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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 3282 条记录，以下是341-350 订阅

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Multi-temporal Domain Feature Adaptation Representation Framework for Website Fingerprinting 3rd

Multi-temporal Domain Feature Adaptation Representation Fram...

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

作者： Lu, Zikui Song, Luona Han, Zhengxuan Ma, Zhixin Wang, Xiaojun Beijing University of Posts and Telecommunications Beijing100876 China Beijing Information Science and Technology University Beijing100192 China Dublin City University Dublin Ireland

ISBN: (纸本)9789819616978

The website fingerprinting (WF) classification focuses on identifying websites accessed by clients over encrypted network tools. By leveraging machine learning technology, methods have demonstrated high accuracy. However, many existing methods heavily rely on specific feature distributions, leading to poor performance on unseen data or data affected by concept drift. In this paper, we design a representation framework based on multi-temporal domain feature adaptation for website fingerprinting identifications, which consists of pre-training and fine-tuning stages. The framework extracts features from multi-temporal traffic data to generate robust representations and requires only a small amount of data for fine-tuning, making it applicable to data with different feature distributions. Experiments demonstrate that this approach effectively addresses concept drift. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： data accuracy

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Automated Number Plate Detection and recognition Using machine learning Techniques 3rd

Automated Number Plate Detection and Recognition Using Machi...

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3rd international conference on Trends in Electronics and Health Informatics, TEHI 2023

作者： Shuva, Taslima Ferdaus Hasan, Nahid Rahman, Md. Tanvir Khan, Risala Tasin Daffodil International University Dhaka Bangladesh Mawlana Bhashani Science and Technology University Tangail Bangladesh Jahangirnagar University Savar Bangladesh

ISBN: (纸本)9789819739363

The official collection of numbers and letters shown on the front and rear of a road vehicle is called a license plate. License plate recognition is crucial for many applications, including traffic monitoring, vehicle tracking, and law enforcement. Automatic License Plate recognition (ALPR) is one of the applications that benefited tremendously from convolutional neural network (CNN) processing, which is now the de facto processing method for complex data. This paper offers a comprehensive analysis and comparison of deep learning-based approaches for identifying license plates. The study also looks a dataset from Kaggle, pre-process the dataset for removing noise and artifact, utilized to train these models into test, train, and validation dataset. Additionally, the study examines the difficulties and prospective possibilities in deep learning-based license plate recognition, highlighting the potential uses of cutting-edge innovations such object detection using attention mechanisms and graph convolutional networks. In this study we utilized six transfer learning model which is VGG16, VGG19, DenseNet121, AlexNet, EfficientNet, and YOLOv4 where DenseNet121 provide the best or highest accuracy 98.66%. The goal of the study is to guide researchers and industry professionals toward powerful license plate detecting systems by employing deep learning approaches by way of this evaluation and analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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The 3rd international Workshop on mining and learning in the Legal Domain 23

The 3rd International Workshop on Mining and Learning in the...

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32nd ACM international conference on Information and Knowledge Management (CIKM)

作者： Makrehchi, Masoud Zhang, Dell Petrova, Alina Armour, John Thomson Reuters Labs Toronto ON Canada Thomson Reuters Labs London England Univ Oxford Oxford England

ISBN: (纸本)9798400701245

The increasing accessibility of legal corpora and databases create opportunities to develop data-driven techniques and advanced tools that can facilitate a variety of tasks in the legal domain, such as legal search and research, legal document review and summary, legal contract drafting, and legal outcome prediction. Compared with other application domains, the legal domain is characterized by the huge scale of natural language text data, the high complexity of specialist knowledge, and the critical importance of ethical considerations. The MLLD workshop aims to bring together researchers and practitioners to share the latest research findings and innovative approaches in employing data mining, machine learning, information retrieval, and knowledge management techniques to transform the legal sector. Building upon the previous successes, the third edition of the MLLD workshop will emphasize the exploration of new research opportunities brought about by recent rapid advances in Large Language Models and Generative AI. We encourage submissions that intersect computer science and law, from both academia and industry, embodying the interdisciplinary spirit of CIKM.

