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共 1150 条记录，以下是201-210 订阅

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Knowledge discovery and supervised machine learning in a construction project database

Knowledge discovery and supervised machine learning in a con...

引用

3rd international conference on data mining

作者： Kim, H Soibelman, L Univ Illinois Dept Civil & Environm Engn Urbana IL 61801 USA

ISBN: (纸本)1853129259

The construction industry is experiencing explosive growth in its capability to, generate and collect data. Advances in data storage technology have allowed the transformation of an enormous amount of data into computerized database systems. Nowadays, there are many efforts to convert the large amounts of data into useful patterns or trends. Knowledge Discovery in database (KDD) is a process that combines data mining (DM) techniques from machine learning, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from a large database. By applying KDD and DM to the analysis of construction project data, this paper presents the results of a research that discovers the knowledge through KDD process to better identify recurring construction problems.

关键词： database systems

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Parallelization implementation of Bayesian algorithms based on Spark platform 4

Parallelization implementation of Bayesian algorithms based ...

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4th IEEE international conference on Smart Cloud (IEEE SmartCloud) / 3rd IEEE international Symposium on Reinforcement learning (IEEE ISRL)

作者： Liu, Hong Guo, Hong Hu, Wei Wuhan Univ Sci & Technol Coll Comp Sci Wuhan Peoples R China

ISBN: (纸本)9781728155050

With the rapid development of Internet technology, all kinds of data are growing exponentially. How to effectively manage and utilize these data has become the focus of research in the era of big data. Under the requirement of massive data processing, aiming at the time requirement of massive data processing which cannot be met by traditional single-machine serial, this paper proposes a Spark computing framework, studies Bayesian algorithm in data mining, realizes the establishment method of parallel Bayesian algorithm and optimizes it. By using Spark memory computing framework, the efficiency of iteration is high. The computational performance of the parallel computing program is investigated. By comparing Spark parallel computing with traditional single machine serial experiments, it is found that the algorithm can effectively improve the speed of text classification. With the expansion of cluster size, the performance of classification accuracy, time performance and acceleration ratio is better. Parallel Bayesian algorithm based on Spark platform is feasible, which solves the problem that traditional single computer cannot handle large-scale data, and can effectively deal with all kinds of classification problems.

关键词： Spark Bayesian Big data Parallel Computing data mining

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High Frequency Forecasting on Stock Market using machine learning 3

High Frequency Forecasting on Stock Market using Machine Lea...

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3rd international conference for Emerging Technology, INCET 2022

作者： Khot, Amruta Bhat, Rashmi Urunkar, Abhijeet Mudegol, Nandinee Walchand College of Engineering Sangli India St. John College of Engineering and Management Palghar India

ISBN: (纸本)9781665494991

Intraday stock trading has become a popular trend in US, Europe, and Indian markets and forecasting these rapid market movements have become an important topic in finance. With the emergence of technology and computing power machine learning and neural network, methodologies are becoming a reality and efficient way to solve problems. The trend of intraday trading has made the researchers and developers explore neural networks approach to forecast the high frequency changing data. We focused on gathering minute to minute changing data of 80 stocks belonging to 8 different sectors from S&P 500 and developed a custom double layer neural network which efficiently captures the dependencies in the stock market and allows us to predict the returns of a specific stock at the end of next 5th minute time interval. © 2022 IEEE.

关键词： data mining

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Predicting IMDb Ratings of Pre-release Movies with Factorization machines Using Social Media 3

Predicting IMDb Ratings of Pre-release Movies with Factoriza...

引用

3rd international conference on Computer Science and Engineering (UBMK)

作者： Cizmeci, Beyza Oguducu, Sule Gunduz Istanbul Tech Univ Fac Comp & Informat Engn Istanbul Turkey

ISBN: (纸本)9781538678930

The film industry has always been a very important sector in the global market. Therefore, it is very important to maximize the profit by predicting the movie success before its release. Although several studies have been done in this field, it is still needed to improve the prediction performance and collect more data. This study aims to explore the use of Factorization machines approach in order to predict movie success by predicting IMDb ratings for newly released movies using social media data and compare it to current studies. Also, a framework has been developed in order to gather the movie data from different sources including social media. Comparison of the Factorization machines to the current models shows that there are promising results.

关键词： machine learning data mining IMDb Social Media Linear Regression Factorization machines Movie Rating Prediction

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Supervised Rainfall learning Model Using machine learning Algorithms 3rd

Supervised Rainfall Learning Model Using Machine Learning Al...

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3rd international conference on Advanced machine learning Technologies and Applications (AMLTA)

作者： Sharma, Amit Kumar Chaurasia, Sandeep Srivastava, Devesh Kumar Manipal Univ Jaipur Rajasthan India

ISBN: (纸本)9783319746906;9783319746890

Unpredictable and uncertain volume of the rainfall is the serious nature disaster. In current, available rainfall forecasting model predict rainfall volume hourly, weekly or monthly. This work proposed a supervised learning model which is based on machine leaning algorithms of data mining. This approach classify the low, mid and high volume of rainfall. Proposed approach is practically implemented on different uncertain heavy rainfall regions and compare the accuracy and measured the accuracy by ROC area of classifiers such as Random Forest, SMO, Naive Bayes and Multilayer Perceptron (MLP).

关键词： Supervised learning Normalization Mean Thresholds Rainfall dataset Feature selection machine learning Naive Bayes Random forest MLP SMO

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Hybrid machine learning system for solving fraud detection tasks 3

Hybrid machine learning system for solving fraud detection t...

