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

424 篇 会议
101 篇 期刊文献
5 册 图书

馆藏范围

530 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

247 篇 工学
- 183 篇 计算机科学与技术...
- 159 篇 软件工程
- 66 篇 信息与通信工程
- 54 篇 控制科学与工程
- 43 篇 生物工程
- 33 篇 电气工程
- 31 篇 生物医学工程（可授...
- 29 篇 网络空间安全
- 22 篇 电子科学与技术（可...
- 21 篇 安全科学与工程
- 17 篇 土木工程
- 14 篇 动力工程及工程热...
- 13 篇 机械工程
137 篇 理学
- 65 篇 数学
- 46 篇 生物学
- 32 篇 物理学
- 18 篇 统计学（可授理学、...
- 17 篇 化学
- 13 篇 系统科学
- 12 篇 海洋科学
85 篇 管理学
- 54 篇 管理科学与工程(可...
- 36 篇 图书情报与档案管...
- 26 篇 工商管理
42 篇 医学
- 33 篇 基础医学(可授医学...
- 32 篇 临床医学
- 23 篇 公共卫生与预防医...
- 15 篇 药学(可授医学、理...
25 篇 法学
- 23 篇 社会学
15 篇 农学
- 14 篇 作物学
14 篇 经济学
- 14 篇 应用经济学
6 篇 教育学
3 篇 文学

主题

53 篇 accuracy
32 篇 convolutional ne...
27 篇 deep learning
22 篇 real-time system...
21 篇 computational mo...
21 篇 feature extracti...
17 篇 support vector m...
17 篇 predictive model...
17 篇 machine learning
16 篇 data models
16 篇 training
14 篇 internet of thin...
14 篇 artificial intel...
13 篇 data mining
13 篇 security
13 篇 cloud computing
12 篇 random forests
12 篇 technological in...
11 篇 medical services
11 篇 optimization

机构

16 篇 lovely professio...
14 篇 maharishi school...
12 篇 centre of interd...
12 篇 department of ma...
10 篇 department of ma...
8 篇 program of maste...
8 篇 master of comput...
7 篇 master program o...
7 篇 school of comput...
7 篇 radiology techni...
7 篇 prince shri venk...
6 篇 department of co...
6 篇 department of co...
6 篇 department of ma...
6 篇 department of co...
5 篇 school of comput...
5 篇 department of co...
5 篇 department of ma...
5 篇 school of comput...
5 篇 department of co...

作者

11 篇 bens pardamean
9 篇 teddy suparyanto
7 篇 pardamean bens
7 篇 thotad puneeth n...
7 篇 asha v.
7 篇 jayadurga r.
7 篇 hiroshi hashimot...
6 篇 r. jayadurga
6 篇 puneeth n. thota...
6 篇 binju saju
6 篇 ginni nijhawan
6 篇 pundru chandra s...
6 篇 manish kumar
6 篇 v. asha
5 篇 j. relin francis...
5 篇 ali albawi
5 篇 gaol ford lumban
5 篇 yasuhiro ohyama
5 篇 krisna adiyarta
5 篇 shanta kallur

语言

432 篇 英文
96 篇 其他
3 篇 日文
3 篇 中文
1 篇 德文

检索条件"机构=Master of Science in Computer Science DigiPen Institute of Technology"

共 530 条记录，以下是61-70 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：

Determining Variables Associated with Annual Oil Palm Yield: An Explainable Gradient Boosting Approach 8

Determining Variables Associated with Annual Oil Palm Yield:...

