检索结果-内蒙古大学图书馆

2024 International Conference on Artificial Intelligence, computer, Data Sciences, and Applications, ACDSA 2024

作者： Soualihou, Soualihou Ngnamsie Kim, Jeong-Dong Zonyfar, Candra Lee, Hyun Lee, Taek Lee, Jung-Been Sun Moon University Dept. of Computer Science and Electronic Convergence Asan Korea Republic of Sun Moon University Dept. of Computer Science and Engineering Asan Korea Republic of Sun Moon University Division of Computer Science and Engineering Asan Korea Republic of

ISBN: (纸本)9798350394528

Chronic diseases present a significant challenge in healthcare, often requiring ongoing medical attention and posing limitations on patients' daily activities. Diagnosis of such diseases is hindered by the absence of specific symptoms making them hard to detect and or prevent. Addressing this issue, researchers have turned to computational approaches, analyzing patients' medical records to predict the presence or absence of chronic diseases with promising results. However, there is potential for further improvement. This paper introduces an ensemble classifier, ELVot-CroDiP, designed to enhance chronic disease prediction. ELVot-CroDiP harnesses the collective strengths of various machine learning algorithms through a majority voting mechanism, thereby boosting predictive accuracy. The model's performance was evaluated using precision, recall, f1-score, and accuracy across five medical datasets, including heart disease, chronic kidney disease, diabetes, and heart stroke. Comparative analysis shows that ELVot-CroDiP offers superior performance in predicting chronic diseases compared to existing models, as evidenced by its higher scores in the mentioned evaluation metrics for each dataset tested. © 2024 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

A Lightweight CNN-SVM Explainable AI Approach for Classification and Visualization of Grape Leaf Disease 3

A Lightweight CNN-SVM Explainable AI Approach for Classifica...

引用

3rd International Conference on Advancement in Electrical and electronic engineering, ICAEEE 2024

作者： Peyal, Hasibul Islam Leion, Zannatul Mawa Abdal, Md. Nazmul Islam, Md. Injamul Miraz, Shafiun Remon, Mahmudur Rashid Kontho, Md. Mahin Rahaman Tasnim, Nishat Rajshahi University of Engineering & Technology Dept. of Electrical & Computer Engineering Rajshahi Bangladesh University of Liberal Arts Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh Uttara University Dept. of Computer Science & Engineering Dhaka Bangladesh Rajshahi University of Engineering & Technology Rajshahi Bangladesh North South University Dept. of Electrical and Electronic Engineering Dhaka Bangladesh Sher-e-Bangla Agricultural University Dept. of Agroforestry and Environmental Science Dhaka Bangladesh

ISBN: (纸本)9798350388282

Grape is highly esteemed as a significant agricultural crop in Bangladesh. Plant diseases primarily result from the presence of pathogens and pest insects, leading to a significant decline in productivity if not properly addressed. The fundamental aim of this research is to employ a streamlined CNN-SVM architecture, utilizing deep learning techniques, to accurately categorize grape leaves into three distinct disease classes and one healthy class. The proposed model surpasses the accuracy of the previously trained transfer learning models VGG-16 and VGG-19 while having approximately 257× to 267× times fewer parameters (0.537 M). On average, the proposed model achieves a classification accuracy of 99.18%, which is significantly higher than the 93.42% and 91.94% achieved by the transfer learning models, respectively. With a precision, recall, and F1 score close to 99%, the suggested model provides excellent results. The model's outstanding performance is further validated by its remarkable Area Under Curve (AUC) score of 99.98%. In addition to using less disc space (about 6 MB), the suggested model because of being lightweight shows a significant decrease in parameters. To visually display the disease identified by the proposed model, a transparent AI methodology has been utilized, specifically the Gradient Weighted Class Activation Mapping (Grad-CAM) technique. To better understand which area was responsible for the classification, a heatmap has been created. © 2024 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Near-sensor computing-assisted simultaneous viral antigen and antibody detection via integrated label-free biosensors with microfluidics

引用

InfoMat 2023年第10期5卷 80-95页

作者： Byungjoon Bae Yongmin Baek Jeongyong Yang Heesung Lee Charana S.S.Sonnadara Sangeun Jung Minseong Park Doeon Lee Sihwan Kim Gaurav Giri Sahil Shah Geonwook Yoo William APetri Kyusang Lee Department of Electrical and Computer Engineering University of VirginiaCharlottesvilleVirginiaUSA School of Electronic Engineering Soongsil UniversitySeoulRepublic of Korea Department of Electrical and Computer Engineering University of MarylandCollege ParkMarylandUSA Department of Chemical Engineering University of VirginiaCharlottesvilleVirginiaUSA Division of Infectious Diseases and International Health Department of MedicineSchool of MedicineUniversity of VirginiaCharlottesvilleVirginiaUSA Department of Material Science and Engineering University of VirginiaCharlottesvilleVirginiaUSA Department of Intelligent Semiconductors Soongsil UniversitySeoulRepublic of Korea

