检索结果-内蒙古大学图书馆

Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Wilfred Blessing N. R Hemalatha Gunasekaran Hariharan B. Jaber Saleh Salim Al Jabri Kavitha S. J Sutherlin Subitha G. College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Sultanate of Oman Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Department of Computer Science and Engineering Stella Mary's College of Engineering Aruthenganvilai Tamil Nadu India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Health care plays a vital responsibility in a human life span. World countries targets to provide better care for their citizens. Digital transformations and innovations in Artificial Intelligence (AI) technologies helps in deep for the novel solutions to speed-up the decision making process in health management and patient consulting. AI branches including Machine Learning (ML), Deep Learning (DL), Neural Networks (NN), Natural Language Processing (NLP) and so forth can support innovations in heal care domain. This paper presents the comprehensive and noble review on recent existing tools and techniques and also recommends advanced techniques for health care industry that are to be used in Sultanate of Oman. Furthermore, overcoming AI challenges are resulted and presented through the study. This work mainly aimed to produce the multiple dimensions and challenges that helps to develop AI powered real-life applications in health care support system.

关键词： Deep learning Technological innovation Ethics Reviews Decision making Surgery Medical services Natural language processing Medical diagnostic imaging Diseases

来源：评论

学校读者我要写书评

暂无评论

Adaptive Vehicle Control System for Improved Safety With Image Processing 5

Adaptive Vehicle Control System for Improved Safety With Ima...

引用

5th IEEE International Conference on Electro-Computing Technologies for Humanity, NIGERCON 2024

作者： Vijayalakshmi, N. Priya, L. Ramaraj, S. Nanjundan, Menakadevi Palanisamy, Satheesh Kumar Olalekan Salau, Ayodeji Braide, Sepiribo Lucky Alliance University Alliance School of Advanced Compuitng Department of Computer Science and Engineering Bangalore India Department of Ece Kinathukadavu Tamilnadu Coimbatore641202 India Karpagam College of Engineering Department of Computer Science and Engineering India Karpagam Institute of Technology Department of Ece Coimbatore India Bms Institute of Technology and Management Department of Ece Karnataka Bengaluru India Afe Babalola University Department of Electrical/Electronics and Computer Engineering Ado-Ekiti Nigeria Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Chennai India Rivers State University Department of Electrical and Electronics Engineering Port Harcourt Nigeria

ISBN: (纸本)9798331542559

We propose the development of an Adaptive Vehicle Control (AVC) system using readily available components such as a Raspberry Pi microcontroller, a motor, a motor driver, and a Raspberry Pi camera. This system aims to enhance safety and convenience by automatically adjusting vehicle speed based on surrounding traffic conditions and recognizing relevant road signs like school zones, speed limits, and hospital zones. The process begins with real-time image capture using the Raspberry Pi camera module. These images are then analyzed using Python-based computer vision algorithms, primarily leveraging OpenCV. The image processing pipeline employs techniques such as object detection and template matching to identify specific road signs within the captured frames. Upon sign detection, the system interprets their meanings and determines the appropriate speed limits associated with them. Using the information from the detected signs and their corresponding speed limits, the system regulates the motor connected to the vehicle's throttle through GPIO pins on the Raspberry Pi. By adjusting the motor's speed, the AVC system ensures compliance with the specified speed limits across various road conditions. Safety measures are paramount, with fail-safe mechanisms implemented to prevent accidents in case of system errors or malfunctions. This AVC system offers a cost-effective and adaptable solution for integrating intelligent cruise control capabilities into vehicles. Leveraging the computational power of the Raspberry Pi and the versatility of image processing techniques, the system can adapt to diverse driving environments, thereby enhancing overall driving safety. This project contributes to the progression of smart transportation systems and lays the foundation for further advancements in autonomous driving technologies. © 2024 IEEE.

关键词： Adaptive cruise control

来源：评论

学校读者我要写书评

暂无评论

AI-Enhanced Healthcare: Densenet Based Alzheimer's Disease Prediction and Online Consultation for Accessible Medical Support

AI-Enhanced Healthcare: Densenet Based Alzheimer's Disease P...