引用

8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Elwirehardja, Gregorius Natanael Suparyanto, Teddy Miftakhurrokhmat Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Faculty of Agricultural Technology Institute of Agriculture STIPER Yogyakarta55282 Indonesia Faculty of Industrial Technology Universitas Islam Indonesia Yogyakarta55584 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

With the growing demands for Precision Agriculture (PA) in Indonesia, researchers have evaluated the utilization of Machine Learning for predicting oil palm yields and determining variables affecting them. Previous studies showed that meteorological variables, including rainfalls and wind speed, are associated with oil palm yields. In this research, the Extreme Gradient Boosting (XGBoost) model and the Shapley Additive exPlanations (SHAP) were deployed for analyzing the importance of 15 agrometeorological variables in predicting oil palm yield. The best model attained 1.911 RMSE and 0.855 RRʹ. By analyzing the weights and gains of the XGBoost model along with the SHAP values, it was found that the yield in the previous year, the age and number of plants, the area of peat lands, and meteorological parameters such as rainfall rates and the number of rainy days in the previous three years were considered important. The previous year's yield in particular possesses the greatest weight and gain values according to the model, and the highest SHAP value among all input variables. However, the meteorological data used in this research are only limited to rainfall rates and the number of rainy days. In the future, more diverse variables can be analyzed. © 2023 The Authors. Published by Elsevier B.V.

关键词： Machine Learning Oil Palm Yield SHAP XAI XGBoost

来源：评论

学校读者我要写书评

暂无评论

Next Generation HR: Transforming Talent Management with AI and ML Innovations 2

Next Generation HR: Transforming Talent Management with AI a...

引用

2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Singh, Niharika Singh, Pooja Chakraborty, Sudeshna Gl Bajaj Institute of Technology & Management Dept. of Master of Computer Application U.P. Greater Noida India School of Computing Science & Engineering Galgotias University Department of Cse Greater Noida India School of Computing Science and Engineering Galgotias University Department of Computer Application Greater Noida India

ISBN: (纸本)9798350371055

Artificial intelligence (AI)-based technology is now accepted as standard. Today, everything is driven by AI, which has changed our way of life. The widespread deployment of AI is helping businesses and companies by streamlining processes, increasing production, increasing effectiveness while cutting expenditures. The way businesses choose, manage, and engage their personnel is evolving as a result of the integration of artificial intelligence (AI) into HRM operations. simulated intelligence (AI) is allowing robots to make judgements that are more accurate than those made by people based on current data sets and behavioral patterns. As a result of this change, machines have taken the place of all manual labor. professionals now play more strategic roles. It is critical for businesses and professionals to comprehend how this technology operates and how it contributes to a variety of HRM activities. In order to determine how AI is bringing about change in the field a study of human resource management evaluates the work of numerous notable scholars. This review outlines the main advantages and unspoken difficulties of The use of AI in HRM and its potential in the future. This essay provides an in-depth analysis. of the utilization of AI and ML techniques in HRM, highlighting their impacts on recruitment, employee performance management, training, and overall organizational success. © 2024 IEEE.

关键词： Artificial intelligence human resource management machine learning Natural Language Processing

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Driven - Pest Classification and Sustainable Agriculture 3

Deep Learning Driven - Pest Classification and Sustainable A...

引用

3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Kumar, M. Sirish Usha, R. Reddy, B. Rama Kantha Ramachandra, S. Prakash, P. Jaya Swetha, B. Mohan Babu University School of Computing Dept. of Data Science India Madanapalle Institute of Technology and Science Madanapalle India Sri Venkateswara College of Engineering Tirupati India Mother Theresa Institute of Engineering and Technology Palamaner India Sri Venkateswara College of Engineering Department of It Tirupati India Mohan Babu University Dept. of Master of Computer Applications India

ISBN: (纸本)9798331509675

This study introduces a system designed to identify pests in crops and classify them as either beneficial or harmful. The project begins by providing a comprehensive overview of various pest identification methods, analyzing their strengths and limitations. To address the problem, a categorization approach is proposed that incorporates convolutional neural networks (CNN) to build the pest identification and classification model. The model was trained using a dataset containing images of different pest species. Extensive testing was conducted on a large dataset, and the system's performance was validated against multiple traditional classification models. The proposed framework achieved a classification accuracy of 90%, outperforming conventional methods. This research highlights agriculture's vital role in the economy and underscores the potential of advanced technologies in supporting sustainable farming practices. © 2025 IEEE.

