检索结果-内蒙古大学图书馆

19th IEEE International Conference on e-science, e-science 2023

作者： Li, Na Zhang, Yangjun Zhao, Zhiming Multiscale Networked System Informatics Institute University of Amsterdam Netherlands School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Amsterdam Netherlands

ISBN: (纸本)9798350322231

The discovery and reutilization of scientific codes are crucial in many research activities. Computational notebooks have emerged as a particularly effective medium for sharing and reusing scientific codes. Nevertheless, effectively locating relevant computational notebooks is a significant challenge. First, computational notebooks encompass multi-modal data comprising unstructured text, source code, and other media, posing complexities in representing such data for retrieval purposes. Second, the absence of evaluation datasets for the computational notebook search task hampers fair performance assessments within the research community. Prior studies have either treated computational notebook search as a code-snippet search problem or focused solely on content-based approaches for searching computational notebooks. To address the aforementioned difficulties, we present DeCNR, tackling the information needs of researchers in seeking computational notebooks. Our approach leverages a fused sparse-dense retrieval model to represent computational notebooks effectively. Additionally, we construct an evaluation dataset including actual scientific queries, computational notebooks, and relevance judgments for fair and objective performance assessment. Experimental results demonstrate that the proposed method surpasses baseline approaches in terms of F1@5 and NDCG@5. The proposed system has been implemented as a web service shipped with REST APIs, allowing seamless integration with other applications and web services. © 2023 IEEE.

关键词： Web services

来源：评论

学校读者我要写书评

暂无评论

Cluster Head Selection Algorithm using Machine Learning 4

Cluster Head Selection Algorithm using Machine Learning

引用

4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Nidhya, M.S. Gurumoorthi, E. Babu, M. Charumathi, A.C. Naved, Mohd Maram, Balajee School of Computer Applications Dayananda Sagar University Bangalore India School of Engineering Malla Reddy University India Chennai Institute of Technology College Dept of Ai & Ds Chennai India Sona College of Technology Dept of Information Technology India Jaipuria Institute of Management Dept of Business Analytics Noida India School of Computer Science and Artificial Intelligence Sr University Ananthasagar Hasanparthy Warangal India

ISBN: (纸本)9798331523923

Wireless Sensor Networks (WSNs) find extensive applications in environmental monitoring, healthcare, and smart cities. Energy efficiency, however, continues to be a significant challenge with the limited lifetime of sensor node batteries. The conventional heuristic-based cluster head (CH) selection techniques tend not to adapt dynamically to network changes, resulting in inefficient energy utilization and decreased network lifetime. This research discusses Machine Learning (ML)-oriented CH selection approaches to improve energy efficiency, network lifetime, and data fusion. Using supervised and unsupervised learning methods, ML algorithms are able to learn optimal CHs dynamically based on residual energy, network structure, and data traffic. The comparative analysis show that ML-based CH selection enhances network stability by 95% and lowers energy consumption by 50% compared to traditional methods. The research points to the promise of ML in WSN performance optimization and opens the doors to intelligent, adaptive clustering technologies. © 2025 IEEE.

关键词： Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

Big data analytics in Healthcare——A Systematic Literature Review and Roadmap for Practical Implementation

引用

IEEE/CAA Journal of Automatica Sinica 2021年第1期8卷 1-22页

作者： Sohail Imran Tariq Mahmood Ahsan Morshed Timos Sellis the Faculty of Computer Science Karachi Institute of Economics and TechnologyKarachi 75190Pakistan the Faculty of Computer Science Institute of Business AdministrationKarachi 75270Pakistan the School of Engineering and Technology CQ UniversityMelbourne 3000Australia the Data Science Research Institute Swinburne University of TechnologyHawthorn 3122Australia

