检索结果-内蒙古大学图书馆

Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： S.A. Shoyusupov Sandeep Bisht Krishna Kant Dixit Sourav Muntather Almusawi Jaishankar Bhatt Department of Physics and Chemistry Tashkent Institute of Irrigation and Agricultural Mechanization Engineers" National Research University Tashkent Uzbekistan School of Management Graphic Era Hill University Bhimtal Uttarakhand India Department of Electrical Engineering GLA University Mathura India Department of Computer Application Chandigarh Engineering College Chandigarh Group of Colleges Jhanjeri Mohali Punjab India Department of Computers Techniques Engineering College Of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department. of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

While various business, industrial, healthcare and economic sectors are deploying the world wide web of things during this current generation of internet evolution. IoT and Intelligence (AI) helps in boosting performance across a wide range of everyday apps with main focus on automation systems for robotics, transportation & commercial/industrial applications. The aim of this essay, is to take a deep dive in both current and future scenarios where the combination between IoT and AI could make possible applications that would significantly improve our way of life. The introductory part of the article provides some background to those who are not familiar with IoT and AI: definition on the "synergy between two magnificent fields", a case study for understanding what brings about an increase in 'IoT enabled by AI' focusing on new analytics toolkit, definitions i.e. Industrial IoT vs Internet About Robotic Things or Industrial Robotics lloT glossary terms etc. This article provides a summary of some case studies followed by few sample use cases of IoT and AI assisted robot & automated manufacturing.

关键词： Privacy Automation Supply chain management Service robots Transportation Telecommunication computing Security Web sites Artificial intelligence Industrial Internet of Things

来源：评论

学校读者我要写书评

暂无评论

Fact-checking based fake news detection: a review

arXiv

引用

arXiv 2024年

作者： Yang, Yuzhou Zhou, Yangming Ying, Qichao Qian, Zhenxing Zeng, Dan Liu, Liang School of Computer Science Fudan University Shanghai200433 China Key Laboratory of Culture & Tourism Intelligent Computing of Ministry of Culture & Tourism Fudan University Shanghai200433 China School of Communication & Information Engineering Shanghai University Shanghai200444 China Technical Application Department of Audiovisual New Media Center China Media Group Beijing100026 China

There are many groundbreaking methods in the field of fake news detection based on deep learning that can automatically detect fake news through feature extraction and detection. A common methodology framework consists of extracting features from news content by pre-trained models and developing algorithms for detection. Major approaches within the scope identify fake news by learning common feature patterns in them, such as writing style, word usage, etc.. The performance of such models highly relies on large well-annotated data sets, but obtaining and annotating fake news data is laborious. Moreover, newly forged fake news often avoids utilizing the writing style of previous fake news, resulting in poor generalization ability in terms of timeliness. In recent research, fake news detection based on fact verification provides new ideas to address the above problems. Approaches within the scope verify the authenticity of the news event, matching between description and factual information, and so on, to provide more reliable and explicable detection, greatly addressing the bias of previous methods that rely on semantic and writing style features. In this paper, we sort out the research findings of fact-based fake news detection from the perspectives of tasks and problems, algorithms, datasets, and so on. First, this paper illustrates the task definition and core problems of fact-based fake news detection. Next, existing approaches are summarized and organized in terms of algorithms. Subsequently, classic and newly published datasets are analyzed, and extended by summarizing experimental results evaluation. Finally, this paper elaborates on the pros and cons of existing approaches from an overall perspective, pointing out some substantial challenges and expectations of this field. It is expected to provide references for future works in the field of fake news detection. © 2024, CC BY.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Product Life Cycle and Business Model Techniques to Promote Marketing in Healthcare Units 2

Product Life Cycle and Business Model Techniques to Promote ...

引用

2nd IEEE Mysore Sub Section International Conference, MysuruCon 2022

作者： Mandala, Gangu Naidu Kasi Raman, R. Sharma, Monika Raj, M. Sundar Lavanyalakshmi, N. Mishra, Nilamadhab SVKM's Narsee Monjee Institute of Management Studies Department of Finance Bengaluru India P.S.R Engineering College Department of Management Studies Sivakasi India The Technological Institute of Textile & Sciences Department of Management Studies Haryana India Panimalar Engineering College Department of Mathematics Chennai India Jayaraj Annapackiam College for Women Department of Commerce with Computer Application Theni India Vit Bhopal University School of Computing Science and Engineering Madhya Pradesh India

