检索结果-内蒙古大学图书馆

Effectiveness of generative adversarial networks in denoising medical imaging (CT/MRI images)

Multimedia Tools and Applications 2024年 1-25页

作者： Ramadass, Sudhir Narayanan, Sreekumar Kumar, Rajiv K, Thilagavathi Data Analytics Sterck Systems Private Limited Chennai Nungambakkam India Network and Infrastructure Management Faculty of Engineering and Technology Botho University Gaborone Botswana School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India Department of Electronics and Communication Engineering Kumaraguru College of Technology Coimbatore India

For clinical analysis or medical intrusion, imaging technology is a potent tool for producing visual images of a body's inside. However, several existing methods include restricted access to high-quality and diverse datasets, overfitting, a lack of interpretability, high computational costs, varying medical imaging data, and ethical issues concerning patient privacy, consent, and bias. To overcome these issues, the effectiveness of generative adversarial networks in denoising medical imaging (CT/MRI images) is proposed. The input CT images are first gathered and preprocessed to reduce undesired noise using Altered Phase Preserving Dynamic Range Compression (APPDRC). The General Adversarial Network (GAN) is then given these preprocessed images to cope with the noisy areas of CT/MRI while also complying with maintaining image structures such as boundary details while preventing over-smoothing, and the weight parameters for the GAN are optimized using the Golden Eagle Optimization (GEO) technique. General Adversarial Network (GAN) has piqued the interest of academics studying medical image-denoising mechanisms and developing sophisticated medical image-denoising algorithms based on deep learning. In light of reducing the difficulty in feature learning and improving final diagnosis accuracy, data preprocessing is necessary and crucial in gan-based medical image denoising methods. This work focuses on GAN-based effective denoising approaches in CT/MRI. The potential challenges and research objectives are in the context of data preprocessing in medical image denoising. Because of how CT/MRI images are acquired, it was observed that noise in CT/MRI data follows the distribution. Generative Adversarial Networks (GANs) for denoising medical imaging, mainly CT and MRI images, have yielded promising results of 99% accuracy. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

HAM: Hybrid Associations Models for Sequential Recommendation (Extended abstract) 39

HAM: Hybrid Associations Models for Sequential Recommendatio...

引用

39th IEEE International Conference on data engineering, ICDE 2023

作者： Peng, Bo Ren, Zhiyun Parthasarathy, Srinivasan Ning, Xia The Ohio State University Department of Computer Science and Engineering United States The Ohio State University Department of Biomedical Informatics United States The Ohio State University Translational Data Analytics Institute Department of Computer Science and Engineering United States The Ohio State University Translational Data Analytics Institute Department of Biomedical Informatics Department of Computer Science and Engineering United States

ISBN: (纸本)9798350322279

Sequential recommendation aims to identify and recommend the next few items of users' interest. It becomes an effective tool to help users select their favorite items from a variety of options. A key challenge in sequential recommendation is to learn the patterns and dynamics, which are most pertinent to inform future interactions of users. With the prosperity of deep learning, many deep models, particularly based on recurrent neural networks [1] and with attention mechanisms [2] , [3] , have been developed for sequential recommendation purposes. However, our analysis demonstrates that, these deep models, particularly those with attention mechanisms, may not always learn meaningful attention weights from the extremely sparse recommendation data, and thus, could degrade the recommendation performance. Therefore, in this study, instead of deep models, we develop novel, effective and efficient hybrid associations models (HAM) to better learn from the sparse and limited recommendation data. This study has been published in IEEE Transactions on Knowledge and data engineering. Please refer to the full manuscript [4] for more details. © 2023 IEEE.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

A Comparative Study on Node Classification Methods For Undirected Social Networks

A Comparative Study on Node Classification Methods For Undir...

引用

2023 Innovations in Intelligent Systems and Applications Conference, ASYU 2023

作者： Ay, Tolga Gulcu, Ayla Bahçeşehir University Big Data Analytics and Management İstanbul Turkey Bahçeşehir University Software Engineering Dept İstanbul Turkey

ISBN: (纸本)9798350306590

Traditional Neural Networks can solve problems with normal 1-dimensional and 2-dimensional Euclidean data such as image and text classification. However, most real-life problems are relationship-based and the corresponding real-world data has a non-Euclidean nature. This paper presents a comparative study between Graph Neural Networks (GNNs) and traditional approaches (K-Nearest Neighbour(KNN)) for solving real-life problems involving non-Euclidean data with complex relationships. The results demonstrate that GNN models outperform KNN in terms of accuracy while maintaining low runtime on graph structured data. The study highlights the strengths and limitations of Graph Convolutional Networks (GCNs) and GraphSAGE, with GraphSAGE offering flexibility and scalability but potentially introducing aggregation bias. Ongoing experiments include investigating the sensitivity of the algorithms to hyperparameter values. Overall, this research contributes to the understanding and practical application of GNN methodologies in various domains. © 2023 IEEE.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

ProbSAINT: Probabilistic Tabular Regression for Used Car Pricing

ProbSAINT: Probabilistic Tabular Regression for Used Car Pri...

