Changjun Zhou¹, Dongfang Chen¹, Chenyang Shi¹, Taiyong Li^2,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073662 - 12 January 2026

Abstract With the rapid development of smart manufacturing, intelligent safety monitoring in industrial workshops has become increasingly important. To address the challenges of complex backgrounds, target scale variation, and excessive model parameters in worker violation detection, this study proposes ADCP-YOLO, an enhanced lightweight model based on YOLOv8. Here, “ADCP” represents four key improvements: Alterable Kernel Convolution (AKConv), Dilated-Wise Residual (DWR) module, Channel Reconstruction Global Attention Mechanism (CRGAM), and Powerful-IoU loss. These components collaboratively enhance feature extraction, multi-scale perception, and localization accuracy while effectively reducing model complexity and computational cost. Experimental results show that ADCP-YOLO achieves a More >

Jing Shan¹, Catherine King^2,3, Harunor Rashid^3,4, Veysel Kayser^1,*

Oncology Research, Vol.34, No.1, 2026, DOI:10.32604/or.2025.070851 - 30 December 2025

Abstract Objectives: B-cell maturation antigen (BCMA)-targeted antibody–drug conjugates (ADCs) have emerged as promising therapies for relapsed/refractory multiple myeloma (RRMM), but the overall efficacy and safety profile is unclear. This study aimed to synthesize the available evidence on the safety and efficacy of BCMA-ADCs in development for RRMM. Methods: A systematic search was conducted using six bibliographic databases and ClinicalTrials.gov up to November 2024. Studies were eligible if they were human clinical trials or animal studies evaluating BCMA-ADCs and reported efficacy and safety outcomes. Data extraction and quality assessments were conducted using validated tools, including ROBINS-I… More >

Xizhou Zhang^1,#, Zetao Zhang^1,#, Jianguang Lin², Jiarong Yi³, Xuxiazi Zou³, Jikun Feng³, Guangsheng Huang¹, Bingfeng Chen¹, Junxi Long¹, Fengjia Wu³, Feng Ye^3,*, Haoming Wu^1,*

Oncology Research, Vol.33, No.9, pp. 2529-2547, 2025, DOI:10.32604/or.2025.065029 - 28 August 2025

Abstract Background: Breast cancer remains a leading cause of morbidity and mortality among women worldwide, with significant geographic disparities in its impact. While human epidermal growth factor receptor 2 (HER2)-targeted therapies, such as trastuzumab, have improved outcomes for HER2-positive breast cancer, challenges like therapy resistance persist, highlighting the need for novel treatments. Recent developments in antibody-drug conjugates (ADCs), particularly disitamab vedotin (RC48), show promising efficacy in targeting both HER2-positive and HER2-low expression tumors, warranting further investigation through real-world studies to assess its broader clinical applicability. Method: This retrospective, multicenter observational study evaluated the real-world efficacy and… More >

Jasem Almotiri^*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2109-2142, 2025, DOI:10.32604/cmc.2025.062923 - 16 April 2025

Abstract The evolving field of Alzheimer’s disease (AD) diagnosis has greatly benefited from deep learning models for analyzing brain magnetic resonance (MR) images. This study introduces Dynamic GradNet, a novel deep learning model designed to increase diagnostic accuracy and interpretability for multiclass AD classification. Initially, four state-of-the-art convolutional neural network (CNN) architectures, the self-regulated network (RegNet), residual network (ResNet), densely connected convolutional network (DenseNet), and efficient network (EfficientNet), were comprehensively compared via a unified preprocessing pipeline to ensure a fair evaluation. Among these models, EfficientNet consistently demonstrated superior performance in terms of accuracy, precision, recall, and… More >

Eunsam Kim¹, Choonhwa Lee^2,*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2555-2581, 2025, DOI:10.32604/cmc.2025.059682 - 17 February 2025

Abstract Personal video recorders (PVRs) have altered the way users consume television (TV) content by allowing users to record programs and watch them at their convenience, overcoming the constraints of live broadcasting. However, standalone PVRs are limited by their individual storage capacities, restricting the number of programs they can store. While online catch-up TV services such as Hulu and Netflix mitigate this limitation by offering on-demand access to broadcast programs shortly after their initial broadcast, they require substantial storage and network resources, leading to significant infrastructural costs for service providers. To address these challenges, we propose… More >

