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

20,096 篇 会议
103 篇 期刊文献
49 册 图书

馆藏范围

20,248 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

14,205 篇 工学
- 11,479 篇 计算机科学与技术...
- 8,852 篇 软件工程
- 3,233 篇 信息与通信工程
- 2,497 篇 生物工程
- 2,267 篇 控制科学与工程
- 1,797 篇 电气工程
- 1,487 篇 生物医学工程（可授...
- 1,047 篇 机械工程
- 1,028 篇 光学工程
- 1,027 篇 电子科学与技术（可...
- 798 篇 安全科学与工程
- 781 篇 动力工程及工程热...
- 761 篇 交通运输工程
- 685 篇 仪器科学与技术
- 663 篇 化学工程与技术
- 484 篇 材料科学与工程（可...
6,793 篇 理学
- 3,492 篇 数学
- 2,659 篇 生物学
- 1,499 篇 统计学（可授理学、...
- 1,368 篇 物理学
- 844 篇 系统科学
- 741 篇 化学
4,238 篇 管理学
- 2,540 篇 管理科学与工程(可...
- 2,021 篇 图书情报与档案管...
- 1,282 篇 工商管理
1,433 篇 医学
- 1,286 篇 临床医学
- 1,240 篇 基础医学(可授医学...
- 764 篇 药学(可授医学、理...
- 631 篇 公共卫生与预防医...
693 篇 教育学
- 667 篇 教育学
559 篇 法学
486 篇 农学
449 篇 经济学
43 篇 军事学
27 篇 文学
8 篇 艺术学
1 篇 哲学

主题

1,325 篇 deep learning
1,122 篇 machine learning
835 篇 accuracy
556 篇 data mining
542 篇 support vector m...
527 篇 feature extracti...
432 篇 training
412 篇 data models
412 篇 convolutional ne...
399 篇 predictive model...
379 篇 internet of thin...
366 篇 real-time system...
327 篇 computational mo...
323 篇 neural networks
314 篇 big data
233 篇 reinforcement le...
200 篇 forecasting
179 篇 object detection
177 篇 machine learning...
170 篇 artificial intel...

机构

227 篇 chitkara univers...
64 篇 centre of interd...
53 篇 maharishi school...
43 篇 maharishi school...
34 篇 college of techn...
31 篇 pune
29 篇 school of comput...
28 篇 school of comput...
28 篇 chitkara univers...
28 篇 the islamic univ...
26 篇 karnataka bangal...
25 篇 uttaranchal inst...
24 篇 computer science...
24 篇 computer science...
23 篇 department of ar...
22 篇 graphic era deem...
22 篇 school of inform...
22 篇 department of in...
22 篇 national univers...
20 篇 department of co...

作者

24 篇 kukreja vinay
22 篇 gill kanwarparta...
21 篇 wang lei
20 篇 shiva mehta
19 篇 liu yang
18 篇 wang jing
16 篇 singh jagendra
15 篇 zhang tao
15 篇 malik bader alaz...
15 篇 joshi kapil
14 篇 yang yang
13 篇 zhang lei
13 篇 li yang
12 篇 meena belwal
12 篇 novais paulo
12 篇 li li
12 篇 liu ying
12 篇 zhang bo
12 篇 wang wei
11 篇 babu tina

语言

17,823 篇 英文
2,406 篇 其他
101 篇 中文
3 篇 土耳其文
2 篇 西班牙文
1 篇 俄文

检索条件"任意字段=3rd International Conference on Intelligent Data Engineering and Automated Learning"

共 20248 条记录，以下是121-130 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：

An intelligent Bio-AI for Optimized Resource Allocation in 5G Networks 3

An Intelligent Bio-AI for Optimized Resource Allocation in 5...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Nimmala, Satyanarayana Chilukuri, Ravindrareddy Janbhasha, Shaik Manasa, Pinnapureddy Mahendar, Maragoni Manoranjini, J. CVR College of Engineering Hyderabad India Department of CSE Neil Gogte Institute of Technology Hyderabad India Department of AI & DS Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9798331527549

