检索结果-内蒙古大学图书馆

Consumer Life Tech (ICLT), IEEE

作者： Sara Sardari Bahareh Nakisa Sara Sharifzadeh Alireza Daneshkhah Seng W. Loke Michael J. Duncan Matteo Crotti Vasile Palade Research Centre for Computational Science and Mathematical Modelling Coventry University Coventry UK School of Information Technology Faculty of Science Engineering and Built Environment Deakin University Geelong Vic Australia Department of Computer Science Swansea University Swansea UK School of Mathematics and Data Science Emirates Aviation University Dubai UAE Centre for Sport Exercise and Life Sciences Coventry University Coventry UK Department of Human and Social Sciences University of Bergamo Bergamo Italy

ISBN: (数字)9798331519339

ISBN: (纸本)9798331519346

Evaluating deep squats accurately during automatic physical rehabilitation monitoring across different subjects remains challenging due to inter-subject variability and limited labelled data. The challenges include: 1) conventional methods presuppose that a "one-model-fits-all" approach works for activity evaluation, ignoring that subject-specific differences can lead to suboptimal results if these differences are not considered. 2) Previous studies focus on offline learning, where models are trained on the entire dataset, which can be updated later through retraining. This approach neglects the need for continual learning, where models adapt sequentially to new subjects while retaining past knowledge to prevent catastrophic forgetting. This study addresses these challenges by proposing a novel continual meta-learning approach and a memory buffer to provide personalized deep squat evaluations. Using Azure Kinect sensors, we collected RGB-D videos and 3D skeletal data from 33 participants performing deep squats, annotated with Functional Movement Screen (FMS) scores. Our model dynamically adapts to new participants while retaining knowledge from previous ones, preventing performance degradation over time. Experimental results demonstrate that our approach outperforms a model without a buffer memory technique by retaining learned knowledge across participants and adapting to new individuals with minimal data.

关键词： Metalearning Continuing education Adaptation models Three-dimensional displays Frequency modulation Skeleton Sensors Quality assessment Monitoring Videos

来源：评论

学校读者我要写书评

暂无评论

Comparative Performance of Intensive Care Mortality Prediction Models Based on Manually Curated Versus Automatically Extracted Electronic Health Record data

SSRN

引用

SSRN 2023年

作者： Jagesar, Ameet R. Otten, Martijn Dam, Tariq A. Biesheuvel, Laurens Dongelmans, Dave A. Brinkman, Sylvia Thoral, Patrick J. Francois-Lavet, Vincent Girbes, Armand R.J. de Keizer, N.F. de Grooth, Harm-Jan S. Elbers, P.W.G. Amsterdam UMC Vrije Universiteit Amsterdam Netherlands Department of Intensive Care Medicine Amsterdam UMC Universiteit van Amsterdam Amsterdam Netherlands Quantitative Data Analytics Group Department of Computer Science Faculty of Science Vrije Universiteit Amsterdam Netherlands foundation Amsterdam Netherlands Netherlands

Introduction: Benchmarking intensive care units for audit and feedback is frequently based on comparing actual mortality versus predicted mortality. Traditionally, mortality prediction models rely on a limited number of input variables and significant manual data entry and curation. Using automatically extracted electronic health record data may be a promising alternative. However, adequate data on comparative performance between these approaches is currently ***: The freely available and highly granular AmsterdamUMCdb intensive care database was used to construct a baseline APACHE IV in-hospital mortality model based on data typically available through manual data curation. Subsequently, new in-hospital mortality models were systematically developed and evaluated. New models differed with respect to the extent of automatic variable extraction, classification method, recalibration usage and the size of collection ***: A total of 13 models were developed based on data from 5,077 admissions divided into a train (80%) and test (20%) cohort. Adding variables or extending collection windows only marginally improved discrimination and calibration. An XGBoost model using only automatically extracted variables, and therefore no acute or chronic diagnoses, was the best performing automated model with an AUC of 0.89 and a Brier score of ***: Performance of intensive care mortality prediction models based on manually curated versus automatically extracted electronic health record data is similar. Importantly, our results suggest that variables typically requiring manual curation, such as diagnosis at admission and comorbidities, may not be necessary for accurate mortality prediction. These proof-of-concept results require replication using multi-centre data. © 2023, The Authors. All rights reserved.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Early Fault Detection in Rolling Element Bearings Using Signal Processing Techniques

Early Fault Detection in Rolling Element Bearings Using Sign...

