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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"

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Construction and application of Aerospace Quality Knowledge Graph 4

Construction and Application of Aerospace Quality Knowledge ...

引用

4th international conference on Signal Processing and computer Science, SPCS 2023

作者： Zhang, Chenyue Dong, Fangxu Long, Qinghua Beijing Institute of Computer Technology and Application Beijing100854 China Beijing Radio Measurement Institute Beijing100854 China

ISBN: (纸本)9781510672505

Aiming at the huge and discrete storage of quality problem data in aerospace model quality management, a method for improving the efficiency of finding aerospace quality information by constructing a knowledge graph of aerospace quality and applying a question-answering system is proposed. the graph construction adopts a top-down construction method. First, the concept-attribute-relationship ontology model of aerospace quality knowledge is constructed. then, based on combining expert knowledge to design the ontology concept of aerospace quality knowledge, the data to be processed is pre-processed by BERT. the training model is combined with deep learning technologies such as BiLSTM-CRF and self-attention mechanism to realize independent knowledge extraction, and then the Dice coefficient combined with the weighted average method is used to fuse the entity-relationship extraction results. Finally, based on the constructed knowledge graph, to improve the accuracy of semantic template matching, a natural language question semantic classification process model based on a weighted naive Bayesian classifier is established, which can further realize intelligent question answering. the results of the paper show that the intelligent application of knowledge graphs in the field of aerospace quality has a good prospect. © 2023 SPIE.

关键词： Deep learning

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A Survey on Drug Suggestion Mechanisms using machine learning Algorithm 7

A Survey on Drug Suggestion Mechanisms using Machine Learnin...

引用

7th international conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Aravind, S. Sivakumar, V. Vellore India Vellore India

ISBN: (纸本)9798350397253

this research study intends to bring convenience to any user to find the appropriate medicine that suits them. the application finds the brand and name of medicine that has the least side-effects and lack harmful chemicals. the primary objective of this research study is to design a prototype for a computer software that would make it simpler and more straightforward for users to evaluate the potential utility of a new pharmaceutical product in an unbiased manner. © 2023 IEEE.

关键词： User interfaces

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Cross-domain Few-shot learning for Chinese Herbal Recognition 4

Cross-domain Few-shot Learning for Chinese Herbal Recognitio...

引用

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

作者： Wu, Nan Guo, Wenjuan Tian, Xia Wu, Xingjiao Yunnan College of Business Management Yunnan China Fudan University Shanghai China

ISBN: (纸本)9798350308082

Traditional Chinese herbal medicine (TCM) is a crucial treatment for various ailments. However, recognizing TCM classes requires specialized knowledge and expertise, limiting accurate identification to experienced medical professionals. As a result, using machine learning to recognize TCM presents a significant challenge. While some studies have proposed TCM datasets, they often focus solely on decoction pieces, overlooking the importance of identifying roots, stems, and leaves. To address this issue, we propose the first dataset concentrating on identifying TCM roots, stems, and leaves. However, labeling this dataset requires extensive labor, particularly when incorporating medical experts' knowledge. therefore, we introduce a cross-domain few-shot TCM recognition method that reduces the need for extensive labeling. Our method utilizes a graph neural network to model feature similarities, improving the model's generalization capabilities. this study is the first to incorporate few-shot learning into TCM recognition, offering a promising approach to address the challenges of TCM recognition using machine learning. © 2023 IEEE.

关键词： Deep learning

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the Improved Maneuvering Model Algorithm Based on Dynamic Feedback Neural Networks for Online learning 4

The Improved Maneuvering Model Algorithm Based on Dynamic Fe...

引用

4th international conference on machine learning and computer application, ICMLCA 2023

作者： Jia, Shuyi Ren, Liqiang Wang, Haipeng Tang, Tiantian Research Institute of Information Fusion Naval Aviation University Yantai China

ISBN: (纸本)9798400709449

Tracking of maneuvering targets plays an important role in sea battlefield situation awareness and threat assessment. To solve the problem of low prediction accuracy of the traditional prediction method and model, an hybrid filter algorithm based on dynamic feedback neural networks for online learning is designed, which embedded the trained neural network with memory function into the state estimation step of the UKF filter to form a hybrid filter. Based on the input eigenvalues, the estimated error is predicted. this estimated error corrects the state estimation in real time and realizes the online monitoring of maneuvering. the simulation results show that the algorithm has a strong adaptability to the target maneuvering form, and has better performance in terms of convergence and filtering accuracy. © 2023 ACM.

关键词： State estimation

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A methods to improve the computational performance of deep learning models 4

A methods to improve the computational performance of deep l...

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4th international conference on computer Vision and Pattern Analysis, ICCPA 2024

作者： Lyu, Cheng School of Mathematical Sciences Qufu Normal University Qufu China

ISBN: (纸本)9781510682528

With the wide application of deep learning in various fields, the computing performance and computing cost of models are required to be higher. this paper introduces some methods to improve the computational performance and reduce the computational cost of deep learning models, including pruning and mixed precision training. We verify the effectiveness of these methods through experiments, and discuss their advantages and disadvantages and application scenarios. the experimental results show that the pruning mixture can not only improve the precision, accuracy and efficiency of the training model, but also improve the performance of the model on multiple data sets, which provides a valuable reference for training and optimization of more complex deep learning models that require a lot of computing power. © 2024 SPIE.

