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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

共 4173 条记录，以下是251-260 订阅

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Quality Evaluation Model of Automatic machine Translation based on Deep learning Algorithm 4

Quality Evaluation Model of Automatic Machine Translation ba...

引用

4th international conference for Emerging Technology, INCET 2023

作者： Peng, Nan Wang, Yue Wei, Yingying Liu, Lu Wang, Lei Wang, Yu East University of HeiLongjiang Department of Basic Teaching and Research Harbin150066 China East University of HeiLongjiang Department of Humanities and Social Sciences Harbin150066 China

ISBN: (纸本)9798350335750

With the continuous development of computer technology, machine translation methods have also experienced a long research process. In recent years, the research of artificial neural network has brought new solutions to machine translation. the application of sequence sequence model has made a qualitative leap in the performance of machine translation. the training of neural machine translation model depends on large-scale bilingual parallel data, which contains sufficient knowledge for machine learning. the training process is the process of data representation and knowledge extraction. How to use data enhancement methods to make model learning easier and knowledge extraction more sufficient is an important research topic. Automatic machine translation quality evaluation model based on deep learning algorithm is a method to evaluate the quality of automatic machine translation. this paper introduces the basic idea of qemt and some examples. We also give an example to illustrate how to use it to evaluate the quality of automatic machine translation. Qemt (quality evaluation model) is a method to evaluate the quality of automatic machine translation by comparing human translations generated by different natural language processing models such as part of speech markers or semantic similarity measures. © 2023 IEEE.

关键词： Semantics

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2023 4th international conference on Intelligent Computing and Human-computer Interaction, ICHCI 2023

2023 4th International Conference on Intelligent Computing a...

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4th international conference on Intelligent Computing and Human-computer Interaction, ICHCI 2023

ISBN: (纸本)9798350343113

the proceedings contain 84 papers. the topics discussed include: YONA: an intelligence question-answering system based on artificial intelligence;a hybrid model based on GAT and TAGCN for node classification;SRGCN: social relationship graph convolutional network-based social network user geolocation prediction;dense pedestrian detection method based on improved YOLOv5;opening the black box: interpretable machine learning reveals the relationship between lexical diversity and writing quality;porting and optimization of electric power consumers anomaly behavior monitoring on ARM platform;short text similarity detection based on graph attention networks and knowledge augmentation;MSFF-Net: a multi-scale feature fusion network for hippocampus segmentation;research on path optimization of automated warehouse based on genetic algorithm;and modeling operational profile for ai systems: a case study on UAV systems.

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Comparative Study of Sentient Anlysis using Modified Pre-trained Deep learning Models for Chinese App Market Comment Texts 4

Comparative Study of Sentient Anlysis using Modified Pre-tra...

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4th international conference on Advances in Electrical Engineering and computer applications, AEECA 2023

作者： Liang, Xinran United Imaging Intelligence Co. Ltd. Shanghai China

ISBN: (纸本)9798350308082

this paper compares sentiment analysis of Chinese user comment text of mobile applications based on both sentiment dictionary and machine learning/deep learning methods. the dataset is unique because of the inclusion of emoji, memes, slangs, 'modernized Chinese,' etc, which are not found in other dataset or dictionary. the evaluation of the sentiment dictionary method shows that the prediction results are consistent with the sentiment polarity of the comments. the prediction results based on the fine-tuned and modified BERT pre-trained model achieve a precision rate of 94.8%. After converting the emoji to text and retraining, the precision rate of the algorithm improved to 95.2%. © 2023 IEEE.

关键词： Sentiment analysis

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Cancer Death Cases Forecasting using Supervised machine learning 4

Cancer Death Cases Forecasting using Supervised Machine Lear...

