检索结果-内蒙古大学图书馆

2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Ahamad, Rayees Mishra, Kamta Nath Birla Institute of Technology Department of Computer Science and Engineering Mesra India

ISBN: (纸本)9798350370188

Knowledge Management (KM) and Artificial Intelligence (AI) at their core are about knowledge. AI provides the mechanism that allows a machine to obtain, acquire, process, and use information to execute tasks, as well as reveal or unlock knowledge that may be transmitted to people for improved strategic decision-making. The use intelligent approaches in the process of KDM (Knowledge Discovery and Management) can improve efficiency in the sense of time and accuracy. Intelligent approaches especially soft computing approaches have the ability to learn in any environment with the help of logic, reasoning, and other computing abilities. In this paper authors have analyses single ICM as well as Combined ICM those provide the correct and reasonable solutions in the form of knowledge. The main aim of this study is to apply intelligent techniques to solve the day-to-day problems of our rural and smart digital societies. © 2024 IEEE.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Review Analysis on Osteoarthritis Using Deep Learning and Machine Learning Technologies for Arthritis Detection 7

A Comprehensive Review Analysis on Osteoarthritis Using Deep...

引用

7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Thomas, Tinu Prince Mary, S. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India St. Joseph's College of Engineering and Technology Department of Electronics and Computer Engineering Palai India

ISBN: (纸本)9798350372816

Osteoarthritis is a prevalent degenerative knee joint disease, causing considerable pain and mobility issues, thus impacting the independence and quality of life of millions. Deep learning and Machine Learning techniques have emerged as promising tools for enhancing the diagnosis, prognosis, and treatment of osteoarthritis (OA). These technologies allow for the extraction of valuable insights from medical images, clinical data, and genetic information, thereby improving patient care and management strategies. By employing advanced algorithms, healthcare professionals can improve early detection, and treatment plans, and accurately predict disease progression, ultimately leading to better outcomes for OA patients. This paper proposes a comprehensive review of various DL-based and ML-based methods for OA, spanning the years 2020 to 2024. It aims to provide valuable insights for researchers interested in exploring different models and potentially inspiring the development of integrated approaches for even more effective OA detection. © 2024 IEEE.

关键词： Joints (anatomy)

来源：评论

学校读者我要写书评

暂无评论

BlasT: Blood Clot Classification Model Using Transfer Learning Based Convolutional Neural Network 16

BlasT: Blood Clot Classification Model Using Transfer Learni...

引用

16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Mishra, Shruti Pal, Sujata Dey, Sweta Indian Institute of Technology Department of Computer Science & Engineering Ropar India

ISBN: (纸本)9798350383119

Blood cells contain a plethora of clotting factors vital for sustaining human life. Blood coagulation manages excessive blood loss from the blood vessels of an injured individual. Blood clots can appear due to various medical reasons in humans, such as platelet aggregation and converting fibrinogen into fibrin. Consequently, blood clots generate multiple disorders and even elevate the risk of death when not promptly diagnosed. Therefore, the prompt identification and treatment of blood clotting are paramount. In this study, we introduced an analytical approach for detecting blood clots. Also, we introduced a blood clot classification model: Blood Clot Classification Model using Transfer Learning and Convolutional Neural Network (BlasT). Our evaluation results demonstrate an impressive accuracy rate of 93.99% for the proposed model: BlasT. The primary objective of this study is to establish an early clot classification model using transfer learning in conjunction with a Convolutional Neural Network (CNN) layer. Our proposed BlasT model can identify blood clots swiftly and accurately. Furthermore, it allows timely notifications to medical professionals. © 2024 IEEE.

关键词： Blood-clot CNN Machine Learning (ML) Red Blood Cells (RBCs) White Blood Cells (WBCs)

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Models for Heart Disease Prediction: Balancing Accuracy and Transparency 2

Machine Learning Models for Heart Disease Prediction: Balanc...

