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

3,712 篇 会议
16 篇 期刊文献
3 册 图书

馆藏范围

3,731 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,749 篇 工学
- 2,566 篇 计算机科学与技术...
- 2,042 篇 软件工程
- 608 篇 信息与通信工程
- 369 篇 生物工程
- 360 篇 生物医学工程（可授...
- 271 篇 电气工程
- 214 篇 控制科学与工程
- 128 篇 光学工程
- 120 篇 化学工程与技术
- 102 篇 安全科学与工程
- 91 篇 机械工程
- 88 篇 电子科学与技术（可...
- 82 篇 农业工程
- 79 篇 材料科学与工程（可...
- 76 篇 网络空间安全
1,209 篇 理学
- 763 篇 数学
- 420 篇 生物学
- 372 篇 统计学（可授理学、...
- 346 篇 系统科学
- 161 篇 物理学
- 77 篇 化学
587 篇 管理学
- 337 篇 图书情报与档案管...
- 261 篇 管理科学与工程(可...
- 203 篇 工商管理
382 篇 医学
- 332 篇 基础医学(可授医学...
- 330 篇 临床医学
- 216 篇 药学(可授医学、理...
- 181 篇 公共卫生与预防医...
154 篇 法学
- 140 篇 社会学
105 篇 农学
- 105 篇 作物学
69 篇 经济学
26 篇 教育学
3 篇 军事学
1 篇 文学

主题

716 篇 machine learning
309 篇 machine learning...
306 篇 adversarial mach...
273 篇 deep learning
252 篇 accuracy
241 篇 support vector m...
154 篇 predictive model...
148 篇 feature extracti...
122 篇 decision trees
119 篇 convolutional ne...
115 篇 random forests
114 篇 training
94 篇 prediction algor...
90 篇 classification a...
84 篇 computational mo...
82 篇 data models
75 篇 forecasting
74 篇 learning algorit...
72 篇 real-time system...
63 篇 reinforcement le...

机构

29 篇 chitkara univers...
15 篇 dept. of compute...
14 篇 j.s. university ...
9 篇 lovely professio...
8 篇 graphic era hill...
8 篇 dept. of humanit...
7 篇 school of comput...
7 篇 pune
6 篇 school of data s...
6 篇 saveetha univers...
6 篇 department of co...
6 篇 computer science...
6 篇 madhav universit...
6 篇 computer science...
6 篇 the islamic univ...
6 篇 global academy o...
5 篇 slp college dept...
5 篇 graphic era deem...
5 篇 chitkara univers...
5 篇 department of co...

作者

16 篇 kodipalli ashwin...
16 篇 rao trupthi
11 篇 devamane shridha...
8 篇 rohini b.r.
7 篇 sharma vikrant
7 篇 sharma himanshu
7 篇 bahaj mohamed
7 篇 kumar rakesh
6 篇 sharma vinod
5 篇 gill kanwarparta...
5 篇 lakshmikantha g....
5 篇 kumar pawan
5 篇 priyanka kaushik
5 篇 mohapatra srikan...
5 篇 sharma madhuri
5 篇 vats satvik
5 篇 madhuri sharma
5 篇 shalu
5 篇 mishra amit kuma...
5 篇 asha v.

语言

3,443 篇 英文
281 篇 其他
169 篇 中文
5 篇 土耳其文

检索条件"任意字段=1st International Conference on Machine Learning Algorithms, ICMLA 2024"

共 3731 条记录，以下是101-110 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：

Enhanced Speaker Accent Classification: An Experimental study of Diverse machine learning Methods 1

Enhanced Speaker Accent Classification: An Experimental Stud...

引用

1st international conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Ravikumar, R.N. Gebremariam, Selam Habtewold Sivakumar, N. Komala, G. Alla, Poorna Chandra Reddy Chauhan, Shailendrasinh Marwadi University Department of Computer Engineering Rajkot India Marwadi University Department of Information Technology Rajkot India

ISBN: (纸本)9798331529246

Speaker Accent Recognition is a supervised learning task where the objective is to predict a speaker's dialect from an audio signal. This research examines linguistic features like pronunciation and intonation to classify speakers based on their unique speech patterns. Three hundred and thirty records in our UCI dataset show six different accents: spoken English from Spanish, German, Italian, French, and both US and UK accents. The model we proposed employs Mel-Frequency Cepstral Coefficients (MFCCs) to transform raw audio into numerical data, enabling the prediction of the speaker's accent within our classification categories. Various machine learning techniques are tested such as K-Nearest Neighbors, Support Vector machines, Decision Trees, Random Forests, Logistic Regression, and Naive Bayes, all with hyperparameter tuning. Results show that Random Forests achieved the highest accuracy at 95.62%, while Naive Bayes had the lowest at 76.09%. After tuning, SVM, Random Forests, and KNN reached accuracies of 97.31%, 95.96%, and 94.61%, respectively. The study highlights the importance of diverse algorithms and optimization techniques for effective accent recognition. © 2025 IEEE.