引用

IEEE International Conference on Big data

作者： Kiran Madhusudhanan Gunnar Behrens Maximilian Stubbemann Lars Schmidt-Thieme Information Systems and Machine Learning Lab VWFS Data Analytics Research Center University Of Hildesheim Data Analytics & AI Engineering Volkswagen Financial Services AG

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Used car pricing is a critical aspect of the automotive industry, influenced by many economic factors and market dynamics. With the recent surge in online marketplaces and increased demand for used cars, accurate pricing would benefit both buyers and sellers by ensuring fair transactions. However, the transition towards automated pricing algorithms using machine learning necessitates the comprehension of model uncertainties, specifically the ability to flag predictions that the model is unsure about. Although recent literature proposes the use of boosting algorithms or nearest neighbor-based approaches for swift and precise price predictions, encapsulating model uncertainties with such algorithms presents a complex challenge. We introduce ProbSAINT, a model that offers a principled approach for uncertainty quantification of its price predictions, along with accurate point predictions that are comparable to state-of-the-art boosting techniques. Furthermore, acknowledging that the business prefers pricing used cars based on the number of days the vehicle was listed for sale, we show how ProbSAINT can be used as a dynamic forecasting model for predicting price probabilities for different expected offer durations. Our experiments further indicate that ProbSAINT is especially accurate in instances where it is highly certain. This proves the applicability of its probabilistic predictions in real-world scenarios where trustworthiness is crucial.

关键词： Industries Accuracy Uncertainty Machine learning algorithms Pricing Predictive models Probabilistic logic Prediction algorithms Automobiles Vehicle dynamics

来源：评论

学校读者我要写书评

暂无评论

Estimation of Eucalyptus DBH from UAV-LiDAR data Utilizing Advanced Point Cloud Processing Techniques 9

Estimation of Eucalyptus DBH from UAV-LiDAR Data Utilizing A...

引用

9th International Conference on Business and Industrial Research, ICBIR 2024

作者： Dangrungroj, Peerapong Udompitaksook, Monton Intarasuk, Natapong Pluempitiwiriyawej, Charnchai Silawan, Teerapol Laboratory Department of Electrical Engineering Bangkok Thailand Sky Visual Imaging Venture Co. Ltd Bangkok Thailand Chulalongkorn University Multimedia Data Analytics and Processing Research Unit Department of Electrical Engineering Bangkok Thailand

ISBN: (纸本)9798350383010

The Diameter at Breast Height (DBH) measurements are essential for forest management and carbon absorption estimation in environmental challenges. Traditional DBH measurements require more precision and efficiency, especially in inaccessible forests. Existing methods, including manual measurement or remote sensing, struggle with precision in low-intensity point cloud areas. Our technique solves these limitations by analyzing the entire tree stem by the process, including convex hull construction and advanced noise reduction using real-world datasets. It has reduced the Root Mean Squared Error (RMSE) to 0.020, or 92.25% improvement, and the bias has decreased to -0.008. These enhancements highlight the accuracy and reliability of our DBH estimation technique. © 2024 IEEE.

关键词： Noise abatement

来源：评论

学校读者我要写书评

暂无评论

INVESTIGATING SUSTAINABLE PRODUCTION PLANNING OF AN INDUSTRIAL PROGRESSIVE DIE STAMPING PROCESS USING DISCRETE EVENT SIMULATION

INVESTIGATING SUSTAINABLE PRODUCTION PLANNING OF AN INDUSTRI...

