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

134 篇 会议
16 篇 期刊文献
8 册 图书

馆藏范围

158 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

93 篇 工学
- 78 篇 计算机科学与技术...
- 69 篇 软件工程
- 18 篇 信息与通信工程
- 18 篇 控制科学与工程
- 15 篇 生物工程
- 12 篇 生物医学工程（可授...
- 10 篇 电子科学与技术（可...
- 9 篇 电气工程
- 8 篇 动力工程及工程热...
- 7 篇 网络空间安全
- 5 篇 仪器科学与技术
- 4 篇 机械工程
- 4 篇 光学工程
- 4 篇 化学工程与技术
- 3 篇 力学（可授工学、理...
- 3 篇 材料科学与工程（可...
55 篇 理学
- 31 篇 数学
- 17 篇 生物学
- 9 篇 物理学
- 7 篇 系统科学
- 7 篇 统计学（可授理学、...
- 4 篇 化学
24 篇 管理学
- 15 篇 管理科学与工程(可...
- 12 篇 图书情报与档案管...
- 4 篇 工商管理
11 篇 医学
- 9 篇 临床医学
- 8 篇 基础医学(可授医学...
- 5 篇 公共卫生与预防医...
- 5 篇 药学(可授医学、理...
4 篇 法学
- 4 篇 社会学
2 篇 农学
1 篇 经济学
1 篇 教育学
1 篇 文学
1 篇 艺术学

主题

9 篇 accuracy
6 篇 deep learning
6 篇 feature extracti...
6 篇 training
4 篇 computational mo...
4 篇 artificial intel...
4 篇 technological in...
3 篇 human computer i...
3 篇 three-dimensiona...
3 篇 transfer learnin...
3 篇 optimization
3 篇 monitoring
3 篇 production
3 篇 analytical model...
3 篇 programming tech...
3 篇 cloud computing
3 篇 measurement
3 篇 data models
3 篇 robustness
3 篇 adaptation model...

机构

11 篇 school of comput...
7 篇 dept. of compute...
7 篇 dept. of compute...
6 篇 department of co...
5 篇 gla university d...
5 篇 department of co...
4 篇 symbiosis centre...
3 篇 center for appli...
3 篇 graphic era hill...
3 篇 dept. of compute...
3 篇 usha martin univ...
3 篇 center for appli...
3 篇 cse dept. graphi...
3 篇 symbiosis centre...
3 篇 dept. of compute...
3 篇 dept. of computi...
3 篇 dept. of computi...
3 篇 dept. of cse cha...
3 篇 dept. of compute...
3 篇 dept. of computi...

作者

8 篇 singh kamred udh...
8 篇 kumar ankit
8 篇 choudhury tanupr...
8 篇 kumar gaurav
6 篇 ankit kumar
6 篇 kamred udham sin...
6 篇 gaurav kumar
6 篇 tanupriya choudh...
5 篇 priyanka kaushik
5 篇 singh teekam
5 篇 ketan kotecha
5 篇 kotecha ketan
4 篇 kaushik priyanka
3 篇 yerramreddy dhan...
3 篇 don s.
3 篇 bhattacharya abh...
3 篇 soumi dutta
3 篇 dhanvanth reddy ...
3 篇 samanta debabrat...
3 篇 abhishek bhattac...

语言

144 篇 英文
13 篇 其他
1 篇 中文

检索条件"机构=Dept. of Scientific Computing and Computer Applications"

共 158 条记录，以下是1-10 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：

Computational Approaches for Drug Discovery: A Review of Artificial Intelligence Techniques for ADME/T Prediction and Virtual Screening 15

Computational Approaches for Drug Discovery: A Review of Art...

引用

15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Menon, Deepak Sharma, Achyut M. Ani, R. Amrita Vishwa Vidyapeetham Amritapuri Amrita School of Computing Dept. of Computer Science and Applications Kollam India

ISBN: (纸本)9798350370249

Drug discovery, traditionally reliant on extensive experimental work and significant financial investments, often faces high failure rates and prolonged timelines. Recent advancements in computational methods and machine learning techniques aim to streamline this process. This study introduces VISMAI, a tool designed for virtual screening with modules for descriptor calculation, fingerprint generation, and drug-likeness prediction. Using SMILES strings and a GCNN-based model, VISMAI accurately assesses drug-likeness. © 2024 IEEE.

