检索结果-内蒙古大学图书馆

International Conference on Intelligent Transportation

作者： Fei Lin Yonglin Tian Yunzhe Wang Tengchao Zhang Xinyuan Zhang Fei-Yue Wang Department of Engineering Science Faculty of Innovation Engineering Macau University of Science and Technology Macau China Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Institute of Automation Beijing China Capital University of Economics and Business Beijing China Department of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Beijing

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

In urban environments, complex transportation demands, including land, air, and maritime transport, are increasingly growing. While significant advancements have been made in land transportation and autonomous driving, research on urban air mobility (UAM) systems is still in its early stages. This paper presents an innovative urban air unmanned aerial vehicle (UAV) framework: AirVista, which is designed and built based on the artificial Systems, Computational experiments, and Parallel execution (ACP) approach, integrated with a multimodal large language model (MLLM) agent. Considering that UAM tasks often require UAVs to possess fine-grained spatial perception and reasoning capabilities, and given that existing MLLMs are somewhat lacking in exploring 3D spaces, this paper further proposes an instruction fine-tuning strategy integrated with 3D spatial knowledge, which has been validated experimentally for its effectiveness. Additionally, to enhance the understanding of the efficiency of MLLM for UAV tasks, this paper delves into prompt fine-tuning templates tailored for UAV task decomposition. Through experimental demonstrations, we showcase that the prompt-tuned MLLM exhibits efficient task decomposition and execution capabilities in handling complex UAV tasks.

关键词： Three-dimensional displays Large language models Urban areas Land transportation Autonomous aerial vehicles Cognition Space exploration Planning

来源：评论

学校读者我要写书评

暂无评论

Quantifying the Benefits of Implementing Big data Analytics on Consumers’ Online Purchase Intentions in e-Commerce

Quantifying the Benefits of Implementing Big Data Analytics ...

引用

Computing (ICOCO), IEEE International Conference on

作者： Justin Ang Kang Wei Mahayaudin M. Mansor Zuraidah Derasit Mohd Aftar Abu Bakar Ahmad Zia Ul-Saufie Firdaus Mohamad Hamzah Faculty of Business Economics and Accounting HELP University Kuala Lumpur Malaysia School of Mathematical Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Malaysia Institute for Big Data Analytics and Artificial Intelligence Universiti Teknologi MARA Shah Alam Selangor Malaysia Department of Mathematical Sciences Faculty of Science & Technology Universiti Kebangsaan Malaysia Malaysia Centre for Defence Foundation Studies Universiti Pertahanan Nasional Malaysia Kuala Lumpur Malaysia

ISBN: (数字)9798331530303

ISBN: (纸本)9798331530310

The digital revolution has reshaped the global economy, especially e-commerce. Innovative technologies such as big data analytics, artificial intelligence, and cloud computing drive this transformation, enabling companies to make informed business decisions and influence consumers’ online purchasing behaviour. This study explores empirical evidence for the benefits of using big data analytics (BDA) to influence consumers’ online purchasing decisions in e-commerce. Primary data was collected via an online survey targeting 300 customers with online shopping experience on platforms that have adopted the digital technologies of BDA. Statistical methods used in this study included questionnaire design, reliability test, descriptive analysis, and multiple regression modelling and diagnostics. The findings indicate that the benefits of implementing BDA in e-commerce for information search, recommendation systems, consumer services, dynamic pricing, security and fraud detection are strongly associated with consumers’ online purchasing intentions. The study underscores the importance of businesses leveraging BDA, as the benefits significantly and positively impact consumers’ purchasing intentions in e-commerce while addressing security and fraud detection concerns that undermine its advantage. Nevertheless, we encourage all companies to leverage BDA implementation in their digital marketplace, which ultimately contributes to achieving e-commerce goals, such as increased market reach, cost reduction, and revenue growth.

关键词： Surveys Statistical analysis Companies Big data Reliability engineering Fraud Electronic commerce Security Recommender systems Business

来源：评论

学校读者我要写书评

暂无评论

Enhanced Cardiovascular Disease Diagnosis Using Multi-Label Machine Learning and Stacking Cross-Validation

Enhanced Cardiovascular Disease Diagnosis Using Multi-Label ...