引用

2nd IEEE International Conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Gupta, Sukriti Tripathi, A. Srivastava, Chaynika Buddha Institute of Technology Department of Computer Science & Engineering Gorakhpur India

ISBN: (纸本)9798350368109

Heart disease continues to be the world's leading cause of death, so precise and trustworthy predictive models are required to enable early diagnosis and individualized treatment. This paper addresses the escalating global burden of heart failure, marked by increasing morbidity, mortality, and strain on healthcare systems, stemming from left ventricular systolic and diastolic dysfunction. When compared to conventional methods, machine learning proves to be a more accurate tool for predicting heart diseases. Machine learning (ML) models have shown great promise in predicting heart disease, but their complexity often results in a trade-off between accuracy and interpretability. This study investigates various interpretable machine learning approaches, including decision trees, Mtree, Random Forest, K-means, to predict heart disease, while maintaining transparency. Utilizing a dataset comprising patient health records, including attributes like age, blood pressure, cholesterol levels, and electrocardiogram results, preprocess the data through normalization and feature selection to enhance model accuracy. Our study showcases the implementation of distinct algorithms, demonstrating their effectiveness in accurately detecting heart failure with an impressive accuracy range of approximately 89% to 92%. The study underscores the importance of balancing accuracy with transparency in developing ML models for healthcare applications, particularly in high-stakes scenarios like heart disease prediction. Keywords: Heart disease, Machine learning (ML), random forest, health records, decision trees, MTree. © 2024 IEEE.

关键词： Heart

来源：评论

学校读者我要写书评

暂无评论

An Enhanced Network Density Based Centrality for Influential Node Detection in Complex Networks

An Enhanced Network Density Based Centrality for Influential...

引用

2024 IEEE Region 10 Conference, TENCON 2024

作者： Mitra, Sourav Kumar Chakraborty, Susanta Kule, Malay Indian Institute of Engineering Science and Technology Department of Computer Science and Technology Shibpur Howrah India

ISBN: (纸本)9798350350821

Comprehending the dynamics of disease or information diffusion requires influential node detection in complex networks. Numerous centrality measures were developed for calculating the spreading capacity of a node. However, they fail to produce a high monotonic ranking of spreaders, which is a highly desired property. Two new measures, local density and extended local density are introduced in this paper to calculate the spreading impact of every node using the density of edges in the neighborhood of the node and the node degree. Our experimental results demonstrate that both of our proposed metrics offer a more consistent monotonic result for node ranking and better rank distribution than other centrality measures. © 2024 IEEE.

关键词： Complex networks

来源：评论

学校读者我要写书评

暂无评论

TruChit: A Blockchain-Based Trusted Chit Fund System with Creditworthiness Evaluation 2

TruChit: A Blockchain-Based Trusted Chit Fund System with Cr...

引用

2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Sharma, Akhilesh Chandrakar, Preeti Department of Computer Science & Engineering National Institute of Technology Raipur India

Chit fund is a peer-to-peer saving and borrowing scheme operated among trusted groups of people. It is a reliable source of funds in emergencies with no guarantor and low-interest rate. Despite their enduring benefits, chit funds face challenges related to trust, transparency, and security. At present, unscrupulous subscribers might join chit-funds, borrow money, and make payment defaults. This leads to disruptions in contribution cycle and affects overall functioning. Additionally, non-transparent record-keeping and transaction processes, hinder participants from verifying fund activities, creating a susceptible environment for fraud and malpractice. These challenges are an obstacle to the sustainability and trustworthiness of the chit-fund system. Therefore, to overcome aforementioned challenges, this paper proposes TruChit, a blockchain-based chit fund system with creditworthiness evaluation framework. It leverages the Adaptive Neuro-Fuzzy Inference System (ANFIS) to assess the creditworthiness of subscribers, which ensures the credibility and reliability of individual participants. Further, proposes permissioned blockchain-assisted chit-fund framework with role-based access control to instill trust, security, and transparency within chit-fund operations. Moreover, the efficacy of TruChit is evaluated by analyzing credit score dataset and achieves 93.5% accuracy, which is better than other approaches. © 2024 Elsevier B.V.. All rights reserved.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Efficient spectrum allocation for secondary users in cognitive radio network using round robin priority algorithm along with reservation channels

引用

Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 16715-16728页

作者： Suganthi, N. Meenakshi, S. Department of Computer Science and Engineering SRM Institute of Science and Technology Faculty of Engineering and Technology Ramapuram Tamil Nadu Chennai600119 India Tamil Nadu Chennai India

