检索结果-内蒙古大学图书馆

8th International Conference on Advanced Computing and Communication Systems, ICACCS 2022

作者： Bhosale, Yogesh H. Zanwar, Shrinivas Ahmed, Zakee Nakrani, Mahendra Bhuyar, Devendra Shinde, Ulhas Dept. of Computer Science and Engineering Mesra Ranchi India Chh. Shahu College of Engineering Dept. of Artificial Intelligence and Data Science Csmss Aurangabad India WeAgile Software Solution Pvt. Ltd Dept. of Computer Vision Engineering Pune India

ISBN: (纸本)9781665408165

The Coronavirus Disease 2019 (COVID19) epidemic, which erupted at the end of 2019, continued rapidly throughout the nations from Wuhan, China. This highly contagious infectious disease is rapidly spreading among the public. Early research on COVID-19-affected patients has revealed distinctive anomalies in chest radiography images. As a result, it is now necessary to identify various risk factors that can move an infected person from a mild to a serious stage of sickness. In Deep Learning (DL), strategies as a subset of Artificial Intelligence (AI) are used to deal with many real-life glitches. This paper introduces a Deep Convolutional Neural Network (DCNN) to perform multiclass classification for COVID-19, Pneumonia, and Normal Patients from radiological imaging of the chest. Also, the work is implemented with an IoT framework, used for communicating user and DCNN model. This Deep Convolutional Neural Network (DCNN) classification mechanism achieved a perfect test accuracy of 94.95% for COVID-19. The used datasets are acquired from Kaggle and GitHub. © 2022 IEEE.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Explainable Multiclass Blood Cell Classification: Combining Custom CNNs with SHAP

Explainable Multiclass Blood Cell Classification: Combining ...

引用

International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Md. Ismiel Hossen Abir Shantanu Dey Anik Hasibul Islam Peyal Dept. of Computer Science & Engineering International Standard University Dhaka Bangladesh Dept. of Software Engineering Daffodil International University Birulia Savar Dhaka Bangladesh Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Blood cell classification is a crucial task in medical diagnostics. This work can identify different blood cell types that play essential roles in the immune system and overall body function. This research introduces a custom Convolutional Neural Network (CNN) model with SHAP for explainable multiclass classification of blood cell images. We classify eight types of blood cells—basophil, eosinophil, erythroblast, immunoglobulin (Ig), lymphocyte, monocyte, neutrophil, and platelet. An ablation study was conducted to optimize the CNN architecture. We compared its performance with eleven transfer learning models, including VGG16, ResNet101, and EfficientNet variants. Our model achieved 92.60% accuracy while maintaining lower complexity. The use of SHAP provides transparency by highlighting crucial features in the model’s decision-making process.

关键词： Training Accuracy Microprocessors Transfer learning Lymphocytes computer architecture Convolutional neural networks Medical diagnosis Blood Testing

来源：评论

学校读者我要写书评

暂无评论

Joint Energy and Computation Workload Management for Geo-distributed Data Centers

IEEE Transactions on Green Communications and Networking

引用

IEEE Transactions on Green Communications and Networking 2025年

作者： Wang, Ran Wu, Rixin Liu, Linfeng Yi, Changyan Zhu, Kun Wang, Ping Niyato, Dusit Nanjing University of Aeronautics and Astronautics Collaborative Innovation Center of Novel Software Technology and Industrialization College of Computer Science and Technology Nanjing China Nanjing University of Posts and Telecommunications School of Computer Science and Technology Nanjing China York University Dept. of Electrical Engineering and Computer Science Lassonde School of Engineering Canada Nanyang Technological University School of Computer Science and Engineering Singapore