引用

International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Dharmesh Dhabliya K.S. Bhuvaneshwari Hitesh Kalra Senthurya S Nitish Vashisht Batani Raghavendra Rao Department of Information Technology Vishwakarma Institute of Technology Pune India Department of Artificial Intelligence and Data Science Karpagam College of Engineering Coimbatore India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of CSE Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Mangement - PG Management JAIN (Deemed to be University) Bangalore Karnataka India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

The necessity for early access to reliable models is heightened by the fact that cardiovascular disease (CVD) remains a leading global killer. This study also looks at how multi-label algorithms for machine learning may be used to better diagnose cardiovascular diseases. In this study we utilized different types of ML procedures to successfully analyze various heart disease datasets, including LR Classifier, SVM, KNN, Choice Tree, Random Forestry, and Gradient Boosting Classifiers. Conventional methods provide the groundwork for prediction, according to comparative research, but they often fall short of what is needed for peak performance and accuracy. We provide a refined model that addresses these limitations by use of a Stacking Cross-Validation (CV) Classifier. To get the best results, this model takes the best parts of many approaches and merges them. The proposed model's prediction power for cardiac illness detection is substantially enhanced by using classifiers like random forest models and Gradient Boosting. By optimizing the reproduction's generalizability, recall, accurateness, correctness, and F1-score, the Stacking CV approach ensures consistent and reliable performance across many datasets. The experimental findings show that the created Stacking CV Classifier outperforms the other methods utilized in this study with an accuracy of 98%. More accurate diagnoses of cardiovascular disease may be possible with this method, and it also outperforms the most advanced ML methods when it comes to predicting CVD in its early stages.

关键词： Heart Support vector machines Accuracy Stacking Predictive models Boosting Prediction algorithms Cardiovascular diseases Reliability Random forests

来源：评论

学校读者我要写书评

暂无评论

LDHP: Experimental Evaluation Of A Secured Blockchain Based data Management Framework Using Lightweight Document Handling Principle

LDHP: Experimental Evaluation Of A Secured Blockchain Based ...

引用

International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： P Sudheer Praveen Talari Prabhakar S Monika Sirigiri S. Saravanan M Dinesh Department of CSE (AI & ML) CVR College of Engineering Hyderabad India Department of CSE Vignana Bharathi Institute of Technology Hyderabad India Department of Mathematics S.A Engineering College Chennai Tamil Nadu India Department of ECE MLR Institute of Technology Hyderabad India Department of Artificial Intelligence & Data Science Jeppiaar Institute of Technology Chennai Tamil Nadu India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

One of the most important technologies that will drive the shift from the Information Internet to the Value Internet is blockchain. How to utilize blockchain for data management is becoming a hot topic as more and more companies investigate block-chain’s potential industrial uses. Blockchain technology's intrinsic scalability issues become apparent, however, while dealing with an enormous amount of IoT devices as well as the vast volumes of data created by these networks. Consequently, to address these issues, this study employs a Lightweight Document Handling Principle (LDHP). To manage data from the Internet of Things (IoT), we provide a blockchain-based platform that can scale to accommodate many devices. The suggested scheme's efficacy is assessed by cross-validating this method with the traditional model known as Decentralized data Security (DDS). The authors of this work presented a solid method for checking database consistency and tracking down errors. To do this, the suggested LDHP is used in a secondary consensus. This method greatly improves the reliability and accuracy of data management by solving the common problem of database mismatches and excelling in other critical areas including data security, verification of information, and error tracing. In order to facilitate future developments in safe and effective data handling, we provide a robust solution through. Modernizing data management using blockchain technology may boost efficiency, enhance data quality, and foster an atmosphere conducive to exchanging information.

关键词： databases data security Document handling data integrity System performance Scalability Smart contracts

来源：评论

学校读者我要写书评

暂无评论

Training of Spiking Neural Network joint Curriculum Learning Strategy

arXiv

引用

arXiv 2023年

作者： Tang, Lingling Chu, Jielei Gong, Zhiguo Li, Tianrui The School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China The State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau China

Starting with small and simple concepts, and gradually introducing complex and difficult concepts is the natural process of human learning. Spiking Neural Networks (SNNs) aim to mimic the way humans process information, but current SNNs models treat all samples equally, which does not align with the principles of human learning and overlooks the biological plausibility of SNNs. To address this, we propose a CL-SNN model that introduces Curriculum Learning(CL) into SNNs, making SNNs learn more like humans and providing higher biological interpretability. CL is a training strategy that advocates presenting easier data to models before gradually introducing more challenging data, mimicking the human learning process. We use a confidence-aware loss to measure and process the samples with different difficulty levels. By learning the confidence of different samples, the model reduces the contribution of difficult samples to parameter optimization automatically. We conducted experiments on static image datasets MNIST, Fashion-MNIST, CIFAR10, and neuromorphic datasets N-MNIST, CIFAR10-DVS, DVS-Gesture. The results are promising. To our best knowledge, this is the first proposal to enhance the biologically plausibility of SNNs by introducing CL. Copyright © 2023, The Authors. All rights reserved.

