检索结果-内蒙古大学图书馆

Architecture, Technologies, and Applications of Location-Based Services

Mobile Information systems 2023年第1期2023卷

作者： Kung, Hsu-Yang Yue, Xiao-Guang Chen, Chi-Hua Hwang, Feng-Jang Department of Management Information Systems National Pingtung University of Science and Technology Taiwan School of Sciences European University Cyprus Cyprus College of Computer and Data Science Fuzhou University China School of Mathematical and Physical Sciences University of Technology Sydney Australia

来源：评论

学校读者我要写书评

暂无评论

Efficient Feature Selection in High Dimensional Data Based on Enhanced Binary Chimp Optimization Algorithms and Machine Learning

引用

Human-Centric Intelligent systems 2023年第4期3卷 558-587页

作者： Ayeche, Farid Alti, Adel Faculty of Technologies Technology Department LMETR University Ferhat Abbas Sétif-1 Sétif Algeria Department of Management Information Systems College of Business Qassim University Buraydah Kingdom of Saudi Arabia Faculty of Sciences Computer Science Department LRSD University Ferhat Abbas Sétif-1 Sétif Algeria

Feature selection with the highest performance accuracy is the biggest win for multidimensional data. The Chimpanzee Optimization Algorithm (ChOA) serves as a crucial technique for dealing with multidimensional global optimization issues. However, ChOA often lacks fast convergence and good selection of sensitive attributes leading to poor performance. To address these issues, most significant features were selected using two variants of ChOA called BChimp1 and BChimp2 (BChimp1 and BChimp are available at : https://***/matlabcentral/fileexchange/133267-binary-chimpoptimization-algorithm-forfeatures-selection . September 22, 202). BChimp1 selects the optimal solution from the four best possible solutions and it applies a stochastic crossover on four moving solutions to deeply speed-up convergence level. BChimp2 uses the sigmoid function to select the significant features. Then, these features were trained using six-well known classifiers. The proposed techniques tend to select the most significant features, speed up the convergence rate and decrease training time for high-dimensional data. 23 standard datasets with six well-known classifiers were employed to assess the performance of BChimp1 and BChimp2. Experimental results validate the efficiency of BChimp1 and BChimp2 in enhancing accuracy by 83.83% and 82.02%, and reducing dimensionality by 42.77% and 72.54%, respectively. However, time-evaluation results of BChimp1 and BChimp2 in all datasets showed fast convergence and surpassed current optimization algorithms such as PSO, GWA, GOA, and GA.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Distributed Learning by Local Training ADMM

Distributed Learning by Local Training ADMM

引用

IEEE Conference on Decision and Control

作者： Xiaoxing Ren Nicola Bastianello Karl H. Johansson Thomas Parisini Department of Electrical and Electronic Engineering Imperial College London London United Kingdom 2School of Electrical Engineering and Computer Science and Digital Futures KTH Royal Institute of Technology Stockholm Sweden Department of Electronic Systems Aalborg University Denmark Department of Engineering and Architecture University of Trieste Trieste Italy

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In this paper, we focus on distributed learning over peer-to-peer networks. In particular, we address the challenge of expensive communications (which arise when e.g. training neural networks), by proposing a novel local training algorithm, LTADMM. We extend the distributed ADMM enabling the agents to perform multiple local gradient steps per communication round (local training). We present a preliminary convergence analysis of the algorithm under a graph regularity assumption, and show how the use of local training does not compromise the accuracy of the learned model. We compare the algorithm with the state of the art for a classification task, and in different set-ups. The results are very promising showing a great performance of LT-ADMM, and paving the way for future important theoretical developments.

关键词： Training computer aided instruction Analytical models Accuracy Distance learning Neural networks Classification algorithms Topology Peer-to-peer computing Convergence

来源：评论

学校读者我要写书评

暂无评论

BSH-Det3D: Improving 3D Object Detection with BEV Shape Heatmap

BSH-Det3D: Improving 3D Object Detection with BEV Shape Heat...