The rapid expansion of 5G networks necessitates sophisticated resource allocation algorithms to tackle the problems posed by fluctuating traffic conditions, varied device requirements, and rigorous Quality of Service (QoS) standards. This work presents the Bio-AI Allocator, a hybrid model that combines Deep Reinforcement learning (DRL) with Ant Colony Optimization (ACO) for effective and adaptable resource management. The DRL model is trained via the publicly accessible 5G Quality of Service dataset from Kaggle, which includes essential parameters like as signal strength, bandwidth utilization, latency, and user mobility patterns. The training utilizes Q-learning with episodic incentives to formulate optimal resource allocation strategies. Experimental findings indicate that the Bio-AI Allocator realizes a 20% decrease in latency, a 25% increase in throughput, and a 15% gain in energy economy relative to traditional approaches such as Round-Robin and Max-Min Fairness scheduling. The comparative analysis demonstrates the superiority of the proposed hybrid model compared to standalone AI and bio-inspired methods, proving the Bio-AI Allocator is a scalable and intelligent solution for next-generation 5G networks. © 2025 IEEE.

关键词： Ant colony optimization

来源：评论

学校读者我要写书评

暂无评论

Vision Assist - Object Detection for the Blind 3

Vision Assist - Object Detection for the Blind

引用

3rd international conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Karthiyayini, J. Farooq, Zeba Syed Sirisha, P. New Horizon College of Engineering Affiliated to Vtu Dept of Information Science and Engineering Bengaluru India

ISBN: (纸本)9798331515911

Blind people need some aid to feel safe while moving. Vision Assist is a smartphone app for visually impaired individuals, using deep learning for intelligent assistance. It enhances accessibility by understanding real-time camera input and providing clear feedback. Compatible with all smartphones, With the help of Vision Assist's real-time object identification and customizable settings, visually impaired people can move more independently and with confidence around their environment. Users are given rapid, understandable feedback on their surroundings by means of text-to-speech communication of this information © 2025 IEEE.

关键词： Smartphones

来源：评论

学校读者我要写书评

暂无评论

Exploring the Potential of MANETs: A Study on Raspberry Pi and UAV Integration 3

Exploring the Potential of MANETs: A Study on Raspberry Pi a...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Archana, B. Mahalakshmi, A. Revathi, K. Ashok Kumar, K. Hema, A.B. Dept. of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore India Dept. of Information Technology SNS College of Engineering Coimbatore India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

This research investigates the feasibility of utilizing Mobile Ad Hoc Networks (MANETs) in conjunction with Raspberry Pi-equipped Unmanned Aerial Vehicles (UAV) swarms. The primary objective is to overcome the limitations of traditional communication infrastructures in dynamic and decentralized environments. Specifically, we address the challenge of establishing reliable and efficient communication links within a swarm of UAVs engaged in various applications, including disaster response, military reconnaissance, and environmental monitoring. By integrating Raspberry Pi devices with UAVs, we aim to create a self-organizing communication network that enables seamless data transmission, coordinated swarm operations, and enhanced mission effectiveness. The research explores different synchronization strategies, including master-slave and decentralized approaches, to optimize swarm coordination. Furthermore, we investigate the potential of incorporating advanced technologies such as machine learning, computer vision, and swarm intelligence to further enhance the autonomy, adaptability, and overall performance of the UAV swarm. © 2025 IEEE.

关键词： Mobile ad hoc networks

来源：评论

学校读者我要写书评

暂无评论

Deep learning-Based Skin type Classification using Fine-Tuned ResNet50 Architecture 3

Deep Learning-Based Skin type Classification using Fine-Tune...

引用

3rd international conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Chauhan, Shanvi Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331515911

Personalized skincare depends critically on the classification of skin types since different skin types - normal, oily, and dry - need different treatment plans to preserve beauty and health. Recommendation of suitable skincare products, prevention of side effects, and optimization of therapies for common dermatological diseases depend on precisely identifying skin types, therefore promoting excellent health and well-being. In this work, we classified skin types from a dataset of 3,152 photos using a deep learning method leveraging ResNet50 architecture. There were 2,522 photos for training, 314 for validation, and 316 for testing out of the dataset. ResNet50, a well-known convolutional neural network (CNN), was tuned for this particular purpose by changing its final fully connected layer to output three classifications matching to normal, oily, and dry skin types. Following training, the model attained an accuracy of 85% on the test set, therefore encouraging infrastructure and creativity in dermatological research. This study emphasizes the relevance of deep learning in dermatological domains and opens the path for automated, customized skincare recommendations, so supporting responsible consumption and manufacturing of skincare products and so helping to lower inequalities by guaranteeing skincare solutions for different skin types. © 2025 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Predicting Customer Lifetime Value in E-commerce: A data-Driven Approach to Enhance Customer Retention Strategies 3

