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Complex industrial automation data stream mining algorithm based on random internet of robotic things

AUTOMATIKA 2019年第5期60卷 570-579页

作者： Cui, Lianhe Qiqihar Univ Dept Engn Qiqihar Heilongjiang Peoples R China

In recent years, with the continuous development of computer application technology, network technology, data storage technology, and the large amount of investment in information technology, enterprises have accumulated a large amount of data while transforming and improving enterprise management modes and means. How to mine useful data, discover important knowledge and extract useful information has become a hot topic of current research. Industrial big data is significantly different from traditional big data. The traditional big data is based on the Internet environment. Although the data has a high degree of discretization and distribution, its association is relatively simple. The collection of industrial process data is relatively easy, but the mathematical and physical and chemical mechanism models involved make the inherent relationship of data complex, so it is difficult to use common analytical models and methods for processing. In this paper, we propose a complex industrial automation data stream mining algorithm based on random internet of robotic things, and experimental results show that the proposed algorithm has higher data mining efficiency and robustness.

关键词： Robot algorithm Stochastic programming algorithm Internet engineering complex industry automation data data mining algorithm

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A novel data mining method based on ant colony algorithm

A novel data mining method based on ant colony algorithm

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1st International Conference on Advanced data mining and Applications

作者： Jiang, WJ Xu, YS Xu, YH Hunan Univ Ind Dept Comp Zhuzhou 412008 Peoples R China Beijing Univ Technol Coll Mech Engn & Appl Elect Beijing 100022 Peoples R China

ISBN: (纸本)354027894X

data mining has become of great importance owing to ever-increasing amounts of data collected by large organizations. This paper propose an data mining algorithm called Ant-Miner(I), which is based on an improvement of Ant Colony System(ACS) algorithm. Experimental results show that Ant-Miner(l) has a higher predictive accuracy and much smaller rule list than the original Ant-Miner algorithm.

关键词： data mining algorithm Ant-Miner(I) ant colony System(ACS) algorithm

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A Comparison of Methods of data mining algorithms Directed Predictive Pharmacosafety Networks for Adverse Drug Event Detection 16

A Comparison of Methods of Data Mining Algorithms Directed P...

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16th IEEE International Conference on Mechatronics and Automation (IEEE ICMA)

作者： Ji, Xiangmin Hua, Liyan Wang, Xueying Zhang, Yunfei Li, Jin Harbin Engn Univ Coll Automat Harbin Heilongjiang Peoples R China Ordos Inst Technol Dept Informat Engn Ordos Inner Mongolia Peoples R China

ISBN: (纸本)9781728116976

Adverse drug events (ADEs) detection is the critical concern in the field of pharmacovigilance, and it is also necessary to optimize the ADEs prediction to reduce the drugrelated morbidity and mortality. Here we propose a novel methods of data mining algorithms directed predictive pharmacosafety networks (PPNs) to compare their predictive performance and investigate the differences between data mining algorithms. The combinations of 152 cancer drugs and 633 ADEs in the 2010 FDA Adverse Event Reporting System(FAERS) data is the training data, and 2011-2015 FAERS data is the validation data. We find that performance of empirical Bayes geometric mean (EBGM) is closer to proportional reporting ratio (PRR), and greater than reporting odds ratio (ROR) in ADE detection. Further, only information component (IC) directed the PPNs have better predictive performance comparing to other data mining algorithms, the predictive performance of which reaches to AUROC=0.908 comparing to the existing AUROC=0.823, and the performance of IC is greater than EBGM in ADE detection.

关键词： Adverse drug event data mining algorithm Disproportionality Analysis Predictive Pharmacosafety Networks Pharmacovigilance

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Supplier Management Decision Support System Under data mining algorithms 3

Supplier Management Decision Support System Under Data Minin...

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3rd IEEE International Conference on Electrical Engineering, Big data and algorithms, EEBDA 2024

作者： Kun, Chen Zehua, Han Rui'an, Lin Xuejun, Wang Dongdong, Song Fujian Great Power Science and Technology Co. Ltd. Fuzhou Fujian350000 China State Grid Information & Telecommunication Group Co. Ltd. Changping Beijing102200 China

ISBN: (纸本)9798350380989

This article proposed a supplier management decision support system based on data mining algorithms, aiming to help enterprises select the best supplier to improve procurement efficiency and reduce procurement costs. The system used data mining algorithms to analyze and mine the historical data of suppliers. It established a supplier evaluation model and combined it with AHP (Analytic Hierarchy Process) to conduct supplier evaluation. At the same time, the system also used fuzzy comprehensive evaluation method and TOPSIS (Technique for Order Preference by Similarity to ideasolution) method to sort and select suppliers. The experimental results indicated that the system could quickly and accurately evaluate suppliers, and improve procurement efficiency and reduce procurement costs, which has practical application value. This article also discussed and prospects the research, and proposed future research directions. © 2024 IEEE.

