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共 1147 条记录，以下是221-230 订阅

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Developing a Computational Tool for Lung Cancer Prediction with data mining Techniques 3

Developing a Computational Tool for Lung Cancer Prediction w...

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3rd IEEE international conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Chohan, Jasgurpreet Singh Madan, Heena Kumar, Himanshu Kapoor, Monit Singh, Devendra Almusawi, Muntather School Of Mechanical Engineering Rayat Bahra University Mohali India Chandigarh Engineering College Chandigarh Group Of Colleges Department Of Computer Science Engineering Mohali Punjab Jhanjeri India Chitkara Centre For Research And Development Chitkara University Himachal Pradesh India Centre Of Research Impact And Outreach Chitkara University Punjab Rajpura India Uttaranchal Institute Of Technology Uttaranchal University Department Of Computer Science & Engineering Dehradun India The Islamic University Najaf Iraq

ISBN: (纸本)9798350318609

machine learning is commonly used to automate disease classification by extracting features from segmented objects, but automated segmentation can be difficult. Model proposed uses average values to compute features based on the whole image, eliminating the need to perform additional steps. Results obtained by the algorithm were encouraging, but implementing it requires considerable computational expertise. Lung cancer detection is a very important field of medical processing. We have included two processes named feature extraction and feature selection processes and evaluated and compared the performance of feature extraction and feature extracting and selection processes. In addition, some benchmark sets can be used to compare the performance of the proposed work model. PCA, ICA, and SURF were used to develop the friendly disease prediction model. The feature selection process is applied after the feature extraction process to select the relative features and will compare with the feature extraction criteria. The feature selection is done using the HPSO-CSO Algorithm. The proposed method in which the work has been done using canny edge operator and feature selection has shown good result values in comparison to feature extraction. The proposed techniques aim to minimise classification errors and maximise accuracy. From the results, it has been concluded that accuracy was achieved at 97.94%. © 2024 IEEE.

关键词： Forecasting

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data mining for Fraud Detection in Telecommunications: Detecting Anomalous Behaviors in Real-Time 3

Data Mining for Fraud Detection in Telecommunications: Detec...

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3rd IEEE international conference on Automation and Computation, AUTOCOM 2025

作者： Mishra, Nidhi Shivaji, Ghorpade Bipin Kalinga University Department of Cs & It Raipur India

ISBN: (纸本)9798331542375

In the recent past, telecom experts have been confronted with so many problems emanating from fraudulent deeds that apart from causing loss of money discredits organizations. To mitigate these problems, real-time fraud detection systems are very essential in detected those unusual and unauthorized activities. This study aims at examining how data mining can be used in the identification of the actual fraud in the telecommunication area through users' interactions in the course of which a huge amount of data is produced. In this study, the case uses different machine learning algorithms including clustering, decision trees, and anomaly detection for mapping out unusual call pattern, unauthorized access and Identity theft. To capture anomalous behavior, the research uses both time-domain and frequency domain data from different telecom networks for training and testing models. The research also intends to extend existing theories and practices of Naive Bayes, Support Vector machines (SVM), and deep learning networks to improve accuracy and effectiveness of the sort of systems used in the detection of fraud. The performance of these models is measured in terms of accuracy in classification and prediction of fraudulent actions thereby reducing potentials false negative and false positive cases. Further, the paper outlines how these models can be integrated into existing network security architecture to assess the prospects for scalability of these solutions within a maturing fraud environment. The findings also affirm that data mining is effective in real-time monitoring and prevention of frauds as such highlighting the importance of frequent updates of the fraud detection solutions within the telecommunication sector. © 2025 IEEE.

关键词： Anomalous data mining Fraud Detection SVM Telecommunication

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An Analysis of data mining Techniques in Software Engineering database Design 3

An Analysis of Data Mining Techniques in Software Engineerin...

引用

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

作者： Lu, Fengwei Jin, Zhongwei Zhang, Xiaolin School of Information Engineering Heilongjiang Polytechnic Heilongjiang Harbin China

With the advent of Internet information technology, data mining technology has been applied in various fields of China's social and industrial development, and has promoted the quality development of the industry [1]. Nowadays, people are widely influenced by Internet computer technology, and the application of computer technology has been indispensable in life, work and study. At the same time, data mining technology arises from Internet communication technology and is used as an important technical means of operation and development by various industries, especially the application of data mining technology in university software engineering teaching is becoming more and more widespread, but there are still many unavoidable problems that require scholars to pay more attention. The article analyzes and discusses the significance of applying data mining technology in software engineering [2], and on this basis, the application path of data mining technology in software engineering database design is proposed, including: mining information, mining loopholes, mining execution records and open source. It is hoped that relevant technology users can efficiently apply data mining technology to make software engineering It is more reasonable and efficient, so that software engineering can be further developed. This paper presents the implementation and performance analysis of the teacher management decision-making system based on data mining, and introduces the implementation process of the system in detail. © 2023 The Authors. Published by Elsevier B.V.

