检索结果-内蒙古大学图书馆

2023 IEEE International Conference on big data, bigdata 2023

作者： Cuzzocrea, Alfredo Hajian, Mojtaba University of Calabria IDEA Lab Rende Italy University of Paris City Dept. of Computer Science Paris France Big Data Management and Analytics at University of Paris City Paris France

ISBN: (纸本)9798350324457

This paper introduces the Indexed Quad-Tree Summary (IQTS) algorithm, along with its concepts, models, and 'philosophy'. IQTS allows us to compress multidimensional OLAP views derived from big OLAP data cubes that populate Mobile Clouds. In addition to this, IQTS effectively and efficiently supports approximate query answering over compressed multidimensional OLAP views. These functionalities turn to be enabling functionalities for big data applications in a wide collection of modern scenarios, such as healthcare analytics. In light of these considerations, this paper provides motivations, anatomy and procedures of IQTS, enriched by several case studies that contribute to pinpoint the benefits coming from our proposed algorithm. © 2023 IEEE.

关键词： Approximate Query Answering big data big OLAP Cubes Intelligent big data Applications Mobile Clouds

来源：评论

学校读者我要写书评

暂无评论

Investigation on Machine Learning Algorithms to Predict High School Students Performance at Risk of Failure

Investigation on Machine Learning Algorithms to Predict High...

引用

2022 International Conference on data analytics for Business and Industry, ICDABI 2022

作者： Almehaiza, Fatema Jaafar, Layla Alatoom, Iyad Ali Mohammad Hewahi, Nabil Mahmood University of Bahrain College of Science MSc. in Big Data Science and Analytics Bahrain University of Bahrain College of Information Technology Department of Computer Science Bahrain

ISBN: (纸本)9781665490580

Successful learning is dependent on the success of the learners;ensuring students' achievement is the responsibility of the educational system. Over the years, educational institutions have utilized Machine Learning (ML) technology to facilitate and support decision-making in a variety of learning areas. Predicting students' performance in the early stages of their learning journey is one of ML's most significant contributions to educational institutions. Using ML technology, the institutes will be able to improve teaching and learning practices for students who are at risk of failing. The objective of this study is to build a model that can predict students' performance and identify students at risk of failure. The study examines the application of three ML algorithms in predicting: Logistic Regression (LR), Decision Tree (DT) and Random Forest (RF). The study used a sample size of 647 with one dependent variable and 33 distinct variables. The study found that the two tree-based methods performed better than LR, although the precession measure out of four measures shows that LR is slightly better. Overall, in this study RF has showed to be the best among them with an accuracy of80.37%, recall of 87.98%, and F1 rate of 91.78%. © 2022 IEEE.

关键词： Logistic regression

来源：评论

学校读者我要写书评

暂无评论

Navigating the Complexities of Municipal Waste Management: Enhancing Cost Prediction for Sustainable Urban Solutions 1st

Navigating the Complexities of Municipal Waste Management: E...

引用

1st International Research Conference on Computing Technologies for Sustainable Development, IRCCTSD 2024

作者： Minu, M.S. Samal, Tushar Bisani, Khushi Tewari, Aishwarya Department of Computer Science and Engineering with Specialisation in Big Data Analytics SRM Institute of Science and Technology Ramapuram Campus Chennai India

ISBN: (纸本)9783031823824

The escalating rates of municipal waste generation in urban areas worldwide present a critical challenge for effective waste management. This paper examines the complexities surrounding municipal waste management, emphasizing the significance of accurate cost prediction for sustainable decision-making. Urbanization, driven by population growth and rising standards of living, intensifies waste generation, while shifts in consumption patterns further complicate waste management efforts. Environmental concerns and regulatory pressures mandate innovative waste reduction strategies. Accurate cost prediction is paramount for budgetary planning and resource allocation, enabling proactive measures to optimize operational efficiency and mitigate financial risks. Emerging methodologies, including data analytics and predictive modeling, offer promising avenues for enhancing cost prediction accuracy by capturing the multifaceted drivers of cost variability. By leveraging these methodologies, decision-makers can forecast municipal waste management costs with precision, informing strategic initiatives to foster sustainable waste management practices in urban areas. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Decentralized finance

来源：评论

学校读者我要写书评

暂无评论

Comparing the Performance of Individual and Hybrid Classifiers for Medical datasets

Comparing the Performance of Individual and Hybrid Classifie...

引用

2022 International Conference on data analytics for Business and Industry, ICDABI 2022

作者： Alkhayyat, Aysha Musaaed, Elham Alheddi, Isa Hewahi, Nabil University of Bahrain MSc. in Big Data Science and Analytics College of Science Bahrain University of Bahrain College of Information Technology Department of Computer Science Bahrain

ISBN: (纸本)9781665490580

An accurate prediction of a certain medical condition is a crucial matter that is of great benefit to patients and healthcare systems. Machine Learning can be of a great help to the medical professionals in assisting them to precisely predict the occurrence of medical conditions. This research paper aims to enhance the prediction capabilities of machine learning models, so it become more adopted in the medical field. To fill in this gap, the capabilities of the ensemble hybrid model were explored in comparison to individual models. The machine learning algorithms were tested using five medical datasets pertaining to five different diseases. The Random Forest model was the best performing individual model. In terms of accuracy, the ensemble hybrid model performance was close to the individual models in all datasets, except for the Liver Disease dataset where it exceled. This research could be extended to experiment the integrated hybrid models along with the optimization techniques. © 2022 IEEE.

