检索结果-内蒙古大学图书馆

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

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

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

Random Security Generators with Enhanced Cryptography for Cybersecurity in Financial Supply Chains 1

Random Security Generators with Enhanced Cryptography for Cy...

引用

1st International Conference on Advances in Computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Thammareddi, Latha Anumolu, Venkateswara Rao Kotte, Kulasekhara Reddy Chowdari Marella, Bhagath Chandra Arun Kumar, K. Bisht, Jatin India ERP Solutions-Information Technology India SLRI Solutions INC India Insights & Data Department of Financial Services India Big Data Architect India Bipin Tripathi Kumaon Institute of Technology Department of Computer Science & Engineering Almora India

ISBN: (纸本)9798331518578

Blockchains, like Ethereum, depend on a distributed system of collaborative servers referred to as inspectors or mine workers to validate transactions and generate novel legal blocks. In today's electronic world, it is critical to secure financial supply chains (FSCs) from complex cyber-attacks. This study combines Quantized Random Security Generators (QRSGs) with Artificial Intelligence (AI) to improve cybersecurity in FSC. QRSGs use quantum physics to produce random outcomes, which is critical for establishing safe cryptography credentials and resolving the flaws of regular predictable RSGs. This study found that the QRSG-based approach outperformed traditional RSG methods. The QRSG demonstrated strong entropy and uncertainty, transferring both NIST SP800-22 and Diehard examinations at much greater levels. The platform's real-time safety features were improved by the AI element's outstanding performance measures in identifying security breaches. More rapid decoding and encryption times, reduced delay, and increased resilience to brute-force, quantum, and prediction assaults were all demonstrated by the coupled QRSG and AI methods. This study emphasizes how crucial it is to use AI and QRSGs to safeguard FSCs. The combined platform provides a complete option to reduce cyber-attacks by offering strong cryptography security and real-time attack identification. These results demonstrate how QRSG and AI innovations can completely transform cybersecurity in the banking industry, safeguarding vital financial systems and guaranteeing the privacy and reliability of financial payments. © 2025 IEEE.

关键词： Quantum optics

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

OSFS-Vague: Online streaming feature selection algorithm based on vague set

引用

CAAI Transactions on Intelligence Technology 2024年第6期9卷 1451-1466页

作者： Jie Yang Zhijun Wang Guoyin Wang Yanmin Liu Yi He Di Wu School of Physics and Electronic Science Zunyi Normal UniversityZunyiChina Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and TelecommunicationsChongqingChina Department of Computer Science Old Dominion UniversityNorfolkVirginiaUSA College of Computer and Information Science Southwest UniversityChongqingChina

Online streaming feature selection(OSFS),as an online learning manner to handle streaming features,is critical in addressing high-dimensional *** real big data-related applications,the patterns and distributions of streaming features constantly change over time due to dynamic data generation ***,existing OSFS methods rely on presented and fixed hyperparameters,which undoubtedly lead to poor selection performance when encountering dynamic *** make up for the existing shortcomings,the authors propose a novel OSFS algorithm based on vague set,named *** main idea is to combine uncertainty and three-way decision theories to improve feature selection from the traditional dichotomous method to the trichotomous ***-Vague also improves the calculation method of correlation between features and ***,OSFS-Vague uses the distance correlation coefficient to classify streaming features into relevant features,weakly redundant features,and redundant ***,the relevant features and weakly redundant features are filtered for an optimal feature *** evaluate the proposed OSFS-Vague,extensive empirical experiments have been conducted on 11 *** results demonstrate that OSFS-Vague outperforms six state-of-the-art OSFS algorithms in terms of selection accuracy and computational efficiency.

关键词： feature selection online feature selection three-way decision vague set

来源：评论

学校读者我要写书评

暂无评论

Identifying the best common factor model via exploratory eactor analysis

引用

Applied Mathematics(A Journal of Chinese Universities) 2024年第1期39卷 24-33页

作者： HE Bao-hua TANG Rui TAGN Qi-yi School of Big Data Fuzhou University of International Studies and TradeFuzhou 350202China School of Mathematics Sun Yat-Sen UniversityGuangzhou 510275China Department of Computer Science University of BathUK Institute of Insect Science Zhejiang UniversityHangzhou 310058China.

