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共 1147 条记录，以下是181-190 订阅

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Vehicle Industry Big data Analysis Using Clustering Approaches 3rd

Vehicle Industry Big Data Analysis Using Clustering Approach...

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3rd international conference on Optimization, learning Algorithms and Applications (OL2A)

作者： Seixas, Lenon Diniz Correa, Fernanda Cristina Siqueira, Hugo Valadares Trojan, Flavio Afonso, Paulo Fed Univ Technol Parana UTFPR R Doctor Washington Subtil Chueire330 Jardim Car BR-84017220 Ponta Grossa PR Brazil Univ Minho Sch Engn Dept Prod & Syst Guimaraes Portugal

ISBN: (纸本)9783031530357;9783031530364

Considering a globalized world economy and industry, data analysis and visualization offer enlightening information for decision-making and strategic planning. data science provides diverse statistical and scientific methods to extract the most value possible from a data set, covering all data preparation, cleaning, aggregation, and manipulation. machine learning (ML) and Artificial Intelligence (AI) come with it to learn and explore the data, uncovering patterns that cannot be seen with only the analyst experience. This work performs a study exploring clustering methods in a trucks data set of logged inclinations on the roadway, a Big data problem. With a good clustering, the data becomes key to improve product development and fuel efficiency, since different environment of truck usage can be identified. Knowledge discovery and data mining methods were used, namely the K-means and Fuzzy C-means (FCM) algorithms and compared to a rule-based method called GTA. The evaluation metrics addressed are the sum of squares within clusters, the sum of squares between clusters, and the silhouette index. The proposed approach showed satisfactory results and demonstrated how the ML application could benefit this real world problem, especially the FCM.

关键词： Clustering K-means Fuzzy C-means Trucks Slope

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Application of text mining technology in power data prediction 3

Application of text mining technology in power data predicti...

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2022 3rd international conference on Computer Information and Big data Applications, CIBDA 2022

作者： Guo, Jian Guo, Wu College of Architecture and Civil Engineering Qiqihar University Qiqihar China College of Communication and Electronic Engineering Qiqihar University Qiqihar China

ISBN: (纸本)9783800758760

This paper analyses the main research methods of power text mining technology in detail, and discusses the research hotspots of power text named entity recognition and named entity relationship extraction based on machine learning and deep learning. On this basis, the limited Boltzmann machine is further used to predict the power data. The test results show that the analysis of power text features in this paper is accurate, can effectively analyse the key text information, and the prediction results meet the requirements. © VDE VERLAG GMBH - Berlin - Offenbach.

关键词： Character recognition

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Sports Training Strategies Based on data mining Technology 3

Sports Training Strategies Based on Data Mining Technology

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3rd international conference on machine learning and Big data Analytics for IoT Security and Privacy, SPIoT2023

作者： Niu, Mengran China Sichuan Chengdu Chengdu Polytechnic Chengdu610041 China

According to the demand of sports athletes' scientific training, combined with data mining technology, a set of sports training model which is suitable for China's national conditions and has a higher level of scientific research is established. Starting from the data mining technology, this paper expounds its connotation and several common methods in detail. Based on the demand of sports training, this paper studies the quality control of sports training and the statistical system of tactical combat. On this basis, an improved output method of Aprilio algorithm is introduced to enhance the correctness of systematic evaluation. On this basis, three algorithms, classic, DC-Apriori and modified Apriori, are simulated and compared. The results show that the improved Apriori algorithm studied in this project can provide better decision support for the training model. © 2023 Elsevier B.V.. All rights reserved.

关键词： Quality control

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Ensemble Feature Selection based on Multiple Metrics and Improved Aggregation Strategies 24

Ensemble Feature Selection based on Multiple Metrics and Imp...

