检索结果-内蒙古大学图书馆

Federated learning in Medical Imaging: Part II: Methods, Challenges, and Considerations

Journal of the American College of Radiology 2022年第8期19卷 975-982页

作者： Darzidehkalani, Erfan Ghasemi-rad, Mohammad van Ooijen, P.M.A. Department of Radiation Oncology University Medical Center Groningen University of Groningen Groningen Netherlands Machine Learning Lab Data Science Center in Health University Medical Center Groningen University of Groningen Netherlands Department of Interventional Radiology Baylor College of Medicine Houston Texas

Federated learning is a machine learning method that allows decentralized training of deep neural networks among multiple clients while preserving the privacy of each client's data. Federated learning is instrumental in medical imaging because of the privacy considerations of medical data. Setting up federated networks in hospitals comes with unique challenges, primarily because medical imaging data and federated learning algorithms each have their own set of distinct characteristics. This article introduces federated learning algorithms in medical imaging and discusses technical challenges and considerations of real-world implementation of them. © 2022 American College of Radiology

关键词： Federated learning medical imaging privacy-preserving machine learning

来源：评论

学校读者我要写书评

暂无评论

Parkinson's Disease Detection using Hand Drawn Spirals and Waves

Parkinson's Disease Detection using Hand Drawn Spirals and W...

引用

2023 International Conference on Recent Advances in science and Engineering Technology, ICRASET 2023

作者： Shashidhara, H.S. Kalbag, Saish K Mathias, Tanya Srinivasa, K.G. Niranjanamurthy, M. Affiliated to Visvesvaraya Technological University M S Ramaiah Institute of Techology and Engineering Department of Informaition Science and Engineering Belgaum Bangalore India International Institute of Information Technology Department of Data Science and Artififial Intelligece Chhattisgarh Naya Raipur India Affiliated to Visvesvaraya Technological University B M S Institute of Technology and Management Department of Artificial Intelligence and Machine Learning Belgaum Bangalore India

ISBN: (纸本)9798350306927

Parkinson's disease, a neurodegenerative disorder, affects millions of people worldwide. Early detection is critical for effective treatment and management. Early detection of Parkinson's disease is challenging due to the lack of accessible and non-invasive screening methods. Current diagnostic techniques often require specialized equipment and invasive procedures, limiting their feasibility for widespread screening. There is a need for cost-effective and accessible approaches that can accurately detect early stages of Parkinson's disease. The model requires a high degree of precision in distinguishing between these patterns, which is a significant challenge to overcome. In this research work, we proposed a novel approach for detecting early stages of Parkinson's disease by analyzing hand-drawn spirals and waves in photographs. © 2023 IEEE.

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

Deep learning Application for Images Augmentation in Electrical Component Classification System 3rd

Deep Learning Application for Images Augmentation in Electri...

引用

3rd International Conference on Advances in Information and Communication Technology, ICTA 2024

作者： Long, Nguyen The Huong, Nguyen Thu Lien, Pham Thi Gen, Anna Shmeleva Viet, Tran Tuan Department of Informatics Institute of Cybersecurity and Digital Technologies MIREA –Russian Technological University Moscow119454 Russia Laboratory of Artificial Intelligence and Machine Learning Institute of Information Technology and Data Science Irkutsk National Research Technical University Irkutsk664074 Russia University of Information and Communication Technology Thai Nguyen University Thai Nguyen70000 Viet Nam

ISBN: (纸本)9783031809422

The possibility of application a convolutional neural network to assess the augmentation of electrical images is proposed. We studied various conditions for sample preparation, optimizer algorithms, the number of pixels in the samples, the size of the training sample, color schemes, compression quality, and other photometric parameters in view of effect on training the neural network. Due to the proposed preliminary data preparation, the optimum of the architecture and hyperparameters of the neural network we achieved a classification accuracy of at least 98%. This paper outlines measurements of several devices and a process for simulating large amounts of annotated data based on those measurements. A convolutional neural net (CNN) algorithm for classifying signals is described. Performance of the CNN is compared to that of dynamic time warping (DTW) and correlation algorithms, with respect to varying training data set size and noise level. The CNN has the best performance for almost all cases considered. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Development of Punjabi Automatic Speech Recognition Application for Mobile Devices

Development of Punjabi Automatic Speech Recognition Applicat...