ISBN: (数字)9781665497909

ISBN: (纸本)9781665497909

Attempting to execute a very intensive and costly eHealth program, judgment in the Indian health service has typically refused to look beyond information and communications technology. As a result, the properly functioning assimilation of scheduled communication and information technology alternatives with different business components of the health framework has largely been ignored, whereas the Indian healthcare program's established and outdated business strategy keeps going to be extremely limited but instead updated towards the vastly increased and widespread infiltration of communication and information technology into the healthcare setting. Such an editorial focus on the description and configuration of the conceptual model helps to identify the approaching consequences of industrialization on the health industry prototype, and eventually propose a method for transforming Slovenia's original healthcare market prototype using information communication technologies. The analysis is focusing on case research findings and structured questionnaires with eminent specialists from many parts of the medical system who are heavily involved with the business model idea. According to the findings, an effective transition of the healthcare market prototype should take into account a list of criteria from the health system, such as a reconceptualization of the operations and relationship issues of and among the business model's main actors, and the retooling of organizational, structural, and technical components in line with long-term company goals. © 2022 IEEE.

关键词： Commerce

来源：评论

学校读者我要写书评

暂无评论

Blockchain-Enabled Smart Contracts: An Evolutionary Algorithm Approach for Automated Financial Agreement

Blockchain-Enabled Smart Contracts: An Evolutionary Algorith...

引用

International Conference on Computing and Networking Technology (ICCNT)

作者： J Chandra Sekhar Arun B Prasad Shantanu Kanade Anuradha Kanade C Dhilipan B Kiran Bala NRI Institute of Technology Guntur India Institute of Law Nirma University Ahmedabad India Symbiosis School for online and Digital Learning Symbiosis International University Pune Maharashtra India School of Computer Science and Application MIT World Peace University Pune Maharashtra India CMS Business School Jain Deemed to be University Bangalore India Department of AI & DS College of Engineering Trichy India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Blockchain technology has revolutionized various industries by introducing decentralized and secure systems. Smart contracts, self-executing contracts with the terms of the agreement directly written into code, represent a significant advancement in automating and enhancing the efficiency of financial agreements. This paper proposes an evolutionary algorithm approach to further optimize the implementation of smart contracts on the blockchain for automated financial agreements. The evolutionary algorithm, inspired by the principles of natural selection and genetic algorithms, aims to enhance the adaptability and performance of smart contracts over time. Through a continuous process of selection, crossover, and mutation, the algorithm evolves the codebase of smart contracts to better suit the dynamic and evolving nature of financial markets. This approach not only improves the efficiency of automated financial agreements but also enables smart contracts to adapt to changing market conditions, regulatory requirements, and user preferences. Furthermore, the security implications and challenges associated with the evolutionary algorithm are examined, along with potential strategies for mitigating risks. In conclusion, the integration of evolutionary algorithms with blockchain-enabled smart contracts represents a promising avenue for advancing the automation of financial agreements. This approach not only addresses the challenges of adaptability and optimization but also contributes to the ongoing evolution of blockchain technology in the realm of decentralized finance. The findings of this research have implications for financial institutions, regulators, and developers seeking to leverage the full potential of smart contracts in an ever-changing financial landscape.

关键词： Industries Regulators Heuristic algorithms Smart contracts Finance Evolutionary computation Blockchains Security Optimization Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

Challenges in metaverse in problem-based learning as a game-changing virtual-physical environment for personalized content development

Challenges in metaverse in problem-based learning as a game-...

引用

6th Smart Cities Symposium (SCS 2022)

作者： P.K. Dutta M. Bose A. Sinha R. Bhardwaj S. Ray S. Roy K.B. Prakash School of Engineering and Technology Amity University Kolkata Kolkata India School of Computer Application Amity University Kolkata Kolkata India Department of Mathematics Amity University Kolkata Kolkata India Senior Researcher Petersburg Polytechnic University Artificial Intelligence & Data Science Jio Institute Navi Mumbai India Department of Computer Science and Engineering Konneru Lakshmaih Education Foundation

The benefits and drawbacks of using the metaverse in the field of education are discussed, along with the possibilities and potential issues it will open up. Utilizing the idea of personalised knowledge transfer and the use of an immersive environment to match students' learning pace using horizon classroom, the Metaverse environment can add a new dimension to the field of educational technologies. We have also highlighted the virtualization-based redesign of educational environments using a personalised cloud, which is anticipated to improve learning for students by making it more affordable, collaborative, and useful. The goal is to build a metaversity that can serve as a communication tool for teachers and students anywhere in the world by providing each with an individual avatar. This platform will offer a virtual setting for students to socialise with their friends and peers and receive peer-to-peer learning, which is lacking in the current e-learning environment. The students won't need an entrance score card; instead, they will need critical learning skills to advance and mature.

