检索结果-内蒙古大学图书馆

5th International Symposium on Artificial Intelligence for Medicine Science, ISAIMS 2024

作者： Zhang, Zhizheng Yang, Qian Du, Yan School of Computer Science and Engineering Southeast University Jiangsu Nanjing China Joint Graduate School Southeast University Jiangsu Nanjing China School of Software Engineering Southeast University Jiangsu Nanjing China

ISBN: (纸本)9798400717826

This paper proposes a medical diagnosis and treatment knowledge question and answer scheme based on clinical practice guidelines, aiming at the need of standardization of medical process and popularization of medical knowledge. The scheme realizes computer intelligent diagnosis and treatment assistance through diagnosis and treatment knowledge base construction, problem analysis, information retrieval and answer generation. The SDA*(State-DecisionAction) modeling method and question formal definition for hypertension diagnose and treatment are proposed, and the answers are generated based on the answer set program (ASP). For the transformation of natural language and ASP rules, large language model (LLM) technology is adopted to automatically identify question terms, generate ASP rules and process the generated answers. To verify the scheme’s ability to generate reliable answers, accuracy for problem classification and ASP rule generations are confirmed. The experimental results show that the scheme could provide the ability to solve medical problems based on the knowledge of clinical practice guidelines. © 2024 Copyright held by the owner/author(s).

关键词： Hyperthermia therapy

来源：评论

学校读者我要写书评

暂无评论

Cost-Optimized Task Offloading for Dependent Applications in Collaborative Edge and Cloud Computing

引用

IEEE Internet of Things Journal 2025年第9期12卷 12975-12988页

作者： Yuan, Haitao Hu, Qinglong Wang, Shen Bi, Jing Buyya, Rajkumar Lu, Jinhu Yang, Jinhong Zhang, Jia Zhou, MengChu Beihang University School of Automation Science and Electrical Engineering Beijing100191 China City University of Hong Kong Department of Computer Science Kowloon Tong Hong Kong Beijing University of Technology School of Software Engineering Faculty of Information Technology Beijing100124 China Laboratory School of Computing and Information Systems MelbourneVIC3010 Australia CSSC Systems Engineering Research Institute Beijing100036 China Southern Methodist University Lyle School of Engineering Department of Computer Science DallasTX75205 United States New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

A collaborative system that includes mobile devices (MDs), edge nodes (ENs), and the cloud is needed where ENs at the network edge can run offloaded tasks of MDs with limited resources and energy for timely processing for latency-sensitive applications. Unlike existing studies, we formulate a total cost minimization problem for the system for applications, which can be divided into several interdependent subtasks. Each subtask can be executed in MDs, ENs, and the cloud. This work formulates a mixed-integer nonlinear program to minimize the total system cost. To address it, a novel meta-heuristic optimization algorithm called Genetic Simulated-annealing-based Particle swarm optimization with Auto-Encoder (GSPAE) is proposed, which innovatively combines feature extraction of deep learning and global search of meta-heuristic optimization. Genetic operations provide diverse solutions, the Metropolis acceptance of annealing offers a robust global search, and autoencoders (AEs) extract distribution characteristics of particles toward high-quality regions for fast convergence. Thus, GSPAE optimizes the associations between ENs and MDs and the scheduling of subtasks among MDs, ENs, and the cloud. Experiments with large-scale Google cluster datasets show that compared to state-of-the-art benchmark methods, GSPAE reduces the total cost by at least 17% while strictly meeting limits of application latency, available energy, computing, and communication resources of ENs and MDs. © 2014 IEEE.

