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

34,611 篇 会议
5,746 篇 期刊文献
295 册 图书

馆藏范围

40,652 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

25,453 篇 工学
- 17,851 篇 计算机科学与技术...
- 14,961 篇 软件工程
- 6,575 篇 信息与通信工程
- 4,225 篇 控制科学与工程
- 3,253 篇 电子科学与技术（可...
- 3,212 篇 电气工程
- 2,620 篇 生物工程
- 2,153 篇 生物医学工程（可授...
- 1,776 篇 光学工程
- 1,680 篇 机械工程
- 1,442 篇 动力工程及工程热...
- 1,416 篇 仪器科学与技术
- 1,251 篇 化学工程与技术
- 949 篇 安全科学与工程
- 847 篇 交通运输工程
- 777 篇 建筑学
13,139 篇 理学
- 7,154 篇 数学
- 3,645 篇 物理学
- 2,879 篇 生物学
- 2,302 篇 统计学（可授理学、...
- 1,596 篇 系统科学
- 1,295 篇 化学
6,447 篇 管理学
- 3,816 篇 管理科学与工程(可...
- 2,944 篇 图书情报与档案管...
- 2,163 篇 工商管理
1,799 篇 医学
- 1,533 篇 临床医学
- 1,431 篇 基础医学(可授医学...
- 943 篇 药学(可授医学、理...
1,163 篇 法学
- 955 篇 社会学
777 篇 经济学
- 776 篇 应用经济学
552 篇 农学
547 篇 教育学
170 篇 文学
93 篇 军事学
46 篇 艺术学
3 篇 哲学
2 篇 历史学

主题

1,121 篇 accuracy
888 篇 computational mo...
877 篇 feature extracti...
736 篇 training
733 篇 computer science
709 篇 deep learning
582 篇 machine learning
529 篇 real-time system...
527 篇 support vector m...
502 篇 predictive model...
436 篇 internet of thin...
400 篇 data models
367 篇 optimization
355 篇 convolutional ne...
351 篇 computer archite...
344 篇 reliability
316 篇 artificial intel...
314 篇 data mining
299 篇 routing
288 篇 visualization

机构

83 篇 institut univers...
82 篇 north-west unive...
81 篇 national centre ...
79 篇 infn sezione di ...
76 篇 infn sezione di ...
76 篇 institute of inf...
67 篇 tno technical sc...
66 篇 università di ca...
59 篇 université de st...
58 篇 dept. of informa...
56 篇 dept. of compute...
53 篇 leiden universit...
52 篇 school of automa...
46 篇 dept. of compute...
46 篇 dept. of compute...
44 篇 dept. of electri...
44 篇 dept. of informa...
42 篇 infn lnf via enr...
42 篇 in2p3 lpc campus...
42 篇 nikhef national ...

作者

85 篇 ferrara g.
85 篇 ardid m.
84 篇 calvo d.
84 篇 gal t.
84 篇 bozza c.
81 篇 chen a.
77 篇 kalekin o.
77 篇 díaz a.f.
74 篇 boumaaza j.
70 篇 coleiro a.
66 篇 huang f.
64 篇 gozzini s.r.
52 篇 kulikovskiy v.
49 篇 dallier r.
42 篇 anguita m.
41 篇 benoit d.m.
41 篇 eddymaoui a.
41 篇 del burgo r.
40 篇 gagliardini s.
40 篇 labalme m.

语言

39,185 篇 英文
931 篇 其他
369 篇 中文
116 篇 日文
47 篇 朝鲜文
4 篇 德文
2 篇 法文
1 篇 葡萄牙文
1 篇 俄文
1 篇 斯洛文尼亚文
1 篇 土耳其文

检索条件"机构=Dept. of Computer Science and Systems Engineering"

共 40652 条记录，以下是51-60 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 Extended Reality Platform Powered by Large Language Models: A Case Study on Teaching Dance Costumes 7

An Extended Reality Platform Powered by Large Language Model...