关键词： database systems

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Construction of Fusion Visualization Platform for Meteorological Station and Monitoring Application Scenarios 3rd

Construction of Fusion Visualization Platform for Meteorolog...

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3rd international conference on machine learning, Cloud Computing and Intelligent mining, MLCCIM 2024

作者： He, Tingting Jiang, Yuantong Hao, Panlin Zheng, Faling He, Yuanrong School of Cultural Industries and Tourism Xiamen University of Technology Xiamen China College of Computer and Information Engineering Xiamen University of Technology Xiamen China Technology Co. Ltd. Xiamen China

ISBN: (纸本)9789819624676

With the continuous development of real-world 3D, the visualization of multi-source real 3D data fusion is significant for digital twins. Based on SketchUp, this study aims to utilize advanced laser point cloud scanning technology to conduct component-level realistic 3D modeling and to construct a high-precision movable meteorological station-based realistic 3D model. Meanwhile, by utilizing the WebGL architecture of a real-time 3D model visualization platform, the meteorological station model is integrated into the large model of the monitoring scene, achieving functions such as browsing, attribute querying, rotating, scaling, dismantling, explosion, and more flexible interaction and spatial analysis of the movable meteorological station 3D model. The real-time 3D model of this study can not only provide intuitive and comprehensive spatial information support for the design, deployment, and operation of meteorological stations, but also provide a visual foundation for environmental monitoring, disaster response, and other fields. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： data fusion

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Intrusion Detection in Computer Networks using Optimized machine learning Algorithms 3

Intrusion Detection in Computer Networks using Optimized Mac...

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3rd international Informatics and Software Engineering conference, IISEC 2022

作者： Yilmaz, Abdullah Asim Republic of Türkiye Ministry of Agriculture and Forestry Department of Strategy Development Ankara Turkey

ISBN: (数字)9781665459952

ISBN: (纸本)9781665459952

Intrusion detection systems (IDSs) are employed to maintain computer networks from cyber attacks. Here, the aim is to detect intrusions once the data is transmitted across the internet. Intrusion detection methods (IDMs) developed in the literature are commonly focused on data mining, statistical and machine learning-based analysis of anomaly situations. In this paper, we suggested machine learning based methodology to detect intrusions in computer networks. The proposed method comprises four main phases, namely, preprocessing, feature selection, parameter optimization and classification. Most significant features are selected by utilizing the Correlation Based Feature Selection. For classification Random Tree, AdaBoost, K-Nearest Neighbor (KNN), Support-vector machine (SVM) are used while for parameter optimization particle swarm optimization is employed. The proposed method was tested on two extensive datasets, namely NSL-KDD and CIC-DDOS2019. The experimental results reveal that the proposed method can effectively classify intrusions with a high detection rate and that it outperforms the other machine learning techniques. © 2022 IEEE.

关键词： Network security

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Research on meteorological risk prediction method based on machine learning algorithm 3

Research on meteorological risk prediction method based on m...

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

作者： Li, Ping Chen, Yu Liu, Shuo Tian, Li Lu, Jinglong Liaoning Provincial Meteorological Observatory Shenyang Liaoning110016 China Liaoning Meteorological Disaster Monitoring and Early Warning Centre Shenyang Liaoning110016 China Institute of Atmospheric Environment China Meteorological Administration Shenyang Liaoning110016 China

ISBN: (纸本)9781510661363

Among all kinds of natural disasters, meteorological disasters cause the greatest economic losses. Meteorological disasters have multiple causes. The characteristics of systematization, social amplification, unpredictability and urgency have caused various. The loss is incalculable. Therefore, the risk assessment and management of meteorological disasters is very important. So far, there is still no universally practical and systematic theory of meteorological disaster risk assessment and management. The research on meteorological disaster risk assessment and management in China started relatively late, Moreover, flood and drought are the main disasters, and there are few studies on other disasters, such as typhoons and hurricanes. Through the method of case analysis, this paper summarizes some internal, hoping to increase the research content in this field. © COPYRIGHT SPIE.

关键词： Disasters

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Application of machine learning Algorithm: Forecasting Heart disease

Application of Machine Learning Algorithm: Forecasting Heart...

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Innovation in Technology (INOCON), IEEE international conference for

作者： Apurva Lohia Arti Pawar ICAS Manipal Academy of Higher Education Manipal India

The healthcare industry produces a huge amount of complex data about patient records. This data can be processed and mined to uncover hidden patterns, which will provide professionals in the healthcare field with additional information and help them in making better informed decisions. Using data in such a way is known as data mining. One such area in the healthcare industry where data mining can be used is early prediction of heart diseases so that patients can get the appropriate treatment. This paper compares the efficiencies of the following supervised machine learning models: Logistic Regression, Naïve Bayes, Support Vector machine, K-Nearest Neighbors, Decision Tree, Neural Networks, Random Forest. It was found that the Neural Networks algorithm provides the highest accuracy of 81.32%.