关键词： Agriculture Sustainability Classification Accuracy Classification Model Convolutional Neural Network (CNN) Pest Classification

来源：评论

学校读者我要写书评

暂无评论

An Enhanced Traffic Incident Detection using Factor Analysis and Weighted Random Forest Algorithm 5

An Enhanced Traffic Incident Detection using Factor Analysis...

引用

5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Kanna, P. Rajesh Vanithamani, S. Karunakaran, P. Pandiaraja, P. Tamilarasi, N. Nithin, P. Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamil Nadu Erode India M. Kumarasamy College of Engineering Department of Master of Computer Applications Tamil Nadu Karur India Nandha Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Erode India Vel Tech Rangarajan Dr. Sagunthala Department of Computer Science and Engineering R&D Institute of Science and Technology Tamil Nadu Chennai India Nehru Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Tamil Nadu Erode India

ISBN: (纸本)9798331518097

Efficient and precise traffic incident detection is essential to reduce casualties and property damage. To address the issue of unbalanced event data, this work offers a novel methodology known as FA-WRF (Factor Analysis and Weighted Random Forest). This approach combines dimensionality reduction through factor analysis with classification using weighted random forests, data preparation through the Synthetic Minority Over-Sampling Technique (SMOTE), and data preparation with SMOTE. The included feature of severity detection is highlighted, as are the performance metrics of the FA-WRF model using well-established metrics such as detection level rate, number of false count alarm rate, classification type rate, and area specification under the receiver operating characteristic curve (AUC). The superiority of the FA-WRF model is illustrated using real-world expressway traffic data represented by imbalanced incidents through thorough comparisons with various machine learning-based algorithms such as Support Vector Machine, k-nearest neighbours algorithms, Logistic Regression algorithms, and decision-making trees. In addition to advancing incident detection, this technique has encouraging prospects for enhancing traffic management procedures and well-informed decision-making procedures in transportation networks. © 2024 IEEE.

关键词： Dimensionality reduction

来源：评论

学校读者我要写书评

暂无评论

An Effective Diagnosis of Alzheimer's Disease with the Use of Deep Learning based CNN Model 7

An Effective Diagnosis of Alzheimer's Disease with the Use o...

引用

7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Sharmili, K.C. Suja, G.P. Pandian, E. Walid, Md. Abul Ala Arunachalam, Sripriya Babu, G.Charles Computer Science Stella Maris College 17 Cathedral Road Chennai India Computer Science Muslim Arts College Thiruvithancode629174 India Sri Muthukumaran Institute of Technology Mangadu Master of Computer Application Chennai69 India Department of Computer Science and Engineering Khulna9203 Bangladesh Computer Applications-PG School of Computing Sciences VELS Institute of Science Technology & Advanced Studies - VISTAS Velan Nagar P.V. Vaithiyalingam Road Pallavaram Chennai600 117 India GRIET Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350397253

In recent years, Alzheimer's disease has become a major health concern. Over 45 million individuals worldwide suffer from this illness. Alzheimer's disease is a neurodegenerative illness of unidentified etiology and process that causes cognitive deterioration, and it mostly affects the elderly. Dementia, the gradual and irreversible loss of brain cells, is the leading cause of Alzheimer's disease. Those with this illness lost the ability to think, read, and do other basic tasks. A machine learning system may aid in this situation by making accurate predictions about the onset of illness. Dementia diagnosis in a broad patient population is the major objective. This research presents the findings and analysis of many Machine Learning models for identifying dementia. To alleviate this obstacle and help in the diagnosis of AD will create a deep-learning architecture that uses stacked auto-encoders and a softmax output layer. The proposed technique can analyze numerous classes in a single setting, unlike earlier procedures, and only needs a small number of labeled training samples and basic domain expertise to get started. In the present studies, a substantial improvement in performance when it came to classifying all diagnostic subgroups. Using the proposed technique can run these time series through a Convolutional Neural Network (CNN) and ResNet50 model to extract the distinguishing patterns across stages. With an F1-score of 0.99 and an accuracy of 99.91 percent, the CNN-based technique outperformed the standard feature-based classifiers by a significant margin. © 2023 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Study of a Conversational AI-Driven Healthcare Bot with Pose Estimation for Precision Fitness Monitoring and Personalized Guidance

A Comprehensive Study of a Conversational AI-Driven Healthca...