引用

IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： You Shen Yunzhou Zhang Yanmin Wu Zhenyu Wang Linghao Yang Sonya Coleman Dermot Kerr College of Information Science and Engineering Northeastern University Shenyang China School of Electronic and Computer Engineering Peking University Shenzhen China Department of Electronic and Computer Engineering Faculty of Robotics and Engineering North-eastern University Technical University in Munich Germany Shenyang China Department of Electronic and Computer Engineering Technical University Munich Germany School of Computing Engineering and Intelligent Systems Ulster University N. Ireland UK

The progress of LiDAR-based 3D object detection has significantly enhanced developments in autonomous driving and robotics. However, due to the limitations of LiDAR sensors, object shapes suffer from deterioration in occluded and distant areas, which creates a fundamental challenge to 3D perception. Existing methods estimate specific 3D shapes and achieve remarkable performance. However, these methods rely on extensive computation and memory, causing imbalances between accuracy and real-time performance. To tackle this challenge, we propose a novel LiDAR-based 3D object detection model named BSH-Det3D, which applies an effective way to enhance spatial features by estimating complete shapes from a bird's eye view (BEV). Specifically, we design the Pillar-based Shape Completion (PSC) module to predict the probability of occupancy whether a pillar contains object shapes. The PSC module generates a BEV shape heatmap for each scene. After integrating with heatmaps, BSH-Det3D can provide additional information in shape deterioration areas and generate high-quality 3D proposals. We also design an attention-based densification fusion module (ADF) to adaptively associate the sparse features with heatmaps and raw points. The ADF module integrates the advantages of points and shapes knowledge with negligible overheads. Extensive experiments on the KITTI benchmark achieve state-of-the-art (SOTA) performance in terms of accuracy and speed, demonstrating the efficiency and flexibility of BSH-Det3D. The source code is available on https://***/mystorm16/BSH-Det3D.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Artificial Intelligence-based Service Chains Scheduling for Medical Emergency in Healthcare

Artificial Intelligence-based Service Chains Scheduling for ...

引用

International Conference on Contemporary Computing and informatics (IC3I)

作者： Mukesh Soni Maher Ali Rusho Haewon Byeon Azzah AlGhamdi Shaikh Abdul Hannan Parth Rameshchandra Dave Dr. D. Y. Patil Vidyapeeth Dr. D. Y. Patil School of Science & Technology Tathawade Pune Marashtra Department of Lockheed Martin Engineering Management University of Colorado Boulder Colorado Department of AI and Software & Medical Big Data Research Centre Inje University Gimhae Republic of Korea Computer Information Systems Department College of Computer Science and Information Technology Imam Abdalrhman Bin Faisal University Khobar Saudi Arabia Faculty of Computing and Information AlBaha University AlBaha Kingdom of Saudi Arabia Department of Computer Engineering L. D. College of Engineering Ahmedabad Gujarat India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

This study analyses the weekly scheduling challenge of health and medical information network service function chains to meet medical emergency categorization and grading needs. Emergency services are divided into four sorts by urgency and service area, with priorities and weights. The health and medical information network architecture, service model, and operation method are built accordingly. To reduce weighted completion time, a service function chain scheduling model is created. The function chain scheduling scheme is solved by matching game and Q -learning reinforcement learning algorithms for different issue scales. Finally, simulation studies demonstrate that this approach can accomplish graded medical emergency service, build health and medical information networks, and progress smart healthcare.

关键词： Schedules Job shop scheduling Service function chaining Hospitals Processor scheduling Reinforcement learning Games Emergency services Network function virtualization Standards

来源：评论

学校读者我要写书评

暂无评论

HomoMGC: Homophily-Enhanced Adaptive Graph Refinement for Multi-View Graph Clustering

HomoMGC: Homophily-Enhanced Adaptive Graph Refinement for Mu...

