (DO) is a well-recognized medicinal and edible plant with a long history of application in traditional medicinal practices across China and Southeast Asia. Recent studies have demonstrated that DO is abundant in diverse bioactive compounds, including polysaccharides (DOP), flavonoids, alkaloids, and bibenzyls thought to exert a range of pharmacological effects, such as anti-tumor and immunomodulatory effects. However, our comprehensive understanding of two key aspects—pharmacological functions and biosynthetic mechanisms—of DO’s major constituents remains limited, especially when considered within the clinical contexts of traditional use. To address this gap, this study reviews DO’s historical applications, clinical effects, and… More > Graphic Abstract

Istvan Berkes^1,*, Bettina Piko^2,*

International Journal of Mental Health Promotion, Vol.27, No.11, pp. 1627-1643, 2025, DOI:10.32604/ijmhp.2025.071931 - 28 November 2025

Abstract Objectives: Parentification, a role reversal where children assume age-inappropriate duties in the family, is a significant childhood adversity often linked to disrupted developmental trajectories and poor mental health outcomes. Yet the complexity of parentification, influenced by various contextual factors, obscures a comprehensive understanding of its psychological consequences and its mental health aspects. The paper aims to map up-to-date research, synthesize key findings, and identify critical knowledge gaps. Methods: To that end, a systematic search was performed in Scopus, PsycINFO, PubMed, and EBSCO databases, and data was extracted and reviewed by two reviewers. The search yielded 29… More >

Theano Makridou¹, Elena Vlastou², Vasilios Kouloulias³, Efstathios P. Efstathopoulos⁴, Kalliopi Platoni^4,*

Oncology Research, Vol.33, No.12, pp. 3611-3632, 2025, DOI:10.32604/or.2025.069101 - 27 November 2025

Abstract Nanomedicine has evolved significantly over the last decades and expanded its applications in pediatric oncology, which represents a special domain with unique patients and distinct requirements. Τhe need for early cancer diagnosis and more effective and targeted therapies aiming to increase the pediatric patients’ survival rates and minimize the treatment-related side effects to survivors is profound. Nanoparticles (NPs) come as a beacon of hope to provide sensitive cancer diagnostic tools and assist contrast agents’ transport to the malignant tumors. Besides, NPs could be designed to deliver targeted drugs and genes to tumors, minimizing the medicine-related… More >

Hiroshi Yoshida^1,*, Mayumi Kobayashi Kato²

Oncology Research, Vol.33, No.12, pp. 3701-3730, 2025, DOI:10.32604/or.2025.068432 - 27 November 2025

Abstract Ovarian endometrioid carcinoma (OEC) accounts for ~10% of epithelial ovarian cancers and displays broad morphologic diversity that complicates diagnosis and grading. Recent data show that the endometrial cancer molecular taxonomy (DNA polymerase epsilon, catalytic subunit [POLE]-ultramutated, mismatch repair-deficient [MMRd], p53-abnormal, no specific molecular profile [NSMP]) also applies to OEC, and that OEC is enriched for Lynch syndrome–associated tumors, supporting routine MMR testing. We aimed to synthesize contemporary evidence spanning epidemiology, histopathology and immunophenotype, diagnostic pitfalls and differential diagnosis, and to evaluate the clinical utility of The Cancer Genome Atlas (TCGA)-surrogate molecular classification for risk stratification; More >

Farhad Mortezapour Shiri^*, Thinagaran Perumal, Norwati Mustapha, Raihani Mohamed

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1389-1485, 2025, DOI:10.32604/cmes.2025.071858 - 26 November 2025

Abstract Human Activity Recognition (HAR) represents a rapidly advancing research domain, propelled by continuous developments in sensor technologies and the Internet of Things (IoT). Deep learning has become the dominant paradigm in sensor-based HAR systems, offering significant advantages over traditional machine learning methods by eliminating manual feature extraction, enhancing recognition accuracy for complex activities, and enabling the exploitation of unlabeled data through generative models. This paper provides a comprehensive review of recent advancements and emerging trends in deep learning models developed for sensor-based human activity recognition (HAR) systems. We begin with an overview of fundamental HAR… More > Graphic Abstract

Abdullrahman A. Al-Shamma’a¹, Hassan M. Hussein Farh^1,*, Ridwan Taiwo², Al-Wesabi Ibrahim³, Abdulrhman Alshaabani¹, Saad Mekhilef ⁴, Mohamed A. Mohamed^5,6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1303-1347, 2025, DOI:10.32604/cmes.2025.071302 - 26 November 2025

Abstract Optimal sizing and allocation of distributed generators (DGs) have become essential computational challenges in improving the performance, efficiency, and reliability of electrical distribution networks. Despite extensive research, existing approaches often face algorithmic limitations such as slow convergence, premature stagnation in local minima, or suboptimal accuracy in determining optimal DG placement and capacity. This study presents a comprehensive scientometric and systematic review of global research focused on computer-based modelling and algorithmic optimization for renewable DG sizing and placement. It integrates both quantitative and qualitative analyses of the scholarly landscape, mapping influential research domains, co-authorship structures, the More >