检索结果-内蒙古大学图书馆

[Wearable Smart Cardiac Care: Take Vipasyana for Example]

Hu li za zhi The journal of nursing 2024年第3期71卷 13-19页

作者： Hsi-Lu Chao Sau-Hsuan Wu PhD Associate Professor Department of Computer Science National Yang Ming Chiao Tung University Taiwan ROC. hsiluchao@nycu.edu.tw. PhD Professor Institute of Communications Engineering National Yang Ming Chiao Tung University Taiwan ROC.

The most direct challenge to the healthcare system in Taiwan associated with population aging is the chronic and growing shortfall in medical and nursing practitioners. The integration of artificial intelligence, Internet of Things, and advanced 5G communication technologies is making healthcare more streamlined and intuitive to improve healthcare service efficiency, encourage cross-disciplinary cooperation to improve patient health management, and introduce and mainstream innovative service models such as telecare. To this end, the wearable smart cardiac care system 'Vipasyana', jointly developed by the interdisciplinary research team of the departments of Electrical engineering and computer science at National Yang Ming Chiao Tung University, not only has an innovative fully functional wireless wearable electrocardiograph but also uses machine learning-based detection/prediction methods to detect and monitor for emergencies such as myocardial infarction, ventricular tachycardia/ventricular fibrillation, and heart failure. To handle the real-time streaming sensing data generated during clinical trials, a big data real-time streaming framework based on Apache Storm and microservices has also been integrated. In addition, a mobile phone app and virtual reality 3D ward round system providing a user-friendly management and monitoring tool for medical staffs were developed and integrated into the system. The 'Vipasyana' system is capable of providing precision medical services to individual patients.

关键词： Apache storm big data real-time streaming framework artificial intelligence heart failure rehabilitation care intelligent cardiac healthcare internet of things virtual reality-based 3D ward round system

来源：评论

学校读者我要写书评

暂无评论

Heart Rate Anomaly Detection in Healthcare Using Elliptic Envelope and Local Forest

引用

Procedia computer science 2025年 258卷 1677-1687页

作者： Chandralekha E S. Vinodhini Kandasamy V Rama P Assistant Professor Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India-600062 Assistant Professor Department of Information Technology Velammal Engineering College Chennai Tamilnadu India-600062 Assistant Professor Panimalar Engineering College Chennai Tamilnadu India – 600123 Assistant Professor Department of Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai Tamilnadu India-603203

Medical professionals use Anomaly Detection(AD) to identify patients with potential health problems in the heart rate data (HRD), an essential metric related to cardiovascular health. In this research, the most effective AD techniques were identified in detecting unusual variations of the heart rate pattern. Synthetic HRD data with anomalous rates of 15%, 20%, and 25% was generated and standardized it using min-max scaling for consistency among the datasets. Normal HR was based on function with a mean of 70 and a standard deviation of 5 while anomalous values were generated with a mean of 120 and a standard deviation of 10. Among the evaluated models (Elliptic Envelope, one-class SVM, Local Outlier Factor, and Isolation Forest), the Elliptic Envelope (EE) and Isolation Forest (IF) models obtain the best scores in terms of recall and precision – 0.92 and 0.89, respectively. Overall, the results of this research indicate that equally robust AD models should be implemented within clinical systems and wearable devices that serve as a digital space for early cardiac risk identification, prompting adequate intervention and, as a result, health improvement.

关键词： Anomaly Detection Heart Rate Data Isolation Forest Local Outlier Factor One-Class SVM Elliptic Envelope

来源：评论

学校读者我要写书评

暂无评论

Performance Prediction of Students Using Machine Learning Algorithms 2nd

Performance Prediction of Students Using Machine Learning Al...

