This paper presents a scientometric analysis of the research landscapes, hotspots, and emerging frontiers of combinatorial optimization algorithms on graphs from 1987 to 2022. The study investigates technical maturity...
This paper presents a scientometric analysis of the research landscapes, hotspots, and emerging frontiers of combinatorial optimization algorithms on graphs from 1987 to 2022. The study investigates technical maturity, country/region collaboration, institutional cooperation, journal co-citation, dual graph overlay, and hotspot frontier analysis based on co-citation and keyword co-occurrence using tools like CiteSpace, Bibliometrics R, and VOSviewer. The analysis reveals that research in this field has entered a mature stage, and countries such as the United States, China, and Japan have produced rich output in this field. The University of Tokyo, MIT, the Chinese Academy of Sciences, Shanghai Jiao Tong University, Stanford University, and the University of California have formed 10 distinct collaborative clusters. The findings also suggest that research in the field of combination optimization mainly comes from two clusters, namely, computer science and mathematics, and that cross-disciplinary knowledge is also involved. In the field of graph-based combination optimization, deep learning, machine learning, graph neural networks, and reinforcement learning are now areas of intense investigation. The frontier direction of research in this field is represented by quantum computing technologies such as coherent ising machines, hybrid quantum-classical algorithms, and quantum algorithms. The research problems in this field are categorized into three main types from a keyword perspective: graph theory problems, classical optimization problems, and open problems. The corresponding methods for solving these problems are also categorized into combinatorial optimization techniques, stochastic optimization methods, and machine learning-based optimization methods. The study highlights the need for interdisciplinary collaboration, more research on quantum computing technologies, advanced optimization techniques, and the exploration of emerging research frontiers in combination o
作者：
Yang, HongtaiLei, BoyiHan, KeLiu, LunaSchool of Transportation and Logistics
National Engineering Laboratory of Integrated Transportation Big Data Application Technology National United Engineering Laboratory of Integrated and Intelligent Transportation Institute of System Science and Engineering Southwest Jiaotong University Chengdu611756 China
Construction waste hauling trucks (CWHTs), as one of the most commonly seen heavy-duty vehicles in major cities around the globe, are usually subject to a series of regulations and spatial-temporal access restrictions...
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Prototype learning is widely used in face recognition, which takes the row vectors of coefficient matrix in the last linear layer of the feature extraction model as the prototypes for each class. When the prototypes a...
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Human facial action units (AUs) are mutually related in a hierarchical manner, as not only they are associated with each other in both spatial and temporal domains but also AUs located in the same/close facial regions...
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Dynamic searchable symmetric encryption (DSSE) enables users to delegate the keyword search over dynamically updated encrypted databases to an honest-but-curious server without losing keyword privacy. This paper studi...
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This note presents a simulation method for investigating the relationship between porosity and particle size distribution in porous media characterization. The method simulates particle packing based on particle size ...
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Today, thanks to the major breakthrough of sequences to sequences model in the field of natural language, most of the dialogue generation tasks are focused on generating more effective responses. However, the response...
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Remote Photoplethysmography (rPPG) is a non-contact method that uses facial video to predict changes in blood volume, enabling physiological metrics measurement. Traditional rPPG models often struggle with poor genera...
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作者：
Feiyu WangJian-tao ZhouCollege of Computer Science
Inner Mongolia University Hohhot Inner Mongolia China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software
Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Engineering Research Center of Ecological Big Data Ministry of Education National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian China
Cloud storage services have been used by most businesses and individual users. However, data loss, service interruptions and cyber attacks often lead to cloud storage services not being provided properly, and these in...
Cloud storage services have been used by most businesses and individual users. However, data loss, service interruptions and cyber attacks often lead to cloud storage services not being provided properly, and these incidents have caused financial losses to users. Second, traditional and single-cloud model disaster recovery services are no longer suitable for the current complex cloud storage systems. Therefore, a scheme to provide disaster recovery for cloud storage services in a multi-cloud storage environment is needed in real production. In this paper, we propose a disaster recovery scheme based on blockchain technology. The proposed scheme outlined in this study aims to address the issue of data availability within the cloud storage landscape. The proposed scheme achieves this goal by dividing data into hot and cold categories, verifying the integrity of copy data via blockchain technology, and utilizing blockchain networks to manage multi-cloud storage systems. Experimental findings demonstrate that the proposed scheme yields superior results in terms of computation and time overheads.
Breast cancer is the most common malignant tumor and the leading cause of cancer-related deaths in women *** means of predicting the prognosis of breast cancer are very helpful in guiding treatment and improving patie...
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Breast cancer is the most common malignant tumor and the leading cause of cancer-related deaths in women *** means of predicting the prognosis of breast cancer are very helpful in guiding treatment and improving patients'*** extracted by radiomics reflect the genetic and molecular characteristics of a tumor and are related to its biological behavior and the patient's ***,radiomics provides a new approach to noninvasive assessment of breast cancer *** is one of the commonest clinical means of examining breast *** recent years,some results of research into ultrasound radiomics for diagnosing breast cancer,predicting lymph node status,treatment response,recurrence and survival times,and other aspects,have been *** this article,we review the current research status and technical challenges of ultrasound radiomics for predicting breast cancer *** aim to provide a reference for radiomics researchers,promote the development of ultrasound radiomics,and advance its clinical application.
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