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

88,833 篇 会议
596 册 图书
350 篇 期刊文献
4 篇 学位论文
1 篇 科技报告

馆藏范围

89,783 篇 电子文献
3 种 纸本馆藏

日期分布

学科分类号

38,499 篇 工学
- 27,692 篇 计算机科学与技术...
- 22,640 篇 软件工程
- 9,358 篇 信息与通信工程
- 8,513 篇 控制科学与工程
- 6,066 篇 电气工程
- 4,318 篇 电子科学与技术（可...
- 3,752 篇 生物工程
- 3,744 篇 机械工程
- 2,734 篇 生物医学工程（可授...
- 2,596 篇 动力工程及工程热...
- 2,174 篇 仪器科学与技术
- 1,872 篇 光学工程
- 1,856 篇 交通运输工程
- 1,638 篇 安全科学与工程
- 1,379 篇 化学工程与技术
- 1,105 篇 网络空间安全
19,409 篇 理学
- 12,578 篇 数学
- 4,022 篇 生物学
- 3,745 篇 系统科学
- 3,691 篇 物理学
- 3,456 篇 统计学（可授理学、...
- 1,386 篇 化学
10,646 篇 管理学
- 7,039 篇 管理科学与工程(可...
- 4,283 篇 图书情报与档案管...
- 3,415 篇 工商管理
2,340 篇 医学
- 2,029 篇 临床医学
- 1,978 篇 基础医学(可授医学...
- 1,266 篇 药学(可授医学、理...
1,355 篇 法学
- 1,187 篇 社会学
1,201 篇 经济学
- 1,198 篇 应用经济学
820 篇 农学
327 篇 教育学
220 篇 文学
194 篇 军事学
48 篇 艺术学
1 篇 哲学

主题

6,017 篇 genetic algorith...
5,118 篇 heuristic algori...
4,452 篇 real-time system...
4,325 篇 accuracy
4,274 篇 algorithm design...
3,864 篇 clustering algor...
3,490 篇 machine learning...
3,412 篇 prediction algor...
3,165 篇 optimization
3,035 篇 feature extracti...
2,936 篇 signal processin...
2,801 篇 computational mo...
2,650 篇 training
2,548 篇 control systems
2,488 篇 classification a...
2,465 篇 predictive model...
2,348 篇 machine learning
2,259 篇 neural networks
2,193 篇 support vector m...
2,118 篇 hardware

机构

193 篇 chitkara univers...
103 篇 school of automa...
98 篇 school of electr...
80 篇 lovely professio...
74 篇 centre of resear...
71 篇 school of comput...
66 篇 school of comput...
47 篇 national mobile ...
46 篇 department of co...
40 篇 chitkara univers...
39 篇 department of co...
38 篇 department of la...
37 篇 school of comput...
35 篇 karnataka bangal...
33 篇 college of compu...
33 篇 school of electr...
32 篇 school of comput...
32 篇 school of inform...
32 篇 department of el...
31 篇 department of co...

作者

36 篇 lei wang
36 篇 jun wang
34 篇 jian wang
30 篇 hisao ishibuchi
29 篇 li yang
26 篇 hui wang
25 篇 yu wang
25 篇 khalgui mohamed
24 篇 li peng
23 篇 wang jing
21 篇 zhang tao
21 篇 jun zhang
20 篇 rui wang
20 篇 wang lei
19 篇 bin wang
18 篇 zhang lei
17 篇 j. relin francis...
17 篇 amandeep nagpal
16 篇 vikram kumar
16 篇 chu-sing yang

语言

87,143 篇 英文
2,203 篇 其他
516 篇 中文
7 篇 俄文
2 篇 德文
2 篇 法文
1 篇 西班牙文
1 篇 土耳其文
1 篇 乌克兰文

检索条件"任意字段=International Conference on Engineering of Reconfigurable Systems and Algorithms"

共 89784 条记录，以下是291-300 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：

Machine Learning-Based Crop Recommendation System for Sustainable Agriculture 4

Machine Learning-Based Crop Recommendation System for Sustai...

