检索结果-内蒙古大学图书馆

1st International Conference on Cognitive computing and engineering Education, ICCCEE 2023

作者： Supriya Menon, M. Spurthi, K. Elanangai, V. Nagaraju, D. Ashokkumar, C. Sundaram, Ajith Koneru Lakshmaiah Education Foundation Computer Science and Engineering Vaddeswaram AP Guntur522501 India Swarna Bharathi Institute of Science and Technology Computer Science and Engineering Khammam507002 India St. Peter's Institute of Higher Education and Research AVADI Department of Electrical and Electronics Engineering Chennai54 India Sri Venkatesa Perumal College of Engineering and Technology Department of CSE Andhra Pradesh Puttur India SRM Institute of Science and Technology School of Computing Department of Computing Technologies Kattankulathur Chennai603203 India Amrita Vishwa Vidyapeetham Amrita School of Business Kerala Kochi India

ISBN: (纸本)9798350332803

Cloud computing (CC) and the Internet of Things (IoT) are important in the modern era for providing new techniques of connection through intelligence perception, M2M, sharing efficient resources, and on-demand use. In a cloud environment, the manufacturing frame has numerous action tasks to perform or manufacture. Various Cloud Manufacturing system (CMfgs) resources influence some difficulties and attacks in manufacturing resources (work units, resource pools, workstations, and so on) and system modeling. To address these issues and meet the deadline, CMfgs proposed a novel Hybrid Grey Wolf-Ant Lion Regression Frame (HGW-ALRF) deep learning technique. In this case, the fitness function of the Ant Lion and Grey Wolf in the recurrent frame is used to effectively schedule cloud resources. During testing, the presented attacks (malware injection, DDoS, bottleneck attacks) can be monitored by the designed IoT application. Finally, the proposed model's obtained results are described, including computation time, accuracy, energy consumption, delay, and resource utilization. © 2023 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Researches Advanced in Data-To-Text Generation Based on Neural Networks 2

Researches Advanced in Data-To-Text Generation Based on Neur...

引用

2nd International Conference on Artificial Intelligence, Automation, and High-Performance computing, AIAHPC 2022

作者： Wang, Shengyu Wang, Yanping School of Computer Science Southwest Petroleum University Chengdu China School of Computing North China Institute of Aerospace Engineering Langfang China

ISBN: (纸本)9781510657717

Data-to-text generation has always been a research hotspot in multimodal machine learning. Given the structured data such as a list of characteristics of a person or statistical data from an exam, the data-to-text generation aims to automatically generate smooth, true and accurate text which can describe the input data. Thanks to the rapid development of convolutional neural networks, the text generation models based on deep learning have made great breakthroughs in performance, but still face many challenges which mainly include confusion of the output location of different information, generation of non-existent content, duplication of informationand data sparseness. In this paper, based on in-depth literature survey, we introduce the representative algorithms from the aspects of improving text evaluation quality, combining deep learning and knowledge, adapting to various expressions and aligning text and structured data. We also summarize the existing problems and prospect the possible future development direction in the data-to-text generation research field. © 2022 SPIE. All rights reserved.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Detection Of Ships in The Ocean Using Deep Learning Algorithm 2

Detection Of Ships in The Ocean Using Deep Learning Algorith...

引用

2nd International Conference on Innovative Sustainable Computational Technologies, CISCT 2022

作者： Mishra, Amit Kumar Pandey, Neeraj Kumar Kumar, Vivek Garg, Umang Singh, Mayank Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Dit University School of Computing Uttarakhand Dehradun India Teerthanker Mahaveer University Computer Science and Engineering Uttar Pradesh Moradabad India

ISBN: (纸本)9781665474160

Maritime transportation is one of the most common and growing transportation schemes in the ocean. Autonomous ships have grown in popularity since the introduction of self-driving autos, particularly the submarines as well as weapon equipped ships that uses by military persons. Ship recognition and categorization in remote sensing photos is important for the security of marine-areas, also some more activities like peoples who use to catch fishes illegally and to control or to view the pollution in the sea. The detection and categorization of ships in remote sensing pictures is vital for maritime safety as well as other applications like traffic surveillance, illicit fishing and their prevention (sea). With the increasing number of satellite image data, automatic ship identification and remote sensor picture classification is an essential application for both the military and civic sectors, but there are many problems like clouds, weather which cause distraction. So, in this paper we have suggested a deep learning model which is based on CNN that is convolutional neural net. By using CNN, we can increase throughput, efficiency & also remote sensing picture which is used for image processing is fast and reliable. © 2022 IEEE.