引用

International Conference on Computing Communication Control and Automation (ICCUBEA)

作者： Puneet Mittal Sukhwinder Sharma Department of Artificial Intelligence & Machine Learning Manipal University Jaipur Jaipur India Department of Data Science & Engineering Manipal University Jaipur Jaipur India

Speech recognition based mobile device applications are gaining popularity due to their ease of use, flexibility as well as ability to provide hands-free access to device features and functions for persons without hands or with disabilities like blindness. The increased accuracy of Automatic Speech Recognition (ASR) systems and support to multiple languages is further motivating the researchers to develop such applications. The development of an application for mobile device helps to validate the applicability of an ASR system in real world environment. An android based speech recognition application for calling and messaging features has been designed and developed in this paper. User can send message or call to any phone number or contact stored in the mobile phone using Punjabi speech. Punjabi based ASR system having pronunciation dictionary, acoustic model and language model is used to implement the underlying speech recognition for Punjabi speakers. Speech user interface has been designed using Android, while AndroidPocketSphinx has been used as speech decoder. The successfully running application has been tested for multiple male and female native Punjabi speakers, followed by thorough performance analysis. The results shown are quite optimistic with a very low combined error of 1.6% for all female speakers and 2.1% for all male speakers. The application gives 98.4% success rate or recognition accuracy for female speakers and 97.9% for male speakers for the underlying Punjabi based ASR system.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Sentiment Analysis Using machine learning Methods

Sentiment Analysis Using Machine Learning Methods

引用

2024 International Conference on Intelligent and Innovative Practices in Engineering and Management, IIPEM 2024

作者： Badholia, Abhishek Diwan, Tarun Dhar Narooka, Preeti Khatkale, Pravin B Vishnoi, Ankit Kaushik, Keshav Shri Shankaracharya Institute of Professional Management and Technology Department of Data Science Raipur India Atal Bihari Vajpayee University Bilaspur India Manipal University Artificial Intelligence and Machine Learning School of Computer and Engineering Jaipur India Sanjivani University Maharashtra Kopargaon India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun India Amity University Punjab Amity School of Engineering and Technology Mohali India

ISBN: (纸本)9798350390049

machine learning (ML) will be utilized to evaluate sentiment analysis in this project. This study aims to do this. Sentiment analysis is a popular natural language processing area. This is a natural language processing subfield. This section identifies textual senses. Due to the growing availability of digital text across platforms, sentiment analysis is now vital for many applications. This is because sentiment analysis has become essential. This technology might be used for market research, political analysis, and consumer feedback. A few instances. This study analyzes several machine learning methods, to name a few. This category includes SVM, Naive Bayes, Random Forest, and RNNs. To clarify, several datasets are used to assess these methodologies. To get more information. Preprocessing and feature engineering are crucial to sentiment analysis model construction, as this paper shows. The research emphasises these two phases. In addition, it provides insights into how these approaches are used in constantly changing digital situations. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Advancing OCT-Based Retinal Disease Classification with XLSTM: A Framework for Variable-Length Volume Processing 22

Advancing OCT-Based Retinal Disease Classification with XLST...

引用

22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Sükei, Emese Oghbaie, Marzieh Schmidt-Erfurth, Ursula Klambauer, Günter Bogunović, Hrvoje Medical University of Vienna Optima Lab Department of Ophthalmology Austria Institute of Artificial Intelligence Medical University of Vienna Center for Medical Data Science Austria Institute for Machine Learning Johannes Kepler University Lit Ai Lab Austria Nxai GmbH Linz Austria

ISBN: (纸本)9798331520526

This paper presents a method for retinal disease classification using optical coherence tomography (OCT) scans, specifically addressing the challenge of variable B-scan density across dataset volumes. Deep learning methods often struggle with fluctuations in the number of B-scans within OCT volumes, which can limit their effectiveness. To address this, we propose an extended long short-term memory (xLSTM) model capable of processing variable-length B-scan sequences, capturing spatial and contextual information across entire volumes regardless of slice count. Leveraging xLSTM's advanced memory structure, our method enhances classification accuracy while maintaining computational efficiency. We evaluated the xLSTM-based approach on public and private datasets, demonstrating its robustness against inter-study variability and domain shifts. Experimental results show that this approach performs on par with or surpasses state-of-the-art baselines and offers a scalable solution for accurate 3D volume classification. © 2025 IEEE.

关键词： 3D volume classification Optical coherence tomography retinal disease classification xLSTM

来源：评论

学校读者我要写书评

暂无评论

Regularity and positivity of solutions of the Consensus-Based Optimization equation: unconditional global convergence

arXiv

引用

arXiv 2025年

作者： Fornasier, Massimo Sun, Lukang Technical University of Munich School of Computation Information and Technology Department of Mathematics Munich Germany Munich Center for Machine Learning Munich Germany Munich Data Science Institute Germany

Introduced in 2017 [28], Consensus-Based Optimization (CBO) has rapidly emerged as a significant breakthrough in global optimization. This straightforward yet powerful multi-particle, zero-order optimization method draws inspiration from Simulated Annealing and Particle Swarm Optimization. Using a quantitative mean-field approximation, CBO dynamics can be described by a nonlinear Fokker-Planck equation with degenerate diffusion, which does not follow a gradient flow structure. In this paper, we demonstrate that solutions to the CBO equation remain positive and maintain full support. Building on this foundation, we establish the unconditional global convergence of CBO methods to global minimizers. Our results are derived through an analysis of solution regularity and the proof of existence for smooth, classical solutions to a broader class of drift-diffusion equations, despite the challenges posed by degenerate diffusion. © 2025, CC BY.