关键词： Drug discovery

来源：评论

学校读者我要写书评

暂无评论

Reinforcement Learning-Based Autonomous Landing of AirSim Simulated Quadcopter in Unreal Engine 15

Reinforcement Learning-Based Autonomous Landing of AirSim Si...

引用

15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Paladugula, Pruthvi Don, S. Vemulapalli, Nehith Sai Prabhat, Guggilam Sai Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Dept. of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350370249

This paper presents a novel approach for landing an autonomous quadcopter using reinforcement learning (RL) in the AirSim simulation environment running on Unreal Engine. The complicated dynamics and unpredictability of quadcopter landing procedures make them difficult to execute and frequently reduce the efficacy of conventional control techniques. In order to overcome these difficulties, the suggested RL model interacts with an incredibly lifelike simulation to learn the best landing techniques, which faithfully replicates physical principles. Impressing with notable improvements in landing precision and resilience in many scenarios, this model holds potential to improve unmanned aerial vehicle operations. By utilizing reinforcement learning within a practical simulation setting, this study advances the creation of more effective and flexible autonomous landing systems for unmanned aerial vehicles (UAVs), potentially leading to benefits in practical uses. ©2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

来源：评论

学校读者我要写书评

暂无评论

Advancements in Cricket Shot Detection: Integrating Human Pose Estimation and Deep Learning for Automated Analysis 15

Advancements in Cricket Shot Detection: Integrating Human Po...

引用

15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Datta, K.S. Sreekar Naidu, G. Narasimha Dasari, Rahul Jayakumar, T.V. Dept. Of Computer Science and Engineering Amrita School Of Computing Amrita Vishwa Vidyapeetham Amritapuri India Dept. Of Computer Science and Applications Amrita School Of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350370249

Cricket, a sport loved worldwide, often presents challenges for aspiring players due to limited access to infrastructure and coaching resources. Cricket’s automated learning solutions are still largely unexplored, despite its popularity. By concentrating on the application of cricket shot detection, a crucial component of the game, this research fills this gap. One essential component of computer vision that becomes apparent in deciphering the subtleties of cricket shots is human stance estimate. Pose estimate provides a detailed examination of batting skills and footwork by tracking important areas on the batsman’s body, such as joints and limbs. MoveNet is a significant framework in this domain that has garnered accolades for its real-time performance and lightweight architecture, which enable accurate identification of human poses, which is a crucial requirement in the dynamic world of cricket. This study explores the combination of deep learning techniques and human pose estimate for cricket shot recognition. More precise and reliable cricket shot identification and classification are made possible by the model’s ability to extract complex patterns and features from the input data by utilizing deep learning techniques. Our goal is to automatically identify and classify cricket shots based on players’ minor motions through theoretical investigation, empirical validation, and real-world applications. Through the integration of cutting-edge technologies, such as deep learning, with traditional cricket analysis, our goal is to gain a deeper understanding of the game and discover new avenues for sports analytics, coaching, and broadcasting. ©2024 IEEE.

关键词： Cricket (Sports)

来源：评论

学校读者我要写书评

暂无评论

A Study of Landmark-Augmented and Landmark-Free Approaches: Yoga Pose Recognition 9

A Study of Landmark-Augmented and Landmark-Free Approaches: ...

引用

9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Marasani, Jayasurya Yerramreddy, Dhanvanth Reddy Karthik, Abburi Sai Karthik, Maddala H. S. M. K. Jayakumar, T.V. Nedungadi, Prema Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science And Engineering Amritapuri India Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science And Applications Amritapuri India Amrita Vishwa Vidyapeetham Amrita School of Computing Amritapuri India

ISBN: (纸本)9798350394474

In the realm of yoga pose recognition, this project delves into a comprehensive investigation of two distinct methodologies, namely landmark-augmented and landmark-free approaches, to enhance the accuracy and efficacy of pose recognition systems. The first approach leverages state-of-the-art pose estimation techniques employing MoveNet, MediaPipe, and YOLO V8 to precisely detect pivotal anatomical landmarks that define yoga postures. Subsequently, these detected landmarks are utilized as foundational cues for the classification process. On the other hand, the landmark-free approach directly employs cutting-edge deep learning architectures, specifically Vision Transformer, ResNet50, and VGG16, to classify yoga poses without the explicit utilization of detected landmarks. By juxtaposing these two methodologies, the project endeavors to uncover the comparative advantages, challenges, and limitations associated with each approach in the domain of yoga pose recognition. Beyond performance evaluation, this study aims to provide valuable insights into the most effective and robust techniques for accurately identifying and classifying yoga poses. The findings are poised to contribute to the development of sophisticated yoga pose recognition systems that hold promise in assisting yoga practitioners in achieving proper alignment and posture during their practice, ultimately promoting their overall well-being and practice effectiveness. © 2024 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Gaze Estimation Using VGG16 Architecture Through XGBoost Classifier 5

Gaze Estimation Using VGG16 Architecture Through XGBoost Cla...