关键词： Remote sensing

来源：评论

学校读者我要写书评

暂无评论

Edge computing and AI for IoT: Opportunities and Challenges

Edge Computing and AI for IoT: Opportunities and Challenges

引用

2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Lim, Ean Heng Yuen Chai, Tong Muniandy, Manoranjitham A.-P. Fui Yong, Tien Ooi, Boon Yaik Lin, Jim-Min Quest International University School of Computing Ipoh Malaysia Quest International University School of Engineering Ipoh Malaysia Universiti Tunku Abdul Rahman Department of Computer Science Kampar Malaysia Universiti Tunku Abdul Rahman Department of Information Systems Kampar Malaysia Feng Chia University Dept of Information Engineering and Computer Science Taichung Taiwan

ISBN: (纸本)9798350324174

The proliferation of Internet of Things (IoT) devices has led to an exponential growth in data generated at the edge of the network. Edge computing, a distributed computing paradigm that enables computation and data storage at the network edge, has emerged as a promising solution for managing this data deluge. With the integration of Artificial Intelligence (AI) technologies, edge computing can provide real-time insights and decision-making capabilities, making it a powerful tool for a variety of IoT applications which poses both opportunities and challenges. © 2023 IEEE.

关键词： Edge computing

来源：评论

学校读者我要写书评

暂无评论

Predicting CircRNA-Disease Associations Through Non-negative Matrix Factorization and Adversarially Regularized Variational Graph Autoencoder

Predicting CircRNA-Disease Associations Through Non-negative...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Lu, Mianshuo Wang, Lei Sun, Jinzhu Li, Yang Wei, Mengmeng Wei, Yu Liu, Changchun Li, Zhengwei China University of Mining and Technology School of Computer Science and Technology Xuzhou221116 China Guangxi Academy of Sciences Big Data and Intelligent Computing Research Center Nanning530007 China Zaozhuang Mental Health Center Zaozhuang277100 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China Zaozhuang Univerity College of Information Science and Enginnering 277100 China

ISBN: (纸本)9798350386226

Circular RNA (circRNA) is an RNA molecule that plays an important role in both pathology and physiology. Accurate identification of associations between circRNAs and diseases is crucial for further physiology research. However, verifying the circRNA-disease associations (CDA) through biological experimental methods is time-consuming. Here, we propose a novel method combines Non-negative Matrix Factorization (NMF) and Adversarially Regularized Variational Graph Autoencoder (ARVGA) to accurately predict CDA. Our model first fuses multi-source information in order to build circRNA similarity, disease similarity and circRNA-disease association matrices. Thereby our model constructs graphs for circRNA and disease respectively and optimize them using a K-means clustering algorithm. We obtain linear features using NMF and non-linear features using ARVGA. Finally, an Extremely Randomized Trees classifier is employed to predict CDA. On the gold standard dataset CircR2Disease, our model achieved a prediction accuracy of 94.8% and an AUC of 0.984 under 5-fold cross-validation. In case study, 19 of top 20 predicted circRNAs associated with Hepatocellular Carcinoma were confirmed in relevant literature. Furthermore, ablation experiment, classifier experiment and independent datasets test fully demonstrate the effectiveness and robustness of our model. © 2024 IEEE.

关键词： Non-negative matrix factorization

来源：评论

学校读者我要写书评

暂无评论

Smart Glove for Impaired People to Convert Sign into Voice with Text

Smart Glove for Impaired People to Convert Sign into Voice w...

引用

2023 Intelligent computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Vijayaraj, N. Nalini, M. Kinol, A. Mary Joy Bommi, R.M. School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Sri Sairam Engineering College Department of Eie Chennai India Institute of Ece Saveetha School of Engineering Simats Chennai India Chennai Institute of Technology Department of Ece Chennai India

ISBN: (纸本)9798350394580

The Smart Glove for Sign Language Translation is an innovative technology that has the potential to improve communication for people who are speech and hearing impaired. The glove uses flex detectors and accelerometers to detect sign language gestures, which are then converted into spoken language that can be understood by others. One of the key advantages of this technology is its portability, enabling users to communicate easily and conveniently anytime and anywhere. The glove also uses Bluetooth technology to wirelessly communicate with an Android phone, which can be used to convert signs into written or spoken language. Despite its potential benefits, there are still some limitations that need to be addressed. The glove may not accurately recognize all sign language gestures, leading to misinterpretation of the user's intended meaning. Additionally, the cost of the technology may be prohibitive for many individuals, especially those living in developing countries. Despite these limitations, the Smart Glove for Sign Language Translation proposed a positive step forward in improving communication for speech and hearing impaire individuals. As technology continues to advance, it islikely that i will see further improvements in this area, resulting in even more efficient and cost-effective solutions. Ultimately, these advancements have the potential to significantly enhance the quality of life for people with speech and hearing impairments, enabling them to better communicate and connect with others. © 2023 IEEE.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Dijkstra's Algorithm with Bellman-Ford for Shortest Path Discovery in Big Data 2

Dijkstra's Algorithm with Bellman-Ford for Shortest Path Dis...