引用

3rd international Symposium on Control Engineering and Robotics, ISCER 2024

作者： Lin, Yumeng Ren, Xiaoying Wang, Shuqin College of Computer and Information Engineering Tianjin Normal University Tianjin300382 China

ISBN: (纸本)9798400709951

Feature selection plays a crucial role in the field of machine learning and data mining. Some feature selection methods use a single evaluation measure to evaluate the classification ability of features, but different measures have their own characteristics. Ensemble learning can be applicable to feature selection for improve the performance by combining the output of several feature selectors. Therefore, we present an ensemble feature selection based on multiple metrics and improved aggregation strategies (EFSMMIAS). Three evaluation metrics, Symmetric Uncertainty (SU), Fisher Discriminant Ratio (FDR) and ReliefF are employed. Each of them is combined with the discriminant structure vector complementarity to construct a base feature selector for selecting multiple subsets of features. Subsequently, we employ an enhanced aggregation strategy to derive the desired feature subset from the feature subset generated by the base feature selectors, which can improve classification capabilities. In order to evaluate the effectiveness of our proposed method, we compare it with other methods on 11 standard datasets through a series of experiments. The results validate that our method is capable of improving classification performance while simultaneously reducing the dimensionality of features. © 2024 ACM.

关键词： Contrastive learning

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machine learning Approach for Discovering Successful Books Based on the Achievement of Award-Winning 3

Machine Learning Approach for Discovering Successful Books B...

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3rd international conference on Advanced Research in Computing, ICARC 2023

作者： Wijekoon, S.M.S.T. Rupasingha, R.A.H.M. Sabaragamuwa University of Sri Lanka Department of Computing and Information Systems Belihuloya Sri Lanka Sabaragamuwa University of Sri Lanka Department of Economics and Statistics Belihuloya Sri Lanka

ISBN: (纸本)9798350347371

Many people are very interested in reading books. Some of them like to read books for various reasons like as a hobby or to learn something new. At present, with modern technology, people have become accustomed to reading books online in addition to physically reading books. The most important factor in identifying which books are the most successful is the award-winning. The goal of this research is to forecast how the most successful books such as award/winning books are identified based on 10 distinct attributes including publishing date, average ratings, author, genre, language, publisher, number of pages, ratings, title, and reviews. A data set was gathered for this purpose through the online community platform Kaggle. The dataset provides information about books gathered from the website 'Goodread' for the period between 2000 and 2021. Firstly, the data set has been pre-processed. For that, duplicate data, unnecessary special characters, and missing values have been removed. To rank the preprocessed data, the Waikato Environment for Knowledge Analysis (WEKA) data mining tool was utilized, and results are improved by hyperparameter tuning. The predictive model is generated using six machine learning approaches at the classification level: Random Forest, Support Vector machine (SVM), Decision Trees, Multilayer Perceptron (MLP), Logistic Regression, and Naive Bayes. A high accuracy of 86.32% was gained with the lowest error rates in addition to high precision, recall, and f-measure values using a Naive Bayes algorithm. Based on the results, the Naive Bayes algorithm makes a reliable prediction of how to find the most successful books using the above features. © 2023 IEEE.

关键词： Forecasting

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Research on metadata management of data lake 3

Research on metadata management of data lake

引用

3rd international conference on Computer Vision and data mining, ICCVDM 2022

作者： Guo, Zhixia Pan, Chenshuo Chang, Ke Li, Xinchi Cheng, Shasha Zhang, XinRui Research Institute of China Telecom Corporation Limited Beijing100000 China University of Wisconsin-Madison MadisonWI999039 United States Beijing Institute of Technology Beijing100000 China Beijing Normal University Beijing100000 China

ISBN: (纸本)9781510661363

Metadata management plays an important role in enterprise information management. The complete metadata management system has directly affected the flexibility and high scalability of the platform. This paper summarizes several important links in metadata management technology in data warehouses and large-scale distributed file systems. This paper organizes the metadata management standards, compares various metadata management architecture technologies,and summarizes the metadata management characteristics and metadata management strategies. At the same time, this paper introduces the applicability of the current mainstream open source metadata management tools in detail, focuses on the research of cognitive metadata directory based on machine learning, and prospects the future research direction. © COPYRIGHT SPIE.

关键词： Information management

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Analyzing the Multifaceted Interactions of Modern Digital Dreamscapes and Deep data Insights 3

Analyzing the Multifaceted Interactions of Modern Digital Dr...