关键词： Mobile edge computing

来源：评论

学校读者我要写书评

暂无评论

Adaptive Local Neighborhood Window Laplacian Eigenmaps 24

Adaptive Local Neighborhood Window Laplacian Eigenmaps

引用

13th International Conference on Computing and Pattern Recognition, ICCPR 2024

作者： Xu, Jiayi Huang, Qirui Zhao, Yang Pei, Jihong Yang, Xuan College of Electronics and Information Engineering Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen Guangdong China College of Computer Science and Software Engineering Shenzhen University Shenzhen Guangdong China

ISBN: (纸本)9798400717482

Manifold learning method is a dimensionality reduction method that treats the data in non-Euclidean space as a Euclidean space in a local scope. However, most existing manifold learning methods cannot obtain the true neighborbood relationship of each sample point well. Therefore, we propose a manifold learning method based on adaptive local neighborhood. This method can adaptively select a neighborhood window. It can be approximated as a local Euclidean space based on the sample characteristics. To implement this method, a specific manifold learning method, named Laplacian Eigenmap of Adaptive Local Neighborhood Window (ALNWLE) is proposed. ALNWLE mainly contains two modules, namely the manifold structure learning module (MSLM) and the manifold structure pattern recognition module (MSPR). MSLM constructs an adaptive local neighborhood adjacency matrix to constrain the learning of the manifold structure. MSPR uses the interpolation method to recognize new sample patterns. The adaptive local neighborhood method proposed in this paper is a general method which is applicable to any manifold learning method. © 2024 Copyright held by the owner/author(s).

关键词： Dimensionality reduction

来源：评论

学校读者我要写书评

暂无评论

Ensemble Classifier for EEG-Based Stress Classification: An Empirical Study on Stacking Classifiers 3rd

Ensemble Classifier for EEG-Based Stress Classification: An ...

引用

3rd International Conference on Computational Electronics for Wireless Communications, ICCWC 2023

作者： Shikha, Shikha Sethia, Divyashikha Indu, S. Department of Computer Science and Engineering Delhi Technological University Delhi India Department of Software Engineering Delhi Technological University Delhi India Department of Electronics and Communication Delhi Technological University Delhi India

ISBN: (纸本)9789819719426

In the last few years, combining multiple algorithms to improve the performance of machine learning models has been a common practice. However, its application to stress detection still needs to be explored. This paper uses a stacking ensemble technique to introduce a novel stress classification approach using electroencephalogram (EEG) signals. Specifically, the study evaluates the effectiveness of various classifiers, including Random Forest (RF), K-Nearest Neighbor (KNN), Gradient Boosting (GB), Naive Bayes (NB), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), and Adaboost (AB), individually and in combination with RF as the base model, for stress classification tasks. The paper uses the SAM40 Dataset consisting of 32-channel EEG data to extract features in time and frequency domains for stress classification. The results show that the stacked classifiers outperform single classifiers, with RF + KNN providing the highest accuracy of 98.55%. The findings suggest that a stacked classifier is a promising approach for stress classification, as it can leverage the strengths of different algorithms and improve generalization performance. It holds promising future applicability in personalized stress management, healthcare interventions, and addressing societal concerns related to mental well-being. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

Guest Editorial: Special Issue on Human-Machine Fusion Decision-Making for Emergency Handling

引用

IEEE Transactions on Automation Science and engineering 2025年 22卷 4427-4433页

作者： Wu, Edmond Q. Li, Jianqiang Chen, Guimin Yuce, Mehmet R. Islam, Shafiqul Ser, Javier Del Yu, Hui Liu, Peter Xiaoping Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China College of Computer and Software Engineering Shenzhen University Shenzhen518060 China State Key Laboratory for Manufacturing Xi'an Jiaotong University Xi'an710049 China Department of Electrical and Computer Systems Engineering Monash University MelbourneVIC3800 Australia Xavier University of Louisiana New OrleansLA70125 United States Tecnalia Institute Bizkaia48160 Spain School of Creative Technologies University of Portsmouth PortsmouthPO1 2UP United Kingdom Department of Systems and Computer Engineering Carleton University OttawaONKIS 5B6 Canada

来源：评论

学校读者我要写书评

暂无评论

When Crowdsensing Meets Smart Cities: A Comprehensive Survey and New Perspectives