Predicting Customer Lifetime Value in E-commerce: A Data-Dri...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Dey, Sonali Radha, D. Kirthika Devi, V.S. Department of Computer Science Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Predicting Customer Lifetime Value (CLV) is one of the most critical tasks that businesses undertake in order to improve customer retention and optimize marketing strategies. The present paper proposes a predictive model for CLV using several machine learning techniques. The features considered include customer demographics, purchasing behavior, and transaction history. All these features were preprocessed using feature engineering and missing value handling. Several regression models, including Linear Regression, Decision Tree, and Random Forest Regressor, were compared in terms of predictive performance. The study includes the decision trees,hash tables as advance data structures such that they can handle large datasets effectively. Of the ones tested, Random Forest Regressor proved to be superior to the others, having the lowest Mean Squared Error (MSE) and highest R2 score, thereby validating its robustness in identifying the complex patterns within the data. The work highlights the time and space complexities of different models. This study contributes to the application of machine learning in customer analytics and provides a foundation for future research to integrate more diverse data sources and enhance model performance through hyperparameter optimization. Future work will also explore real-time prediction capabilities and the integration of multimodal data to further refine CLV forecasting. © 2025 IEEE.

关键词： Sales

来源：评论

学校读者我要写书评

暂无评论

RNNSczNet: Recurrent Neural Network with attention and ReUL for the Detection of Schizophrenia using EEG Signals 3

RNNSczNet: Recurrent Neural Network with attention and ReUL ...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Reetha, L. Gnanajeyaraman, R. Department of CSE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences TN Chennai602105 India Department of CSE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences TN Chennai602105 India

ISBN: (纸本)9798331527549

The activation function of the Rectified Linear Unit (ReLU), integrated with Recurrent Neural Networks (RNNs), GooLuNet, and other networks, has greatly improved the early identification of schizophrenia (SZ) using EEG data. RNNs’ ability to capture temporal dependencies, combined with GooLuNet's inception modules for spatial feature extraction, enables accurate classification of SZ patients. ReLU is pivotal in ensuring efficient gradient propagation, reducing training time, and preventing vanishing gradient issues, thus improving model performance. The proposed model achieves a remarkable accuracy of 98.6%, outperforming more traditional machine learning techniques like Random Forest (RF) and Support Vector Machines (SVM). The architecture can also detect SZ around six months before symptoms appear, which shows that it might be used in real-time clinical settings. This combination of deep learning techniques offers a robust framework for early diagnosis, improving patient outcomes through timely intervention and treatment. © 2025 IEEE.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

AI based Emergency Assist System for Ambulance 3

AI based Emergency Assist System for Ambulance

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Dev, Abishai Sakthi Dhammodharan, S. Angelin Ponrani, M. Department Of ECE St. Joseph’s College of Engineering Chennai India

ISBN: (纸本)9798331527549

India is one of the most popular countries in the world and provides significant health care support to people. Managing the emergency vehicles with lifesaving benefits are highly demanded. During uncertain situations, the need for life saving support. The proposed approach incorporates a microcontroller-enabled smart system capable of detecting a patient’s critical condition using a heartbeat sensor, a respiratory sensor, and an AI-driven voice assistant. This system facilitates automated control and immediate activation of necessary life-saving measures. Real-time patient data is continuously monitored and uploaded to an Internet of Things (IoT)-enabled cloud platform, ensuring secure access through fingerprint authentication. In emergency scenarios, the system grants authorized access to provide timely medical intervention to the patient. Additionally, the AI-driven voice assistant sends alerts about accidents, including the patient's authorized demographic details, to relevant authorities such as the police and the nearest hospital. This ensures that the Intensive Care Unit (ICU) is preemptively notified and prepared for the patient's arrival. The automated framework is designed to eliminate delays associated with manual processing, enabling swift and efficient responses during emergencies. By streamlining communication and providing immediate support, the system significantly enhances the likelihood of saving lives in critical situations. © 2025 IEEE.

关键词： Hospital data processing

来源：评论

学校读者我要写书评

暂无评论

intelligent Interaction Design and User Experience of VR Platform Based on intelligent Perception and Deep learning 24

Intelligent Interaction Design and User Experience of VR Pla...