引用

Machine Learning and Autonomous Systems (ICMLAS), International Conference on

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Improvements in patient care and early disease detection possibilities are two of the ways artificial intelligence is transforming the healthcare industry. Alzheimer's disease, a neurodegenerative disease that worsens with time, is among the most difficult diseases to exactly identify in early stages. Patients thus delay in treatment and experience a decline in their quality of life. Research is still under progress, thus knowledge about how AI-driven prediction models could be combined with readily available healthcare services remains lacking. This work presents a special artificial intelligence-powered system consisting of DenseNet-based deep learning for Alzheimer's disease prediction with an online consultation platform. The proposed approach analyzes brain MRI data and classifies Alzheimer's stages with higher accuracy using a DenseNet architecture, well-known for its exceptional performance in the field of feature extraction. Comprising three thousand magnetic resonance imaging (MRI) images, the dataset has been preprocessed for noise reduction and tuned for models performance. At 98.5%, the DenseNet model exceeded other methods including VGGNet and ResNet in accuracy.

关键词： Accuracy Magnetic resonance imaging Redundancy Medical services Predictive models Brain modeling Real-time systems Alzheimer's disease Medical diagnostic imaging Overfitting

来源：评论

学校读者我要写书评

暂无评论

Economical Convex Coverings and Applications

arXiv

引用

arXiv 2023年

作者： Arya, Sunil da Fonseca, Guilherme D. Mount, David M. Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Aix-Marseille Université LIS Lab France Department of Computer Science Institute for Advanced Computer Studies University of Maryland College ParkMD United States

Coverings of convex bodies have emerged as a central component in the design of efficient solutions to approximation problems involving convex bodies. Intuitively, given a convex body K and Ε > 0, a covering is a collection of convex bodies whose union covers K such that a constant factor expansion of each body lies within an Ε expansion of K. Coverings have been employed in many applications, such as approximations for diameter, width, and Ε-kernels of point sets, approximate nearest neighbor searching, polytope approximations with low combinatorial complexity, and approximations to the Closest Vector Problem (CVP). It is known how to construct coverings of size nO(n)/Ε(n−1)/2 for general convex bodies in Rn. In special cases, such as when the convex body is the p unit ball, this bound has been improved to 2O(n)/Ε(n−1)/2. This raises the question of whether such a bound generally holds. In this paper we answer the question in the affirmative. We demonstrate the power and versatility of our coverings by applying them to the problem of approximating a convex body by a polytope, where the error is measured through the Banach-Mazur metric. Given a well-centered convex body K and an approximation parameter Ε > 0, we show that there exists a polytope P consisting of 2O(n)/Ε(n−1)/2vertices (facets) such that K ⊂ P ⊂ K(1+Ε). This bound is optimal in the worst case up to factors of 2O(n). (This bound has been established recently using different techniques, but our approach is arguably simpler and more elegant.) As an additional consequence, we obtain the fastest (1 + Ε)-approximate CVP algorithm that works in any norm, with a running time of 2O(n)/Ε(n−1)/2 up to polynomial factors in the input size, and we obtain the fastest (1 + Ε)-approximation algorithm for integer programming. We also present a framework for constructing coverings of optimal size for any convex body (up to factors of 2O(n)). Copyright © 2023, 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

The Impact of SBOM Generators on Vulnerability Assessment in Python: A Comparison and a Novel Approach

arXiv

引用

arXiv 2024年

作者： Benedetti, Giacomo Cofano, Serena Brighente, Alessandro Conti, Mauro Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Italy IMT School for Advanced Studies Lucca Italy Department of Mathematics University of Padua Italy Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Netherlands

The Software Supply Chain (SSC) security is a critical concern for both users and developers. Recent incidents, like the SolarWinds Orion compromise, proved the widespread impact resulting from the distribution of compromised software. The reliance on open-source components, which constitute a significant portion of modern software, further exacerbates this risk. To enhance SSC security, the Software Bill of Materials (SBOM) has been promoted as a tool to increase transparency and verifiability in software composition. However, despite its promise, SBOMs are not without limitations. Current SBOM generation tools often suffer from inaccuracies in identifying components and dependencies, leading to the creation of erroneous or incomplete representations of the SSC. Despite existing studies exposing these limitations, their impact on the vulnerability detection capabilities of security tools is still unknown. In this paper, we perform the first security analysis on the vulnerability detection capabilities of tools receiving SBOMs as input. We comprehensively evaluate SBOM generation tools by providing their outputs to vulnerability identification software. Based on our results, we identify the root causes of these tools' ineffectiveness and propose PIP-sbom, a novel pip-inspired solution that addresses their shortcomings. PIP-sbom provides improved accuracy in component identification and dependency resolution. Compared to best-performing state-of-the-art tools, PIP-sbom increases the average precision and recall by 60%, and reduces by ten times the number of false positives. © 2024, CC BY-SA.

