检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Deng, Jamie Wu, Yusen Yesha, Yelena Nguyen, Phuong Department of Computer Science University of Miami MiamiFL United States Frost Institute for Data Science & Computing University of Miami MiamiFL United States Department of Radiology University of Miami MiamiFL United States

Venous thromboembolism (VTE) is a critical cardiovascular condition, encompassing deep vein thrombosis (DVT) and pulmonary embolism (PE). Accurate and timely identification of VTE is essential for effective medical care. This study builds upon our previous work, which addressed VTE detection using deep learning methods for DVT and a hybrid approach combining deep learning and rule-based classification for PE. Our earlier approaches, while effective, had two major limitations: they were complex and required expert involvement for feature engineering of the rule set. To overcome these challenges, we utilize the Mamba architecture-based classifier. This model achieves remarkable results, with a 97% accuracy and F1 score on the DVT dataset and a 98% accuracy and F1 score on the PE dataset. In contrast to the previous hybrid method on PE identification, the Mamba classifier eliminates the need for hand-engineered rules, significantly reducing model complexity while maintaining comparable performance. Additionally, we evaluated a lightweight Large Language Model (LLM), Phi-3 Mini, in detecting VTE. While this model delivers competitive results, outperforming the baseline BERT models, it proves to be computationally intensive due to its larger parameter set. Our evaluation shows that the Mamba-based model demonstrates superior performance and efficiency in VTE identification, offering an effective solution to the limitations of previous approaches. © 2024, CC BY.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Deep Learning for Automated Detection of Lung Cancer from Medical Imaging data 1

Deep Learning for Automated Detection of Lung Cancer from Me...

引用

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

作者： Devarajan, Harshitha Raghavan Balasubramanian, Sivasubramanian Kumar Swarnkar, Suman Kumar, Purushottam Jallepalli, Venkateswara Rao Millennium Management Llc New York United States University of Washington Department of Data Science Seattle United States Shri Shankaracharya Institute of Professional Management and Technology Dept of Computer Science and Engineering Raipur India Amity University Department of Computer Science and Cyber Security Jharkhand India School of Pharmaceutical Sciences Lovely Professional University Department of Pharmacology Jalandhar India

ISBN: (纸本)9798350330915

Early and accurate detection of lung cancer is essential for improving patient outcomes and reducing mortality rates. On a range of medical image processing applications in recent years, deep learning techniques have demonstrated exceptional effectiveness. This study used medical imaging data, specifically CT scans and chest X-rays., to conduct a thorough research into the use of deep learning for automated lung cancer diagnosis. The early and accurate detection of lung cancer plays a pivotal role in improving patient outcomes and reducing mortality rates. In recent years, deep learning techniques have demonstrated remarkable performance in various medical image analysis tasks. This research paper presents a comprehensive study on the application of deep learning for the automated detection of lung cancer from medical imaging data, primarily focusing on chest X-rays and computed tomography (CT) scans. We explore different deep learning architectures, data preprocessing techniques, and training strategies to enhance the accuracy and efficiency of lung cancer detection. Through rigorous experimentation and validation on diverse datasets, we showcase the potential of deep learning models in identifying lung cancer During its initial phases. The findings highlight the significance of implementing effective data augmentation techniques., transfer learning, and model interpretability in achieving reliable and clinically applicable outcomes. This study contributes to the ongoing efforts in harnessing the power of deep learning for effective lung cancer diagnosis and establishes a foundation for further advancements in the field of medical image analysis. © 2023 IEEE.

