检索结果-内蒙古大学图书馆

3rd International Conference on Disruptive Technologies, ICDT 2025

作者： Arcot, Siva Venkatesh Buvaneswari, T. Kavitha, K.R. Manickavasagam, R. Suguna, M. Muthulekshmi, M. Contact Center Business Unit Cisco Systems Inc Little ElmTX United States Department of Computer Science and Engineering Tamil Nadu Salem India Sona College of Technology Department of Electronics and Communication Engineering Tamil Nadu Salem India Alpha College of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India Department of Computer Science Tamil Nadu Salem India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331519582

Inflammation and joint destruction are hallmarks of Rheumatoid Arthritis (RA), a chronic autoimmune condition. To improve patient outcomes, it is essential to optimize treatment plans. According to this research, integrating deep learning (DL) methods into a Convolutional Neural Network (CNN) provides a new method to optimize RA treatments. The CNN model is used to decipher intricate medical data, such as imaging and patient records, find trends, and predict how patients react to treatment. To assess the efficacy of the proposed model in predicting ideal treatment methods, it was trained on a large dataset that included patient images and clinical data. The CNN model offers a potential tool for doctors to adapt RA management plans since results show it significantly improves prediction accuracy and treatment personalization. This method allows for data-driven decisions in RA management, improving patient care and enhancing treatment performance. © 2025 IEEE.

关键词： Theranostics

来源：评论

学校读者我要写书评

暂无评论

Visual Sensor-Based Trajectory Multimodality Prediction via Anchor-Free Query Attention Network

引用

IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Wang, Ruiping Cheng, Jun Yu, Junzhi Nanyang Technological University School of Computer Science and Engineering Jurong West Singapore639798 Singapore Peking University College of Engineering Beijing100871 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics College of Engineering Beijing100871 China

As a core component of intelligent surveillance and autonomous driving systems, visual sensor-based trajectory multimodality prediction can significantly improve their perception and decision-making capabilities. Numerous existing trajectory prediction methods are dedicated to enhance prediction performance. However, they still fail to effectively model the complex spatiotemporal interactions among pedestrians and trajectory multimodality. To address the above challenges, this article presents one new trajectory multimodality prediction method via anchor-free query-based attention network, which can more effectively capture spatiotemporal interactions and model trajectory multimodality. First, the initial trajectory proposal module employs target-centered query-based attention to capture complex spatiotemporal interactions and generate multiple trajectory proposals corresponding to the multiple modes of the future trajectory, which allows the model to use different interaction information when decoding trajectory points at different time steps. Second, the novel trajectory refinement module utilizes the learnable trajectory proposals to construct proposal-level interaction mechanisms to refine future trajectories. Experimental results conducted on ETH and UCY datasets demonstrate that the proposed model can provide the state-of-the-art prediction performance. © 1963-2012 IEEE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Multi-Criteria Fuzzy-Based Decision Making Algorithm to Optimize the VHO Performance in Hetnets

引用

computers, Materials & Continua 2022年第1期70卷 323-341页

作者： A.Prithiviraj A.Maheswari D.Balamurugan Vinayakumar Ravi Moez Krichen Roobaea Alroobaea Saeed Rubaiee Sankar Sennan Department of CSE Sona College of TechnologySalem636005India Department of CSE Dr.M.G.R.Educational and Research InstituteChennai600095India Center for Artificial Intelligence PrinceMohammad Bin Fahd UniversityKhobarSaudi Arabia Faculty of CSIT Al-Baha UniversitySaudi Arabia ReDCAD laboratory University of SfaxTunisia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Industrial and Systems Engineering College of EngineeringUniversity of JeddahJaddahSaudi Arabia

Despite the seemingly exponential growth of mobile and wireless communication,this same technology aims to offer uninterrupted access to different wireless systems like Radio Communication,Bluetooth,and Wi-Fi to achieve better network connection which in turn gives the best quality of service(QoS).Many analysts have established many handover decision systems(HDS)to enable assured continuous mobility between various radio access *** is one of themost significant problems considered in wireless communication *** application needs a distinct QoS,so the network choice may shift *** achieve this objective and to choose the finest networks,it is important to select a best decision making algorithm that chooses the most effective network for every application that the user requires,dependent on QoS ***,the main goal of the proposed system is to provide an enhanced vertical handover(VHO)decision making programby using aMulti-CriteriaFuzzy-Based algorithm to choose the best *** Multi-Criteria algorithms and a Fuzzy-Based algorithm is implemented successfully for optimal network selection and also to minimize the probability of false ***,a double packet buffer is utilized to decrease the packet loss by 1.5%and to reduce the number of handovers up to 50%compared to the existing *** addition,the network setup has an optimized mobilitymanagement system to supervise the movement of the mobile nodes.