关键词：

来源：评论

学校读者我要写书评

暂无评论

CONIC10K: A Challenging Math Problem Understanding and Reasoning Dataset

arXiv

引用

arXiv 2023年

作者： Wu, Haoyi Hui, Wenyang Chen, Yezeng Wu, Weiqi Tu, Kewei Zhou, Yi School of Information Science and Technology ShanghaiTech University China Shanghai Engineering Research Center of Intelligent Vision and Imaging China School of Information Science and Technology University of Science and Technology of China China National Engineering Laboratory for Brain-inspired Intelligence Technology and Application China Key Laboratory of Brain Cognition and Education Sciences Ministry of Education China Shanghai Innovation Center for Processor Technologies China Department of Computer Science and Engineering Shanghai Jiao Tong University China

Mathematical understanding and reasoning are crucial tasks for assessing the capabilities of artificial intelligence (AI). However, existing benchmarks either require just a few steps of reasoning, or only contain a small amount of data in one specific topic, making it hard to analyse AI's behaviour with reference to different problems within a specific topic in detail. In this work, we propose CONIC10K, a challenging math problem dataset on conic sections in Chinese senior high school education. Our dataset contains various problems with different reasoning depths, while only the knowledge from conic sections is required. Since the dataset only involves a narrow range of knowledge, it is easy to separately analyse the knowledge a model possesses and the reasoning ability it has. For each problem, we provide a high-quality formal representation, the reasoning steps, and the final solution. Experiments show that existing large language models, including GPT-4, exhibit weak performance on complex reasoning. We hope that our findings could inspire more advanced techniques for precise natural language understanding and reasoning. Our dataset and codes are available at https://***/whyNLP/Conic10K. © 2023, CC BY.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Robust DL Approach for Detection of Invasive Ductal Carcinoma in Whole Slide Images using DenseNet169

A Robust DL Approach for Detection of Invasive Ductal Carcin...

引用

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

作者： Menaka C Sunil Sharma Preeti Naval Khushmeen Brar Rohit Anand Haider Alabdeli Shivani Pant Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Department of Computer Science & Application Vivekananda Global University Jaipur India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Electronics and Communication Engineering Chandigarh University Mohali Punjab India Department of ECE G.B Pant Okhla-1 Campus (formerly GBPEC) New Delhi India The Islamic University Najaf Iraq Uttaranchal Institute of Technology Uttaranchal University Uttarakhand India

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

The present research introduces a deep learning method for automatically recognizing and evaluating tissue areas that correspond to invasive ductal carcinoma (IDC) in whole slide images (WSI) of breast cancer (BCa). Learn-from-data techniques which utilize computational simulations of the learning process are called deep learning approaches. It has been demonstrated that these methodologies outperform conventional methods in resolving the challenging issues in a number of domains, particularly object and speech recognition. The main reason why making an invasive breast cancer evaluation is an exhausting and laborious process is that, it necessitates a pathologist to scan a huge number of benign regions with the aim to determine the spots that are malignant. Effectively characterizing IDC in WSI is a prerequisite for evaluating the severity of tumors and predicting patient prognosis. In order to support diagnosis, the DL approach in this study draws out many convolutional neural networks (CNNs) for perceptible semantic examination of tumor locations. A WSI database of 162 patients with IDC diagnoses is exploited to assess the technique, and 170 slides were chosen for training. The objective of the experimental analysis was to assess the accuracy of the classifier in identifying IDC tissue areas in WSI. A fusion of optimization techniques namely Best First and Evolutionary Search has been employed for feature reduction and improvement. When using DenseNet169, our approach produced the highest quantitative outcomes for the computerized identification of IDC sections in WSI with regard to accuracy 87.46% in comparison with an approach using EfficientNetB0 and ML classifiers for invasive tumor categorization employing Random Forest, BayesNet, SVM, Naïve Bayes and Decision Tree with accuracy of 80.65%.

