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

22,544 篇 会议
8,486 篇 期刊文献
271 册 图书

馆藏范围

31,301 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

17,673 篇 工学
- 10,086 篇 计算机科学与技术...
- 8,019 篇 软件工程
- 5,401 篇 信息与通信工程
- 4,122 篇 电子科学与技术（可...
- 3,728 篇 电气工程
- 3,067 篇 控制科学与工程
- 1,973 篇 光学工程
- 1,841 篇 生物工程
- 1,703 篇 生物医学工程（可授...
- 1,442 篇 动力工程及工程热...
- 1,394 篇 机械工程
- 1,383 篇 仪器科学与技术
- 1,264 篇 化学工程与技术
- 1,180 篇 材料科学与工程（可...
- 919 篇 网络空间安全
- 825 篇 安全科学与工程
- 715 篇 交通运输工程
10,357 篇 理学
- 4,704 篇 物理学
- 4,161 篇 数学
- 2,077 篇 生物学
- 1,389 篇 统计学（可授理学、...
- 1,360 篇 化学
- 880 篇 系统科学
3,840 篇 管理学
- 2,627 篇 管理科学与工程(可...
- 1,362 篇 图书情报与档案管...
- 1,123 篇 工商管理
1,749 篇 医学
- 1,401 篇 临床医学
- 1,265 篇 基础医学(可授医学...
- 815 篇 公共卫生与预防医...
- 784 篇 药学(可授医学、理...
708 篇 法学
500 篇 经济学
408 篇 农学
178 篇 教育学
148 篇 文学
75 篇 军事学
30 篇 艺术学
2 篇 历史学

主题

1,092 篇 accuracy
825 篇 deep learning
736 篇 feature extracti...
727 篇 internet of thin...
677 篇 real-time system...
502 篇 computational mo...
500 篇 machine learning
474 篇 support vector m...
437 篇 training
431 篇 convolutional ne...
389 篇 optimization
387 篇 predictive model...
340 篇 reliability
301 篇 computer science
293 篇 wireless sensor ...
273 篇 wireless communi...
269 篇 artificial intel...
249 篇 scalability
238 篇 monitoring
237 篇 security

机构

433 篇 department of ph...
366 篇 cavendish labora...
269 篇 infn e laborator...
264 篇 department for p...
260 篇 oliver lodge lab...
241 篇 physikalisches i...
239 篇 west university ...
223 篇 department of mo...
206 篇 faculty of scien...
206 篇 department of ph...
203 篇 department of ph...
200 篇 irfu cea univers...
199 篇 school of physic...
190 篇 yerevan physics ...
190 篇 infn sezione di ...
184 篇 infn sezione di ...
182 篇 iccub universita...
180 篇 department of ph...
180 篇 infn sezione di ...
176 篇 massachusetts in...

作者

185 篇 m. lu
162 篇 c. alexa
157 篇 j. m. izen
156 篇 c. amelung
154 篇 g. bella
154 篇 d. calvet
152 篇 j. strandberg
143 篇 n. orlando
137 篇 y. tayalati
135 篇 g. spigo
133 篇 v. chiarella
131 篇 f. siegert
130 篇 s. veneziano
130 篇 b. konya
125 篇 a. dimitrievska
125 篇 f. deliot
123 篇 r. ströhmer
121 篇 m. rijssenbeek
121 篇 s. jin
120 篇 a. grummer

语言

26,427 篇 英文
4,463 篇 其他
373 篇 中文
47 篇 日文
10 篇 法文
8 篇 德文
7 篇 朝鲜文
5 篇 阿拉伯文
3 篇 斯洛文尼亚文
1 篇 荷兰文
1 篇 土耳其文

检索条件"机构=Electronics and Computer Engineering Technology"

共 31301 条记录，以下是4711-4720 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：

Naive Bayes-based Autonomous Illumination System for Urban Green Spaces with Cloud Assistance 4

Naive Bayes-based Autonomous Illumination System for Urban G...

