检索结果-内蒙古大学图书馆

Deep learning approach for diabetes prediction using PIMA Indian dataset

JOURNAL OF DIABETES AND METABOLIC DISORDERS 2020年第1期19卷 391-403页

作者： Naz, Huma Ahuja, Sachin Chitkara Univ Chitkara Univ Inst Engn & Technol Rajpura Punjab India

Purpose International Diabetes Federation (IDF) stated that 382 million people are living with diabetes worldwide. Over the last few years, the impact of diabetes has been increased drastically, which makes it a global threat. At present, Diabetes has steadily been listed in the top position as a major cause of death. The number of affected people will reach up to 629 million i.e. 48% increase by 2045. However, diabetes is largely preventable and can be avoided by making lifestyle changes. These changes can also lower the chances of developing heart disease and cancer. So, there is a dire need for a prognosis tool that can help the doctors with early detection of the disease and hence can recommend the lifestyle changes required to stop the progression of the deadly disease. Method Diabetes if untreated may turn into fatal and directly or indirectly invites lot of other diseases such as heart attack, heart failure, brain stroke and many more. Therefore, early detection of diabetes is very significant so that timely action can be taken and the progression of the disease may be prevented to avoid further complications. Healthcare organizations accumulate huge amount of data including Electronic health records, images, omics data, and text but gaining knowledge and insight into the data remains a key challenge. The latest advances in Machine learning technologies can be applied for obtaining hidden patterns, which may diagnose diabetes at an early phase. This research paper presents a methodology for diabetes prediction using a diverse machine learning algorithm using the PIMA dataset. Results The accuracy achieved by functional classifiers Artificial Neural Network (ANN), Naive Bayes (NB), Decision Tree (DT) and Deep Learning (DL) lies within the range of 90-98%. Among the four of them, DL provides the best results for diabetes onset with an accuracy rate of 98.07% on the PIMA dataset. Hence, this proposed system provides an effective prognostic tool for healthcare of

关键词： Diabetes prediction Deep learning data mining algorithms Neural network PIMA Indian dataset

来源：评论

学校读者我要写书评

暂无评论

A Business Intelligence Solution for Frequent Pattern mining on Social Networks 14

A Business Intelligence Solution for Frequent Pattern Mining...

引用

14th IEEE International Conference on data mining (IEEE ICDM)

作者： Jiang, Fan Leung, Carson Kai-Sang Univ Manitoba Dept Comp Sci Winnipeg MB Canada

ISBN: (纸本)9781479942749

Frequent pattern mining is an important data mining task. Since its introduction, it has drawn attention from many researchers. Consequently, many frequent pattern mining algorithms have been proposed to mine large varieties of high-value data such as high volumes of shopper market basket data. In this paper, we propose a business intelligence (BI) solution for frequent pattern mining on social network data. Evaluation results show that our proposed BI solution is both space-and time-efficient. Moreover, we also discuss the benefits and practicality of our BI solution-which reveals frequent social patterns-in real-life business applications.

关键词： Association analysis business applications business intelligence data mining algorithms frequent patterns

来源：评论

学校读者我要写书评

暂无评论

DISC: Efficient Uncertain Frequent Pattern mining with Tightened Upper Bounds 14

DISC: Efficient Uncertain Frequent Pattern Mining with Tight...

引用

14th IEEE International Conference on data mining (IEEE ICDM)

作者： MacKinnon, Richard Kyle Strauss, Teagan D. Leung, Carson Kai-Sang Univ Manitoba Dept Comp Sci Winnipeg MB Canada

ISBN: (纸本)9781479942749

UF-growth is a tree-based exact algorithm for mining frequent patterns from uncertain data. While it directly calculates the expected support of a pattern, it requires a significant amount of storage space to capture all existential probability values among the items. To eliminate the extra space requirement of UF-growth, the CUF-growth algorithm combines nodes with the same item by storing an upper bound on expected support. In this paper, we (i) introduce a new concept of domain item-specific capping (DISC) and (ii) propose three new scalable data analytics algorithms that use this concept to achieve a tighter upper bound than CUF-growth. Experimental results show the effectiveness of uncertain frequent pattern mining with tightened upper bounds provided by using the concept of DISC.

关键词： Association analysis data mining algorithms expected support frequent itemsets tree structures uncertain data

来源：评论

学校读者我要写书评

暂无评论

Critical Components Identification for Cyber-Physical Power Systems Considering Time-Varying Operational States 9

Critical Components Identification for Cyber-Physical Power ...