关键词： automated diagnosis chest X-rays computed tomography data preprocessing deep learning deep neural networks early detection lung cancer detection medical imaging model interpretability

来源：评论

学校读者我要写书评

暂无评论

Transversal Hamilton cycles in digraph collections

arXiv

引用

arXiv 2025年

作者： Cheng, Yangyang Li, Heng Sun, Wanting Wang, Guanghui Mathematical Institute University of Oxford Oxford United Kingdom School of Mathematics Shandong University Jinan250100 China Data Science Institute Shandong University Jinan250100 China

Given a collection D = {D1,D2, . . . ,Dm} of digraphs on the common vertex set V , an medge digraph H with vertices in V is transversal in D if there exists a bijection φ : E(H) → [m] such that e ∈ E(Dφ(e)) for all e ∈ E(H). Ghouila-Houri proved that any n-vertex digraph with minimum semi-degree at least n 2 contains a directed Hamilton cycle. In this paper, we provide a transversal generalization of Ghouila-Houri's theorem, thereby solving a problem proposed by Chakraborti, Kim, Lee and Seo [6]. Our proof utilizes the absorption method for transversals, the regularity method for digraph collections, as well as the transversal blow-up lemma [9] and the related machinery. As an application, when n is sufficiently large, our result implies the transversal version of Dirac's theorem, which was proved by Joos and Kim [21].MSC Codes 05C20, 05C38 © 2025, CC BY.

关键词： Set theory

来源：评论

学校读者我要写书评

暂无评论

Automated Assignment Management System: Integrating Email Inbox Parsing, database Updates, and Notification

Automated Assignment Management System: Integrating Email In...

引用

Advances in computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： P Varun R.S. Rashika S Kayalvizhi D Preetha P Naveen T A Mohanaprakash Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Department of CSE Panimalar Engineering College Chennai Department of Computer Science and Engineering Velammal Engineering College Department of Information Technology Alpha College of Engineering Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur Chennai Department of CSE School of Engineering & Technology CMR University Bengalunu Karnataka

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

This paper presents the design and implementation of an automated assignment management system utilizing a bot interface. The system’ s primary objective is to streamline the process of collecting and tracking student assignments, ensuring timely updates to a central database, and facilitating communication between students and teachers. The bot employs email inbox parsing techniques to identify incoming assignment submissions, extracting relevant data such as student names, assignment details, and submission timestamps. Upon receiving a submission, the bot updates an Excel database with the pertinent information, maintaining an organized record of completed ***, the system incorporates a notification feature to promptly alert students who have not yet submitted their assignments by the deadline. Leveraging email integration, the bot sends personalized notifications reminding students of impending deadlines and urging them to submit their work promptly. Additionally, the bot generates a list of incomplete assignments and forwards it to the respective teacher, providing a consolidated overview of students who require *** automating the assignment management process, this system reduces manual intervention, minimizes administrative burden, and enhances efficiency in monitoring student progress. The integration of email parsing, database updates, and notification functionalities ensures seamless communication and facilitates timely submission of assignments, contributing to improved academic outcomes and instructor-student engagement

关键词： Automation databases Manuals Chatbots Feature extraction data mining Informatics

来源：评论

学校读者我要写书评

暂无评论

HPE Transformer: Learning to Optimize Multi-Group Multicast Beamforming Under Nonconvex QoS Constraints

arXiv

引用

arXiv 2024年

作者： Li, Yang Liu, Ya-Feng The Shenzhen Research Institute of Big Data Shenzhen518172 China The State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China

This paper studies the quality-of-service (QoS) constrained multi-group multicast beamforming design problem, where each multicast group is composed of a number of users requiring the same content. Due to the nonconvex QoS constraints, this problem is nonconvex and NP-hard. While existing optimization-based iterative algorithms can obtain a suboptimal solution, their iterative nature results in large computational complexity and delay. To facilitate real-time implementations, this paper proposes a deep learning-based approach, which consists of a beamforming structure assisted problem transformation and a customized neural network architecture named hierarchical permutation equivariance (HPE) transformer. The proposed HPE transformer is proved to be permutation equivariant with respect to the users within each multicast group, and also permutation equivariant with respect to different multicast groups. Simulation results demonstrate that the proposed HPE transformer outperforms state-of-the-art optimization-based and deep learning-based approaches for multi-group multicast beamforming design in terms of the total transmit power, the constraint violation, and the computational time. In addition, the proposed HPE transformer achieves pretty good generalization performance on different numbers of users, different numbers of multicast groups, and different signal-to-interference-plus-noise ratio targets. Copyright © 2024, The Authors. All rights reserved.