关键词： Vertical handover mobility management handover decision systems multi-criteria fuzzy logic QoS false handover

来源：评论

学校读者我要写书评

暂无评论

Content Moderation and the Formation of Online Communities: A Theoretical Framework

arXiv

引用

arXiv 2023年

作者： Dwork, Cynthia Hays, Chris Kleinberg, Jon Raghavan, Manish Department of Computer Science Harvard University United States Institute for Data Systems and Society Massachusetts Institute of Technology United States Departments of Computer Science and Information Science Cornell University United States Sloan School of Management Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States

We study the impact of content moderation policies in online communities. In our theoretical model, a platform chooses a content moderation policy and individuals choose whether or not to participate in the community according to the fraction of user content that aligns with their preferences. The effects of content moderation, at first blush, might seem obvious: it restricts speech on a platform. However, when user participation decisions are taken into account, its effects can be more subtle - and counter-intuitive. For example, our model can straightforwardly demonstrate how moderation policies may increase participation and diversify content available on the platform. In our analysis, we explore a rich set of interconnected phenomena related to content moderation in online communities. We first characterize the effectiveness of a natural class of moderation policies for creating and sustaining communities. Building on this, we explore how resource-limited or ideological platforms might set policies, how communities are affected by differing levels of personalization, and competition between platforms. Our model provides a vocabulary and mathematically tractable framework for analyzing platform decisions about content moderation. Copyright © 2023, The Authors. All rights reserved.

关键词： Online systems

来源：评论

学校读者我要写书评

暂无评论

An Intelligent IoT-based COVID-19 Contact Tracing System for Ashesi University

An Intelligent IoT-based COVID-19 Contact Tracing System for...

引用

Utility Conference and Exposition (IUCE), IEEE/IET International

作者： Alhassan Issifu David Ebo Adjepon-Yamoah Engineering Department Ashesi University Ghana Computer Science and Information Systems Department Ashesi University Ghana

The outbreak of contagious diseases demand isolation and quarantining of infected persons and people they have been in close proximity with. This can be easily achieved if technology-based systems are designed to facilitate contact tracing. The aim of this research project is to develop a privacy focused IoT-based COVID-19 contact tracing system that leverages mobile devices and artificial intelligence for the Ashesi University community. To achieve this, we divided the project into two main parts: the software sub-system and the hardware subsystem. The software sub-system comprises of a cross-platform mobile application that tracks users, and an admin portal to monitor user activities. The hardware sub-system is an IoT-based system that uses a Raspberry Pi to capture indoor images with the aid of a Raspberry Pi camera module. It processes the images to determine whether the occupants of the room have been in close proximity with one another or not while relaying feedback to them via its actuators and at the same time updates the admin portal. Through system testing, it was identified that 32% our system users considered privacy during the pandemic as critical even though 95% confirmed that the system assures very high level of privacy.

关键词： COVID-19 Privacy System testing Pandemics Software Hardware Mobile handsets

来源：评论

学校读者我要写书评

暂无评论

Whole-Body Image-to-Image Translation for a Virtual Scanner in a Healthcare Digital Twin

arXiv

引用

arXiv 2025年

作者： Guarrasi, Valerio Di Feola, Francesco Restivo, Rebecca Tronchin, Lorenzo Soda, Paolo Research Unit of Computer Systems and Bioinformatics Department of Engineering Università Campus Bio-Medico di Roma Rome Italy Department of Computing Science Umeå University Umeå Sweden Department of Diagnostics and Intervention Biomedical Engineering and Radiation Physics Umeå University Umeå Sweden