关键词： Random forests

来源：评论

学校读者我要写书评

暂无评论

5G Wireless Technology Throughput Prediction Using Ensemble machine learning Approach 1st

5G Wireless Technology Throughput Prediction Using Ensemble ...

引用

1st international conference on machine learning algorithms, icmla 2024

作者： Sharma, Abhilasha Talluri, Salman Raju Pandit, Shweta Department of Electronics and Communication Engineering Jaypee University of Information Technology Himachal Pradesh Solan Waknaghat India School of Engineering and Technology Chitkara University Himachal Pradesh Baddi India

ISBN: (纸本)9783031758607

It is expected that mobile traffic will increase rapidly in the coming years due to high demand of real-time communication. The fifth generation (5G) is seen as a new generation of technology that can improve the quality of services (QoS) and enable user satisfaction by providing higher data rates and low latency. An advance prediction of 5G throughput improves QoS. The authors of this paper propose an ensemble machine learning scheme to predict 5G throughput to improve the QoS. The support vector machine (SVM), decision tree (DT), and K nearest neighbor (KNN) machine learning models are trained separately in this ensemble machine learning approach. The output of each model is combined and the final predicted throughput of 5G is calculated based on the maximum voting rule. The proposed ensemble model approach obtains optimal results with an accuracy of 88.80%, a precision of 89.19%, a recall of 88.71%, and F1-score of 88.71%. The ensemble model obtains an area under curve (AUC) of 94.90%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

IoT-Inspired Smart Drought Prediction Framework: machine learning Approach 1st

IoT-Inspired Smart Drought Prediction Framework: Machine Lea...

引用

1st international conference on machine learning algorithms, icmla 2024

作者： Bhardwaj, Diksha Kaur, Gagninder Chandigarh University Punjab Chandigarh India CSE Chandigarh University Punjab Chandigarh India

ISBN: (纸本)9783031758607

A severe drought may have devastating effects on a region’s hydrological balance, agriculture, animal habitat, and economy, among other things. As a result, a reliable method of drought forecasting is required. Several drought indices purport to measure drought severity, but many of them ignore crucial details. This research presents a fog-based paradigm for drought predictive analysis, made possible by the IoT. Singular vector decomposition is used to compress data at the fog level in this architecture. Future drought conditions are predicted using the Holt-Winters approach, while the severity of individual drought occurrences is evaluated using an Adaptive Neuro-fuzzy Inference System (ANFIS) mechanism. The usefulness of the suggested system is proved by its implementation utilizing the online UCI dataset in terms of forecasting efficacy, and Prediction efficiency. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Fog computing

来源：评论

学校读者我要写书评

暂无评论

Adaptive machine learning for Resource-Constrained Environments 1st

Adaptive Machine Learning for Resource-Constrained Environm...

引用

1st international Workshop on Discovering Drift Phenomena in Evolving Landscapes, DELTA 2024

作者： Ordóñez, Sebastián A. Cajas Samanta, Jaydeep Suárez-Cetrulo, Andrés L. Carbajo, Ricardo Simón University College Dublin Dublin Ireland

ISBN: (纸本)9783031823459

The Internet of Things is an example domain where data is perpetually generated in ever-increasing quantities, reflecting the proliferation of connected devices and the formation of continuous data streams over time. Consequently, the demand for ad-hoc, cost-effective machine learning solutions must adapt to this evolving data influx. This study tackles the task of offloading in small gateways, exacerbated by their dynamic availability over time. An approach leveraging CPU utilization metrics using online and continual machine learning techniques is proposed to predict gateway availability. These methods are compared to popular machine learning algorithms and a recent time-series foundation model, Lag-Llama, for fine-tuned and zero-shot setups. Their performance is benchmarked on a dataset of CPU utilization measurements over time from an IoT gateway and focuses on model metrics such as prediction errors, training and inference times, and memory consumption. Our primary objective is to study new efficient ways to predict CPU performance in IoT environments. Across various scenarios, our findings highlight that ensemble and online methods offer promising results for this task in terms of accuracy while maintaining a low resource footprint. Code is available at https://***/sebasmos/AML4CPU. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Feature Based machine learning Models for Cardiovascular Disease Diagnosis: An Experimental Analysis 1st

Feature Based Machine Learning Models for Cardiovascular Dis...

