检索结果-内蒙古大学图书馆

IEEE International Symposium on Biomedical Imaging

作者： Haoshen Li Zifan Chen Jie Zhao Heyun Chen Hexin Dong Mingze Yuan Bin Dong Li Zhang Center for Data Science Peking University China National Engineering Laboratory for Big Data Analysis and Applications Peking University China Peking University Changsha Institute for Computing and Digital Economy China Beijing International Center for Mathematical Research Peking University China Center for Machine Learning Research Peking University China

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Hypertension is a major global health concern, linked to various cardiovascular diseases and associated with distinct ocular manifestations. While recent advances in artificial intelligence have enabled accurate diagnosis of current hypertension through fundus images, predicting the future onset of hypertension remains an uncharted domain. In this study, we introduce the multi-scale clinical-guided binocular fusion framework (MCBO), designed to predict the likelihood of developing hypertension within the next four years. MCBO uniquely integrates left and right fundus images and clinical data, utilizing a shared-weight multi-stage Transformer-based encoder. Our multi-scale clinical-guided module (MCM) ensures image feature extraction is clinically contextualized based on clinical information, and our binocular fusion module (BFM) fuses binocular information. Comparative performance against seven baseline models establishes MCBO’s supremacy, with improvements of 6.7% in Area Under Curve (AUC), 6.9% in Accuracy (ACC), 5.1% in Sensitivity (SEN) and 5.5% in Specificity (SPE). This approach offers a promising avenue for proactive hypertension management, underscoring the potential of integrating Deep learning with clinical data for enhanced healthcare outcomes. Our code is available at https://***/HaoshenLi/MCBO.

关键词： Hypertension Deep learning Accuracy Sensitivity Codes Fuses Feature extraction

来源：评论

学校读者我要写书评

暂无评论

machine learning-Based Security for Cloud Computing Challenges and Implications 3

Machine Learning-Based Security for Cloud Computing Challeng...

引用

3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Dattangire, Rahul Burle, Rushikesh Biradar, Divya Dewangan, Leelkanth Data Engineering Independent Researcher HoustonTX77002 United States Datta Meghe Institute of Higher Education and Research Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Maharashtra Wardha442001 India University of Texas at Arlington Computer Science ArlingtonTX76013 United States G H Raisoni College of Engineering Maharashtra Nagpur India

ISBN: (纸本)9798350364569

Cloud computing"is a computer model that provides end users with quantifiable, scalable, and on-demand services. These days, almost every organization uses computer technology extensively for infrastructure, cost-cutting, advanced platforms, data processing, data analytics, and other uses. End users can use any online application from anywhere to access the cloud service providers' (CSP) services. It is imperative to guarantee the security of cloud infrastructure, and a multitude of research employing various technologies is used to provide more accurate and potent defenses against cloud threats. machine learning has shown to be a more useful technology in recent years for protecting cloud infrastructures. machine learning algorithms are trained to generate models that mechanize the process of recognizing cloud threats more precisely than any other technology by using a range of actual datasets as training grounds. A couple of the most recent research that has worked on machine learning to defend against cloud attacks are examined in this paper. © 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

Optimal bounds for p sensitivity sampling via 2 augmentation

arXiv

引用

arXiv 2024年

作者： Munteanu, Alexander Omlor, Simon Dortmund Data Science Center Faculties of Statistics and Computer Science TU Dortmund University Dortmund Germany Faculty of Statistics and Lamarr Institute for Machine Learning and Artificial Intelligence TU Dortmund University Dortmund Germany