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Construction of Business English Needs Analysis Model Based on Computer Corpus 3rd

Construction of Business English Needs Analysis Model Based ...

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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： Lu, Yao Zhang, Li Ma, Lingya Shandong Management University Shandong Jinan250357 China Hubei Three Gorges Polytechnic Hubei Yichang443000 China

ISBN: (纸本)9783031631290

In order to meet the needs of business English, this article constructs a computer corpus based business English requirement analysis model. By collecting and organizing relevant corpus in the field of business English, establish a large-scale and diverse corpus. Then, using natural language processing technology and machine learning algorithms, the corpus is analyzed and processed to extract common vocabulary, phrases, and grammatical structures in business English. Next, by clustering and mining association rules on the texts in the corpus, the actual needs and characteristics of business English are discovered. In the process of model construction, we will also consider different branches and specialties in the business field, such as marketing, international trade, business communication, etc., to adapt to the needs of different fields. Finally, based on the results of the needs analysis, we will provide targeted suggestions and guidance for business English teaching and training. The construction of this model helps educators and learners better understand the characteristics and needs of business English, in order to carry out business English teaching and training more effectively. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Big data

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3rdWorkshop on Patent Text mining and Semantic Technologies (PatentSemTech2022) 22

3rdWorkshop on Patent Text Mining and Semantic Technologies ...

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45th international ACM SIGIR conference on Research and Development in Information Retrieval (SIGIR)

作者： Krestel, Ralf Aras, Hidir Andersson, Linda Piroi, Florina Hanbury, Allan Alderucci, Dean ZBW Leibniz Informat Ctr Econ Kiel Germany Univ Kiel Kiel Germany FIZ Karlsruhe Karlsruhe Germany Artificial Researcher IT GmbH Vienna Austria TU Wien Vienna Austria Res Studios Austria Vienna Austria Carnegie Mellon Univ Ctr AI & Patent Anal Pittsburgh PA 15213 USA

ISBN: (纸本)9781450387323

Steadily increasing numbers of patent applications per year and large amounts of available patent data necessitate highly efficient and interactive next-generation information retrieval systems in the patent domain. AI and machine learning (ML) methods such as Deep learning (DL) are successfully adopted in many domains, so patent researchers and practitioners start to employ AI-based approaches as well, to support experts in the patenting process or to automate patent analysis and retrieval processes. AI-enhanced Information Retrieval systems can improve patent search and analysis but also require millions of annotated sample data for training the ML models. When working with patent data, particular challenges arise that call for adaption of existing IR and AI methods as well as development of novel approaches suited for the patent domain. The focus of the 3rd edition of this workshop will be on two-way communication between industry and academia from all areas of Information Retrieval, such as Natural Language Processing (NLP), Text and data mining (TDM), and Semantic Technologies (ST). We want to bring together novel research results and the latest systems and methods employed by the Intellectual Property (IP) industry.

关键词： patent analysis text mining semantic technology deep learning

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Cyber Security Fraud Detection Using machine learning Approach 3

Cyber Security Fraud Detection Using Machine Learning Approa...

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3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Raman, Ramakrishnan Tiwari, Mohit Buddhi, Dharam Trivedi, Snehal Bothe, Shivaji Ponnusamy, R. Symbiosis Institute of Business Management Maharashtra Pune India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Paschim Vihar Delhi India Uttaranchal University Uttaranchal Institute of Technology India Parul University Department of Mech India Department of IT & Business Analytics India Chennai Institute of Technology Department of Computer Science & Engineering India

ISBN: (纸本)9798350399264

It is notoriously harder to identify fraudsters for its fluid nature in terms of recognizable trends. Latest technological breakthroughs were used by scammers. Individuals overcome the protection, incurring millions of dollars of lost revenue. data mining approaches could be employed to crunch numbers and discover out-of-the-ordinary behaviors, allowing it to be pursued to its cause, in this case a fraudulent payment, activities. In this study, we really would like to evaluate and contrast various popular ml algorithms, namely k-nearest peer (KNN), randomized forest (RF), and support vector (SVM), along with popular deep neural networks, including auto - encoder, Classifiers, Multiple solutions, and multi - layer perceptron (DBN). The European Union (EU), Canada, and Netherlands information will be used. The measures utilized for evaluations are the Region That under Fitted Model (AUC), the Matthew R Squared (MCC), and the cost of failing. © 2023 IEEE.

关键词： Multilayer neural networks

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