引用

5th International Conference on Electrical, Control and Instrumentation Engineering, ICECIE 2023

作者： Yerramreddy, Dhanvanth Reddy Addanki, Veerababu Durgapu, Sathvik Don, S. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Amritapuri India Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Applications Amritapuri India

ISBN: (纸本)9798350325041

Gaze Estimation, a technology that has evolved in the modern era that revolves around analysing the movement of eyes and their patterns which covers a wide array of fields and domains. It transformed human-computer interaction by providing hands-free device control and navigation through visual focus. In this study, an unique approach has been introduced for estimating the gazes of the eyes through VGG16 model by modifying it's architecture that preserves it's base layers but replacing the softmax layer with an XGBoost classifier for better classification. For estimating it's efficiency, a comparative analysis has been conducted with various deep learning architectures. Through the implementation of the architecture, the primary aim is to outperform the benchmark models with notable performance improvements. The outcomes are highly encouraging and the modified architecture provides more precise estimation of gazes than the benchmark models. © 2023 IEEE.

关键词： Human computer interaction

来源：评论

学校读者我要写书评

暂无评论

Analysis of Image Restoration techniques on Lunar Surface Images

Analysis of Image Restoration techniques on Lunar Surface Im...

引用

2023 Innovations in Power and Advanced computing Technologies, i-PACT 2023

作者： Yerramreddy, Dhanvanth Reddy Marasani, Jayasurya Gowtham, Ponnuru Sathwik Venkata Don, S. Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Dept. of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350325188

Lunar surface images captured by Chandrayaan-II, leveraging advanced image restoration techniques. The images obtained from the space craft can subject to various noise influence depending on the data acquisition system. Our goal is to investigate the effect of various types of noise in the image, such as Gaussian, Salt and Pepper, and Speckle noise, necessitating advanced restoration approaches for optimal data extraction. Our primary objective is to employ three restoration techniques such as Wiener, Median and Gaussian filters to restore the lunar images. Through the implementation of these restoration techniques, we aim to mitigate the adverse effect of sensor noise, thermal noise, cosmic ray noise, transmission noise and compression artifacts, all of which impede the precise interpretation of lunar surface features. These restoration methods are then compared in various noise levels and has been evaluated in terms of SNR, PSNR, image fidelity and MSE. The outcomes are encouraging and the suggested technique provides more precise restoration for images of the lunar surface. © 2023 IEEE.

关键词： Chandrayaan-II filter Image restoration lunar data noise suppression

来源：评论

学校读者我要写书评

暂无评论

Virtual Illusions: Unleashing Deepfake Expertise for Enhanced Visual Effects in Film Production 11

Virtual Illusions: Unleashing Deepfake Expertise for Enhance...

引用

11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Mittal, Sonu Joshi, Mukesh Vats, Prashant Upadhayay, Govind Murari Vats, Shailender Kumar Kumar, Shalendra Amity University Dept. of Aiit Uttar Pradesh Noida India School of Computing Graphic Era Hill University Bhimtal Campus Uttarakhand India Manipal University Jaipur Faculty of Engineering Dept. of Cse Rajasthan Jaipur India Manipal University Jaipur Dept. of Computer Applications Rajasthan Jaipur India Dept. of Computer Applications Uttar Pradesh Noida India New Delhi Inst. of Mgmt. Ggsipu Dept. of Computer Applications New Delhi India

ISBN: (纸本)9798350350357

As technology continues to evolve, the realm of visual effects in film production has witnessed a significant transformation. Deepfake technology, once associated with controversial uses, has found a legitimate and innovative application within the filmmaking industry. This paper explores the integration of deepfake expertise into film production processes, focusing on its potential to revolutionize visual effects and storytelling. By harnessing deep learning algorithms, filmmakers can create hyper-realistic digital assets, seamlessly blending fiction with reality. This paper delves into the ethical considerations, creative possibilities, and technical challenges associated with the utilization of deepfake technology in filmmaking. Furthermore, it examines case studies and industry trends to provide insights into the current landscape and prospects of incorporating deep fake expertise for enhanced visual effects in film production. © 2024 IEEE.