关键词： Particle swarm optimization (PSO)

来源：评论

学校读者我要写书评

暂无评论

Position: insights from survey methodology can improve training data 24

Position: insights from survey methodology can improve train...

引用

Proceedings of the 41st International Conference on machine learning

作者： Stephanie Eckman Barbara Plank Frauke Kreuter Social Data Science Center University of Maryland College Park MD Center for Information and Language Processing (CIS) LMU Munich Germany and Computer Science Department IT University of Copenhagen Denmark and Munich Center for Machine Learning (MCML) LMU Munich Germany Institute for Statistics and Munich Center for Machine Learning (MCML) LMU Munich Germany and Social Data Science Center and Joint Program in Survey Methodology University of Maryland College Park MD

Whether future AI models are fair, trustworthy, and aligned with the public's interests rests in part on our ability to collect accurate data about what we want the models to do. However, collecting high-quality data is difficult, and few AI/ML researchers are trained in data collection methods. Recent research in data-centric AI has show that higher quality training data leads to better performing models, making this the right moment to introduce AI/ML researchers to the field of survey methodology, the science of data collection. We summarize insights from the survey methodology literature and discuss how they can improve the quality of training and feedback data. We also suggest collaborative research ideas into how biases in data collection can be mitigated, making models more accurate and human-centric.

关键词：

来源：评论

学校读者我要写书评

暂无评论

-summability and Fourier series of B-splines with respect to their knots

引用

Mathematische Zeitschrift 2024年第3期306卷 1-16页

作者： Buhmann, Martin Jäger, Janin Xu, Yuan Justus-Liebig University Lehrstuhl Numerische Mathematik Giessen Germany Mathematical Institute for Machine Learning and Data Science Catholic University of Eichstätt-Ingolstadt Ingolstadt Germany Department of Mathematics University of Oregon Eugene USA

We study the $$\ell ^1$$ -summability of functions in the d-dimensional torus $${{\mathbb {T}}}^d$$ and so-called $$\ell ^1$$ -invariant functions. Those are functions on the torus whose Fourier coefficients depend only on the $$\ell ^1$$ -norm of their indices. Such functions are characterized as divided differences that have $$\cos {\theta }_1,\ldots ,\cos {\theta }_d$$ as knots for $$({\theta }_1\,\ldots , {\theta }_d) \in {{\mathbb {T}}}^d$$ . It leads us to consider the d-dimensional Fourier series of univariate B-splines with respect to its knots, which turns out to enjoy a simple bi-orthogonality that can be used to obtain an orthogonal series of the B-spline function.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Leveraging Patient Flow data to Reduce Hospital Readmissions: A Study Using Medical Dashboards

Leveraging Patient Flow Data to Reduce Hospital Readmissions...

引用

Sentiment Analysis and Deep learning (ICSADL), International Conference on

作者： Santoshi Deshmukh Utkarsha Pacharaney Department of Artificial Intelligence and Data Science Datta Meghe Institute of Higher Education and Research Wardha India Department of Artificial Intelligence and Machine Learning Datta Meghe Institute of Higher Education and Research Wardha India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Transparent reporting of patient outcomes is becoming more and more common as the inequalities in health outcomes amongst groups are being more widely acknowledged. In addition, documenting health outcomes over time across subgroups will be necessary for pay-for-equity programs like those put forth by the Centers for Medicare and Medicaid. One way to visualize data in the context of healthcare is through dashboards, which may be used to identify important differences in clinical outcomes, direct focused efforts toward quality improvement, and eventually enhance health equity. We assess every study that has documented the effective creation of a disparity dashboard in this viewpoint, and we also publish the information gathered and any unexpected repercussions that have been mentioned. We offer a framework for the systematic enhancement of equity via reward systems to lessen health inequalities and incentives for gathering and reporting health data. essential elements of efficient hospital management. A sophisticated approach is necessary to address the impact that hospital leadership has on patient outcomes. Because they embody patient-centered care in their everyday lives, these executives serve as the company's role models. The beliefs, vision, and leadership style of healthcare professionals greatly influence their behavior and performance. Competent leaders who inspire and empower their staff foster a culture of responsibility, creativity, and cooperation. They exhort healthcare professionals to put their patients' safety, satisfaction, and well-being first. Strategic planning, resource allocation, and decision-making are all components of good leadership that support the hospital's goal of offering the highest quality of care. essential elements of efficient hospital management. A sophisticated approach is necessary to address the impact that hospital leadership has on patient outcomes. Because they embody patient-centered care in their everyday lives, these ex

关键词： Leadership Sentiment analysis Systematics Hospitals Decision making Strategic planning Safety Resource management Predictive analytics Creativity

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：