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Efficient Vertical Mining of Frequent Quantitative Patterns

Efficient Vertical Mining of Frequent Quantitative Patterns

引用

2023 IEEE international conference on Dependable, Autonomic and Secure computing, 2023 international conference on Pervasive Intelligence and computing, 2023 international conference on Cloud and Big data computing, 2023 international conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Czubryt, Thomas J. Hryhoruk, Connor C.J. Leung, Carson K. Pazdor, Adam G.M. University of Manitoba Department of Computer Science WinnipegMB Canada

ISBN: (纸本)9798350304602

Frequent pattern mining has become popular in big data analytics and knowledge discovery as it discovers sets of items (e.g., merchandise items or events) that co-occur frequently. These frequent patterns are discovered by either horizontally by transaction-centric mining algorithms or vertically by item-centric mining algorithms. Regardless of the mining algorithms used, traditional frequent pattern mining algorithms focus on discovering Boolean frequent patterns, which reveal the presence or absence of specific items within the discovered patterns. However, in many real-life scenarios, the quantities of items within the patterns are crucial. For instance, the quantity of items can significantly impact the profitability of selling the items found in the discovered patterns. An existing quantitative algorithm called Q-VIPER (2022) mined frequent quantitative patterns by representing the big data as a collection of item-centric bitmaps. Each bitmap captures the presence or absence of a transaction containing the item, together with the quantity of that item in each transaction. It then mines quantitative frequent patterns vertically. It works well with small quantity. However, when dealing with large quantity, it generates a large number of sets of candidate quantitative frequent patterns (aka sets of item expressions, or itemexpsets for short). Given that large quantities are not unusual in numerous real-life applications, we design a scalable solution in this paper. The resulting scalable quantitative frequent pattern algorithm called SQ-VIPER significantly reduces the number of candidates to be generated, and thus speeds up the mining process. Evaluation results show that superiority of our SQ-VIPER over the existing Q-VIPER and MQA-M algorithms, which respectively mine quantitative frequent patterns vertically and horizontally. © 2023 IEEE.

关键词： Big data

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An intelligent optimization framework to predict the vulnerable range of tumor cells using Internet of things 2

An intelligent optimization framework to predict the vulnera...

引用

2nd IEEE international conference on Industrial Electronics: Developments and Applications, ICIDeA 2023

作者： Gorantla, Venkata Ashok K Sriramulugari, Shiva Kumar Mewada, Amit Hasmukhbhai Logeshwaran, J. Solution Architect United States Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Coimbatore641202 India

ISBN: (纸本)9798350381979

The intelligent optimization framework for predicting the vulnerable range of tumor cells using Internet of Things (IoT) provides a robust and efficient solution for monitoring, assessing, and managing cancer treatment. This framework is based on a combination of deep learning algorithms, big data analytics, cloud computing, and IoT technology. The main purpose of this framework is to develop an optimal solution for detecting the vulnerable range of tumor cells using the latest advances in image processing, machine learning and natural language processing. This framework collects data from various medical or health-related sources such as patient records, imaging results, and medical reports and stores them on the cloud. It filters real-time data at micro-levels to generate a more accurate analysis of the vulnerable range of tumor cells in order to make better proactive healthcare decisions. In this framework, the deep learning algorithms are used to develop predictive models that can accurately detect the vulnerable range of tumor cells. The big data analytics platform is used to analyze the collected data patterns for more refined predictions. Finally, the cloud computing technology provides a distributed computing platform to take advantage of the data from different sources. By integrating the latest advances in machine learning, natural language processing, and cloud computing, the intelligent optimization framework for predicting the vulnerable range of tumor cells using IoT provides an efficient and accurate solution for optimizing the diagnosis and treatment of cancer, enabling doctors to provide better proactive care to their patients. © 2023 IEEE.

关键词： data Analytics

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A Methodological Review on EEG data Reduction in Edge/Fog computing-based IoMT networks 14

A Methodological Review on EEG Data Reduction in Edge/Fog co...

引用

14th international conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Alwash, Riyam Faisal Kadhum Idrees, Ali Al-Obaidi, Salah University of Babylon Department of Computer Science Babylon Iraq University of Babylon Department of Information Networks Babylon Iraq

ISBN: (纸本)9798350335095

This paper presents a methodological review of electroencephalography (EEG) data reduction in edge/fog computing-based IoMT networks. The review focuses on the existing EEG-based signal reduction methods. The EEG components and their framework for capturing the signal generated by the EEG and the signal analysis process are explained. In addition, the public EEG datasets and their characteristics are illustrated. Various signal processing algorithms, including data reduction, compression, and feature extraction, which are used to reduce the size of the captured data, are investigated. The feature-based machine learning techniques are proposed to achieve two goals: reducing the size of the data and extracting a meaningful representation for signal classification. This work can serve as a fundamental plan and a road map for scholars interested in EEG data reduction on edge/fog computing based on INTERNET OF MEDICAL THINGS (IOMT) networks. © 2023 IEEE.

