Tanvir Fatima Naik Bukht^1,2, Yanfeng Wu¹, Nouf Abdullah Almujally³, Shuoa S. AItarbi⁴, Hameedur Rahman², Ahmad Jalal^2,5,*, Hui Liu^1,6,7,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4013-4036, 2025, DOI:10.32604/cmes.2025.071850 - 23 December 2025

Abstract Human activity recognition (HAR) is crucial in fields like robotics, surveillance, and healthcare, enabling systems to understand and respond to human actions. Current models often struggle with complex datasets, making accurate recognition challenging. This study proposes a quantum-integrated Convolutional Neural Network (QI-CNN) to enhance HAR performance. The traditional models demonstrate weak performance in transferring learned knowledge between diverse complex data collections, including D3D-HOI and Sysu 3D HOI. HAR requires better extraction models and techniques that must address current challenges to achieve improved accuracy and scalability. The model aims to enhance HAR task performance by combining… More >

Carlos Rojas¹, Cristian Bravo¹, Carlos Enrique Montenegro-Marín¹, Rubén González-Crespo^2,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5489-5507, 2025, DOI:10.32604/cmc.2025.067394 - 23 October 2025

Abstract The increasing prevalence of violent incidents in public spaces has created an urgent need for intelligent surveillance systems capable of detecting dangerous objects in real time. While traditional video surveillance relies on human monitoring, this approach suffers from limitations such as fatigue and delayed response times. This study addresses these challenges by developing an automated detection system using advanced deep learning techniques to enhance public safety. Our approach leverages state-of-the-art convolutional neural networks (CNNs), specifically You Only Look Once version 4 (YOLOv4) and EfficientDet, for real-time object detection. The system was trained on a comprehensive… More >

Szymon Piątek¹, Anna Dańska-Bidzińska^2,*, Paweł Derlatka², Bartosz Szymanowski³, Renata Duchnowska³, Aleksandra Zielińska⁴, Natalia Sawicka⁴, Aleksander Gorzeń⁵, Wojciech Michalski⁶, Mariusz Bidziński¹

Oncology Research, Vol.33, No.11, pp. 3405-3416, 2025, DOI:10.32604/or.2025.068609 - 22 October 2025

Abstract Objectives: Monitoring of Cancer Antigen 125 (CA125) during ovarian cancer (OC) maintenance treatment with poly(ADP-ribose) polymerase inhibitors (PARPis) may be insufficient when using Gynecologic Cancer Intergroup (GCIG) biochemical progression criteria. This study aimed to evaluate the usefulness of CA125 monitoring in detecting OC recurrence during PARPis maintenance treatment. Methods: This multicenter retrospective cohort study included patients with primary OC who achieved complete or partial response after first-line platinum-based chemotherapy followed by PARPis maintenance treatment. Progression was defined using Response Evaluation Criteria in Solid Tumors (RECIST) and GCIG biochemical criteria. New biochemical progression definitions, based on… More >

Lakshmi Alekhya Jandhyam^1,*, Ragupathy Rengaswamy¹, Narayana Satyala²

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3679-3714, 2025, DOI:10.32604/cmes.2025.068941 - 30 September 2025

Abstract Human Activity Recognition (HAR) has become increasingly critical in civic surveillance, medical care monitoring, and institutional protection. Current deep learning-based approaches often suffer from excessive computational complexity, limited generalizability under varying conditions, and compromised real-time performance. To counter these, this paper introduces an Active Learning-aided Heuristic Deep Spatio-Textural Ensemble Learning (ALH-DSEL) framework. The model initially identifies keyframes from the surveillance videos with a Multi-Constraint Active Learning (MCAL) approach, with features extracted from DenseNet121. The frames are then segmented employing an optimized Fuzzy C-Means clustering algorithm with Firefly to identify areas of interest. A deep ensemble More >

Xinyao Huang^1,#, Renjun Gu^2,3,#, Ziyun Li^4,*, Fangyu Wang^3,*

Oncology Research, Vol.33, No.10, pp. 2857-2902, 2025, DOI:10.32604/or.2025.066805 - 26 September 2025

Abstract Cold tumors, defined by insufficient immune cell infiltration and a highly immunosuppressive tumor microenvironment (TME), exhibit limited responsiveness to conventional immunotherapies. This review systematically summarizes the mechanisms of immune evasion and the therapeutic strategies for cold tumors as revealed by multi-omics technologies. By integrating genomic, transcriptomic, proteomic, metabolomic, and spatial multi-omics data, the review elucidates key immune evasion mechanisms, including activation of the WNT/β-catenin pathway, transforming growth factor-β (TGF-β)–mediated immunosuppression, metabolic reprogramming (e.g., lactate accumulation), and aberrant expression of immune checkpoint molecules. Furthermore, this review proposes multi-dimensional therapeutic strategies, such as targeting immunosuppressive pathways (e.g.,… More > Graphic Abstract