引用

4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Tamilselvi, M. Neelima, A. Amarnath, J. Jegan Lalitha, K. Ganesh, E.N. Thamizhamuthu, R. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Department of Computer Science and Engineering Andhra Pradesh Bhimavaram India Sri Sairam Engineering College Department of Computer Science And Engineering Tamil Nadu Chennai India Panimalar Engineering College Department of Information Technology Tamil Nadu Chennai India R&d Department of Electronics and Communication Engineering Avadi Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Campus-Chengalpattu Tamil Nadu Chennai India

ISBN: (纸本)9798331540364

Improving urban cities' quality of life is one of the primary functions of urban green spaces. Efficient lighting solutions in these areas enhance safety and ambiance. This research introduces a new method for autonomously controlling the lighting in urban parks with the help of a Naive Bayes classifier and cloud computing. The system uses information gathered from several sensors to adapt the brightness of the lights in response to changes in the surrounding environment and the presence or absence of people. The system can generate accurate choices in real-time using machine learning methods, including the Naive Bayes algorithm. With the help of the cloud, remote monitoring and administration are made easier, which improves efficiency and scalability. The experimental findings prove that the proposed approach is reliable and efficient in enhancing urban green areas' energy efficiency and user enjoyment. Intelligent algorithms combined with cloud computing provide a viable option for smart city lighting control, opening the door to more responsive and environmentally friendly cityscapes. © 2024 IEEE.

关键词： Ambiance Energy efficiency Environmental conditions Lighting management Real-time control Remote monitoring Safety

来源：评论

学校读者我要写书评

暂无评论

Sustainable Energy Evolution Using IoT-Enhanced Carbon Capture and Storage for Oil Recovery 6

Sustainable Energy Evolution Using IoT-Enhanced Carbon Captu...

引用

6th International Conference on Energy, Power and Environment, ICEPE 2024

作者： Valli, K. Vijaya Naga Swathi, G. Lumina Julie, R. Saravanan, S. Sujatha, S. Malarvizhi, E. Srkr Engineering College Department of Computer Science and Engineering Andhra Pradesh Bhimavaram India Cmr Technical Campus Department of Computer Science and Engineering Telangana Hyderabad India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Management Studies Tamil Nadu Chennai India Muthayammal Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Namakkal India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India St. Joseph's College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350379297

The global energy environment is shifting toward sustainability and carbon neutrality. Internet of Things (IoT) technology with Carbon Capture and Storage (CCS) in Enhanced Oil Recovery (EOR) may reduce carbon emissions and optimize hydrocarbon recovery. This paper examines IoT-enabled CCS for EOR and carbon-neutral oil and gas operations. Real-time data capture and analysis using IoT technologies optimize CCS operations. Installing IoT sensors and devices across the EOR and CCS infrastructure lets operators monitor CO2 injection rates, reservoir pressure, temperature, and fluid composition. The CCS process is precisely controlled using data, improving carbon capture efficiency and lowering operating costs. Remote monitoring and management of carbon storage sites using IoT in CCS for EOR improves environmental sustainability. The collected CO2 is securely and efficiently contained, reducing leakage and environmental effects. Preventing CCS infrastructure problems with IoT-driven predictive maintenance ensures long-term operational dependability. Securely storing collected CO2 offsets oil and gas emissions and contributes to carbon neutrality. EOR process optimization with IoT technologies may boost hydrocarbon recovery, making the switch to greener energy sources easier. IoT-enabled CCS for EOR has the potential to produce carbon-neutral oil and gas operations. The system improves CCS efficiency and sustainability using real-time data, remote monitoring, and predictive maintenance. Integration of IoT with CCS reduces carbon emissions and promotes the industry's worldwide sustainability objectives. © 2024 IEEE.

关键词： Carbon capture and utilization

来源：评论

学校读者我要写书评

暂无评论

AI-Driven Circuit Optimization for Energy-Efficient electronics Design 5

AI-Driven Circuit Optimization for Energy-Efficient Electron...

引用

5th IEEE International Conference on Recent Trends in computer Science and technology, ICRTCST 2024

作者： Horsley Solomon, P. Pattanaik, Balachandra Pattanaik, Ojasvi Pavithra, G. Elamvazhudhi, D. Shaik, Bazani SRM Arts and Science College Department of Electronics Science Tamilnadu Kattankulathur India Saveetha Institute of Medical and Technical Sciences Adjunct Faculty Saveetha School of Engineering Tamilnadu Chennai India Vignan Institute of Management and Technology for Women Department of Computer Science and Engineering Hyderabad Ghatkesar India Dayananda Sagar College of Engineering Department of Electronics & Communication Engineering Karnataka Bangalore India Dharmamurthi Rao Bahadur Calavala Cunnan Chetty Hindu College Department of Electronics & Communication Science Tamilnadu Chennai India Ramachandra College Of Engineering Department of Mechanical Engineering Andhra Pradesh Eluru India

