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Risk prediction and factors risk analysis based on IFOA-GRNN and apriori algorithms: Application of artificial intelligence in accident prevention

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PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2019年第Part B期122卷 169-184页

作者： Xie Xuecai Fu Gui Xue Yujingyang Zhao Ziqi Chen Ping Lu Baojun Jiang Song China Univ Min & Technol Beijing Coll Resources & Safety Engn Beijing 100083 Peoples R China China Natl Oil & Gas Explorat & Dev Co Ltd Beijing 100034 Peoples R China Xian Univ Architecture & Technol Coll Management Xian 710055 Shaanxi Peoples R China

Risk prediction of disasters is one of the most effective ways to prevent accidents. To solve the problems in multi-factor complex disaster prediction, this paper proposes a new method for risk prediction and factorial risk analysis. Coal and gas outburst accidents were selected as research objects. First, a new coal and gas outburst prediction model was established that consists of 4 levels and 14 factors. Then, the Improved Fruit Fly Optimization Algorithm (IFOA) and the General Regression Neural Network (GRNN) algorithm were combined to establish the IFOA-GRNN prediction model. After that, the sensitivity analysis method was applied to the analysis of the sensitive factors of coal and gas outbursts. Finally, an apriori algorithm was used to mine the disaster information. The method proposed in this paper was applied to the Pingdingshan No. 8 Min. The application results show that the IFOA-GRNN algorithm proposed in this paper has an accuracy rate of 100% for the prediction of accident risk levels. Compared with the Back Propagation (BP), GRNN and FOA-GRNN algorithms,IFOA-GRNN has the characteristics of a smaller prediction error, higher stability and faster convergence. The sensitivity analysis method can judge the sensitive factors of coal and gas outbursts without knowing the mechanisms of the accident. The a priori algorithm can perform good data mining on the combination of high frequency factors leading to accidents and the relationships between the coal and gas outburst levels and factors. The data mining results are very helpful for the prevention and management of coal and gas outbursts. (C) 2018 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

关键词： Risk prediction Factors risk analysis IFOA-GRNN algorithms apriori algorithms Artificial intelligence Accident prevention

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A Fast Algorithm Based on apriori algorithms to Explore the Set of Repetitive Items of Large Transaction Data 2018

A Fast Algorithm Based on Apriori Algorithms to Explore the ...

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2nd International Conference on Compute and Data Analysis (ICCDA)

作者： Ghofrani, Javad Bozorgmehr, Arezoo Panah, Amir Leibniz Univ Hannover Software Engn Grp Hannover Germany DZHW Hannover Germany Hadaf Inst Higher Educ Sari Iran

ISBN: (纸本)9781450363594

Parallel data mining is utilized to improve the performance of analyzing large databases within a reasonable time frame. Exploring associative rules is an important task in data mining with various practical applications that can be used to explore knowledge in the form of a set of repetitive items or associative rules. Parallel algorithms divide the data superficially and then using different distribution approach, like data distribution, candidate and numerical candidate distribution, extract the set of repetitive items, and ultimately explore strong association laws. In this paper, a parallel algorithm is proposed to explore the collection of repetitive items from big and dense transaction databases. In this algorithm, the numerical candidate distribution together with the using of two processes at each level of parallel processing increases the speed of extraction of data patterns. Two steps, extracting the repetitive items, which include generating a set of candidate counts, and computing a set of repetitive items, are performed simultaneously using both processes at each level. The proposed algorithm by us is compared in terms of runtime and accelerated with basic apriori algorithms and numerical distribution on four datasets. Experimental results prove that the proposed algorithm has better optimization in terms of maximizing the performance and has better run time than the two other algorithms for transactional data in different scales and dense data.

关键词： Big Data Data Mining apriori algorithms Association rules

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Analysis of FP-Growth and apriori algorithms on Pattern Discovery from Weblog Data

Analysis of FP-Growth and Apriori Algorithms on Pattern Disc...

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IEEE International Conference on Advances in Computer Applications (ICACA)

作者： Dharmaraajan, K. Dorairangaswamy, M. A. Bharathiar Univ Ctr Res & Dev Chennai Tamil Nadu India

ISBN: (纸本)9781509037704

Web usage mining, is the method of mining for user browsing and access patterns. Usage data captures the identity or origin of Web users along with their surfing behavior at a Web site. This paper aims to classify user behavior in identifying the patterns of the browsing and navigation data of web users and also measure the performance of the Frequent Pattern (FP) Growth algorithm and apriori algorithm by comparing their performances. The apriori algorithm and FP Growth algorithm are compared by applying the rapid miner tool to discover frequent user patterns along with user behavior in the web log. Both the algorithms help to the analyze the patterns of web site usage and the features of user behavior knowledge obtained from web usage. This can be used to enhance web design, introduce personalization service and facilitate more effective browsing. The experimental results mainly focus on number of instance and execution time to be calculated on the two algorithms. FP growth algorithm gives the better performance in terms of time complexity.

