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检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3245 条记录，以下是1-10 订阅

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3rd international conference on machine learning and data mining in pattern recognition, MLDM 2003

引用

3rd international conference on machine learning and data mining, MLDM 2003

ISBN: (纸本)3540405046

The proceedings contain 37 papers. The special focus in this conference is on Decision Trees, Clustering and Its Application. The topics include: Introspective learning to build case-based reasoning CBR knowledge containers;graph-based tools for data mining and machine learning;simplification methods for model trees with regression and splitting nodes;learning multi-label alternating decision trees from texts and data;a discretization method of continuous attributes with guaranteed resistance to noise;on the size of a classification tree;a comparative analysis of clustering algorithms applied to load profiling;similarity-based clustering of sequences using hidden markov models;a fast parallel optimization for training support vector machine;a ROC-based reject rule for support vector machines;remembering similitude terms in CBR;authoring cases from free-text maintenance data;classification boundary approximation by using combination of training steps for real-time image segmentation;simple mimetic classifiers;novel mixtures based on the dirichlet distribution;estimating a quality of decision function by empirical risk;efficient locally linear embeddings of imperfect manifolds;dissimilarity representation of images for relevance feedback in content-based image retrieval;a rule-based scheme for filtering examples from majority class in an imbalanced training set;coevolutionary feature learning for object recognition;generalization of pattern-growth methods for sequential pattern mining with gap constraints;discover motifs in multi-dimensional time-series using the principal component analysis and the MDL principle and optimizing financial portfolios from the perspective of mining temporal structures of stock returns.

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Research on the outlier value analysis algorithm of water conservancy engineering quality detection based on machine learning 24

Research on the outlier value analysis algorithm of water co...

引用

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

作者： Chu, Dongdong Wang, Xiaoyong Zou, Dan Jiangsu Hydraulic Research Institute Jiangsu Nanjing China Yangzhou City Urban River Management Office Jiangsu Nanjing China

ISBN: (纸本)9798400718120

This study aims to improve the accuracy of the quality detection of water conservancy projects, and to identify and analyze the outliers in the detection data by introducing machine learning algorithms. A variety of technical techniques, including data preprocessing, feature extraction, supervised learning and unsupervised learning, were used to construct and optimize the outlier detection model. The experimental results show that the machine learning-based detection method significantly improves the accuracy and efficiency of outlier identification, especially when using deep learning models. The research conclusion points out that the machine learning algorithm has obvious advantages in processing complex and massive monitoring data, and can be widely used in the quality monitoring and management of water conservancy projects, providing a strong guarantee for the engineering safety. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Research on Intelligent Signal Detection in the Internet of Things Based on Deep learning Algorithms 24

Research on Intelligent Signal Detection in the Internet of ...

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

作者： Liu, Xingcen School of Information and Communication Engineering Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9798400718120

With the rapid development of Internet of Things technology, intelligent signal detection has become a highly challenging key issue. Traditional manual feature extraction methods are no longer able to cope with complex and ever-changing practical environments. In response to this issue, this study proposes an intelligent signal detection model for the Internet of Things based on deep learning. This model adopts a convolutional neural network structure, which can automatically learn local pattern features in signals, thereby improving its adaptability to complex environments. Research a large-scale wireless signal dataset constructed based on real IoT scenarios, and adopt reasonable data preprocessing and augmentation strategies. Through scientific model training and multi-dimensional evaluation index settings, a comprehensive evaluation of the proposed model was conducted on the test set. The experimental results show that the model has achieved excellent performance over traditional methods in key indicators such as accuracy, recall, and F1 score, especially in complex signal recognition scenarios. © 2024 Copyright held by the owner/author(s).

关键词： Deep learning algorithms

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Research on a Bridge Health Monitoring System Based on machine learning 24

Research on a Bridge Health Monitoring System Based on Machi...

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

作者： Zhang, DaYong Changjiang Survey Planning Design and Research Limited Company Chengdu China

ISBN: (纸本)9798400718120

This study designs and implements a bridge health monitoring system based on machine learning. The system adopts a four-layer architecture, including data acquisition, processing, analysis and decision-making, and user interaction layers. Core functions encompass data preprocessing, feature extraction, model training, health assessment, and early warning. It integrates algorithms such as Support Vector machines, Random Forests, and Deep Neural Networks to enhance the accuracy of damage identification and status prediction. The development utilizes modern tools to improve efficiency. Test results show that the system is stable, responsive, and has strong processing capabilities. This system provides an intelligent solution for bridge health monitoring, improving the automation level and management efficiency of monitoring, and offers strong support for bridge safety management. © 2024 Copyright held by the owner/author(s).

关键词： Damage detection

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Research on Economic Growth Forecasting Models Based on Big data 24

Research on Economic Growth Forecasting Models Based on Big ...

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

作者： Sun, Yihan Zhang, Yijian Zhejiang University Zhejiang Hangzhou China Wuhan Business University Hubei Wuhan China

ISBN: (纸本)9798400718120

Traditional economic growth forecasting methods, though somewhat successful, have limitations such as assuming a linear data generation process and ignoring the nonlinear relationships between economic variables. This study aims to integrate big data technology and machine learning methods to construct an efficient and accurate economic growth forecasting model. By systematically collecting and processing multi-source data, we apply machine learning algorithms such as linear regression, support vector machines (SVM), random forests, and gradient boosting decision trees (GBDT) for modeling and evaluation. The experimental results show that GBDT performs the best across various evaluation metrics, with the lowest mean squared error (MSE) and mean absolute error (MAE), as well as the highest R2 value. Further validation through 5-fold cross-validation and time series split methods demonstrates the robustness and generalization ability of the model, showing that the GBDT model outperforms other models in different data partitions and time series predictions. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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data Processing and pattern recognition for Electronic Nose 3