引用

IEEE Communications Surveys and Tutorials 2025年第2期27卷 1101-1151页

作者： Wang, Zhenning Cao, Yue Jiang, Kai Zhou, Huan Kang, Jiawen Zhuang, Yuan Tian, Daxin Leung, Victor C. M. Wuhan University School of Cyber Science and Engineering Wuhan430072 China Northwestern Polytechnical University College of Computer Xi’an710129 China Guangdong University of Technology School of Automation Guangzhou510006 China Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan430079 China Beihang University School of Transportation Science and Engineering Beijing100191 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518061 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Crowdsensing has received widespread attention in recent years. It is extensively employed in smart cities and intelligent transportation systems. This paper comprehensively surveys the latest research advancements in crowdsensing for smart cities from a novel perspective. Specifically, this paper is categorized according to sensing entities in smart cities, including human-oriented sensing, vehicle-oriented sensing, and infrastructure-oriented sensing. Meanwhile, the development of Unmanned Aerial Vehicle (UAV)-assisted sensing in recent years is also summarized, accompanied by a timeline of related research. To facilitate easy comprehension, we have positioned the reading flow into the corresponding architectures, resolved problems, existing technical solutions, and specific application scenarios for different sensing entities. In particular, the problems to be solved are further analyzed from four technical perspectives, namely mathematics and operational research, artificial intelligence and machine learning, incentive mechanisms, security and privacy protection. Based on the proposed taxonomy, recent studies are thoroughly investigated to illustrate the current state of research in crowdsensing. Furthermore, this paper highlights the emerging applications of human-oriented and vehicle-oriented sensing in smart cities, as well as the frameworks, platforms, simulators, and datasets involved in crowdsensing. Finally, this paper discusses research directions related to crowdsensing in smart cities, such as digital twins, metaverses, and artificial intelligence-generated content. The primary goal of this survey is to review and synthesize prior research, identify potential avenues for future research, and explore opportunities for collaboration with other relevant research domains. © 1998-2012 IEEE.

关键词： Smart city

来源：评论

学校读者我要写书评

暂无评论

A Survey of Deep Learning Approaches for the Monitoring and Classification of Seagrass

引用

Ocean Science Journal 2025年第2期60卷 1-24页

作者： Nawaz, Uzma Anees-ur-Rahaman, Mufti Saeed, Zubair Department of Computer and Software Engineering College of Electrical and Mechanical Engineering NUST University Rawalpindi Pakistan Department of Software Engineering Military College of Signals NUST University Rawalpindi Pakistan Department of Electrical and Computer Engineering Texas A&M University College Station USA

Seagrass meadows are essential to the health of coastal ecosystems. They support carbon storage, provide habitats for marine species, and help stabilize coastlines. Monitoring underwater seagrass is vital for understanding the conditions of the ecosystem. Researchers have been interested in identifying and classifying underwater seagrasses. However, traditional monitoring methods can be labor-intensive and costly, especially in complex underwater environments. Deep learning approaches have made significant progress in digital image processing, particularly in object recognition and classification, and are among the most popular computer vision tools. The collection of digital images for monitoring underwater habitats, such as seagrass meadows, has increased significantly as recent progress in imaging technology has made it easier to collect high-resolution data. The increase in imagery data has in turn created a demand for automated detection and classification using deep neural network-based techniques. This study reviews the current deep-learning techniques used for monitoring and classification of the seagrass. It discusses the key methodologies, datasets, and progress in this area. This study not only examines the well-known challenges such as limited availability of data but provides a novel, structured taxonomy of deep learning techniques tailored for the monitoring of seagrass, highlighting their unique advantages and limitations within diverse marine environments. By synthesizing findings across various data sources and model architectures, we offer critical insights into the selection of context-aware algorithms and identify key research gaps, an essential step for advancing the reliability and applicability of AI-driven seagrass conservation efforts.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A novel surrogate model with deep learning for predicting spacial-temporal pressure in coalbed methane reservoirs

引用

Natural Gas Industry B 2025年第2期12卷 219-233页

作者： Dong, Yukun Zhang, Xiaodong Zhang, Jiyuan Wu, Kuankuan Liu, Shuaiwei Qingdao Institute of Software College of Computer Science and Technology China University of Petroleum (East China) Qingdao 266580 China College of Petroleum Engineering China University of Petroleum (East China) Qingdao 266580 China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software China