The increasing demands of data computation and storage for cloud-based services motivate the development and deployment of large-scale data centers (DCs). The energy demand of these devices is rising rapidly and becoming a noticeable challenge for current power networks. The smart grid (SG) is deemed as the future power system paradigm enabling more affordable and sustainable energy supply, which can effectively relieve the load pressure from DCs. Moreover, with growing concerns regarding harmful emissions due to combustion of fossil fuels, the exploitation of renewable energy sources (RES) has attracted extensive attention, which can benefit SGs and DCs, as well as society at large. However, the geo-distributed property of DCs and SGs and the uncertain nature of RES production pose severe challenges to the optimal management of computation and energy resources in such a tripartite coupling system. Focusing on these issues, a joint energy and computation workload management framework is proposed for enabling a sustainable DC paradigm with distributed RES. Specifically, a three-layer game is formulated to model the iterations among entities including the energy market, data center operators (DCOs), and SGs. The market includes a certain amount of RES that must be dispatched. The SG offers the DCO an electricity selling price while simultaneously importing RES from the market at a buying price in order to maximize the benefit. The DCO allocates the workload to different DCs, aiming to minimize the costs of energy consumption and carbon emissions. The interactive processes between different entities are further decomposed into two coupling Stackelberg games. We obtain the equilibrium state of the game and prove its uniqueness and optimality. Simulation experiments are conducted to evaluate the performance of the joint energy and computation workload management scheme and show its superiority over counterparts in utilizing renewable energy and reducing emissions. Furthe

关键词： Smart power grids

来源：评论

学校读者我要写书评

暂无评论

AlgoCert: Adopt Non-transferable NFT for the Issuance and Verification of Educational Certificates using Algorand Blockchain

AlgoCert: Adopt Non-transferable NFT for the Issuance and Ve...

引用

2022 IEEE Asia-Pacific Conference on computer science and Data engineering, CSDE 2022

作者： Tahlil, Tahlil Gomasta, Sarmistha Sarna Ali, A B M Shawkat Institute of Information Technology University of Dhaka Dhaka Bangladesh Daffodil International University Dept of Software Engineering Dhaka Bangladesh The University of Fiji Dept of Computer Science & Engineering Fiji

ISBN: (纸本)9781665453059

In addition to tanking the economy and society, credential fraud frequently damages the credibility of higher education. As the majority of conventional verification and issuance procedures are arduous, expensive, and obsolete, fraud has proliferated in numerous forms, ranging from paper-based counterfeit certificates to replicas enabled by database manipulation, and has reached exponential quantities. In this paper, the authors propose AlgoCert, a comprehensive credential verification solution that incorporates blockchain, self-sovereign identity (SSI), and decentralized identifier (DID) to introduce a more proficient, attack-resistant system that is user-friendly as well as effective at reducing widespread credential fraud. In addition, AlgoCert leverages stateful smart contracts on the Algorand blockchain and non-transferable non-fungible tokens (NFT), and neither students nor organizations are required to handle credentials in order to use the system. This paper provides a prototype of our solution and detail our attempts to develop a verification service with features, including data security, transcript authentication, and selective data disclosure. © 2022 IEEE.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Designing and Simulating a Smart Water Harvesting System Using PLC Techniques

Designing and Simulating a Smart Water Harvesting System Usi...

引用

2023 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2023

作者： Thabet, Hassaan Th. H. Al-Hilali, Aqeel A. Qasim, Haitham B. Thabit, Thabit H. Mahmood, Firas M. Al-Hadba University College Techniques Engineering Dept. of Medical Instruments Mosul Iraq Al-Farahidi University Dept. of Medical Instruments Engineering Techniques Baghdad Iraq Al-Esraa University College Dept. of Computer Science Baghdad Iraq Ninevah University Dept. of Computer Engineering and Information Mosul Iraq Cihan University-Erbil Dept. of Informatics and Software Engineering Iraq

ISBN: (纸本)9798350307771

The presence of water in human life is crucial, and God has blessed humans much with its availability. This paper tries to reduce the amount of water that people use for washing in all aspects of daily life and instead utilize it to irrigate gardens and maintain hygienic facilities. The importance of water as a fundamental ingredient and the range of social and age groups that regularly take advantage of the washing process made it an ideal location for water waste during the washing process. This is why the authors choose this region to conduct their research. The monitoring and management of water usage during the washing process are simulated using PLC techniques. © 2023 IEEE.

关键词： Washing

来源：评论

学校读者我要写书评

暂无评论

Investigating Online Searching Behavior Based on Google Trends in MENA Region Before and After COVID-19 53

Investigating Online Searching Behavior Based on Google Tren...