关键词： Curricula

来源：评论

学校读者我要写书评

暂无评论

Exploring the Impact of Non-Verbal Virtual Agent Behavior on User Engagement in Argumentative Dialogues

arXiv

引用

arXiv 2024年

作者： Aicher, Annalena Bea Minker, Wolfgang Matsuda, Yuki André, Elisabeth Yasumoto, Keichii Ultes, Stefan Human-Centered Artificial Intelligence University of Augsburg Augsburg Germany Institute of Communications Engineering Ulm University Ulm Germany Faculty of Environmental Life Natural Science and Technology Okayama University Okayama Japan Ubiquitous Computing Systems Laboratory NAIST Ikoma Nara Japan Natural Language Generation and Dialogue Systems University of Bamberg Bamberg Germany

Engaging in discussions that involve diverse perspectives and exchanging arguments on a controversial issue is a natural way for humans to form opinions. In this process, the way arguments are presented plays a crucial role in determining how engaged users are, whether the interaction takes place solely among humans or within human-agent teams. This is of great importance as user engagement plays a crucial role in determining the success or failure of cooperative argumentative discussions. One main goal is to maintain the user’s motivation to participate in a reflective opinion-building process, even when addressing contradicting viewpoints. This work investigates how non-verbal agent behavior, specifically co-speech gestures, influences the user’s engagement and interest during an ongoing argumentative interaction. The results of a laboratory study conducted with 56 participants demonstrate that the agent’s co-speech gestures have a substantial impact on user engagement and interest and the overall perception of the system. Therefore, this research offers valuable insights for the design of future cooperative argumentative virtual agents. © 2024, CC BY-NC-ND.

关键词： Chatbots

来源：评论

学校读者我要写书评

暂无评论

Hybrid ANFIS Model For Depression Recognition Consuming Three-Channel EEG data

Hybrid ANFIS Model For Depression Recognition Consuming Thre...

引用

Recent Advances in science and engineering technology (ICRASET), International Conference on

作者： Dharmesh Dhabliya K.S. Bhuvaneshwari Prakriti Kapoor S Sowmiya Avni Garg L Yashoda Department of Information Technology Vishwakarma Institute of Technology Pune India Department of Artificial Intelligence and Data science Karpagam College of Engineering Coimbatore India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of CSE Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Mangement UGManagement JAIN (Deemed to be University) Bangalore Karnataka India

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

The goal of this effort is to make it more widely used by detecting depression with fewer EEG modes, namely Fp1, Fp2, and Fz. Band power (delta, theta, alpha, and beta) and sub-bands obtained through the selected channels are then used in the research. It also assesses the use of Hybrid ANFIS as well as its variants as classification systems, including GA-ANFIS, PSO-ANFIS, and FA-ANFIS. Filtering and re-referencing to CAR, ICA, and visually are some of the methods used to reduce artifacts and noise in fMRI information. A publicly available information is used in the process, which is then partitioned into a set for training and a testing set. Using ANFIS-GA, this research achieved a high classification accuracy of ${8 5 . 5 9 \%}$, indicating that the suggested framework might be helpful for physicians when detecting depressive disorders. Potentially beneficial uses are demonstrated by the findings, which indicate that depressive disorders may be effectively and easily identified using a minimal number of channels and characteristics. Consequently, the project’s methodology and the implementation of the enhanced hybrid ANFIS systems provide useful data about the use of EEG for depressive disorders diagnosis. Given these findings, it’s reasonable to assume that the suggested technique has the potential to contribute to the enhancement of depressive disorders testing methods.

