检索结果-内蒙古大学图书馆

1st International Conference on Healthcare Innovations, Software and engineering Technologies, HISET 2024

作者： Kulkarni, Mukund Kashinath, Amruta Garagate Momin Irshad, Anjuman Sekhar, G Chandra Patil, P.B. Phatak, Pratiksha Institute of Management Kolhapur India Jaywant College of Engineering and Polytechnic Department of Computer Science Engineering Karad India Jaywant College of Management Karad India Institute of Aeronautical Engineering Department of Computer Science and Engineering Hyderabad India Krishna Vishwa Vidyapeeth Deemed to Be University Department of Anaesthesiology Karad India Jaywant College of Engineering and Polytechnic Department of Electrical Engineering Karad India

ISBN: (纸本)9798350360707

Clinical the healthcare and awareness towards it is now most prominent among the living beings in the world. The mobile health and healthcare are now increasing among the populations over the worlds. Thus, the focus of study is to know the importance of mobile healthcare and its data management, storage and security. The utilization of IoT gadgets and mHealth apps is only two examples of how digital medicine is becoming more and more common in daily medical practice. It has the ability to promote improved patient health outcomes, facilitate care coordination, and increase communication with lower costs. The security to healthcare helps to protect the health-related data from the hackers by providing high security to the devices and the applications. Thus, with time hacking will be getting more intense and will provide more damage so it is necessary to improve the security and data management through mHealth. © 2024 IEEE.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

Enhanced Vehicle Detection by Optimized Image Compression in NextG Wireless Network Autonomous Vehicles System 100

Enhanced Vehicle Detection by Optimized Image Compression in...

引用

100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Monir, Md Fahad Hossain, Md Junayed Barkatullah, Mohammad Hoque, Md Mozammal Hassan, Zoheb Ahmed, Tarem Virginia Polytechnic Institute and State University Department of Electrical and Computer Engineering United States Department of Computer Science & Engineering Bangladesh University of Houston Department of Engineering Technology United States Université Laval Department of Electrical and Computer Engineering Canada

ISBN: (纸本)9798331517786

Autonomous Vehicle System (AVS) is rapidly advancing and is expected to completely transform the transportation industry, bringing about a new era of mobility. As digital data proliferation strains network resources, the demand for performing resource-intensive edge-assisted Deep Learning tasks in AVS with limited computational resources becomes increasingly challenging. The advent of 5G and future cellular networks (NextG) offers the promise of facilitating the seamless execution of these tasks. This research aims to diminish dataset size, in harmony with the integration capabilities of the Semantic and Flexible Open Radio Access Network (SEMO-RAN) framework, which is anticipated to reduce latency and refine resource allocation in vehicle image processing. The focus of this study is on optimizing image compression without compromising the accuracy of vehicle classification, leveraging esteemed Convolutional Neural Network models like YOLOv5. By employing Generative Adversarial Network compression and Wavelet Image Compression methods, our study achieves an impressive 81.82% reduction in data size while maintaining a 96.97% accuracy in classification tasks. This underscores the potential for significant efficiency gains in AVS through improved data management, supported by Digital Twins (DT), Integrated Sensing and Communication (ISAC), and O-RAN. © 2024 IEEE.

关键词： Radio access networks

来源：评论

学校读者我要写书评

暂无评论

Exploring emerging spreaders through GCN-based link prediction and a novel centrality method 17

Exploring emerging spreaders through GCN-based link predicti...

引用

17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Nandi, Suman Maji, Giridhar Dutta, Animesh National Institute of Technology Dept. of Computer Science and Engineering West Bengal Durgapur India Asansol Polytechnic Dept. of Electrical Engineering West Bengal India

ISBN: (纸本)9798331531195

Exploring influential spreaders and predicting missing links in complex networks is essential for understanding and effectively controlling network dynamics. This paper presents a Graph Convolutional Network (GCN)-based link prediction method to estimate the probability of future link formation. We incorporate node features that capture local and global topological connectivity structures and feed these into the GCN model, where convolutional layers aggregate neighboring information and transform node features. This approach enables the model to capture structural patterns by integrating local and global information from neighboring nodes. In the final layer, the GCN model computes a prediction score representing the likelihood of an edge's existence, using insights gained during training. Finally, considering the predicted links, we update the network structure and introduce a novel centrality method called Emerging Spreader Centrality (ESC) to identify emerging spreaders within this augmented network. We conduct two separate experiments to evaluate the performance of the GCN-based link prediction and the ESC method, comparing their effectiveness with various state-of-the-art methods. Results demonstrate that our approach not only effectively predicts future links but also identifies emerging spreaders in the augmented networks. © 2025 IEEE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Enabling Cross-Language Data Integration and Scalable Analytics in Decentralized Finance

Enabling Cross-Language Data Integration and Scalable Analyt...

