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共 1150 条记录，以下是11-20 订阅

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Enhanced data mining Algorithms for Sybil Attack Prevention in Truth Discovery 24

Enhanced Data Mining Algorithms for Sybil Attack Prevention ...

引用

3rd international conference on Algorithms, data mining, and Information Technology, ADMIT 2024

作者： Shao, Yanjing Zhao, Wei Liu, Jianghua Xu, Lei Xu, Chungen School of Mathematics and Statistics Nanjing University of Science and Technology China National Key Laboratory of Security Communication China School of Computer Science and Engineering Nanjing University of Science and Technology China

ISBN: (纸本)9798400718120

In crowdsourcing systems, where substantial amounts of data from various contributors are aggregated to discern reliable information, privacy concerns are often managed through differential privacy techniques. However, these systems are vulnerable to Sybil attacks, where attackers create multiple fake contributors to gain undeserved rewards. The presence of differential privacy noise complicates the detection of these fraudulent contributors, thereby reducing the accuracy of truth discovery. To address this challenge, we introduce an enhanced data mining algorithm, Truth Discovery against Sybil Attacks (TDSA). This algorithm is specifically designed to improve truth discovery by effectively mitigating Sybil attacks and reducing transmission errors in data mining contexts, utilizing machine learning clustering algorithms and lattice-based linear coding techniques. TDSA optimizes the mining process to better handle malicious data contributions and reduces errors associated with data aggregation. Experimental results on real-world datasets demonstrate that TDSA improves accuracy by approximately 20% compared to conventional methods lacking Sybil attack defenses, highlighting its efficacy in advanced data mining applications. © 2024 Copyright held by the owner/author(s).

关键词： data aggregation

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A Tennis Momentum Analysis Method Based on Gaussian Dynamics and machine learning 24

A Tennis Momentum Analysis Method Based on Gaussian Dynamics...

引用

3rd international conference on Algorithms, data mining, and Information Technology, ADMIT 2024

作者： Long, Zheng Li, Na Sun, Mengqing Luo, Jiawen Pan, Ting Yin, Yujie Yang, Xinhua School of Computer and Communication Lanzhou University of Technology Gansu Lanzhou China School of Economics and Management Lanzhou University of Technology Gansu Lanzhou China College of Energy and Power Engineering Beihang University Beijing Beijing China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing Beijing China College of Automation Engineering Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing China College of Electrical and Information Engineering Lanzhou University of Technology Gansu Lanzhou China

ISBN: (纸本)9798400718120

Momentum analysis in tennis matches has predominantly focused on qualitative methods, lacking systematic quantitative approaches. This study proposes a novel method that integrates Gaussian dynamics models with machine learning techniques to quantitatively analyze and predict momentum changes in tennis matches. By selecting and defining key indicators such as serve success rate and scoring rate, we developed an algorithm to identify and predict momentum shifts. The proposed method employs ensemble learning techniques, specifically Stacking, to combine multiple machine learning models, enhancing prediction accuracy and execution efficiency. Experimental validation using data from major tennis tournaments, including the 2023 Wimbledon Championships, demonstrates the effectiveness of the proposed approach. The results show that the Stacking model outperforms individual models in accuracy and robustness, providing scientific decision support for coaches and players. This method has low computational requirements, is simple to implement, and considers a wide range of variables, making it highly efficient. Future research will focus on further optimizing the model and applying it to other sports and match types. © 2024 Copyright held by the owner/author(s).

关键词： Gaussian dynamics model

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Construction and Validation of Big data Analytics in Air Traffic Control Flow Prediction Models 24

Construction and Validation of Big Data Analytics in Air Tra...

引用

3rd international conference on Algorithms, data mining, and Information Technology, ADMIT 2024

作者： Wang, Minghua Qiao, Jiantong Air Force Engineering University ATC&GCL College Shaanxi Xi'an China

ISBN: (纸本)9798400718120

Air traffic control flow prediction is one of the key issues in aviation transportation management, crucial for optimizing resource allocation, enhancing control efficiency, and ensuring flight operation safety. This paper proposes a solution for air traffic control flow prediction based on big data analysis and machine learning techniques. Through extensive feature engineering and model construction using large-scale historical data, multiple prediction models including Support Vector Regression, Random Forest, Gradient Boosting Decision Tree, and an ensemble model were trained. Comprehensive performance evaluations using cross-validation and multiple metrics were conducted. Experimental results demonstrate that the proposed ensemble model effectively combines the strengths of multiple base models, significantly outperforming individual models in terms of prediction accuracy, stability, and generalization ability. This provides an efficient and reliable technical path for air traffic control flow prediction, with broad application prospects. © 2024 Copyright held by the owner/author(s).

关键词： Resource allocation

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Big data mining of Website Recruitment data Based on Multi-source data Fusion and machine learning Methods 3rd

Big Data Mining of Website Recruitment Data Based on Multi-s...