引用

1st international conference on machine learning algorithms, icmla 2024

作者： Agrawal, Alok Kumar Vajpayee, Amit Saxena, Merry Sarangi, Pradeepta Kumar Bajaj, Karan Sahoo, Ashok Kumar Chitkara University School of Engineering and Technology Chitkara University Baddi India Parul Institute of Engineering and Technology Parul University Gujrat Vadodara India Department of Interdisciplinary Courses in Engineering Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computer Science and Engineering Lovely Professional University Jalandhar India Graphic Era Hill University Dehradun India

ISBN: (纸本)9783031758607

The heart is widely regarded as the most vital organ within the human body. Cardiovascular diseases are prevalent in contemporary society;hence it is imperative to anticipate and forecast these conditions in advance. The accurate prognosis and prediction of coronary heart diseases necessitate a high level of precision and efficiency, since even a minor error might result in fatality for the individual. In order to address this condition, it is imperative to develop a model that possesses the capability to forecast and generate awareness pertaining to certain illnesses. Determining the disease manually based solely on indications and risk factors presents a significant challenge. However, machine learning techniques can address this issue. This work implements and evaluates the efficiency of machine learning algorithms in predicting cardiovascular diseases. The algorithms used in this work are K-Nearest Neighbors (KNN) and Support Vector machine (SVM) and the data set used is the UCI repository data. The experimental results prove that the KNN and SVM models exhibit the highest levels of suitability, as evidenced by their respective accuracies of 88% and 84% respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Predicting stroke Through Retinal Graphs and Multimodal Self-supervised learning

Predicting Stroke Through Retinal Graphs and Multimodal Sel...

引用

Workshop on Longitudinal Disease Tracking and Modeling with Medical Images and Data, LDTM 2024, 5th international Workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, 1st Workshop on machine learning for Multimodal/-sensor Healthcare Data, ML4MHD2024 and Workshop on Multimodal learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th international conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024

作者： Huang, Yuqing Wittmann, Bastian Demler, Olga Menze, Bjoern Davoudi, Neda Department of Computer Science ETH Zürich Zürich Switzerland ETH AI Center ETH Zürich Zürich Switzerland Department of Quantitative Biomedicine University of Zurich Zürich Switzerland Harvard Medical School BostonMA United States

ISBN: (纸本)9783031845246

Early identification of stroke is crucial for intervention, requiring reliable models. We proposed an efficient retinal image representation together with clinical information to capture a comprehensive overview of cardiovascular health, leveraging large multimodal datasets for new medical insights. Our approach is one of the first contrastive frameworks that integrates graph and tabular data, using vessel graphs derived from retinal images for efficient representation. This method, combined with multimodal contrastive learning, significantly enhances stroke prediction accuracy by integrating data from multiple sources and using contrastive learning for transfer learning. The self-supervised learning techniques employed allow the model to learn effectively from unlabeled data, reducing the dependency on large annotated datasets. Our framework showed an AUROC improvement of 3.78% from supervised to self-supervised approaches. Additionally, the graph-level representation approach achieved superior performance to image encoders while significantly reducing pre-training and fine-tuning runtimes. These findings indicate that retinal images are a cost-effective method for improving cardiovascular disease predictions and pave the way for future research into retinal and cerebral vessel connections and the use of graph-based retinal vessel representations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

An Analysis of Deep learning Models for Conversational Agents in Healthcare 1st

An Analysis of Deep Learning Models for Conversational Agent...

引用

1st international conference on machine learning algorithms, icmla 2024

作者： Lal, Mily Neduncheliyan, S. Dr. D. Y. Patil School of Science and Technology Dr. D. Y. Patil Vidyapeeth Sant Tukaram Nagar Pimpri Pune India Tamil Nadu Chennai India

ISBN: (纸本)9783031758607

In the healthcare industry, natural language processing, or NLP, is essential for sifting through the massive volume of textual data and extracting important insights. The use of deep learning models in healthcare is thoroughly examined in this work, with a focus on improving different facets of natural language processing (NLP). Through an examination of these models’ efficacy, constraints, and moral implications, the study fills in important knowledge gaps and improves usability. It addresses issues with managing context, having insufficient data, and ethical complexity while showcasing the benefits of various deep learning models in healthcare discussions. It also describes future directions for research, focusing on methods such as adversarial machine learning, transfer learning, reinforcement learning, and multi-task learning. These paths have the potential to enhance the context, adaptability, and security comprehension of convergent agents across domains, especially in the fields of healthcare and customer service. This thorough analysis seeks to offer recommendations for the moral application of AI-powered conversational agents in healthcare, encouraging their advantageous integration, with a particular emphasis on their responsible implementation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Chatbots

来源：评论

学校读者我要写书评

暂无评论

Artificial Intelligence in Drone and Anti-drone Systems: Cutting-Edge Advances and Applications 1st

Artificial Intelligence in Drone and Anti-drone Systems: Cut...