引用

2023 IEEE International Conference on Big Data, BigData 2023

作者： Flynn, Conor Bennett, Kristin P. Erickson, John S. Green, Aaron Seneviratne, Oshani Rensselaer Polytechnic Institute Electrical and Computer Systems Engineering Troy United States Rensselaer Polytechnic Institute Mathematical and Computer Sciences Troy United States Rensselaer Polytechnic Institute Future of Computing Institute Troy United States Rensselaer Polytechnic Institute Mathematical Sciences Troy United States Rensselaer Polytechnic Institute Computer Science Troy United States

ISBN: (纸本)9798350324457

With the agile development process of most academic and corporate entities, designing a robust computational back-end system that can support their ever-changing data needs is a constantly evolving challenge. We propose the implementation of a data and language-agnostic system design that handles different data schemes and sources while subsequently providing researchers and developers a way to connect to it that is supported by a vast majority of programming languages. To validate the efficacy of a system with this proposed architecture, we integrate various data sources throughout the decentralized finance (DeFi) space, specifically from DeFi lending protocols, retrieving tens of millions of data points to perform analytics through this system. We then access and process the retrieved data through several different programming languages (R-Lang, Python, and Java). Finally, we analyze the performance of the proposed architecture in relation to other high-performance systems and explore how this system performs under a high computational load. © 2023 IEEE.

关键词： Database management Multi-user channels Sockets

来源：评论

学校读者我要写书评

暂无评论

Adaptive Fixed-Time Command Filtered Tracking Control for Parametric Nonlinear Systems With Unknown Nonlinear Control Coefficient and Unknown Disturbances

引用

IEEE Access 2025年 13卷 37839-37849页

作者： Zhang, Xiyu Zhou, Youjun Feng, Chun Deng, Xiongfeng Guangxi Science and Technology Normal University School of Mathematics and Computer Science Laibin546199 China Guang’an Institute of Technology School of Mechanical and Electrical Engineering Guang’an638550 China Anhui Polytechnic University Key Laboratory of Electric Drive and Control Anhui Higher Education Institute Wuhu241000 China

This paper addresses the issue of adaptive fixed-time tracking control (FTTC) for a category of parametric nonlinear systems characterized by unknown nonlinear control coefficient (UNCC) and unknown external disturbances (UEDs). To effectively handle unknown parameters and unknown disturbances, the adaptive parameter estimation method is introduced into the control law design. Meanwhile, some parameter adaptive laws are presented to estimate these unknown parameters. To eliminate repeated differentiation of virtual control laws, a class of nonlinear command filters is introduced. Furthermore, the filtering errors are compensated through the designed adaptive compensation control laws. Subsequently, the Nussbaum gain function technique is further used to deal with the UNCC in the system. Leveraging command-filtering backstepping control (BC) and fixed-time control method, an adaptive command filtered FTTC law is final presented to achieve the fixed time tracking. The theoretical analysis results indicate that the designed control law guarantees the convergence of tracking error to a small neighborhood of zero within a fixed time, while ensuring that all signals within the closed-loop system remain bounded. The proposed control law’s effectiveness is demonstrated through three simulation examples. © 2025 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License.

关键词： Backstepping

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Based Earlier Identification of Liver Disease Using Ultrasound Images 1st

Machine Learning Based Earlier Identification of Liver Disea...