引用

9th Workshop on Modeling and Simulation of Cyber-Physical Energy Systems (MSCPES)

作者： Liu, Yigu Semertzis, Ioannis Stefanov, Alexandru Palensky, Peter Delft Univ Technol Dept Elect Sustainable Energy Delft Netherlands

ISBN: (纸本)9781450386081

The security issues of Cyber-Physical power Systems (CPS) have attracted widespread attention from scholars. Vulnerability assessment emerges as an effective method to identify the critical components and thus increase the system resilience. While efforts have been made to study the vulnerability features of power systems under the occurrence of a single, discrete disturbance or failure at a specific time instant, this paper focuses on identifying the critical components of the cyber-physical system considering time-varying operational states. To investigate the potentially ever-changing CPS vulnerability features, in this paper we construct a database of cascading failure chains using quasi-dynamic simulations to capture the vulnerability relationships among components under time-varying operational states. Then, by adopting sequential mining algorithms, we mine the most frequent cascading failure patterns and identify the critical components based on the data mining results. Simulation studies are conducted on IEEE 39-bus and IEEE RTS-96 systems to evaluate the effectiveness of the proposed method for the identification of critical components at both cyber and physical layers.

关键词： cyber-physical systems vulnerability assessment data mining algorithms

来源：评论

学校读者我要写书评

暂无评论

Simulation and Analysis of Buying Behavior in Supermarkets

Simulation and Analysis of Buying Behavior in Supermarkets

引用

15th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Schwenke, Clemens Vasyutynskyy, Volodymyr Kabitzsch, Klaus Tech Univ Dresden Chair Tech Informat Syst D-01062 Dresden Germany

ISBN: (纸本)9781424468508

Future supermarkets will provide instrumented shopping carts, shelves and products so that large amounts of event data of customer's actions inside the store will be recorded. Special data mining algorithms will be necessary for analyzing this data effectively. At present, respective event data is not available. In order to create test data sets for developing, implementing and testing respective data mining algorithms presently, a simulation of buying behavior in supermarkets is currently being developed by the authors. It will be used to investigate tailored data mining methods.

关键词： buying behavior consumer behaviour data mining data mining algorithms instrumented shopping carts supermarkets

来源：评论

学校读者我要写书评

暂无评论

Parental stress in the relationship with the child and personality traits that parents shape in their children

引用

EARLY CHILD DEVELOPMENT AND CARE 2021年第2期191卷 198-209页

作者： Szymanska, Agnieszka Aranowska, Elzbieta Cardinal Stefan Wyszynski Univ Inst Psychol Ul Woycickiego 1-3 Bud 14 PL-01938 Warsaw Poland

The aim of the research was to determine what relations occur between the stress experienced by parents in the relationship with their children and the personality traits that they shape in them. Personal characteristics that parents shape in their children have been classified into metatraits described in the Circumplex of Personality model. The study was carried out on a sample of 319 parents of preschool children. Two data mining methods were used: text mining algorithms and cluster analysis carried out by data mining algorithms. Parents experiencing higher stress more often shaped Self-restraint (Delta Plus) personality features. They tried to ensure that their children did not develop Sensation-Seeking (Delta Minus) features associated with impulsivity and dominance over others. Parents experiencing lower parental stress developed Stability (Alpha Plus) qualities related to social adaptation in their children and tried to ensure that they did not develop the antisocial features of Disinhibition (Alpha Minus).

关键词： Parental stress parental goals development of personality traits data mining algorithms

来源：评论

学校读者我要写书评

暂无评论

Automated Flow-based Rule Generation for Network Intrusion Detection Systems 24

Automated Flow-based Rule Generation for Network Intrusion D...

引用

24th Iranian Conference on Electrical Engineering (ICEE)

作者： Fallahi, Naser Sami, Ashkan Tajbakhsh, Morteza Shiraz Univ Dept Comp Sci & Engn Shiraz *** Iran Shiraz Univ IT Shiraz *** Iran

ISBN: (纸本)9781467387897

Snort is a popular open-source Intrusion Detection System (IDS). Since rules are updated offline and network environment changes dynamically, Snort has a low detection rate especially for new types of attacks. Since attack signatures are not stored in the system, attackers could intrude without being detected. The aim of this research is to automate rule generation for system by use of logs of performed attacks. This approach has been implemented using two data mining algorithms called Ripper and C5.0. Automatic rule generation improves security of Snort and attacks are detected better. Five types of attacks, like Denial of service and Brute Force, have been investigated in this work and tested on newly released ISCX 2012 dataset which has 84.42 Gigabytes. By processing the dump, it can be used to generate general rules and eight new features from known features of streams. Detection rate of more than 99 percent was obtained for some attacks, which represent sensible impact of this approach on Snort software.