引用

ASME 2024 International Design engineering Technical Conferences and Computers and Information in engineering Conference, IDETC-CIE 2024

作者： Borbolla, Adrián Aboujarra, Mohamed Nezar Ramanujan, Devarajan Advanced Manufacturing Engineering Grundfos A/s Bjerringbro Denmark AIRA Laboratory Smart Factory and Robotics Université Hassan 1er Settat Morocco Department of Mechanical and Production Engineering Aarhus University Aarhus Denmark Center for Digitalisation Big Data and Data Analytics Aarhus University Aarhus Denmark

ISBN: (纸本)9780791888391

This paper investigates the use of discrete event simulation (DES) as a potential decision-support tool for environmentally sustainable production planning. We propose a comprehensive methodology that integrates DES design, dynamic CO2 intensity calculations, and in-depth scenario analysis. This approach facilitates simulation-driven production planning with an emphasis on evaluating and reducing the environmental impacts of manufacturing. Our methodology is applied to the evaluation of an industrial progressive die stamping process (PDSP). The PDSP is a part of a production line in a large Danish manufacturing company. The performance of the PDSP was simulated and evaluated for two working days: 09 Jan 2024 and 10 Jan 2024. Key performance indicators like utilization rate and lead time are assessed alongside environmental sustainability metrics such as process CO2 emissions and CO2 emissions per product. Building upon this baseline, we then simulate and compare two alternative production scenarios: i) increased production capacity and ii) variations in planning orders. These scenarios are then contrasted with the baseline model. Our results demonstrate the potential of DES in identifying new production strategies. The first scenario demonstrates a 7% increase in production capacity, corresponding to a 2.6% increase in process CO2 emissions. The second scenario demonstrates a 5% reduction in process CO2 emissions while retaining the same production capacity as the baseline model. These findings illustrate how DES can uncover production plans that prioritize sustainable manufacturing. Copyright © 2024 by ASME.

关键词： Stamping

来源：评论

学校读者我要写书评

暂无评论

Energy Efficient ECG Classifier Using Smart Lead Switching and Apache TVM 22

Energy Efficient ECG Classifier Using Smart Lead Switching a...

引用

22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Ayad, Ahmad Barhoush, Mahdi Volker, Benedikt Leonhardt, Steffen Schmeink, Anke Information Theory and Data Analytics Chair or Medical Information Technology Helmholtz-Institute for Biomedical Engineering Rwth Aachen University AachenD-52074 Germany

ISBN: (纸本)9798350345346

The classification of Electrocardiograms (ECG) sig-nals is vital for diagnosing and monitoring cardiovascular diseases. Recently, there has been increasing interest in leveraging efficient machine learning models for ECG classification. Op-timizing computational resources in this domain is crucial for real-time analysis and reducing power consumption. This paper explores two approaches to enhance the efficiency of ECG classification on edge devices. Firstly, a smart lead-switching mechanism intelligently switches between a high-power model and a low- power model. Secondly, Apache Tensor Virtual Machine (TVM) is employed to optimize the implementation of the developed models on the Jetson Nano single-board computer. Experimental results demonstrate noticeable achievements, including up to 96% reduction in FLOPS and 95% reduction in inference time. Our results hold significant promise for improving ECG classification on edge devices, enabling early detection of abnormalities, and enhancing patient care. © 2023 IEEE.

关键词： Deep learning Electrocardiogram Energy-efficient Temporal Convolutional Network Tensor Virtual Machine

来源：评论

学校读者我要写书评

暂无评论

Sustainable Energy Efficient Routing Protocol using Chimp algorithm and SOM for Underwater Wireless Sensor Networks

Sustainable Energy Efficient Routing Protocol using Chimp al...

引用

2023 International Conference on Recent Advances in Science and engineering Technology, ICRASET 2023

作者： Philip Paul Arunodhayam, T. Vadde, Akhil Raj Kumar, Thimmampali Prasanna Silas, Salaja Gandu, Shalem Preetham Harshith Kumar, Chamarti Karunya Institute of Technology and Sciences Dept. of Computer Science and Engineering Coimbatore India College of Engineering and Computing George Mason University Data Analytics Engineering VA Fairfax22030 United States

