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共 3165 条记录，以下是201-210 订阅

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Research on the Application of Big data mining Algorithms in Reading Analysis of WeChat Public Numbers 5

Research on the Application of Big Data Mining Algorithms in...

引用

5th international conference on Applied machine learning, ICAML 2023

作者： Zhang, Wanliang Guangdong University of Science & Technology Dongguan523000 China

ISBN: (纸本)9798350341416

Due to the increase in the number and types of public numbers, it has become increasingly difficult to boost the readership of WeChat public numbers. the study analyses the mining algorithm and constructs a WeChat readership analysis model based on random forest. the results show that the NMSE value of random forest is 0.457 in terms of the reading volume data of WeChat public numbers. With the same data set, random forest uses the shortest time and occupies the least memory, i.e. random forest has the best fitting effect. As the single frequency increases, the degree of influence on the total number of reads becomes greater;while the degree of influence gradually decreases when the average daily number of posts first increases;the higher the proportion of originality, the higher the reading rate;the increased punctuation index of titles is detrimental to the readers' reading effect;the increase in the proportion of videos helps to increase the total number of reads;and the lower information entropy has a higher number of reads for its public number. It shows that the random forest can effectively mine the data information and provide reference significance for the improvement of the reading volume of WeChat public numbers. © 2023 IEEE.

关键词： Big data

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Construction of data domain based cascade ensemble learning algorithm for pathological image diagnosis 5

Construction of data domain based cascade ensemble learning ...

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5th international conference on Computer Vision, Image and Deep learning, CVIDL 2024

作者： Hua, Jiajun Shanghai University College of Computer Engineering and Science Shanghai China

ISBN: (纸本)9798350373820

the use of medical images is an important link in the early detection of cancer. Artificial intelligence theories and technologies such as machine learning and neural networks have gradually become important tools to assist in solving the classification problems of pathological images. However, traditional methods ignore the irreversible cost caused by misdiagnosis when the tumor is classified as other types in the prediction process, and it is difficult to obtain a classifier with high robustness for all data sources due to the differences in the manipulations of different operators in the process of slicing, staining and extracting small images. In this paper, we construct a cascade ensemble learning algorithm BDD-Boost (Based on data Domin-Boost). A pathological image classification method of cascading multiple classifiers is proposed and used. through the feature extraction and processing on the neurons of the preorder classifier, the adaptive threshold was generated to divide the data set. the postorder classifier was trained on the specified image set depending on the data set division result of the preorder classifier. Finally, the optimization results were obtained by integration. We verify the effectiveness of our algorithm by comparing experiments without BDD-Boost ensemble learning algorithm and with BDD-Boost ensemble learning algorithm. BDD-Boost improves the recognition performance by 1% on MobileNet_V2 and 2.2% on ResNet50. In the comparison of voting method and Bagging, it also shows better performance. the model proposed in this paper combines the advantages and characteristics of various convolutional neural networks, and has better performance and reliability than using a single convolutional neural network to directly predict the classification results of medical images under the condition of reducing computing resource consumption and calculation time. It can be used for computer-aided diagnosis and provide diagnostic reference for medical staff

关键词： Image recognition

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Comprehensive Survey of Event Extraction Methods in Natural Language Processing 5

Comprehensive Survey of Event Extraction Methods in Natural ...

引用

5th international conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Kulkarni, Amit Dogra, Varun School of Computer Applications Lovely Professional University Punjab Phagwara144411 India School of Computer Science and Engineering Lovely Professional University Punjab Phagwara144411 India

ISBN: (纸本)9798331530013

Event Extraction (Event Extraction) plays an essential role in NLP (Natural Language Processing) by automating the identification and extraction of events from unstructured textual data. this paper provides a comprehensive overview of event extraction, highlighting its significance in various domains such as security, law, and financial analysis. the paper categorizes event extraction into closed-domain and open-domain approaches. It also talks about various techniques like argument identification, event trigger identification, as well as event type classification. It explores methodologies including machine learning, pattern matching, & deep learning, emphasizing their application in different domains. Evaluation metrics to evaluate the effectiveness of EE systems are explored, including precision, recall, as well as the F1 score. through a systematic review of the literature, the paper underscores the advancements, challenges, and future directions in EE, positioning it as a critical component in extracting actionable knowledge from textualsources. © 2024 IEEE.

关键词： Deep learning

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A Two-stage Heterogeneous Ensemble learning Algorithm Based on MLP-SVR-LGBM 5

A Two-stage Heterogeneous Ensemble Learning Algorithm Based ...

