检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

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

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

47,795 篇 会议
7,576 篇 期刊文献
327 册 图书

馆藏范围

55,698 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

34,424 篇 工学
- 21,276 篇 计算机科学与技术...
- 17,610 篇 软件工程
- 9,594 篇 信息与通信工程
- 6,349 篇 电子科学与技术（可...
- 6,184 篇 电气工程
- 5,875 篇 控制科学与工程
- 3,121 篇 生物工程
- 2,649 篇 光学工程
- 2,640 篇 生物医学工程（可授...
- 2,308 篇 机械工程
- 2,304 篇 动力工程及工程热...
- 2,180 篇 仪器科学与技术
- 1,693 篇 化学工程与技术
- 1,274 篇 材料科学与工程（可...
- 1,187 篇 安全科学与工程
- 1,113 篇 交通运输工程
17,984 篇 理学
- 9,486 篇 数学
- 5,864 篇 物理学
- 3,396 篇 生物学
- 3,102 篇 统计学（可授理学、...
- 2,212 篇 系统科学
- 1,800 篇 化学
7,437 篇 管理学
- 4,567 篇 管理科学与工程(可...
- 3,206 篇 图书情报与档案管...
- 2,476 篇 工商管理
2,136 篇 医学
- 1,814 篇 临床医学
- 1,686 篇 基础医学(可授医学...
- 1,116 篇 药学(可授医学、理...
1,369 篇 法学
- 1,127 篇 社会学
926 篇 经济学
- 925 篇 应用经济学
598 篇 农学
593 篇 教育学
264 篇 文学
149 篇 军事学
51 篇 艺术学
3 篇 哲学
2 篇 历史学

主题

1,393 篇 accuracy
1,248 篇 computational mo...
1,069 篇 feature extracti...
955 篇 training
789 篇 deep learning
719 篇 computer science
719 篇 real-time system...
692 篇 machine learning
636 篇 predictive model...
614 篇 optimization
519 篇 support vector m...
510 篇 computer archite...
510 篇 data models
508 篇 internet of thin...
444 篇 convolutional ne...
442 篇 neural networks
402 篇 wireless sensor ...
401 篇 bandwidth
398 篇 reliability
389 篇 monitoring

机构

168 篇 dept. of electri...
82 篇 north-west unive...
81 篇 institut univers...
79 篇 infn sezione di ...
79 篇 national centre ...
76 篇 infn sezione di ...
76 篇 institute of inf...
66 篇 università di ca...
62 篇 dept. of electr....
58 篇 dept. of electri...
57 篇 dept. of informa...
56 篇 dept. of compute...
52 篇 dept. of electri...
52 篇 school of automa...
51 篇 leiden universit...
44 篇 dept. of compute...
43 篇 dept. of electri...
42 篇 infn lnf via enr...
42 篇 in2p3 lpc campus...
39 篇 dept. of compute...

作者

85 篇 ferrara g.
85 篇 ardid m.
84 篇 calvo d.
84 篇 gal t.
84 篇 bozza c.
81 篇 chen a.
76 篇 kalekin o.
64 篇 díaz a.f.
63 篇 huang f.
53 篇 boumaaza j.
52 篇 kulikovskiy v.
41 篇 benoit d.m.
41 篇 eddymaoui a.
40 篇 gagliardini s.
40 篇 labalme m.
37 篇 androulakis g.
36 篇 coleiro a.
34 篇 de palma m.
34 篇 khatun a.
33 篇 eirini eleni tsi...

语言

48,531 篇 英文
6,296 篇 其他
639 篇 中文
212 篇 朝鲜文
59 篇 日文
4 篇 德文
4 篇 斯洛文尼亚文
1 篇 阿拉伯文
1 篇 法文
1 篇 葡萄牙文
1 篇 土耳其文

检索条件"机构=Dept. of Computer and Electrical Engineering and Computer Science"

共 55698 条记录，以下是331-340 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Detection of Primary Open Angle Glaucoma Based on Deep CNN Using Fundus Images 26

Detection of Primary Open Angle Glaucoma Based on Deep CNN U...