ISBN: (纸本)9798350351378

The process of enhancing electronic circuits in order to improve energy efficiency is currently being made more efficient with the application of artificial intelligence (AI). In this day and age, where energy consumption is a big problem, the exploitation of techniques driven by artificial intelligence has shown to be an indispensable tool in the construction of electronic devices that reduce power consumption without sacrificing performance. This is achieved through the deployment of ways that are driven by artificial intelligence. Within the scope of this, the application of machine learning techniques is being researched for the purpose of analyzing and modifying circuit designs. There are several other variables that are taken into consideration, such as voltage, current, and the specifications of the components. The purpose of this research is to make a significant contribution to the development of electronic systems that are efficient in terms of energy consumption by utilizing circuit optimization that is driven by artificial intelligence capabilities. Because of this, the research will be able to satisfy the growing need for technology that is favourable to the environment. These discoveries not only offer valuable insights into the synergy that exists between artificial intelligence and the design of electronics, but they also pave the way for future improvements in technology that are aware of the amount of energy that is being consumed. © 2024 IEEE.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Automated Metal Waste Segregation Using IoT-Connected Robotics Using Convolutional Neural Networks for Efficient Sorting 1

Automated Metal Waste Segregation Using IoT-Connected Roboti...

引用

1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Vimaladevi, S. Sekar, Satheeshkumar Pradeepa, H. Mubarakali, Azath Sendhilkumar, N.C. Murugan, S. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology School of Commerce Chennai India Mphasis Corporation ChandlerAZ United States Thiagarajar College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Madurai India King Khalid University College of Computer Science Saudi Arabia Sri Indu College of Engineering and Technology Department of Electronics and Communication Engineering Hyderabad India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376517

Sustainable environmental practices depend on effective waste management. Combining Convolutional Neural Networks (CNNs) with Internet of Things (IoT)-connected robots, a new method for automatic metal trash sorting is presented in this research. By automatically detecting and separating metallic items from mixed waste streams, our technology aims to improve the efficiency of trash sorting operations. Robotic arms with sensors to collect data in real-time and CNNs models trained to identify different metal items make up the suggested system. The sorting processes are carried out with perfect coordination and adaptive decision-making due to the IoT integration, which allows the robots system to connect with a centralized control unit. By showcasing the function of CNNs in facilitating reliable metal classification it provides a comprehensive design of the integrated system. The results of the experiments show that our method can efficiently and precisely detect and sort metal waste. IoT connection also allows for remote monitoring and control, which lets user optimize sorting tactics in real-time and make modifications as needed. Our suggested approach provides an encouraging solution to the problem of automating the segregation of metal trash, which may lead to better and more long-term waste management in both industrial and municipal sectors. © 2024 IEEE.

关键词： Robotic arms

来源：评论

学校读者我要写书评

暂无评论

SYNERGIZING AI AND BLOCKCHAIN FOR EFFICIENT, TRANSPARENT PATIENT DATA MANAGEMENT AND SECURE ONLINE SHARING

SYNERGIZING AI AND BLOCKCHAIN FOR EFFICIENT, TRANSPARENT PAT...

引用

International Conference on Green Energy, Computing and Intelligent technology 2024, GEn-CITy 2024

作者： Bathula, Archana Gupta, Suneet K. Merugu, Suresh Sanagala, Skandha S. Department of CSE Bennett University UP Greater Noida India Telangana Secunderabad India School of Computer Science UPES Uttarakhand Dehradun India Department of Electronics & Computer Science CIRG University of Southampton Southampton Malaysia Department of CSE CMR College of Engineering & Technology Telangana Hyderabad India

ISBN: (纸本)9781837242689

This paper aims to develop a decentralized hospital records and image sharing platform on blockchain, integrating an Inter Planetary File System (IPFS) and AI-driven Feedback. Addressing the critical need for secure and efficient sharing of patient data and medical images in healthcare, the platform utilizes IPFS and blockchain technology to ensure the utmost security and tamper-proof storage. The user-friendly interface, built with *** and ***, enables seamless access and management of medical records. Smart contracts written in Solidity enhance security and transparency, providing an effective means for authorized sharing of medical records. The platform includes an AI feedback section for securely submitting feedback via smart contracts, with MetaMask ensuring secure interactions and offering valuable insights into healthcare management. The solution aims to revolutionize healthcare data sharing, prioritizing privacy, and improved patient outcomes. This comprehensive solution aims to revolutionize healthcare data sharing, ensuring privacy, security, and fostering improved patient outcomes. Leveraging K10 cross-validation, we evaluated the performance of our AI models, including Random Forest (RF), which demonstrated the highest accuracy at 92.5% ± 3.25% and an AUC of 0.925, with a critical value of p © The Institution of engineering & technology 2024.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

Cloud-Based Foot Pressure Analysis for Diabetic Care Using ANN 5

Cloud-Based Foot Pressure Analysis for Diabetic Care Using A...