引用

IEEE International Conference on Data Mining (ICDM)

作者： Man-Sheng Chen Xiao-Sha Cai Chang-Dong Wang Dong Huang Min Chen Mohsen Guizani School of Computer Science and Engineering Sun Yat-sen University Guangzhou China School of Mathematics (Zhuhai) Sun Yat-sen University Zhuhai China College of Mathematics and Informatics South China Agricultural University Guangzhou China School of Computer Science and Engineering South China University of Technology Guangzhou China Pazhou Laboratory Guangzhou China Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) Abu Dhabi UAE

ISBN: (数字)9798331506681

ISBN: (纸本)9798331506698

Due to the emergency of multi-view graph data, considerable attention is focused on the multi-view graph clustering. Although great efforts have been made in developing the multi-view graph clustering methods, most of them implicitly follow the homophily assumption, where the connected nodes with edges tend to be in the same category. As a matter of fact, such an ideal assumption is hard to be satisfied in the real-world graph data, and there are some heterogeneous edges connecting dissimilar nodes in graph. How to well consider the homophily and refine the noisy/heterogeneous edges in multi-view graph clustering still remains an under-explored challenge. Therefore, in this paper, we propose a Homophily-enhanced Adaptive Graph Refinement for Multi-view Graph Clustering (HomoMGC) method, where an adaptive graph refinement strategy is seamlessly designed. Specifically, a feature-oriented graph is constructed based on the shared feature, and an integrated graph is computed by averagely fusing all the input adjacent graphs. Then, the feature-oriented graph and integrated graph are stacked into a graph tensor with a low-rank tensor constraint, where a refined affinity probability matrix can be adaptively recovered from the integrated graph by considering multiple graph information as well as the semantics features. Extensive experiments on several benchmark datasets demonstrate the superiority of HomoMGC compared with the state-of-the-art graph clustering methods. For the code reproducibility, the source code of HomoMGC is public available at https://***/ManshengChen/Code-for-HomoMGc-master.

关键词： Tensors Codes Clustering methods Source coding Semantics Benchmark testing Reproducibility of results Noise measurement Data mining

来源：评论

学校读者我要写书评

暂无评论

Contrastive learning of T cell receptor representations

arXiv

引用

arXiv 2024年

作者： Nagano, Yuta Pyo, Andrew Milighetti, Martina Henderson, James Shawe-Taylor, John Chain, Benny Tiffeau-Mayer, Andreas Division of Medicine University College London United Kingdom Division of Infection and Immunity University College London United Kingdom Center for the Physics of Biological Function Princeton University United States Cancer Institute University College London United Kingdom Institute for the Physics of Living Systems University College London United Kingdom Department of Computer Science University College London United Kingdom

Computational prediction of the interaction of T cell receptors (TCRs) and their ligands is a grand challenge in immunology. Despite advances in high-throughput assays, specificity-labelled TCR data remains sparse. In other domains, the pre-training of language models on unlabelled data has been successfully used to address data bottlenecks. However, it is unclear how to best pre-train protein language models for TCR specificity prediction. Here we introduce a TCR language model called SCEPTR (Simple Contrastive Embedding of the Primary sequence of T cell Receptors), capable of data-efficient transfer learning. Through our model, we introduce a novel pre-training strategy combining autocontrastive learning and masked-language modelling, which enables SCEPTR to achieve its state-of-the-art performance. In contrast, existing protein language models and a variant of SCEPTR pre-trained without autocontrastive learning are outperformed by sequence alignment-based methods. We anticipate that contrastive learning will be a useful paradigm to decode the rules of TCR specificity. © 2024, CC BY.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

An Improved Machine Learning Technique with Effective Heart Disease Prediction System

引用

computers, Materials & Continua 2021年第12期69卷 4169-4181页

作者： Mohammad Tabrez Quasim Saad Alhuwaimel Asadullah Shaikh Yousef Asiri Khairan Rajab Rihem Farkh Khaled Al Jaloud College of Computing and Information Technology University of BishaBisha67714Saudi Arabia King Abdulaziz City for Science and Technology P.O.Box 6086Riyadh11442Saudi Arabia College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia College of Engineering Muzahimiyah BranchKing Saud UniversityRiyadh11451Saudi Arabia Department of Electrical Engineering Laboratory for AnalysisConception and Control of SystemsLR-11-ES20National Engineering School of TunisTunis El Manar University1002Tunisia

Heart disease is the leading cause of death *** heart disease is challenging because it requires substantial experience and *** research studies have found that the diagnostic accuracy of heart disease is *** coronary heart disorder determines the state that influences the heart valves,causing heart *** indications of coronary heart disorder are strep throat with a red persistent skin rash,and a sore throat covered by tonsils or strep *** work focuses on a hybrid machine learning algorithm that helps predict heart attacks and arterial *** first,we achieved the component perception measured by using a hybrid cuckoo search particle swarm optimization(CSPSO)*** this perception measure,characterization and accuracy were improved,while the execution time of the proposed model was *** CSPSO-deep recurrent neural network algorithm resolved issues that state-of-the-art methods *** proposed method offers an illustrative framework that helps predict heart attacks with high *** proposed technique demonstrates the model accuracy,which reached 0.97 with the applied dataset.