引用

26th International Conference on computer and Information Technology, ICCIT 2023

作者： Ratul, Md. Toyaha Rahman Afroge, Shyla Peyal, Hasibul Islam Zafrin, Dia Ahmed, Kazi Fardeen Abdal, Md. Nazmul Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Bangladesh Uttara University Dept. of Computer Science & Engineering Bangladesh Khulna University Dept. of Computer Science & Engineering Discipline Bangladesh

ISBN: (纸本)9798350359015

The early and accurate diagnosis of glaucoma, a primary cause of permanent blindness, is critical for efficient treatment and prevention of vision loss. Although the exact causes of glaucoma are not yet fully understood, it is thought to be a result of several factors, including raised pressure inside the eye and decreased blood supply to the optic nerve. We have developed a convolutional neural network model for accurate detection of glaucoma. Methods based on deep learning have been effective at classifying diseases in retinal fundus images, facilitating in the evaluation of the growing number of images. The goal of this work is to create and train a unique deep CNN model that makes use of the connections between related eye-fundus tasks and metrics used to identify glaucoma. We have meticulously selected two distinct datasets to underpin this research endeavor: the ACRIMA dataset and the LAG dataset. Notably, our model attains a remarkable accuracy score of 99.29% on the ACRIMA dataset and an equally commendable accuracy score of 97.22% on the LAG dataset. This performance eclipses that of the majority of contemporary deep CNN models, underscoring the prowess and sophistication of our approach. © 2023 IEEE.

关键词： CNN deep learning fundus image glaucoma image classification

来源：评论

学校读者我要写书评

暂无评论

IoT-based Automatic Pan Evaporimeter Monitoring and Irrigation Control System for Crops 8

IoT-based Automatic Pan Evaporimeter Monitoring and Irrigati...

引用

8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Kabir, Md Shahriar Niloy, Khalid Mahmud Hossen, Md Imon Afrose, Sumaiya Mofazzol, Md. Ismail Hossain American International University-Bangladesh Dept. of Electrical & Electronics Engineering Dhaka Bangladesh Uttara University Dept. of Computer Science Engineering Dhaka Bangladesh

ISBN: (纸本)9798350376425

This research study intends to automate the manual pan evaporimeter system and scheduling irrigation by considering soil water content and crop evapotranspiration. Automating the pan evaporimeter means performing tasks without human intervention, such as measuring the daily water evaporation amount, calculating and storing data, and refilling the pan with water. A waterproof ultrasonic sensor was used to measure the water evaporation amount. The ultrasonic sensor was installed on an innovatively designed structure facing the pan's water surface. The sensor measured the distance between the water surface and the sensor. The surface level decreased as the water evaporated over time, which caused an increment in the distance between the ultrasonic and water surfaces. By this, the sensor calculated the daily evaporation amount. The pan needed to be refilled when all water evaporated;a submersible pump was used to refill the pan automatically. A relay switch controlled the pump, and a microcontroller turned on the switch based on sensor value. The entire setup was IoT-enabled to monitor and control the system remotely on a mobile device. The entire system was backed up by solar energy to run on clean energy. A solenoid valve was used to control the flow of irrigation water. Pan evaporation estimated the amount of water that could be lost owing to evaporation and transpiration. Soil moisture sensors provided a direct measurement of the amount of water present in the soil. The two combined generated a dynamic and responsive irrigation plan that maximized water use and promoted the healthy development of crops. That approach was used. The ultrasonic sensor accurately measured water evaporation, but the installation process was complex. So, a need arises to develop a simple system or scale-type sensors to overcome the complexity. © 2024 IEEE.

关键词： Solenoid valves

来源：评论

学校读者我要写书评

暂无评论

Federated Learning for Enhanced Cell Nuclei Segmentation in Histopathological Images

Federated Learning for Enhanced Cell Nuclei Segmentation in ...

