检索结果-内蒙古大学图书馆

Cloud-Based Framework for Data Exchange to Enhance Global Healthcare

Procedia computer science 2024年 241卷 570-575页

作者： Ammerha Naz Muhammad Ali Sehrish Munawar Cheema Ivan Miguel Pires Dept. of Computer Science University of Sialkot Sialkot Punjab Dept. of Software Engineering Superior University Lahore Pakistan Dept. of Computer Science University of Management and Technology Lahore Pakistan Instituto de Telecomunicações Escola Superior de Tecnologia e Gestão de Águeda Universidade de Aveiro Águeda Portugal

Healthcare is a global pillar, with a surge in the adoption of information technology, particularly in hospital information systems (HIS). However, global protocols are needed to meet the growing demand for data interchange, practical implementations for sharing healthcare data among facilities, and a pressing need for processing and storage infrastructure to handle the escalating volume of healthcare data. This study proposes a solution for efficient data transmission using electronic health records (EHR) and Platform-as-a-Service (PaaS) to leverage cloud computing resources. This framework's architecture boasts robustness and adaptability, providing all registered software programs access to data interchange services. Through a comprehensive examination of the framework's structure, the essay also explores the most effective data-sharing methods. It identifies the healthcare system's optimal EHR data model. According to multiple healthcare experts, the operational building of this framework is expected to catalyze the growth of healthcare institutions both nationally and within specific industries.

关键词： Healthcare Hospital information systems (HIS) Electronic health records (EHRs) Cloud computing. Data transmission

来源：评论

学校读者我要写书评

暂无评论

Mayfly Optimization Algorithm with Bidirectional Long-Short Term Memory for Intrusion Detection System in Internet of Things 3

Mayfly Optimization Algorithm with Bidirectional Long-Short ...

引用

3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Pillai, Sanjaikanth E. Vadakkethil Somanathan Polimetla, Kiran Alsalami, Zaid Pareek, Piyush Kumar Kumar, Deepak School Of Electrical Engineering And Computer Science University Of North Dakota Grand Forks United States Adobe Inc. Dept. Of Information Technology AustinTX United States College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute Of Technology Department Of Aiml And Ipr Cell Bengaluru India University Of The Cumberlands Department Of Information Technology WilliamsburgKY United States

ISBN: (纸本)9798350318609

The rapid development of number of connected devices exchange sensitive and personal significant data through Internet of Things (IoT)-assisted global network, attacks which are targeting security services are also enhancing every day. This paper proposed a Mayfly Optimization Algorithm (MOA) with Bidirectional Long-Short Term Memory (BiLSTM) for Intrusion Detection System (IDS) in IoT. The MOA is used for feature selection which select relevant features through balancing exploration and exploitation. The BiLSTM is used for classifying network into attack and normal which process the data in both forward and backward directions, allowing them to capture dependencies between events that might be far apart in sequence. The min-max normalization is used to normalize the continuous features from NSL-KDD, ToN-IoT and UNSW-NB15 datasets. The recall, accuracy, f1-score and precision are considered to evaluate MOA-BiLSTM performance. The MOA-BiLSTM realizes accuracy of 99.25%, 89.61% and 99.35% accuracy for NSL-KDD, ToN-IoT and UNSW-NB15 datasets when compared to existing techniques such as LSTM and Deep Neural Network (DNN). © 2024 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Technique for Real-Time Forest Fire Detection from Video Streams

Deep Learning Technique for Real-Time Forest Fire Detection ...

引用

Intelligent Systems and Embedded Design (ISED), International Conference on

作者： Nidhi Gupta Sujata Chakravarty Satyabrata Dash Dept. of Computer Science & Engineering Centurion University of Technology & Management Odisha India Dep. of Computer Science and Engineering Gandhi Institute of Technology & Management (GITAM Deemed to be University) Andhra Pradesh India

Forest fires are currently the most common issue that we are dealing with. The resources that have been lost are irreplaceable. Forest fires are uncontrollable fires that occur in nature as a result of human involvement or any other natural event. Forest fires can be so large that putting them out takes a long time. Contrasting with previous years, there has been a striking surge in the frequency of forest fires. Changes in climatic conditions are the key factor affecting the scope of forest fires. Long dry spells in the spring and summer make the woodlands particularly vulnerable. The extent of forest fires is mostly determined by weather conditions such as precipitation and wind, as well as the terrain's architecture. Long dry spells in the spring and summer make the woodlands particularly vulnerable. The extent of forest fires is mostly determined by weather conditions such as precipitation and wind, as well as the terrain's architecture. The incorporation of a Video Fire Detection model significantly enhances the efficiency of fire detection systems, particularly in large spaces. Establishing a video fire detection system involves focusing on spatial, spectral, and temporal indicators, as well as flame picture segmentation, recognition, tracking, and prediction. Analytical techniques, specifically utilizing ResNet50 and VGG16 models, can be applied to identify pool fire images through these segmentation processes, contributing to more robust fire detection capabilities.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Design and Characterization of a Ka-Band Parasitic Patch Antenna for 5G Applications