Generating positron emission tomography (PET) images from computed tomography (CT) scans via deep learning offers a promising pathway to reduce radiation exposure and costs associated with PET imaging, improving patient care and accessibility to functional imaging. Whole-body image translation presents challenges due to anatomical heterogeneity, often limiting generalized models. We propose a framework that segments whole-body CT images into four regions—head, trunk, arms, and legs—and uses district-specific Generative Adversarial Networks (GANs) for tailored CT-to-PET translation. Synthetic PET images from each region are stitched together to reconstruct the whole-body scan. Comparisons with a baseline non-segmented GAN and experiments with Pix2Pix and CycleGAN architectures tested paired and unpaired scenarios. Quantitative evaluations at district, whole-body, and lesion levels demonstrated significant improvements with our district-specific GANs. Pix2Pix yielded superior metrics, ensuring precise, high-quality image synthesis. By addressing anatomical heterogeneity, this approach achieves state-of-the-art results in whole-body CT-to-PET translation. This methodology supports healthcare Digital Twins by enabling accurate virtual PET scans from CT data, creating virtual imaging representations to monitor, predict, and optimize health outcomes. © 2025, CC BY-SA.

关键词： Positron emission tomography

来源：评论

学校读者我要写书评

暂无评论

Sepsis Risk Assessment with XGBoost: A Data-Intensive Approach

Sepsis Risk Assessment with XGBoost: A Data-Intensive Approa...

引用

Innovative Computing, Intelligent Communication and Smart Electrical systems (ICSES), International Conference on

作者： M. Gayathri V. Kavitha Department of Computer Science and Engineering School of Computing SRM Institute of Science and Technology Kattankulathur Tamilnadu India Department of DataScience and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Tamilnadu India

In sepsis, immunological response to infection fails, causing systemic inflammation and organ failure. Sepsis progresses quickly and is commonly misdiagnosed, making early discovery and treatment crucial for patient outcomes. Augmented patient care and earlier intervention are possible using machine learning sepsis prediction systems. This research investigates if the well-known gradient boosting technique XGBoost can predict sepsis. Our goal is to construct a viable predictive model that can identify sepsis patients at risk of deterioration utilizing a huge dataset of EHRs from various healthcare facilities. The information includes vitals, test results, and patient details. Methodology includes "data preprocessing," "feature engineering," and model development". Train the XGBoost model to predict sepsis onset and then build a threshold-based system to alert patients when their risk surpasses a certain level. Some indicators of model efficacy include sensitivity, specificity, and AUC-ROC. Sepsis prediction appears promising using the XGBoost model. If doctors can detect at-risk patients early and administer early treatments, fatality rates and patient outcomes may decrease. The study emphasizes the need of machine learning in healthcare to improve clinical decision support systems and assist critical care physicians.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of Machine Learning Models in Online Payment Fraud Prediction 2

Comparative Analysis of Machine Learning Models in Online Pa...

引用

2nd International Conference of Intelligent Methods, systems and Applications, IMSA 2024

作者： Shdefat, Ahmed Younes Mohamed, Mennatallah Khaled, Shahd Hany, Farrah Fathi, Hanaa Abdelminaam, Diaa Salama College of Engineering and Technology American University of the Middle East Egaila54200 Kuwait Misr International University Faculty of Computer Science Cairo Egypt Applied Science Private University Applied Science Research Center Amman Jordan Jadara University Jadara Research Center Irbid21110 Jordan Benha University Faculty of Computers and Artificial Intelligence Information Systems department Benha Egypt MEU Research Unit Middle East University Amman11831 Jordan

ISBN: (纸本)9798350362633

With the substantial increase in Cybercrime, the pressing need for effective solutions in the light of the rising amount of corporate financial losses, is a concern f or many researchers. The widespread adoption of ecommerce and diverse online payment methods has contributed to the surge in a major form of cybercrime - online payment fraud. This further highlights the critical need to control and reduce the widespread danger of fraudulent behaviour on the internet. However, predicting online payment fraud is difficult as fraudsters are constantly changing their tactics, transactions happen quickly and in large volumes, transactions involve different regions and countries, stolen data is often used and other factors. Our goal is to improve the accuracy and usability of online payment fraud prediction through the use of machine learning. A well-established and widely recognized dataset specifically designed for studying online payment fraud was used. The six proposed algorithms trained and tested were (SVM), Decision Tree, Naïve Bayes, Random Forest, (KNN) and Logistic Regression. After rigorous testing, Decision Tree presented the highest accuracy in performance of 98.58%. However, as ten-fold cross validation was implemented on the dataset, Decision Tree suffered a loss while Random Forest had an increase in accuracy making it the best model with accuracy of 98.47%. As shown in this paper, machine learning has proven to be a reliable and ever-evolving approach for the prediction of Online Payment Fraud and results can be further improved in collaboration with financial and cybercrime specialists. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Towards Evidence-Based Cybersecurity Assessment of Programmable systems to Ensure the Protection of Critical IT Infrastructure 12