引用

7th IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality, AIxVR 2025

作者： Garzarella, Silvia Vallasciani, Giacomo Cascarano, Pasquale Hajahmadi, Shirin Cervellati, Elena Marfia, Gustavo University of Bologna Dept. of the Arts Bologna Italy University of Bologna Dept. of Computer Science and Engineering Bologna Italy

ISBN: (纸本)9798331521578

In this paper, we present an Extended Reality (XR) platform powered by Large Language Models (LLMs), designed to support education in dance history and cultural heritage. The platform here applied to a case study on a dance costume, allows users to interact with 3D models, annotate them, and explore their historical and cultural contexts collaboratively. Using features like image-to-LLM queries, users can gain insights into costume details and performance history, and the annotation tools enable uploading multimedia resources, thereby simulating an archival research environment. By integrating LLMs, the platform provides tailored information and context on demand, enriching the user experience with detailed explanations about objects, such as costume construction, usage, and cultural significance. © 2025 IEEE.

关键词： Hosiery manufacture

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of Machine Learning Algorithms for Binary Classification of Tea Leaf Diseases 5

Comparative Analysis of Machine Learning Algorithms for Bina...

引用

5th IEEE International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Shekhar, Nidhi Megha, J.S. Varsha, J.S. Revathi, G.P. Pesu - Isfcr Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350356236

India stands as one of the world's largest tea exporters. This requires for an implementation of an efficient and robust system for disease detection and prevention. The most important challenge faced by the tea industry is tea leaf diseases due to continuous pathogen infestation. These diseases can cause full-size crop losses, adversely affecting the economic system and the livelihood of tea farmers. The ability to detect these diseases early is essential. Early detection can cause timely intervention that can notably reduce harm to tea plants and keep them at their best possible quality. Unfortunately, traditional diagnostic techniques often depend on visible inspection and are hence inefficient as those methods can be gradual, labour in-dept. and vulnerable to human errors, reducing the effectiveness in larger fields. This research paper aims to classify whether a leaf is diseased or healthy through a two-step process involving feature extraction using a pretrained Convolutional Neural Network (CNN) and classification using four machine learning models - LightGBM, CatBoost, Autoencoders and Self Organized Maps (SOMs). This paper utilizes a pre-trained VGG16 model, which is trained on the ImageNet dataset. This model is capable of extracting high-level features from images. © 2025 IEEE.

关键词： Self organizing maps

来源：评论

学校读者我要写书评

暂无评论

Stomach Cancer Prediction and Detection using Deep Learning: A Review 2

Stomach Cancer Prediction and Detection using Deep Learning:...

引用

2nd International Conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Kaur, Sharandeep Goel, Paurav Kapoor, Nitika Chandigarh University dept. of Computer Science Engineering Mohali India

ISBN: (纸本)9798331505745

Stomach cancer, sometimes referred to as gastric cancer, is still one of the most common and widespread deadly disease worldwide offers a significant challenge in oncology due to late detection and high mortality rates. The advent of deep learning has revolutionized stomach cancer prediction by leveraging sophisticated models such as convolutional neural net-works (CNNs), multimodal learning and hybrid frameworks that integrate multiple data sources like medical imaging, histological slides, clinical records and genetic profiles and provide better performance with high accuracy. This review comprehensively ex-plores recent advances in deep learning applications for the detection of stomach cancer, highlighting their performance, accuracy, and clinical implications. Comparative analysis demonstrates that different deep learning models, such as ResNet50, DenseNet121, EfficientNetB5, and MobileNetV2, provide impressive results with accuracy up to 99% and higher sensitivity and specificity. It also indicates that the system accuracy needs to be improved to 100% in order to detect stomach cancer as early as possible. Early detection helps in the stoppage of further spreading of the disease and lowers the death rates. © 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

Plant Disease Detection using Image Processing and Generative Adversarial Network 14

Plant Disease Detection using Image Processing and Generativ...