Cognitive Radio Network provides opportunistic spectrum access to unlicensed users in the absence of licensed users using dynamic spectrum access technology. The key challenge here is to impartially share the existing spectrum to all the secondary users in the network. A Round Robin Priority (RRP) algorithm along with reservation spectrum is proposed in this paper to impartially schedule available spectrum band and to provide continuous uninterrupted service to all secondary users. The QoS of secondary users are evaluated and compared with traditional spectrum allocation methods. The QoS parameters considered are blocking probability, the forced termination probability, spectrum utilization, average waiting time and the average delay of secondary users. The proposed Round Robin Priority Algorithm with Reservation scheme results reveals 22.82% reduction in Blocking Probability, 15.28% reduction in Forced Termination Probability, 12.54% reduction in average waiting time and 24.58% increase in Spectrum utilization when compared with traditional Constant Channel Aggregation Scheme. On comparing RRP with reservation with Variable Channel Aggregation schemes shows 19.32% reduction in Blocking Probability, 12.74% reduction in Forced Termination Probability and 10.57% increase in Spectrum Utilization. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Blocking probability

来源：评论

学校读者我要写书评

暂无评论

Biomedical signal processing for health monitoring applications: a review

引用

International Journal of Applied Systemic Studies 2023年第1期10卷 44-69页

作者： Sonawani, Shilpa Patil, Kailas Natarajan, Prabhu School of Computer Engineering and Technology Dr. Vishwanath Karad MIT World Peace University India Department of Computer Engineering Vishwakarma University India Department of Computer Science DigiPen Institute of Technology Singapore

Biomedical health monitoring systems are evolving rapidly and using non-invasive and cost effective sensors. These systems can monitor physiological parameters of the body to monitor health conditions and provide feedback. These new generation systems can use advance technologies to build intelligent systems for health monitoring and timely diseases detection and diagnosis. Biomedical signal processing and analysis of the patterns in the signals plays important role in building efficient systems, life sciences and research and is rapidly expanding in this domain. This review paper is significant as no existing review paper is giving complete information on biomedical signal processing phases as a whole. It makes four contributions;first, it gives activity flow for developing biomedical signal processing systems. Second, reviews various recent applications researched and kinds of low cost, non-invasive sensors used for biomedical health monitoring. Third, categorisation and enlisting of signal processing techniques like signal sampling, segmentation, filtering, feature extraction, dimensionality reduction and machine learning techniques in healthcare domain. Fourth, it reviews challenges in using signal processing techniques and gives future perspectives which can be helpful for the researchers to use advance techniques to build intelligent systems for reliable decision making. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Cost effectiveness

来源：评论

学校读者我要写书评

暂无评论

Detection of Neurodegenerative Disorder 8

Detection of Neurodegenerative Disorder

引用

8th IEEE International Conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Dhumane, Amol Mukherjee, Jasmita Kapadia, Jil Nayak, Aditi Jain, Himanshi Patil, Rahul Symbiosis Institute of Technology Department of Computer Science and Engineering Pune India Pimpri Chinchwad College of Engineering Department of Computer Engineering Pune India

ISBN: (纸本)9798350391770

Globally, neurodegenerative diseases like dementia and Alzheimer's represent a major danger to our healthcare system. Clinical evaluations, which may or may not be able to identify the modest cognitive changes that occur in the early stages of the disease, have historically played a major role in diagnosis procedures. Recent developments in machine learning technologies have demonstrated potential paths for enhancing diagnostic precision and have demonstrated that they are more than capable of identifying these illnesses. Thus, we employed CNN, SVM, and logistic regression to identify dementia in an image dataset. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

AVATAR-RETAIL: The Digital ShoppingWorld 15

AVATAR-RETAIL: The Digital ShoppingWorld

引用

15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Gahana, R. Ekeshwar, J. Nihal Jain, T.P. Shanthakumara, A.H. Department of Computer Science and Engineering Siddaganga Institute of Technology Tumakuru India

ISBN: (纸本)9798331300579

With cutting-edge functionality, 3D Shopping (Avatar Retail) promises to revolutionize the online shopping experience by seamlessly integrating gamification, social media, and augmented reality features. By immersing users in a realistic virtual shopping environment, the application aims to bridge the gap between traditional in-store purchases and the convenience of online buying. Through dynamic 3D navigation, avatar customization, and gamification components, Avatar Retail enhances user engagement and ensures seamless performance scaling on various platforms. Augmented reality plays a central role, allowing users to interact with products in a fully immersive online environment while facilitating instant communication and collaboration. Extensively tested across multiple platforms and devices, Retail Realm sets new standards for immersive online shopping, combining technology and user-centric design to make a significant impact on the e-commerce sector. © Grenze Scientific Society, 2024.

关键词： Gamification

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：