The advent of healthcare information management systems(HIMSs)continues to produce large volumes of healthcare data for patient care and compliance and regulatory requirements at a global *** of this big data allows for boundless potential outcomes for discovering *** data analytics(BDA)in healthcare can,for instance,help determine causes of diseases,generate effective diagnoses,enhance Qo S guarantees by increasing efficiency of the healthcare delivery and effectiveness and viability of treatments,generate accurate predictions of readmissions,enhance clinical care,and pinpoint opportunities for cost ***,BDA implementations in any domain are generally complicated and resource-intensive with a high failure rate and no roadmap or success strategies to guide the *** this paper,we present a comprehensive roadmap to derive insights from BDA in the healthcare(patient care)domain,based on the results of a systematic literature *** initially determine big data characteristics for healthcare and then review BDA applications to healthcare in academic research focusing particularly on No SQL *** also identify the limitations and challenges of these applications and justify the potential of No SQL databases to address these challenges and further enhance BDA healthcare *** then propose and describe a state-of-the-art BDA architecture called Med-BDA for healthcare domain which solves all current BDA challenges and is based on the latest zeta big data *** also present success strategies to ensure the working of Med-BDA along with outlining the major benefits of BDA applications to ***,we compare our work with other related literature reviews across twelve hallmark features to justify the novelty and importance of our *** aforementioned contributions of our work are collectively unique and clearly present a roadmap for clinical administrators,practitioners and professionals to successf

关键词： Big data analytics(BDA) big data architecture healthcare NoSQL data stores patient care roadmap systematic literature review

来源：评论

学校读者我要写书评

暂无评论

Beyond Scaleup: Knowledge-aware Parsimony Learning from Deep Networks

arXiv

引用

arXiv 2024年

作者： Yao, Quanming Zhang, Yongqi Wang, Yaqing Yin, Nan Kwok, James Yang, Qiang Department of Electronic Engineering the Tsinghua University China Department of Data Science and Analytics Thrust the Hong Kong University of Science and Technology Guangzhou China Beijing Institute of Mathematical Sciences and Applications China Department of Computer Science and Engineering the Hong Kong University of Science and Technology Hong Kong

The brute-force scaleup of training datasets, learnable parameters and computation power, has become a prevalent strategy for developing more robust learning models. However, due to bottlenecks in data, computation, and trust, the sustainability of this strategy is a serious concern. In this paper, we attempt to address this issue in a parsimonious manner (i.e., achieving greater potential with simpler models). The key is to drive models using domain-specific knowledge, such as symbols, logic, and formulas, instead of purely relying on scaleup. This approach allows us to build a framework that uses this knowledge as "building blocks" to achieve parsimony in model design, training, and interpretation. Empirical results show that our methods surpass those that typically follow the scaling law. We also demonstrate our framework in AI for science, specifically in the problem of drug-drug interaction prediction. We hope our research can foster more diverse technical roadmaps in the era of foundation models. © 2024, CC BY.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Minimize the Energy Consumption to Increase the Network Lifetime for Green IoT Environment

Minimize the Energy Consumption to Increase the Network Life...

引用

International Conference on Machine Learning and Big data analytics, ICMLBDA 2023

作者： Dekka, Satish Raju, K. Narasimha Sai, D. Manendra Pallavi, M. Sambana, Bosubabu JNTU-GV Andhra Pradesh Vizianagaram India Andhra University Andhra Pradesh Visakhapatnam India Department of Computer Science and Engineering Vignan’s Institute of Engineering for Women Visakhapatnam JNTU-GV Andhra Pradesh Vizianagaram India Department of Data Science School of Computing Mohan Babu University Andhra Pradesh Tirupathi India

ISBN: (纸本)9783031513374

One of the most important and complex issues in the IoT context is energy usage. Because the many nodes utilized in IoT contexts are energy-constrained and must operate for long periods of time without human interaction, their batteries quickly run out of power. As a result, nodes must manage their energy effectively. Sensing, communicating, and processing are regarded as the primary functions of IoT sensors. IoT connectivity uses more energy at the node level than at other times. The main motto of this study is to reduce energy consumption and lengthen network life. Although routing also has a major impact on energy usage, the first one deals with employing an energy model, and the second one deals with selecting an effective routing protocol. When picking a protocol, various other performance indicators such as throughput, delay, packet delivery ratio, and packet loss are also considered. The Network Simulator 3 is used as a tool for testing these strategies (NS3). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Energy Model Green IoT Green networks MANET NS3 Routing

来源：评论

学校读者我要写书评

暂无评论

Comparative Evaluation of Attribute-Enabled Supervised Classification in Predicting the Air Quality