关键词： Beamforming

来源：评论

学校读者我要写书评

暂无评论

A Modeling Study Recurrent Tuberculosis Towards End TB Strategy—China,2025-2035

引用

China CDC weekly 2024年第35期6卷 885-890页

作者： Tao Li Xin Du Zhongwei Jia Yanlin Zhao Department of Epidemiology and Biostatistics School of Public HealthPeking UniversityBeijingChina Chinese Center for Disease Control and Prevention BeijingChina National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases Chinese Center for Disease Control and PreventionBeijingChina Center for Intelligent Public Health Institute for Artificial IntelligencePeking UniversityBeijingChina Department of Global Health School of Public HealthPeking UniversityBeijingChina Center for Drug Abuse Control and Prevention National Institute of Health Data SciencePeking UniversityBeijingChina

Summary What is already known about this topic?The recurrence of tuberculosis(TB)following successful treatment presents a significant *** is added by this report?Achieving the global End TB Strategy milestones and targets with the current strategies in China is ***,interventions following recovery to prevent recurrence,in conjunction with preventive treatment for latent TB infection(LTBI),will aid in meeting these objectives.

关键词： Strategy tuberculosis treatment

来源：评论

学校读者我要写书评

暂无评论

So Far Yet So Near: Time Series data Augmentation with Exploring non-Semantic Boundaries based on Reinforcement Learning

So Far Yet So Near: Time Series Data Augmentation with Explo...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Haoran Li Zhibo Zhang Jiarong Kang Xun Jiang Xiaoli Gong Jin Zhang Zhe Sun Andrzej Cichocki College of Computer Science Nankai University Tianjin China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Faculty of Medicine and Faculty of Health Data Science Juntendo University Japan Polish Academy of Science Systems Research Institute Warsaw Poland

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

data augmentation effectively expands feature distribution in time series classification, enhancing downstream task performance. However, existing techniques often fail to maintain semantic consistency between augmented and original time series data, causing label noise and thereby degrading downstream task performance. We argue that data augmentation should preserve time series semantic consistency and expand the non-semantic information space. In this paper, we reformulate data augmentation as a semantic path planning problem between original data and augmented data, modeled as a Markov Decision Process (MDP). We propose a reinforcement learning-based algorithm (RL) named FreqSYN, where the action space is defined by a set of learnable Gaussian kernels that perturbs the frequency domain of the original data to generate augmented samples. The confidence coefficients of augmented data in semantically relevant classification tasks are used as a reward to iteratively refine the FreqSYN. Our method is validated across four datasets, achieving state-of-the-art performance, with a 2% improvement in F1 score over the SimPSI method. The code and models are available at https://***/NKU-EmbeddedSystem/FreqSYN.

关键词： Semantics Time series analysis Signal processing algorithms Reinforcement learning data augmentation Prediction algorithms Path planning data models Kernel Speech processing

来源：评论

学校读者我要写书评

暂无评论

Attention towards chemistry agnostic and explainable battery lifetime prediction

引用

npj Computational Materials 2024年第1期10卷 2182-2197页

作者： Fuzhan Rahmanian Robert M.Lee Dominik Linzner Kathrin Michel Leon Merker Balazs B.Berkes Leah Nuss Helge Sören Stein Helmholtz Institute Ulm Applied ElectrochemistryHelmholtzstr.1189081 UlmGermany Karlsruhe Institute of Technology Institute of Physical ChemistryFritz-Haber-Weg 276131 KarlsruheGermany Technische Universität München School of Natural SciencesDepartment of ChemistryLichtenbergstr 485748 GarchingGermany Technische Universität München Munich Data Science InstituteWalther-von-Dyck-Straße 10485748 GarchingGermany Technische Universität München Munich Institute for Robotic and Machine IntelligenceGeorg-Brauchle-Ring 60-6280992 MunichGermany BASF SE LudwigshafenGermany