引用

International Symposium on High Capacity Optical Networks and Enabling Technologies, HONET

作者： Ali Saeed M. Usman Akram Muazzam A. Khan M. Belal Afsar Department of Computer Science ICESCO Chair Big Data Analytics and Edge Computing Quaid-i-Azam University Islamabad Pakistan Department of Computer and Software Engineering National University of Sciences & Technology Islamabad Pakistan Faculty of Electrical Engineering and Information Technology Otto-von-Guericke University Maddeburg DE

ISBN: (数字)9798350378078

ISBN: (纸本)9798350378085

Many studies show that bearings are the most vulnerable components in low-voltage motors. While advanced bearing diagnostic systems exist, their cost can be a barrier for non-critical machinery due to the potential wait time to see a return on investment. This research explores the feasibility of using basic signal processing techniques on vibration data for bearing condition monitoring. The primary goal is to analyze benchmark data from the Case Western Reserve University (CWRU) dataset and establish a baseline performance for this data-driven approach. The results suggest that, with appropriate signal processing, it's possible to achieve early detection of bearing faults, leading to more efficient time-to-fault identification with higher detection accuracies ranging from 83 % to 100 % for each method.

关键词： Vibrations Fault diagnosis Low voltage Smart cities Rolling bearings Signal processing Motors Robots Monitoring Investment

来源：评论

学校读者我要写书评

暂无评论

Novel Framework for Alzheimer Early Diagnosis using Inductive Transfer Learning Techniques 2

Novel Framework for Alzheimer Early Diagnosis using Inductiv...

引用

2nd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2022

作者： Sahu, Himanshu Kumar Kumar, Santosh Alsamhi, Saeed Hamood Chaube, Mithilesh Kumar Curry, Edward Shri Shankaracharya Technical Campus Bhilai India IIIT-Naya Raipur Department of Computer Science and Engineering Chhattisgarh India University of Galway Insight Centre for Data Analytics Galway Ireland IBB Univeristy Faculty of Engineering Ibb Yemen IIIT-Naya Raipur Department of Mathematical Sciences Chhattisgarh India

ISBN: (数字)9781665461818

ISBN: (纸本)9781665461818

The early diagnosis of Alzheimer's disease (AD) is gaining popularity due to massive applications in the medical field. The estimated 50 million people affected by AD worldwide are increasing rapidly, and the condition has an approximately 1 dollar trillion economic cost. This necessitates developing scalable, economical, and reliable methods for detecting Alzheimer's disease. We describe a unique architecture that uses auditory, cognitive, and linguistic aspects to create a multimodal ensemble system. Scores from the Mini-Mental State Examination are used to assess AD severity, which is detected using specialized artificial neural networks with temporal properties. We first test it using the ADReSS challenge dataset, a balanced dataset available through Dementia Bank that contains no biases against any patient. Our system achieves state-of-the-art test accuracy, precision, recall, and F1-score of 93.30% each, as well as a state-of-the-art test Root Mean Squared Error (RMSE) of 4.60, when it comes to the classification of AD. To our knowledge, the algorithm achieves cutting-edge AD classification accuracy of 88.0 % when tested against the benchmark Dementia Bank Pitt database. In order to build a robust inductive transfer learning model, our study highlights the applicability and transferability of spontaneous speech and demonstrates generalizability by utilising a task-agnostic feature space. © 2022 IEEE.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

A Conversational Human-computer Interface for Smart Energy System Simulation Environments

A Conversational Human-Computer Interface for Smart Energy S...

引用

Simulation Winter Conference

作者： Gabriel Dengler Peter Bazan Reinhard German Pooia Lalbakhsh Ariel Liebmann Laboratory of Computer Networks and Communication Systems Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen BY Germany Department of Data Science and AI Faculty of Information Technology Monash University Clayton VIC Australia

This paper introduces a conversational framework that enhances the usability of smart energy system simulations. This study is centered around OpenAI’s Generative Pre-trained Transformer (GPT), a fine-tuned conversational model that allows users to communicate with the system in a natural way. Therefore, users can describe their simulation scenarios in plain language and GPT seamlessly translates these descriptions into Python scripts, used as inputs to the simulation environment, in our case, AnyLogic Simulation Software. Our framework is based on the i7-AnyEnergy core framework to compute distribution flows and relevant statistics. The proposed human-machine interface facilitates and accelerates simulation modeling, as demonstrated through the two scenarios we have provided in this paper. Overall, our conversational framework has the potential to significantly improve the user experience of smart energy system simulation environments. By simplifying the interaction between users and complex simulation models, we enable users to obtain valuable insights rapidly and more easily.