Currently,there is no solid criterion for judging the quality of the estimators in factor *** paper presents a new evaluation method for exploratory factor analysis that pinpoints an appropriate number of factors along with the best method for factor *** proposed technique consists of two steps:testing the normality of the residuals from the fitted model via the Shapiro-Wilk test and using an empirical quantified index to judge the quality of the factor *** are presented to demonstrate how the method is implemented and to verify its effectiveness.

关键词： factor analysis Shapiro-Wilk normality residuals

来源：评论

学校读者我要写书评

暂无评论

Smart Contract Vulnerability Detection Using Large Language Models and Graph Structural Analysis

引用

Computers, Materials & Continua 2025年第4期83卷 785-801页

作者： Ra-Yeon Choi Yeji Song Minsoo Jang Taekyung Kim Jinhyun Ahn Dong-Hyuk Im Department of Artificial Intelligence Application Kwangwoon UniversitySeoul01897Republic of Korea Department of Artificial Intelligence Convergence Kwangwoon UniversitySeoul01897Republic of Korea Department of Big Data Analytics KyungHee UniversitySeoul02447Republic of Korea Department of Management Information Systems Jeju National UniversityJeju63243Republic of Korea School of Information Convergence Kwangwoon UniversitySeoul01897Republic of Korea

Smart contracts are self-executing programs on blockchains that manage complex business logic with transparency and ***,their immutability after deployment makes programming errors particularly critical,as such errors can be exploited to compromise blockchain *** vulnerability detection methods often rely on fixed rules or target specific vulnerabilities,limiting their scalability and adaptability to diverse smart contract ***,natural language processing approaches for source code analysis frequently fail to capture program flow,which is essential for identifying structural *** address these limitations,we propose a novel model that integrates textual and structural information for smart contract vulnerability *** approach employs the CodeBERT NLP model for textual analysis,augmented with structural insights derived from control flow graphs created using the abstract syntax tree and opcode of smart *** graph node is embedded using Sent2Vec,and centrality analysis is applied to highlight critical paths and nodes within the *** extracted features are normalized and combined into a prompt for a large language model to detect vulnerabilities *** results demonstrate the superiority of our model,achieving an accuracy of 86.70%,a recall of 84.87%,a precision of 85.24%,and an F1-score of 84.46%.These outcomes surpass existing methods,including CodeBERT alone(accuracy:81.26%,F1-score:79.84%)and CodeBERT combined with abstract syntax tree analysis(accuracy:83.48%,F1-score:79.65%).The findings underscore the effectiveness of incorporating graph structural information alongside text-based analysis,offering improved scalability and performance in detecting diverse vulnerabilities.

关键词： Blockchain smart contract vulnerability detection large language model

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

Transaction fee mechanisms with farsighted miners

Transaction fee mechanisms with farsighted miners

引用

作者： Leth Hougaard, Jens Pourpouneh, Mohsen Han, Haiting IFRO University of Copenhagen Copenhagen Denmark Department of Computer Science University of Oxford Oxford United Kingdom Department of Data Analytics and Digitalisation Maastricht University Maastricht Netherlands

We investigate the recent fee mechanism EIP1559 of the Ethereum network. Whereas previous studies have focused on myopic miners, we here focus on strategic miners in the sense of miners being able to reason about the future blocks. We derive expressions for optimal miner behavior (in terms of setting block sizes) in the case of two-block foresight and varying degrees of hashing power. Results indicate that a sufficiently large mining pool will have enough hashing power to gain by strategic foresight. We further use a simulation study to examine the impact of both two-block and three-block foresight. In particular, the simulation study indicates that for realistic levels of hashing power, mining pools do not gain from being able to reason more than two blocks ahead. Moreover, even though the presence of strategic miners increases the variation in block sizes and potentially empty blocks, overall system throughput tends to increase slightly compared with myopic mining. We further analyze the effect of varying the base fee updating rule. © 2024 John Wiley & Sons Ltd.

关键词： Blockchain

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：