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4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Degadwala, Sheshang Vyas, Dhairya Kothari, Ankita Khunt, Urmila Sigma University Computer Engineering Department Gujarat Vadodara India University of Baroda The Maharaja Sayajirao Gujarat Vadodara India Krishna School of Emerging Technology and Applied Research Kpgu Dept. of Comp. Sci. & Eng. Vadodara India Itm Vocational University Computer Science Engineering Vadodara India

ISBN: (纸本)9798350300093

In India, like in the rest of the world, cancer is a major killer. this research objective is to predict cancer mortality in India, using supervised machine learning methods. Cancer mortality rates in India between 1990 and 2017 are provided by age group, gender, and region using data from the Global Burden of Disease Study. We employ three distinct supervised learning algorithms - linear regression, decision tree regression, and random forest regression - after performing data preprocessing, which includes missing value imputation and feature engineering. Using a variety of criteria, we analyze the effectiveness of these models and conclude that the random forest regression model is superior to the other two. the scope of research is provide a long-term prediction of cancer mortality in India using the best model so it will help health department to work on it. Our research has implications for policymakers and healthcare providers in India, where it may inform efforts to reduce cancer rates and improve cancer care. © 2023 IEEE.

关键词： Forecasting

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Real Time Network threat Detection Using machine learning and Kafka in Middleware 4

Real Time Network Threat Detection Using Machine Learning an...

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4th international conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Jaspin, K. Yogesh Shamlin Shinanth, J.S. Srihari Kishore, K. St. Joseph's Institute of Technology Tamil Nadu Chennai India

ISBN: (纸本)9798331529635

the increasing complexity of network traffic resulting from internet-based services and data exchanges presents formidable obstacles to real-time analysis and decision-making in the field of network security. A real-time network traffic analysis system was created using Kafka, Redpanda, and machine learning to overcome these difficulties. Flow-based values were collected from CICFlowmeter. To efficiently classify network traffic, these attributes are subsequently examined using a machine learning model that has already been trained. Our integration of a Flask application Programming Interface (API) with Kafka guarantees efficient, real-time data handling and processing. Efficient deployment is made possible via Docker, which also makes the system compatible with any existing server infrastructure. Performance analysis was conducted to assess how well the system handles real-time traffic and its accuracy in classifying network data, an accuracy of 99.4% is achieved using the Random Forest Classifier algorithm. the findings show that using Redpanda for Kafka, alongside machine learning, greatly improves the speed and effectiveness of data processing and classification. this approach provides a solid and adaptable solution for enhancing network security. © 2024 IEEE.

关键词： Middleware

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proceedings of the 4th IEEE international conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2023

Proceedings of the 4th IEEE International Conference on Smar...

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4th IEEE international conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2023

ISBN: (纸本)9798350384628

the proceedings contain 90 papers. the topics discussed include: severity stratification of brinjal leaf diseases: a federated learning CNNs approach;experimental analysis of malware detection and classification system using intelligent deep learning methodology;exploring the thematic clusters of artificial intelligence applications in supply chains using topic modelling and text mining: a machine learning insight;predicting quality of ground water using different deep learning models;BananaLeafNet: federated learning CNNs for multiclass disease identification in banana crops;the detection of adverse drug reactions in clinical text data using transformer models;energy consumption forecasting in buildings based on heating and cooling loads using regression models;towards sustainable ICT: a bibliometric review of green communications;leveraging machine learning approach for large-scale unstructured satellite image analysis: a comprehensive review and future predictions;and the food preservation using a solar dryer monitoring system.

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Cryptocurrency volatility forecasting using commonality in intraday volatility 23

Cryptocurrency volatility forecasting using commonality in i...

引用

4th ACM international conference on AI in Finance (ICAIF)

作者： Djanga, Emmanuel Cucuringu, Mihai Zhang, Chao Univ Oxford Dept Stat Oxford England Univ Oxford Alan Turing Inst Oxford Dept Stat Oxford England Univ Oxford Alan Turing Inst Oxford Math Inst Oxford England Univ Oxford Alan Turing Inst Oxford Oxford Man Inst Quantitat Finance Oxford England Univ Oxford Oxford Man Inst Quantitat Finance Oxford England

ISBN: (纸本)9798400702402

We investigate the benefits of using intraday realized volatility (RV) commonality, and propose a novel non-parametric framework for forecasting one-day ahead intraday RV (1D-ahead intraday RV). Specifically, we train multiple models using machine learning (ML) techniques under various training settings (single-asset, cluster-driven, and cross-asset), where commonality gradually enters model dynamics as training schemes become more complex. We conclude that models that leverage the cryptocurrency commonality outperform models that do not explicitly account for it, regardless of the market regime considered. the source code of this project is available at: ***/edjanga/crypto_volatility_commonality.