引用

14th IEEE International Conference on Communication systems and Network Technologies, CSNT 2025

作者： Teresha, Y. Shaji, Nikhil John Jarial, Manav Mirjankar, Ved Vishal Patil, Swetha Computer Science Engineering Dept. PES University Bengaluru India

ISBN: (纸本)9798331531935

Agriculture is a key contributor to the GDP of our country, India. Plants play a very important role in the ecosystem and plant diseases can negatively impact the agricultural industry by causing huge losses to the food supply. Plant leaves are prone to a variety of diseases, including blights, leaf spots, and other infections. Therefore, identifying diseases efficiently is crucial to pave the way for sustainable agriculture. In the field of agriculture, traditional and manual methods for detecting diseases in plants have sometimes been found to be inefficient and error prone. We therefore present a novel solution to these issues by introducing a method to automate plant disease detection in the Tomato plant leaf by using advanced Convolutional Neural Network (CNN) frameworks, image processing libraries and Generative Adversarial Networks (GANs). The pipeline of our work involves preprocessing plant image datasets, generating synthetic images using GANs, segmenting region of interests via a U-Net model, and classifying diseases with a CNN-based classifier. A deployed model ensures accurate disease identification from user input. © 2025 IEEE.

关键词： Photointerpretation

来源：评论

学校读者我要写书评

暂无评论

PhishVQC: Optimizing Phishing URL Detection with Correlation Based Feature Selection and Variational Quantum Classifier 3

PhishVQC: Optimizing Phishing URL Detection with Correlation...

引用

3rd International Conference on Intelligent systems, Advanced Computing, and Communication, ISACC 2025

作者： Shahriyar, Farhan Tanbhir, Gazi Raihan Chy, Abdullah Md Tanzin, Mohammed Abdul Al Arafat Mashrafi, Jisan World University of Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh Southern University Bangladesh Dept. of Computer Science and Engineering Chittagong Bangladesh Brac University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331523893

Phishing URL detection is crucial in cybersecurity as malicious websites disguise themselves to steal sensitive information. Traditional machine learning techniques struggle to perform well in complex real-world scenarios due to large datasets and intricate patterns. Motivated by quantum computing, this paper proposes using Variational Quantum Classifiers (VQC) to enhance phishing URL detection. We present PhishVQC, a quantum model that combines quantum feature maps and variational ansatzes such as RealAmplitude and EfficientSU2. The model is evaluated across two experimental setups with varying dataset sizes and feature map repetitions. PhishVQC achieves a maximum macro average F1-score of 0.89, showing a 22% improvement over prior studies. This highlights the potential of quantum machine learning to improve phishing detection accuracy. The study also notes computational challenges, with execution wall times increasing as dataset size grows. © 2025 IEEE.

关键词： Correlation Based Feature Selection Cybersecurity Feature Selection Phishing Detection QML Quantum Classical Hybrid Model Quantum Computing Quantum Machine Learning URL Detection Variational Quantum Classifier VQC

来源：评论

学校读者我要写书评

暂无评论

Smart Agriculture: A Crop Monitoring System to Empower Agricultural Field Using IOT

Smart Agriculture: A Crop Monitoring System to Empower Agric...

引用

2025 International Conference on Emerging Technologies for Intelligent systems, ETIS 2025

作者： Madhu, Surya Sujith Kumar, P.S. Supriya, L.P. Sree Buddha College of Engineering Dept. of Computer Science and Engineering Kerala India