关键词： Logistic regression

来源：评论

学校读者我要写书评

暂无评论

A Comparative Study Of Heart Disease Prediction Using Tree-Based Ensemble Classification Techniques

A Comparative Study Of Heart Disease Prediction Using Tree-B...

引用

2022 International Conference on data analytics for Business and Industry, ICDABI 2022

作者： Aldossary, Yasmeen Ebrahim, Majeed Hewahi, Nabil University of Bahrain MSc. in Big Data Science and Analytics College of Science Bahrain University of Bahrain College of Information Technology Department of Computer Science Bahrain

ISBN: (纸本)9781665490580

In recent years, the prediction of heart disease has been one of the most complicated tasks in the medical field. Approximately one person dies per minute due to heart disease in the modern era. To help the healthcare industry experts give an early detection in preventing the progression of the disease, Machine Learning offers various algorithms and techniques to achieve this goal. This study compares the performance of tree-based models Random Forest, Decision Tree, Extra Trees, and Gradient Boosting using the Cleveland heart dataset. It investigates whether applying two different ensemble techniques Voting and Stacking, to tree-based models improves heart disease diagnosis performance. The obtained results revealed that the Extra Trees model outweighs the other three models with an accuracy of 92%, whereas the Decision Tree model has the lowest accuracy with 84%. Applying feature selection has enhanced the performance of Random Forest and Gradient Boosting models. As for the ensemble techniques, the Staking ensemble model had the same performance as the Extra trees, with an accuracy of 92%. In contrast, the Voting ensemble model has a lower performance with a 90% accuracy. © 2022 IEEE.

关键词： Heart

来源：评论

学校读者我要写书评

暂无评论

Classification of Water Potability Using Machine Learning Algorithms

Classification of Water Potability Using Machine Learning Al...

引用

2022 International Conference on data analytics for Business and Industry, ICDABI 2022

作者： Yusuf, Husain Alhaddad, Salman Yusuf, Salman Hewahi, Nabil University of Bahrain MSc. in Big Data Science and Analytics College of Science Bahrain University of Bahrain College of Information Technology Department of Computer Science Bahrain

ISBN: (纸本)9781665490580

Water is the most important resource for sustaining life. Everyone has the right to have access to pollution-free water. The achievement of safe drinking water leads to tangible benefits to health. According to the World Health organization (WHO), the number of global deaths due to water diseases is about two and a half million deaths per year. Therefore, it is important to study new applications for analyzing and classifying safe water quality. In this research, the classification algorithms of K Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Artificial Neural Network (ANN), Logistic Regression (LR), and Support Vector Machine (SVM) are used to classify the potability of the drinking water. RF and DT achieved the highest performance with an accuracy of 83.78% and 74.98% respectively. The lowest classifier accuracy is LR with 48.74%. In addition, the most important features of the RF model are pH, followed by Hardness, followed by Sulfate. On other hand, the least important feature is Turbidity. © 2022 IEEE.

关键词： Neural networks

来源：评论

学校读者我要写书评

暂无评论

Semantic grounding of LLMs using knowledge graphs for query reformulation in medical information retrieval

Semantic grounding of LLMs using knowledge graphs for query ...

引用

IEEE International Conference on big data

作者： Antonela Tommasel Ira Assent ISISTAN CONICET-UNCPBA Argentina Department of Computer Science Aarhus University DIGIT Aarhus University Centre for Digitalisation Big Data and Data Analytics Denmark

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

The widespread adoption of electronic health records has generated a vast amount of patient-related data, mostly presented in the form of unstructured text, which could be used for document retrieval. However, querying these texts in full could present challenges due to their unstructured and lengthy nature, as they may contain noise or irrelevant terms that can interfere with the retrieval process. Recently, large language models (LLMs) have revolutionized natural language processing tasks. However, despite their promising capabilities, their use in the medical domain has raised concerns due to their lack of understanding, hallucinations, and reliance on outdated knowledge. To address these concerns, we evaluate a Retrieval Augmented Generation (RAG) approach that integrates medical knowledge graphs with LLMs to support query refinement in medical document retrieval tasks. Our initial findings from experiments using two benchmark TREC datasets demonstrate that knowledge graphs can effectively ground LLMs in the medical domain.