关键词： Association Rules Fp-Growth algorithms apriori algorithms

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Fuzzy association rule mining approaches for enhancing prediction performance

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EXPERT SYSTEMS WITH APPLICATIONS 2013年第17期40卷 6928-6937页

作者： Sowan, Bilal Dahal, Keshav Hossain, M. A. Zhang, Li Spencer, Linda Univ Bradford Sch Comp Informat & Media Bradford BD7 1DP W Yorkshire England Northumbria Univ Computat Intelligence Grp Fac Engn & Environm Newcastle Upon Tyne NE1 8ST Tyne & Wear England

This paper presents an investigation into two fuzzy association rule mining models for enhancing prediction performance. The first model (the FCM-apriori model) integrates Fuzzy C-Means (FCM) and the apriori approach for road traffic performance prediction. FCM is used to define the membership functions of fuzzy sets and the apriori approach is employed to identify the Fuzzy Association Rules (FARs). The proposed model extracts knowledge from a database for a Fuzzy Inference System (FIS) that can be used in prediction of a future value. The knowledge extraction process and the performance of the model are demonstrated through two case studies of road traffic data sets with different sizes. The experimental results show the merits and capability of the proposed KD model in FARs based knowledge extraction. The second model (the FCM-MSapriori model) integrates FCM and a Multiple Support apriori (MSapriori) approach to extract the FARs. These FARs provide the knowledge base to be utilized within the FIS for prediction evaluation. Experimental results have shown that the FCM-MSapriori model predicted the future values effectively and outperformed the FCM-apriori model and other models reported in the literature. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： apriori algorithms Data mining Fuzzy C-Mean Knowledge discovery Prediction Fuzzy association rules

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Evaluation of Flotation Working Condition Recognition Based on An Improved apriori Algorithm

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IFAC-PapersOnLine 2018年第21期51卷 129-134页

作者： Wang, Xiaoli Song, Chen Xiong, Wei Lv, Xingxiao School of Information Science and Engineering Central South University Hunan410083 China Changsha Research Institute of Mining and Metallurgy Co. Ltd Hunan410012 China

Based on machine vision and image processing technology, a large number of methods have been proposed for flotation working condition recognition in froth flotation industry. However, in these condition recognition methods, there lacks internal or external evaluation criteria, so that the reliability of the recognition results obtained by the current recognition models is not high for operators. In order to solve this problem, by taking time series of working condition data as the research object, an improved apriori algorithm based on time series is proposed for the evaluation of flotation working condition recognition. Based on the idea of transaction database, the time series data set of working condition is divided into working condition transaction database by an auto-regressive model. Then the improved apriori algorithm is used to find the association rules that contain relations between the time-series of the working condition transaction database to form a rule base for the working condition, which provides the reliability evaluation of the online working condition recognition results based on the association rules. © 2018

关键词： Froth flotation Association rules Database systems Image processing Learning algorithms Time series apriori algorithms Auto regressive models Condition recognition Image processing technology Improved apriori algorithms Reliability Evaluation Transaction database Working condition evaluation

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Model Study of Segment Market on Internet of Vehicles Big Data

Model Study of Segment Market on Internet of Vehicles Big Da...

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Asia Conference on algorithms, Computing and Machine Learning (CACML))

作者： Wang, Huajun Yang, Liang Wang, Wenbin Zhang, Xiaolin China Auto Informat Technol Co Ltd Tianjin 300399 Peoples R China Tianjin Univ Finance & Econ Sch Accountancy Tianjin 300222 Peoples R China

ISBN: (数字)9781665482905

ISBN: (纸本)9781665482905

At present, there are many problems in China's commercial vehicle enterprise, such as large subjective judgment deviation and lack of scientific calculation method. In order to solve the above problems, this paper takes the data of internet of vehicles in June 2021 collected by heavy truck of a commercial vehicle based on GB/T 17691-2018 as the experimental sample, and mining the scientific calculation model and system of commercial vehicle market segment by using algorithms such as parking point analysis and processing, word segmentation processing, DBSCAN clustering and apriori association. The experimental results show that the effectiveness and accuracy of the model are 98.18% and 94.4% respectively. At the same time, we designed a _ 100,000magnitude platform technology scheme, which reached the enterprise-level usability requirements and_ verified the feasibility of the research method in this paper.