Ji-Yuan Ding¹, Wang-Su Jeon², Sang-Yong Rhee^2,*, Chang-Man Zou^1,3

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3989-4014, 2024, DOI:10.32604/cmc.2024.057473 - 19 December 2024

Abstract In complex agricultural environments, cucumber disease identification is confronted with challenges like symptom diversity, environmental interference, and poor detection accuracy. This paper presents the DM-YOLO model, which is an enhanced version of the YOLOv8 framework designed to enhance detection accuracy for cucumber diseases. Traditional detection models have a tough time identifying small-scale and overlapping symptoms, especially when critical features are obscured by lighting variations, occlusion, and background noise. The proposed DM-YOLO model combines three innovative modules to enhance detection performance in a collective way. First, the MultiCat module employs a multi-scale feature processing strategy with… More >

Yumin Jo¹, Jongho Paik^2,*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4153-4176, 2024, DOI:10.32604/cmc.2024.047046 - 26 March 2024

Abstract Broadcasting gateway equipment generally uses a method of simply switching to a spare input stream when a failure occurs in a main input stream. However, when the transmission environment is unstable, problems such as reduction in the lifespan of equipment due to frequent switching and interruption, delay, and stoppage of services may occur. Therefore, applying a machine learning (ML) method, which is possible to automatically judge and classify network-related service anomaly, and switch multi-input signals without dropping or changing signals by predicting or quickly determining the time of error occurrence for smooth stream switching when… More >

Yanjun Yu¹, Lei Yu^1,*, Huiqi Wang², Haodong Zheng¹, Yi Deng¹

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2225-2243, 2024, DOI:10.32604/cmc.2024.047641 - 27 February 2024

Abstract Bone age assessment (BAA) helps doctors determine how a child’s bones grow and develop in clinical medicine. Traditional BAA methods rely on clinician expertise, leading to time-consuming predictions and inaccurate results. Most deep learning-based BAA methods feed the extracted critical points of images into the network by providing additional annotations. This operation is costly and subjective. To address these problems, we propose a multi-scale attentional densely connected network (MSADCN) in this paper. MSADCN constructs a multi-scale dense connectivity mechanism, which can avoid overfitting, obtain the local features effectively and prevent gradient vanishing even in limited… More >

Jungyeon Lee¹, Malik Summair Asghar^1,2, HyungWon Kim^1,*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4357-4375, 2023, DOI:10.32604/cmc.2023.031372 - 31 March 2023

Abstract As deep learning techniques such as Convolutional Neural Networks (CNNs) are widely adopted, the complexity of CNNs is rapidly increasing due to the growing demand for CNN accelerator system-on-chip (SoC). Although conventional CNN accelerators can reduce the computational time of learning and inference tasks, they tend to occupy large chip areas due to many multiply-and-accumulate (MAC) operators when implemented in complex digital circuits, incurring excessive power consumption. To overcome these drawbacks, this work implements an analog convolutional filter consisting of an analog multiply-and-accumulate arithmetic circuit along with an analog-to-digital converter (ADC). This paper introduces the… More >

Moeen Tayyab¹, Ayyaz Hussain^2,*, Usama Mir³, M. Aqeel Iqbal⁴, Muhammad Haneef⁵

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6177-6193, 2023, DOI:10.32604/cmc.2023.034669 - 28 December 2022

Abstract The news ticker is a common feature of many different news networks that display headlines and other information. News ticker recognition applications are highly valuable in e-business and news surveillance for media regulatory authorities. In this paper, we focus on the automatic Arabic Ticker Recognition system for the Al-Ekhbariya news channel. The primary emphasis of this research is on ticker recognition methods and storage schemes. To that end, the research is aimed at character-wise explicit segmentation using a semantic segmentation technique and words identification method. The proposed learning architecture considers the grouping of homogeneous-shaped classes. More >