引用

53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Belkacem, Abdelkader Nasreddine Jamil, Nuraini Alrabaee, Saed College of It Uae University Dept. of Computer and Network Engineering Al Ain United Arab Emirates College of It Uae University Dept. of Computer Science and Software Engineering Al Ain United Arab Emirates College of It Uae University Dept. of Information Systems and Security Al Ain United Arab Emirates

ISBN: (纸本)9798350336429

The outbreak of the coronavirus disease (COVID-19) has had a profound impact on education worldwide. The rise of remote learning is one of the most significant changes in this regard, as many schools and universities were forced to close down by regional health authorities. This has also caused people to become more conservative in trade-offs between healthcare and education. Google Trends is the most common tool for analyzing online search behaviors. It is a free resource that provides information on the trends and changes in users' online interests over time based on certain terms and subjects. The online search queries on Google can be used to assess users' behaviors concerning online learning to forecast their choices regarding online education. This paper examines the frequency of users' web searches for online communication tools, courses, and learning terms. We statistically compared users in the Middle East and North Africa regions by using the volumes of searches recorded on Google Trends from January 2016 to August 2022. Moreover, we used machine learning techniques to identify differences among the keywords used. The findings statistically show that COVID-19 has led to an increase in the extent of students' attention to and interest in online learning. © 2023 IEEE.

关键词： COVID-19 Google Trends MENA Online learning Remote learning Web Behavior

来源：评论

学校读者我要写书评

暂无评论

PRAGyan - Connecting the Dots in Tweets

arXiv

引用

arXiv 2024年

作者： Ravi, Rahul Ginde, Gouri Rokne, Jon Dept. of Computer Science University of Calgary Canada Dept. of Electrical and Software Engineering University of Calgary Canada

As social media platforms grow, understanding the underlying reasons behind events and statements becomes crucial for businesses, policymakers, and researchers. This research explores the integration of Knowledge Graphs (KGs) with Large Language Models (LLMs) to perform causal analysis of tweets dataset. The LLM aided analysis techniques often lack depth in uncovering the causes driving observed effects. By leveraging KGs and LLMs, which encode rich semantic relationships and temporal information, this study aims to uncover the complex interplay of factors influencing causal dynamics and compare the results obtained using GPT-3.5 Turbo. We employ a Retrieval-Augmented Generation (RAG) model, utilizing a KG stored in a Neo4j (a.k.a PRAGyan) data format, to retrieve relevant context for causal reasoning. Our approach demonstrates that the KG-enhanced LLM RAG can provide improved results when compared to the baseline LLM (GPT-3.5 Turbo) model as the source corpus increases in size. Our qualitative analysis highlights the advantages of combining KGs with LLMs for improved interpretability and actionable insights, facilitating informed decision-making across various domains. Whereas, quantitative analysis using metrics such as BLEU and cosine similarity show that our approach outperforms the baseline by 10%. © 2024, CC BY.

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

Towards Eco-Sustainability and Green Analytics Model to Measure the Performance of Big Data Systems 9

Towards Eco-Sustainability and Green Analytics Model to Meas...

引用

9th International Conference on Information Management, ICIM 2023

作者： Jusoh, Yusmadi Yah Abdullah, Rusli Nor, Rozi Nor Haizan Yahaya, Jamaiah Hj. Muhammad, Shireen Arunachalam, Aishwharya Raani Universiti Putra Malaysia Faculty of Computer Science and Information Technology Dept. of Software Engineering and Information Systems Serdang Malaysia Universiti Kebangsaan Malaysia Faculty of Information Science and Technology Bangi Malaysia Faculty of Communication Selangor Malaysia

ISBN: (纸本)9798350336382

Many BDS is doomed to failure because of the missing knowledge on measuring the performance of BDS. The failure to identify the performance measurement and correct will make the problems worsen. This will complicate the efforts of fixing such problems and more importantly, will affect the quality of knowledge, insights, and results expected by the users in the future. In achieving durable performance, the organization needsto understand the factors that contribute to the performance of the eco-sustainability and green analytics metrics for BDS. Although the existence of its importance and awareness was largely neglected. Therefore, the eco-sustainability and green analytic measurement of the BDS is examined in this research. Measuring the BDS performance has the benefit of identifying problems and launching corrective actions before these problems happen and become worsen. The research will investigate the eco-sustainability and green analytic measures which will be proposed and used to capture performance for each process of BDS. Then, based on such measures and metrics, as well as existing performance concepts, frameworks, and models, a measurement model for BDS will be created. The model will be validated through expert evaluations and confirmatory studies with users and practitioners of BDS users. The research has a greater significance in leveraging existing performance concepts in BDS settings and the research proposes users' participation in the sustainability and continuous performance evaluation of theirBDS. © 2023 IEEE.