关键词： Internet of Vehicles Big Data Model of Segment Market Clustering algorithms Word Segmentation apriori algorithms

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DM Data Mining Based on Improved apriori Algorithm

DM Data Mining Based on Improved Apriori Algorithm

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4th International Conference on Information Computing and Applications (ICICA)

作者： Wang, Yongping Jin, Yanfeng Li, Ying Geng, Keming Shi Jiazhuang Post & Telecommun Tech Coll Shijiazhuang 050021 Peoples R China

ISBN: (纸本)9783642537035;9783642537028

Association rules are the key technology in data mining;it has a very broad applying foreground in many industries. An improved association rules algorithms based on apriori was proposed in this paper. And it will be used in direct mail data mining. By analyzing the normative database of users' sets, we can get item set which satisfy the minimal support degree, and form the rule set. We can get more accurate DM data mining results than other methods by testing the post DM database. Experiments indicate the validity of the method.

关键词： data mining association rules apriori algorithms support degree

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Development of an adaptive business insolvency classifier prototype (AVICENA) using hybrid intelligent algorithms

Development of an adaptive business insolvency classifier pr...

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Student Conference on Research and Development (SCOReD2002)

作者： Ab Aziz, A Siraj, F Zakaria, A Univ Utara Malaysia Sch Informat Technol Artificial Intelligence Special Interest Grp Sintok 06010 Kedah Malaysia

ISBN: (纸本)0780375653

Confronted by an increasingly competitive environment and chaotic economy conditions, businesses are facing with the need to accept greater risk. Businesses do not become insolvent overnight, rather many times creditors, investors and the financial community will receive either direct or indirect indications that a company is experiencing financial distress. Thus, this paper analyzed the ability of AVICENA in classifying business insolvency performance events. Neural networks (Multi layer Perceptron - Backpropagation) serves as a classifier mechanism while apriori algorithms (Auto Association Rules) supports the decision made by the neural networks, in which rules are generated. The conventional model in predicting business performances, called as Altman- Z Scores model is used for performance comparison.

关键词： neural networks business insolvency apriori algorithms discriminant analysis financial ratios

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DATABASE INTRUSION DETECTION USING SEQUENTIAL DATA MINING APPROACHES 9

DATABASE INTRUSION DETECTION USING SEQUENTIAL DATA MINING AP...

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9th International Conference on Computer Engineering & Systems (ICCES)

作者： Abd Elaziz, Pakinam Elamein Sobh, Mohamed Mohamed, Hoda K. Ain Shams Univ Fac Engn Dept Comp & Syst Engn Cairo Egypt

ISBN: (纸本)9781479965946

The procedure of detecting any violation or trespass on the level of information in a database depends on placing the normal behaviors and practices of operations done by a transaction Afterwards, any identified pattern or behavior other than those normal patterns could be of high potential of being considered as an intrusion or violation. One of the known problems in this process is that, the accuracy of the process of detecting the frequent patterns in the database, as the algorithm applied may not detect all the patterns and this would affect in two ways. First, the database of the normal patterns would be missing. Second, some new patterns would be missed in the detection process. This paper studies and implements different sequential data mining techniques, and then proposes a new enhanced algorithm. The proposed algorithm increases the accuracy of the process and the number of detected patterns. Finally, the paper proposes a model for database intrusion detection based on the modified algorithm. The paper uses a realistic huge database for evaluating the performance and the accuracy.

关键词： Sequential data mining apriori algorithms intrusion detection predictive data mining descriptive data mining host-based intrusion detection network-based intrusion detection computer security malicious transaction misuse detection anomaly detection

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Impact of the Service Sector on the Creation of Companies in Poland

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Procedia Economics and Finance 2015年 24卷 523-532页

作者： Korneliusz Pylak Dariusz Majerek Lublin University of Technology Nadbystrzycka St. 38 Lublin 20-618 Poland

This paper's aim is to analyze the role of the service sector in creating companies from different industries. This paper is one of the first to contribute to revealing mechanisms of company creation in the Polish economy through utilizing association rule mining to find patterns in relationships between development paths of industries. The study's results reveal the complexity of the processes of creating new companies and the huge impact of the service sector on businesses creation. In addition, the study identifies the rules governing the creation of service companies and reveals the irrelevancy of urbanization economies.

关键词： service sector company creation development paths regional development urbanization economies data mining association rules apriori algorithms

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