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

3,079 篇 会议
141 册 图书
24 篇 期刊文献

馆藏范围

3,243 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

2,107 篇 工学
- 1,924 篇 计算机科学与技术...
- 1,068 篇 软件工程
- 399 篇 信息与通信工程
- 285 篇 电气工程
- 265 篇 生物工程
- 215 篇 控制科学与工程
- 170 篇 生物医学工程（可授...
- 79 篇 化学工程与技术
- 72 篇 机械工程
- 69 篇 光学工程
- 68 篇 仪器科学与技术
- 63 篇 电子科学与技术（可...
- 62 篇 安全科学与工程
- 55 篇 动力工程及工程热...
- 53 篇 网络空间安全
760 篇 理学
- 424 篇 数学
- 291 篇 生物学
- 148 篇 统计学（可授理学、...
- 140 篇 物理学
- 127 篇 系统科学
- 90 篇 化学
499 篇 管理学
- 301 篇 图书情报与档案管...
- 229 篇 管理科学与工程(可...
- 93 篇 工商管理
216 篇 医学
- 191 篇 临床医学
- 123 篇 基础医学(可授医学...
- 87 篇 药学(可授医学、理...
- 53 篇 公共卫生与预防医...
70 篇 法学
- 68 篇 社会学
51 篇 教育学
- 46 篇 教育学
35 篇 经济学
30 篇 农学
6 篇 艺术学
4 篇 军事学
1 篇 文学

主题

568 篇 data mining
542 篇 machine learning
241 篇 pattern recognit...
184 篇 deep learning
173 篇 feature extracti...
154 篇 artificial intel...
152 篇 support vector m...
113 篇 accuracy
113 篇 machine learning...
111 篇 training
93 篇 data models
80 篇 neural networks
77 篇 data mining and ...
65 篇 classification
62 篇 sentiment analys...
61 篇 classification a...
59 篇 big data
57 篇 predictive model...
50 篇 face recognition
48 篇 prediction algor...

机构

7 篇 maharishi school...
6 篇 guangdong univer...
5 篇 centre of interd...
5 篇 chitkara univers...
5 篇 uttaranchal univ...
5 篇 uttaranchal inst...
5 篇 institute of aut...
4 篇 shanghai jiao to...
4 篇 chitkara univers...
4 篇 school of inform...
4 篇 indian statistic...
4 篇 south china univ...
4 篇 galgotias univer...
4 篇 zhejiang univers...
4 篇 maharishi school...
4 篇 department of cs...
4 篇 pune
4 篇 university of ca...
4 篇 school of comput...
4 篇 department of co...

作者

9 篇 de-shuang huang
7 篇 coenen frans
7 篇 venkatesh svetha
7 篇 petra perner
7 篇 tianrui li
6 篇 ryszard tadeusie...
6 篇 marcin korytkows...
6 篇 jacek m. zurada
6 篇 krasotkina olga
6 篇 lotfi a. zadeh
6 篇 mottl vadim
6 篇 leszek rutkowski
5 篇 vitoantonio bevi...
5 篇 sankar k. pal
4 篇 waiyamai kitsana
4 篇 sozo inoue
4 篇 sergei o. kuznet...
4 篇 duoqian miao
4 篇 shengli xie
4 篇 hepu deng

语言

3,105 篇 英文
129 篇 其他
32 篇 中文
1 篇 西班牙文
1 篇 土耳其文

检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3244 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：

Corn Crop Yield Prediction using Deep learning 3

Corn Crop Yield Prediction using Deep Learning

引用

3rd international conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Iniyan, S. Keerthi, Pidikiti Pawar, Shruti Department of Computing Technologies SRM Institute of Science and Technology Kaatankulathur Tamil Nadu Chennai603203 India Department of Computer Science and Engineering SRM Institute of Science and Technology Kaatankulathur Tamil Nadu Chennai603203 India

ISBN: (纸本)9798331532420

Accurate crop yield prediction has emerged as a critical area of research, especially in light of global food security challenges and the need for sustainable agricultural practices. In this study, we focus on predicting the yield of US corn, a staple crop with significant economic and nutritional value. By leveraging data mining techniques, we extract meaningful patterns from large agricultural datasets, aiming to enhance our understanding of the factors influencing crop yields. Accurate predictions can guide farmers and policymakers in making informed decisions, such as optimizing planting schedules, managing resources more effectively, and anticipating potential shortages. In this study, we utilized a variety of machine learning models, such as Logistic Regression, Decision Trees, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Random Forest, Support Vector machines (SVM), and Long Short Term Memory (LSTM) networks. These models were selected to explore their varying capabilities in handling complex agricultural data and providing accurate predictions. The final outcome, Linear Regression, achieved an impressive accuracy of 93%, demonstrating its effectiveness in predicting crop yields based on the given dataset. © 2024 IEEE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Generation and detection technology of intelligent building engineering based on digital twin and deep learning 3

Generation and detection technology of intelligent building ...