Design and Characterization of a Ka-Band Parasitic Patch Ant...

引用

International Conference on Communication and Electronics Systems (ICCES)

作者： Kahkashan Kouser Mohammad Hannan Faizan Shafi Mohammad Aknan Dept. of Computer Science & Engineering Motihari College of Engineering (MCE) Motihari Motihari Bihar India Dept. of Electrical and Electronics Engineering Rajiv Gandhi Institute of Petroleum Technology Amethi Uttar Pradesh India Dept. of Electrical Electronics and Communication Engineering Indian Institute of Technology Dharwad India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

He design of parasitic patch antennas for high-frequency applications, such as 5G millimeter-wave (mm-wave) communication, presents several challenges, including achieving high gain, wide bandwidth, and efficient impedance matching within a compact structure. Recent techniques, such as the use of parasitic elements and stepped impedance feedlines, have shown promise in enhancing antenna performance. However, these methods still face limitations related to fabrication complexity, signal degradation, and reliability under varying conditions. This work introduces a circular patch antenna with a parasitic reflector element specifically designed for Ka-band applications at 28 GHz. The proposed design addresses these challenges by optimizing the placement of parasitic elements to achieve high gain, a wide bandwidth of 2 GHz, and stable VSWR performance across the operating range. The single-layer, single-side etching structure further simplifies fabrication, making it suitable for compact wireless *** design of parasitic patch antennas for high-frequency applications, such as 5G millimeter-wave (mm-wave) communication, presents several challenges, including achieving high gain, wide bandwidth, and efficient impedance matching within a compact structure. Recent techniques, such as the use of parasitic elements and stepped impedance feedlines, have shown promise in enhancing antenna performance. However, these methods still face limitations related to fabrication complexity, signal degradation, and reliability under varying conditions. This work introduces a circular patch antenna with a parasitic reflector element specifically designed for Ka-band applications at 28 GHz. The proposed design addresses these challenges by optimizing the placement of parasitic elements to achieve high gain, a wide bandwidth of 2 GHz, and stable VSWR performance across the operating range. The single-layer, single-side etching structure further simplifies fabrication, making it suitable

关键词： Wireless communication Fabrication Performance evaluation 5G mobile communication Patch antennas Bandwidth Impedance Ka-band Gain Periodic structures

来源：评论

学校读者我要写书评

暂无评论

RSZero-CSAT: Zero-Shot Scene Classification in Remote Sensing Imagery using a Cross Semantic Attribute-guided Transformer

RSZero-CSAT: Zero-Shot Scene Classification in Remote Sensin...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Damalla Rambabu Swetha G Rajeshreddy Datla Vishnu Chalavadi C Krishna Mohan Department of Computer Science and Engineering Indian Institute of Technology Hyderabad Dept. of Space Advanced Data Processing Research Institute (ADRIN) Secunderabad India Indian Institute of Technology Tirupati India

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Zero-shot learning (ZSL) based scene classification aims to recognize unseen classes by transferring semantic information from seen classes. The applicability of ZSL for scene classification in remote sensing images becomes challenging due to the complexity of scenes. Earlier attention-based methods are ineffective for extracting discriminative region-based features within a single image. This limitation hinders their ability to achieve transferability and accurately localize object attributes, which is essential for extracting discriminative region-based features. Hence, we propose a method for zero-shot scene classification in remote sensing images using a cross-semantic attribute-guided Transformer named RSZero-CSAT. Firstly, the semantic information is acquired using shared remote sensing semantic attributes to localize object attributes that characterize discriminative region features. Then, a Transformer in RSZero-CSAT is employed to localize object attributes within visual features accurately, enhancing the effectiveness of semantic information transfer in ZSL. Specifically, the RSZero-CSAT employs a semantic attribute → visual Transformer (SAVT) and a visual → semantic attribute Transformer (VSAT) components to extract visual features guided by semantic attributes and semantic attribute features guided by visual features, respectively. Further, SAVT and VSAT mutually learn and collaborate to obtain semantically enriched visual representations, leveraging prediction-level and feature-level semantic collaborative losses for capturing crucial semantic information. Finally, the semantically enriched visual representations obtained from SAVT and VSAT are combined to facilitate visual-semantic interactions in collaboration with class semantic vectors to classify ZSL. Our experimental results demonstrate the impact of RSZero-CSAT in improving the performance of unseen classes on four scene classification benchmark datasets in remote sensing images. The code is avai

