Results 101 to 110 of about 5,438,478 (196)

A Network Traffic Prediction Model Based on Layered Training Graph Convolutional Network

IEEE Access
Routing deployment and resource scheduling in communication networks require accurate traffic prediction. Neural network-based models that extract the time-correlated or space-correlated features of traffic flow have been developed for traffic prediction.
Yulian Li, Yang Su
doaj   +1 more source

A Novel DDoS Detection and Multi-Class Classification Method: A Graph Convolutional Network Approach [PDF]

Distributed Denial of Service (DDoS) is an attack that overwhelms the cyber critical infrastructure system with malicious packets causing it to become unresponsive, which precludes legitimate users from accessing the target system.
Saunders, Braden
core  

Multi-level graph learning network for hyperspectral image classification

, 2022
Graph Convolutional Network (GCN) has emerged as a new technique for hyperspectral image (HSI) classification. However, in current GCN-based methods, the graphs are usually constructed with manual effort and thus is separate from the classification task,
Wan, Sheng, Zhong, Shengwei, Yang, Jian, Gong, Chen, Zhan, Yibing, Pan, Shirui, Yang, Jie, Chen Gong, Yibing Zhan, Jie Yang, Shirui Pan, Shengwei Zhong, Jian Yang, Sheng Wan   +13 more
core   +1 more source

A Supervised Learning Framework for Accelerating Solvers and Metaheuristics in Routing Problems

Networks, EarlyView.
ABSTRACT Routing problems such as the traveling salesman problem (TSP) and the capacitated vehicle routing problem (CVRP) are computationally challenging due to the exponential growth of their feasible solution spaces with increasing instance size.
Johannes Gückel, Stefan Voigt, Pirmin Fontaine   +2 more
wiley   +1 more source

IA-GCN: Interactive Graph Convolutional Network for Recommendation [PDF]

Recently, Graph Convolutional Network (GCN) has become a novel state-of-art for Collaborative Filtering (CF) based Recommender Systems (RS). It is a common practice to learn informative user and item representations by performing embedding propagation on
He, Jie, Jin, Junsheng, Liu, Congcong, Shao, Jingping, Lin, Zhangang, Qi, Hao, Peng, Changping, Zhang, Yinan, Wang, Pei, Zhao, Xiwei   +9 more
core  

Machine learning‐driven advances in carbon‐based quantum dots: Opportunities accompanied by challenges

Responsive Materials, EarlyView.
Machine learning provides a unifying framework to connect structure, fluorescence properties, and applications of carbon‐based quantum dots. This review highlights how data‐driven strategies enable fluorescence regulation, reveal underlying mechanisms, and accelerate the rational design of functional carbon dots.
Liangfeng Chen, Siwei Yang, Xiangqian Cui, Ziheng Liu, Hang Wang, Yongqiang Li, Peng He, Miao Wang, Guqiao Ding   +8 more
wiley   +1 more source

Research on Finger-vein Recognition Based on Deep Graph Convolutional Network with Dual-Branch [PDF]

Jisuanji gongcheng
This study presents a finger-vein recognition method based on a Graph Convolutional Neural Network (GCNN) to overcome the low recognition rates and high computational cost of traditional methods.
CHENG Junjun, WANG Mingwen
doaj   +1 more source

LST-GCN: Long Short-Term Memory Embedded Graph Convolution Network for Traffic Flow Forecasting

, 2022
Traffic flow prediction is an important part of the intelligent transportation system. Accurate traffic flow prediction is of great significance for strengthening urban management and facilitating people’s travel.
Shicai Gong, Xu Han
core   +1 more source

Network Latency Estimation for Telesurgery Using Deep Reinforcement Learning

SmartBot, EarlyView.
Overview of the proposed two‐stage deep reinforcement learning framework for network latency prediction in telesurgery. The pipeline includes data collection from simulated catheter navigation sessions (Philippines–Botswana), feature engineering, DQN‐based direction prediction (85.8% accuracy), direction‐to‐value transformation, and value forecasting ...
Bakang Kgopolo, Rodrigo S. Jamisola Jr., Molaletsa Namoshe   +2 more
wiley   +1 more source

Artificial intelligence empowers targeted protein degradation: Core technological innovations, multi‐scenario applications, and translational prospects

Smart Molecules, EarlyView.
AI is transforming TPD by improving the design, prediction, and optimization of degraders such as PROTACs, molecular glues, and LYTACs. This review summarizes key AI‐driven advances, highlights applications across drug discovery stages, and discusses remaining challenges and future directions for accelerating the development of therapies against ...
Shuanglin Qin, Rui Peng, Guangshuai Zhang, Si Yan, Jin Xiao, Zishu Liu, Ziyu Tan, Qingqing Xie, Shangjie Li, Wei Luo, Xiaohe Xiao   +10 more
wiley   +1 more source