检索结果-内蒙古大学图书馆

An Immutable Framework for Smart Healthcare Using Blockchain Technology

computer Systems science & engineering 2023年第7期46卷 165-179页

作者： Faneela Muazzam A.Khan Suliman A.Alsuhibany Walid El-Shafai Mujeeb Ur Rehman Jawad Ahmad Department of Computer Science Quaid-i-Azam UniversityIslamabad45320Pakistan Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Computer Science Security Engineering LaboratoryPrince Sultan UniversityRiyadh11586Saudi Arabia James Watt School of Engineering University of GlasgowG128QQUK School of Computing Edinburgh Napier UniversityEdinburghEH105DTUK

The advancements in sensing technologies,information processing,and communication schemes have revolutionized the healthcare *** Healthcare Records(EHR)facilitate the patients,doctors,hospitals,and other stakeholders to maintain valuable data and medical *** traditional EHRs are based on cloud-based architectures and are susceptible to multiple cyberattacks.A single attempt of a successful Denial of Service(DoS)attack can compromise the complete healthcare *** article introduces a secure and immutable blockchain-based framework for the Internet of Medical Things(IoMT)to address the stated *** proposed architecture is on the idea of a lightweight private blockchain-based network that facilitates the users and hospitals to perform multiple healthcare-related operations in a secure and trustworthy *** efficacy of the proposed framework is evaluated in the context of service execution time and *** experimental outcomes indicate that the proposed design attained lower service execution time and higher throughput under different control parameters.

关键词： Blockchain technology healthcare applications cybersecurity services IoMT DoS EHR

来源：评论

学校读者我要写书评

暂无评论

On the Challenge of Generating Multivariate Time Series Data from Distributed Sensors in IoT-enabled Scenarios 24

On the Challenge of Generating Multivariate Time Series Data...

引用

14th International Conference on the Internet of Things, IoT 2024

作者： Bañuelos, Julián Jerónimo Costa-Requena, Jose He, Jiayuan Salim, Flora D. Sigg, Stephan Department of Information and Communications Engineering School of Electrical Engineering Aalto University Espoo Uusimaa Finland School of Computing Technologies RMIT University MelbourneVIC Australia Cumucore Oy Espoo Uusima Finland School of Computer Science and Engineering University of New South Wales Sydney Australia

ISBN: (纸本)9798400712852

Smart sensing environments supported by an Internet of Things (IoT) infrastructure are enabling a wide array of possibilities for data-intensive remote monitoring applications. Although nowadays it is possible to access and deploy complex setups of sophisticated sensors, the telecommunications infrastructures are struggling to deal with the massive amounts of generated data for transmission, processing, and storage from the diverse range of IoT devices. As data corruption and the loss of data are realistic scenarios in current IoT deployments, it is relevant to find an alternative solution that circumvents the limitations of the physical infrastructure for dealing with potential missing data in remote sensing environments. In this paper, we analyze the feasibility of reproducing missing sensor data due to a communication failure from correlated sources in the same experimental context. We rely on generative adversarial models for the prediction of acceleration data and evaluate the usefulness of the synthetic data with a standard human activity recognition (HAR) classifier. © 2024 Copyright held by the owner/author(s).

关键词： Time series

来源：评论

学校读者我要写书评

暂无评论

Comprehensive Architectural Framework for the engineering and Optimization of Mobile Software Ecosystems 4

Comprehensive Architectural Framework for the Engineering an...

引用

4th IEEE International Conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2024

作者： Tomar, Riya Chauhan, Naman Chauhan, Chhavi Tomar, Yash Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India University of Petroleum and Energy Studies Dehradun India CMR University Bengaluru Department of Computer Science and Engineering India

ISBN: (纸本)9798331528171

Mobile application development has become increasingly popular due to the widespread use of mobile devices. Mobile applications cater to various sectors such as business, gaming, social networking, and digital governance. This paper presents a framework to assist in the design and development of mobile applications, with a focus on selecting appropriate components from multiple alternatives. The framework is based on the COTS (Commercial Off-The-Shelf) paradigm and employs a structured first-order predicate calculus to formalize component requirements. It introduces an acceptance indicator equation that evaluates components based on vendor viability, maintainability, and matching features with system specifications. The framework aims to facilitate informed decision-making in selecting the best component for mobile applications. A case study demonstrates the practical application of the framework, which ultimately resulted in the selection of Certicom's Security Builder SSL for a wireless data transmission application. The framework's contribution lies in integrating maintainability into the early stages of the evaluation process, enabling modular specification, and assisting developers in making informed choices. © 2024 IEEE.