引用

2024 IEEE International Conference on Big Data and Machine Learning, ICBDML 2024

作者： Sinha, Anurag Kumar, Biresh Kundu, Roumo Sharma, Amit Sharma, Mukesh Raza, Asif ICFAI University ICFAI Tech School Computer Science Department Jharkhand Ranchi India Amity University Department of Computer Engineering and Application Jharkhand India Amity Institute of Information Technology Amity University Jharkhand Jharkhand Ranchi India Lovely Professional University Department of Computer Science Phagwara India Amity University Department of Master of Computer Application Jharkhand Ranchi India

ISBN: (纸本)9798350374100

This research investigates a new healthcare paradigm through the analysis of a conversational AI-powered healthcare bot that has posture estimation capabilities for accurate fitness tracking and customized recommendations. User motions during workout routines are accurately and thoroughly assessed thanks to the incorporation of powerful posture estimation technology. With the help of individualized exercise advice, this thorough study explores the capabilities of the system and shows how it may transform wellness practices. The healthcare bot provides an innovative method of fitness tracking by using conversational AI and accurate posture prediction to customize recommendations based on user requirements. This novel combination promises to enhance wellbeing and provide consumers with a more efficient and customized workout experience. The study emphasizes this point. © 2024 IEEE.

关键词： Health care

来源：评论

学校读者我要写书评

暂无评论

Model using GAN for Text to Image synthesis

Model using GAN for Text to Image synthesis

引用

2022 IEEE North Karnataka Subsection Flagship International Conference, NKCon 2022

作者： Patil, Kumari Reshma Biradar, Vidyadevi G Mohan, S.G. Dileep, M.R. Clara Kanmani, A. Pramanick, Monalisa Information Science Engineering Nitte Meenakshi Institute of Technology Bengaluru India Nitte Meenakshi Institute of Technology Master of Computer Applications Bengaluru India Information Science Engineering Presidency University Bengaluru India

ISBN: (纸本)9781665453424

Due to its remarkable data-generating capabilities, generative adversarial networks have gained significant relevance in unsupervised learning. Generative adversarial networks are very much useful in generating synthetic images from the given text description. The research work presented in this paper aims at developing a text-image synthesis model to generate high resolution synthetic images based on the scenario description given in input text. The paper proposes a novel model Df-GAN which is derived from VQGAN and CLIP using transfer learning. This model is evaluated on the COCO dataset. the experimental results show that the model can generate high resolution images. The model achieves FID score of 18.26 which is satisfactory. © 2022 IEEE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

An Exploration of Deep Learning-Based Object Detection and Classification in Yakshagana Imagery with YOLOv3

An Exploration of Deep Learning-Based Object Detection and C...

引用

Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： Anantha Murthy Prarhwini Sanjeev Kulkarni Department of Master of Computer Applications Karkala and Research Scholar NMAM Institute of Technology Nitte (Deemed to be University) Institute of Computer Science and Information Science Srinivas University India Department of Master of Computer Applications NMAM Institute of Technology Nitte (Deemed to be University) Karkala India Department of Computer Science and Engineering Srinivas University Institute of Engineering and Technology Mangalore India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

The Indian theatrical genre known as yakshagana has its roots in the coastal districts of Karnataka and parts of Kerala. With a distinct style and shape, it is a colorful fusion of dance, music, conversation, clothing, makeup, and stage tactics. This research uses YOLOv3, a cutting-edge object detection framework, to recognize people and crown jewelry in order to tackle the problem of identifying Yakshagana characters. Yakshagana, a traditional Indian theatrical style, is distinguished by its distinctive and ornate headdresses and costumes, which complicates automated identification. In order to do this, three classifications were taken into consideration: “person,” “yedekattu,” and “kireeta”. A carefully annotated dataset was used to train the model, which allowed it to capture all of the fine characteristics of these classifications. With a mean Average Precision (mAP) of 93.83%, our method showed impressive accuracy. With the improvement of picture identification and classification technologies, deep learning approaches have the ability to preserve and promote cultural heritage, as demonstrated by this high accuracy.