关键词： legal data mining legal information retrieval legal natural language processing legal knowledge management large language models

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machine learning based human recognition via robust Features from audio signals 3

Machine Learning based human recognition via robust Features...

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3rd IEEE international conference on Artificial Intelligence, ICAI 2023

作者： Sadique, Umar Khan, Muhammad Suleman Anwar, Shahzad Ahmad, Mehran University of Engineering and Technology AI in Healthcare Lab National Centre of AI Peshawar Pakistan University of Engineering and Technology Department of Electrical Engineering Bannu Campus Peshawar Pakistan University of Engineering and Technology Department of Mechatronics Engineering Peshawar Pakistan

ISBN: (纸本)9798350322125

Biometric verification techniques are commissioned throughout the world in different applications. Human voice recognition is one of the biometric techniques. This technique consists of identifying a human from their voice characteristic. This popular and beneficial biometric technique could be employed for identity human, security purposes, and many different applications. Human audio signal recognition consists of two phases i.e., Features Extraction and Classification. The proposed work consists of extracting features through the Mel Frequency Cepstral-Coefficient (MFCC) from the human audio signal, selecting robust features through Principal Component Analysis PCA, and classifying the selected features by comparing seven machine learning and proposed deep learning algorithms. Finally, compare the performance of different algorithms with different percentages of selected features to evaluate the acceptance rate of the correlated features. Support Vector machine SVM shows the best performance with an Accuracy of 99.27% with F1-score and ROC values of 1.00. In the comparison with other methods, the Random Forest and CNN-ANN are 2nd top robust models with an accuracy of 98.7%. Some of the algorithm's accuracy decreased with fewer features, including Naïve Bayes accuracy suddenly decreases to 60% on 20% of total features. The experiment concludes the acceptance rate of correlated features in different ML and DL algorithms are different in speech processing data. © 2023 IEEE.

关键词： learning algorithms

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Big data Query Processing Approach UsingMongoDB 3

Big Data Query Processing Approach UsingMongoDB

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3rd international conference on Innovative Practices in Technology and Management, ICIPTM 2023

作者： Keshav Rani, Sangeeta World College of Technology and Management Gurgaon India

ISBN: (纸本)9798350336238

The 'Big data' phrase describes to the management of a wide range of organized and unstructured data with increasing speed and quantity. These datasets are conventional, large, and difficult to maintain. However, these datasets are used within a number of companies to perform various tasks on them as well as for organizational purposes and to provide a summary of the data currently being used. More precise and accurate business judgments can be make as a result of the growing volume of big data, which is now more affordable and available. The objective of this research paper is to demonstrate how to identify and use only the most significant and important data to be used in a follow-up investigation, help other researchers perform additional analysis, take into account only a limited number of data, ensuring that the study will always provide the best results. Although there are other methods and tools to extract data with certain filters. MongoDB uses the NoSQL model as the basis for query processing. To obtain data from a large data collection, query processing is used and it will continue to play an important role in future research and strategies for this work. The behaviour of extraction of data from Big data and query processing on the bases input parameters that are going to use in machine learning. This process also termed as data mining which will show the behaviour of mining data from large amount of combine data. This paper show the behaviour implementation of Mongo DB on required parameter and will produce the efficient result. © 2023 IEEE.

关键词： data mining

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IoT and machine learning-Based Monitoring of the Growth of Crops Using Blockchain Technology 3rd

IoT and Machine Learning-Based Monitoring of the Growth of C...