引用

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

作者： Dong, Xiaoyang Wang, Jing Strategic Assessments and Consultation Institute Academy of Military Sciences Beijing100091 China

ISBN: (纸本)9789819616978

With the development of information technology, a large amount of data is generated and correlated. Realizing the integration of data from different channels, comprehensive analysis and data mining of multi-source data have gradually become a significant research issue. As the needs of various types of talent recruitment increases, website recruitment has been universal, along with multi-source data environment. It is a typical scenario for data fusion and data mining. The research proposes data comprehensive mining framework based on data fusion and machine learning methods. After that, taking website data analysis as an example, the research integrates various open-source data such as website data, city developing data, time data and other relevant data as well as displays the distribution characteristics of website recruitment of talents. It systematically excavates the inherent law of website recruitment of talents, identifies the factors affecting position popularity, and puts forward policy suggestions. It provides the theoretical reference and case application for the comprehensive framework of data fusion and data mining. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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From data to Decisions: An Ontology-Driven Method for Opinion mining 3rd

From Data to Decisions: An Ontology-Driven Method for Opin...

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3rd international conference on Language Processing and Knowledge Management, LPKM 2024

作者： Belguith, Mehdi Khabou, Wafa Aloulou, Chafik Gargouri, Bilel ANLP-RG MIRACL Laboratory FSEGS University of Sfax Sfax Tunisia MIRACL Laboratory FSEGS University of Sfax Sfax Tunisia

ISBN: (纸本)9783031850660

In this paper, we propose an ontology-driven method for opinion mining in social media. We first construct our Tunisian datasets from YouTube and social media content, mainly Twitter and Facebook. Then, based on natural language techniques and machine learning, we built four ontologies related to the Mobile phone, Election, Restaurant, and TV channel domains. We also propose an opinion mining method based on our domain ontologies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Aspects Domain ontologies machine learning NLP Opinion mining Tunisian dialect

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3rd international conference on Intelligent data Engineering and Automated learning, IDEAL 2002

引用

3rd international conference on Intelligent data Engineering and Automated learning, IDEAL 2002

ISBN: (纸本)9783540440253

The proceedings contain 89 papers. The special focus in this conference is on data mining and Knowledge Engineering. The topics include: mining frequent sequential patterns under a similarity constraint;pre-pruning classification trees to reduce overfitting in noisy domains;data mining for fuzzy decision tree structure with a genetic program;co-evolutionary data mining to discover rules for fuzzy resource management;discovering temporal rules from temporally ordered data;automated personalisation of internet users using self-organising maps;data abstractions for numerical attributes in data mining;calculating aggregates with range-encoded bit-sliced index;a classification algorithm for data mining;a hierarchical model to support Kansei mining process;indexing and mining of the local patterns in sequence database;a knowledge discovery by fuzzy rule based hopfield network;fusing partially inconsistent expert and learnt knowledge in uncertain hierarchies;organisational information management and knowledge discovery in email within mailing lists;design of multi-drilling gear machines by knowledge processing and machine simulation;approach based on hierarchically structured subject domain;a knowledge-based information extraction system for semi-structured labeled documents;measuring semantic similarity between words using lexical knowledge and neural networks;extraction of hidden semantics from web pages;self-organising maps for hierarchical tree view document clustering using contextual information;schema discovery of the semi-structured and hierarchical data;indexing and retrieving web document using computational and linguistic techniques and an intelligent mobile commerce system with dynamic contents builder and mobile products browser.

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3rd international conference on machine learning, Optimization, and Big data, MOD 2017

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3rd international conference on machine learning, Optimization, and Big data, MOD 2017

ISBN: (纸本)9783319729251

The proceedings contain 49 papers. The special focus in this conference is on machine learning, Optimization, and Big data. The topics include: Nonlinear methods for design-space dimensionality reduction in shape optimization;a differential evolution algorithm to develop strategies for the iterated prisoner’s dilemma;automatic creation of a large and polished training set for sentiment analysis on twitter;forecasting natural gas flows in large networks;a differential evolution algorithm to semivectorial bilevel problems;evolving training sets for improved transfer learning in brain computer interfaces;hybrid global/local derivative-free multi-objective optimization via deterministic particle swarm with local linesearch;artificial bee colony optimization to reallocate personnel to tasks improving workplace safety;multi-objective genetic algorithm for interior lighting design;improving support vector machines performance using local search;an elementary approach to the problem of column selection in a rectangular matrix;A simple and effective lagrangian-based combinatorial algorithm for S3VMs;A heuristic based on fuzzy inference systems for multiobjective IMRT treatment planning;data-driven machine learning approach for predicting missing values in large data sets: A comparison study;mineral: Multi-modal network representation learning;visual perception of mixed homogeneous textures in flying pigeons;estimating dynamics of honeybee population densities with machine learning algorithms;SQG-differential evolution for difficult optimization problems under a tight function evaluation budget;age and gender classification of tweets using convolutional neural networks;approximate dynamic programming with combined policy functions for solving multi-stage nurse rostering problem;projective approximation based quasi-newton methods;a data mining tool for water uses classification based on multiple classifier systems.