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3rd IEEE international conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Mothilal, T. Balaji, V. Natrayan, L. Yamsani, Nagendar Kcg College of Technology Department of Automobile Engineering Karapakkam TN Chennai India Loyola Institute of Technology Department of Mechanical Engineering TN Chennai India Saveetha School of Engineering Simats Department of Mechanical Engineering TN Chennai India School of Computer Science & Artificial Intelligence Sr University Telangana Warangal India

ISBN: (纸本)9798350343274

Through a methodologically exhaustive approach, this study investigates the complex relationships between contemporary digital dreamscapes and deep data insights. Natural Language Processing (NLP), Social Network Analysis (SNA), machine learning Algorithms (MLA), data mining (DM), Topic Modeling (TM), and Sentiment Analysis (SA) are among the six original methods compared to the proposed method, which combines ethnographic research with cutting-edge data analysis techniques. To emphasize the relative performance and ethical implications of the methodologies, four different visualizations are offered, including a bar chart, scatter plot, pie chart, and box plot. According to the results, the suggested technique is superior to the conventional ones in many respects, including accuracy, precision, recall, F1-Score, processing time, privacy, data security, ethics, cultural sensitivity, scalability, and adaptability. Considering these findings, an interdisciplinary approach is necessary for evaluating digital dreamscapes, one that takes into consideration cultural, ethical, and data-driven factors. © 2023 IEEE.

关键词： Scalability

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Evaluation Model and Algorithm of Physical Education Teaching 3rd

Evaluation Model and Algorithm of Physical Education Teachin...

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3rd EAI international conference on Application of Big data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2023

作者： Li, Lei Suiying, Liu Nanchang University College of Science and Technology Jiangxi Nanchang330001 China Foreign Languages Schoolichuan University Jinjiang Collegeeishan Sichuan620800 China

ISBN: (纸本)9783031631320

This article proposes an evaluation model and algorithm based on data mining and machine learning for physical education teaching evaluation, based on the needs of educational evaluation. In this study, we quantified the key factors and elements in the physical education teaching process by developing relevant indicators and standards, and transformed them into a form that can be used for data analysis and processing for evaluation and analysis. The system consists of four steps. The first step is to plan and evaluate. In this step, we should plan our activities based on the goals of each lesson or unit. We can also use some simple tools, such as scales, checklists, etc., to facilitate the preparation of the evaluation. The evaluation teaching model of physical education teaching refers to the process in which teachers evaluate and provide feedback on students’ performance in physical education teaching. This process helps teachers improve their skills and physical education knowledge. The feedback provided by the teacher should be positive so that students can learn from it. This also helps to improve their performance in sports. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： data mining

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Fabrication of a Tool for Prediction of Crops Using machine learning 3rd

Fabrication of a Tool for Prediction of Crops Using Machine ...

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3rd international conference on Mechanical and Energy Technologies, ICMET 2023

作者： Khan, Nausad Firdosh, Nasima Department of Mechanical Engineering Galgotias College of Engineering and Technology Knowledge Park—II Uttar Pradesh Greater Noida201013 India Department of Applied Sciences GGDSD PC College Haryana Palwal121102 India

ISBN: (纸本)9789819727155

Climate change is a major problem all over the world in the current scenario that affects many things but its great impact on agricultural crops in India past 20–25 years has been consistent. The crop yield prediction can be more beneficial and helpful with policymakers and all communities of farmers for making informed decisions regarding the marketing purpose and storage area. This paper aims to develop a K-nearest neighbors (KNN) model for an interactive crop prediction system specifically for the purpose of prediction of crops for providing a better environment by using a machine learning algorithm. This constructive model system will give accurate crop yield predictions to the farmers before the cultivation of crops and also enables them to provide the appropriate decisions for different types of crops for cultivation purposes. This implementation of this KNN model includes a user-friendly web-based interface for easy access the weather conditions. After analyzing all such weather conditions as temperature, humidity, draught, rainfall, and moisture, the KNN model algorithm predicts the effectiveness of crop yield. This data mining approach summarizes important information by analyzing data from various perspectives and conditions. KNN model is a popular machine learning tool algorithm that is adequate for performing reversion and their grouping by making decision trees with training programmed and creating the outcome-based data on the majority of order or mean value for the purpose used. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Crops

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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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