关键词： Deep learning Training Support vector machines Accuracy Semantics Speech recognition Breast cancer

来源：评论

学校读者我要写书评

暂无评论

Class-Imbalanced Semi-Supervised Learning for Large-Scale Point Cloud Semantic Segmentation Via Decoupling Optimization

SSRN

引用

SSRN 2023年

作者： Li, Mengtian Lin, Shaohui Wang, Zihan Shen, Yunhang Zhang, Baochang Ma, Lizhuang Visual Media Intelligent Processing Research Group Department of Film and Television Engineering Shanghai University Shanghai200072 China School of Computer Science and Technology East China Normal University Shanghai200062 China Key Laboratory of Advanced Theory and Application in Statistics and Data Science Ministry of Education China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Youtu Lab Tencent China

Semi-supervised learning (SSL), thanks to the significant reduction of data annotation costs, has been an active research topic for large-scale 3D scene understanding. However, the existing SSL-based methods suffer from severe training bias, mainly due to class imbalance and long-tail distributions of the point cloud data. As a result, they lead to a biased prediction for the tail class segmentation. In this paper, we introduce a new decoupling optimization framework, which disentangles feature representation learning and classifier in an alternative optimization manner to shift the bias decision boundary effectively. In particular, we first employ two-round pseudo-label generation to select unlabeled points across head-to-tail classes. We further introduce multi-class imbalanced focus loss to adaptively pay more attention to feature learning across head-to-tail classes. We fix the backbone parameters after feature learning and retrain the classifier using ground-truth points to update its parameters. Ex-✩Fully documented templates are available in the elsarticle package on CTAN. © 2023, The Authors. All rights reserved.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Pushing the Performance Limit of Scene Text Recognizer without Human Annotation

arXiv

引用

arXiv 2022年

作者： Zheng, Caiyuan Li, Hui Rhee, Seon-Min Han, Seungju Han, Jae-Joon Wang, Peng School of Computer Science Ningbo Institute Northwestern Polytechnical University China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology China China Korea Republic of

Scene text recognition (STR) attracts much attention over the years because of its wide application. Most methods train STR model in a fully supervised manner which requires large amounts of labeled data. Although synthetic data contributes a lot to STR, it suffers from the real-to-synthetic domain gap that restricts model performance. In this work, we aim to boost STR models by leveraging both synthetic data and the numerous real unlabeled images, exempting human annotation cost thoroughly. A robust consistency regularization based semi-supervised framework is proposed for STR, which can effectively solve the instability issue due to domain inconsistency between synthetic and real images. A character-level consistency regularization is designed to mitigate the misalignment between characters in sequence recognition. Extensive experiments on standard text recognition benchmarks demonstrate the effectiveness of the proposed method. It can steadily improve existing STR models, and boost an STR model to achieve new state-of-the-art results. To our best knowledge, this is the first consistency regularization based framework that applies successfully to STR. © 2022, CC BY.

关键词： Character recognition

来源：评论

学校读者我要写书评

暂无评论

Adaptive Backdoor Attacks with Reasonable Constraints on Graph Neural Networks

arXiv

引用

arXiv 2025年

作者： Dong, Xuewen Li, Jiachen Li, Shujun You, Zhichao Qu, Qiang Kholodov, Yaroslav Shen, Yulong School of Computer Science and Technology Xidian University The Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of Education China The Shaanxi Key Laboratory of Blockchain and Secure Computing Xi’an710071 China University of Kent CanterburyCT2 7NP United Kingdom Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Intelligent Transportation Systems Lab Innopolis University Innopolis Russia School of Computer Science and Technology Xidian University China The Shaanxi Key Laboratory of Network and System Security Xi’an710071 China

Recent studies show that graph neural networks (GNNs) are vulnerable to backdoor attacks. Existing backdoor attacks against GNNs use fixed-pattern triggers and lack reasonable trigger constraints, overlooking individual graph characteristics and rendering insufficient evasiveness. To tackle the above issues, we propose ABARC, the first Adaptive Backdoor Attack with Reasonable Constraints, applying to both graph-level and node-level tasks in GNNs. For graph-level tasks, we propose a subgraph backdoor attack independent of the graph’s topology. It dynamically selects trigger nodes for each target graph and modifies node features with constraints based on graph similarity, feature range, and feature type. For node-level tasks, our attack begins with an analysis of node features, followed by selecting and modifying trigger features, which are then constrained by node similarity, feature range, and feature type. Furthermore, an adaptive edge-pruning mechanism is designed to reduce the impact of neighbors on target nodes, ensuring a high attack success rate (ASR). Experimental results show that even with reasonable constraints for attack evasiveness, our attack achieves a high ASR while incurring a marginal clean accuracy drop (CAD). When combined with the state-of-the-art defense randomized smoothing (RS) method, our attack maintains an ASR over 94%, surpassing existing attacks by more than 7%. Copyright © 2025, The Authors. All rights reserved.

关键词： Graph neural networks

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：