Precise diagnosis and immunity to viruses,such as severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)and Middle East respiratory syndrome coronavirus(MERS-CoV)is achieved by the detection of the viral antigens and/or corresponding antibodies,***,a widely used antigen detection methods,such as polymerase chain reaction(PCR),are complex,expensive,and time-consuming Furthermore,the antibody test that detects an asymptomatic infection and immunity is usually performed separately and exhibits relatively low *** achieve a simplified,rapid,and accurate diagnosis,we have demonstrated an indium gallium zinc oxide(IGZO)-based biosensor field-effect transistor(bio-FET)that can simultaneously detect spike proteins and antibodies with a limit of detection(LOD)of 1 pg mL–1 and 200 ng mL–1,respectively using a single assay in less than 20 min by integrat-ing microfluidic channels and artificial neural networks(ANNs).The near-sensor ANN-aided classification provides high diagnosis accuracy(>93%)with significantly reduced processing time(0.62%)and energy consumption(5.64%)compared to the software-based *** believe that the development of rapid and accurate diagnosis system for the viral antigens and antibodies detec-tion will play a crucial role in preventing global viral outbreaks.

关键词： artificial intelligence biosensor edge-computing integration microfluidic

来源：评论

学校读者我要写书评

暂无评论

Reinforcement Learning Applied to Very Small Size Soccer Decision-Making, Trajectory Planning and Control In Penalty Kicks 16

Reinforcement Learning Applied to Very Small Size Soccer Dec...

引用

16th Brazilian Symposium on Robotics and 15th Workshop on Robotics in Education, SBR-WRE 2024

作者： Pires Baldão, Thayna Maximo, Marcos R.O.A. Yoneyama, Takashi Computer Science Division Aeronautics Institute of Technology São José dos Campos Brazil Electronic Engineering Division Aeronautics Institute of Technology São José dos Campos Brazil

ISBN: (纸本)9798350391084

In this work, we propose a Reinforcement Learning (RL) training methodology using Proximal Policy Optimization (PPO) and Curriculum Learning (CL) to make simulated Very Small Size Soccer robots learn to convert penalty kicks against two types of opponents: the "line follow" goalkeeper, which defends the goal by following the goal line, and the offensive goalkeeper, which uses the univector field navigation technique to attack the ball. The RL agents trained using this methodology demonstrated sophisticated penalty kick skills with high conversion rates. The best-evaluated RL agent achieved a conversion rate of over 90% against the "line follow" goalkeeper and of over 71% against the offensive goalkeeper. Comparisons with the univector agent showed that the RL agents significantly outperformed it, with conversion rates 78.71% higher against the "line follow" goalkeeper and 1997.66% higher against the offensive goalkeeper. ©2024 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

First-order reversal curve analysis of superparamagnetic nanoparticles with oriented easy axis of magnetization

引用

Journal of the Magnetics Society of Japan 2023年第4期47卷 115-117页

作者： Sasaoka, Eisuke Yang, Chao Trisnanto, Suko Bagus Song, Zenglu Ota, Satoshi Takemura, Yasushi Division of Electrical and Computer Engineering Yokohama National University Yokohama240-8501 Japan School of Electrical Engineering Nanjing Vocational University of Industry Technology Nanjing210023 China Department of Electrical and Electronic Engineering Shizuoka University Hamamatsu432-8561 Japan

We analyzed the magnetization process of magnetic nanoparticles using first-order reversal curve (FORC) analysis and verified a superparamagnetic feature and the orientation of the easy axis of the particles. The FORCs and FORC diagram of magnetic nanoparticles dispersed in water exhibited a superparamagnetic feature. The differences in the magnetizations and distributions of the coercive force of the solid-state samples were used to identify the orientation of the easy axes of magnetization. © 2023 by the Magnetics Society of Japan.

关键词： Magnetic anisotropy

来源：评论

学校读者我要写书评

暂无评论

Vision-Based Fixed-Time Uncooperative Aerial Target Tracking for UAV

引用

IEEE/CAA Journal of Automatica Sinica 2023年第5期10卷 1322-1324页

作者： Peng Sun Siqi Li Bing Zhu Zongyu Zuo Xiaohua Xia the Seventh Research Division Beihang UniversityBeijing 100191China the School of Automation Beijing Institute of TechnologyBeijing 100081China the Department of Electrical Electronic and Computer EngineeringUniversity of PretoriaPretoria 0002South Africa

Dear Editor,In this letter,a vision-based fixed-time control is proposed for an unmanned aerial vehicle(UAV)with actuator saturation to track an uncooperative *** fixed-time control is designed in backstepping *** rel... 详细信息

关键词： Fixed cooperative letter

来源：评论

学校读者我要写书评

暂无评论

WiFo: wireless foundation model for channel prediction

引用

Science China(Information Sciences) 2025年第6期68卷 372-384页

作者： Boxun LIU Shijian GAO Xuanyu LIU Xiang CHENG Liuqing YANG State Key Laboratory of Photonics and Communications School of Electronics Peking University Internet of Things Thrust The Hong Kong University of Science and Technology (Guangzhou) Department of Electronic and Computer Engineering and Department of Civil and Environmental Engineering The Hong Kong University of Science and Technology