引用

4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Sriharsha, Kantheti Chandan Joshi, Kotapuri Rahul Reddy, Vummadi Ravi Sankar Chowdary, Vadlapudi Kalyan Hanuman Hajarathaiah, Koduru Rajak, Shaik School of Computer Science and Engineering VIT-AP University Amaravati India Amrita Vishwa Vidyapeetham Department of Electronics and Communication Engineering Amaravati India

ISBN: (纸本)9798331523923

This research paper addresses the critical challenges in agricultural crop recommendation systems through machine learning approaches. Current systems face significant limitations including limited integration of real-time environmental data, inconsistent prediction accuracy across diverse geographical regions, difficulty in handling multiple conflicting parameters simultaneously, and lack of interpretability in recommendation decisions. Recent techniques like Random Forest, Support Vector Machines, and Deep Neural Networks have attempted to address these issues but with varying degrees of success. This paper proposes an enhanced crop recommendation system that leverages multiple machine learning algorithms to overcome these limitations. The system implements real-time soil and weather parameter integration, while providing interpretable recommendations to farmers. Our approach achieves 99.54% accuracy in crop recommendations, significantly improving upon existing systems. The proposed model serves as a comprehensive solution for sustainable agriculture, contributing to improved crop yield and farmer decision-making processes. The system's effectiveness is validated across different agricultural zones, demonstrating its practical applicability and scalability in diverse farming conditions. © 2025 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

A Survey of Advanced algorithms and Experimental Approaches for Kidney Abnormality Detection in Ultrasound Imaging 3

A Survey of Advanced Algorithms and Experimental Approaches ...

引用

3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Chitra, N. Thulasi Gogula, Sreenivasulu Satapathy, Sukla Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Telangana Hyderabad500075 India Telangana Hyderabad501218 India Koneru Lakshmaiah Education Foundation Department of Artificial Intelligence and Machine Learning Telangana Hyderabad500075 India

ISBN: (纸本)9798331509675

Kidney abnormalities, such as cysts, tumors, and stones, are critical health concerns requiring early and precise detection. This study reviews advanced algorithms and experimental approaches for kidney abnormality detection in ultrasound imaging, emphasizing deep learning (DL) methodologies and explainable AI (XAI) techniques. Contributions include exploring CNN-based segmentation frameworks like SDFNet and U-Net, multi-scale feature fusion, and transfer learning to enhance diagnostic accuracy. Data augmentation and adversarial training are employed to address dataset bias, while Grad-CAM and CAManim provide interpretability, fostering clinician trust. Results demonstrate significant improvements in diagnostic performance, with sensitivity and specificity exceeding 95% and Dice coefficients surpassing 95% on diverse clinical datasets. The findings highlight the transformative potential of DL to automate kidney abnormality detection, reducing dependency on operator expertise while improving accuracy and efficiency. Challenges such as dataset scarcity and computational complexity persist, but future research on multimodal imaging, CNN optimization, and emerging technologies like quantum machine learning presents promising opportunities. This study serves as a guide for advancing diagnostic tools, paving the way for next-generation solutions in clinical applications. © 2025 IEEE.

关键词： Ultrasonic imaging

来源：评论

学校读者我要写书评

暂无评论

Blockchain-Based Secure Cloud Storage for IoT Healthcare Using BiGRU and BiLSTM Models 3

Blockchain-Based Secure Cloud Storage for IoT Healthcare Usi...

引用

3rd international conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Chougule, Prachi Arihant Sarumathi, S. Kant, Ravi Gupta, Raj Kumar Latha, U. Pushpa Perada, Anvesh Dkte Society's Tetile and Engineering Institute Department of Electrical Engineering Ichalkaranji India Hkbk College of Engineering Department of Computer Science and Engineering Bangalore India Ysaicds Shoolini University Department of Computer and Data Sciences Solan India Sardar Vallabhbhai Patel College Bhabua Vksu Department of Physics Ara India Department of English Hyderabad India Drexel University Department of Electrical and Computer Engineering Philadelphia United States