Towards Evidence-Based Cybersecurity Assessment of Programma...

引用

12th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing systems: Technology and Applications, IDAACS 2023

作者： Illiashenko, Oleg Kharchenko, Vyacheslav Odarushchenko, Oleg National Aerospace University 'KhAI' Department of Computer Systems and Networks Kharkiv61070 Ukraine Institute of Information Science and Technologies 'Alessandro Faedo' - Isti Cnr Software Engineering & Dependable Computing Laboratory Pisa56124 Italy Research and Production Enterprise Rpc Radics Llc Kropyvnytskyi25009 Ukraine

ISBN: (纸本)9798350358056

The paper is devoted to developing scientific principles, methods, means, and information technology of model-oriented verification and evidence-based assessment using functional safety and cybersecurity cases for programmable systems of critical applications (PSCA). In particular, information and control systems important for the safety of nuclear power plants (NPPs), aerospace systems, railway domains, etc., developed using Field Programmable Gate Arrays (FPGAs) and hardware platforms are reviewed. The goal is to ensure the guaranteed completeness and reliability of its functional safety and cybersecurity assessment by developing and implementing a set of formal and semi-formal methods and tools that consider defects of different nature - physical, design, trojans, and vulnerabilities that can be attacked and lead to a fatal system failure, which results in damaging the critical IT infrastructure. The methods shown are based on integrating algebraic, tabular, graph models, and case assessment methodology. The methods are implemented as appropriate technologies for evidence-based verification and evaluation of PSCA. For formal methods, a prototype of a translator of Very High-Speed Integrated Circuits Hardware Description Language (VHDL) code is developed into an algebra of behaviors. It provides evidence-based verification and a framework and tools to design reports on assessing the cybersecurity and functional safety of programmable systems. © 2023 IEEE.

关键词： critical IT infrastructure cybersecurity FPGA functional safety programmable systems of critical applications

来源：评论

学校读者我要写书评

暂无评论

Universal Process Migration Solution of MAGICAL for Analog IC Layout Automation

Universal Process Migration Solution of MAGICAL for Analog I...

引用

China Semiconductor Technology International Conference (CSTIC)

作者： Yufeng Wei Yifan Xu Keren Zhu Ye Lu State Key Lab. of Integrated Chips and Systems School of Information Science and Technology Fudan University Shanghai China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong China

ISBN: (数字)9798350362190

ISBN: (纸本)9798350362206

Analog IC layout automation has been a hot topic in the field in recent years[1]. An open-source software MAGICAL developed by the University of Texas team is one of the most effective means to achieve this goal[2]. However, so far MAGICAL is only able to support one process library currently, i.e., TSMC 40nm, and this imposes limitations of its practical application. This work proposes a solution for the technology migration of MAGICAL, and this greatly broadens its application scenarios. Firstly, the MAGICAL's device generator is rewritten to take in the general design rule information of different technology nodes. Second, the parameters of a given technology's tech file are extracted by customerized Parameter Extractor. Finally, the extracted parameters are set into the device generator of MAGICAL as well as the LEF file used for routing. As demonstrations, layouts of multiple circuits such as inverter, Foleded Cascode, Nbooster and Pbooster are generated sucessfully using this new flow at different technology nodesincluding but not limited to TSMC 65nm and TSMC 130nm. In addition, all GDS files generated with this method are DRC-clean and LVS-clean. This work paves the way for MAGICAL to be used in design automation at various technology nodes.

关键词： Integrated circuits Automation Design automation Layout Routing Generators Libraries

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：