关键词： Input output programs

来源：评论

学校读者我要写书评

暂无评论

AI-Aided Height Optimization for NOMA-UAV Networks

AI-Aided Height Optimization for NOMA-UAV Networks

引用

computer Science, Information Technology and engineering (ICCoSITE), International Conference on

作者： Amira O. Hashesh Adly S. Tag Eldien Mostafa M. Fouda Rokaia M. Zaki Department of Electrical Engineering Faculty of Engineering at Shoubra Benha University Cairo Egypt High Institute for Engineering and Technology - Obour Egypt Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA Center for Advanced Energy Studies (CAES) Idaho Falls ID USA Higher Institute of Engineering and Technology Kafrelsheikh Egypt

Currently, Unmanned Aerial Vehicles (UAVs) are gaining significant attention due to their potential to effectively carry out a variety of tasks with superior performance through the use of fifth-generation (5G) and sixth-generation (6G) networks. Non-orthogonal multiple access (NOMA) techniques can further improve the performance and efficiency while reducing the interference. In this paper, we propose the application of machine learning (ML) techniques to evaluate the outage performance of a NOMA-enabled UAV network. Specifically, this study investigates the optimal UAV height that allows two users on the ground to receive the best service when they are simultaneously served by one UAV. We generated our own dataset which included several network parameters. We then trained various machine learning techniques on this dataset, including artificial neural networks (ANN), support vector regression (SVR), and linear regression (LR). Our results indicate that ANN provides the best accuracy compared with SVR and LR, with an average root mean squared error (RMSE) of 0.0931.

关键词： Support vector machines NOMA Linear regression Artificial neural networks Machine learning Interference Autonomous aerial vehicles

来源：评论

学校读者我要写书评

暂无评论

Geological Time Series Analysis for Spatial Seismic Forecasting in Japan

Geological Time Series Analysis for Spatial Seismic Forecast...

引用

Artificial Intelligence and Emerging Technology (Global AI Summit), International Conference on

作者： Prerit Sharma Bhawna Saxena Rhythm Rajat Khandelwal Adwitiya Sinha Department of Computer Science and Engineering & Information Technology Jaypee Institute of Information Technology Noida Uttar Pradesh India Natural & Applied Sciences TERI School of Advanced Studies Delhi India

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

Earthquakes pose significant threats to lives and infrastructure worldwide, underscoring the critical need for effective prediction models. Our study represents a meticulous examination of seismic data sourced from the United States Geological Survey, encompassing earthquake occurrences in Japan over five years. Employing a refined zoning approach based on historical seismicity, we tailored our analysis to account for regional variations in earthquake frequency and intensity. By integrating these discoveries into the ARIMA model, we aim to fortify earthquake prediction frameworks, thereby fostering more effective disaster mitigation strategies on a global scale. Furthermore, our analysis using SMA, EMA, and ARIMA models demonstrated that the EMA model provided superior performance. Leveraging the predictive capabilities of the EMA model, we identified twelve geographic coordinates in Japan with a higher likelihood of future earthquakes. This study underscores the potential of EMA in earthquake prediction and provides a foundational approach for integrating machine learning with geospatial analysis for natural disaster forecasting.

关键词： Surveys Analytical models Geology Disasters Earthquakes Time series analysis Machine learning Predictive models Data models Forecasting

来源：评论

学校读者我要写书评

暂无评论

Cell-Free MIMO Perceptive Mobile Networks: Cloud Vs. Edge Processing

引用

IEEE Transactions on Vehicular Technology 2025年

作者： Jeong, Seongah Kang, Jinkyu Simeone, Osvaldo Shamai, Shlomo University of Seoul School of Advanced Fusion Studies Department of Intelligent Semiconductor Engineering Seoul02504 Korea Republic of Myongji University Department of Information and Communication Engineering Gyeonggi-do Korea Republic of LondonWC2B 4BG United Kingdom Technion Department of Electrical and Computer Engineering Haifa3200003 Israel

Perceptive mobile networks (PMNs) implement sensing and communication by reusing existing cellular infrastructure. Thanks to the cooperation among distributed access points, cell-free multiple-input multiple-output (MIMO) systems support the deployment of multistatic radar sensing, while providing high spectral efficiency for data communication services. To this end, in a cell-free MIMO system, distributed access points (APs) communicate over fronthaul links with a central processing unit (CPU) in the cloud. This work proposes four different types of PMN uplink solutions based on cell-free MIMO systems, in which the sensing and decoding functionalities are carried out at either the cloud or the edge. Accordingly, we develop and compare joint cloud-based decoding and sensing (CDCS), hybrid cloud-based decoding and edge-based sensing (CDES), hybrid edge-based decoding and cloud-based sensing (EDCS) and edge-based decoding and sensing (EDES). In all cases, we target a unified design problem formulation for fronthaul quantization to maximize the achievable rate under sensing and fronthaul capacity constraints. Via numerical results, the four implementation scenarios are compared as a function of the available fronthaul resources by highlighting the relative merits of edge- and cloud-based sensing and communications. This study provides guidelines on the optimal functional allocation in fronthaul-constrained networks implementing integrated sensing and communications. © 2025 IEEE.

