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

63,298 篇 会议
10,938 篇 期刊文献
577 册 图书

馆藏范围

74,813 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

48,330 篇 工学
- 37,175 篇 计算机科学与技术...
- 30,994 篇 软件工程
- 11,455 篇 信息与通信工程
- 7,027 篇 控制科学与工程
- 4,679 篇 电气工程
- 4,622 篇 生物工程
- 4,449 篇 电子科学与技术（可...
- 3,620 篇 生物医学工程（可授...
- 2,778 篇 光学工程
- 2,609 篇 机械工程
- 2,081 篇 仪器科学与技术
- 1,945 篇 动力工程及工程热...
- 1,844 篇 化学工程与技术
- 1,554 篇 安全科学与工程
- 1,366 篇 建筑学
- 1,290 篇 交通运输工程
24,955 篇 理学
- 15,310 篇 数学
- 5,650 篇 物理学
- 5,159 篇 生物学
- 4,593 篇 统计学（可授理学、...
- 2,814 篇 系统科学
- 1,977 篇 化学
12,857 篇 管理学
- 7,015 篇 管理科学与工程(可...
- 6,504 篇 图书情报与档案管...
- 4,017 篇 工商管理
2,921 篇 医学
- 2,549 篇 临床医学
- 2,392 篇 基础医学(可授医学...
- 1,536 篇 药学(可授医学、理...
2,321 篇 法学
- 1,950 篇 社会学
1,328 篇 经济学
- 1,327 篇 应用经济学
1,204 篇 教育学
969 篇 农学
315 篇 文学
164 篇 军事学
94 篇 艺术学
41 篇 哲学
17 篇 历史学

主题

1,866 篇 computer science
1,839 篇 accuracy
1,660 篇 computational mo...
1,601 篇 feature extracti...
1,299 篇 training
1,183 篇 deep learning
1,065 篇 machine learning
898 篇 predictive model...
895 篇 data mining
895 篇 real-time system...
832 篇 support vector m...
818 篇 semantics
774 篇 artificial intel...
759 篇 data models
714 篇 computer archite...
632 篇 convolutional ne...
625 篇 visualization
624 篇 internet of thin...
617 篇 optimization
571 篇 neural networks

机构

90 篇 institute of inf...
83 篇 north-west unive...
81 篇 dept. of compute...
81 篇 infn sezione di ...
78 篇 dept. of compute...
76 篇 infn sezione di ...
72 篇 institut univers...
66 篇 università di ca...
64 篇 national centre ...
60 篇 dept. of compute...
57 篇 dept. of informa...
56 篇 dept. of compute...
56 篇 dept. of compute...
52 篇 school of automa...
44 篇 dept. of compute...
42 篇 infn lnf via enr...
42 篇 in2p3 lpc campus...
40 篇 dept. of compute...
40 篇 leiden universit...
37 篇 dept. of informa...

作者

94 篇 ardid m.
93 篇 ferrara g.
87 篇 calvo d.
81 篇 chen a.
78 篇 gal t.
78 篇 bozza c.
65 篇 ahmad jalal
63 篇 kulikovskiy v.
54 篇 kalekin o.
42 篇 benoit d.m.
42 篇 gagliardini s.
42 篇 eddymaoui a.
41 篇 labalme m.
38 篇 huang f.
37 篇 androulakis g.
36 篇 díaz a.f.
35 篇 de palma m.
29 篇 guru prasad m.s.
28 篇 sharma neha
27 篇 zhang lei

语言

68,252 篇 英文
5,861 篇 其他
521 篇 中文
106 篇 日文
61 篇 朝鲜文
11 篇 德文
7 篇 法文
6 篇 西班牙文
5 篇 斯洛文尼亚文
3 篇 葡萄牙文

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

共 74813 条记录，以下是201-210 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：

SobelMNet: A Lightweight CNN based Approach for Median Filtering Detection in compressed images 27

SobelMNet: A Lightweight CNN based Approach for Median Filte...

引用

27th International Conference on Advanced Communications Technology, ICACT 2025

作者： Kaushal, Anukriti Kumar, Rajeev Kumar, Sanjay Dept. of Computer Science & Engineering Delhi Technological University Delhi India

ISBN: (纸本)9791188428137

Digital image forgery is the process of manipulating an image to deceive or mislead observers with real or manipulated content. Median filtering is widely used to smooth images and obscure traces of tampering, making its detection critical for image forensics. However, identifying median filtering becomes more complex when additional operations, such as compression, resampling, or noise addition, are applied. To address this issue, we propose a lightweight convolutional neural network (CNN) model named SobelMNet, specifically designed for detecting median filtering in compressed images. The proposed model utilises a Sobel filter-based preprocessing step to enhance the residual differences between the original and manipulated images. These residuals, which capture subtle features indicative of median filtering, are analysed by CNN for classification. Further, the proposed model is evaluated on grayscale low-resolution images generated from the Dresden dataset for both binary and multiclass classification tasks. The model achieved a remarkable detection accuracy of 99.43% in median filter detection and outperformed state-of-the-art methods in various scenarios, including combinations of median filtering with Gaussian blur, resampling, and additive white Gaussian noise (AWGN) with an average accuracy of 98.32%. Finally, its lightweight architecture ensures computational efficiency, making it practical for real-world forensic applications. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.