关键词： Cryptocurrency markets Volatility forecasting machine learning

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application of machine learning Based Life Prediction Method in Aviation Enterprises 4

Application of Machine Learning Based Life Prediction Method...

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4th international conference on Electronic Information Engineering and computer Technology, EIECT 2024

作者： Wang, Yuning Xie, Daye Yin, Haijun Yin, Mingang Co. Ltd Industrial Engineering Office Shenyang China School of mechanical engineering Shenyang university of technology Shenyang China

ISBN: (纸本)9798331528850

In order to quickly predict the life indicators of aircraft wires, a modeling and parameter estimation method based on different surrogate models is proposed for aircraft wires. the predicted values are used to predict the full cycle life and reliable life of aircraft wires during their service period through finite difference method, determine the working life of the product, and achieve vibration in the high vibration zone of the aircraft tail. © 2024 IEEE.

关键词： Prediction models

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Exploring the Potential of machine learning for Early Diagnosis of Parkinson's Disease: A Comparative Study 4

Exploring The Potential of Machine Learning for Early Diagno...

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4th international conference on Intelligent Engineering and Management, ICIEM 2023

作者： Devi Padma, K. Harshitha, P. Nafiza, R. Lilian, J. Felicia Malini, A. Thiagarajar College of Engineering Department of Computer Science and Business Systems Madurai625 015 India

ISBN: (纸本)9798350341126

the neurological ailment Parkinson's disease affects millions of individuals globally. Contrarily, an early detection of the illness will aid in its efficient treatment. With machine learning, which is still in its infancy, there are a number of prospects for computer-aided identification and diagnosis that may lessen inescapable medical issues and intrinsic clinical uncertainty, offer direction, and enhance decision-making. In an effort to improve the technique for diagnosing Parkinson's disease, we have proposed a model for early detection of the condition. Performance and speed were taken into consideration when XGBoost, a new machine learning technique, was developed. A decision tree-based technique called Extreme Gradient Boosting, or XGBoost, was developed. © 2023 IEEE.

关键词： Decision trees

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Sybil Attacks detection for dynamic environment in Federated learning 4

Sybil Attacks detection for dynamic environment in Federated...

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4th international conference on Telecommunications, Optics, and computer Science, TOCS 2023

作者： Lin, Lihuang Zhu, Xi Wang, Junbo The School of Intelligent Systems Engineering Sun Yat-Sen University Shenzhen518107 China The School of System Science and Engineering Sun Yat-Sen University Guangzhou510275 China

ISBN: (数字)9781510679849

ISBN: (纸本)9781510679832

Federated learning can utilize its distributed structure to protect data privacy security of clients and improve efficiency of machine learning. However, its distributed framework also make itself be susceptible to sybil attacks. While previous research has already proposed defense methods to address this issue, they often fail to guarantee effective performance in a dynamic federated learning system, where some clients dynamically join in and out. To tackle this problem, our paper introduces a novel defense method specifically designed to mitigate sybil attacks in dynamic federated learning scenario. Our proposed method consists of three mechanisms: similarity mechanism, validation mechanism, and reputation mechanism. these mechanisms can address the problem of missing information and effectively resist sybil attacks in dynamic federated learning. We evaluate the performance of our method on the MNIST and KDDCup datasets and demonstrate its advanced ability in defending against sybil attacks in dynamic federated learning compared to existing methods. © 2024 SPIE.

关键词： Dynamics

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