引用

2023 IEEE 3rd international conference on data Science and Computer Application, ICDSCA 2023

作者： Hongqing, Liu Sheng, Kong Lin, Li China Tobacco Shandong Industrial Co. Ltd Tengzhou China

ISBN: (纸本)9798350341546

With the rapid development of building information modeling and deep learning technology, as well as people's increasing demand for virtual reality and intelligent construction projects, time-consuming and inefficient manual building modeling methods are far from meeting engineering needs. Plane detection is also gradually unable to meet the needs of building earthquake resistance. This paper proposes a generation and detection technique for intelligent construction projects based on digital twins and deep learning. First, using digital twin technology, the building plane is obtained through the Boolean intersection operation between geometric objects, and then the main body, external working conditions, environment and other entities are faithfully reproduced in the building information model;Contour;Finally, based on the Boltzmann machine of deep learning, the contour outside the building plane is input into the trained anomaly detection neural network algorithm model, and the recognition result is fed back. The experimental results show that the algorithm model has greatly improved the recognition rate of the abnormal detection of concave-convex irregular building plans, and is more in line with the actual needs of intelligent building construction. © 2023 IEEE.

关键词： Anomaly Detection Boltzmann machine Building Information Modeling Deep learning Digital Twin Smart Building

来源：评论

学校读者我要写书评

暂无评论

3rd international conference on Smart Computing and Cyber Security—Strategic Foresight, Security Challenges and Innovation, SMARTCYBER 2023

3rd International Conference on Smart Computing and Cyber Se...

引用

3rd international conference on Smart Computing and Cyber Security—Strategic Foresight, Security Challenges and Innovation, SMARTCYBER 2023

ISBN: (纸本)9789819705726

The proceedings contain 49 papers. The special focus in this conference is on Smart Computing and Cyber Security—Strategic Foresight, Security Challenges and Innovation. The topics include: A Comparative Analysis of machine learning Using Classification Algorithms for the Detection of Breast;A Classification of Attacks in an IDS Using Sparse Convolutional Autoencoder and DNN;sentiment Analysis of Tweets Using Bi-Directional Long Short-Term Memory and Word Embeddings;QOS Performance Analysis of DDOS Attack on IoT-Based MANET;Speech recognition and Transformation in Sign Language Using CNN;Development of a Lower-Cost Surveillance System Using an ESP32-Cam, IoT, and Twilio Application Programming Interface;comparative Study of machine learning Algorithms;routing Mechanisms for Unmanned Aerial Vehicle;implementation of Augmented Reality to Promote Civil Engineering Through Laboratory Facilities;a Comparative Evaluation of Deep learning Algorithms: Assessing Effectiveness and Performance;analysis of Creative Economic Situation in Pekanbaru: Statistical Analysis and Word Cloud;best Practices for Enabling a Successful Digital Transformation Initiative;songket Cloth Promotion from Riau Province, Indonesia, Using Augmented Reality;toward Crops Prediction in Indonesia;Windows System Vulnerability Testing and Exploitation with Reverse TCP and Metasploit;book Genre Classification System Through Supervised learning Technique;an Overview of Edge Computing for Big data Analytics;video Game Special Effects Simulation Based on Particle System of Unity;deep learning and machine learning Algorithms Methods in Cyber Security;identification and Mitigation Process for the Air Quality Using machine learning Techniques;Conversation AI for Verbal Children with ASD to Facilitate Social Interaction;a Wavelet Transformation Technique and Cryptographic Encryption for Image Steganography.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Real Time Air-Writing and recognition of Tamil Alphabets Using Deep learning 3rd

Real Time Air-Writing and Recognition of Tamil Alphabets Usi...