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3rd IEEE international conference on data Stream mining and Processing, DSMP 2020

作者： Vynokurova, Olena Peleshko, Dmytro Bondarenko, Oleksandr Ilyasov, Vadim Serzhantov, Vladislav Peleshko, Marta GeoGuard Kharkiv Ukraine Lviv State University of Life Safety Lviv Ukraine

ISBN: (数字)9781728132143

ISBN: (纸本)9781728132143

In parallel with technological development the problem of fraud detection is becoming more and more important. Increasing number of electronic transactions in various business environments, on the one hand, and software and technology development, on the other hand, lead to an active increase in electronic crime. In the paper the hybrid system of machine learning for solving tasks of anomalies detection has been proposed. This hybrid system consists of two subsystems - anomalies detection subsystem (based on unsupervised learning) and the interpretation subsystem of anomaly type (based on supervised system). The advantage of proposed hybrid system is the high-speed data processing when the data are fed in real time. The effectiveness of the proposed approach was confirmed during the solution of the detecting anomalies problem based on real data streams. © 2020 IEEE.

关键词： Hybrid systems

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Enhancing Management of Diverse Taiwanese Hotels Through machine learning and data mining 3

Enhancing Management of Diverse Taiwanese Hotels Through Mac...

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3rd IEEE international conference on Social Sciences and Intelligence Management, SSIM 2023

作者： Huang, Yu-Hsiang John Yao, Jenq-Foung Yang, Cheng-Ying Information Systems and Computer Science Georgia College & State University MilledgevilleGA United States Computer Science University of Taipei Taipei Taiwan

ISBN: (纸本)9798350327786

Using the capabilities of machine learning and data mining techniques, we formulated a Decision Tree model to aid hotel management in optimizing resource allocation for profit maximization. Additionally, we constructed an Artificial Neural Network (ANN) model to predict hotel occupancy rates based on various known predictors. To establish a baseline for correct prediction, we employed the ZeroR model that achieved an accuracy rate of 42.22%. In contrast, the developed Decision Tree model outperformed with a correct prediction rate of 73.33%, while the ANN model showed a rate of 70.56%. These results showed the effectiveness of both models in predicting outcomes for Taiwanese international tourist hotels. © 2023 IEEE.

关键词： Neural networks

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The 3rd Workshop on Graph learning Benchmarks (GLB 2023) 23

The 3rd Workshop on Graph Learning Benchmarks (GLB 2023)

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29th ACM SIGKDD conference on Knowledge Discovery and data mining (KDD)

作者： Ma, Jiaqi Zhu, Jiong Dong, Yuxiao Koutra, Danai He, Jingrui Mei, Qiaozhu Tsitsulin, Anton Zhang, Xingjian Zitnik, Marinka Univ Illinois Champaign IL 61820 USA Univ Michigan Ann Arbor MI 48109 USA Tsinghua Univ Beijing Peoples R China Amazon Ann Arbor MI USA Google Res Seattle WA USA Harvard Univ Cambridge MA USA

ISBN: (纸本)9798400701030

Recent years have witnessed a surge of research interest in graph machine learning. However, the benchmark datasets available to the field are rather limited in both quantity and diversity, an issue particularly notable given the immense potential applications of graph learning. The lack of diverse benchmark datasets may have biased the development of graph machine learning techniques towards narrow directions. By crowdsourcing novel tasks and datasets, this workshop aims to increase the diversity of graph learning benchmarks, identify new demands of graph machine learning in general, and gain a better synergy of how concrete techniques perform on these benchmarks. Moreover, this workshop offers a platform for discussions of best practices in curating graph learning benchmarks and data-centric approaches for graph learning.

关键词： graph machine learning datasets benchmarks data-centric AI

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Traditional Work Station System with AI based Crypto mining 3

Traditional Work Station System with AI based Crypto Mining

引用

3rd international conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Vanitha, M. Harivanth, A. Govardhan, K. Saveetha Engineering College India

ISBN: (纸本)9798331532420

This research explores the feasibility of AI-powered cryptocurrency mining on traditional workstations. Traditional workstations, while widely available, face challenges in terms of computational efficiency and energy consumption compared to specialized hardware like ASICs and GPUs. This project aims to address these challenges by leveraging machine learning techniques to optimize mining performance. By analyzing real-time data such as hash rates, power usage, and network difficulty, the system will dynamically adjust mining parameters to maximize profitability while minimizing resource consumption. Furthermore, the project will investigate the use of reinforcement learning to enable the system to continuously adapt to changing mining conditions and optimize its performance over time. This approach aims to democratize cryptocurrency mining by making it accessible to users with traditional workstations, enabling them to participate in a sustainable and efficient manner. © 2024 IEEE.

关键词： Self-supervised learning

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AI-Enhanced data mining for Fraud Detection in Financial Transactions 3

AI-Enhanced Data Mining for Fraud Detection in Financial Tra...

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3rd international conference on Sentiment Analysis and Deep learning, ICSADL 2024

作者： Gupta, Rajan Goyal, Raghav Malik, Kanta Sahu, Indu School of IT Vivekananda Institute of Professional New Delhi India Gemini Solutions Private Limited. Application Development Haryana Gurugram India

ISBN: (纸本)9798350396157

Financial frauds are on the rise globally, causing significant financial losses. This issue has far-reaching consequences, impacting the investment industry, government, and corporate sectors alike. Manual verifications and inspections are inaccurate, expensive, and time consuming for detecting such fraudulent operations. Artificial Intelligence (AI) and machine learning (ML) techniques should be used intelligently to identify fraudulent transactions by evaluating enormous amounts of financial data. The paper outlines a process of detecting financial fraud with the application of AI and machine learning. Finally, the paper discusses how organizations can leverage AI techniques for the detection of financial frauds. © 2024 IEEE.

关键词： machine learning

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