ISBN: (纸本)9798350306927

Underwater Wireless Sensor Networks (UWSNs) are increasingly being used for various applications, such as environmental monitoring, underwater exploration, and oceanographic research. protocols that consume less energy for underwater wireless sensor networks (UWSNs) are still a difficult undertaking because of the harsh conditions present underwater and the restricted battery capacity of the sensor nodes. This research paper proposes an optimized multi-Hop routing protocol using Chimp Optimization and Self-Organizing Map (SOM). The proposed technique is compared with three popular routing protocols - AODV, DSDV, and DSR in terms of network lifetime and packet delivery ratio. Chimp Optimization algorithm as well as SOM are used to improve network performance. The findings indicate that the put-forward technique exceeds the performance of existing routing protocols, as it increases network lifetime, PDR and PLR while decreasing packet loss ratio. The network lifetime of the put-forward technique is 1039.42 seconds, which is higher than AODV, DSDV, and DSR. Similarly, the PDR of the proposed technique is 97.38%, which is comparatively higher than the three routing protocols. Moreover, the PLR of the proposed technique is 2.62%, which is much lower than AODV, DSDV, and DSR. The proposed technique based on Chimp Optimization and SOM can effectively optimize the multi-Hop routing protocol in underwater wireless sensor networks to achieve sustainable energy efficiency, thus providing important insights for researchers and practitioners to enhance the performance of underwater wireless sensor networks. © 2023 IEEE.

关键词： Energy efficiency

来源：评论

学校读者我要写书评

暂无评论

AKDC: Ambiguous Kernel Distance Clustering Algorithm for COVID-19 CT Scans Analysis

引用

IEEE Transactions on Systems, Man, and Cybernetics: Systems 2024年第10期54卷 6218-6229页

作者： Singh, Pritpal Huang, Yo-Ping The Quantum Optimization Research Lab Department of Data Science and Analytics Central University of Rajasthan Ajmer305817 India National Taipei University of Technology Department of Electrical Engineering Taipei10608 Taiwan National Taipei University Department of Computer Science and Information Engineering New Taipei City23741 Taiwan Chaoyang University of Technology Department of Information and Communication Engineering Taichung41349 Taiwan

Conventional soft clustering algorithms perform well on linearly distributed features, but their performance degrades on nonlinearly distributed features in high-dimensional space. In this study, a novel soft clustering algorithm, the ambiguous kernel distance clustering (AKDC) algorithm, is presented. This algorithm is developed by applying ambiguous set theory and the Gaussian kernel function. The ambiguous set theory defines the ambiguities inherent in each feature with four membership values: 1) true;2) false;3) true-ambiguous;and 4) false-ambiguous. The degree of membership values here forms a low-dimensional feature space that is not linearly distributed. Therefore, these nonlinearly distributed membership values are mapped into a high-dimensional feature space using the Gaussian kernel function. This study focuses on performing cluster analysis of computerized tomography scans of COVID-19 (CTSC-19) cases using AKDC. COVID-19, recognized as one of the most life-threatening diseases of this century, is highly contagious, and early diagnosis may prevent one-to-one transmission. Extensive empirical studies have been conducted with different types of CTSC-19 to demonstrate its effectiveness against existing kernel-based clustering and nonkernel-based clustering algorithms, namely mercer kernel fuzzy c-mean (MKFCM), kernel generalized FCM (KGFCM), kernel intuitionistic fuzzy entropy c-means (KIFECMs), morphological reconstruction and membership filtering clustering (FRFCM), and intuitionistic FCM based on membership information transferring and similarity measurements (IFCM-MS). The effectiveness of the proposed algorithm compared to the existing algorithms is evaluated using standard statistical metrics, such as dice index (DI), Jaccard index (JI), structural similarity index (SI), and correlation coefficient (CC). The empirical results show that AKDC is more effective than existing algorithms based on DI, JI, SI, and CC. © 2013 IEEE.

关键词： Clustering algorithms

来源：评论

学校读者我要写书评

暂无评论

Influencing Factors of Healthy Aging Risk Assessed Using Biomarkers:A Life Course Perspective

引用

China CDC weekly 2024年第11期6卷 219-224,I0014-I0017页

作者： Cedric Zhang Bo Lua Yajie Gao Jinming Li Xingqi Cao Xinwei Lyu Yinuo Tu Shuyi Jin Zuyun Liu Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Healththe Key Laboratory of Intelligent Preventive Medicine of Zhejiang ProvinceZhejiang University School of MedicineHangzhou CityZhejiang ProvinceChina Institute of Epidemiology and Health Care University College LondonLondonUK College of Chemical and Biological Engineering Zhejiang UniversityHangzhou CityZhejiang ProvinceChina

Assessing individual risks of healthy aging using biomarkers and identifying associated factors have become important areas of *** this study,we conducted a literature review of relevant publications between 2018 and 2023 in both Chinese and English *** studies have predominantly used single biomarkers,such as C-reactive protein,or focused on specific life course stages and factors such as socioeconomic status,mental health,educational levels,and unhealthy *** summarizing the progress in this field,our study provides valuable insights and future directions for promoting healthy aging from a life course perspective.

关键词： such insight directions

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：