ISBN: (纸本)9798331507541

Agriculture has long been the cornerstone of India's economy and way of life, contributing significantly to the nation's GDP and employment. With a diverse range of crops and climatic conditions, India's agricultural sector is both vast and complex, demanding innovative approaches to meet the growing needs of its population. The sector faces numerous challenges, including climate variability, soil degradation, water scarcity, and pest infestations, which impact productivity and sustainability. In response to these challenges, modern agricultural system in India increasingly focuses on integrating technology to enhance efficacy and eco-friendly. One such initiative is the development of smart agricultural systems that leverage advanced technologies. This paper presents an advanced Internet of Things (IoT) based crop monitoring system designed to enhance agricultural efficacy and conciliatory. The system integrates multiple sensors to monitor critical environmental parameters, including pH levels, soil properties, and temperature. By continuously measuring these variables, the system provides real-time data to optimize crop health and growth conditions. Additionally, the paper incorporates pest control mechanisms, leveraging IoT technology to detect and manage pest populations effectively. This comprehensive approach not only ensures the optimal condition for crops but also minimizes the need for manual intervention and reduces the reliance on chemical treatments. The data collected through this system is analysed and visualized through a user-friendly interface, offering farmers actionable insights and enabling timely decision-making. The integration of these technologies aims to promote sustainable agricultural practices, improve crop yield, and reduce environmental impact, demonstrating the potential of IoT in modernizing agricultural operations. © 2025 IEEE.

关键词： Insect control

来源：评论

学校读者我要写书评

暂无评论

AI-Driven Solutions with CNN and IoT for Enhancing Visual Impairments 5

AI-Driven Solutions with CNN and IoT for Enhancing Visual Im...

引用

5th International Conference on Emerging systems and Intelligent Computing, ESIC 2025

作者： Prasad, M. Lakshmi Varshitha, Veeramalli Goud, A. Praneeth Bindu, R. Srinivasulu, Ch. Rao, S. Janardhana Institute of Aeronautical Engineering Dept. of Computer Science and Engineering Dundigal Hyderabad India

ISBN: (纸本)9798331522100

Visual impairment is a problem that affects millions of people worldwide, preventing them from walking independently and performing daily tasks. Some of the challenges that blind people face include crossing the street, reading, driving, and socializing. Despite the scientific community's continuous efforts to treat the eye, these solutions have not yet been found. Traditional aids such as canes are useful but limited. Get in the groove. The main focus of the project is to integrate the YOLOv5 object detection model with a convolutional neural network (CNN). Known for its fast and accurate target detection, YOLOv5, together with CNN, improves the physical ability to detect and classify targets in difficult areas with high accuracy. The eye uses the camera module to continuously monitor the environment and identify various objects in real time;the detected data is converted into speech by text-to-speech (TTS) converters such as Google Text- to-Speech (gTTS) and pyttsx3 This description allows users to get an instant description of their surroundings, unlike helping the blind, it improves their ability to navigate independently rather than just providing references, but there are also good ways to work with better and more reliable assistive technologies. © 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

Implementing HR Management System with an AI Powered ChatBot 3

Implementing HR Management System with an AI Powered ChatBot

引用

3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Susitra, D. Pratheesh, G. Karthiga, J.D. Dhanalakshmi, K. Shamreen Ahamed, B. Sylvia Grace, J. Dept. of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai600119 India

ISBN: (纸本)9798331527549

Human Resource (HR) management is one important section that increases the success of an organization to handle some important functions like employee data manipulation, attendance tracking, payroll processing, and performance evaluation. With the management of these processes becoming key to the success of small-scale startups, it poses a very big challenge for many of them to be able to manage these processes efficiently and cost efficient. By contrast, the old era of traditional HR systems was based on database driven architectures that are expensive in terms of operational cost, require storage space and complex infrastructural needs. These limitations often make traditional HR systems impossible for a startup that would prefer lightweight, affordable, and scalable solutions. Startups need robust platforms to streamline HR work streams, but often lack with financial resources for building complex database driven systems. In this paper, an AI powered HR management system is developed to simplify employee management processes for small scale startups. In this system. cache memory is used instead of traditional databases, thereby the operational cost of the system will be reduced for storage and retrieval of employee details. Automated Attendance Management enhanced with Face Recognition technology, Payroll Management, Leave Management, Feedback Submission modules are included. It is integrated with an AI powered chatbot, which facilitates a smooth interaction by the employees with query for information, submit leave requests and pull up all payroll information etc. A user-friendly dashboard for a consolidated view of employee profiles, attendance records, leave approvals, and payroll data can be served over an optimized and time efficient HR workflow. This system addresses the needs of the startups such as flexible usage, cost effective operations and improved resource management. © 2025 IEEE.