引用

5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Pradeepa, H. Amarnath, Raveendra N. Sivakumar, V.G. Thamizhamuthu, R. Manikandan, G. Bhuvaneswari, G. Thiagarajar College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Madurai India Mphasis Campbell Dr MelissaTX United States Vidya Jyothi Institute of Technology Department of Electronics and Communication Engineering Telangana Hyderabad India Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Campus-Chengalpattu Tamil Nadu Chennai India Rmk Engineering College Department of Artificial Intelligence and Data Science RSM Nagar Tamil Nadu Chennai India Saveetha Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350389609

It introduces a new method for caring for diabetic feet combines smart pressure monitoring for the feet with wearable gear that is cloud-based and uses artificial neural networks (ANNs). It is essential to recognize diabetic foot ulcers early and monitor them continuously to avoid complications. Wearable pressure sensors record data on the user's foot pressure in real time make up the proposed system. ANNs trained to detect patterns recommend a possible risk of ulceration by analyzing this data once it is transferred to a cloud-based platform. By using cloud-based architecture, healthcare practitioners can remotely monitor patients and intervene as necessary. Clinical trials show the technology improves diabetic foot care by detecting irregularities in pressure distribution and lowering the risk of ulcers and their sequel. Improved patient outcomes and decreased healthcare expenses are achieved via the preventive treatment of diabetic feet made possible by this system, which makes use of breakthroughs in wearable technology and artificial intelligence (AI). © 2024 IEEE.

关键词： Remote patient monitoring

来源：评论

学校读者我要写书评

暂无评论

Leveraging Machine Learning Methods for Multiple Disease Prediction using Python ML Libraries and Flask API 1

Leveraging Machine Learning Methods for Multiple Disease Pre...

引用

1st International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2022

作者： Narayanan, Srikanth Balamurugan, N.M. Maithili, K. Palas, P. Bini Cognizant Chennai India Sri Venkateswara College of Engineering Department of Computer Science and Engineering Chennai India Kg Reddy College of Engineering and Technology Department of Computer Science and Engineering Telangana India Eswari Engineering College Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9781665497107

Disease prediction has become one of the most sophisticated problems in the Machine Learning field in computer Science. Almost all of the existing machines learning models are fixated on single disease prediction. For Example:-Machine learning model for predicting Malaria, ML model for predicting Brain Cancer, ML Model for predicting fever et cetera. This research paper proposes a system using which multiple diseases can be predicted. At present, the proposed system can predict Malaria, Alzheimer's Disease, Tuberculosis, Pancreatic Cancer The proposed system used CNN, Random Forest and Logistic Regression for analysis and prediction of multiple diseases. The system is implemented using Flask API which is a framework for creating web applications in Python and using ML Libraries in Python such as TensorFlow, Scikit Learn, Pandas et cetera. The main purpose of this Research Paper is to provide a system which Medical Professionals can use to predict diseases present in the patient in a very quick manner. © 2022 IEEE.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Vehicle Detection in UAV Images Using Data Augmentation and Transfer Learning with Modified YOLOv9

Vehicle Detection in UAV Images Using Data Augmentation and ...

引用

Remote Sensing Symposium (InGARSS), 2020 IEEE India Geoscience and

作者： Vyomika Singh Vedant Kashyap Laxman Singh Khangarot Dharmendra Singh Department of Electronics & Communication Engineering Indian Institute of Technology Roorkee Roorkee Computer Science and engineering Graphic Era deemed to be University Dehradun Department of Computer Science and Engineering Indian Institute of Technology Roorkee India

ISBN: (数字)9798350390346

ISBN: (纸本)9798350390353

Vehicle detection in UAV images is constrained by the differences in environmental and technical conditions such as target type and size, degree of occlusion, acquisition altitude, illumination, and weather. High levels of false alarms and missed detections are the most prominent challenges faced by traditional detection algorithms, particularly in the case of occluded or tiny targets acquired by UAV. This research sustains such problems by employing data augmentation and transfer learning techniques with an adjustable YOLOv9 model on UAV image dataset. We apply focused data augmentation techniques to enhance model performance over varied use cases, and transfer learning turns out to be effective in adapting the model to work for UAV-based occluded vehicle detection. The targeted improvements in the modified architecture of YOLOv9 enables effective detection of vehicles, including occluded ones with different sizes and orientations, thus, minimizing false alarms. The effectiveness of the proposed approach was experimentally validated, outperforming the existing ones and providing an architecturally and practically adequate solution for vehicle detection on real UAVs.