关键词： Feature extraction

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Intelligent Community Cloud Education Service Platform System Based on Artificial Intelligence algorithms

Intelligent Community Cloud Education Service Platform Syste...

引用

2023 international conference on Artificial Intelligence and Automation Control, AIAC 2023

作者： Liu, Hongli Shandong Institute of Commerce and Technology Jinan250103 China

ISBN: (纸本)9798350383805

As the cloud computing and the Internet of Things (IoT) rise, community management is moving towards the path of intelligence, and smart communities have become the focus of great attention of the state and society. The purpose of this paper is to study the construction of a two-way interactive platform based on artificial intelligence (AI) algorithms to realize the effective communication between the intelligent terminal and the server. This platform is based on big data to conduct personalized analysis of a large number of users, and store and classify the obtained large amount of information. Through various technological means, there is a need to achieve big data interaction and automatic management of cloud computing. Finally, this article tested the performance of the smart community cloud service platform, and the test results are as follows: the platform processing data takes 4 seconds, 4 seconds, 5 seconds, 4 seconds, 6 seconds, 5 seconds, 4 seconds, 5 seconds, and 5 seconds respectively;the resource utilization rate of its platform is within 5%;the stability of the platform remains above 80%. © 2023 IEEE.

关键词： Big data

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Multimodal data Trajectory Prediction: A Review 10

Multimodal Data Trajectory Prediction: A Review

引用

10th IEEE international conference on Cyber Security and Cloud computing and 9th IEEE international conference on Edge computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Wang, Xiaoliang Yue, Hao Yang, Qing Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan China Guangzhou Maritime University School of Information and Communication Engineering Guangzhou China

ISBN: (纸本)9798350312461

Trajectory prediction refers to predicting the future movement of an object, person, or vehicle based on past motion trajectory information and other relevant environmental information. Traditional trajectory prediction solutions are suitable for simple driving scenarios and only applicable for short-term predictions. With the improvement of computing power and data processing speed, people can analyze and utilize large-scale datasets faster, use more data and more complex algorithms to build models, and therefore better predict future trends and behaviors. In addition, different solutions focus on how to efficiently extract features from different types of information and how to more accurately predict the future trajectory of the target object. Our goal is to analyze the impact of different information on prediction, and to classify and compare deep learning-based trajectory prediction methods for vehicles or surrounding pedestrians based on the same data. According to the type and quantity of inputs, they are divided into five categories, and the use and prediction effect of different information solutions are elaborated in detail. © 2023 IEEE.

关键词： Deep neural networks

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FSKY-Miner: Fast Mining of Skyline Patterns 21

FSKY-Miner: Fast Mining of Skyline Patterns

引用

21st IEEE international Symposium on Parallel and Distributed processing with Applications, 13th IEEE international conference on Big data and Cloud computing, 16th IEEE international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Chen, Chengdong Gan, Wensheng Chen, Jiahui Wan, Shicheng Guangdong University of Technology School of Computers Guangzhou51000 China Jinan University College of Cyber Security Guangzhou510632 China South China University of Technology School of Business Administration Guangzhou510641 China

ISBN: (纸本)9798350329223

Frequent pattern mining is one of the major tasks to ascertain the frequency of itemsets in a transaction database. Utility-driven pattern discovery takes into account additional factors like interestingness or unit profit. However, we may consider more factors. For instance, while choosing hotels, they may carefully examine both price and distance. In recent years, skyline pattern mining, which focuses on frequency and utility aspects, has garnered substantial attention, and several algorithms have been developed, enabling people to make better decisions when considering multiple factors. The issue of mining skyline patterns from different perspectives is explored in further detail in this work. We provided an efficient algorithm called FSKY-Miner with an effective initialization strategy, two pruning strategies involving subtree utility and local utility, and projected database as well as transaction merging technologies, which collectively aim to efficiently prune unpromising itemsets for the purpose of avoiding generating large numbers of candidates. To demonstrate the comparison results of FSKY-Miner with cutting-edge algorithms and prove the impact of different strategies on FSKY-Miner, numerous experiments were conducted. FSKY-Miner performs better than other algorithms in terms of runtime, memory usage, search space, and scalability. © 2023 IEEE.

关键词： data mining

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Research on improved algorithm of FP-growth based on data strong association

Research on improved algorithm of FP-growth based on data st...

