Nuh Azginoglu^*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2026.075834 - 29 January 2026

Abstract Breast cancer screening programs rely heavily on mammography for early detection; however, diagnostic performance is strongly affected by inter-reader variability, breast density, and the limitations of conventional computer-aided detection systems. Recent advances in deep learning have enabled more robust and scalable solutions for large-scale screening, yet a systematic comparison of modern object detection architectures on nationally representative datasets remains limited. This study presents a comprehensive quantitative comparison of prominent deep learning–based object detection architectures for Artificial Intelligence-assisted mammography analysis using the MammosighTR dataset, developed within the Turkish National Breast Cancer Screening Program. The dataset comprises… More >

Huda Talib Najm^1,2, Ahmed Sabah Al-Araji³, Nur Syazreen Ahmad^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.071837 - 29 January 2026

Abstract Mobile service robots (MSRs) in hospital environments require precise and robust trajectory tracking to ensure reliable operation under dynamic conditions, including model uncertainties and external disturbances. This study presents a cognitive control strategy that integrates a Numerical Feedforward Inverse Dynamic Controller (NFIDC) with a Feedback Radial Basis Function Neural Network (FRBFNN). The robot’s mechanical structure was designed in SolidWorks 2022 SP2.0 and validated under operational loads using finite element analysis in ANSYS 2022 R1. The NFIDC-FRBFNN framework merges proactive inverse dynamic compensation with adaptive neural learning to achieve smooth torque responses and accurate motion control.… More >

Williams Kyei^*, Chunyong Yin, Kelvin Amos Nicodemas, Khagendra Darlami

Journal on Artificial Intelligence, Vol.8, pp. 19-38, 2026, DOI:10.32604/jai.2026.074988 - 20 January 2026

Abstract The COVID-19 pandemic has underscored the need for rapid and accurate diagnostic tools to differentiate respiratory infections from normal cases using chest X-rays (CXRs). Manual interpretation of CXRs is time-consuming and prone to errors, particularly in distinguishing COVID-19 from viral pneumonia. This research addresses these challenges by proposing a customized EfficientNet-B0 model for ternary classification (COVID-19, Viral Pneumonia, Normal) on the COVID-19 Radiography Database. Employing transfer learning with architectural modifications, including a tailored classification head and regularization techniques, the model achieves superior performance. Evaluated via accuracy, F1-score (macro-averaged), AUROC (macro-averaged), precision (macro-averaged), recall (macro-averaged), inference… More >

Faisal Mahmud¹, Fahim Mahmud², Rashedur M. Rahman^1,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073519 - 12 January 2026

Abstract Federated Learning (FL) has become a leading decentralized solution that enables multiple clients to train a model in a collaborative environment without directly sharing raw data, making it suitable for privacy-sensitive applications such as healthcare, finance, and smart systems. As the field continues to evolve, the research field has become more complex and scattered, covering different system designs, training methods, and privacy techniques. This survey is organized around the three core challenges: how the data is distributed, how models are synchronized, and how to defend against attacks. It provides a structured and up-to-date review of… More >

Lifu He¹, Zhongchu Huang¹, Haidong Shao^2,*, Zhangbo Hu¹, Yuting Wang¹, Jie Mei¹, Xiaofei Zhang³

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073227 - 12 January 2026

Abstract Deep learning-based wind turbine blade fault diagnosis has been widely applied due to its advantages in end-to-end feature extraction. However, several challenges remain. First, signal noise collected during blade operation masks fault features, severely impairing the fault diagnosis performance of deep learning models. Second, current blade fault diagnosis often relies on single-sensor data, resulting in limited monitoring dimensions and ability to comprehensively capture complex fault states. To address these issues, a multi-sensor fusion-based wind turbine blade fault diagnosis method is proposed. Specifically, a CNN-Transformer Coupled Feature Learning Architecture is constructed to enhance the ability to More >

Samira Rastbod¹, Mehdi Jahangiri^2,*, Behrang Moradi¹, Haleh Nazari¹

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.070089 - 27 December 2025

Abstract Curtain wall systems have evolved from aesthetic façade elements into multifunctional building envelopes that actively contribute to energy efficiency and climate responsiveness. This review presents a comprehensive examination of curtain walls from an energy-engineering perspective, highlighting their structural typologies (Stick and Unitized), material configurations, and integration with smart technologies such as electrochromic glazing, parametric design algorithms, and Building Management Systems (BMS). The study explores the thermal, acoustic, and solar performance of curtain walls across various climatic zones, supported by comparative analyses and iconic case studies including Apple Park, Burj Khalifa, and Milad Tower. Key challenges—including… More > Graphic Abstract

Mo Hou^1,2,3,#,*, Bin Xu^4,#, Wen Shang^1,2,3

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-19, 2026, DOI:10.32604/cmc.2025.071282 - 09 December 2025

Abstract Image captioning, a pivotal research area at the intersection of image understanding, artificial intelligence, and linguistics, aims to generate natural language descriptions for images. This paper proposes an efficient image captioning model named Mob-IMWTC, which integrates improved wavelet convolution (IMWTC) with an enhanced MobileNet V3 architecture. The enhanced MobileNet V3 integrates a transformer encoder as its encoding module and a transformer decoder as its decoding module. This innovative neural network significantly reduces the memory space required and model training time, while maintaining a high level of accuracy in generating image descriptions. IMWTC facilitates large receptive… More >

Sabina-Cristiana Necula^*, Napoleon-Alexandru Sireteanu

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-34, 2026, DOI:10.32604/cmc.2025.070861 - 09 December 2025

Abstract Modern business information systems face significant challenges in managing heterogeneous data sources, integrating disparate systems, and providing real-time decision support in complex enterprise environments. Contemporary enterprises typically operate 200+ interconnected systems, with research indicating that 52% of organizations manage three or more enterprise content management systems, creating information silos that reduce operational efficiency by up to 35%. While attention mechanisms have demonstrated remarkable success in natural language processing and computer vision, their systematic application to business information systems remains largely unexplored. This paper presents the theoretical foundation for a Hierarchical Attention-Based Business Information System (HABIS)… 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 >

Kunpeng Zhou¹, Tao Wu^1,*, Jia Zhang²

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-27, 2026, DOI:10.32604/cmc.2025.068160 - 10 November 2025

Abstract Over the past few years, video live streaming has gained immense popularity as a leading internet application. In current solutions offered by cloud service providers, the Group of Pictures (GOP) length of the video source often significantly impacts end-to-end (E2E) latency. However, designing an optimized GOP structure to reduce this effect remains a significant challenge. This paper presents two key contributions. First, it explores how the GOP length at the video source influences E2E latency in mainstream cloud streaming services. Experimental results reveal that the mean E2E latency increases linearly with longer GOP lengths. Second, More >