关键词： Adaptation models Vehicle detection Transfer learning Refining Object detection Autonomous aerial vehicles Data augmentation Data models Optimization Meteorology

来源：评论

学校读者我要写书评

暂无评论

Underwater Image Enhancement using CLAHE Fusion in YIQ and HSI Color Model

Underwater Image Enhancement using CLAHE Fusion in YIQ and H...

引用

Emerging Frontiers in Electrical and Electronic Technologies (ICEFEET), International Conference on

作者： Ravi Kumar Manikant Kumar Shweta Kumari Ashish Kumar Mithilesh Kumar Rai Vishal Mourya Electronics and Communication Engineering Muzaffarpur Institute of Technology Muzaffarpur Leather Technology Muzaffarpur Institute of Technology Muzaffarpur Information Technology Muzaffarpur Institute of Technology Muzaffarpur Computer Science Engineering Muzaffarpur Institute of Technology Muzaffarpur National Institute of Electronics and Information Technology Patna

ISBN: (数字)9798331528614

ISBN: (纸本)9798331528621

Underwater image enhancement has been employed in a variety of areas, such as terrain scanning, underwater microscopic detection, telecommunication cables, mine detection, and autonomous underwater vehicles. However, underwater imagery suffers from strong scattering, noise, color distortion, and absorption from artificial light sources, causing haziness, image blur, and a greenish or bluish texture. To address these issues, the proposed work introduces a new enhancement approach based on Contrast Limited Adaptive Histogram Equalization (CLAHE) used for images taken underwater. The proposed method employs CLAHE to adaptively enhance local contrast while constraining the amplification of noise and artifacts. The fusion method is proposed to enhance the complementary advantages of different color spaces in YIQ and HSI color models. The YIQ color space separates intensity from chrominance, allowing for independent processing of luminance information, which is crucial for preserving image details and contrast. The proposed algorithm improves low-contrast underwater images with intact color and a natural appearance with reliability according to the results of the qualitative analysis. The proposed method outperforms the state-of-the-art methods in terms of image performance measures like GMSD, VSI, MCSD, and PSNR.

关键词： Visualization Translation Image color analysis Absorption Scattering Distortion Reflection Reliability Colored noise Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Synergizing Futures: Precision Career Mapping with Llama 2 and AI Fine-Tuning for Personalized Path Prediction and Guided Navigation

Synergizing Futures: Precision Career Mapping with Llama 2 a...

引用

Communication, computer Sciences and engineering (IC3SE), International Conference on

作者： Anuradha Singh Himanshu Sharma Kanika Jindal Ankur Chaudhary Department of Computer Science and Engineering Noida Institute of Engineering and Technology Greater Noida India Department of Electronics and Communication Engineering Noida Institute of Engineering and Technology Greater Noida India

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

These days, a lot of students struggle in how to choose the career. As they progress through their studies, students must recognize their abilities and assess their areas of interest to determine the most appropriate job for them. With the aid of this approach, today's youngsters will be able to determine which career route will yield the best outcomes in the long run if they choose the suggested vocation. This will assist in raising the student's performance and spark their interest to keep them concentrated on their desired job. This system is based on an exam that students must complete; based on their responses, it will produce a summary of the test results. The primary goal of this system is to give a summary of the artificial intelligence methods that we employed to forecast the student's performance. This structure likewise be emphasizing how we are identifying characteristics in student data by employing prediction systems. For educators, educational institutions, and students alike, using this system has proven to be advantageous. In this paper we have used various technologies like Flutter, Llama-2 generative A.I. model, Firebase and UI/UX design tools. Our experimental results of proposed system reduce the career searching effort by 80% when tested for Lyman people.

关键词： Employee welfare engineering profession Navigation Scalability Large language models Design tools Sparks

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 << < 468 469 470 471 472 473 474 475 476 477 > >>

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：