引用

2023 international conference on algorithms, High Performance computing, and Artificial Intelligence, AHPCAI 2023

作者： Ye, Qianhao Ning, Shiyong School of Computer and Information Engineering Harbin University of Commerce Heilongjiang Harbin150028 China Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing China

ISBN: (纸本)9781510671881

FP-Growth algorithm is a classic algorithm in the field of data mining. Compared with Apriori algorithm, all frequent item sets can be obtained by traversing the data set only twice, which improves the efficiency of data mining. However, in practical use, if there are many Frequent one item sets in the data set, the data set is scattered, and a lot of time will be wasted. Moreover, it will cause the problem that there are too many child nodes when FP-Tree is formed, and it is easy to overflow. In view of the above shortcomings, in this paper, a new FP-Growth algorithm based on data Strong Association (SDA-FP-Growth algorithm) is proposed., which uses frequent two items set for operation integration and merging of the same leaf nodes, which is conducive to the subsequent frequent item set search. Experiments show that the SDA-FP-Growth algorithm reduces the running time and memory space. © 2023 SPIE. All rights reserved.

关键词： data mining

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Cyber vulnerabilities surveillance via web-scraped specialized documents 25

Cyber vulnerabilities surveillance via web-scraped specializ...

引用

25th international Symposium on Symbolic and Numeric algorithms for Scientific computing (SYNASC)

作者： Sirbu, Alexandru-Gabriel Babes Bolyai Univ Comp Sci Cluj Napoca Romania

ISBN: (纸本)9798350394139;9798350394122

Cyber security articles represent an important source of information of up-to-date data about software vulnerabilities, including data like Indicators of Compromise, highly valuable to any security researcher. The articles can be collected using Crawlers and Scrapers, and can be later analyzed in order to create keyword-type searches. This paper conducts a study towards the extraction of articles from forum-like websites, and of relevant information from the articles, while highlighting different types of Crawlers, Scrapers and analysis algorithms, such as the extraction of Indicators of Compromise, keyword text search and Natural Language processing methods. Thus, we will present the types of forum websites with their characteristics, alongside different types of Crawlers, while also presenting some of the benefits of ethical scraping and some of its most important principles, such as postponing a request to server and changing the User-Agent header. The extracted information can be saved in an Elasticsearch Index, in order to create various statistics about the information extracted, providing an up-to-date overview over the current state of the cybersecurity space.

关键词： Crawlers Scrapers data extraction data analysis cyber vulnerability cyber attack malware Indicator of Compromise

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Blockchain-Based System for Secure data Sharing in Cloud Using Machine Learning: Current Researches and Challenges

Blockchain-Based System for Secure Data Sharing in Cloud Usi...

引用

2023 IEEE international conference on Innovative data Communication Technologies and Application, ICIDCA 2023

作者： Shrivastav, Pooja Sadasivan, Manju Cmr Institute of Technology Department of Mca Bengaluru India School of Science Studies Cmr University Bengaluru India

ISBN: (纸本)9798350397208

The adoption of blockchain is accelerating globally. Cloud computing and secure data sharing have emerged as new technologies, owing to current advances in machine learning. Conventional machine learning algorithms need the collection and processing of training information on centralized systems. With the introduction of new decentralized machine learning algorithms and cloud computing (CC), Machine Learning (ML) on-device is now a reality. IoT gadgets may outsource training from cloud computing services to incorporate Artificial Intelligence (AI). Furthermore, these distributed edge frameworks bring additional issues, including data confidentiality and information safety. To overcome these challenges, blockchain has been proposed as a viable alternative. Blockchain, as a distrusted framework has evolved as a revolutionary innovation due to its decentralized, accessible, and secured structure. This system also includes trustworthy automatic scripting and static information recordings. The complexity of blockchain technology and the level of skill needed to work with it is one of its drawbacks. This issue can be successfully resolved by using Machine Learning (ML) frameworks. It can increase the user-friendliness of Blockchain. The existing blockchain-based cloud computing, machine learning, and secure data sharing are summarizedin this research study. © 2023 IEEE.

关键词： Blockchain

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Recursive Calculation of Space Multi body System Dynamics Based on data Analysis

Recursive Calculation of Space Multi body System Dynamics Ba...

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2023 international conference on algorithms, High Performance computing, and Artificial Intelligence, AHPCAI 2023

作者： Huang, Heyouzi Zhongshan Jizhong Sanxin International Department High School Department Three towns Guangdong Province Zhongshan City528463 China

ISBN: (纸本)9781510671881

The recursive result of multi body space system dynamics is a dynamic process. In this process, multiple discrete points can be used to analyze the research objects. The space system dynamics of multiple objects is a discipline that studies the motion laws of multiple objects interacting in space. In the development of Space technology, the application of space system dynamics includes spacecraft design and orbit control. This article explored a recursive calculation method for space multi body system dynamics based on data analysis, with the aim of providing new computational ideas for the aerospace field. This article mainly analyzed and designed spatial multibody systems, and compared them using recursive algorithms and Lagrange methods to obtain the advantages of recursive computation. The experimental results showed that the longest CPU (Central processing Unit) time consumed by the recursive method was 9.3 seconds, and the shortest was 1.9 seconds. © 2023 SPIE. All rights reserved.

关键词： Program processors

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