关键词： Multistatic radars

来源：评论

学校读者我要写书评

暂无评论

Power Supply Induced Jitter (PSIJ) Modeling, Analysis, and Optimization of High Bandwidth Memory (HBM) I/O Interface

Power Supply Induced Jitter (PSIJ) Modeling, Analysis, and O...

引用

IEEE International Symposium on Electromagnetic Compatibility (EMC)

作者： Hyunwook Park Taein Shin Seongguk Kim Keeyoung Son Keunwoo Kim Boogyo Sim Hyungmin Kang Seonguk Choi Jiwon Yoon Hyunwoo Kim Chulsoon Hwang Joungho Kim School of Electrical Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon South Korea Department of Electrical and Computer Engineering Missouri University of Science and Technology (MST) Rolla MO USA

Power supply induced jitter (PSIJ) in high bandwidth memory (HBM) I/O interface is modeled, analyzed, and optimized for different HBM generations. Precise models for VDDQ power distribution networks (PDNs), simultaneous switching current (SSC), and jitter sensitivities of the clock and I/O buffers are implemented for PSIJ estimation. Compared to the SPICE, the average error rate of the estimated PSIJ is 4.26 %. The critical frequency bands in the jitter spectrum where large jitters occur are derived by comparing the relative impact of the modeled interface factors in the frequency domain. For the optimization, on-chip and on-interposer decoupling capacitor (decap) placement strategies using machine learning (ML) are applied. The decap effects in the critical ranges are analyzed. Finally, based on the integrated analysis of the limitation of the decap solution and all the I/O interface factors, the major challenges of high-frequency PSIJ are characterized.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Longitudinal velocity control of autonomous driving based on extended state observer

引用

CAAI Transactions on Intelligence Technology 2025年第1期10卷 36-46页

作者： Hongbo Gao Hanqing Yang Xiaoyu Zhang Xiangyun Ren Fenghua Liang Ruidong Yan Qingchao Liu Mingmao Hu Fang Zhang Jiabing Gao Siyu Bao Keqiang Li Deyi Li Danwei Wang Department of Automation University of Science and Technology of ChinaHefeiChina State Key Laboratory of Intelligent Vehicle Safety Technology ChongqingChina Institute of Advanced Technology University of Science and Technology of ChinaHefeiChina School of Electrical and Electronic Engineering Nanyang Technological UniversitySingaporeSingapore School of Traffic and Transportation Beijing Jiaotong UniversityBeijingChina Automotive Engineering Research Institute Jiangsu UniversityZhenjiangChina School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingaporeSingapore School of Automotive Engineering Hubei University of Automotive TechnologyShiyanChina Beijing Idriverplus Technology Co. Ltd.BeijingChina Chery Automobile Co. Ltd.WuhuChina State Key Laboratory of Automotive Safety and Energy Tsinghua UniversityBeijingChina Department of Computer Science and Technology Tsinghua UniversityBeijingChina

Active Disturbance Rejection Control(ADRC)possesses robust disturbance rejection capabilities,making it well-suited for longitudinal velocity ***,the conventional Extended State Observer(ESO)in ADRC fails to fully exploit feedback from first-order and higher-order estimation errors and tracking error simultaneously,thereby diminishing the control performance of *** address this limitation,an enhanced car-following algorithm utilising ADRC is proposed,which integrates the improved ESO with a feedback *** comparison to the conventional ESO,the enhanced version effectively utilises multi-order estimation and tracking ***,it enhances convergence rates by incorporating feedback from higher-order estimation errors and ensures the estimated value converges to the reference value by utilising tracking error *** improved ESO significantly enhances the disturbance rejection performance of ***,the effectiveness of the proposed algorithm is validated through the Lyapunov approach and experiments.

关键词： active disturbance rejection control(ADRC) autonomous vehicle extended state observer(ESO) intelligent control longitudinal velocity control

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：