Liang Yu^1,#, Mingrui Zhao^1,#, Jinhui Liu², Yuqin Yang¹, Lin Zhang², Wenjun Cheng^2,*

Oncology Research, Vol.33, No.10, pp. 3007-3022, 2025, DOI:10.32604/or.2025.065137 - 26 September 2025

Abstract Background: The benefit of adjuvant chemotherapy for stage I ovarian endometrioid carcinoma (OEC) remains controversial. Hence, the study sought to explore its value in stage I OEC patients. Methods: Stage I OEC patients (1988–2018) were identified from the Surveillance, Epidemiology, and End Results (SEER) database. Multivariate Cox analysis was used to control confounders. Logistic regression was used to explore factors associated with adjuvant chemotherapy. Cox regression analysis and Kaplan-Meier curves were used to assess the survival benefits. Single-center clinical data and meta-analysis following PRISMA guidelines provided external validation. Result: Adjuvant chemotherapy correlated with improved survival (Hazard… More >

Zeeshan Ali¹, Jihoon Moon², Saira Gillani³, Sitara Afzal⁴, Maryam Bukhari⁵, Seungmin Rho^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2263-2286, 2025, DOI:10.32604/cmes.2025.067743 - 31 August 2025

Abstract Surveillance systems can take various forms, but gait-based surveillance is emerging as a powerful approach due to its ability to identify individuals without requiring their cooperation. In the existing studies, several approaches have been suggested for gait recognition; nevertheless, the performance of existing systems is often degraded in real-world conditions due to covariate factors such as occlusions, clothing changes, walking speed, and varying camera viewpoints. Furthermore, most existing research focuses on single-person gait recognition; however, counting, tracking, detecting, and recognizing individuals in dual-subject settings with occlusions remains a challenging task. Therefore, this research proposed a… More >

Irfan Abbas, Robertas Damaševičius^*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 483-502, 2025, DOI:10.32604/cmc.2025.066578 - 29 August 2025

Abstract Forests are vital ecosystems that play a crucial role in sustaining life on Earth and supporting human well-being. Traditional forest mapping and monitoring methods are often costly and limited in scope, necessitating the adoption of advanced, automated approaches for improved forest conservation and management. This study explores the application of deep learning-based object detection techniques for individual tree detection in RGB satellite imagery. A dataset of 3157 images was collected and divided into training (2528), validation (495), and testing (134) sets. To enhance model robustness and generalization, data augmentation was applied to the training part… More >

Nayeemul Islam Nayeem¹, Shirin Mahbuba¹, Sanjida Islam Disha¹, Md Rifat Hossain Buiyan¹, Shakila Rahman^1,*, M. Abdullah-Al-Wadud², Jia Uddin^3,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1541-1557, 2025, DOI:10.32604/cmc.2025.065061 - 29 August 2025

Abstract Human activity recognition is a significant area of research in artificial intelligence for surveillance, healthcare, sports, and human-computer interaction applications. The article benchmarks the performance of You Only Look Once version 11-based (YOLOv11-based) architecture for multi-class human activity recognition. The article benchmarks the performance of You Only Look Once version 11-based (YOLOv11-based) architecture for multi-class human activity recognition. The dataset consists of 14,186 images across 19 activity classes, from dynamic activities such as running and swimming to static activities such as sitting and sleeping. Preprocessing included resizing all images to 512 512 pixels, annotating them… More >

Gracia Cristina Villodres^1,#,*, Federico Salvador-Pérez², José Joaquín Muros¹, Rocío Vizcaíno-Cuenca^3,4,#

International Journal of Mental Health Promotion, Vol.27, No.7, pp. 877-899, 2025, DOI:10.32604/ijmhp.2025.066137 - 31 July 2025

Abstract Background: University students face significant challenges in maintaining healthy physical activity (PA) and dietary habits, and they often fall short of global health recommendations. Psychological factors such as social physique anxiety, body image concerns, and self-objectification may act as barriers to PA engagement, influencing both mental and physical health. The present study constructed a structural equation model (SEM) to examine the relationship between body image-related psychological factors and resistance to PA in university students from southern Spain. Methods: A cross-sectional and correlational study was conducted with 519 university students (74% females, 26% males; Mean age… More >