关键词： Grounding Law Retrieval augmented generation Semantics Pipelines Noise Knowledge graphs Benchmark testing Natural language processing Resource management

来源：评论

学校读者我要写书评

暂无评论

Joint Routing and Scheduling for In-Vehicle Networks: A Deterministic Perspective

IEEE Transactions on Intelligent Vehicles

引用

IEEE Transactions on Intelligent Vehicles 2024年第11期9卷 1-15页

作者： Wang, Wenxun Wang, Wenyong Yu, Haisheng Wu, Bochun Zou, Sai Ni, Wei Zhang, Ji School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China International Institute of Next Generation Internet Macau University of Science and Technology Macau China State Key Laboratory of Integrated Chips and Systems Fudan University Shanghai China College of Big Data and Information Engineering Guizhou University Guiyang China Data61 Business Unit CSIRO Canberra ACT Australia PA Company SC Branch China Telecom Co. LTD Chendu China

Deterministic transmission refers to the guaranteed delivery of data to its destination within a specific time frame along an optimal route, making it an indispensable prerequisite for the operation of autonomous vehicles. The in-vehicle networks (IVNs) face significant challenges in the deterministic transmission of large volumes of perception and control data from and to various vehicle components. In this paper, the cooperative scheduling and routing problem among multi-area control units (ACUs) in IVN is discussed from the view of deterministic transmission, and a ResVmix method is proposed to find the optimal two-layer assignment with ACU cooperation. The multi-cycle queuing and forwarding (multi-CQF) mechanism is employed to schedule the perceived data according to different quality of service (QoS) requirements. The cooperation among multiple ACUs enables deterministic routing of data. The two-layer optimization problems on multi-CQF and ACU are formulated as a+ decentralized partially observable Markov decision processes (Dec-POMDP) and solved using the enhanced multi-agent deep reinforcement learning (MADRL) method. Simulation results demonstrate that ResVmix significantly enhances the deterministic transmission performance of IVN. Compared to traditional shortest path algorithms and state-of-the-art MADRL methods, ResVmix achieves a 22.4% increase in arrival rate and a 20.1% increase in confirmed arrivals, respectively. IEEE

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

On Counterfactual Explanations of Cardiovascular Risk in Adolescent and Young Adult Breast Cancer Survivors 3

On Counterfactual Explanations of Cardiovascular Risk in Ado...

引用

3rd AIxIA Workshop on Artificial Intelligence For Healthcare, HC@AIxIA 2024

作者： Bernasconi, Alice Balordi, Antonio Zanga, Alessio Cabañas, Rafael University of Milano-Bicocca Milan Italy Evaluative Epidemiology Unit Department of Epidemiology and Data Science Fondazione IRCCS Istituto Nazionale dei tumori Milan Italy Data Science and Advanced Analytics F. Hoffmann - La Roche Ltd. Basel Switzerland Department of Mathematics and CDTIME University of Almería Almería Spain

In the last decades, the growing population of cancer survivors has shifted researchers' focus from primary toward tertiary prevention. Particularly, adolescents and young adults (AYAs) breast cancer (BC) survivors may face long-term outcomes as a result of their treatments, among which cardiovascular diseases (CVDs) are the most life-threatening ones. To plan effective follow-up guidelines for preventing and treating these events, it is essential to disentangle the causal role of cancer treatments in these patients. In this work, we aim to extend the current state of BC treatment guidelines by leveraging on the estimate of the risk of CVDs in AYAs who underwent BC treatments, as provided by a causal Bayesian network. In these regards, we provide counterfactual explanations of a causal query, using real-world data, algorithms and methods from the causal inference domain. We show that while ovarian suppression combined with tamoxifen may be a necessary cause for ischemic heart disease, it is not a sufficient one, i.e., this treatment alone is not enough to cause the disease, other factors must also be present. These findings can contribute to support clinicians in the treatment choice and help in refining treatment strategies and follow-up protocols for AYAs, advancing personalised healthcare in oncology. © 2024 CEUR-WS. All rights reserved.

关键词： Lung cancer

来源：评论

学校读者我要写书评

暂无评论

MatchPrompt: Prompt-based Open Relation Extraction with Semantic Consistency Guided Clustering

MatchPrompt: Prompt-based Open Relation Extraction with Sema...

引用

2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Wang, Jiaxin Zhang, Lingling Liu, Jun Liang, Xi Zhong, Yujie Wu, Yaqiang Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering School of Computer Science and Technology Xi'an Jiaotong University China National Engineering Lab for Big Data Analytics Xi'an Jiaotong University China Lenovo Research Beijing China

Relation clustering is a general approach for open relation extraction (OpenRE). Current methods have two major problems. One is that their good performance relies on large amounts of labeled and pre-defined relational instances for pre-training, which are costly to acquire in reality. The other is that they only focus on learning a high-dimensional metric space to measure the similarity of novel relations and ignore the specific relational representations of clusters. In this work, we propose a new prompt-based framework named MatchPrompt, which can realize OpenRE with efficient knowledge transfer from only a few predefined relational instances as well as mine the specific meanings for cluster interpretability. To our best knowledge, we are the first to introduce a prompt-based framework for unlabeled clustering. Experimental results on different datasets show that MatchPrompt achieves the new SOTA results for OpenRE. © 2022 Association for Computational Linguistics.

关键词： Extraction

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：