Robot-assisted axillary lymph node dissection in patients with node-positive breast cancer: techniques, learning curve, and preliminary results by Zhjie Wu, Zuxiao Chen, Zongyan Li, Xiaoyan Fu, Yunxiang Luo, Qiongyu Hu, Haiyan Li

“…In patients with breast cancer that have positive lymph nodes, we present the initial outcomes of RALND. …”
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Detecting and Analyzing Botnet Nodes via Advanced Graph Representation Learning Tools by Alfredo Cuzzocrea, Abderraouf Hafsaoui, Carmine Gallo

“…In this study, we also present SIR-GN, a structural iterative representation learning methodology for graph nodes. …”
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Machine learning prediction model for lateral lymph node metastasis in rectal cancer by Longchun Dong, Shiyong Du, Hongjie Yang, Hongjie Yang, Hongjie Yang, Hongjie Yang, Xipeng Zhang, Xipeng Zhang, Xipeng Zhang, Xipeng Zhang, Zhichun Zhang, Zhichun Zhang, Zhichun Zhang, Zhichun Zhang, Shuan Geng, Shuan Geng, Shuan Geng, Shuan Geng, Yuanda Zhou, Yuanda Zhou, Yuanda Zhou, Yuanda Zhou, Peng Li, Peng Li, Peng Li, Peng Li, Qingsheng Zeng, Qingsheng Zeng, Qingsheng Zeng, Qingsheng Zeng, Yi Sun, Yi Sun, Yi Sun, Yi Sun, Peishi Jiang

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Transformer optimization with meta learning on pathology images for breast cancer lymph node micrometastasis by Jing Huang, Jingtao Wang, Junhai Shi, Hengli Ni, Shan Xu, Ping Wu, Yuexiang Ren, Lijuan Bian, Chenhan Su, Yuxuan Xu, Xinyu He, Xinjian Chen, Jianming Li

“…However, the limited size of these hidden lesions restricts dataset expansion, presenting a significant challenge for manual examination and conventional deep learning techniques. …”
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Efficient Visual-Aware Fashion Recommendation Using Compressed Node Features and Graph-Based Learning by Umar Subhan Malhi, Junfeng Zhou, Abdur Rasool, Shahbaz Siddeeq

“…Current recommendation systems often struggle to incorporate high-dimensional visual data into graph-based learning models effectively. This limitation presents a substantial opportunity to enhance the precision and effectiveness of fashion recommendations. …”
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Epithelioid sarcoma presenting as recurrent thumb ulcer: A lesson to learn by Lakkavalli Krishnappa Rajeev, Vikas Asati, Suresh Babu Mallekavu, K Govind Babu, G Champka

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Hybrid machine learning-based 3-dimensional UAV node localization for UAV-assisted wireless networks by Workeneh Geleta Negassa, Demissie J. Gelmecha, Ram Sewak Singh, Davinder Singh Rathee

“…This paper presents a hybrid machine-learning framework for optimizing 3-Dimensional (3D) Unmanned Aerial Vehicles (UAV) node localization and resource distribution in UAV-assisted THz 6G networks to ensure efficient coverage in dynamic, high-density environments. …”
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Predicting central lymph node metastasis in papillary thyroid microcarcinoma: a breakthrough with interpretable machine learning by Weijun Zhou, Lijuan Li, Xiaowen Hao, Lanying Wu, Lifu Liu, Binyu Zheng, Yangzheng Xia, Yong Liu

“…ObjectiveTo develop and validate an interpretable machine learning (ML) model for the preoperative prediction of central lymph node metastasis (CLNM) in papillary thyroid microcarcinoma (PTMC).MethodsFrom December 2016 to December 2023, we retrospectively analyzed 710 PTMC patients who underwent thyroidectomies. …”
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AutoTarget: Disease-Associated druggable target identification via node representation learning in PPI networks by Hyunseung Kong, Inyoung Kim, Byoung-Tak Zhang

“…Drug target discovery, a pivotal early stage in drug development, is resource-intensive and crucial for ensuring drug efficacy. This study presents AutoTarget, a novel computational pipeline designed to identify disease-associated druggable targets by applying node representation learning to protein–protein interaction (PPI) networks. …”
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Anomalous Node Detection in Blockchain Networks Based on Graph Neural Networks by Ze Chang, Yunfei Cai, Xiao Fan Liu, Zhenping Xie, Yuan Liu, Qianyi Zhan