引用

2nd Conference of Innovative Product Design and Intelligent Manufacturing System, IPDIMS 2020

作者： Gadde, Sai Sudha Anand, Dama Sasidhar Babu, N. Pujitha, B.V. Sai Reethi, M. Pradeep Ghantasala, G.S. Assistant Professor Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Student Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Vijayawada India

ISBN: (纸本)9789811902956

To predict the accurate future performance of the students, we consider the data like student’s past and present academic records. It is very important to complete student’s academics in a successful way. The problem is to identify the students whose performance is poor in their courses. So according to their performance and capability there will be a chance to take some remedial actions. There are many literatures on predicting the grades and improving their accuracy. In this paper, we predict the final grades using naive Bayes algorithm and linear regression to predict the final performance of students so that we can get more accurate values. It issues a forecast for every student exclusively, when the expected exactness of the prediction is adequate. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Linear regression Naive Bayes algorithm Performance prediction

来源：评论

学校读者我要写书评

暂无评论

Bi-path Architecture of CNN Segmentation and Classification Method for Cervical Cancer Disorders Based on Pap-smear Images

IAENG International Journal of Computer Science

引用

IAENG International Journal of computer science 2021年第3期48卷 1-9页

作者： Desiani, Anita Erwin, Member Suprihatin, Bambang Yahdin, Sugandi Putri, Ajeng I. Husein, Fathur R. Assistant Professor of Mathematics Department and Phd candidate of Mathematics and Natural Science Faculty Universitas Sriwijaya Indralaya30662 Indonesia Associate Professor of Computer Engineering Department Computer Science Faculty Universitas Sriwijaya Indralaya30662 Indonesia Associate Professor of Mathematics Department Mathematics and Natural Science Faculty Universitas Sriwijaya Indralaya30662 Indonesia Head of Mathematics Department Mathematics and Natural Science Faculty Universitas Sriwijaya Indralaya30662 Indonesia Undergraduate student of Mathematics Department Mathematics and Natural Science Faculty Universitas Sriwijaya Indralaya30662 Indonesia An undergraduate student of Mathematics Department Mathematics and Natural Science Faculty Universitas Sriwijaya Indralaya30662 Indonesia

This paper is concern about segmentation on pap smear images to use for classification cervical cancer. A cervical cancer screening by recognizing the cell shape pattern on the Pap-smear image can provide information about the presence of cervical cancer Pap-smear. The manual screening process for classifying cells is a challenging endeavor prone to the risk of error. There are several studies on the classification of cervical cell images which are sometimes not segmented first. Segmentation is very important to get the features contained in the image of cervical cells including the cell nucleus and cytoplasm. However, the segmentation results are also determined by the quality of images. This study proposed 2 paths that had a combination of image segmentation and classification. Before segmentation, image enhancement was carried out to improve image quality using Normalization, CLAHE and Adaptive Gamma Correction. The first path, the segmentation based on the CNN architecture. The second path is a classification process to test the segmentation results used by applying the KNN and ANN methods. The accuracy (ACC) result at the segmentation path was used to measure the match between the location of the segmented pixels and the results on ground truth, while the accuracy at the classification path was the success of the machine in classifying or predicting the segmentation results based on the class label for each group according normal and abnormal Pap-smear's label. The ACC result obtained in segmentation was only 0.77. However, the results of the segmentation when used in classification to classify which Pap-smears were normal and which are abnormal could provide excellent accuracy results, which were above 0.9. The results of the performance of Sensitivity (SN), Specificity (SP) and F1-score on the segmentation path also only gave results below 0.72, but the performance on the classification path that used the results on the segmentation path gave well results, wh

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of ARIMA Time Series Model and Other Techniques for Cloud Workloads Performance Prediction 5th

Comparative Analysis of ARIMA Time Series Model and Other Te...

引用

5th International Conference on computer and Communication Technologies, IC3T 2023

作者： Mishra, Vishnu Kumar Mishra, Megha Dewangan, Bhupesh Kumar Amulya, P. Choudhury, Tanupriya Kotecha, Ketan Shri Shankaracharya Technical Campus Chhattisharh Bhilai India Department of Computer Science and Engineering OP Jindal University Raigarh India Department of Computer Science and Engineering Malla Reddy College of Engineering Telangana Secunderabad India CSE Department Symbiosis Institute of Technology Symbiosis International University Lavale Campus Maharashtra Pune412115 India Pune412115 India Research Professor CSE Dept. Graphic Era Deemed to be University Uttarakhand Dehradun248002 India