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3rd international conference on Advanced Network Technologies and Intelligent Computing (ANTIC)

作者： Yadav, Randheer Mishra, Ashish Kumar Pandey, Mahima Shanker Pandey, Brijesh REC Ambedkar Nagar Dept IT Ambedkar Nagar Uttar Pradesh India Sharda Univ Dept CS & Applicat Greater Noida Uttar Pradesh India Lovely Profess Univ Dept CSE Jalandhar Punjab India

ISBN: (纸本)9783031640759;9783031640766

Agriculture is the cultivation of soil, the cultivation of crops, and the raising of livestock. Agriculture is essential to the economic development of any country. Despite significant advancement in the service sector, agriculture remains India's most important employer and source of income. The agricultural sector has great potential to improve food needs and provide healthy and nutritious food. But nowadays, the situation is not favoring the farmers. Farmers have to bear a lot of loss due to diseases, pests, and lack of moisture in soil, destroying the crop. Detecting insects and diseases in plants is one of the most challenging tasks for farmers, as large portions of crops are damaged, and quality is affected as a result. Farmers have so far adopted conventional farming methods. These techniques were imprecise, slowing productivity and taking a lot of time. Precision agriculture helps improve productivity by identifying steps that need to be taken at different phases of crop production. Precision farming uses advanced techniques such as collecting data, training systems, and predicting outcomes using IoT, data mining, data analytics, machine learning, and more. With the help of emerging technologies, precise farming reduces manual work and increases productivity. The application of machine learning with IoT data analytics in the agricultural sector brings new benefits to improve the quantity and quality of production from crop fields and meet the growing demand for food. The proposed model will predict the task needed to be performed at the time of disease in the crop, lack of moisture in the soil, in case of pests, etc. The goal of this work is to enable individuals to grow crops efficiently and achieve high productivity at low cost.

关键词： Agriculture IoT machine learning Blockchain Disease Detection Pest Control

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data mining Technology-Based Algorithms for Evaluating English Language Teaching Indicators 3rd

Data Mining Technology-Based Algorithms for Evaluating Engli...

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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： Qian, Wang Jiaxin, Wang Huixing, Zhou Beijing Polytechnic Beijing100176 China

ISBN: (纸本)9783031631290

TE is a judgement on the value of teachers’ teaching (TT) and students’ learning (SL), and has become an important part of the teaching management and teaching process in universities. There are many common TE systems, most of which evaluate the behavioural performance of teachers, while the learning process and effectiveness of students are rarely mentioned. At the same time, the workflow of implementing TE is tedious and often requires the completion of a large number of data calculation tasks. Therefore how to use modern science and technology to establish a sound, objective and feasible classroom TE system and optimise the evaluation process is an important issue that needs to be addressed urgently. The main objective of this paper is to conduct a study on the evaluation algorithm of English teaching indicators based on data mining (DM) technology. Starting from the construction of a learning-centred university TE system, this paper optimises student TE indicators by using data correlation analysis and association rules. At the same time, machine learning algorithms are introduced into the TE process to build TE models and automate the TE process. Through clustering, the experiment can divide all teachers into corresponding categories, analyse the overall characteristics of teachers in each category, and obtain the performance of teachers in different categories in each indicator, and teachers can focus on their lower level indicators according to the performance of each indicator in their respective categories. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： data mining

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Heart Disease Prediction Using machine learning Algorithms: Performance Analysis 3

Heart Disease Prediction Using Machine Learning Algorithms: ...

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3rd international conference on Advancement in Electrical and Electronic Engineering, ICAEEE 2024

作者： Saha, Subrata Rahman, Md. Motinur Suki, Tahmid Tamrin Alam, Md. Mahbub Alam, Md. Shah Haque, Mohammod Abu Sayid Bangladesh Atomic Energy Commission Institute of Electronics Dhaka Bangladesh Bangladesh University of Professionals Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350388282