“…Fraudulent nodes in the transaction network are referred to as anomalous nodes. …”
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A machine learning model for predicting lymph node positivity in ovarian cancer: development, validation, and clinical application by QingYong Guo, Jinji Wang, Ru Chen, LiPing Hu, Wenqiang You

“…The model demonstrated high sensitivity and robust performance in identifying lymph node-positive cases. Tumor size ≥5 cm, histological subtype, and chemotherapy were key predictive features, with SHAP analysis identifying tumor size as the most influential factor.ConclusionWe present the first machine learning model specifically developed for predicting lymph node positivity in OC, validated across large, diverse cohorts. …”
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An Anomaly Node Detection Method for Wireless Sensor Networks Based on Deep Metric Learning with Fusion of Spatial–Temporal Features by Ziheng Wang, Miao Ye, Jin Cheng, Cheng Zhu, Yong Wang

“…To address these challenges, this paper presents an anomaly detection approach that integrates deep learning with metric learning. …”
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Windows Malware Detection via Enhanced Graph Representations with Node2Vec and Graph Attention Network by Nisa Vuran Sarı, Mehmet Acı, Çiğdem İnan Acı

“…The proposed model generates initial embeddings using Node2Vec, which uses a random walk approach to understand structural relationships between nodes. …”
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Cloud Computing-Based Security Analysis on Wireless Sensor Nodes Cluster Using Predictive Technique by Muhammed Zaharadeen Ahmed, Aisha Hassan Abdallah Hashim, Othman Omran Khalifa, Aliyu Muhammad Wakil, Zeinab E. Ahmed, Khmaies Ouhada

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Dynamic Edge Loading Balancing with Edge Node Activity Prediction and Accelerating the Model Convergence by Wen Chen, Sibin Liu, Yuxiao Yang, Wenjing Hu, Jinming Yu

“…However, the lack of a priori knowledge regarding the current load state of edge nodes for user devices presents a significant challenge in multi-user, multi-edge node scenarios. …”
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Investigating and Optimizing MINDWALC Node Classification to Extract Interpretable Decision Trees from Knowledge Graphs by Maximilian Legnar, Joern-Helge Heinrich Siemoneit, Gilles Vandewiele, Jürgen Hesser, Zoran Popovic, Stefan Porubsky, Cleo-Aron Weis

“…This work deals with the investigation and optimization of the MINDWALC node classification algorithm with a focus on its ability to learn human-interpretable decision trees from knowledge graph databases. …”
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Node-Based Graph Convolutional Network With SLIC Method for Breast Cancer Ultrasound Images Classification by Kien Trang, Fung Fung Ting, Bao Quoc Vuong, Chee-Ming Ting

“…This research presents a novel node-based Graph Convolutional Network (GCN) approach for the classification of breast cancer from ultrasound images. …”
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Enhancing the reliability and accuracy of wireless sensor networks using a deep learning and blockchain approach with DV-HOP algorithm for DDoS mitigation and node localization by Bhupinder Kaur, Deepak Prashar, Leo Mrsic, Ahmad Almogren, Ateeq Ur Rehman, Ayman Altameem, Seada Hussen

“…Abstract Wireless sensor networks (WSNs) are subject to distributed denial-of-service (DDoS) attacks that impact data dependability, mobility of nodes, and energy drain. The remedy to these challenges in this work is a solution based on deep learning integrated with a blockchain-aided distance-vector hop (DV-HOP) localization algorithm for reliable and secure node localization. …”
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CT-based machine learning model integrating intra- and peri-tumoral radiomics features for predicting occult lymph node metastasis in peripheral lung cancer by Xiaoyan Lu, Fan Liu, Jiahui E, Xiaoting Cai, Jingyi Yang, Xueqi Wang, Yuwei Zhang, Bingsheng Sun, Ying Liu

“…Abstract Background Accurate preoperative assessment of occult lymph node metastasis (OLNM) plays a crucial role in informing therapeutic decision-making for lung cancer patients. …”
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Preoperative lymph node metastasis risk assessment in invasive micropapillary carcinoma of the breast: development of a machine learning-based predictive model with a web-based cal... by Yan Zhang, Nan Wang, Yuxin Qiu, Yingxiao Jiang, Peiyan Qin, Xiaoxiao Wang, Yang Li, Xiangdi Meng, Furong Hao

“…Abstract Background Invasive micropapillary carcinoma (IMPC) is a rare subtype of breast cancer characterized by a high risk of lymph node metastasis (LNM). The study aimed to identify predictors of LNM and to develop a machine learning (ML)-based risk prediction model for patients with breast IMPC. …”
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