关键词： Visualization Scene classification Zero-shot learning Semantics Collaboration Benchmark testing Feature extraction

来源：评论

学校读者我要写书评

暂无评论

A Review On Applications of Blockchain

A Review On Applications of Blockchain

引用

Cybernetics, Cognition and Machine Learning Applications (ICCCMLA), IEEE International Conference on

作者： G. Sowmya Dept of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Any organisation that saves structured data has the potential to transform as a result of blockchain technology. Blockchain is a system of shared distributed ledgers used to store internet data. The way the information is handled makes it special. It is enduring, decentralised, open, and safe. Given that the blockchain's initial foray into the finance sector is regarded as largely successful with bitcoin and cryptocurrencies, think tanks and technocrats are looking into the potential applications of this revolutionary technology in a variety of industrial sectors, including health care, retail, real estate, and others. Some governments' goals and ambitions have already been altered by blockchain technology. Countries like the US, UK, China, Australia, and Singapore are investing in blockchain.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Deep Transfer Learning Training Strategy for Inter-Subject Classification of EEG Signal 28

A Deep Transfer Learning Training Strategy for Inter-Subject...

引用

28th National and 6th International Iranian Conference on Biomedical engineering, ICBME 2021

作者： Sameri, Javad Zarooshan, Hesam Jahed-Motlagh, M.R. Iran University of Science and Technology Dept. Computer Engineering Tehran Iran

ISBN: (纸本)9781665499842

Electroencephalogram(EEG) signals are generally available in small limited quantities, and there are considerable variabilities between individual and recording sessions. Thus it is crucial to obtain a model capable of finding discriminative features invariant to these differences. For this purpose, Convolutional Neural Networks (CNN) proved to be suited for detecting Spatio-Temporal features that are discriminative for these signals. This paper introduces Stage-Training as a variation of fine-Tuning to train end-To-end CNN models explicitly designed for learning discriminative features from EEG recordings. The Stage-Training approach specifically addresses the challenges of finding a model invariant to inter-subject differences when fine-Tuning fails due to the particular structure of these models. Using this approach, which conducts multiple stages of fine-Tuning in transfer learning, we significantly increased the overall accuracy of the EEGNet by 9%. Our strategy's implementation can be found at: https://***/mj-sam/stage-Trans. © 2021 IEEE.

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

A Deep Learning based Bangladeshi Vehicle Classification using Fine-Tuned Multi-class Vehicle Image Network (MVINet) Model

A Deep Learning based Bangladeshi Vehicle Classification usi...

引用

Next-Generation Computing, IoT and Machine Learning (NCIM), International Conference on

作者： Tanvir Ahmed Farzana Sharmin Mou Abu Anas Ibn. Samad Dept. of Computer Science and Engineering Dhaka University of Engineering & Technology Dhaka Gazipur Bangladesh Dept. of Computer Science University of Birmingham Birmingham UK Computer Vision Engineering Lab Sigmind.ai Limited Dhaka Bangladesh

The classification of vehicles is an important task in many applications, including traffic management, surveillance, and law enforcement. In Bangladesh, vehicle classification has become increasingly challenging due to the rapid growth in the number of vehicles on the roads. In this study, a novel deep learning-based Bangladeshi vehicle classification model using fine-tuned Multi-class Vehicle Image Network (MVINet) based on DenseNet201 with four added layers has been proposed. Our approach uses convolutional neural networks (CNNs) to extract features from images of vehicles. To make our dataset, we employ a YOLO (You Only Look Once) model for initial vehicle detection, followed by manual filtering and augmentation of the dataset to improve classification accuracy. The proposed method demonstrates superior performance compared to manual data collection methods. We evaluated our approach using standard metrics such as accuracy, precision, recall, and F1 score. Our model achieved an accuracy of 97.06%, precision of 97.17%, recall of 97.24%, and F1-score of 97.12% indicating high performance in classifying vehicles. We also compared our approach with state-of-the-art techniques and found that the proposed deep learning-based approach outperformed them significantly. The proposed approach has the potential to improve traffic management and law enforcement in Bangladesh. It can be used to monitor traffic flow, detect traffic violations, and identify stolen vehicles. Overall, this study demonstrates the effectiveness of deep learning in Bangladeshi vehicle detection and classification and highlights the importance of using advanced technologies to address the challenges of a rapidly changing transportation landscape.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Global Asymptotic Stabilty Analysis of DC-DC Buck Converters