引用

1st International Conference on Intelligent Computing, Smart Communication, and Network Technologies, ICICSCNT 2023

作者： Saravanakumar, C. Prakash, M. Deepak Kumar, A. Ashokkumar, C. Department of Computer Engineering Government Polytechnic College Purasawalkam Chennai600012 India School of Computer Science and Engineering Vellore Institute of Technology Vellore632014 India Department of Computer Science and Engineering St. Joseph’s Institute of Technology Chennai India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai603203 India

ISBN: (纸本)9783031759567

Liver disease begins in the ovaries and is especially hazardous for women. As a result, abnormal cells can potentially spread to other organs. Liver disease is a type of dangerous development that affects the ovaries in females, and it is difficult to detect at an early stage, which is why it remains one of the leading causes of death from disease. Unquestionable confirmation of intrinsic and natural components is crucial for the development of novel systems to detect and eliminate hazard. We propose the detection and categorization of liver illness in ultrasound pictures using hybrid machine learning techniques. Using Machine Learning Algorithms and a vote-based classifier to classify liver tissues as fatty or normal based on ultrasound picture attributes and voting. Our created method provides four major contributions: first, the classification of liver pictures as normal or fatty is performed without a segmentation phase. Secondly, compared to our suggested effort, the datasets in earlier works were inadequate. The third contribution is a collection of 26 characteristics. On the basis of the presented methods, sigmoid radial basis function neural network (SRBFNN) and Gray-Level Co-Occurrence Matrix are the recovered features (GLCM). The fourth contribution is the voting classifier that is utilised to determine the type of liver tissue. Fruit Fly Optimization concludes the categorization process (FFO). We have achieved a classification accuracy of 98.24% using our proposed approach. Convolutional Neural Network (CNN), Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Random Forest (RF), and Naive Bayes are compared with our proposed system (NB). The experimental results demonstrate that the classification accuracy of our suggested system is superior to that of the leading classification strategies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Radial basis function networks

来源：评论

学校读者我要写书评

暂无评论

Application of Recursive Query on Structured Query Language Server

引用

Journal of Donghua University(English Edition) 2023年第1期40卷 68-73页

作者：荀雪莲 ABHIJIT Sen 姚志强 Library North China Institute of Aerospace EngineeringLangfang 065000China Department of Computer Science&Information System Kwantlen Polytechnic UniversitySurreyCanada School of Computer Science and Engineering North China Institute of Aerospace EngineeringLangfang 065000China

The advantage of recursive programming is that it is very easy to write and it only requires very few lines of code if done *** query language(SQL)is a database language and is used to manipulate *** Microsoft SQL Server 2000,recursive queries are implemented to retrieve data which is presented in a hierarchical format,but this way has its *** table expression(CTE)construction introduced in Microsoft SQL Server 2005 provides the significant advantage of being able to reference itself to create a recursive *** data structures,organizational charts and other parent-child table relationship reports can easily benefit from the use of recursive *** recursive query is illustrated and implemented on some simple hierarchical *** addition,one business case study is brought forward and the solution using recursive query based on CTE is *** the same time,stored procedures are programmed to do the recursion in *** results show that recursive queries based on CTEs bring us the chance to create much more complex queries while retaining a much simpler syntax.

关键词： structured query language(SQL)server common table expression(CTE) recursive query stored procedure hierarchical data

来源：评论

学校读者我要写书评

暂无评论

IoT-Enhanced Gesture Recognition System for Healthcare Applications

IoT-Enhanced Gesture Recognition System for Healthcare Appli...

引用

2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Krithika, L.B. Nallakaruppan, M.K. Baza, Mohamed Badr, Mahmoud Deora, Yuvraj Singh School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India College of Charleston Deparment of Computer Science CharlestonSC United States Polytechnic Institute Department of Networks and Computer Security UticaNY12201 United States