关键词： Mobile applications

来源：评论

学校读者我要写书评

暂无评论

VitalLink: NodeMCU IoT Solution for Vital Signs Surveillance 7

VitalLink: NodeMCU IoT Solution for Vital Signs Surveillance

引用

7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Ramitha Rajeswari, J. Chamanthi, Reddy Mary Posonia, A. Albert Mayan, J. Sathyabama Institute of Science And Technology Department of Computer Science And Engineering Chennai India Sathyabama Institute of Science And Technology School of Computing Chennai India

ISBN: (纸本)9798350359299

This research study proposes the development of comprehensive vital sign surveillance designed for real-time patient assessment. The system integrates multiple sensors, including the LM35 temperature sensor, Heartbeat Rate (HBR) and Blood Oxygen (SpO2) Sensor, DHT11 humidity and temperature sensor, OLED display, and gyroscope sensor, to provide a holistic approach to vital signs. The LM35 sensor is employed for accurate temperature measurement, providing crucial data for monitoring the patient's thermal status. This information is essential for detecting fever or abnormal temperature variations, aiding in the early identification of potential health issues. The HBR and SpO2 sensor plays a vital role in cardiovascular vital signs. The system can assess the patient's cardiac and respiratory well-being by measuring the heart rate and blood oxygen levels. Abnormalities in these parameters can trigger timely alerts, enabling swift medical intervention. The DHT11 sensor enhances the system's capabilities by monitoring ambient temperature and humidity. This additional environmental data contributes to a more comprehensive understanding of the patient's surroundings, ensuring that external factors are considered in the health assessment process. Integrating an OLED display serves as an intuitive user interface, providing real-time feedback on the measured parameters. This ensures that patients and healthcare providers can easily interpret and respond to the health data presented by the system. The display can showcase temperature, humidity, heart rate, blood oxygen levels, and other relevant information in a user-friendly format. Furthermore, the gyroscope sensor adds a layer of sophistication to vital sign surveillance by enabling the assessment of body movement and posture. This feature is particularly beneficial for patients with mobility issues or those prone to falls. The gyroscope data can be analyzed to detect sudden movements or abnormal postures, triggering alerts

关键词： Heart

来源：评论

学校读者我要写书评

暂无评论

A Novel Neural Network Approach to Proactive 3-D Beamforming

引用

IEEE Transactions on Cognitive Communications and Networking 2024年第3期11卷 1576-1596页

作者： Mallioras, Ioannis Yioultsis, Traianos V. Kantartzis, Nikolaos V. Lazaridis, Pavlos I. Zaharis, Zaharias D. Aristotle University of Thessaloniki School of Electrical and Computer Engineering Thessaloniki54124 Greece Santarcangelo di Romagna R and D Department Maggioli SpA Rimini47822 Italy University of Huddersfield School of Computing and Engineering HuddersfieldHD1 3DH United Kingdom

This study explores three-dimensional proactive beamforming at millimeter wave frequencies using transformer neural networks (TNNs), long short-term memory networks (LSTMs) and gated-recurrent units (GRUs). The proposed scheme aims to reduce beamforming latency by predicting future directions of arrival (DoAs) based on past observations, allowing the system to prepare beamforming weights proactively. We simulate an urban environment using OpenStreetMap data to generate realistic movement paths, creating a comprehensive dataset for training and evaluation. Our focus is on the predictive capacity of TNNs, LSTMs and GRUs to anticipate future DoAs, even in non-line-of-sight scenarios influenced by urban infrastructure. We detail the environment simulation setup, the ray-tracing mechanism as well as the movement generation process for pedestrians and vehicles. A statistical analysis on the prediction accuracy and response time is presented to assess the most accurate model and discuss the trade-offs between the architectures. In addition, an end-to-end AI-based proactive beamforming scenario is examined where zero-forcing is applied on moving users. This is to further demonstrate and evaluate the capabilities and the performance of each model. Our findings suggest that proactive beamforming can significantly enhance performance in dynamically changing urban landscapes, offering a promising avenue for future research and development in adaptive communication systems. © 2024 IEEE.

