Zhuhong Chu¹, Jianxun Zhang^1,*, Chengdong Wang², Changhui Yang³

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1143-1161, 2024, DOI:10.32604/cmc.2024.052269

Abstract Rail fasteners are a crucial component of the railway transportation safety system. These fasteners, distinguished by their high length-to-width ratio, frequently encounter elevated failure rates, necessitating manual inspection and maintenance. Manual inspection not only consumes time but also poses the risk of potential oversights. With the advancement of deep learning technology in rail fasteners, challenges such as the complex background of rail fasteners and the similarity in their states are addressed. We have proposed an efficient and high-precision rail fastener detection algorithm, named YOLO-O2E (you only look once-O2E). Firstly, we propose the EFOV (Enhanced Field… More >

Pablo Fernández Saura^1,*, José M. Bernabé Murcia¹, Emilio García de la Calera Molina¹, Alejandro Molina Zarca², Jorge Bernal Bernabé¹, Antonio F. Skarmeta Gómez¹

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 163-193, 2024, DOI:10.32604/cmc.2024.051307

Abstract The management of network intelligence in Beyond 5G (B5G) networks encompasses the complex challenges of scalability, dynamicity, interoperability, privacy, and security. These are essential steps towards achieving the realization of truly ubiquitous Artificial Intelligence (AI)-based analytics, empowering seamless integration across the entire Continuum (Edge, Fog, Core, Cloud). This paper introduces a Federated Network Intelligence Orchestration approach aimed at scalable and automated Federated Learning (FL)-based anomaly detection in B5G networks. By leveraging a horizontal Federated learning approach based on the FedAvg aggregation algorithm, which employs a deep autoencoder model trained on non-anomalous traffic samples to recognize… More >

Amna Khatoon¹, Weixing Wang^1,*, Asad Ullah², Limin Li^3,*, Mengfei Wang¹

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 527-546, 2024, DOI:10.32604/cmc.2024.051147

Abstract Integrating Tiny Machine Learning (TinyML) with edge computing in remotely sensed images enhances the capabilities of road anomaly detection on a broader level. Constrained devices efficiently implement a Binary Neural Network (BNN) for road feature extraction, utilizing quantization and compression through a pruning strategy. The modifications resulted in a 28-fold decrease in memory usage and a 25% enhancement in inference speed while only experiencing a 2.5% decrease in accuracy. It showcases its superiority over conventional detection algorithms in different road image scenarios. Although constrained by computer resources and training datasets, our results indicate opportunities for More >

Oncology Research Editorial Office

Oncology Research, Vol.32, No.8, pp. 1387-1387, 2024, DOI:10.32604/or.2024.055039

Abstract This article has no abstract. More >

Oncology Research Editorial Office

Oncology Research, Vol.32, No.8, pp. 1381-1381, 2024, DOI:10.32604/or.2024.055035

Abstract This article has no abstract. More >

SNJEŽANA KAŠTELAN^1,2, ANA DIDOVIĆ PAVIČIĆ³, DARIA PAŠALIĆ⁴, TAMARA NIKUŠEVA-MARTIĆ⁵, SAMIR ČANOVIĆ^3,7, PETRA KOVAČEVIĆ^1,6,*, SUZANA KONJEVODA^3,7

Oncology Research, Vol.32, No.8, pp. 1265-1285, 2024, DOI:10.32604/or.2024.048437

Abstract Uveal and conjunctival melanomas are relatively rare tumors; nonetheless, they pose a significant risk of mortality for a large number of affected individuals. The pathogenesis of melanoma at different sites is very similar, however, the prognosis for patients with ocular melanoma remains unfavourable, primarily due to its distinctive genetic profile and tumor microenvironment. Regardless of considerable advances in understanding the genetic characteristics and biological behaviour, the treatment of uveal and conjunctival melanoma remains a formidable challenge. To enhance the prospect of success, collaborative efforts involving medical professionals and researchers in the fields of ocular biology… More > Graphic Abstract

RUIFANG LI¹, XINRONG NAN^2,*, MING LI^3,*, OMAR RAHHAL³

Oncology Research, Vol.32, No.8, pp. 1323-1334, 2024, DOI:10.32604/or.2024.046965

Abstract Background: Fibroblast activation protein (FAP), a cell surface serine protease, plays roles in tumor invasion and immune regulation. However, there is currently no pan-cancer analysis of FAP. Objective: We aimed to assess the pan-cancer expression profile of FAP, its molecular function, and its potential role in head and neck squamous cell carcinoma (HNSC). Methods: We analyzed gene expression, survival status, immune infiltration, and molecular functional pathways of FAP in The Cancer Genome Atlas (TCGA) and Genotype Tissue Expression (GTEx) tumors. Furthermore, to elucidate the role of FAP in HNSC, we performed proliferation, migration, and invasion assays… More >

MING ZHUANG, XUE ZHANG, LU LI, LIMING WEN, JIAMIN QIN^*

Oncology Research, Vol.32, No.8, pp. 1347-1357, 2024, DOI:10.32604/or.2024.046170

Abstract Hepatocellular carcinoma (HCC) poses a significant threat to human health. Resistance to sorafenib in the chemotherapy of HCC is a common and significant issue that profoundly impacts clinical treatment. While several members of the transmembrane (TMEM) protein family have been implicated in the occurrence and progression of HCC, the association between TMEM39b and HCC remains unexplored. This study revealed a significant overexpression of TMEM39b in HCC, which correlated with a poor prognosis. Subsequent investigation revealed that RAS-selective lethal 3 (RSL3) induced pronounced ferroptosis in HCC, and knocking down the expression of TMEM39b significantly decreased its More >

Brij B. Gupta^1,2,3,*, Akshat Gaurav⁴, Razaz Waheeb Attar⁵, Varsha Arya^6,7, Ahmed Alhomoud⁸, Kwok Tai Chui⁹

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2689-2706, 2024, DOI:10.32604/cmes.2024.050825

Abstract This study introduces a long-short-term memory (LSTM)-based neural network model developed for detecting anomaly events in care-independent smart homes, focusing on the critical application of elderly fall detection. It balances the dataset using the Synthetic Minority Over-sampling Technique (SMOTE), effectively neutralizing bias to address the challenge of unbalanced datasets prevalent in time-series classification tasks. The proposed LSTM model is trained on the enriched dataset, capturing the temporal dependencies essential for anomaly recognition. The model demonstrated a significant improvement in anomaly detection, with an accuracy of 84%. The results, detailed in the comprehensive classification and confusion More >

Weijian Song^1,, Xi Li^1,, Peng Chen^1,*, Juan Chen¹, Jianhua Ren², Yunni Xia^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 3001-3016, 2024, DOI:10.32604/cmes.2024.048563

Abstract With the rapid development of Internet of Things (IoT) technology, IoT systems have been widely applied in healthcare, transportation, home, and other fields. However, with the continuous expansion of the scale and increasing complexity of IoT systems, the stability and security issues of IoT systems have become increasingly prominent. Thus, it is crucial to detect anomalies in the collected IoT time series from various sensors. Recently, deep learning models have been leveraged for IoT anomaly detection. However, owing to the challenges associated with data labeling, most IoT anomaly detection methods resort to unsupervised learning techniques.… More >