Global Asymptotic Stabilty Analysis of DC-DC Buck Converters

引用

Innovative engineering sciences and Technological Research (ICIESTR), International Conference on

作者： Kanimozhi Kannabiran B. Raja Mohamed Rabi Booma Jayapalan Krismadinata Thangapalani Lakshmanan Rajalakshmi K Dept. of Electrical and Electronics Engg. NPR College of Engineering & Technology Natham India Dept. of Mechanical Engineering Rathinam Technical Campus Coimbatore India Dept. of Electronics and Communication Engg. PSNA College of Engineering and Technology Dindigul India Centre for Energy and Power Electronics Research Universitas Negeri Pedang Indonesia Dept. of Computer Science and Engineering Sri Sairam Institute of Technology Chennai India Dept. of Computer Science and Engineering NPR College of Engineering & Technology Natham India

ISBN: (数字)9798350348637

ISBN: (纸本)9798350348644

Stability of power electronic converters is one of the most important problems related to their dynamics. The problems related to global asymptotic stability is discussed here. The design example used is DC-DC buck converter with Adaptive sliding mode control. An averaged small signal model is derived and transfer functions are obtained. Stability is also verified using conventional techniques by MATLAB simulation. Simulation results and mathematical proof shows that closed loop system designed is stable and a fast response is obtained. Simulated results show that the simulated converter system acquires the excellent robustness feature of achieving a stable steady-state and fast transient response under variable operating points. A prototype model has been developed and simulated results are compared with experimental results. The results validate the stability analysis of Dc-Dc buck converter

关键词： Asymptotic stability Transient response Buck converters Simulation Prototypes Stability analysis Mathematical models Robustness Steady-state Load modeling

来源：评论

学校读者我要写书评

暂无评论

Sign Language Detection Using Action Recognition LSTM Deep Learning Model

Sign Language Detection Using Action Recognition LSTM Deep L...

引用

Network, Multimedia and Information technology (NMITCON) International Conference on

作者： N Snehalatha S Mohana Kumar H Kavitha S Nisha Puttabore Gowda Sj Krishna Prasad Dept. of Computer Science and Engineering JSS Academy of Technical Education Bangalore India Dept. of CSE(Cyber Security) Ramaiah Institute of Technology Bangalore India Dept. of Information Science Siddaganga Institute of Technology Tumukur India Dept. of CSE(AI and ML) Ramaiah Institute of Technology Bangalore India Dept. of ME Ramaiah Institute of Technology Bangalore India Dept. of EC University Visvesvaraya College of Engineering Bangalore India

ISBN: (数字)9798350372892

ISBN: (纸本)9798350372908

This project aims to develop a system that can recognize sign language gestures in real-time using computer vision techniques. The system is designed to bridge the communication gap between individuals with different hearing abilities by enabling accurate and effective communication through sign language. To achieve this, an image datasets is created, consisting of labeled images of sign language gestures for alphabets, digits, or other signs. This datasets is used to train a deep neural network using transfer learning techniques. Transfer learning involves leveraging the knowledge gained from pre-trained models, such as VGG-16 and ResNet, to accelerate the training process and improve the accuracy of the model. The trained deep neural network is then deployed in a real- time setting where it processes video feeds or live camera feeds to recognize and interpret sign language gestures. The system is designed to be user-friendly, with an intuitive interface that can be easily used by individuals with hearing difficulties or those who do not understand sign language The accuracy of the system is evaluated using various performance metrics such as accuracy, precision, recall, and F1 score. The system is tested in different environments, including noisy and low-light settings, to ensure that it can recognize sign language gestures accurately. Overall, this work demonstrates the potential of computer vision and deep learning techniques to improve communication for individuals with hearing difficulties. It also highlights the importance of developing accessible technologies that can enable effective communication and improve the quality of life for individuals with disabilities

关键词： Deep learning Sign language computer vision Accuracy Face recognition Transfer learning Auditory system Real-time systems Mobile applications Feeds

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：