关键词： Direction of arrival

来源：评论

学校读者我要写书评

暂无评论

Spatially Attentive Scale Invariant Feature Modelling for Alzheimer's Disease Detection 9

Spatially Attentive Scale Invariant Feature Modelling for Al...

引用

9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Tripathy, Santosh Kumar Behera, Chandan Kumar Pandey, Pranjal Mohan Pandey, Vedant Verma, Pratishtha Vit Bhopal University School of Computing Science And Engineering Kothri-Kalan Sehore MP Bhopal India Nit Kurukshetra Department of Computer Science And Engineering Haryana India

ISBN: (纸本)9798350394474

The prevalence of Alzheimer's Disease (AD) patients has significantly increased recently, and early diagnosis is crucial. AI-aided solutions for diagnosing AD have been rising for decades. The majority of these solutions show the application of deep learning methods, both with and without transfer learning. However, a few issues have been noted that require attention in order to detect AD: a. there is a lack of scale-invariant feature modelling, and b. there is a lack of spatial attention paid to scale-invariant features. The suggested model addresses these issues in two ways: first, an efficient multiscale feature modelling module (EMFMM) is designed to exploit scale invariant features through multilayers each having multicolumn of dilated convolution layers. At each layer the multicolumn features are concatenated and forwarded to its subsequent layer. Secondly, the spatial convolution block attention (SCBA) is plugged in different layers of concatenated features to provide spatial attention over the multiscale features. The spatially attentive feature maps from several SCBAs are further concatenated and given to a multilayer neural network to employ disease forecasting. Two open-source datasets, OASIS and AD-Dataset, are used to demonstrate extensive experiment. The suggested model performs better than current best practices, with accuracy values on OASIS and AD-Dataset of 99.75% and 96.20%, respectively. © 2024 IEEE.

关键词： Multilayer neural networks

来源：评论

学校读者我要写书评

暂无评论

Precision Forecasting of Stock Prices: Leveraging XGBoost and Technical Indicator for Advanced Predictive Modeling 1

Precision Forecasting of Stock Prices: Leveraging XGBoost an...

引用

1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Yuvaraj, S. Chenni Kumaran, J. Vinston Raja, R. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Faculty of Engineering and Technology School of Computing Department of Computational Intelligence Kattankulathur Chennai India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529246

The investigation aims to evaluate the performance of machine learning techniques, particularly the XGBoost regression method, for stock price prediction with the help of technical indicators. The research targets the adjusted closing price to reduce the autocorrelation problem in time series. The Five Days and Ten Days Exponential Moving Averages (EMA-5 and EMA-10) are involved in refining feature selection. The performance of the model was evaluated using different error metrics, namely, Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R2). The results indicate that the XGBoost model, combined with EMA-5 and EMA-10, achieves high predictive performance, obtaining an r2 value of 0.98. It implies that incorporating short-term EMAs into advanced machine learning techniques drastically improves stock price forecasting. © 2025 IEEE.

关键词： Time series

来源：评论

学校读者我要写书评

暂无评论

Unravelling State-wise Crime Patterns with Ensemble Machine Learning and Data Visualization for Public Safety 2

Unravelling State-wise Crime Patterns with Ensemble Machine ...