引用

Spatial Information Research 2023年第4期31卷 399-407页

作者： Subbulakshmi, P. Vimal, S. Robinson, Y. Harold Verma, Amit Nayak, Janmenjoy School of computer science and Engineering VIT Chennai campus Tamilnadu India Data Analytics Lab Department of Artificial Intelligence and Data Science Ramco Institute of Technology Rajapalayam India Department of Computer Science and Engineering Francis Xavier Engineering College Tirunelveli India Department of Computer Science & Engineering University Centre for Research & Development Chandigarh University Gharuan Mohali India Department of Computer Science Maharaja Sriram Chandra Bhanja Deo University Odisha India

Air pollution demonstrates the appearance of toxins into the air which is blocking human prosperity and the earth. It will portray as potentially the riskiest threats that humanity anytime faced. It makes hurt animals, harvests to thwart these issues in transportation territories need to expect air quality from pollutions utilizing AI systems and IoT. Along these lines, air quality evaluation and assumption has become a huge target for human health factors and also affect internal organs related to respiratory. The accuracy of Air Pollution prediction has been involved with the machine learning techniques and the best accuracy model is identified. The air quality prediction dataset is used for identifying the meteorology air pollution data while the predicted model is involved the decision tree computation for predicting the toxin contents in the region, the Air quality indicator is used to assess the pollution level and monitoring the air quality. The performance analysis shows that the decision tree technique has produced the better results in the performance metrics of Accuracy, precision, recall, and F1-score with the minimized error values while the comparative evaluation of Attribute-enabled classification has identified the best technique for predicting the air quality.

关键词：

来源：评论

学校读者我要写书评

暂无评论

GCN-LSTM Based Transient Angle Stability Assessment Method for Future Power Systems Considering Spatial-temporal Disturbance Response Characteristics

引用

Protection and Control of Modern Power Systems 2024年第6期9卷 108-121页

作者： Shiwei Xia Chenhui Zhang Yahan Li Gengyin Li Linlin Ma Ning Zhou Ziqing Zhu Huan Ma the School of Electrical and Electronic Engineering North China Electric Power UniversityBeijing 102206China the State Grid Shandong Dispatch and Control Center Jinan 250003China the State Grid Shandong Electric Power Research Institute Jinan 250001China the Department of Computer Science Hong Kong Baptist UniversityHong Kong 999077China

Traditional transient angle stability analysis methods do not fully consider the spatial characteristics of the network topology and the temporal characteristics of the time-series ***,a data-driven method is proposed in this study,combining graph convolution network and long short-term memory network(GCN-LSTM)to analyze the transient power angle sta-bility by exploring the spatiotemporal disturbance char-acteristics of future power systems with high penetration of renewable energy sources(wind and solar energy)and power *** key time-series electrical state quantities are considered as the initial input feature quantities and normalized using the Z-score,whereas the network adjacency matrix is constructed according to the system network *** normalized feature quan-tities and network adjacency matrix were used as the inputs of the GCN to obtain the spatial features,reflecting changes in the network ***,the spa-tial features are inputted into the LSTM network to ob-tain the temporal features,reflecting dynamic changes in the transient power angle of the ***,the spatiotemporal features are fused through a fully con-nected network to analyze the transient power angle stability of future power systems,and the softmax activa-tion cross-entropy loss functions are used to predict the stability of the *** proposed transient power angle stability assessment method is tested on a 500 kV AC-DC practical power system,and the simulation results show that the proposed method could effectively mine the spatiotemporal disturbance characteristics of power sys-tems. Moreover, the proposed model has higher accuracy, higher recall rate, and shorter training and testing times than traditional transient power angle stability algo-rithms.

关键词： Future power system spatiotemporal disturbance characteristics transient power angle stabil-ity graph convolutional network long short-term memory network.

来源：评论

学校读者我要写书评

暂无评论

Artificial Intelligence: A Promising Prospect for Better Management of Diabetic Patients 5

Artificial Intelligence: A Promising Prospect for Better Man...