关键词： Median filters

来源：评论

学校读者我要写书评

暂无评论

Plant Disease Detection for Guava and Mango using YOLO and Faster R-CNN 2

Plant Disease Detection for Guava and Mango using YOLO and F...

引用

2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Shetty, Kruthi U. Javed Kutty, Rida Donthi, Khushi Patil, Anuj Subramanyam, Natarajan Pes University Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350360523

Traditional methods of plant disease detection are cumbersome and prone to errors that cannot be avoided. Plant disease detection can help prevent crop losses and ensure food security. The system employs state-of-the-art deep learning techniques to automatically detect and classify plant disease in agricultural fields. The proposed system addresses the challenges associated with identifying plant diseases, which often have similar visual characteristics, by using a combination of deep learning and computer vision methods, specifically employing the YOLO (You Only Look Once) and Faster Region-Convolutional Neural Network with a ResNet-152 backbone. The system is trained on a custom dataset where the images were captured in an uncontrolled environment, specifically focusing on two plant leaves, namely Guava and Mango, to achieve high accuracy in identifying diseases in real-world field conditions. The proposed system has the potential to revolutionize plant disease detection and ensure food security. © 2024 IEEE.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

Enhancing Movie Recommendation Efficiency Using Ensemble Learning Techniques

Enhancing Movie Recommendation Efficiency Using Ensemble Lea...

引用

2024 International Conference on Emerging Innovations and Advanced Computing, INNOCOMP 2024

作者： Nitasha Pandey, Aniket Kumar Yadav, Ankit Mishra, Subrat Medhanshi Chandigarh University Dept. of Computer Science and Engineering Mohali India

ISBN: (纸本)9798350376470

This research paper explores the 'Movie Dataset' to create a better movie recommendation. The data includes many features such as genre, ratings, players, and user preferences. Our main goal is to find the best combination model combining SVD (Singular Value Decomposition), SVD++ (SVD with Implicit Feedback) and KNN Baseline (K Nearest Neighbors with Baseline) recommendation algorithms to improve prediction accuracy and recommendations. The rapid expansion of digital media platforms has created a lot of information in the entertainment industry, making movie recommendations very important for the development of consumers. Through our research, we focus on the development of personalized movie recommendations and user satisfaction in the field of digital entertainment. © 2024 IEEE.

关键词： Collaborative filtering

来源：评论

学校读者我要写书评

暂无评论

Sentient Sound waves: Elevating Emotional Communication with AI-Generated Speech Technology 5

Sentient Sound waves: Elevating Emotional Communication with...

引用

5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Roshan, Rahul G. Roshan, Rohit Sohan, M.H. Raj, S.M. Sutharsan Prasad, V.R. Badri Dept. of Computer Science and Engineering PES University Bangalore India

ISBN: (纸本)9798350361155

This research-based project is about a new way to put feelings into computer-generated speech. It has two stages: text emotion detection and emotional speech synthesis. In the former part, labeled text data is used to build models to spot the emotions. This model learns to change pitch, volume, and rhythm based on the given emotion. During the latter phase, the text is changed into neutral speech using text-to-speech (TTS) methods. The model is used for practice to find the emotion in the text, tagging the text with that emotion. Also, a deep-learning model known as a tacotron is employed to make an emotional-sounding speech. The model perfectly uses machine learning strategies to blend feelings into speech. This opens up possibilities for turning plain text into speech with feelings of varied emotions. The proposed system makes a novice and important contribution to natural language processing for emotion identification and deep learning for emotional speech synthesis. © 2024 IEEE.

关键词： Speech synthesis

来源：评论

学校读者我要写书评

暂无评论

A Rubric-Centric Approach for Automated Test Correction Utilizing RAG and Fine Tuning 4

A Rubric-Centric Approach for Automated Test Correction Util...