关键词： Big data

来源：评论

学校读者我要写书评

暂无评论

A Systematic Review on Pattern-based GUI Testing of Android and Web Apps: State-of-the-Art, Taxonomy, Challenges and Future Directions

A Systematic Review on Pattern-based GUI Testing of Android ...

引用

IEEE International Conference on Multi Topic

作者： Ambreen Kousar Saif Ur Rehman Khan Shahid Hussain M. Abdul Basit Ur Rahim Wen-Li Wang Naseem Ibrahim Dept of Computer Science COMSATS University Islamabad Islamabad Pakistan Dept. of Software Engineering Shifa Tameer-e-Millat University Islamabad Pakistan School of Computer Science and Software Engineering Penn State University Behrend Pennsylvania USA Dept. of Computer Engineering and Computer Science California State University Long Beach USA Dept. of Computer Science and software engineering Penn State Erie USA

Graphical User Interface (GUI) designing and testing of different applications is significant from a user point of view. GUI testing ensures that the user interface of apps is visually appealing, user-friendly, and enhances user satisfaction. Pattern-Based GUI Testing (PBGT) is a new model-based testing approach that aims to enhance reusability while reducing the workload associated with modeling user interfaces and testing applications. The objective of conducting a Systematic Literature Review (SLR) on pattern-based GUI testing is to consolidate existing knowledge and provide insights for improving the effectiveness of PBGT methods. The objectives of conducting an SLR are the identification of current state-of-the-art approaches and tools in the targeted research context. To accomplish the mentioned objectives, A hybrid methodology combining Kitchenham’s SLR and PRISMA guidelines was used to conduct this SLR. We performed a keyword-based search on well-known databases IEEE, ACM, science Direct, Wiley, and Springer Link to determine the potential studies published in the last 10 years. Following a comprehensive systematic analysis, we identified a total of twenty-four studies employing varied approaches for pattern-based GUI testing. Additionally, we identified 11 tools that have been used and proposed in PBGT for Android and web apps and made a taxonomy of tools. We also identified challenges and research directions. The implications of this study are significant. The current study is helpful for researchers intending to work in pattern-based GUI testing. The comprehensive knowledge about the current state-of-the-art Tools, techniques, and approaches will help practitioners to effectively apply them in industry and enable researchers to extend their work in this context in future research.

关键词：

来源：评论

学校读者我要写书评

暂无评论

An Integrated Approach to Enhance Legal Information Retrieval of Sri Lankan Supreme Court Verdicts 5

An Integrated Approach to Enhance Legal Information Retrieva...

引用

5th International Conference on Advancements in Computing, ICAC 2023

作者： Jayasooriya, Asel Ahamed, Akeel Bandara, Yomal Gavindya, Chamod Kasthurirathna, Dharshana Abeywardhana, Lakmini Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept.of Computer Science & Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Legal professionals and law students often grapple with the arduous task of locating prior Supreme Court rulings for argument preparation and academic study, leading to a laborious and resource-intensive process. This study introduces an innovative approach to streamline the retrieval of legal information from Sri Lankan Supreme Court verdicts. The method centers on developing a Custom Named Entity Recognition (NER) model, boasting a remarkable accuracy exceeding 90%. This model efficiently extracts crucial legal entities from Supreme Court rulings, readily available on the Ministry of Justice's Supreme Court website. The high accuracy ensures precise entity extraction, followed by systematic organization within a dedicated database. Subsequently, a knowledge graph is formed by linking recorded legal entities, reducing information retrieval time to a mere 179 milliseconds, significantly outperforming existing methods. Moreover, a BART summarization model is crafted to generate concise, accurate, and insightful summaries of Supreme Court decisions, boasting an impressive ROGUE1 score of 85%. This approach revolutionizes legal information retrieval, delivering a user-friendly platform that enhances the identification of cases and fosters a deeper understanding, ultimately elevating the quality of legal research and practice. © 2023 IEEE.

关键词： Knowledge graph

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：