关键词： YOLO Deep learning Accuracy Art Clothing Real-time systems Cultural differences Character recognition Object recognition Global communication

来源：评论

学校读者我要写书评

暂无评论

Exploring the Power of Deep Learning in Anomaly Detection: A Comprehensive Review and Analysis 12th

Exploring the Power of Deep Learning in Anomaly Detection: A...

引用

12th International Conference on Soft Computing for Problem Solving, SocProS 2023

作者： Kumar, Gaurav Kumar, Ankit Pandy, Saroj Kumar Singh, Kamred Udham Singh, Teekam Kumar, Lalan Choudhury, Tanupriya Department of Computer Engineering and Applications GLA University UP Mathura India School of Computing Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Master of Computer Application GL Bajaj Institute of Technology and Management Greater Noida India Department of Computer Science and Engineering University of Petroleum and Energy Studies Dehradun India

ISBN: (纸本)9789819732913

This paper aims to provide a comprehensive overview of existing and state-of-the-art fraud and intrusion detection strategies, specifically focusing on incorporating neural networks. We begin by discussing the fundamental concept of data mining and its significance in detecting anomalies. By leveraging advanced machine learning and neural network techniques, we can effectively uncover subtle patterns and irregularities within complex datasets. We explore how the development of anomalies has enhanced our ability to identify and mitigate various threats, such as malware attacks and unlawful practices. Traditional detection strategies have proven effective, but as deep learning progresses, we uncover new opportunities and insights that can significantly enhance the accuracy and efficiency of anomaly detection systems. Throughout this study, we delve into the most cutting-edge approaches, ranging from broad neural networks to shallower architectures, and their applicability to fraud and intrusion detection. We analyze the strengths and limitations of these strategies, shedding light on their performance characteristics, scalability, and interpretability. By addressing these key issues, we aim to provide a comprehensive summary of the latest fraud and intrusion detection advancements driven by deep learning methodologies. This research contributes to the broader understanding of data mining's potential in uncovering anomalies and bolstering security measures across diverse domains. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Intrusion detection

来源：评论

学校读者我要写书评

暂无评论

Distributed Analytics in Smart Grids with Edge Computing and Computational Intelligence

Distributed Analytics in Smart Grids with Edge Computing and...

引用

2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Joshi, Apoorva Preethi, D. Gill, Amandeep Noida Institute of Engineering & Technology Master of Computer Application Uttar Pradesh Greater Noida India Department of Computer Science Bangalore India Vivekananda Global University Department of R&d Cell Jaipur India

ISBN: (纸本)9798350357769

Conventional methods like classification or clustering have limits when it comes to handling the complex task for dispersed data mining across heterogeneous sources and divergent data formats. This is especially true in scenarios where stochastic models or mathematical formulas may not be feasible. Data centralization is a prevalent trend that exacerbates problems including communication channel congestion and privacy difficulties related to sensitive data flows. In order to overcome these obstacles, this paper presents the Distributed Academic Engine (DAE), an adaptive edge-based scattered analytic engine designed for distributed networks. The DAE routinely breaks down and divides computations that need data from multiple sources among the nodes that are already in place, enabling the sharing of partial results without disclosing the original data. To get the full expression output, the master node combines these partial results. The expression is decomposed and processing jobs are distributed among nodes using three computational intelligence techniques: genetic algorithm, genetic algorithm using evolution control, the particle swarm optimization. This method's effectiveness is illustrated with a case study on smart grid KPI identification, which shows a noteworthy (over 91%) decrease in communication messages over conventional approaches. This demonstrates how well the suggested method works to reduce the difficulties related to distributed data mining. © 2023 IEEE.

关键词： Computational Intelligence Data Integration and Heterogeneous Data Source Integration Distributed Analytics Edge Computing Genetic Algorithm Particle Swarm Optimization Smart Grid

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53页 << < 3 4 5 6 7 8 9 10 11 12 > >>

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：