引用

2024 IEEE International Conference on Big Data, BigData 2024

作者： Usai, Marco Loddo, Andrea Putzu, Lorenzo Ruberto, Cecilia Di University of Cagliari Dept. of Mathematics and Computer Science Cagliari Italy University of Cagliari Dept. of Electrical and Electronic Engineering Cagliari Italy

ISBN: (纸本)9798350362480

This study addresses the critical challenge of cell nuclei segmentation in histopathological image analysis, which is essential for cancer diagnosis and prognosis. Traditional segmentation methods often struggle with complexities such as overlapping nuclei and variations in shape and size. Recent advancements in machine learning and deep learning, particularly convolutional neural networks, have improved segmentation accuracy but face limitations due to the need for large annotated datasets, often constrained by privacy regulations in the medical field. This research explores federated learning as a solution, enabling collaborative model training across institutions without sharing sensitive patient data. By leveraging diverse datasets while maintaining privacy, federated learning enhances model robustness and generalization capabilities. The study evaluates various federated learning techniques on in- and out-of-domain test sets, aiming to improve the reliability of segmentation models in clinical settings. The findings suggest that federated learning techniques can effectively address the challenges of data scarcity and domain shift, paving the way for more accurate and widely applicable segmentation algorithms in computational pathology. © 2024 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Preserving Pre-trained Representation Space: On Effectiveness of Prefix-tuning for Large Multi-modal Models

Preserving Pre-trained Representation Space: On Effectivenes...

引用

2024 Findings of the Association for Computational Linguistics, EMNLP 2024

作者： Kim, Donghoon Lee, Gusang Shim, Kyuhong Shim, Byonghyo Dept. of Electrical and Computer Engineering Seoul National University Korea Republic of

ISBN: (纸本)9798891761681

Recently, we have observed that Large Multi-modal Models (LMMs) are revolutionizing the way machines interact with the world, unlocking new possibilities across various multi-modal applications. To adapt LMMs for downstream tasks, parameter-efficient fine-tuning (PEFT) which only trains additional prefix tokens or modules, has gained popularity. Nevertheless, there has been little analysis of how PEFT works in LMMs. In this paper, we delve into the strengths and weaknesses of each tuning strategy, shifting the focus from the efficiency typically associated with these approaches. We first discover that model parameter tuning methods such as LoRA and Adapters distort the feature representation space learned during pre-training and limit the full utilization of pre-trained knowledge. We also demonstrate that prefix-tuning excels at preserving the representation space, despite its lower performance on downstream tasks. These findings suggest a simple two-step PEFT strategy called Prefix-Tuned PEFT (PT-PEFT), which successively performs prefix-tuning and then PEFT (i.e., Adapter, LoRA), combines the benefits of both. Experimental results show that PT-PEFT not only improves performance in image captioning and visual question answering compared to vanilla PEFT methods but also helps preserve the representation space of the four pre-trained models. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

来源：评论

学校读者我要写书评

暂无评论

Using Semeru Cloud Compiler to Enhance Cloud-Native Java Application Performance 34

Using Semeru Cloud Compiler to Enhance Cloud-Native Java App...

引用

34th Annual International Conference on Collaborative Advances in Software and Computing, CASCON 2024

作者： Liu, Ryan Shinde, Shreya Tahvildari, Ladan Stoodley, Mark Sundaresan, Vijay Pirvu, Marius University of Waterloo Dept. of Electrical and Computer Engineering Canada IBM Canada