关键词： Training Accuracy Protocols Instruments Noise Medical services Functional magnetic resonance imaging Depression Electroencephalography Hybrid power systems

来源：评论

学校读者我要写书评

暂无评论

Federated learning for privacy-preserving open innovation future on digital health

arXiv

引用

arXiv 2021年

作者： Long, Guodong Shen, Tao Tan, Yue Gerrard, Leah Clarke, Allison Jiang, Jing Australian Artificial Intelligence Institute Faculty of Engineering and Information Technology University of Technology Sydney Data and Analytics Branch Health Economics and Research Division Australian Department of Health

Privacy protection is an ethical issue with broad concern in artificial intelligence (AI). Federated learning is a new machine learning paradigm to learn a shared model across users or organisations without direct access to the data. It has great potential to be the next-general AI model training framework that offers privacy protection and therefore has broad implications for the future of digital health and healthcare informatics. Implementing an open innovation framework in the healthcare industry, namely open health, is to enhance innovation and creative capability of health-related organisations by building a next-generation collaborative framework with partner organisations and the research community. In particular, this game-changing collaborative framework offers knowledge sharing from diverse data with a privacy-preserving. This chapter will discuss how federated learning can enable the development of an open health ecosystem with the support of AI. Existing challenges and solutions for federated learning will be discussed. Copyright © 2021, The Authors. All rights reserved.

关键词： E-learning

来源：评论

学校读者我要写书评

暂无评论

Federated Learning for 6G Communications:Challenges,Methods,and Future Directions

引用

China Communications 2020年第9期17卷 105-118页

作者： Yi Liu Xingliang Yuan Zehui Xiong Jiawen Kang Xiaofei Wang Dusit Niyato School of Data Science of Technology Heilongjiang UniversityHarbinChina Faculty of Information Technology Monash UniversityAustralia Alibaba-NTU Joint Research Institute and also School of Computer Science and Engineering NTUSingapore Energy Research Institute Nanyang Technological UniversitySingapore College of Intelligence and Computing Tianjin UniversityTianjinChina School of Computer Science and Engineering NTUSingapore

As the 5G communication networks are being widely deployed worldwide,both industry and academia have started to move beyond 5G and explore 6G *** is generally believed that 6G will be established on ubiquitous artificial intelligence(AI)to achieve data-driven Machine Learning(ML)solutions in heterogeneous and massive-scale ***,traditional ML techniques require centralized data collection and processing by a central server,which is becoming a bottleneck of large-scale implementation in daily life due to significantly increasing privacy *** learning,as an emerging distributed AI approach with privacy preservation nature,is particularly attractive for various wireless applications,especially being treated as one of the vital solutions to achieve ubiquitous AI in *** this article,we first introduce the integration of 6G and federated learning and provide potential federated learning applications for *** then describe key technical challenges,the corresponding federated learning methods,and open problems for future research on federated learning in the context of 6G communications.

关键词： 6G communication federated learning security and privacy protection

来源：评论

学校读者我要写书评

暂无评论

RecCoder: Reformulating Sequential Recommendation as Large Language Model-Based Code Completion

RecCoder: Reformulating Sequential Recommendation as Large L...

引用

IEEE International Conference on data Mining (ICDM)

作者： Kai-Huang Lai Wu-Dong Xi Xing-Xing Xing Wei Wan Chang-Dong Wang Min Chen Mohsen Guizani School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Contribution during internship at NetEase Games UX Center NetEase Games Guangzhou China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangzhou China School of Computer Science and Engineering South China University of Technology Guangzhou China Pazhou Laboratory Guangzhou China Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) Abu Dhabi UAE

ISBN: (数字)9798331506681

ISBN: (纸本)9798331506698

In the evolving landscape of sequential recommendation systems, the application of Large Language Models (LLMs) is increasingly prominent. However, current attempts typically utilize general-purpose LLMs, which present a mismatch in capability and a large semantic gap relative to the specialized needs of recommendation tasks. To tackle these issues, we introduce RecCoder, an innovative model that reformulates sequential recommendation as a code completion task. This approach leverages the superior reasoning capability of code LLMs as a backbone, aligning well with the requirements of recommendation systems. To bridge the semantic gap, RecCoder creates extra tokens for each item and employs item content to initialize token embeddings. Furthermore, we have developed a suite of Semantic Adaptation Fine-tuning tasks, tailored to enhance the model's acquisition of both content and collaborative semantic information, thus aligning the model's intrinsic capabilities with the unique demands of recommendation tasks. Through extensive testing on three public datasets, RecCoder has shown remarkable improvements over existing models in terms of recommendation accuracy and efficiency. This success highlights the substantial yet previously underexplored potential of code LLMs in improving recommendation accuracy and efficiency, suggesting a promising new direction for future research in this area. The implementation code is accessible at https://***/AllminerLab/Code-for-RecCoder-master.

关键词： Adaptation models Codes Accuracy Large language models Semantics Cognition data models data mining Recommender systems Testing

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：