Channel prediction permits to acquire channel state information(CSI) without signaling overhead. However,almost all existing channel prediction methods necessitate the deployment of a dedicated model to accommodate a specific configuration. Leveraging the powerful modeling and multi-task learning capabilities of foundation models, we propose the first space-time-frequency(STF) wireless foundation model(WiFo) to address time-frequency channel prediction tasks in a unified manner. Specifically, WiFo is initially pre-trained over massive and extensive diverse CSI datasets. Then, the model will be instantly used for channel prediction under various CSI configurations without any fine-tuning. We propose a masked autoencoder(MAE)-based network structure for WiFo to handle heterogeneous STF CSI data, and design several mask reconstruction tasks for self-supervised pre-training to capture the inherent 3D variations of CSI. To fully unleash its predictive power, we build a large-scale heterogeneous simulated CSI dataset consisting of 160k CSI samples for *** validate its superior unified learning performance across multiple datasets and demonstrate its state-of-the-art(SOTA) zero-shot generalization performance via comparisons with other full-shot baselines.

关键词： channel prediction channel state information foundation model self-supervised pre-training zero-shot learning

来源：评论

学校读者我要写书评

暂无评论

Enhanced Learned Query Driven Cardinality Estimator with Active Learning

Enhanced Learned Query Driven Cardinality Estimator with Act...

引用

2023 International Conference on Algorithms, Computing and Data Processing, ACDP 2023

作者： Tang, Yiheng Zhan, Siyu Zhou, Li School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Panzhihua Institute of Ecological and Environmental Protection Panzhihua China

ISBN: (纸本)9798350326680

Recent research has shown the excellent per-formance of using machine learning algorithms to optimize database query optimizer, especially the cardinality estimator. Cardinality estimation is a crucial component of query op-timization, as it directly affects the accuracy of cost model and play a critical role in evaluating query execution plans. Learned query-driven cardinality estimation methods aim to capture query features and data distribution, learning the regression from query to cardinality, resulting in more pre-cise cardinality predictions. However, in dynamic production environments, the performance of supervised models can significantly decrease due to data change operations such as insertion, deletion, and update. To address this, We introduce an active learning based model updating strategy that enhances the accuracy of the learned query-driven cardinality estimator, while reducing the model update time in dynamic data change scenarios. We selected two classic, learned query-driven cardinality estimation methods (LW-NN and MSCN) and tested the impact of our proposed model updating strategy on them in the PostgreSQ L database. The experimental results show that our method achieved an effective balance between model accuracy and updating time. This indicates that our method can promote the application of learned query-driven cardinality estimator in the dynamic production deployment environment. © 2023 IEEE.

关键词： Database systems

来源：评论

学校读者我要写书评

暂无评论

Detecting the Number of Bite Prehension of Ggrazing Cows in an Extensive System Using an Audio Recording Method 8

Detecting the Number of Bite Prehension of Ggrazing Cows in ...

引用

8th International Conference of Yearly Reports on Informatics, Mathematics, and engineering, ICYRIME 2023

作者： Avanzato, Roberta Avondo, Marcella Beritelli, Francesco Franco, Francesco Di Tumino, Serena Department of Electrical Electronic and Computer Engineering University of Catania Catania Italy Department of Agricultural Food and Environmental Science University of Catania Catania Italy

In the context of cattle farming, understanding the feeding behavior of animals is essential to ensure their welfare and maximize productivity. However, monitoring and interpret- ing the acoustic signals associated with grazing, particularly the sound events related to grass intake, pose a significant challenge. This study proposes an innovative method based on 1D convolutional neural networks to automatically classify such sound events during grazing. The approach was developed using a balanced dataset composed of 322 prehension samples and 1000 non-prehension samples, extracted from audio recordings of grazing cattle in real conditions. The results obtained show a high accuracy of 100% during the testing and validation phases of the model. However, there is concern about overfitting of the model due to the limited size of the dataset used. Consequently, future expansion of the dataset is suggested by collecting a larger and more diverse number of audio recordings to improve the generalization and robustness of the model in real cattle farming contexts. © 2023 Copyright for this paper by its authors.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

The opportunistic relaying scheme design and symbol error rate analysis for PLC networks in smart homes

引用

Science China(Information Sciences) 2021年第8期64卷 250-252页

作者： Linlin SUN Jiahui YAN Yuwen QIAN Feng SHU Xiangwei ZHOU The School of Electronic and Optical Engineering Nanjing University of Science and Technology The Division of Electrical and Computer Engineering Louisiana State University

Dear editor,Power line communication(PLC) is treated as a retrofit technology, transmitting messages via conductors without extra infrastructure, which reduces the cost of implementation and maintenance [1]. Therefore, PLC is extensively adopted in smart grid applications. However, PLC encounters a hostile communication environment caused by the fast signal attenuation and severe impulsive noise [2].

关键词： SER The opportunistic relaying scheme design and symbol error rate analysis for PLC networks in smart homes

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：