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5th international conference on Industrial Artificial Intelligence, IAI 2023

作者： Yang, Chunpeng Zhang, Xurui Wang, Yiping Zhao, Jianjun Sun, Kai Automation in Mining & Metallurgy State Key Laboratory of Process Automation in Mining & Metallurgy Beijing Key Laboratory of Process Beijing China School of Information and Automation Jinan China Automation in Mining & Metallurgy Beijing Key Laboratory of Process Beijing China

ISBN: (纸本)9798350325294

A single machine learning approaches often encounter numerous challenges and limitations when addressing complex problems. In this study, we propose a novel stacking ensemble learning model for heterogeneous soft sensors, utilizing multi-layer perceptron (MLP), support vector regression (SVR), and light gradient boosting machine (LGBM) as base learners, with gradient boosting decision tree (GBDT) as the meta-learner. First, the proposed heterogeneous soft sensor model fully considers the diversity of the base learners and their learning performance to effectively exploit the advantages of various machine learning methods. Second, the meta-learner, GBDT, is an integrated learning algorithm that can better handle different data of the base learners and improve the performance of the overall model. Subsequently, we demonstrate the superiority of the proposed method is confirmed through an artificial dataset case study. © 2023 IEEE.

关键词： Decision trees

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Application of Chord Length Histogram to Enhance the Accuracy of Pulmonary Nodule recognition on Computed Tomograms 5

Application of Chord Length Histogram to Enhance the Accurac...

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5th international conference on Neural Networks and Neurotechnologies, NeuroNT 2024

作者： Smirnova, Daria S. Pchitskaya, Ekaterina I. Chukanov, Vyacheslav S. Institute of Physics and Mechanics Peter the Great St. Petersburg Polytechnic University Laboratory of Biomedical Imaging and Data Analysis St.Petersburg Russia Institute of Biomedical Systems and Biotechnology Peter the Great St. Petersburg Polytechnic University Laboratory of Biomedical Imaging and Data Analysis St.Petersburg Russia

ISBN: (纸本)9798350363739

the advancement of computed tomography (CT) technologies in pulmonary nodule medical diagnostics necessitates the development of efficient classification and segmentation methods. this study assesses the utility of the Chord Length Distribution Histogram (CLDH) in conjunction with machine learning techniques, including SVR, random forest, and gradient boosting, for comprehensive analysis of the three-dimensional (3D) geometry of pulmonary nodules. the results indicate that CLDH serves as a promising shape descriptor, providing high accuracy in classifying pulmonary nodules. Compared to neural network approaches, the CLDH-based method maintains effectiveness even when confronted with limited training data, thus optimizing computational resource utilization. the proposed methodology for characterizing pulmonary nodule morphology showcases robust data separability and achieves a classification accuracy rate of 0.8 on the LUNA16 dataset. these results emphasize the importance of integrating classical machine learning methodologies into the analysis of three-dimensional geometric structures to refine lung cancer diagnosis accuracy. © 2024 IEEE.

关键词： Computerized tomography

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Process mining Techniques for Collusion Detection in Online Exams 5th

Process Mining Techniques for Collusion Detection in Online ...

引用

5th international conference on Process mining (ICPM)

作者： Maldonado, Andrea Zellner, Ludwig Strickroth, Sven Seidl, thomas Ludwig Maximilians Univ Munchen Munich Germany Munich Ctr Machine Learning MCML Munich Germany

ISBN: (纸本)9783031561061;9783031561078

Honesty and fairness are essential. As many skills, practicing those values starts in the classroom. Whether students are examined online or on-site, only testing their knowledge righteously, educators can assess their skills and room for improvement. As online exams increase, we are provided with more suitable data for analysis. Process mining methods as anomaly detection and trace clustering techniques have been used to identify dishonest behavior in other fields, as e.g. fraud detection. In this paper, we investigate collusion detection in online exams as a process mining task. We explore trace ordering for anomaly detection (TOAD) as well as hierarchical agglomerative trace clustering (HATC). Promising preliminary results exemplify, how process mining techniques empower teachers in their decision making, while via flexible configuration of parameters, leaves the last word to them.