ISBN: (纸本)9798331508456

Machine Learning (ML) algorithms have experienced a significant increase in popularity owing to the digitisation of analogue processes and other technological advancements, like the Internet of Things (IoT). Dependable datasets are crucial for the precise forecasting and resolution abilities of these algorithms, which are indispensable in healthcare, cloud computing, engineering, and finance. Conversely, training datasets are vulnerable to manipulation, thereby skewing the results. To address this issue, blockchain-based approaches have been proposed to enhance the reliability and security of cloud-stored E-Health data generated by the Internet of Things. To improve the accuracy and efficacy of healthcare prediction models, our study focusses on an advanced strategy that integrates feature selection with model training. Optimal input data is refined by feature selection employing entropy and correlation coefficient methodologies. The proposed model, employing BiGRU and BiLSTM, surpasses conventional CNNs and BiRNNs, with an average accuracy of 94.57%. When integrated with the Internet of Things (IoT), blockchain technology ensures the secure storage and transmission of electronic health records (EHRs). This strategy enhances predictive accuracy and safeguards sensitive health data in cloud infrastructures by amalgamating feature selection techniques with robust deep learning models. © 2025 IEEE.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

Short-Term Load Forecasting Using Deep Learning algorithms with Hyperparameter Optimization 4

Short-Term Load Forecasting Using Deep Learning Algorithms w...

引用

4th international conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2025

作者： Uddin, Raihan Hazari, Md. Rifat Ahmad, Shameem Hossain, Chowdhury Akram Rahman Zishan, Md. Saniat Ahmed, Abir American International University-Bangladesh Faculty of Engineering Department of Electrical and Electronic Engineering Dhaka1229 Bangladesh American International University-Bangladesh Faculty of Engineering Department of Computer Engineering Dhaka1229 Bangladesh

ISBN: (纸本)9798331530938

The accurate forecasting of electrical load in short-term intervals is crucial for the efficient and secure operation of modern power systems. The dynamic nature of energy demand necessitates sophisticated forecasting techniques that can adapt to real-time changes, thereby optimizing power generation and distribution. This study proposes a Long Short-Term Memory (LSTM) model optimized using Hyperparameter Optimization (HPO) to address the inherent non-linearities in load data and enhance predictive performance. The LSTM with HPO achieved a Mean Absolute Percentage Error (MAPE) of 2.63%, substantially outperforming other models such as k-Nearest Neighbors (3.77%), Random Forest (3.46%), and Artificial Neural Networks (3.58%). It also recorded superior performance in Mean Squared Error (MSE) and Root Mean Squared Error (RMSE), with values of 1.1423 and 1.0688, and an R2 of 0.9442. Additionally, experiments were conducted combining LSTM with other models to form hybrid architectures without applying HPO. These LSTM-based hybrids, such as RF-LSTM, consistently outperformed non-LSTM models, achieving a MAPE as low as 2.81%. © 2025 IEEE.

关键词： Mean square error

来源：评论

学校读者我要写书评

暂无评论

M-Patch: Adversarial Patch-based Physical Attack against Multi-modal Fusion Detectors 24

M-Patch: Adversarial Patch-based Physical Attack against Mul...

引用

3rd international conference on algorithms, Data Mining, and Information Technology, ADMIT 2024

作者： Chen, Yixin Zheng, Haibin Chen, Jinyin Huang, Haojie College of Information Engineering Zhejiang University of Technology China Institute of Cyberspace Security Zhejiang University of Technology China

ISBN: (纸本)9798400718120

As reliance on multimodal fusion object detection models grows in critical tasks, ensuring their robustness and security is essential before deployment. However, research on adversarial attacks in multimodal target detection is still in its early stages, making it crucial for evaluating and improving system resilience. To address these challenges, we propose M-Patch, an adversarial patch-based attack method specifically designed for multimodal fusion object detection models. M-Patch can execute attacks in both digital and physical environments and is applicable to various multimodal detection models. In digital tests, M-Patch achieved success rates exceeding 60%, with a notable 97.6% success rate against the MFEIF+YOLOv3 multimodal detection model. Physical experiments further validated the patch’s effectiveness, demonstrating strong attack results. More importantly, M-Patch reveals significant vulnerabilities in multimodal systems, offering a practical and efficient approach to enhancing their security. © 2024 Copyright held by the owner/author(s).

