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检索条件"任意字段=5th International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3136 条记录，以下是51-60 订阅

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Predicting the Need for Mental Treatment Across Various Age Groups Using machine learning Algorithm 5th

Predicting the Need for Mental Treatment Across Various Age ...

引用

5th international conference on data Science, machine learning and Applications

作者： Pal, Surya Kant Rawat, Himani Roy, Rita Khan, Ameer Lareb Kumari, Nisha Srivastava, Prashasya Sharda Univ Dept Math SSBSR Noida UP India GITAM GITAM Inst Technol Dept Comp Sci & Engn Visakhapatnam Andhra Pradesh India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

the extent of mental health is much more than what the general public knows. Many studies have found that women, the elderly, those who had recently survived a disaster, those who worked in the industrial sector, children, adolescents, and persons with long-term medical illnesses were at a higher risk of mental difficulties. Early recognition helps patients treat them more effectively and enhance their lives. Better living conditions, healthcare, and women's empowerment are all necessary. Mental health treatment is required for every age group, from children to seniors. this research will illuminate the need for mental health treatment across different age groups. this prediction is evaluated on the publicly available dataset. the dataset consists of 1259 entries and 27 attributes. Various accuracy metrics were used to evaluate the performance of machine learning techniques in identifying mental health concerns in this study. A comparison of these methods has been done and put into practice and also found that the Logistic Regression method, based on a forecast accuracy of 83.07%, was the most accurate. the machine learning algorithms used in this study include K-NN Classifier, Logistic Regression, Decision Tree Classifier, AdaBoost and Random Forest, which are discussed with their algorithm procedure.

关键词： Mental Health machine learning Logistic Regression Method Random Forest Age

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Deep learning-Based Frequency Offset Estimation 5

Deep Learning-Based Frequency Offset Estimation

引用

5th IEEE international conference on pattern recognition and machine learning, PRML 2024

作者： Chen, Tao Zheng, Shilian Zhu, Jiawei Xuan, Qi Yang, Xiaoniu Institute of Cyberspace Security Zhejiang University of Technology Hangzhou China Innovation Studio of Academician Yang National Key Laboratory of Electromagnetic Space Security Jiaxing China

ISBN: (纸本)9798350355925

In wireless communication systems, the asynchro-nization of the oscillators in the transmitter and the receiver along with the Doppler shift due to relative movement may lead to the presence of carrier frequency offset (CFO) in the received signals. Estimation of CFO is crucial for subsequent processing such as coherent demodulation. In this brief, we demonstrate the utilization of deep learning for CFO estimation by employing a residual network (ResNet) to learn and extract signal features from the raw in-phase (I) and quadrature (Q) components of the signals. We use multiple modulation schemes in the training set to make the trained model adaptable to multiple modulations or even new signals. In comparison to the commonly used traditional CFO estimation methods, our proposed IQ-ResNet method exhibits superior performance across various scenarios including different oversampling ratios, various signal lengths, and different channels. © 2024 IEEE.

关键词： Frequency estimation

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Facial expression recognition algorithm based on deep learning 5

Facial expression recognition algorithm based on deep learni...

引用

5th international conference on Signal Processing and Computer Science, SPCS 2024

作者： Luo, Senhang China University of Petroleum Beijing China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

Facial expression recognition (FER) plays a crucial role within the realm of computer vision. In the last several years, machine learning-powered FER methods have been fully developed. In contrast to conventional feature-based FER algorithms, FER algorithms based on machine learning have gained advantages. In this paper, we review the progress of FER algorithms driven by deep learning, and compare the horizontal effects of different network architectures based on CK+ data sets and VGG16 and other pre-trained deep learning models. At the same time, different data enhancement methods and learning rate adaptive adjustment methods are used to introduce attention mechanisms. A significant quantity of experimental data and model evaluation parameters show that the optimized facial expression recognition algorithm can improve the accuracy of facial expression recognition. Our method and optimization are effective, and good results are obtained on CK+ data set. © 2025 SPIE.

关键词： Contrastive learning

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Intelligent Operation and Maintenance System Design of Financial Product Management Background Based on Intelligent Algorithm 5

Intelligent Operation and Maintenance System Design of Finan...

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5th IEEE international conference for Emerging Technology, INCET 2024

作者： Pu, Na Chengdu Polytechnic Sichuan Chengdu610041 China

ISBN: (纸本)9798350361155

Intelligent algorithms, as a comprehensive method utilizing mathematics, computer science, and artificial intelligence technology, simulate human thinking processes and problem-solving abilities to achieve analysis and processing of complex data. It involves various technical fields such as machine learning, deep learning, data mining, etc. Its core goal is to extract valuable information and patterns through learning and analyzing large amounts of data, providing strong support for decision-making. Intelligent algorithms have multiple significant characteristics. Firstly, it has powerful data processing capabilities, capable of processing massive amounts of data and extracting useful information from it. Secondly, intelligent algorithms have high adaptability and flexibility, and can self adjust and optimize according to the characteristics of specific tasks and data. In addition, intelligent algorithms also have good predictive ability, which can predict future trends and changes based on historical data. © 2024 IEEE.