Cardiovascular disease (CVD) is one of the major causes of death worldwide. Early detection of this disease can decrease the mortality rate significantly and it can also reduce the costs of medical treatment. Researchers are trying to develop automated systems using data mining techniques that can help in this regard. Although they would be extremely difficult to build, machine learning (ML) based methods for heart disease prediction might be very useful in clinical settings. Moreover, the majority of nations lack doctors specialized in cardiovascular treatments, and a high percentage of cases are misdiagnosed. These issues could be resolved by creating an accurate and effective early-stage heart disease prediction system using ML. In this paper, we propose an ML technique to identify cvd patients depending on different features. We collected a heart disease dataset from the Center for Disease Control and Prevention (CDC) then applied different pre-processing steps, and transformed several attributes to make them readable for the ML algorithms. 5 supervised ML algorisms were implemented and the Random Forest algorithm provided the best outcomes in all the performance metrics that we studied, with maximum accuracy of 91% and, ROC-AUC (Area under the ROC Curve) of 0.91. It also surpasses the performance of other algorithms with a maximum True Positive Rate (TPR) of 0.922 and a minimum False Negative Rate (FNR) of.078. © 2024 IEEE.

关键词： Forecasting

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A Comparative Study of Clustering Algorithms for Profiling Researchers in Universities Through Google Scholar 3

A Comparative Study of Clustering Algorithms for Profiling R...

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3rd international conference on Computing and machine Intelligence (ICMI)

作者： Al-Kerboly, Doreyed Muhammed Ahmed Al-Kerboly, Dhamyaa Zohdi Fatah Univ Anbar Coll Educ Pure Sci Anbar Iraq Minist Educ Al Anbar Distinguished Sch Anbar Iraq

ISBN: (纸本)9798350372977;9798350372984

This study compares the clustering algorithms used by four outstanding Iraqi institutions in Anbar, including Anbar University, Fallujah University, Al-Marf University and Al-Huda University. Meta-studies for clustering, studies by themselves ensure clustering, clustering Reinforcement learning, definition including clustering, and deep clustering include methods to be considered The analysis is based on three basic metrics of cluster performance: the Silhouette score, the Calinski-Harabasz index, and the Davies-Bouldin index. The collected evaluations show unique performance characteristics for each algorithm, which provides helpful information for algorithm selection in an educational setting. Meta-getting to know for clustering is an effective method. It works well on all parameters with a silhouette rating of 0.4655, a Calinski-Harabasz index of 189.8185, and a Davies-Bouldin index of 0.8202, just like 0.7287. The locating, which is supported by way of silhouette rankings and equal Calinski-Harabasz and Davies-Bouldin indexes, suggests that the efficacy of all 3 of those methods compares nicely inside the context of experimental measurements from the s. Deep clustering, alternatively, plays in another way, with an extensively decreased silhouette score of 0.0880 and extensively higher Calinski-Harabasz (83.4026) and Davies-Bouldin (3.2493) indices. This shows that its clustering performance may be more severe when compared to the other strategies studied. The most successful method is meta-gaining knowledge of for clustering, which plays nicely on all parameters with a silhouette rating of 0.4655, a Calinski-Harabasz index of 189.8185, and a Davis- Bouldin index of 0.8202, just like 0.7287 Finding this, similarly determined by silhouette scores and almost same Calinski-Harabasz, and Davis- Bouldin indices, shows that the effectiveness of these three technology is comparable in the context of experimental measures of the. On the other hand, deep clustering has un

关键词： data Minim Web mining Clustering Silhouette score the Calinski-Harabasz index the Davies-Bouldin index

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Research on the application of data mining technology in software engineering 3

Research on the application of data mining technology in sof...

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

作者： Yitao, Lin School of Software Heilongjiang University Heilongjiang Province Harbin450000 China

The role of data mining in software engineering is obvious, but there is a lack of mining depth. In the past, engineering layout and function distribution problems in software development, and the framework construction was unreasonable. Therefore, this paper proposes a software system based on data mining technology. First, the software engineering project is divided, and the software engineering project design is carried out according to the interactive requirements to realize the preprocessing of software engineering. Then, according to the design criteria, the design collection of software engineering is formed, and the information system is analyzed in depth. MATLAB simulation shows that the design interactivity and accuracy of data mining are superior to traditional design methods under the condition that software engineering requirements are consistent. © 2023 Elsevier B.V.. All rights reserved.

关键词： data mining

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