ISBN: (纸本)9798331504830

Java-based applications rely on just-in-time (JIT) compilation to convert bytecode into machine code at run-time to improve throughput. However, JIT compilation is achieved at the cost of additional CPU cycles and memory. In the context of modern cloud, where applications have limited computing resources due to containerization, JIT compilation may potentially become a pain-point. The Semeru Cloud Compiler (SCC) is a promising solution that offloads JIT compilation to a remote cloud compiler to reduce the local overhead of JIT compilation in containers. In this paper, we outline novel research directions to further leverage the advantages of remote cloud compilation by extending the SCC to achieve two goals: (i) reduce application start-up time and (ii) improve JIT compilation decision making. To reduce application start-up time, the proposed enhancement is to extract unused or unlikely to be used Java class files from container images. The SCC can still provide the extracted class files to the client JVM if any turn out to be needed. Our preliminary experiments show that this approach can potentially reduce container image sizes by up to 22.1% and start-up times by up to 9.2% for certain applications. To reduce JIT compilation overhead, the proposed enhancement is to perform dynamic analysis of the Java application using the additional resources on the server-side. In turn, the remote JIT compilation can make more informed decisions regarding which methods to compile and optimizations to apply. We are also developing a novel visualization tool designed to help JIT compiler developers identify pain points in current JIT compilation heuristics. Specifically, we have measured that up to 18.9% of JIT compilations for certain applications may be sub-optimal due to later method inlining, and we are investigating how the SCC could help to avoid these potentially wasted compile-time computations by using information learned from other JVM clients. © 2024 IEEE.

关键词： Program compilers

来源：评论

学校读者我要写书评

暂无评论

An Intelligent Forecasting Classification Model for Traffic Analysis using Machine Learning Techniques 4

An Intelligent Forecasting Classification Model for Traffic ...

引用

4th International Conference on Advances in electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Kishor, Bodla Tiwari, Rajesh Patra, Raj Kumar Pinjakar, Latika Singh, Mahesh Cmr Engineering College Dept. of Computer Science and Engineering Hyderabad India Cmr Technical Campus Dept. of Computer Science and Engineering Hyderabad India Symbiosis Institute of Technology Dept. of Computer Science and Engineering Nagpur India Shri Shankaracharya Technical Campus Dept. of Eee Durg Bhilai India

ISBN: (纸本)9798350343670

In addition to taking care of the normal citizens, Road Traffic protection is also one of the main issue for corporations governing the transportation. We use data mining techniques to get the hidden matter from the database. The applications of Data mining is there in various fields such as fraud detection, marketing and many more. Many countries were affected due to many decisions that were taken by the governments one such issue is globalization. This has brought some drastically changes in the intake level and the financial related activities and this has given a boost to transportation department. Understanding the importance of the street protection the authorities are putting effort to know the reasons behind the street accidents. The increase in the street injuries made it difficult to determine the limitations of the street injuries. This paper explains the street injuries frequency from analysing the database. This work is about 95% efficient. © 2024 IEEE.

关键词： classification data mining machine learning traffic analysis Transportation models

来源：评论

学校读者我要写书评

暂无评论

Fault Detection and Classification Methodology for Photovoltaic Systems Through AI-based Digital Twin 3

Fault Detection and Classification Methodology for Photovolt...

引用

3rd International Conference on Energy Transition in the Mediterranean Area, SyNERGY MED 2024

作者： Kothona, Despoina Kouliousis, Asterios Christoforidis, Georgios C. Univeristy of Wesern Macedonia Dept. of Electrical and Computer Engineering Kozani Greece

ISBN: (纸本)9798350375923

This paper presents a Fault Detection and Classification (FDC) methodology using an Artificial Intelligence-based (AI-based) Digital Twin (DT). Initially, the AI-based DT simulates the generated photovoltaic (PV) power under specific environmental conditions. Subsequently, the FDC methodology assesses the system state and classifies faults. The case study in the paper considers four types of faults (shading, open circuit in one string, open circuit in two strings, and open circuit in three strings). Additionally, the time series of PV power production used in this study were simulated using MATLAB Simulink, considering real meteorological conditions with a recording frequency of one hour. The simplicity of the proposed methodology ensures its applicability to large-scale PV systems, with results indicating up to 93.6% fault classification accuracy and 100% fault detection accuracy. © 2024 IEEE.

关键词： Photovoltaics

来源：评论

学校读者我要写书评

暂无评论

MHC : Multi-flit HBM Crossbar with Enhancing Performance and Resource Utilization 21

MHC : Multi-flit HBM Crossbar with Enhancing Performance and...