关键词： Machine learning deep recurrent neural network effective heart disease prediction framework

来源：评论

学校读者我要写书评

暂无评论

Personalized Meal Planning in Inpatient Clinical Dietetics Using Generative Artificial Intelligence: System Description

Personalized Meal Planning in Inpatient Clinical Dietetics U...

引用

IEEE International Conference on Healthcare informatics (ICHI)

作者： Leon Kopitar Gregor Stiglic Leon Bedrac Jiang Bian Faculty of Health Sciences Faculty of Electrical Engineering and Computer Science University of Maribor Maribor Slovenia Faculty of Health Sciences University of Maribor Maribor Slovenia The NU B.V. Leiden The Netherlands Department of Health Outcomes and Biomedical Informatics College of Medicine University of Florida FL USA

ISBN: (数字)9798350383737

ISBN: (纸本)9798350383744

This study addresses the limitations of traditional prescribed meal plans, which lack personalization and often prove monotonous and challenging for patients to follow. We propose a novel approach employing generative artificial intelligence in the context of a learning health system, with an emphasis on inpatient clinical dietetics. The system incorporates two key models: the Meal Plan Generation Model, MeaIGM, and the Meal Plan Image Generation Model, MealImageGM, leveraging state-of-the-art large language models. Patient information from electronic health records and clinical dietetics guidelines are incorporated into prompts for MeaIGM, which is refined through nutritionist validations and users' feedback. On the other hand, MealImageGM generates visual representations of meal plans to enhance patient engagement, utilizing crowd-sourced feedback to optimize image generation prompts. The overall system process includes extracting data from electronic health records, pre-designed user meal generation prompts, and the generation of personalized meal plans and images. Nutritionists play a crucial role in monitoring patient adherence and preferences, contributing to a continuous learning health system cycle. The proposed framework ensures clinically appropriate and personalized meal plans, aligning with dynamic dietary recommendations. The study emphasizes the importance of patient-physician co-creation for constant optimization and highlights the potential positive impact on health outcomes.

关键词： Visualization Image synthesis Generative AI Medical services Learning (artificial intelligence) Planning Electronic medical records

来源：评论

学校读者我要写书评

暂无评论

Laser written optical Physical Unclonable Functions: a route to commercialization.

Laser written optical Physical Unclonable Functions: a route...

引用

International Conference on Transparent Optical Networks

作者： Dimitris Alexandropoulos Christos Tselios Simone Mazzucato Panagiotis Rizomiliotis Konstantinos Moustakas Thomas Kamalakis Research Centre ATHENA Industrial Systems Institute Patras Greece Department of Materials Science University of Patras Patras Greece SISMA S.p.A. via dell’Industria 1 - 36013 Piovene Rocchette VI Italy Department of Informatics and Telematics Harokopio University of Athens Athens Greece Department of Electrical and Computer Engineering University of Patras Patras Greece

ISBN: (数字)9798350377323

ISBN: (纸本)9798350377330

This paper presents a fabrication approach towards commercialization of optical Physical Unclonable Functions (PUFs) for anti-counterfeiting. We address the limitations of existing complex and expensive implementations by demonstrating strong, intrinsic optical PUFs using computer-Generated Holograms (CGHs) fabricated with industrial-friendly lasers. This novel method offers exceptional security with very low probability of cloning through a low-cost, production-line friendly set-up and a robust signature encoding scheme that utilizes nonlinear dimensionality reduction techniques, to differentiate legitimate variations from counterfeiting attempts. This research paves the way for widespread adoption of PUF technology for real-world applications.

关键词： Integrated optics Laser theory Optical device fabrication Supply chains Cloning Physical unclonable function Adaptive optics

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：