data subsampling is one of the most natural methods to approximate a massively large data set by a small representative proxy. In particular, sensitivity sampling received a lot of attention, which samples points proportional to an individual importance measure called sensitivity. This framework reduces in very general settings the size of data to roughly the VC dimension d times the total sensitivity S while providing strong (1 ± Ε) guarantees on the quality of approximation. The recent work of Woodruff and Yasuda (2023c) improved substantially over the general Õ(Ε−2Sd) bound for the important problem of p subspace embeddings to Õ(Ε−2S2/p) for p ∈ [1, 2]. Their result was subsumed by an earlier Õ(Ε−2Sd1−p/2) bound which was implicitly given in the work of Chen and Derezinski (2021). We show that their result is tight when sampling according to plain p sensitivities. We observe that by augmenting the p sensitivities by 2 sensitivities, we obtain better bounds improving over the aforementioned results to optimal linear Õ(Ε−2(S + d)) = Õ(Ε−2d) sampling complexity for all p ∈ [1, 2]. In particular, this resolves an open question of Woodruff and Yasuda (2023c) in the affirmative for p ∈ [1, 2] and brings sensitivity subsampling into the regime that was previously only known to be possible using Lewis weights (Cohen and Peng, 2015). As an application of our main result, we also obtain an Õ(Ε−2µd) sensitivity sampling bound for logistic regression, where µ is a natural complexity measure for this problem. This improves over the previous Õ(Ε−2µ2d) bound of Mai et al. (2021) which was based on Lewis weights subsampling. Copyright © 2024, The Authors. All rights reserved.

关键词： Importance sampling

来源：评论

学校读者我要写书评

暂无评论

Pass: Peer-Agreement Based Sample Selection for Training with Instance-Dependent Noisy Labels

SSRN

引用

SSRN 2024年

作者： Garg, Arpit Nguyen, Cuong Felix, Rafael Do, Thanh-Toan Carneiro, Gustavo Australian Institute for Machine Learning University of Adelaide Australia Centre for Vision Speech and Signal Processing University of Surrey United Kingdom Department of Data Science and AI Monash University Australia

The prevalence of noisy-label samples poses a significant challenge in deep learning, inducing overfitting effects. This has, therefore, motivated the emergence of learning with noisy-label (LNL) techniques that focus on separating noisy- and clean-label samples to apply different learning strategies to each group of samples. Current methodologies often rely on the small-loss hypothesis or feature-based selection to separate noisy- and clean-label samples, yet our empirical observations reveal their limitations, especially for labels with instance dependent noise (IDN). An important characteristic of IDN is the difficulty to distinguish the clean-label samples that lie near the decision boundary (i.e., the hard samples) from the noisy-label samples. We, therefore, propose a new noisy-label detection method, termed Peer-Agreement based Sample Selection (PASS), to address this problem. Utilising a trio of classifiers, PASS employs consensus-driven peer-based agreement of two models to select the samples to train the remaining model. PASS is easily integrated into existing LNL models, enabling the improvement of the detection accuracy of noisy- and clean-label samples, which increases the classification accuracy across various LNL benchmarks. © 2024, The Authors. All rights reserved.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Advances in Glaucoma Detection through machine learning Technology 1

Advances in Glaucoma Detection through Machine Learning Tech...

引用

1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Burle, Rushikesh Gaurkhede, Sanskruti Dewangan, Leelkanth Datta Meghe Institute of Higher Education and Research Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Mahartashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Computer Science and Design Faculty of Engineering and Technology Mahartashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Artificial Intelligence and Data Science Faculty of Engineering and Technology Maharashtra Wardha442001 India

ISBN: (纸本)9798350330915

In the event that glaucoma is not treated promptly, blindness may become permanent. It ranks second among the factors that might lead to blindness in the eyes. One example of a recent imaging technology that looks within the eye is the fundus camera. Glaucoma is diagnosed using a variety of techniques, such as the retinal nerve fibre layer analyzer, The Topcon image net technique and optical coherence tomography. Despite its expensive cost and the dearth of available research in this field, the optic cup to disc ratio is used to diagnose glaucoma. The degree of glaucomatous damage is mostly determined by the appearance of the optic cup. The beaker slowly gets better and inhabits most of the judgement as the glaucoma becomes worse. If glaucoma is not pickled trendy a timely manner, blindness may become irreversible. It is the second most common cause of ocular blindness. A contemporary imaging tool called the fundus camera is utilised to view within the eye. Among the methods used to diagnose glaucoma include the retinal nerve fibre layer analyzer, optical coherence tomography, and the Topcon image net approach. Despite its high cost and paucity of research, glaucoma is diagnosed using the optic cup to discus relation. The degree of glaucomatous damage is mostly determined by the appearance of the optic cup. The cup expands as glaucoma progresses until it finally occupies the majority of the eye. © 2023 IEEE.