引用

1st international conference on Electrical Systems and Smart Technologies, ICESst 2024

作者： Ghazlane, Yasmine Hajar, Bnouachir Hajar, El Maghraoui Saida, Haidrar Mourad, El Yadari Casablanca Morocco ENSAM Mohammed V University Rabat Rabat Morocco

ISBN: (纸本)9783031867040

With the increasing proliferation of drones, or Unmanned Aerial Vehicles (UAVs), across various sectors, both opportunities and challenges arise, particularly concerning security and privacy. The misuse of drones for malicious activities has necessitated the development of advanced anti-drone systems to detect, identify, and neutralize these threats effectively. This article discusses the critical aspects of Artificial Intelligence (AI) in enhancing anti-drone systems, highlighting recent advances in AI applications for both drones and anti-drone technologies. The integration of AI techniques such as machine learning deep learning, computer vision, and multi-agent systems has significantly improved the detection, identification, tracking, and interception capabilities of anti-drone systems. This work provides an overview of the state-of-the-art AI technologies driving the evolution of both drones and counter-drone measures. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

1st international conference on Advanced Computing, machine learning, Robotics and Internet Technologies, AMRIT 2023

1st International Conference on Advanced Computing, Machine ...

引用

1st international conference on Advanced Computing, machine learning, Robotics and Internet Technologies, AMRIT 2023

ISBN: (纸本)9783031472237

The proceedings contain 46 papers. The special focus in this conference is on Advanced Computing, machine learning, Robotics and Internet Technologies. The topics include: Galactic Simulation: Visual Perception of Anisotropic Dark Matter;Protocol Anomaly Detection in IIoT;Agricultural Informatics & ICT: The Foundation, Issues, Challenges and Possible Solutions—A Policy Work;a Guava Leaf Disease Identification Application;text to Image Generation Using Attentional Generative Adversarial Network;attention-CoviNet: A Deep-learning Approach to Classify Covid-19 Using Chest X-Rays;a Deep learning Framework for Violence Detection in Videos Using Transfer learning;multi-focus Image Fusion Methods: A Review;cache Memory and On-Chip Cache Architecture: A Survey;authenticating Smartphone Users Continuously Even if the Smartphone is in the User’s Pocket;Comparative Analysis of machine learning algorithms for COVID-19 Detection and Prediction;machine learning Classifiers Explanations with Prototype Counterfactual;a Systematic study of Super-Resolution Generative Adversarial Networks: Review;stance Detection in Manipuri Editorial Article Using CRF;deep learning Based Software Vulnerability Detection in Code Snippets and Tag Questions Using Convolutional Neural Networks;a Comprehensive study of the Performances of Imbalanced Data learning Methods with Different Optimization Techniques;Smart Parking System Using Arduino and IR Sensor;quMaDe: Quick Foreground Mask and Monocular Depth Data Generation;fine-Grained Air Quality with Deep Air learning;enhancing Melanoma Skin Cancer Detection with machine learning and Image Processing Techniques;Image Processing Technique and SVM for Epizootic Ulcerative Syndrome Fish Image Classification.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Discovering Causal Relationships in Noisy Web Data for Sentiment Classification Using Attention Mechanisms

Discovering Causal Relationships in Noisy Web Data for Sen...

引用

PhD Symposium, Posters, Demos, and A Web for more inclusive, sustainable and prosperous societies, WEB-for-GOOD 2024 and 1st international Workshop on AI and Web Data Analytics, AIWDA 2024 form the 25th international conference on Web Information Systems Engineering, WISE 2024

作者： Mihoubi, Miloud Zerkouk, Meriem Chikhaoui, Belkacem Artificial Intelligence Institute University of Téluq 5800 rue Saint-Denis bureau 1105 MontrealQCH2S 3L5 Canada

ISBN: (纸本)9789819614820

In the era of big data, sentiment analysis on social media platforms presents unique challenges due to the noisy and unstructured nature of the data. This study introduces a novel deep learning approach for discovering causal relationships within such noisy web data using an attention-enhanced architecture. The proposed model leverages an embedding layer, bidirectional LstM, attention mechanisms, global average pooling, and dropout techniques, followed by a dense layer with sigmoid activation for binary classification. We validated our model on the Sentiment140 dataset, achieving a test accuracy of 82.36% and an F1-score of 0.8246, outperforming traditional machine learning models such as Logistic Regression, Support Vector machines (SVM), and Random Forests. An extensive ablation study was conducted to assess the contributions of key model components, confirming the critical role of attention mechanisms and bidirectional LstM in improving performance and interpretability. Beyond its superior performance in sentiment classification, our model provides insights into the causal dynamics driving sentiment shifts on social media. This work significantly contributes to the fields of web data mining and causal inference by offering a robust framework capable of handling the complexities of noisy, high-dimensional data commonly encountered in online environments.. © 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374页 << < 7 8 9 10 11 12 13 14 15 16 > >>

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：