引用

3rd international conference on Cyber Security, Artificial Intelligence and Digital Economy (CSAIDE)

作者： Zhang, Yadong Zheng, Yujuan Liu, Meifang Sun, Lixia Shandong Vocat Univ Foreign Affairs Sch Informat Engn Weihai 264504 Shandong Peoples R China Shandong Drug & Food Vocat Coll Informat Ctr Weihai 264210 Shandong Peoples R China

ISBN: (纸本)9798400718212

Based on the theory of intelligent perception and deep learning, this paper discusses the interactive design method of virtual reality (VR) platform based on artificial intelligence, and carries out in-depth analysis of its modeling and evaluation process. The research shows that the multi-user collaborative recommendation algorithm can effectively reduce the concurrency of users in the interactive system. At the same time, the deep learning algorithm model can predict the data flow and feature information in the virtual reality scene, and the performance results are obtained through iterative operations. The results show that the initial stability of the system is about 90%, and the fluctuation range is between 80% and 100% as time goes by. Under the premise of maintaining stability of 80% to 100%, small vibration caused by random noise leads to stability fluctuations. The system shows a certain degree of robustness and elasticity in the fluctuation range of 80% to 100%, and can maintain stability in response to external interference, and realize functions such as intelligent roaming and environmental monitoring of the VR platform.

关键词： intelligent Perception Deep learning VR Platform intelligent Interaction

来源：评论

学校读者我要写书评

暂无评论

Optimizing Telecom Operations with Segmentation and Churn Prediction 3

Optimizing Telecom Operations with Segmentation and Churn Pr...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Neha, Dandu Ameesha, Mohammed Dheeraj, Mukka Sangeeta, K. Dept.Computer Science and Engineering Institute of Aeronautical Engineering Telangana Hyderabad India Institute of Aeronautical Engineering Telangana Hyderabad India

ISBN: (纸本)9798331527549

Client turnover is a significant issue for large organizations, particularly within the telecom sector, where it has a direct impact on profitability. To address this, telecom providers are increasingly focusing on developing predictive models to forecast customer churn and reduce its adverse effects. Identifying the key factors influencing churn is critical for implementing effective retention strategies. This research presents the development of a churn prediction model designed to help telecom companies identify customers at a high risk of leaving. The study involves exploring various data analysis techniques and presenting the results through graphical visualizations. Using machine learning methods, the model leverages large-scale datasets and introduces an innovative approach to feature design and selection. Historical customer data from previous months was utilized for training, evaluation, and validation of the model. Four machine learning algorithms were tested: Logistic Regression, XGBoost, Gradient-Boosted Machines (GBM), Random Forest, and Decision Trees. To further improve model accuracy, parameter tuning was applied during the training phase. The primary objective of this work is to enhance telecom operations by optimizing customer segmentation and churn prediction, ultimately improving both operational efficiency and customer retention. © 2025 IEEE.

关键词： Sales

来源：评论

学校读者我要写书评

暂无评论

intelligent Hotel Recommendation System Using Dynamic Graph learning 3

Intelligent Hotel Recommendation System Using Dynamic Graph ...

引用

3rd international conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Kiruthiga, G. Kamalesh, S. Jegadeesan, S. Manojkumar, S. Kavinesh, K.K. Ruban, A.S. Karpagam College of Engineering Department of Artificial Intelligence And Data Science Coimbatore India Velammal College of Engineering and Technology Department of Information Technology Tamilnadu Madurai India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

Inherently, the framework of Inn Suggestion presents an entirely hybrid approach to making hotel recommendations. The framework encompasses content-based and collaborative filtering techniques into a single system by analyzing user behaviors, including past bookings and reviews, besides features, which are generally associated with hotels, such as location and price, to get relevant suggestions. At the heart of the framework lies the Adaptive Meta-learning Hybrid algorithm that coordinates several machine learning techniques, like RNN, CNN, KNN, and Node2Vec. This meta-learning layer can recalculate the selection of algorithms dynamically based on performance in targeted subsets of users. The feature affords rapid adaptation to shifts in the user behavior and underlying data distributions, thus significantly enhancing both accuracy and diversity of recommendations over time. Key features include real-time updates, an intuitive user interface, and a robust feedback system. The technologies which architecture is based on including ***, Python, ***, and cloud services, with which scalability and security are the essence of their execution. Introducing these methodologies with AMLH, the Brilliantly Inn Suggestion Framework aims to change the entire experience of booking hotels. Which means a more dynamic personalized experience through booking in respect of each user's needs and overall satisfaction toward travel planning. © 2025 IEEE.

关键词： Collaborative filtering

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 << < 9 10 11 12 13 14 15 16 17 18 > >>

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：