关键词： Collusion Detection Hierarchical Agglomerative Clustering Positive Unlabeled data Teacher in the Loop

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Ensemble Models for Multi-class Classification of Diabetic Retinopathy 1

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9th international conference on pattern recognition and machine Intelligence (PReMI)

作者： Sahayam, Subin Manasa, Tutturu Lakshmi Jayaraman, Umarani Indian Inst Informat Technol Design & Mfg Chennai 600127 Tamil Nadu India

ISBN: (数字)9783031127007

ISBN: (纸本)9783031126994;9783031127007

Artificial intelligence-based methods help in the automatic detection and diagnosis of various diseases in the medical field. Diabetes affects 5 in 10 people in the world. It can cause diabetic retinopathy(DR) that damages the retina, leading to irreversible vision loss. DR is generally diagnosed by an Ophthalmologist using a patient's fundus image. the Ophthalmologist grades the disease based on severity. Early intervention can delay the progression of the disease. Automatic disease detection can help large-scale patient screening, early detection of diabetic retinopathy, and reduce human error. the work aims to study various ensemble learning models and their ensemble voting methods for the DR classification task. the study also focuses on the effects of data augmentation along with preprocessing. the performance of each of the models has been studied using the Aptos-blindness detection 2019 dataset.

关键词： Multi-class Classification Microaneurysm Transfer learning Ensemble learning

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5th EAI international conference on Multimedia Technology and Enhanced learning, ICMTEL 2023

5th EAI International Conference on Multimedia Technology an...

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5th EAI international conference on Multimedia Technology and Enhanced learning, ICMTEL 2023

ISBN: (纸本)9783031505706

the proceedings contain 121 papers. the special focus in this conference is on Multimedia Technology and Enhanced learning. the topics include: Security Awareness Method of Opportunistic Network Routing Protocol Based on Deep learning and Knowledge Graph;research on Pedestrian Intrusion Detection Method in Coal Mine Based on Deep learning;personalized Recommendation Method of College Art Education Resources Based on Deep learning;global Planning Method of Village Public Space Based on Deep Neural Network;a Multi Stage data Attack Traceability Method Based on Convolutional Neural Network for Industrial Internet;machine learning Based Method for mining Anomaly Features of Network Multi Source data;data Anti-jamming Method for Ad Hoc Networks Based on machine learning Algorithm;a data Fusion Method of Information Teaching Feedback Based on Heuristic Firefly Algorithm;research on Pain Information Management System Based on Deep learning;research on database Language Query Method Based on Cloud Computing Platform;reliability Evaluation Method of Intelligent Transportation System Based on Deep learning;forecasting Method of Power Consumption Information for Power Users Based on Cloud Computing;power Consumption Behavior Analysis Method Based on Improved Clustering Algorithm of Big data Technology;anomaly Detection of Big data Based on Improved Fast Density Peak Clustering Algorithm;data Privacy Access Control Method Based on Ciphertext Policy Attribute-Based Encryption Algorithm;Evaluation Method of Enterprise Circular Economy Development Level Based on AHP Fuzzy Inference;research on Performance Evaluation of Industrial Economic Management Based on Improved machine learning;application of Big data Processing Technology in Power Consumption Information Acquisition.

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5th EAI international conference on Multimedia Technology and Enhanced learning, ICMTEL 2023

5th EAI International Conference on Multimedia Technology an...

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5th EAI international conference on Multimedia Technology and Enhanced learning, ICMTEL 2023

ISBN: (纸本)9783031505799

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Analysis on Modulation and Demodulation Techniques for Visible Light Communication 5

Analysis on Modulation and Demodulation Techniques for Visib...

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5th international conference on Innovative Trends in Information Technology (ICITIIT)

作者： Dharshini, K. R. Priya Kalaivani, R. Sampoornam, K. P. Logamurthy, P. Ragulgandhi, S. Vishal, S. V. Anna Univ Affiliated Erode Sengunthar Engn Coll Autonomous Elect & Commun Engn Erode India Bannari Amman Inst Technol Elect & Commun Engn Sathyamangalam India Anna Univ Affiliated Nandha Engn Coll Autonomous Elect & Commun Engn Erode India

ISBN: (纸本)9798350386813;9798350386820

Visible Light Communications (VLC) is a wireless communication system for enhanced 5G and 6G high speed wireless networks. VLC is one of the most accepted varieties of short-range wireless communication. With a large number of mobile devices, high-speed data transmission is an essential one. VLC uses an intensity modulation as well as direct recognition for illumination and data transmit. VLC is a disruptive form of wireless communication that supplements radio frequency communication. Demodulation of radio signals plays an important role in VLC systems. Artificial intelligence (AI) addressed the distinctive challenges in VLC. AI techniques are explored to overcome the unique challenges in VLC, whereas significant barriers are found in practical VLC systems applied with intelligent learning approaches. machine learning (ML) illustrates potential challenges in non-linear extenuating for their natural capability to fit every sort of transfer function in VLC. Different modulation methods are studied for performing efficient visible light communication. However, the bit error rate was not minimized as well as accuracy level was not enhanced. To address these issues, different modulation and demodulation methods were reviewed.

关键词： Visible light communication intensity modulation data transmission radio frequency communication

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