关键词： CNN ConvNets Glaucoma machine learning MATLAB Rim-One

来源：评论

学校读者我要写书评

暂无评论

Turnstile p leverage score sampling with applications

arXiv

引用

arXiv 2024年

The turnstile data stream model offers the most flexible framework where data can be manipulated dynamically, i.e., rows, columns, and even single entries of an input matrix can be added, deleted, or updated multiple times in a data stream. We develop a novel algorithm for sampling rows ai of a matrix A ∈ n×d, proportional to their p norm, when A is presented in a turnstile data stream. Our algorithm not only returns the set of sampled row indexes, it also returns slightly perturbed rows ãi ≈ ai, and approximates their sampling probabilities up to Ε relative error. When combined with preconditioning techniques, our algorithm extends to p leverage score sampling over turnstile data streams. With these properties in place, it allows us to simulate subsampling constructions of coresets for important regression problems to operate over turnstile data streams with very little overhead compared to their respective offline subsampling algorithms. For logistic regression, our framework yields the first algorithm that achieves a (1 + Ε) approximation and works in a turnstile data stream using polynomial sketch/subsample size, improving over O(1) approximations, or exp(1/Ε) sketch size of previous work. We compare experimentally to plain oblivious sketching and plain leverage score sampling algorithms for p and logistic regression. Copyright © 2024, The Authors. All rights reserved.

关键词： Approximation algorithms

来源：评论

学校读者我要写书评

暂无评论

Rainfall Prediction using Novel Approach for Enhancing Crop Yield 2

Rainfall Prediction using Novel Approach for Enhancing Crop ...

引用

2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Seelam, Visweswararao Vikruthi, Sriharsha Bhagavatham, Naresh Kumar Nagendrudu, P.V.V.S.D. Srinivasarao, Goda Chakravarthi M, Ayyappa PACE Institute of Technology and Sciences Dept of CSE India B V Raju Institute of Technology Dept of CSE Telangana Narsapur Medak502313 India Vignana bharathi Institute of Technology Dept of CSE Hyderabad India Aditya University Department of Artificial Intelligence and Machine Learning Surampalem India KL University Vaddeswaram Dept of CSE Andhra Pradesh Guntur India KKR & KSR Institute of Technology & Sciences Dept of CSE-Data Science Andhra Pradesh Guntur India

ISBN: (纸本)9798331519056

In India, agriculture is one of the fastest-growing fields to improve the country's economy. Based on several factors like climatic conditions, predicting rainfall in these stages is one of the most significant tasks. In recent years, unexpected and unpredictable weather conditions have been observed in some places. This study mainly focused on developing the Hybrid algorithm (HA) to predict rainfall in the early stages and find accurate weather conditions. The proposed approach also called as Novel Approach that improves the algorithm's strength by adopting the advanced preprocessing and feature extraction technique. The training model random forest (RF) also used to find the accurate patterns from the given dataset samples. The preprocessing technique focused on cleaning and transforming the raw data to remove the noise from the input dataset, improving the data quality. The proposed approach combines Autoencoders and Linear Regression (LR) to accurately predict rainfall. Extensive experiments are carried out to evaluate the performance of the hybrid algorithm using historical meteorological data from multiple regions with varying climatic conditions. The results show that the hybrid algorithm outperforms traditional methods and individual machine-learning models in terms of accuracy, robustness, and generalization. © 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 Alzheimer's Nodule Detection Through a Comprehensive Multi-Scene Deep learning Framework 10

Advancing Alzheimer's Nodule Detection Through a Comprehensi...