引用

21st International System-on-Chip Design Conference, ISOCC 2024

作者： Jung, Jaehun Park, Jin-Hyeong Jeong, Young-Gil Park, Jeongwoo Sungkyunkwan University Dept. of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9798350377088

The bottleneck problem arises from the internal crossbar architecture within the HBM. Challenges related to the internal crossbar's bandwidth in HBM primarily revolve around efficiently utilizing the memory's considerable bandwidth on Field-Programmable Gate Array (FPGA) platforms. To tackle this issue, we propose the Multi-flit HBM Crossbar(MHC), which offers customized interconnections tailored for FPGA HBM boards, targeting high performance. The crossbar stage is designed using the Butterfly-Fat-Tree(BFT) structure, with multi-flit data format adoption. The effectiveness of MHC has been demonstrated through its application on the Xilinx Alveo U280 board. We have enhanced bandwidth across diverse addressing policies and noted that employing multi-flit leads to notable band-width improvements, especially with increasing burst lengths. © 2024 IEEE.

关键词： Integrated circuit interconnects

来源：评论

学校读者我要写书评

暂无评论

Robust Exercise-Based Telerehabilitation for Elderly Healthcare Survives 19

Robust Exercise-Based Telerehabilitation for Elderly Healthc...

引用

19th International Conference on Emerging Technologies, ICET 2024

作者： Awan, Aftab Ahmad Najam, Shaheryar Jalal, Ahmad Riphah International University Dept. of Biomedical Engineering Islamabad Pakistan Riphah International University Dept. of Electrical & Computer Eng Islamabad Pakistan Air University Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350379587

Improving motor function post-injury or surgery often relies on physical therapy, but long waiting lists can impede effective monitoring of patient progress. Tele-rehabilitation addresses this issue by providing equitable access to therapy without the need for travel or costly in-home treatments. This paper introduces a machine learning system capable of autonomously detecting exercise correctness during patient performance, thereby enhancing rehabilitation outcomes. Utilizing the IRDS dataset, which includes 29 participants performing nine exercises, extensive feature engineering was conducted, and five machine learning algorithms coupled with various dimensionality reduction techniques were evaluated such as Random Forests (RF), K-Nearest Neighbors (KNN), Logistic Regression (LR), Support Vector Machines (SVM) and Decision Trees (DT). Random Forests combined with correlation-based feature selection emerged as the most effective model with 98% accuracy in exercise recognition, 91% accuracy in exercise correctness detection, and a combined accuracy of 95%. © 2024 IEEE.

关键词： Remote patient monitoring

来源：评论

学校读者我要写书评

暂无评论

Systematic Comparison of Machine Learning for Activity Recognition in Cross-Subject vs. NonCross-Subject Scenarios: A Preliminary Analysis 8

Systematic Comparison of Machine Learning for Activity Recog...

引用

8th IEEE International Forum on Research and Technologies for Society and Industry Innovation, RTSI 2024

作者： Pe, Samuele Nicora, Giovanna Guerra, Bruna Maria Vittoria Sozzi, Stefania Parimbelli, Enea University of Pavia Dept. of Electrical Computer and Biomedical Engineering Pavia Italy

ISBN: (纸本)9798350362138

Human Activity Recognition (HAR) is pivotal for automating the detection and classification of human movements. Technological advances in data collection from sensors and cameras is making automatic HAR increasingly available to support robotics-assisted rehabilitation, where the detection of abnormal patterns in movement and gait can aid clinical evaluation of patients with diverse disorders. HAR typically employs Machine Learning (ML) or Deep Learning (DL) techniques to analyze sensor data patterns for various activities. However, the generalizability of ML/DL models remains underexplored, particularly concerning the impact of different training and evaluation settings. Overfitting is a common challenge, especially in medical applications where user-dependent training data hinder generalization. Investigating the performance of ML and DL algorithms after a cross-subject (CS) and a noncross-subject (NCS) division of the dataset into training and test sets is crucial. This study aims to systematically compare various ML and DL algorithms for HAR, evaluating their performance across the CS and NCS settings to discern the impact of inter-subject variability on model generalizability. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共500页 << < 30 31 32 33 34 35 36 37 38 39 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

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：