关键词： adversarial attack

来源：评论

学校读者我要写书评

暂无评论

Raspberry Pi: A Cost-Effective System for Real-Time Number Plate Recognition using Rear-View Cameras

Raspberry Pi: A Cost-Effective System for Real-Time Number P...

引用

2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Akurathi, Ravi Raja Venkata Ratna Prabha, K. Dasam, Sridevi Yallamandaiah, S. Usha Rani, M. Pushpavathi, Aravapalli V. Velagapudi Ramakrisshna Siddhartha Engineering College Deemed to be University Department of Ece AP Vijayawada India Velagapudi Ramakrisshna Siddhartha Engineering College Deemed to be University Department of English AP Vijayawada India Vignan's Nirula Institute of Technology & Science for Women Department of Ece AP Guntur India Gokaraju Ranga Raju Institute of Engineering and Technology Department of It TS Hyderabad India

ISBN: (纸本)9798331509828

In today's traffic management landscape, precise vehicle identification via number plate detection is vital for road safety and regulatory compliance. This strategy focuses on creating a robust detection system using Raspberry Pi and rear-view cameras for real-time monitoring. Utilizing custom canny edge detection algorithms optimized for Raspberry Pi's computational capabilities ensures accurate number plate recognition(NPR) from live video feeds. Integration of rear-view camera hardware with Raspberry Pi involves setup, configuration, and optimization for seamless communication and enhanced functionality. The paper aims to advance road safety and traffic management through a cost-effective solution for automatic vehicle identification. By combining Raspberry Pi technology with rear-view cameras, the goal is to establish new standards, enabling proactive measures to reduce risks on roads and highways. © 2025 IEEE.

关键词： Automatic vehicle identification

来源：评论

学校读者我要写书评

暂无评论

Data Protection in Enhanced EXplainable Digital Twins: Insights from Hybrid AI 2

Data Protection in Enhanced EXplainable Digital Twins: Insig...

引用

2nd international conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Gupta, Prashant K. Mazumdar, Bireshwar Dass Pillai, Subash Nataraja School of Computer Science & Engineering Technology Bennett University Uttar Pradesh Greater Noida India Division of Agricultural Physics ICAR-Indian Agricultural Research Institute Pusa Campus New Delhi110012 India

ISBN: (纸本)9798331530389

Deep Learning (DL) based artificial intelligence (AI) is becoming pervasive in almost all the day to day applications. These DL algorithms require large amounts of data, which can be easily collected nowadays by deploying sensors in the environment of the system under observation. These DL algorithms however, suffer from drawbacks such as being criticised as 'black-boxes', being biased to generating predictions based on past data and ignoring the human expertise. Hence, an architecture of Enhanced eXplainable Digital Twins (EXDTs) was proposed. EXDTs remove 'black-boxness' of DL algorithms by using the XAI approaches. Further, the digital twins (DTs) underlying the EXDTs are constructed through the sensor data and depict the real-time simulatability. Also, to improve the explainability and use the human expertise/experience, EXDTs use the domain experts' qualitative data. EXDTs are a class of Hybrid AI systems, that use the numeric data for underlying DL algorithms and subjective knowledge of the domain expert. This multimodal data in EXDTs opens up new attacks fronts for data manipulation and change the EXDTs' outputs. In this manuscript, we discuss about the data protection in EXDTs, by simulating various types of attacks and suggesting possible ways the system can counter them. We feel our proposed framework is useful for the involved stakeholders in various domains like precision agriculture, healthcare, manufacturing, etc. © 2025 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Parkinson’s Disease Verdict and Cataloguing by Using Various Machine Learning and Deep Learning Techniques: A Technical Review 3rd

Parkinson’s Disease Verdict and Cataloguing by Using Variou...