引用

3rd international conference on machine learning, Internet of Things and Big data, ICMIB 2023

作者： Preethi, S. Meeradevi, T. Kaif, K. Mohammed Hema, S. Monikraj, M. Department of Electronics and Communication Engineering Kongu Engineering College Tamil Nadu Erode India

ISBN: (纸本)9789819939312

Writing has always been a prominent way of communication. The way in which the letters are written has been varying with time. From the conventional pen and paper to touch pad and stylus, the way of writing has evolved. Air- Writing is another development in which the characters are written in free space without being limited to a specific tool. This method of writing makes the hand movement easier compared to the conventional methods. Therefore, the air writing and recognition model will be of great help for children who start learning a language. The trajectory of the air written characters is obtained by mapping the focal point using Optical flow in OpenCV. The obtained trajectory is then preprocessed and given to Dense Net 121 which is a type of CNN model widely used for pattern matching along with the dataset from HP labs which contains 3000 images for 11 Tamil vowels. The model which is trained obtained a maximum training and validation accuracy of 98.2% and 91.83% respectively with minimum training and validation loss of 6.35% and 21.04% respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Optical flows

来源：评论

学校读者我要写书评

暂无评论

3rd international conference on machine learning, Image Processing, Network Security and data Sciences, MIND 2021

3rd International Conference on Machine Learning, Image Proc...

引用

3rd international conference on machine learning, Image Processing, Network Security and data Sciences, MIND 2021

ISBN: (纸本)9789811958670

The proceedings contain 68 papers. The special focus in this conference is on machine learning, Image Processing, Network Security and data Sciences. The topics include: Analyzing Wearable data for Diagnosing COVID-19 Using machine learning Model;comparative Analysis of Classification Methods to Predict Diabetes Mellitus on Noisy data;a Robust Secure Access Entrance Method Based on Multi Model Biometric Credentials Iris and Finger Print;region Classification for Air Quality Estimation Using Deep learning and machine learning Approach;neuroevolution-Based Earthquake Intensity Classification for Onsite Earthquake Early Warning;detection of Credit Card Fraud by Applying Genetic Algorithm and Particle Swarm Optimization;traditional Indian Textile Designs Classification Using Transfer learning;classification of Electrocardiogram Signal Using Hybrid Deep learning Techniques;automated Detection of Type 2 Diabetes with Imbalanced and machine learning Methods;fault Diagnosis in Wind Turbine Blades Using machine learning Techniques;real-Time Detection of Vehicles on South Asian Roads;stock Market Prediction Using Ensemble learning and Sentimental Analysis;Multiple Feature-Based Tomato Plant Leaf Disease Classification Using SVM Classifier;a Methodological Review of Time Series Forecasting with Deep learning Model: A Case Study on Electricity Load and Price Prediction;unexpected Alliance of Cardiovascular Diseases and Artificial Intelligence in Health Care;a Novel Smartphone-Based Human Activity recognition Using Deep learning in Health care;An Enhanced Deep learning Approach for Smartphone-Based Human Activity recognition in IoHT;classification of Indoor–Outdoor Scene Using Deep learning Techniques;prediction of the Reference Evapotranspiration data from Raipur Weather Station in Chhattisgarh using Decision Tree-Based machine learning Techniques;deep Transfer learning and Intelligent Item Packing in Retail Management;preface.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Research on Feature learning Algorithm of Social Media based on Deep Network 3

Research on Feature Learning Algorithm of Social Media based...

引用

3rd IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Bai, Haoming Wu, Linjiao Chengdu University of Technology College of Earth Science Sichuan Chengdu China University of Shanghai for Science and Technology College of Communication and Art Design Shanghai Shanghai China

ISBN: (纸本)9798350317022

Deep learning is a branch of machine learning, which has been used to solve many problems in the fields of computer vision, speech recognition, natural language processing and so on. Deep neural networks are trained by inputting large amounts of data into them, and then they learn how to recognize patterns from these data. This process is called supervised training because the network learns from tagged examples (such as images) or other inputs (such as audio files or text documents). The social media based performed by two models such as Deep network structure feature learning and Feature learning of social media based on deep network. The suggested approach achieved the better transmission of social media by M10, DBLP and Cora of values about 0.70, 0.75 and 0.80 respectively. The problem with deep learning is that it is difficult to train these networks on new tasks without accessing a large amount of tag data for each task. In addition, there are many challenges in teaching the knowledge learned in the learning process. © 2023 IEEE.

关键词： deep learning learning algorithm social media features

来源：评论

学校读者我要写书评

暂无评论

Protecting Contactless Credit Card Payments from Fraud through Ambient Authentication and machine learning 3

Protecting Contactless Credit Card Payments from Fraud throu...