引用

2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Sri Harsha, M.V.R. Akhil, M.H.S. Ronald Doni, A. Albert Mayan, J. Yovan Felix, A. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India Sathyabama Institute of Science and Technology School of Computing Chennai India

ISBN: (纸本)9798350349221

This study, detailed below, introduces a new method of handling crimes in particular states, using ensemble machine learning and data visualization techniques in particular, as a way to enhance public safety. Crime detection is a hard task for two concerns, the complexities of the crime and the enormity of the data. By means of random forest and AdaBoost ensemble learning techniques we can model sophisticated problems of the relationship between features such as crime data as a way to match predictions. The other point we stress is data visualization which allows us to comprehend the patterns and connections that have been derived from the ensemble learning process. In addition to the analysis of local crime data, the hybrid approach that we propose involves the inclusion of national crime data to make the identification of hidden crime patterns at state level possible. This move will enable the law enforcement agencies to gain a better understanding of the activities and to effectively respond to the crimes. The proposed methodology represents a, robust and efficient solution to statewise crime detection utilizing the advantages of machine learning and mapping tools. © 2024 IEEE.

关键词： Crime

来源：评论

学校读者我要写书评

暂无评论

An Automated Model for Predicting Weekly Tea Auction Prices at Colombo Tea Auction for Specific Elevations and Grades 6

An Automated Model for Predicting Weekly Tea Auction Prices ...

引用

6th International Conference on Advancements in computing, ICAC 2024

作者： Ariyaratne, Chandika Arachchi, Samantha Mathara Pallewatta, Pandula Karunanayaka, Kasun Seneviratne, Gihan Halloluwa, Thilina University of Colombo School of Computing Colombo Sri Lanka Nanjing University of Information Science and Technology Nanjing China The University of Queensland School of Electrical Engineering and Computer Science Australia

ISBN: (纸本)9798331517878

Sri Lanka's tea business has existed since the 1800s and maintains a high standard worldwide. However, rivalry is increasing as more competitors enter the market, and tea exporters strive to make their operations more cost-effective. The discovery led to the development of a weekly tea price auction prediction model for a selected tea grade, Broken Orange Pekoe Fannings (BOPF), across different elevations. The existing research suggested using Box and Jenkins models over Machine Learning (ML) models such as Artificial Neural Networks (ANN) and Convolution Neural Networks (CNN). The research also suggested using external factors to improve accuracy. Supply, Demand, and Rainfall were the primary factors. This analysis required data from multiple sources to be combined into one dataset for model building. The tea auction data was from another source, but the rest was in the public domain. For the fourth elevation, the research group employed the SARIMAX variation of Box and Jenkins models to analyze them separately. The results ranged from a Mean Absolute Percentage Error (MAPE) of 0.26% to 6.03%, a manageable and acceptable level of risk. For structured Comma Separated (CSV) file inputs, a new function was created to easily extract predictions from the generated model, improving model usability. © 2024 IEEE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Developing Efficient Game Theory Approaches for Spectrum Sharing in 6G Networks 5th

Developing Efficient Game Theory Approaches for Spectrum Sha...

引用

5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Ramola, Abhishek C. Francis, Jerrin Joe Savita Murugan, R. Department of Electrical Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India

ISBN: (纸本)9789819780426

This paper provides the development of green game idea procedures for spectrum sharing in upcoming 6G cellular networks. As dynamic spectrum get entry to keeps to advantage commercial interest, the want to broaden a sustainable framework to make sure powerful and relaxed spectrum sharing arises. To this stop, sport principle methods are seen as a legitimate candidate to facilitate robust spectrum sharing in 6G networks. This paper offers unique attention to the area of spectrum sharing and how the underlying game theory procedures may be leveraged to percentage spectrum efficiently. After an intensive review of existing methods and strategies, an overall performance evaluation of the numerous strategies is provided. Subsequently, the paper gives insights at the potential of game concept strategies in future 6G networks. Moreover, much game theoretic equipment are used for analytical as well as simulation experiments if you want to higher apprehend the dynamics of the spectrum sharing. Basic, this paper examines the effectiveness of each analytical and simulation-based totally game theoretic fashions for spectrum sharing in 6G networks, and gives a new version which may be used to improve the spectrum sharing dynamics in such networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Spread spectrum communication

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：