关键词： data mining

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Analysis of machine learning Based Big data mining System for Enterprise Businesses 5

Analysis of Machine Learning Based Big Data Mining System fo...

引用

5th international conference on machine learning, Big data and Business Intelligence, MLBDBI 2023

作者： Dan, Luo University of Malaya Kuala Lumpur Malaysia

ISBN: (纸本)9798350359930

the primary objective of this paper is to conduct an in-depth analysis of a machine learning-based enterprise business big data mining system. Firstly, the introduction section presents the background, significance, and objectives of the research. Secondly, we provide an overview of the fundamental concepts of data mining and machine learning, including data mining methods and machine learning algorithms. Specifically, we discuss data mining methods such as the Apriori algorithm, clustering algorithm, and decision tree, as well as machine learning algorithms such as the Naive Bayes algorithm and K-nearest neighbors method. Furthermore, we introduce the principles and applications of the big data processing framework Hadoop. Subsequently, the design and implementation of the enterprise big data mining system are discussed. Finally, we conduct experiments on the enterprise big data mining system. We present a detailed description of the experimental process, including the introduction of experimental data, the experimental environment, and parameter settings, as well as specific experimental steps and procedures. Additionally, we showcase the experimental results and conduct evaluation and analysis. the research in this paper aims to provide valuable guidance and practical experience for enterprises in the field of business big data mining, promoting the intelligence and precision of decision-making, and enhancing the healthy development and competitive advantage of businesses. © 2023 IEEE.

关键词： Big data

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Accident Severity Detection Using machine learning Algorithms 5th

Accident Severity Detection Using Machine Learning Algorithm...

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5th international conference on data Science, machine learning and Applications

作者： Kumar, B. Naveen Kumar, N. Sunil Kumar, U. Naresh Annamacharya Inst Technol & Sci Autonomous Dept CSE Rajampet 516126 Andhra Pradesh India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

the mobility management skills, including the capacity to make scientific decisions can be improved as well as the reduction of personal injuries and property losses by timely and precise identification of traffic events. the detection effect is significantly impacted by the imbalance in the statistics on traffic incidents. Consequently, An technique for identifying traffic events inferred via factor analysis and adjusted random forests (FA-WRF) is developed. Analyze the flow of traffic parameters change rule to establish the first incident variable. the component analysis (FA) approach is used to minimize the original incident variables' dimension. Using the Bootstrap improved technique, establishing the information collection criteria for the training set. Upon training, the decision tree's categorization impact is measured using the MCC coefficient value. the random forest (RF) procedure's overall ability to discriminate for imbalanced data can be improved by using the tree is used as a weight value to make sure that specimens with better classification skills have more influence during the voting phase. the area under the receiver operating characteristic curve, the classification rate, and the false alarm rate, and the recognition rate are typical metrics used to evaluate the effectiveness of detecting systems. the results of the experiment show how well the FA-WRF-based model performs in terms of categorization. It does just as well when classifying imbalanced data as the Support Vector machine does.

关键词： Random Forest Logistic Regression XGBoost Support Vector machines

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Sentiment Analysis of Chinese Bullet Comments Based on Enhanced Deep learning Model 5

Sentiment Analysis of Chinese Bullet Comments Based on Enhan...

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5th IEEE international conference on pattern recognition and machine learning, PRML 2024

作者： Kai, Zhao Qibo, Wang Lining, Wan Linlin, Zhang Xilei, Lu Zhiwei, Wang XinJiang University School of Computer Science and Technology School of Cyberspace Security XinJiang Urumqi China XinJiang University School of Software XinJiang Urumqi China

ISBN: (纸本)9798350355925

In the Internet environment, bullet comments texts expressing emotions are an important kind of data with clear sentiment polarity, and this particular text can be used to analyse the trend of public opinion in the video media for the reference of decision makers. Aiming at today's sentiment analysis models that suffer from poor quality and uneven levels of natural language data annotation, as well as low accuracy and weak generalization in identifying the sentiment of very short texts, a sentiment analysis augmented hybrid deep learning model, RBMHSA (RoBERTa-BiGRU-Multi-Head-Self-Attention), is proposed for sentiment analysis of Chinese bullet comments texts. the data were collected from the BiliBili video library and manually annotated for integration. Accuracy and F1 value were used as the evaluation indexes, and comparative experiments were conducted on three datasets, compared to more than deep learning models. the experimental results show that RBMHSA (RoBERTa-BiGRU-Multi-Head-Self-Attention) is the most effective deep learning model for the analysis of Chinese bullet comments texts. deep learning models, the experimental results show that the RBMHSA model has higher accuracy and F1 value in identifying the polarity of bullet comments text views. © 2024 IEEE.