ISBN: (纸本)9789819997060

Accurate performance prediction of cloud workloads is essential for optimizing resource allocation, meeting service level agreements (SLAs), and ensuring efficient cloud service delivery. In this research paper, we conduct a comparative analysis of the ARIMA (Auto Regressive and Integrated Moving Average) time series model with other popular techniques for cloud workloads performance prediction. We evaluate the performance of ARIMA in comparison with other models, including machine learning algorithms and statistical methods, using real-world cloud workload performance data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

When Computing Follows Vehicles: Decentralized Mobility-Aware Resource Allocation for Edge-to-Cloud Continuum

引用

IEEE Internet of Things Journal 2025年

作者： Nezami, Zeinab Chaniotakis, Emmanouil Pournaras, Evangelos University of Leeds School of Electronic and Electrical Engineering Leeds United Kingdom University College London Associate Professor with the Ucl Energy Institute London United Kingdom University of Leeds Professor at the School of Computer Science Leeds United Kingdom

The transformation of smart mobility is unprecedented-Autonomous, shared and electric connected vehicles, along with the urgent need to meet ambitious net-zero targets by shifting to low-carbon transport modalities result in new traffic patterns and requirements for real-time computation at large-scale, for instance, self-driving cars and augmented reality applications. The cloud computing paradigm can neither respond to such low-latency requirements nor adapt resource allocation to such dynamic spatio-temporal service requests. This paper addresses this grand challenge by introducing a novel decentralized optimization framework for mobility-aware edge-to-cloud resource allocation, service offloading, provisioning and load-balancing. In contrast to related work, this framework comes with superior efficiency and cost-effectiveness under evaluation in real-world traffic settings and mobility datasets. This breakthrough capability of 'computing follows vehicles' proves to be superior to balance resource utilization in a highly cost-effective way, while preventing service deadline violations by 14%-34%. © 2014 IEEE.

关键词： Distributed Optimization Dynamic Resource Allocation Edge-to-Cloud Computing Multi-agent System Smart Mobility

来源：评论

学校读者我要写书评

暂无评论

Design of a Multiple Phased Convolution Model for Pre-emption of Breast Cancer via Extensive Learning Operations (MPC2EL)

引用

Procedia computer science 2024年 233卷 638-650页

作者： Pravin Malve Dr. Vijay Gulhane Research Scholar Department of Computer Science & Engineering Sipna College of Engineering & Technology Amravati 444607 India Professor Department of Information Technology Sipna College of Engineering & Technology Amravati 444607 India

Breast cancer diagnosis from mammographic scans is a complex challenge, demanding accurate and efficient methodologies. Existing approaches often rely on time-consuming manual feature extraction or lack scalability when employed with diverse clinical image sets. To address these limitations, this paper presents an innovative solution: the MPC2EL (Multiple Phased Convolution model for Pre-emption of Breast Cancer via Extensive Learning Operations). The core problem tackled by this research is the need for improved breast cancer detection, especially concerning scalability, accuracy, and practicality. To achieve this, MPC2EL leverages extensive learning operations and multiple phased convolutions to process extensive clinical datasets containing both cancerous and benign images. Our novel approach begins with the conversion of these images into multimodal feature sets, encompassing frequency, cosine, wavelet, and Gabor domains. These feature sets enable enhanced classification of images into different cancer stages. The proposed methodology lies in a Dual 1D Convolutional Neural Network (D1D CNN) architecture, capable of categorizing images initially into malignant and benign types and subsequently refining classification for malignant images. Experimental results reveal a substantial 5.9% increase in classification accuracy and a 4.5% improvement in recall compared to conventional deep learning methods. Moreover, the model demonstrates lower processing delay and higher precision, making it a promising candidate for real-world clinical deployment.

关键词： Cancer Mammogram Multiple Phased CNN Gabor Cosine

来源：评论

学校读者我要写书评

暂无评论

An efficacy analysis of data encryption architecture for cloud platform

引用

Procedia computer science 2023年 218卷 989-1002页

作者： Sheenam Malhotra Williamjeet Singh Research Scholar Department of Computer Science and Engineering Faculty of Engineering and Technology Punjabi University Patiala Punjab India Assistant Professor Department of Computer Science and Engineering Faculty of Engineering and Technology Punjabi University Patiala Punjab India