引用

3rd international conference on Advances in Computing, Communication, Embedded and Secure Systems, ACCESS 2023

作者： Singh, Divneet Chitkara University Institute of Engineering & Technology Chitkara University Punjab India

ISBN: (纸本)9798350346169

Credit card fraud (CCF) is a persistent issue in the financial sector with serious consequences. data mining has proven to be extremely useful in detecting fraud in online transactions. However, detecting CCF through data mining is quite a difficult task because of two causes: constant changes in the profiles of normal and fraudulent behaviour, and the highly skewed nature of the data sets. The outcome of fraud detection in credit card transactions depends on the sampling approach, detection techniques, and variable selection. This work studies the performance of K-Nearest Neighbor, Naive Bayes, Logistic Regression and Random Forest algorithms on a highly skewed dataset. The dataset contains 2,84,807 transactions and has been collected from European cardholder transactions. A hybrid of under-sampling and oversampling techniques has been used on the skewed data. The four techniques were utilized on both data namely preprocessed and raw, and the results are evaluated using specificity, accuracy, sensitivity, and F1-score. The outcomes show that the optimal accuracy for Naive Bayes, Logistic Regression, K-Nearest Neighbor and Random Forest classifiers are 98.72%, 52.34%, 96.89%, 91.67%, respectively. The comparative results indicate that K-Nearest Neighbor performs better than Logistic Regression, Random Forest and Naive Bayes techniques. © 2023 IEEE.

关键词： machine learning

来源：评论

学校读者我要写书评

暂无评论

Tell Me Why - A Systematic Literature Review on Outlier Explanation for Tabular data 3

Tell Me Why - A Systematic Literature Review on Outlier Expl...

引用

3rd international conference on pattern recognition and machine learning, PRML 2022

作者： Nonnenmacher, Jakob Holte, Nils-Christoph Gomez, Jorge Marx University of Oldenburg Oldenburg Germany

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

Outlier explanation approaches are used to support analysts in investigating outliers, especially those detected by methods which are not intuitively interpretable such as deep learning or ensemble approaches. There have been several studies on outlier explanation and visualization in the last years. Nonetheless, there has been no overarching survey of existing outlier explanation approaches. We investigate what kind of explanation approaches exist for tabular data and how they can be characterized. For this, we perform a literature review of 418 papers uncovering 26 existing studies which we analyze and categorize according to: approach, model dependence, supported data, output and evaluation. Based on our categorization, we identify trends within outlier explanation. This includes the limited number of underlying approaches and the lack of a common evaluation method. We are also able to highlight avenues for further research such as utilizing different datasets for evaluation or exploring visualization approaches. © 2022 IEEE.

关键词： Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

Long-Short Term Memory Analysis of EEG data Using Python for Emotion Classification 3

Long-Short Term Memory Analysis of EEG Data Using Python for...

引用

3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Singh, Anmol Rattan Singh, Gurjinder Saluja, Nitin Chitkara Uiversity Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350319125

Python has made significant progress and its adaptability allows it to be utilized in nearly all fields recognized by humanity. The utilization of this technology in the field of research and development has left a distinctive impact. Advancements in brain research have occurred at a rapid pace, resulting in a continuous stream of novel results being unveiled on a daily basis. Electroencephalogram (EEG) signals refer to the electrical impulses generated by the brain in reaction to specific visual stimuli, such as positive, negative, neutral, or soothing stimuli. The utilization of Python, along with its robust modules and libraries, facilitates the efficient identification and subsequent study of human emotions, hence enabling emotion recognition. In this discussion, we will explore Long Short Term memory a machine learning (ML) models that demonstrate efficacy in extracting and conveying significant insights pertaining to the human brain and EEG signals. © 2023 IEEE.

关键词： Deep Analysis EEG signals Exploratory data Analysis (EDA) Long-Short Term Memory (LSTM) machine learning Python

来源：评论

学校读者我要写书评

暂无评论

Integrating Bi-LSTM and Extended Geological Constraints Method for Automatic Logging Curve Stratification 3

Integrating Bi-LSTM and Extended Geological Constraints Meth...

引用

3rd international conference on Cloud Computing, Big data Application and Software Engineering, CBASE 2024

作者： Li, Xiang Zhang, XiaLin Li, ZhangLin Weng, ZhengPing School of Computer Science China University of Geosciences Wuhan China

ISBN: (纸本)9798331506582

Logging curve stratification plays a crucial role in the interpretation of geological data. As the volume of logging data increases, logging curve stratification becomes increasingly challenging and time-consuming. Current automated log stratification methods primarily rely on pattern recognition techniques, which are limited to processing single curves and overlook the complex nonlinear relationships between different logging curves, resulting in suboptimal performance in thin layer stratification. In this study, we propose a deep learning-based approach that simultaneously handles multiple logging curves while incorporating geological constraints to enhance the impact of spatial features on logging curve stratification. Furthermore, we optimize the loss function and introduce a new evaluation metric to better assess the model's performance. Experimental results demonstrate that our proposed method performs better in terms of stratification error and thin layer detection. © 2024 IEEE.

关键词： Deep 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325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：