ISBN: (纸本)9798350351408

In response to the prevalent language barriers in medical settings, this paper proposes an innovative solution leveraging gesture recognition technology. Through the integration of deep learning models and Internet of Things (IoT) tools, the system aims to facilitate seamless communication between patients and healthcare providers. Initially, a deep learning model is developed using a personalized dataset containing various medical gestures, allowing for the recognition of nuanced patient expressions such as 'I have Fever' or 'Shoulder pain.' Additionally, the system incorporates IoT sensors to monitor vital signs like body temperature and pulse in real-time, ensuring comprehensive patient assessment. With the capability to continuously evolve and adapt to new gestures, this solution not only enhances accessibility but also improves the quality of healthcare delivery, particularly in underserved communities. By bridging the gap between language barriers and medical communication, this innovative approach holds promise for fostering inclusive and effective healthcare practices worldwide. In addressing the challenges posed by language barriers in medical interactions, this paper presents a comprehensive solution that combines gesture recognition technology with deep learning models and IoT devices. By capturing and interpreting patient gestures, such as indicating specific symptoms or discomfort, the system facilitates clear communication between patients and doctors. Furthermore, the integration of IoT sensors enables continuous monitoring of vital signs, allowing for timely interventions and personalized care. With its ability to adapt and expand to accommodate new gestures and medical scenarios, this solution represents a significant advancement in healthcare accessibility and quality. By leveraging cutting-edge technology, this approach has the potential to revolutionize healthcare delivery, particularly in areas with limited access to linguistic resources or medi

关键词： Gesture recognition

来源：评论

学校读者我要写书评

暂无评论

PrivShape: Extracting Shapes in Time Series under User-Level Local Differential Privacy 40

PrivShape: Extracting Shapes in Time Series under User-Level...

引用

40th IEEE International Conference on Data engineering, ICDE 2024

作者： Mao, Yulian Ye, Qingqing Hu, Haibo Wang, Qi Huang, Kai Southern University of Science and Technology Department of Computer Science and Engineering China The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Hong Kong Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology China School of Computer Science and Engineering Macau University of Science and Technology China

ISBN: (纸本)9798350317152

Time series have numerous applications in finance, healthcare, IoT, and smart city. In many of these applications, time series typically contain personal data, so privacy infringement may occur if they are released directly to the public. Recently, local differential privacy (LDP) has emerged as the state-of-the-art approach to protecting data privacy. However, existing works on LDP-based collections cannot preserve the shape of time series. A recent work, PatternLDP, attempts to address this problem, but it can only protect a finite group of elements in a time series due to ?-event level privacy guarantee. In this paper, we propose PrivShape, a trie-based mechanism under user-level LDP to protect all elements. PrivShape first transforms a time series to reduce its length, and then adopts trie-expansion and two-level refinement to improve utility. By extensive experiments on real-world datasets, we demonstrate that PrivShape outperforms PatternLDP when adapted for offline use, and can effectively extract frequent shapes. © 2024 IEEE.

关键词： Time series

来源：评论

学校读者我要写书评

暂无评论

Attention-Guided CNN with LSTM for Temporal Gastrointestinal Disease Progression Prediction in Endoscopic Video Sequences 4

Attention-Guided CNN with LSTM for Temporal Gastrointestinal...

引用

4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Mohamad, Fayazoddin Dhumpati, Raghu Reenadevi, R. Rao, P.S.V. Srinivasa Sutaria, Kamal Kulkarni, Shirish NY United States Bahrain Polytechnic Department of Computer Science & Engineering Bahrain City Bahrain Salem India Joginpally B R Engineering College Department of Computer Science and Engineering Hyderabad India Parul institute of Engineering and Technology Parul University Faculty of Engineering and Technology Department of Computer Science and Engineering Post Limda India Faculty of Law Pune India

ISBN: (纸本)9798350352931

The detection and tracking of changes in the progression of GI disease using endoscopic video analysis remains difficult due to temporal changes and image complexity. Proper models of prediction are critical in diagnosing individuals at an early stage and offering them treatment. In the work presented in this paper, we introduce novel Attention-Guided Convolutional Neural Network (AG-CNN) combined with LSTM networks to analyze temporal endoscopic video sequences. It uses spatial attention to catch necessary areas and temporal attention to capture assertive illness transformation in video frames. For disease progression prediction the proposed model yield 94.8% accuracy with 93.2% sensitivity and 95.6% specificity. The attention mechanism increased the feature extraction speed by 22 percent when compared to traditional CNN-LSTM classes. Independent validation checks confirmed that the two models produced a prediction concordance of 92.7% based on different patient samples. The overall performance of temporal GI disease progression prediction by the AG-CNN-LSTM framework shows potential usage in clinical diagnosis by providing automated disease progression monitoring and treatment planning. © 2024 IEEE.

关键词： Video analysis

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：