关键词：

来源：评论

学校读者我要写书评

暂无评论

PRIVACY-PRESERVING LINKAGE OF DISTRIBUTED dataSETS USING THE PERSONAL HEALTH TRAIN

arXiv

引用

arXiv 2023年

作者： Jugl, Maximilian Welten, Sascha Mou, Yongli Yediel, Yeliz U. Beyan, Oya D. Sax, Ulrich Kirsten, Toralf Chair of Computer Science 5 RWTH Aachen University Aachen Germany Department of Medical Data Science University Medical Center Leipzig Leipzig Germany Department of Data Science and Artificial Intelligence Fraunhofer FIT Sankt Augustin Germany Institute for Medical Informatics Faculty of Medicine University Hospital Cologne Cologne Germany Department of Medical Informatics University Medical Center Göttingen Göttingen Germany

With the generation of personal and medical data at several locations, medical data science faces unique challenges when working on distributed datasets. Growing data protection requirements in recent years drastically limit the use of personally identifiable information. Distributed data analysis aims to provide solutions for securely working on highly sensitive data while minimizing the risk of information leaks, which would not be possible to the same degree in a centralized approach. A novel concept in this field is the Personal Health Train (PHT), which encapsulates the idea of bringing the analysis to the data, not vice versa. data sources are represented as train stations. Trains containing analysis tasks move between stations and aggregate results. Train executions are coordinated by a central station which data analysts can interact with. data remains at their respective stations and analysis results are only stored inside the train, providing a safe and secure environment for distributed data analysis. Duplicate records across multiple locations can skew results in a distributed data analysis. On the other hand, merging information from several datasets referring to the same real-world entities may improve data completeness and therefore data quality. In this paper, we present an approach for record linkage on distributed datasets using the Personal Health Train. We verify this approach and evaluate its effectiveness by applying it to two datasets based on real-world data and outline its possible applications in the context of distributed data analysis tasks. The source code for the services and analysis scripts mentioned in this paper is open source and available from the authors1 © 2023, CC BY.

关键词： Sensitive data

来源：评论

学校读者我要写书评

暂无评论

Multimodal Knowledge Graph Completion Method Based on Integrated Modality Adversarial Training and Relation-Enhanced Attention Mechanism

Multimodal Knowledge Graph Completion Method Based on Integr...

引用

International Conference on computer Supported Cooperative Work in Design

作者： Ruijia Zhang Zheng Dong Jianqiang Zhang Gongpeng Song Qin Lu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Negative sampling (NS) is widely used in knowledge graph completion (KGC) to generate negative triples for contrastive learning during training. However, existing NS methods are not suitable when multimodal information is incorporated into KGC models. Due to their complex design, these methods are also inefficient. In this paper, we propose the integration of Modality-Aware Adversarial Training (IMAT) to generate higher-quality negative samples for Multimodal Knowledge Graph Completion (MMKGC), and introduce a Relation-Enhanced Cross-modal Attention (RECA) mechanism to evaluate bidirectional attention weights between multimodal features using relational information, thereby improving the model's ability to identify hard negative samples. Our approach represents a joint design of MMKGC models and training strategies, surpassing 16 recent MMKGC methods and achieving new state-of-the-art results on three public MMKGC benchmarks.

关键词： Training Federated learning Design methodology Computational modeling Semantics Focusing Knowledge graphs Contrastive learning Sampling methods Real-time systems

来源：评论

学校读者我要写书评

暂无评论

Learnable Graph Convolutional Network and Feature Fusion for Multi-view Learning

arXiv

引用

arXiv 2022年

作者： Chen, Zhaoliang Fu, Lele Yao, Jie Guo, Wenzhong Plant, Claudia Wang, Shiping College of Computer and Data Science Fuzhou University Fuzhou350116 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China Faculty of Computer Science University of Vienna Vienna1090 Austria Research Network Data Science @ Uni Vienna University of Vienna Vienna1090 Austria