关键词： AI powered chatbot for Small-scale startups using cache memory attendance management face recognition HR Management System leave management payroll management

来源：评论

学校读者我要写书评

暂无评论

Quantum-Enhanced Machine Learning for Precision Breast Cancer Diagnosis 17

Quantum-Enhanced Machine Learning for Precision Breast Cance...

引用

17th International Conference on COMmunication systems and NETworkS, COMSNETS 2025

作者： Banerjee, Agniva Indian Institute of Science Education and Research Bhopal Dept. Electrical Engineering and Computer Science Bhopal India

ISBN: (纸本)9798331531195

Cancer is the leading cause of premature mortality. Globally, the prevalence of cancer is on the rise, and early-stage diagnosis is crucial for cancer recovery and survival. However, the presence of circulating tumor cells (CTCs) and heterogeneity within the tumor makes it hard to track how cancer changes over time and how resistance to treatment develops. Unfortunately, the cancer diagnosis precision remains inadequate, endangering precious human lives and necessitating costly procedures for accurate treatment. Machine learning (ML) has proven capable of discovering meaningful information and making predictions, particularly with large datasets. Moreover, quantum computers (QCs) inherently possess superposition and parallel computation, features not found in classical computing. Researchers have demonstrated that quantum neural networks (QNNs) achieve a significantly better effective dimension than classical NNs. In this context, this study investigates the application of classical and quantum machine learning (QML) algorithms to classify breast cancer images. Region-based segmentation is used to identify regions of interest and image processing algorithms are performed on segmented breast cancer images to extract meaningful features (e.g., perimeter, area, compactness). An user interface (UI) based software is created to swiftly distinguish benign and malignant instances during image analysis. Although QNNs perform poorly than the classical NNs, there is still a long way to go in developing feature maps and techniques for better performance. In contrast, the classical NNs have gone a long way with well-established algorithms and packages available to us. This research shows that with further development, QML has the potential to enhance cancer diagnosis accuracy and speed, ultimately improving early detection and treatment outcomes. © 2025 IEEE.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

Deep Defense: A Hybrid LSTM-ANN Architecture for Intelligent Intrusion Detection System 2

Deep Defense: A Hybrid LSTM-ANN Architecture for Intelligent...

引用

2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Shadeja, Mohit Singh, Shashwat Prakash, Ayushi Dept. of Computer Science and Engineering Ajay Kumar Garg Engineering College Uttar Pradesh Ghaziabad India

ISBN: (纸本)9798331530389

The rapidly evolving cyber threat landscape requires innovative and adaptive intrusion detection solutions. Traditional signature-based intrusion detection systems, despite their high accuracy, are inherently inflexible and susceptible to evasion tactics, making them ineffective against novel and sophisticated attacks. In contrast, machine learning-powered anomaly-based intrusion detection systems demonstrate improved detection capabilities, but their advantages are often outweighed by substantial drawbacks, including significant computational resource requirements when the dataset and its features for behavior analysis increase. Also, there are high power consumption and latency issues in communication that compromise network performance. This study proposes a hybrid ANN-LSTM approach for performing deep packet inspection and efficiently detecting malicious network packets. By analyzing binary container data, our model reduces time and space complexity, thereby mitigating the computational resources and power consumption challenges but also increasing the efficiency of intrusion detection by 3%, making it more intelligent than various established IDS. © 2025 IEEE.

关键词： Network intrusion

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 << < 2 3 4 5 6 7 8 9 10 11 > >>

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：