Data Processing and Pattern Recognition for Electronic Nose

引用

3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Raymond, David Chaudhri, Shivnath Shaikh, Kahkasha Datta Meghe Institute of Higher Education and Research Faculty of Enfineering and Technology Department of Computer Science and Medical Engineering Wardha India Datta Meghe Institute of Higher Education and Research Faculty of Enfineering and Technology Depertment of Computer Science and Design Wardha India

ISBN: (纸本)9798331509675

Electronic nose (e-nose) technology has become a powerful tool for identifying and evaluating complex scents in a variety of contexts, such as environmental monitoring, medical diagnostics, and food quality control. This review study looks at how important pattern recognition and data processing are to improving e-nose system performance. This study covered challenges, including data complexity, cross-sensitivity and interference, sensor drift, and a lack of standardization. The article covers several future directions, such as the use of artificial intelligence (AI) and machine learning to advance pattern recognition and predictive analytics, integration with the Internet of Things (IoT) and big data for real-time monitoring, and advancements in sensor technology for improved sensitivity and *** research work is underway towards the development of low power flexible sensors, improved multi sensor fusion Methodologies, smart adaptive algorithms, Application for personalized health monitoring and diagnostics as well to make it green technologies. To move forward with emerging technologies and for e-nose technology to be applicable, it will have to overcome these weaknesses. © 2025 IEEE.

关键词： Electronic nose

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machine learning methods for identifying destructive texts in the Kazakh language 24

Machine learning methods for identifying destructive texts i...

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

作者： Bolatbek, Milana Mussiraliyeva, Shynar Sagynay, Moldir Zhastay, Yeltay Al-Farabi Kazakh National University Kazakhstan

ISBN: (纸本)9798400718120

In the digital age, society is faced with an increasing number of destructive messages on the Internet, including insults, racism, violent extremism and national extremism. This paper presents a machine learning-based approach to classify destructive texts in the Kazakh language, collected from social networks. During the study a text corpora has been accumulated on social networks: YouTube, Vkontakte, Telegram. The study uses advanced text vectorization methods, including Bag of Words, TF-IDF, and Word2Vec, to preprocess text data. Our findings demonstrate that Logistic Regression outperforms other models, achieving an accuracy of 86%. These results contribute to the development of automated systems for identifying destructive content, promoting safer online communication". © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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PL-attack: Projected Light-based Adversarial Attacks against Deep Neural Networks in the Physical World 24

PL-attack: Projected Light-based Adversarial Attacks against...

引用

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

作者： Huang, Haojie Chen, Jinyin Zheng, Haibin Chen, Yixin Huang, Haoben College of Information Engineering Zhejiang University of Technology China Institute of Cyberspace Security Zhejiang University of Technology China

ISBN: (纸本)9798400718120

The rapid development of artificial intelligence has led to the increasing application of image recognition in autonomous driving and surveillance. However, these systems are often vulnerable to adversarial attacks. In this paper, a multi-target optimized projection attack method based on image recognition is designed and implemented to evaluate and improve the security of image recognition system. By controlling the projection parameters, you can control the color, clarity, shape, and size of the projection interference, projecting it onto the object to test the recognition effect. We built a physical platform, implemented the existing attack, and then designed a multi-target optimized projection attack method to optimize the projection properties through particle swarm optimization. Tests on ResNet18 and custom fruit models have shown a 95% success rate for indoor and 90% outdoor dark attacks. This highlights the vulnerability of image recognition to physical adversarial attacks and highlights the need to enhance the system’s anti-interference capabilities, thus providing insights for protecting intelligent systems. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Technical Evaluation of the Impact of Industry-Education Integration on College Students’ Employment Rate Based on machine learning Models 24

Technical Evaluation of the Impact of Industry-Education Int...

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

作者： Zhuang, Yan Su, Ruiqian Xiamen Institute of Technology Fujian Xiamen China

ISBN: (纸本)9798400718120

This study explores the impact of industry-education integration on college students’ employment rate using machine learning models. The original data was preprocessed through feature engineering, and models such as logistic regression, decision tree, and random forest were used, with hyperparameters optimized using techniques like grid search. The experimental results show that, among 58,342 graduate data, the neural network model performs best in evaluation metrics such as accuracy, recall, precision, and F1 score. The model is most accurate in predicting medium employment quality, with errors stemming from feature noise, nonlinear relationships, and data imbalance. Analysis based on major categories and school types indicates that the model performs better on data from science and engineering disciplines and key universities. This study contributes to the formulation of relevant educational policies. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Application of Deep learning-Based Malware Detection in Video Conferencing Systems 24

Application of Deep Learning-Based Malware Detection in Vide...

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

作者： Wang, Weitao Lei, Lei Shao, Lisong Peng, Wang Chang, Jiale Beijing KeDong Electric Power Control System Co. Ltd Beijing China China Electric Power Research Institute Beijing China Nari Group Corporation Beijing China State Grid Electric Power Research Institute Beijing China

ISBN: (纸本)9798400718120

This paper explores the application of deep learning-based malware detection models in video conferencing systems. By constructing a large-scale dataset of malware and training deep neural network models such as CNN, RNN, and LSTM, high-precision detection of malware is achieved, outperforming traditional machine learning models. Integrating this deep learning model into the video conferencing system, a comprehensive malware defense system is built, effectively blocking a large number of attacks through real-time monitoring and intelligent analysis, significantly improving the security of video conferencing. The research results demonstrate that deep learning technology has broad prospects for application in this field, providing strong support for building a more secure video conferencing environment. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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