关键词： Analytic Hierarchy Process data mining algorithm Decision Support System Fuzzy Comprehensive Evaluation Method Supplier Management TOPSIS Method

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A STUDY ON OPTIMIZING ERROR DETECTION AND CORRECTION STRATEGIES IN PHYSICAL EDUCATION AND SPORT TEACHING USING data mining algorithmS

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SCALABLE COMPUTING-PRACTICE AND EXPERIENCE 2023年第4期24卷 1223-1229页

作者： Gao, Ziyao Hu, Shengfei Yu, Guo Li, Yinhui South Cent Minzu Univ Phys Educ Coll Wuhan 430074 Peoples R China Wuhan Univ Technol Minist Educ Sch Phys Educ Wuhan 430070 Peoples R China Zhongnan Univ Econ & Law Phys Educ Dept Wuhan 430073 Peoples R China

In the fiercely competitive realm of sports and physical education, the application of data mining algorithms has emerged as a vital solution. Machine learning has streamlined processes, offering a seamless means of elevating the quality of education and training provided to students, particularly in the context of sports. This technological support empowers the sports education system to make more informed decisions pertaining to the physical development of aspiring athletes. In this comprehensive study, a blended approach of qualitative methods has been leveraged to gather intricate insights, enriching the overall understanding of the subject. Additionally, an in-depth exploration of articles and journals has been undertaken to scrutinize the practical implementation of data algorithm techniques geared towards enhancing physical training. The resultant findings underscore a substantial and tangible nexus between data algorithms and the domain of sports education. Of paramount significance is the central role played by data mining algorithms in augmenting performance. Notably, the National Sports Board (NSB) has extensively harnessed this technology to meticulously monitor players' on-field performance, ultimately leading to a granular comprehension of each player's capabilities. This paper emphasizes the methods of optimizing mistake detection and its joining systems for increasing the punishment in the operational procedures.

关键词： Optimizing Error Detection data mining algorithm sports teaching physical education

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A multi-stage Smart Energy Management System under multiple uncertainties: A data mining approach

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RENEWABLE ENERGY 2017年第PartA期102卷 178-189页

作者： Parvizimosaed, M. Farmani, F. Monsef, H. Rahimi-Kian, A. Univ Tehran Sch ECE Coll Engn Tehran Iran

Smart Energy Management Systems (SEMS) have become indispensable in Micro-Grid (MG) infrastructure for saving energy usage costs and system control considering the time-varying parameters. In this paper, a new multi-stage SEMS architecture is proposed for optimal energy management in MGs considering various resource uncertainties. The proposed SEMS performs various tasks such as data acquisition/mining/refinement, pattern recognition, learning parameters and offline/online decision making. To meet the energy consumption suitably, the multi-objective SEMS operates in multi-stage scheduling problem, i.e. day-ahead, hour-ahead, and real-time markets. Moreover, some data mining algorithms have been applied to reduce the huge amount of raw data, to recognize patterns for analysis, and to learn the given parameters. From the stochastic point of view, the proposed architecture also takes into account the uncertainties of weather conditions, energy consumption and the spot market price in the risk analysis. To handle these uncertainties, a stochastic scheduling approach which includes the mean and variance of energy cost is considered in the optimization process. The simulation results illustrate the efficiency of the proposed SEMS in different case studies. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Multi-stage energy management data mining algorithm Multi-objective optimization Offline and online decision making

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mining top-K frequent itemsets from data streams

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data mining AND KNOWLEDGE DISCOVERY 2006年第2期13卷 193-217页

作者： Wong, Raymond Chi-Wing Fu, Ada Wai-Chee Chinese Univ Hong Kong Dept Comp Sci & Engn Shatin Hong Kong Peoples R China

Frequent pattern mining on data streams is of interest recently. However, it is not easy for users to determine a proper frequency threshold. It is more reasonable to ask users to set a bound on the result size. We study the problem of mining top K frequent itemsets in data streams. We introduce a method based on the Chernoff bound with a guarantee of the output quality and also a bound on the memory usage. We also propose an algorithm based on the Lossy Counting algorithm. In most of the experiments of the two proposed algorithms, we obtain perfect solutions and the memory space occupied by our algorithms is very small. Besides, we also propose the adapted approach of these two algorithms in order to handle the case when we are interested in mining the data in a sliding window. The experiments show that the results are accurate.