关键词： network intrusion detection systems signature-based detection automatic rule generation computer security data mining algorithms

来源：评论

学校读者我要写书评

暂无评论

Forestry Economic Forecasting Methods, Models and Computer Support Systems 3

Forestry Economic Forecasting Methods, Models and Computer S...

引用

3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Liu, Yanzhen Liulin County Forestry Bureau Shanxi Liulin030599 China

ISBN: (纸本)9798350319125

With the rapid development of computer technology, computer technology is widely used in all walks of life. The role of computer-supported systems in forestry economic development decision-making is important, but there may be lagging problems in practical applications. Traditional forecasting techniques cannot solve some problems in computer support systems in forestry economic development decision-making, and the evaluation is unreasonable. Therefore, this paper proposes a data mining algorithm for innovative optimization of computer-supported system analysis. Firstly, forestry economic theory is used to evaluate forestry managers, and indicators are divided according to the requirements of the computer support system to reduce them the computer supports the interference factor in the system. Then, the forestry economic theory analyzes the computer support system of forestry economic forecasting, forms a computer support system scheme, and supports the computer system Comprehensive analysis of the program. MATLAB simulation shows that under the condition of certain evaluation criteria, the accuracy of the computer support system of data mining algorithm for forestry economic forecasting is accurate the rationality is better than traditional forecasting techniques. © 2023 IEEE.

关键词： Computer technology data mining algorithms economic forecasts forestry economic theory

来源：评论

学校读者我要写书评

暂无评论

Research and Application of Community Pension Service data Analysis System Based on data mining Algorithm

Research and Application of Community Pension Service Data A...

引用

第二十届分布式计算及其应用国际学术研讨会

作者： Xiaofeng Yao Cheng Jiang Jiangsu Key Lab of IoT Application Technology Wuxi Taihu University

In recent years,with the development of China's aging population,pension issue has become a major issue related to the national economy and people's livelihood continues to be *** paper aims to study the design and implementation of data analysis system of community elderly care service based on data mining ***,this paper introduces the theory of data mining,and designs three modules of community pension ***,the gender,age,education level,the number of people living with the elderly and the situation of chronic diseases are used as variables for model design,and finally a community service pension system is designed in accordance with the national *** experimental results of this paper show that,on the basis of data mining algorithm,the construction of community pension service system satisfies the needs of the elderly,among which,the highest reaches 91.8%,which also shows that the pension service system brings great convenience to the elderly.

关键词： data mining algorithms community pension services data analysis systems

来源：评论

学校读者我要写书评

暂无评论

Pinpoint: Problem determination in large, dynamic Internet services

Pinpoint: Problem determination in large, dynamic Internet s...

引用

International Conference on Dependable Systems and Networks

作者： Chen, MY Kiciman, E Fratkin, E Fox, A Brewer, E Univ Calif Berkeley Div Comp Sci Berkeley CA 94720 USA

ISBN: (纸本)0769515975

Traditional problem determination techniques rely on static dependency models that are difficult to generate accurately in today's large, distributed, and dynamic application environments such as e-commerce systems. In this paper, we present a dynamic analysis methodology that automates problem determination in these environments by 1) coarse-grained tagging of numerous real client requests as they travel through the system and 2) using data mining techniques to correlate the believed failures and successes of these requests to determine which components are most likely to be at fault. To validate our methodology, we have implemented Pinpoint, a framework for root cause analysis on the J2EE platform that requires no knowledge of the application components. Pinpoint consists of three parts: a communications layer that traces client requests, a failure detector that uses traffic-sniffing and middleware instrumentation, and a data analysis engine. We evaluate Pinpoint by injecting faults into various application components and show that Pinpoint identifies the faulty components with high accuracy and produces few false-positives.

关键词： problem determination problem diagnosis root cause analysis data clustering data mining algorithms

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：