Predicting and monitoring battery life early and across chemistries is a significant challenge due to the plethora of degradation paths,form factors,and electrochemical testing *** models typically translate poorly across different electrode,electrolyte,and additive materials,mostly require a fixed number of cycles,and are limited to a single discharge ***,an attention-based recurrent algorithm for neural analysis(ARCANA)architecture is developed and trained on an ultralarge,proprietary dataset from BASF and a large Li-ion dataset gathered from literature across the *** generalizes well across this diverse set of chemistries,electrolyte formulations,battery designs,and cycling protocols and thus allows for an extraction of data-driven knowledge of the degradation *** model’s adaptability is further demonstrated through fine-tuning on Na-ion *** advances the frontier of large-scale time series models in analytical chemistry beyond textual data and holds the potential to significantly accelerate discovery-oriented battery research endeavors.

关键词： battery electrolyte lifetime

来源：评论

学校读者我要写书评

暂无评论

FACE EMOTION BASED MUSIC RECOMMENDATION SYSTEM USING MODIFIED CONVOLUTION NEURAL NETWORK

FACE EMOTION BASED MUSIC RECOMMENDATION SYSTEM USING MODIFIE...

引用

2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering, RMKMATE 2023

作者： Jaya Mabel Rani, A. Nivetha, M.S. Jothi Swaroopan, N.M. Hari Kumar, K. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Department of Artificial Intelligence and Data Science Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Department of EEE R.M.K. Engineering College Chennai India Department of Computer Science St Jude's College KanyaKumari District Tamil Nadu Thoothoor India

ISBN: (纸本)9798350305708

In this paper, proposed a new vision to personify music recommendation by face emotion recognition technology. Emotions play a crucial role in the way humans perceive and respond to music. This system aims to enhance the user experience by recommending music based on their real-time facial expressions, allowing for a more immersive and emotionally resonant listening experience. So here employed deep learning techniques to detect facial emotions accurately and use them as input to our music recommendation algorithm. The proposed method modified convolution neural network ensures user privacy and data security by processing emotions locally on the user's device without uploading any sensitive information to external servers. Through experimental evaluation, here demonstrate the effectiveness and usability of the emotion-based music recommendation system and finally shown this method is very effective with the help of accuracy and F1 score. © 2023 IEEE.

关键词： data Security Deep Learning Face Emotion Modified Convolution neural network Music Recommendation

来源：评论

学校读者我要写书评

暂无评论

OVEL: Large Language Model as Memory Manager for Online Video Entity Linking

arXiv

引用

arXiv 2024年

作者： Zhao, Haiquan Wang, Xuwu Chen, Shisong Li, Zhixu Zheng, Xin Xiao, Yanghua Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China Shanghai Institute of AI for Education School of Computer Science and Technology East China Normal University China iFLYTEK CO. LTD Suzhou China

In recent years, multi-modal entity linking (MEL) has garnered increasing attention in the research community due to its significance in numerous multi-modal applications. Video, as a popular means of information transmission, has become prevalent in people’s daily lives. However, most existing MEL methods primarily focus on linking textual and visual mentions or offline videos’s mentions to entities in multi-modal knowledge bases, with limited efforts devoted to linking mentions within online video content. In this paper, we propose a task called Online Video Entity Linking (OVEL), aiming to establish connections between mentions in online videos and a knowledge base with high accuracy and timeliness. To facilitate the research works of OVEL, we specifically concentrate on live delivery scenarios and construct a live delivery entity linking dataset called LIVE. Besides, we propose an evaluation metric that considers timelessness, robustness, and accuracy. Furthermore, to effectively handle OVEL task, we leverage a memory block managed by a Large Language Model and retrieve entity candidates from the knowledge base to augment LLM performance on memory management. The experimental results prove the effectiveness and efficiency of our method. Copyright © 2024, The Authors. All rights reserved.

关键词： Knowledge based systems

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：