In practical applications, multi-view data depicting objectives from assorted perspectives can facilitate the accuracy increase of learning algorithms. However, given multi-view data, there is limited work for learning discriminative node relationships and graph information simultaneously via graph convolutional network that has drawn the attention from considerable researchers in recent years. Most of existing methods only consider the weighted sum of adjacency matrices, yet a joint neural network of both feature and graph fusion is still under-explored. To cope with these issues, this paper proposes a joint deep learning framework called Learnable Graph Convolutional Network and Feature Fusion (LGCN-FF), consisting of two stages: feature fusion network and learnable graph convolutional network. The former aims to learn an underlying feature representation from heterogeneous views, while the latter explores a more discriminative graph fusion via learnable weights and a parametric activation function dubbed Differentiable Shrinkage Activation (DSA) function. The proposed LGCN-FF is validated to be superior to various state-of-the-art methods in multi-view semi-supervised classification. Copyright © 2022, The Authors. All rights reserved.

关键词： Learning algorithms

来源：评论

学校读者我要写书评

暂无评论

Exploring the Benefits of DNS-Based Authentication for Wireless Network Security 3

Exploring the Benefits of DNS-Based Authentication for Wirel...

引用

3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Swarup, Lakshya Jayadurga, R. Borade, Monali R. Ramachandran, T. Singh, Akhilendra Pratap Prabha, Savita Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Master of Business Administration Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India

ISBN: (纸本)9798350319125

Wi-wireless networks are becoming more pervasive and are frequently the primary goal of malicious attackers. Therefore, it is miles crucial that wireless networks are nicely secured to defend their users from malicious moves. DNS-based total authentication is a promising answer for attaining ease of authentication of wi-wireless customers in an open Wi-Fi community. This paper surveys numerous current works that discover the wi-fits wireless of DNS-based total authentication for Wi-Fi wireless community safety. especially this paper opinions three one-of-a-kindwireless strategies of using DNS for authentication purposes: making use of DNS's cache for identifying windows-primarily based Wi-Fi wireless clients;Open Mobile Alliance (OMA)'s tool control (DM) projects to relaxed get right of entry to;and a modern scheme for protecting the use of the DNS-based techniques. Wi-finally, this paper discusses a few safety-demanding situations and future studies directions. © 2023 IEEE.

关键词： authentication initiatives management protecting secured

来源：评论

学校读者我要写书评

暂无评论

Deep Learning based Brain Tumor Classification for MR Images using ResNet50

Deep Learning based Brain Tumor Classification for MR Images...

引用

International Symposium on Digital Forensic and Security (ISDFS)

作者： Ömer Miraç Kökçam Aytuğ Boyaci Muhammed Emre Çolak Department of Software Engineering Faculty of Technology Fırat University Elazığ Türkiye Department of Computer Engineering Air Force Academy National Defence University İstanbul Türkiye Department of Artificial Intelligence and Data Science Engineering Faculty of Engineering Fırat University Elazığ Türkiye

ISBN: (数字)9798350330366

ISBN: (纸本)9798350330373

Brain tumors are abnormal cell growths that occur in various parts of the brain, and the accurate classification of these tumors plays a critical role in determining treatment methods. Classification and diagnosis of brain tumors based on artificial intelligence and deep learning models have been made possible due to advances in medical image processing technologies. For instance, such technologies facilitate quick detection of tumors by health experts thereby improving early diagnosis rate and hence enhancing treatment outcomes. Importantly, however, one considers improvement or breakthroughs in artificial intelligence-based classification systems as a crucial step forward towards brain tumor diagnosis as well as its treatment strategy. In this study we use Google Research's ImageNet-21k pre-trained ResNet50 model for classifying brain tumor cases. This model is a deep learning-based image classification tool and is capable of learning from large data sets. The model is specifically trained for high-resolution image recognition and is designed to achieve high accuracy rates. The results showed a classification accuracy of 99.9 percent, which is an exceptional accuracy rate for this model. This accuracy rate indicates that the model is highly effective in detecting and classifying brain tumors. High accuracy rates allow for early diagnosis of patients' diseases and, as it is often said in medicine, “Early Diagnosis Saves Lives”. This result is one of the many proofs of how effective artificial intelligence and deep learning are in medical image analysis.

关键词： Deep learning Image recognition Brain modeling Internet Security Artificial intelligence Medical diagnostic imaging

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：