关键词： Contrastive learning

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A Scene-Aware Prompt Template of Chinese Text data Augmentation Method Based on Large Language Model 5

A Scene-Aware Prompt Template of Chinese Text Data Augmentat...

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5th IEEE international conference on pattern recognition and machine learning, PRML 2024

作者： Kong, Lingfei Wang, Gang He, Jingheng Wang, Ying Gao, Yunpeng School of Data Science and Applications Inner Mongolia University of Technology Hohhot China

ISBN: (纸本)9798350355925

In Natural Language Processing (NLP) tasks, the target domain data is usually limited and the quality is poor. Text data augmentation is one of the effective methods to solve the problem of insufficient sample size in many NLP tasks. At present, on the one hand, the text data augmentation method is difficult to achieve the diversity and authenticity of the generated data text structure, on the other hand, it is also a challenge for the screening and elimination of low-quality samples in the generated data. therefore, this paper proposes a scene-aware template of chinese text data augmentation method (Scene-Aware Prompt Template, SAPT) based on large language model. the SAPT method first uses prompt learning to construct a Scene-Aware Prompt (SAP)template. Secondly, combined with the text understanding and generation ability of the large language model, the SAP is improved by analyzing the semantic and contextual features of the original data to generate scene prompt words. then, the original data is re-expressed into multiple data samples with different text structures but similar semantics by using the perfect SAP and large language model. Further, the quality of generated data samples is evaluated by fusing Rouge score and cosine similarity score, and low-quality data samples are filtered. the final generated data will be used for training the target domain task model. the experimental results on the Chinese news dataset thUCNews show that the overall performance of the classification model trained by the data generated by the SAPTDA method proposed in this paper reaches 89.89 %, which is 2.79 % higher than the baseline model, and has superior performance compared with the existing text data augmentation methods. © 2024 IEEE.

关键词： Semantics

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Automated Penetration Testing Based on LSTM and Advanced Curiosity Exploration 5

Automated Penetration Testing Based on LSTM and Advanced Cur...

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5th IEEE international conference on pattern recognition and machine learning, PRML 2024

作者： Ren, Qiankun Liu, Jingju Xiong, Xinli Lu, Canju College of Electronic Engineering National University of Defense Technology Anhui Province Key Laboratory of Cyberspace Security Situation Awareness and Evaluation Hefei230037 China

ISBN: (纸本)9798350355925

With the rapid development of artificial intelligence technology, cybersecurity issues have become increasingly prominent, particularly the escalating threats posed by malicious cyber behavior to users and systems. Automated penetration testing is increasingly important as a crucial means of maintaining cybersecurity. However, existing automated penetration testing methods based on reinforcement learning algorithms exhibit deficiencies in leveraging historical experience information and exploring unknown environments. this paper proposes an innovative algorithm - PLACE (PPO algorithm based on LSTM architecture and advanced curiosity exploration mechanism) to address these issues. By incorporating an LSTM architecture and an advanced curiosity module, this algorithm enhances the efficiency of utilizing historical experience information and improves the exploration capability in unknown environments. the experimental results show that the cumulative reward of the PLACE algorithm in the penetration test scenario composed of 40 nodes is 13.84\% higher than that of the CPPO algorithm, 12.95\% higher than that of the PPO-Curious-advanced algorithm, and 13.29\% higher than that of PPO algorithm. Cumulative rewards and success rates in other network scale scenarios have also improved, providing new insights and approaches to solving complex problems in the field of cybersecurity. © 2024 IEEE.

关键词： Adversarial machine learning

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machine learning Application for Diabetes Prediction Using Ensemble Classification 5

Machine Learning Application for Diabetes Prediction Using E...

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5th international conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Dhiman, Tanu Goyal, Shefali Singh, Nongmeikapam thoiba Singhal, Rohit Kumar Chandigarh University Department of Computer Science Engineering Punjab India

ISBN: (纸本)9798350321425

data mining is a useful method for extracting meaningful information from massive volumes of records. Statistics and machine learning may be utilised to evaluate large-scale patterns in massive databases. Numerous researchers use image processing methods to predict disease, utilising data mining and machine learning approaches. Disease data can be collected from various sources. A bioinformatics technology will be used for keeping, retrieving, and interpreting information with biological molecules. A forecast can be explained as a description of upcoming events based on current conditions. the aim of this study is to create machine learning and data mining algorithms for diabetes prediction. there are various steps in predicting diabetes. In this study, a vote-based classifier is developed to predict diabetes. the proposed algorithm, is used to optimize the diabetes prediction performance up to 2%. © 2023 IEEE.

关键词： data mining

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