关键词： Blending

来源：评论

学校读者我要写书评

暂无评论

Looking into the Black Box: Monitoring computer Architecture Simulations in Real-Time with AkitaRTM 57

Looking into the Black Box: Monitoring Computer Architecture...

引用

57th Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2024

作者： Mosallaei, Ali Isaacs, Katherine E. Sun, Yifan University of Michigan Electrical and Computer Engineering Dept. Ann Arbor United States Scientific Computing and Imaging Institute University of Utah Salt Lake City United States William & Mary Dept. of Computer Science Williamsburg United States

ISBN: (纸本)9798350350579

computer architecture simulators are essential for validating novel chip designs. However, they often provide little transparency during execution. This opaqueness limits the ability of users to identify issues during the simulation, leading to both wasted computational and human time. We address these issues by providing an intuitive user experience during simulation execution. Particularly, we reveal the status of executions and allow users to control the execution of a computer architecture simulator through AkitaRTM, an interactive web-based tool for real-time monitoring of computer architecture simulations. We based its design on the design workflow inefficiencies experienced by computer architects when using simulations. We demonstrate AkitaRTM's utility through two case studies, the second leading to a patch in the simulator. Additionally, we conducted a user study with computer architects, aiming to validate AkitaRTM. We found that, in addition to solving the observed problems, AkitaRTM also provided an educational benefit by making simulators more transparent to users. Based on these findings, we reflect upon the design of AkitaRTM and provide guidance for future human-centered tools in this space. © 2024 IEEE.

关键词： computer monitors

来源：评论

学校读者我要写书评

暂无评论

Empowering computing: AI and CPU Synergy 4

Empowering Computing: AI and CPU Synergy

引用

4th International Conference on Advancement in Electronics and Communication Engineering, AECE 2024

作者： Kaushik, Priyanka Chamarthi, Subbarao Wasim, Javed Khatoon, Naghma Kumar, B Rajesh Tyagi, Nancy Chandigarh University Dept. of Cse Chandigarh India Lingayas Vidyapeeth Dept. of Mechanical Engineering Faridabad India Mangalayatan University Dept. of Computer Engineering and Applications Aligarh India Usha Martin University Dept. of Computing & It Ranchi India Dhanalakshmi Srinivasan University Dept. of Cse Coimbatore India

ISBN: (纸本)9798350364729

The objective of this research is to redefine the future of computing systems, focusing on enhancing performance, efficiency, and adaptability by identifying the cutting-edge innovations driven by AI in CPU design. This study conducts a comprehensive literature review and empirical evaluations to survey front-end optimizations, new architectural concepts, and emerging paradigms that could potentially revolutionize CPU technology. By leveraging advanced artificial intelligence techniques such as machine learning, deep learning, and reinforcement learning, novel CPU architectures and optimization strategies can be explored, unlocking unprecedented computational capabilities. The integration of AI methods into CPU architecture addresses key concerns including scalability, energy efficiency, security, and reliability. Moreover, real- world case studies across various domains such as autonomous systems, healthcare informatics, and edge computing demonstrate the transformative potential of AI- driven CPUs. This paper provides insights into the roadmap for the next wave of innovation in AI-infused computing, outlining potential avenues for further research and collaboration. © 2024 IEEE.

关键词： Deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

A Design of Software Architecture for Validate and Verification of Linear-Optical Quantum computing Platform 4

A Design of Software Architecture for Validate and Verificat...

引用

4th IEEE International Conference on Quantum computing and Engineering, QCE 2023

作者： Lee, Duwon Kwon, Yong Kim, Kanghyeon Lee, Jaehyeok Choi, Byung-Soo Pukyong National University Dept. of Physics Korea Republic of Pukyong National University Dept. of Computer Engineering Korea Republic of Pukyong National University Dept. of Scientific Computing Korea Republic of

ISBN: (纸本)9798350343236

Compared to other semiconductor quantum computing platform, it is relatively untouched about the verification of linear-optical quantum computing (LOQC) platform. To overcome this problem, we propose a universal and versatile software platform which can support any variation of LOQC layout structure. It also supports the user-friendly interface for smooth experiment. © 2023 IEEE.

关键词： Verification

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16页 << < 1 2 3 4 5 6 7 8 9 10 > >>

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：