Bo Cao^1,*, Qinghua Xing², Longyue Li², Huaixi Xing¹, Zhanfu Song¹

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1251-1275, 2024, DOI:10.32604/cmc.2024.052842

Abstract As a core part of battlefield situational awareness, air target intention recognition plays an important role in modern air operations. Aiming at the problems of insufficient feature extraction and misclassification in intention recognition, this paper designs an air target intention recognition method (KGTLIR) based on Knowledge Graph and Deep Learning. Firstly, the intention recognition model based on Deep Learning is constructed to mine the temporal relationship of intention features using dilated causal convolution and the spatial relationship of intention features using a graph attention mechanism. Meanwhile, the accuracy, recall, and F1-score after iteration are introduced More >

Bingyi Ren^1,4, Juwei Zhang^2,3,4,*, Tong Wang^2,4

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1425-1440, 2024, DOI:10.32604/cmc.2024.052667

Abstract Autonomous driving technology has entered a period of rapid development, and traffic sign detection is one of the important tasks. Existing target detection networks are difficult to adapt to scenarios where target sizes are seriously imbalanced, and traffic sign targets are small and have unclear features, which makes detection more difficult. Therefore, we propose a Hybrid Feature Fusion Traffic Sign detection algorithm based on YOLOv7 (HFFT-YOLO). First, a self-attention mechanism is incorporated at the end of the backbone network to calculate feature interactions within scales; Secondly, the cross-scale fusion part of the neck introduces a… More >

Yongguo Li, Yuanrong Wang, Jia Xie^*, Caiyin Xu, Kun Zhang

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 467-486, 2024, DOI:10.32604/cmc.2024.051426

Abstract To address the challenges of missed detections in water surface target detection using solely visual algorithms in unmanned surface vehicle (USV) perception, this paper proposes a method based on the fusion of visual and LiDAR point-cloud projection for water surface target detection. Firstly, the visual recognition component employs an improved YOLOv7 algorithm based on a self-built dataset for the detection of water surface targets. This algorithm modifies the original YOLOv7 architecture to a Slim-Neck structure, addressing the problem of excessive redundant information during feature extraction in the original YOLOv7 network model. Simultaneously, this modification simplifies… More >

Oncology Research Editorial Office

Oncology Research, Vol.32, No.8, pp. 1379-1379, 2024, DOI:10.32604/or.2024.055034

Abstract This article has no abstract. More >

Oncology Research Editorial Office

Oncology Research, Vol.32, No.8, pp. 1371-1371, 2024, DOI:10.32604/or.2024.055030

Abstract This article has no abstract. More >

HUI SUN, MASANORI KAWANO^*, TATSUYA IWASAKI, ICHIRO ITONAGA, YUTA KUBOTA, HIROSHI TSUMURA, KAZUHIRO TANAKA

Oncology Research, Vol.32, No.8, pp. 1369-1370, 2024, DOI:10.32604/or.2024.052652

Abstract This article has no abstract. More >

GERHARD HAMILTON^*, MARIE-THERESE EGGERSTORFER, SANDRA STICKLER

Oncology Research, Vol.32, No.8, pp. 1257-1264, 2024, DOI:10.32604/or.2024.051653

Abstract The Kirsten rat sarcoma virus—son of sevenless 1 (KRAS-SOS1) axis drives tumor growth preferentially in pancreatic, colon, and lung cancer. Now, KRAS G12C mutated tumors can be successfully treated with inhibitors that covalently block the cysteine of the switch II binding pocket of KRAS. However, the range of other KRAS mutations is not amenable to treatment and the G12C-directed agents Sotorasib and Adragrasib show a response rate of only approximately 40%, lasting for a mean period of 8 months. One approach to increase the efficacy of inhibitors is their inclusion into proteolysis-targeting chimeras (PROTACs), which… More > Graphic Abstract

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

HAIQING HU^1,#,*, HAO YANG^2,#, SHUAISHUAI FAN³, XUE JIA³, YING ZHAO³, HONGRUI LI³

Oncology Research, Vol.32, No.8, pp. 1335-1346, 2024, DOI:10.32604/or.2024.044174

Abstract Long non-coding RNAs (lncRNAs) have been implicated in cancer progression and drug resistance development. Moreover, there is evidence that lncRNA HOX transcript antisense intergenic RNA (HOTAIR) is involved in colorectal cancer (CRC) progression. The present study aimed to examine the functional role of lncRNA HOTAIR in conferring radiotherapy resistance in CRC cells, as well as the underlying mechanism. The relative expression levels of HOTAIR were examined in 70 pairs of CRC tumor and para-cancerous tissues, as well as in radiosensitive and radioresistant samples. The correlations between HOTAIR expression levels and clinical features of patients with… More >

Isaac Asare Boateng^1,2,*, Lord Amoah², Isogun Toluwalase Adewale³

Journal of Information Hiding and Privacy Protection, Vol.6, pp. 1-19, 2024, DOI:10.32604/jihpp.2024.051354

Abstract Contrast enhancement in medical images has been vital since the prevalence of image representations in healthcare. In this research, the PRDHMCE (pairwise reversible data hiding for medical images with contrast enhancement) algorithm is proposed as an automatic contrast enhancement (CE) method for medical images based on region of interest (ROI) and non-region of interest (NROI). The PRDHMCE algorithm strategically enhances the ROI after segmentation using histogram stretching and data embedding. An initial histogram evaluation compares histogram bins with their neighbours to select the bin with the maximum pixel count. The selected bin is set as More >