引用

2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Karthikeyan, M. Sekar, Satheeshkumar Basha, H. Anwer Epsiba, P. Ganesh, E.N. Meenakshi, B. Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Computing Technologies Kattankulathur Tamil Nadu Chennai India Mphasis Corporation ChandlerAZ United States Saveetha College of Liberal Arts & Sciences Simats University Department of Computer Science Tamil Nadu Chennai India Sri Indu College of Engineering and Technology Department of Electronics and Communication Engineering Telangana Hyderabad India Department of Electronics and Communication Engineering Avadi Tamil Nadu Chennai India Sri Sairam Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350368413

Many applications, from logistics to social networks, rely on quickly navigating the shortest pathways inside large-scale graphs. For shortest route finding in huge data settings, this study aims to compare Dijkstra's method with the Bellman-Ford algorithm. Applying these methods to large datasets will allow us to compare their speed, accuracy, and scalability. Graph density and negative weight cycles are two of the intrinsic properties of big data settings that will be used to determine which method is most suited to each. Optimal algorithm selection for given data situations is guided by insights into each algorithm's operational strengths and shortcomings, which are revealed via rigorous testing and performance metrics analysis. This comparison helps real-world applications of each method and provides valuable information for future advancements in large data processing shortest route calculation algorithms. In a study comparing Dijkstra's Algorithm and Bellman-Ford using the RandomGraph1 dataset, the results showed that for nodes 100-500, the former's ms value ranged from 5.2 to 52.3 and the latter's ms value ranged from 6.8 to 68.9. Similarly, for nodes 100-500, the MB value range for Dijkstra's Algorithm was 10.2-46.7 and for Bellman-Ford, it was 14.8-71.9. Data from a different SparseGraph1 dataset reveals that, for values between 100 and 500, Dijkstra's Algorithm has a percentage range of 98.5 to 99.8, while for Bellman-Ford, it varies from 97.2 to 99.3. © 2024 IEEE.

关键词： Graph algorithms

来源：评论

学校读者我要写书评

暂无评论

Detection and Taxonomy of Aircraft using Synthetic Aperture Radar Imaging

Detection and Taxonomy of Aircraft using Synthetic Aperture ...

引用

Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： D. V. Sairam M Tamilselvi S Gayathri Devi R Bhuvaneswari Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Computer Science and Engineering Roever Engineering College Perambalur Tamilnadu India Department of Computer Science and Engineering Sri Ramakrishna Institute of Technology Coimbatore India

Improvements in aircraft detection are necessary to improve surveillance. This work investigates the exact detection and classification of airplanes using YOLOv8 in conjunction with Synthetic Aperture Radar (SAR) photos. SAR imaging performs well in dimly lit and unfavorable environments. The objective is to use YOLOv8's object detection to identify aircraft kinds and categories in SAR pictures. A variety of SAR datasets are gathered, preprocessed, and trained on the model to allow for a thorough assessment of YOLOv8's performance in differentiating between different types of aircraft. Measures that assess model performance across aircraft classes include mean average precision (mAP), recall, and loss. The results demonstrate the effectiveness of YOLOv8 and show promising results in terms of speed and precision. This work drives progress in aerial surveillance by highlighting the robustness of SAR when combined with cutting edge deep learning for aircraft taxonomy. The achieved 98.5% mean average precision (mAP) attests to exceptional performance in aircraft detection and categorization from SAR images, reinforcing the utility of this integrated approach in bolstering security systems.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Requirements engineering Practices Model to Incorporate 'Power' Human Value 12

Requirements Engineering Practices Model to Incorporate 'Pow...

引用

12th International Workshop on Quantitative Approaches to Software Quality, QuASoQ 2024

作者： Minhas, Nasir Mehmood Iqbal, Javed Khan, Saif Ur Rehman School of Innovation Design and Engineering Mälardalen University Västerås Sweden Department of Computer Science COMSATS University Islamabad Pakistan Department of Computing Shifa Tameer-e-Millat University Islamabad Pakistan

Integrating human values into software development, particularly in Requirements engineering (RE), is essential for ensuring systems' alignment with stakeholder concerns, yet this area has not received due attention in research. To bridge this gap,this study presents a model based on Schwartz's value framework, focusing on incorporating the human value of’Power’ into the RE process. This model addresses five sub-values: preserving public image, social power, social recognition, authority, and wealth. Leveraging two focus-group sessions, the model integrates the sub-values of Power using 53 RE practices, 48 identified through a literature review and five from a survey with practitioners. These practices operationalize the sub-values ensuring that critical aspects like public image, recognition, and authority are addressed within the RE process. Preliminary evaluations by an expert panel indicate that the model is comprehensive, practical, and effective. Further validation through real-world testing will assess its impact on RE practices and outcomes. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Software design

来源：评论

学校读者我要写书评

暂无评论

Guest Editorial Special Section on Generative AI and Large Language Models Enhanced 6G Wireless Communication and Sensing

IEEE Open Journal of the Communications Society

引用

IEEE Open Journal of the Communications Society 2025年 6卷 0-2页

作者： Jiacheng Wang Geng Sun Dusit Niyato Hina Tabassum Gabriel-Miro Muntean Nelson Fonseca College of Computing and Data Science Nanyang Technological University Jurong West Singapore College of Computer Science and Technology Jilin University Changchun China Department of Electrical Engineering and Computer Science Lassonde School of Engineering York University Toronto ON Canada School of Electronic Engineering Dublin City University Dublin 9 Ireland Institute of Computing State University of Campinas Campinas Brazil

来源：评论

学校读者我要写书评

暂无评论

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：

请选择收藏分类：

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

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：