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Content Recommendations Using machine learning-Based Approaches 3rd

Content Recommendations Using Machine Learning-Based Approac...

引用

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

作者： Indian, Ajay Meena, Gaurav Mohbey, Krishna Kumar Yadav, Bharati Central University of Rajasthan Ajmer India

ISBN: (纸本)9789819624676

In today’s society, recommendation systems are critical. Because there is a huge amount of data around us, whether music, book, or movie data, numerous programs or businesses, including Netflix, Amazon, and Spotify, focus on recommendation systems such as music apps. Systems for recommending content are intended to assist viewers by reducing the need for them to choose from a large variety that may number in the thousands or even millions. With the help of the movie lens dataset, this work aims to conduct a study to analyze the effectiveness of machine learning techniques, like K-NN, XG-Boost algorithms with Pearson Correlation Coefficient, and Count Vectorizer model to develop the Con-tent Recommender System for suggesting the movie. The performance of the proposed systems is evaluated in this study using the RMSE of 1.1062 and 1.1089 for KNN and XG-Boost algorithms, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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machine learning based Fault Distance Estimation of Underground Transmission using Impedance Relay 3

Machine Learning based Fault Distance Estimation of Undergro...

引用

3rd international conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Mathi, Sri Chaithanya Tammisettti, Pooja Naidu Akthar, Shaik Rohid Manitha, P.V. Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Fast development of electric transmission infrastructure necessitates efficient methods of transmission and distribution for reliable cost effectiveness. Between the two main types of transmission, Overhead Transmission Lines involve extensive maintenance cost, use of extensive protection schemes, earthing, and lightning protection. In contrast, Underground Transmission Lines with their insulated and securely laid design are highly resistant to external factors such as lightning, wind, and open conductor faults, showing higher reliability and efficiency. However, faults such as symmetrical and unsymmetrical faults can occur due to conductors being bound together. The traditional fault distance estimation techniques are often inefficient for high-power transmission when using impedance-based fault distance relays. The constant variation in fault characteristics leads to impedance fluctuations, and therefore, fault distance calculations become inaccurate. The current research introduces a machine learning-based Fault Distance Estimation system for Underground Transmission Lines using Impedance Relays to address the above-mentioned challenges. Through data fed into the training machine learning model based on various fault scenarios, data mining is extracted for predicting precise impedance in faulty states and accurately determining fault locations by utilizing trained models with high-performance K-Nearest Neighbour (KNN), Decision Trees (DT), Support Vector Regression (SVR), Random Forest (RF), Long Short-Term Memory (LSTM), and Artificial Neural Networks (ANNs). Through these algorithms and approaches, implementations that emphasize optimal preprocessing, effective feature selection and tuning of specific hyperparameters determine their respective modeling efficiencies. The results show that the models generate very accurate distance estimations, allowing for more accurate fault locations and quick rectification. Not only does it bypass the deficiency of the method

关键词： Nearest neighbor search

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3rd international conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

3rd International Conference on Intelligent Systems and Sust...

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3rd international conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

ISBN: (纸本)9789819783540

The proceedings contain 52 papers. The special focus in this conference is on Intelligent Systems and Sustainable Computing. The topics include: Supervised machine learning Method for Bone Cancer Detection and Classification;heart Disease Identification Using Ensemble learning and Deep learning;Eigenvalue-Based MRAC for All-Quadrant Vector-Controlled Induction Motor Drives Using Fictitious Resistance;PCOS Screening Using machine learning and Ensemble learning Techniques in a Smart Way;MRI-Based Brain Tumour Detection and Classification Using Random Forest Algorithm;decentralizing Rides: A Blockchain Approach to Carpooling;a Privacy Preserving Decision Tree Classification Algorithm for Prediction of Health Care Monitoring System;drug Review Classification by Using Sentiment Analysis;dynamic Resource Management of Cloud Resources Using machine learning Classification Approaches;deciphering Diabetes: Illuminating Prognostic Insights with an Interpretable machine learning Framework;integrating Blockchain for Supply Chain Management of Agricultural and Food Products;a Comprehensive Survey on Traffic Light Detection Sensors for Vehicle Safety and Automatic Emergency Braking Systems Using Deep learning Techniques;a Multi-objective Smart Grid Optimization: Integrating Swarm Intelligence with Domain-Specific Heuristics for Superior Energy Efficiency;generative Adversarial Networks for Semi-Supervised learning in Clinical Decision Support Systems;Efficient Temporal data mining Technique Using Dynamic Time Warped LSTM for E-Commerce Recommendation Systems;optimized Task Scheduling in Diverse Distributed Systems with a Hybrid Genetic Algorithm Approach;scalable Real-Time Anomaly Detection in Financial Markets Using a Novel machine learning Model;AI Enabled Swarm Application for Fire-Fighting Bots Over Web-Bluetooth APIs;disaster Management: Sentimental Analysis Using Tweets Based on Experience and Rehabilitation;movie Sense—Hybrid Movie Recommendation System with Sentimental An

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