Coalbed methane (CBM) is a vital unconventional energy resource, and predicting its spatiotemporal pressure dynamics is crucial for efficient development strategies. This paper proposes a novel deep learning–based data-driven surrogate model, AxialViT-ConvLSTM, which integrates AxialAttention Vision Transformer, ConvLSTM, and an enhanced loss function to predict pressure dynamics in CBM reservoirs. The results showed that the model achieves a mean square error of 0.003, a learned perceptual image patch similarity of 0.037, a structural similarity of 0.979, and an R² of 0.982 between predictions and actual pressures, indicating excellent performance. The model also demonstrates strong robustness and accuracy in capturing spatial–temporal pressure features. © 2025 Sichuan Petroleum Administration

关键词： AxialAttention Vision Transformer Coalbed methane ConvLSTM Deep learning Spatial-temporal pressure prediction Surrogate models

来源：评论

学校读者我要写书评

暂无评论

Evaluating BERT-based language models for detecting misinformation

引用

Neural Computing and Applications 2025年 1-32页

作者： Anggrainingsih, Rini Hassan, Ghulam Mubashar Datta, Amitava Information Technology and Data Science Faculty Sebelas Maret University Ir Sutami 36A Central Java Surakarta57126 Indonesia Computer Science and Software Engineering The University of Western Australia 35 Stirling Highway PerthWA6009 Australia

Online misinformation poses a significant challenge due to its rapid spread and limited supervision. To address this issue, automated rumour detection techniques are essential for countering the negative impact of false information. Previous research primarily focussed on extracting text features, which proved time-consuming and less effective. In this study, we contribute substantially to two domains: rumour detection on Twitter and the evaluation of text embeddings. We thoroughly analyse rumour detection models and compare the quality of text embeddings generated by various fine-tuned BERT-based models. Our findings indicate that our proposed models outperform existing techniques. Notably, when we test these models on combined datasets, we observe significant performance improvements with larger training and testing data sizes. We conclude that carefully considering the dataset, data splitting, and classification techniques is crucial for evaluating solution performance. Additionally, we find that differences in the quality of text embeddings between RoBERTa, BERT, and DistilBERT are insignificant. This challenges existing assumptions and highlights the need for future research to explore these nuances further. © The Author(s) 2025.

关键词： BERT-based models Comparative analysis Rumour detection Text embeddings

来源：评论

学校读者我要写书评

暂无评论

Migrant Resettlement by Evolutionary Multiobjective Optimization

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2025年第1期6卷 51-65页

作者： Liu, Dan-Xuan Gu, Yu-Ran Qian, Chao Mu, Xin Tang, Ke Nanjing University National Key Laboratory for Novel Software Technology School of Artificial Intelligence Nanjing210023 China Peng Cheng Laboratory Shenzhen518000 China Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen518055 China

Migration has been a universal phenomenon, which brings opportunities as well as challenges for global development. As the number of migrants (e.g., refugees) increases rapidly, a key challenge faced by each country is the problem of migrant resettlement. This problem has attracted scientific research attention, from the perspective of maximizing the employment rate. Previous works mainly formulated migrant resettlement as an approximately submodular optimization problem subject to multiple matroid constraints and employed the greedy algorithm, whose performance, however, may be limited due to its greedy nature. In this article, we propose a new framework called migrant resettlement by evolutionary multiobjective optimization (MR-EMO), which reformulates migrant resettlement as a biobjective optimization problem that maximizes the expected number of employed migrants and minimizes the number of dispatched migrants simultaneously, and employs a multiobjective evolutionary algorithm (MOEA) to solve the biobjective problem. We implement MR-EMO using three MOEAs: the popular nondominated sorting genetic algorithm II (NSGA-II), MOEA based on decomposition (MOEA/D) as well as the theoretically grounded global simple evolutionary multiobjective optimizer (GSEMO). To further improve the performance of MR-EMO, we propose a specific MOEA, called GSEMO using matrix-swap mutation and repair mechanism (GSEMO-SR), which has a better ability to search for feasible solutions. We prove that MR-EMO using either GSEMO or GSEMO-SR can achieve better theoretical guarantees than the previous greedy algorithm. Experimental results under the interview and coordination migration models clearly show the superiority of MR-EMO (with either NSGA-II, MOEA/D, GSEMO or GSEMO-SR) over previous algorithms, and that using GSEMO-SR leads to the best performance of MR-EMO. © 2024 IEEE.

关键词： Multiobjective optimization

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：