引用

4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Harshavardhan, G. Singh, Kulvinder Chandigarh University Dept. of Computer Science & Engineering Punjab India

ISBN: (纸本)9798350387490

Assessments are a popular method to quantify a person's knowledge, skill, or cognitive ability. However, to grade such assessments, particularly subjective questions requires a significant amount of time and effort for the examiners. There is also a degree of variance due to inconsistency from different examiners. Thus institutions often resort to taking objective-type questions, which may not completely capture a person's understanding. Unfortunately, many other proposed solutions, tend to be difficult to implement and scale. To address these issues, this paper proposes an innovative method to automate the grading of subjective assessments by leveraging recent advances in artificial intelligence. This approach aims to replicate the grading style of human experts, offering significant benefits for educational institutions, faculty, and students by enhancing the efficiency and quality of grading. Widespread adoption can help increase the quality of education overall, resulting in more qualified graduates. This method involves using a rubric to fine- tune pre-trained language models, providing them with clear guidelines to ensure consistent and accurate grading. Inspired by how rubrics guide human examiners to minimize variation, this approach will use rubrics to instruct the model on the most relevant grading criteria. Utilizing such clearly defined and precise grading criteria, users can obtain reliable results, comparable to human experts. © 2024 IEEE.

关键词： Natural language processing systems

来源：评论

学校读者我要写书评

暂无评论

A Study on Deep Fake Face Detection Techniques 3

A Study on Deep Fake Face Detection Techniques

引用

3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Arya, Mudit Priyanshu Upwan Akash Goyal, Umang Chawla, Simran Chandigarh University Dept. of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350375190

Advanced technology and the widespread use of deep fake technology has rendered the digital landscape vulnerable to deceptive manipulations, particularly through the creation of synthetic face images. This comprehensive review precisely traces the historical trajectory of deep fake faces, encompassing their evolution and its impact on various domains. It also investigates into the growing concerns regarding misinformation and privacy breaches. The literature review analyzes important works, pivotal milestones, and notable case studies, providing a complete understanding of the dynamic landscape of deep fake faces. This study analyzes existing detection techniques, ranging from neural networks to machine learning approaches, offering insights into the complexities of current methodologies. It sheds light on both the strengths and limitations of these techniques, emphasizing the need for robust solutions to counter adversarial attacks and address data scarcity. Building on this analysis, this study explores breakthroughs within the field of deep fake detection, highlighting instances where identification has been successful. It also underscores the ethical considerations that come with the evolution of these technologies. In proposing innovative solutions and discussing potential optimizations for existing techniques, the review explores interdisciplinary approaches and emerging technologies like blockchain and explainable AI. The findings provide a summary of key insights collected from the literature, identify research gaps, and project the future of deep fake face detection. This study concludes by emphasizing the ongoing societal impact of these developments in the digital era. © 2024 IEEE.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

An IoT Solution for Enhanced Tray Management Systems in Food Service and Industrial Settings

An IoT Solution for Enhanced Tray Management Systems in Food...

引用

2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Chaudhary, Vipin Kumar Singh, Suchita Singh, Nagendra Pratap Afreen, Sana Yadav, Manmohan Singh Gupta, Durgesh Kumar Lovely Professional University Dept. of Computer Science and Engineering Punjab Phagwara India Buddha Institute of Technology Dept. of Computer Science and Engineering Uttar Pradesh Gorakhpur India Dr. B R Ambedkar National Institute of Technology Dept. of Computer Science and Engineering Punjab Janlandhar India Telangana Hyderabad India Sharda University Dept. of Computer Science and Engineering Uttar Pradesh Greater Noida India Institute of Engineering and Technology Dept. of Computer Science and Engineering Uttar Pradesh Sitapur India

ISBN: (纸本)9798350385199

This study introduces TraySmart, a cutting-edge Internet of Things (IoT) tool meant to completely transform the effectiveness and administration of tray systems in industrial, food court, and cafeteria environments. TraySmart employs cutting-edge technologies like weight sensing, RFID tag reading, temperature monitoring, NFC for user interaction, and Wi-Fi Bluetooth for data transmission to tackle the widespread issues associated with tray management, such as inefficient operations, concerns about food safety, and less-than-ideal user experiences. The weight-sensing feature of the device makes it possible to identify objects on the tray and monitor the contents of the tray in real time. Reading RFID tags makes identifying and tracking items easier, improving inventory control and lowering loss. Food safety and quality assurance depend on food items maintaining their ideal temperatures, which is ensured via temperature monitoring. Furthermore, NFC technology enhances the eating experience by enabling users to engage with the smartphone for payments or feedback. The communication options on the device guarantee smooth data transfer to a central system, facilitating thorough data analysis and system optimization. This essay covers the creativity, design, and development of the TraySmart prototype, the difficulties faced, and the strategies used in the process. The report assesses TraySmart's effect on operational efficiency, food safety standards, and customer happiness by carefully examining the outcomes of pilot testing. With its scalable solution to improve tray management systems, TraySmart marks a significant leap in the application of IoT technology in the food service and industrial sectors. The results highlight how IoT gadgets like TraySmart have the power to revolutionize operational procedures and service delivery, establishing a new benchmark for innovation in the industry. © 2024 IEEE.