关键词： data mining algorithm data stream top K frequent itemset mining sliding window Chernoff bound probabilistic algorithm

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Implement web learning environment based on data mining

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KNOWLEDGE-BASED SYSTEMS 2009年第6期22卷 439-442页

作者： Guo, Qinglin Zhang, Ming N China Elect Power Univ Sch Comp Sci & Technol Beijing 102206 Peoples R China Peking Univ Dept Comp Sci & Technol Beijing 100871 Peoples R China

The need for providing learners with web-based learning content that match their accessibility needs and preferences, as well as providing ways to match learning content to user's devices has been identified as an important issue in accessible educational environment. For a web-based open and dynamic learning environment, personalized support for learners becomes more important. In order to achieve optimal efficiency in a learning process, individual learner's cognitive learning style should be taken into account. Due to different types of learners using these systems, it is necessary to provide them with an individualized learning support system. However, the design and development of web-based learning environments for people with special abilities has been addressed so far by the development of hypermedia and multimedia based on educational content. In this paper a framework of individual web-based learning system is presented by focusing on learner's cognitive learning process, learning pattern and activities, as well as the technology support needed. Based on the learner-centered mode and cognitive learning theory, we demonstrate an online course design and development that supports the students with the learning flexibility and the adaptability. The proposed framework utilizes data mining algorithm for representing and extracting a dynamic learning process and learning pattern to support students' deep learning, efficient tutoring and collaboration in web-based learning environment. And experiments do prove that it is feasible to use the method to develop an individual web-based learning system, which is valuable for further study in more depth. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Web-based learning Individual data mining algorithm Learning technologies data mining

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RETRACTED: Research on economic function data and entrepreneurship analysis based on machine learning and computer interaction platform (Retracted Article)

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第4期39卷 5635-5647页

作者： Nie, Xueling Univ Sci & Technol Liaoning Sch Business Adm Anshan Peoples R China

Big data has the characteristics of rapid data flow, massive data scale, dynamic data system, and various data types, and it has become increasingly apparent in improving innovation and entrepreneurship data analysis, trend prediction, and decision support. In this paper, the authors analyze the economic function data and entrepreneurship analysis based on machine learning. The support vector pair is very sensitive to the choice of parameters, and the parameters obtained using the genetic algorithm will greatly improve the accuracy of the model prediction. When using the genetic algorithm to find parameters, the cv method is used for verification. By applying big data technologies and platforms, it can provide strong data support to establish entrepreneurship education;integrate and integrate various types of innovation and entrepreneurship data, improve the quality of data *** the same time, through big data mining and analysis, accurately determine market demand hotspots and innovation and entrepreneurship trends, and promote scientific planning of innovation and entrepreneurship strategies. The research results show that this research model can be applied to actual projects in the future, and help investors better understand the changes of market economy.

关键词： Machine learning data mining algorithm driving innovation entrepreneurship education

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data mining-based model for motion target trajectory prediction

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019年第1期37卷 371-379页

作者： Liu, Lizheng Liu, Fangai Ky, Byron Shandong Normal Univ Jinan 250014 Shandong Peoples R China Shandong Univ Finance & Econ Jinan Shandong Peoples R China Tarrant Cty Coll Ft Worth TX 76102 USA

Due to the increased development and applications of satellite communication, GPS equipment, video tracking and other communication technologies, the trajectory prediction of various SIs can be accurately predicted and tracked. Based on these technologies, mobile targets are extensively used in many applications. A large amount of trajectory data predicted by trajectory can be collected from the positioning terminal of the moving target by the signal-receiving device. This paper analyzes the application of computer technology in trajectory prediction of moving objects in data mining algorithm and proposes a trajectory analysis method based on structure and cloud computing motion capture algorithm. The motion and trajectory characteristics of moving target trajectories are analyzed from microcosmic angle. By comparing the structural features of the extracted trajectory and the trajectory predicted by the trajectory of the moving object, the motion characteristics of the object can be analyzed more comprehensively. In addition, the sensitivity of the trajectory structure can be flexibly adjusted by setting the weight of the trajectory structure, so that the trajectory of the moving target can be analyzed and predicted in an effective and flexible way.

关键词： data mining algorithm moving target trajectory prediction computer technology application

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