引用

5th IEEE International Conference on Artificial Intelligence and Big data, ICAIBD 2022

作者： Hossin, Md Altab Islam, Md Safiqul Hassan, Sani Abubakar Ukwuoma, Chiagoziem Chima Nuri, Most. Miftahul Jannat Siddiquee, Md. Tofael Alam Chengdu University School of Innovation and Entrepreneurship Chengdu China Thakurgaon Polytechnic Institute Department of Computer Technology Thakurgaon Bangladesh University of Electronic Science and Technology of China School of Electronics Science and Engineering Chengdu China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China Hajee Mohammad Danesh Science and Technology University Department of Electronic and Communication Engineering Dinajpur Bangladesh Mymensingh Polytechnic Institute Department of Computer Science and Engineering Mymensingh Bangladesh

ISBN: (纸本)9781665499132

Diabetic as an incurable chronic disease is increasing rapidly over time, and its impacts on other diseases are also striking. Indeed, science and technology have drastically developed, and it is also in the healthcare section. Getting diabetes education to help self-management is essential to help patients enhance their metabolic control and quality of life. Using artificial intelligence (AI) technologies, we can make significant progress in transforming available genetic data and clinical information into valuable knowledge. In disease education, given its potential benefits in encouraging individualization and full-course education intervention, AI would be extremely advantageous based on the unique situations of different individuals. The use of AI in different facets of diabetes education was explored based on collected data. © 2022 IEEE.

关键词： Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Self-Paced Neutral Expression-Disentangled Learning for Facial Expression Recognition

Self-Paced Neutral Expression-Disentangled Learning for Faci...

引用

Artificial Intelligence Technology (ACAIT), Asian Conference on

作者： Zhenqian Wu Xiaoyuan Li Yazhou Ren Xiaorong Pu Xiaofeng Zhu Lifang He School of Computer Science and Engineering University of Electronic Science and Technology of China School of Computer Science and Engineering Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Department of Computer Science and Engineering Lehigh University

ISBN: (数字)9798350359145

ISBN: (纸本)9798350359152

The accuracy of facial expression recognition is typically influenced by the following factors: high similarities across different expressions, disturbing factors, and the subtle and rapid micro-movements of the face. One potential solution to overcome these obstacles is to leverage the hidden neutral information present in neutral expression images. In this paper, we propose a Self-Paced Neutral Expression-Disentangled Learning (SPNDL) model, which aims to disentangle the neutral information from facial expressions, facilitating the extraction of crucial features and variations. This approach enables the capture of discriminative details within similar expressions and the detection of micro-facial movements. To enhance the learning of these neutral expression-disentangled features (NDFs) and mitigate the challenges posed by non-convex optimization, we propose a self-paced learning (SPL) strategy based on NDFs during the training phase. SPL learns samples from easy to complex by increasing the number of samples selected into the training process, which enables to effectively suppress the negative impacts caused by low-quality samples and inconsistently distributed NDFs. Experiments on three popular databases (i.e., CK+, Oulu-CASIA, and RAF-DB) show the effectiveness of our proposed method.

关键词： Training databases Face recognition Learning (artificial intelligence) Feature extraction data mining Optimization

来源：评论

学校读者我要写书评

暂无评论

Parm: Efficient Training of Large Sparsely-Activated Models with Dedicated Schedules

arXiv

引用

arXiv 2024年

作者： Pan, Xinglin Lin, Wenxiang Shi, Shaohuai Chu, Xiaowen Sun, Weinong Li, Bo Data Science and Analytics Thrust The Hong Kong University of Science and Technology Guangzhou China Department of Computer Science Hong Kong Baptist University Hong Kong School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

Sparsely-activated Mixture-of-Expert (MoE) layers have found practical applications in enlarging the model size of large-scale foundation models, with only a sub-linear increase in computation demands. Despite the wide adoption of hybrid parallel paradigms like model parallelism, expert parallelism, and expert-sharding parallelism (i.e., MP+EP+ESP) to support MoE model training on GPU clusters, the training efficiency is hindered by communication costs introduced by these parallel paradigms. To address this limitation, we propose Parm, a system that accelerates MP+EP+ESP training by designing two dedicated schedules for placing communication tasks. The proposed schedules eliminate redundant computations and communications and enable overlaps between intra-node and internode communications, ultimately reducing the overall training time. As the two schedules are not mutually exclusive, we provide comprehensive theoretical analyses and derive an automatic and accurate solution to determine which schedule should be applied in different scenarios. Experimental results on an 8-GPU server and a 32-GPU cluster demonstrate that Parm outperforms the state-of-the-art MoE training system, DeepSpeed-MoE, achieving 1.13× to 5.77× speedup on 1296 manually configured MoE layers and approximately 3× improvement on two real-world MoE models based on BERT and GPT-2. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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：