关键词： Customer satisfaction

来源：评论

学校读者我要写书评

暂无评论

Forensic science: AI-Powered Image and Audio Analysis 5

Forensic Science: AI-Powered Image and Audio Analysis

引用

5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Renukadevi, P. John, Sincy Shivani, N. Jain University Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331504403

Forensic science demands the precise application of advanced scientific principles and procedures to investigate crimes and ensure justice. This process involves the analysis of vast and complex data. The research explores the potential of modern science and technologies, particularly Artificial Intelligence (AI), to develop innovative techniques across various branches of forensic science. It provides a comprehensive examination of AI applications in forensic fields, focusing on image and audio analysis to identify instruments used in crimes and determine causes of death. By integrating AI, the research aims to enhance the accuracy and efficiency of forensic investigations while maintaining ethical standards. The paper also discusses the difficulties with AI in forensic science, highlighting the need for open and moral AI systems. It highlights that forensic errors leading to wrongful convictions often result from factors such as incompetence, spam, weak scientific foundations, or organizational deficiencies, rather than blunders made by forensic scientists. These issues highlight the importance of addressing the root causes of forensic errors to improve the reliability of forensic practices. The study also delves into the specific challenges AI encounters, such as data quality, interpretability of AI models, and the unification of AI into current forensic procedures. By tackling these challenges, the research aims to ensure that AI applications in forensic science are effective and trustworthy. Overall, this work highlights the critical role of AI in advancing forensic science and the importance of addressing the underlying issues that contribute to forensic errors, ultimately striving for a more reliable and just forensic process © 2024 IEEE.

关键词： Digital forensics

来源：评论

学校读者我要写书评

暂无评论

KoDialogBench: Evaluating Conversational Understanding of Language Models with Korean Dialogue Benchmark 30

KoDialogBench: Evaluating Conversational Understanding of La...

引用

Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Jang, Seongbo Lee, Seonghyeon Yu, Hwanjo Dept. of Computer Science and Engineering POSTECH Pohang Korea Republic of

ISBN: (纸本)9782493814104

As language models are often deployed as chatbot assistants, it becomes a virtue for models to engage in conversations in a user's first language. While these models are trained on a wide range of languages, a comprehensive evaluation of their proficiency in low-resource languages such as Korean has been lacking. In this work, we introduce KoDialogBench, a benchmark designed to assess language models' conversational capabilities in Korean. To this end, we collect native Korean dialogues on daily topics from public sources, or translate dialogues from other languages. We then structure these conversations into diverse test datasets, spanning from dialogue comprehension to response selection tasks. Leveraging the proposed benchmark, we conduct extensive evaluations and analyses of various language models to measure a foundational understanding of Korean dialogues. Experimental results indicate that there exists significant room for improvement in models' conversation skills. Furthermore, our in-dept. comparisons across different language models highlight the effectiveness of recent training techniques in enhancing conversational proficiency. We anticipate that KoDialogBench will promote the progress towards conversation-aware Korean language models. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Computational linguistics

来源：评论

学校读者我要写书评

暂无评论

Advances in Remote Sensing Technologies for Ecological Monitoring and Applications in Carbon Stock Estimation 3

Advances in Remote Sensing Technologies for Ecological Monit...

引用

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

作者： Sadhasivam, Shankaripriya Gayathri, G. Poongothai, P. Pavithra, S. Dept. of Computer Science and Engineering Chennai Institute of technology Chennai India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

This review explores recent advancements that are made in remote sensing, enabling detailed analysis of our ecosystem through high resolution and multispectral imaging. The recent advancements in remote sensing includes developments useful for generation of synthetic images using GAN such as StyleGAN3 and for estimation of carbon stocks in forest using OBIA. Ecotones like mangroves and saltmarsh are crucial for carbon sequestration but mapping them accurately was challenging due to spectral similarity and seasonal variations. Focusing on coastal protection by this review examines the mapping methodologies such as random forest classification, OBIA and FID for habitat distinction, canopy area assessment and analysis. Through these technologies data accuracy can be enhanced for better monitoring. This review concludes with a discussion of AI-powered image synthesis and classification. © 2025 IEEE.

关键词： Carbon sequestration

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 << < 17 18 19 20 21 22 23 24 25 26 > >>

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：