引用

3rd international conference on Intelligent systems and Sustainable Computing, ICISSC 2023

作者： Kumari, G. Rohini Phaneendra Kanth, M. Ravi Kamal, M.V. Mallareddy University Hyderabad India Malla Reddy College of Engineering and Technology Hyderabad India

ISBN: (纸本)9789819783540

Reduced dopamine exudation is the cause of the neurodegenerative condition Parkinson's disease (PD), which progresses over time. It has evolved into a global health issue in recent years. It will be possible to lessen the severity of the illness by identifying the source of the issue early on. A number of deep learning and algorithms for machine learning were evaluated to raise the predictability of Parkinson's disease in people who may be at risk. This study provides an in-depth analysis of various Deep learning and machine learning algorithms that are used in Parkinson's disease classification and prediction. This review also includes specifics of findings from numerous researchers that are published in the literature and pertain to the prediction process. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

MediGuide AI using Machine Learning 2

MediGuide AI using Machine Learning

引用

2nd international conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Pooja, R. Elugubanti, Nikitha Mary Shiba, C. Kalai priya, V. Sasirekha, K. Sabapathy, S Rathana R.M.K Engineering College Computer Science and Business Systems Tiruvallur District Kavaraipettai India R.M.K Engineering College Department Of Computer Science and Business Systems Kavaraipettai India R.M.K College of Engineering and Technology Department Of Computer Science and Engineering Tiruvallur District Puduvoyal India R.M.D Engineering College Department Of Computer Science and Business Systems Kavaraipettai India Dhaanish Ahmed College of Engineering Department Of Computer Science and Engineering Vanchuvanchery Chennai India

ISBN: (纸本)9798331505745

Providing diverse patient groups with their specific medication requirements, using a wide range of drugs, poses meaningful challenges for prescribing. Standardized approaches often lead to large waste, many side effects and suboptimal outcomes. Therefore, we need improved, more personalized healthcare. MediGuide AI's drug recommendation system is powered by machine learning and it offers a thorough solution. Optimized data pre-processing and analysis, leading to the desired outcome, is achieved using advanced machine learning algorithms like Naive Bayes, TF-IDF and the Natural Language Toolkit. Highly personalized prescriptions are generated using thorough patient data, including demographics and meaningful clinical factors to achieve substantially better accuracy. Important enhancements include dual recommendation outputs, which provide many drug recommendations alongside a large number of alternative Ayurvedic treatments presented side by side for effortless comparison. An advanced personalized dosage calculation module precisely determines drug dosages using advanced regression models based on age, weight and metabolism. This system helps patients track and record their medications and it also suggests several nearby Ayurvedic shops based on their location. MediGuide AI employs exceptionally powerful algorithms and its advanced tools deliver remarkably precise and exceptionally user-friendly healthcare solutions. Current systems make use of rule-based methods that cannot learn from new data, whereas MediGuide AI leverages a hybrid Naïve Bayes and PassiveAggressiveClassifier model for higher accuracy and real-time adaptability. It also includes Ayurvedic options to ensure personalized healthcare experience. © 2025 IEEE.

关键词： Drug dosage

来源：评论

学校读者我要写书评

暂无评论

A Precise and Comprehensive Assessment of Various Machine Learning algorithms for the Detection of Plant Diseases 12th

A Precise and Comprehensive Assessment of Various Machine Le...

引用

12th international conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Tiwari, Rajesh Nayak, Himanshu Malsoru, Vankudothu Madhukar, G. Anuradha, B. Surekha, N. Department of CSE CMR Engineering College Telangana Hyderabad India CMR Engineering College Telangana Hyderabad India Department of IT CMR Technical Campus Telangana Hyderabad India Department of CSE CMR Technical Campus Telangana Hyderabad India Department of CSE CMR College of Engineering and Technology Telangana Hyderabad India

ISBN: (纸本)9789819601462

Early detection and identification of plant diseases are critical—and currently very difficult—for the agriculture industry. Major utilization of AI that aids in our successful achievement of this goal is machine learning. It analyzes and interprets data using a collection of algorithms so that it can be used to learn from and make intelligent judgments. In order to complete this research, we employed a dataset that included pictures of both healthy and sick plant leaves. We then used image processing to derive the features from the photos. Next, we employ various machine learning methods, such as random forest, support vector machine, Naïve Bayes, and others, to model this dataset. The objective is to conduct a comparison analysis to determine which of those algorithms has the highest accuracy in disease prediction. We evaluate various machine learning algorithms’ projection times as well as their precision and error rates. With all of these comparisons, insightful findings for the current endeavor can be drawn. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine 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 << < 26 27 28 29 30 31 32 33 34 35 > >>

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：