In recent times, cloud computing is being utilized largely for storage and information sharing purposes in several established commercial segments, particularly those where online businesses are prevalent, such as Google, Amazon, etc. Cloud system presents several benefits to users in terms of easy operations, low implementation, and maintenance expenses. However, significant risks are encountered in the data security procedures of cloud systems. Although the area is frequently being analyzed and reformed, the concern of cloud data protection and user reliability remains under uncertainty due to growing cyber-attack schemes as well as cloud storage system errors. To deal with this risk and contribute to the endeavor of providing optimal data security solutions in cloud data storage and retrieval system, this paper proposes a Symmetric Searchable Encryption influenced Machine Learning based cloud data encryption and retrieval model. The proposed model enhances data security and employs an effective keyword ranking approach by using an Artificial Neural Network. The comparative assessment of the proposed model against multiclass SVM and Naïve Bayes has established the better operational potentiality of the model. The effectiveness of the proposed work is justified by the association between high TPR and low FPR. Further, a low CCR of 0.6973 adds up to the success of the proposed work.

关键词： Cloud Computing Symmetric Searchable Encryption Machine Learning

来源：评论

学校读者我要写书评

暂无评论

Evaluative Analysis of Secure approaches in Simulated Real-World Contexts

Evaluative Analysis of Secure approaches in Simulated Real-W...

引用

International Conference on ICT in Business Industry & Government (ICTBIG)

作者： A Kannagi Pavan Chaudhary Muthupandi G Associate Professor Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Assistant Professor Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Associate Professor Department of Electronics and Communication Engineering Presidency University Bangalore Karnataka India

This paper introduces a novel methodological approach to evaluating security measures within simulated real-world contexts. By leveraging three distinct algorithms Vulnerability Scoring Algorithm (VSA), the Attack Simulation Algorithm (ASA), and the Impact Assessment Algorithm (IAA) this provides an exhaustive analysis of potential vulnerabilities, simulate cyberattacks based on these vulnerabilities, and gauge the consequences of successful breaches. The Proposed method's foundational algorithm, VSA, quantifies vulnerabilities' severity, whereas ASA simulates realistic attacks and calculates the likelihood of their success. Lastly, the IAA assesses the potential impact of successful attacks. Collectively, these algorithms offer a rigorous and holistic evaluation of security measures. The findings visualized and underscored the superior performance of our proposed method compared to traditional approaches. This research contributes a valuable tool set for organizations seeking to fortify their security posture in an ever-evolving threat landscape.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Optimized Prognostic Models for Oral Cancer Survival using Feature Selection Methods

引用

Procedia computer science 2024年 235卷 1832-1840页

作者： Deepali Neelam Goel Padmavati University Institute of Engineering and Technology Panjab University Chandigarh 160014 India Assistant Professor Department of Computer Science Guru Nanak College Budhlada 151502 India Department of Computer Science Punjab Engineering College (deemed to be university) Chandigarh 160012 India

Accurately predicting survival time holds significant importance in various medical and scientific domains. This study proposes a novel survival prediction model for oral cancer using multi-omics data including clinical, DNA methylation, copy number alteration, and mRNA data. The proposed model incorporates four feature selection methods, namely LASSO, Elastic Net, Random Forest Regressor, and Recursive Feature Elimination (RFE), to improve the accuracy. The findings reveal that RFE outperforms the other methods, achieving the highest C-Index of 0.944. This is a significant improvement over previous models, which have a c-index of 0.694 using only clinical data and 0.916 using multi-omics data. In terms of precision, the Random Forest Regressor is the most precise with an MSE of 18.37, followed by RFE of 21.61. Notably, both RFE and Random Forest Regressor outperformed LASSO and Elastic Net in predictive accuracy. Additionally, the results highlight the consistency of RFE, as indicated by its low Median Absolute Error (MedAE) of 2.86. Random Forest Regressor also performed well in this regard, with a MedAE of 4.05. In contrast, Elastic Net exhibited a higher MedAE of 4.55, while LASSO showed more variability, with a MedAE of 8.58. These findings offer valuable insights into survival analysis and feature selection, helping researchers and practitioners select appropriate approaches for survival time prediction, and improving accuracy and reliability in critical applications.

关键词： Oral Cancer Survival Time Feature Selection Accuracy

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：