引用

10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Premkumar, C. Rajeshwari, R. Remya, R. Cholaathiraj, M.S. Reethika, A. Bharathidasan, S. Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Sathyamangalam India Cmr Institute of Technology Department of Computer Science and Engineering Bengaluru India Vel Tech Rangarajan Dr Sagunthala R&d institute of science and Technology Department of Electronics and Communication Engineering Chennai India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Sathyamangalam India Sri Ramakrishna Engineering College Department of Electronics and Communication Engineering Coimbatore India Erode Sengunthar Engineering College Department of Electronics and Communication Engineering Erode India

ISBN: (纸本)9798350384369

Determining the precise location of Alzheimer's nodules is essential for estimating the risk of brain cancer. Conventional CAD modules, including MRI, PET, and CT, struggle with feature extraction and segmentation due to time limits and complexity. This study proposes an efficient brain nodule detection technique based on the Multi-Scene Deep learning Framework (MSDLF) that makes use of the vesselness filter. A four-channel CNN model is developed using information from two image sequences to enhance radiologists' ability to recognise four-stage nodules. This adaptable model may be used in two different classes. The outcomes demonstrate how well the MSDLF performs while processing substantial volumes of image data for brain nodule recognition, improving with accuracy 98.86% and 98.45% of sensitivity by using decreasing false positives. © 2024 IEEE.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Social Distancing Detection AI System using YOLO

Social Distancing Detection AI System using YOLO

引用

Computer, Electronics & Electrical Engineering & their Applications (IC2E3), International Conference on

作者： M. Karthiga D. Kavitha Dept of Artificial Intelligence and Data Science Coimbatore Institute of Technology Coimbatore India Dept. of Artificial Intelligence and Machine Learning Coimbatore Institute of Technology Coimbatore India

The number of reported COVID-19 cases has once again increased due to the recent spread of the H3N2 virus. Even with vaccinations and medications, social distancing remains the most effective way to prevent the spread of these viruses. To address this issue, this paper proposes an automated system for monitoring social distancing in public places. The system utilizes a YOLOv3 object detection model to detect pedestrians in video frames captured by a camera. The frames are then converted to a top-down perspective to measure the distances between individuals in a 2D plane. If any individuals are too close to each other, the system displays a warning by highlighting them with a red frame and a red line.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A machine learning and Explainable AI Framework Tailored for Unbalanced Experimental Catalyst Discovery

arXiv

引用

arXiv 2024年

作者： Semnani, Parastoo Bogojeski, Mihail Bley, Florian Zhang, Zizheng Wu, Qiong Kneib, Thomas Herrmann, Jan Weisser, Christoph Patcas, Florina Müller, Klaus-Robert Machine Learning Group TU Berlin Berlin Germany Berlin Institute for the Foundations of Learning and Data Berlin Germany BASLEARN-TU Berlin BASF Joint Lab for Machine Learning TU Berlin Berlin Germany Georg-August-University Göttingen Statistics and Campus Institute Data Science Göttingen Germany BASF SE Ludwigshafen Germany Max Planck Institute for Informatics Saarbrücken Germany Department of Artificial Intelligence Korea University Seoul Korea Republic of

The successful application of machine learning in catalyst design depends on high-quality and diverse data to ensure effective generalization to novel compositions, thereby aiding in catalyst discovery. However, due to the complex interactions of catalyst components, the design of novel catalysts has long relied on trial-and-error, a costly and labor-intensive process that results in scarce data that is heavily biased towards undesired, low-yield catalysts. Despite the increasing popularity of machine learning applications in this field, most of the efforts so far have not focused on dealing with the challenges presented by such experimental data. To address these challenges, we introduce a robust machine learning and explainable AI framework to accurately classify the catalytic yield of various compositions and identify the contributions of individual components to the yield. This framework combines a series of ML practices designed to handle the scarcity and imbalance of catalyst data. We apply the framework to the task of determining the yield of different catalyst combinations in oxidative methane coupling, and use it to evaluate the performance of a range of ML models: tree-based models (such as decision trees, random forest, and gradient boosted trees), logistic regression, support vector machines, and neural networks. These experiments demonstrate that the methods used in our framework lead to a significant improvement in the performance of all but one of the evaluated models. Additionally, the decision-making process of each ML model is analyzed by identifying the most important features for predicting catalyst performance using explainable AI (XAI) methods. Our analysis found that XAI methods, which provide class-aware explanations, such as Layer-wise Relevance Propagation, managed to identify key components that contribute specifically to high-yield catalysts. These findings align with chemical intuition and existing literature, reinforcing their validity.

关键词： Adversarial machine learning

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：