关键词： Deep reinforcement learning

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CSR & Sentiment Analysis: A New Customized Dictionary 4th

CSR & Sentiment Analysis: A New Customized Dictionary

引用

4th international conference on Deep learning theory and applications (DeLTA)

作者： Zavarrone, Emma Forciniti, Alessia IULM Univ I-20143 Milan Italy

ISBN: (纸本)9783031390586;9783031390593

Communication concerning the CSR pillars is key to sustainable corporate development. Sentiment analysis (SA) is a sub-area of natural language processing for studying communication through the classification of negative or positive opinions. Measuring sentiment is characterized by pitfalls related to: a) the context, where the polarity classification depends on the domain;b) the methods, if lexicon-based, machine learning, or their combination;c) the language, where the lack of resources (different from English) in literature was observed. Strategic communication based on CSR has no domain resources for investigating sentiment, neither in English nor in other languages. Our contribution is placed within the methodological setting of SA for the sustainability framework. We combined lexicon-based methods with machine-learning ones to build a customized lexicon for analyzing the CSR. the innovation concerns: 1) a domain corpus-based approach for improving a general pre-constructed dictionary;2) the application for Italian;and 3) the performance assessment through machine learning. We developed an algorithm characterized by a multi-stage model that combines text analysis with network analysis and captures semantic concordances through an index of keyword content in the text. To validate our model from a machine learning perspective, we divided our data collection into five random samples: one sample was utilized as a train set or baseline for the lexicon's implementation, and four were used as test sets. the study showed a notable increase in performance metrics across all samples, demonstrating the effectiveness of our proposal in building a customized lexicon for analyzing CSR in the Italian context.

关键词： CSR Sentiment analysis Lexicon-based and machine learning methods

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theoretical Evaluation of Transfer learning Approaches for the Identification of Lung Respiratory Sounds 4

Theoretical Evaluation of Transfer Learning Approaches for t...

引用

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

作者： Dubey, Prarthna Tripathi, Paritosh Dr. Rammanohar Lohia Avadh University Institute of Engineering and Technology Department of Information and Technology U.P. Ayodhya India

ISBN: (纸本)9798350300093

machine learning approaches for lung respiratory sound identification and categorization have increased in study. Transfer learning uses pre-trained models to extract relevant attributes from input data, one of the most promising strategies for high accuracy in this activity. In this review, many transfer learning algorithms for lung breathing sound identification are described. First, discuss respiratory sounds and their clinical significance. Next discuss the challenges of recognizing these sounds, such as the vast variety of sound characteristics and the requirement for trustworthy feature extraction techniques. Next, examine transfer learning methods such fine-tuning pre-trained convolutional neural networks (CNNs), learning from comparable tasks, and unsupervised pre-training. Focusing on current research on lung respiratory sound identification. Also compare each method's benefits and limitations. the discussion finishes with the field's most urgent difficulties, including the need for bigger and more diversified datasets, enhanced feature extraction techniques, and clinical information in machine learning models. In conclusion, this research examines the latest transfer learning algorithms for lung breathing sound recognition. It emphasizes these technologies potential to improve clinical diagnostic accuracy and efficiency and recommends numerous interesting research opportunities. © 2023 IEEE.

关键词： Feature extraction

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Collaborative Prediction of Baseline Functions of Wind, Coal and Water Based on Stacked Generalized Integrated learning for a Coal-fired Plant 4

Collaborative Prediction of Baseline Functions of Wind, Coal...

引用

4th IEEE international conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Zhang, Zhihao Wang, Lin Deng, Kai Liu, Bingyuan Kuigao, Yao Xianglei, Yang Li, Jun Gao, Lin Liu, Ming Xi'an Thermal Power Institute. Co. Ltd Xi'an China Shannxi Yanchang Petroleum Fuxian Power Generation Co. Ltd Yan'an China Xi'an Jiaotong University School of Energy and Power Engineering Xi'an China

ISBN: (纸本)9798350349252

Under the background of China's construction of a new type of power system, thermal power is faced with the new situation of frequent peak and frequency adjustments, which puts forward higher requirements for the flexibility. Wind, coal and water are three key variables in the control of coal-fired units. With time going on, they mismatch with each other for the shift of combustion characteristics of the boiler and the deviation between the coal type in use and the designed coal type;and then economy and flexibilities decrease. To solve the problem, a method based on machine learning to predict the wind-coal-water coordination is proposed. Firstly, the R-Value algorithm and local outlier algorithm are used to eliminate the anomalies;secondly, the Least Squares algorithm, the Support Vector machine algorithm and the Polynomial Fitting algorithm are used as the basis function to fit the wind-coal-water function under different load conditions, respectively;finally, the stacking generalization ensemble learning model is used as the second-level integration algorithm to make the final prediction. the application of the modified wind-coal-water matching function in a 1000MW ultra-supercritical unit shows that the wind-coal-water matching function is more coordinated, critical parameters' tracking performance is improved, and the unit operates more smoothly and economically. © 2024 IEEE.

关键词： Support vector machines

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Hierarchical Multi-Scale Synergistic Fusion Network for Pathological Myopia ATN Classification 24

Hierarchical Multi-Scale Synergistic Fusion Network for Path...

引用

4th international conference on computer, Internet of things and Control Engineering

作者： Jiang, Han Zhao, Xiujuan Zhang, Wei Dong, Zhicheng Yu, Shanshan Song, Huiying Qin, Haonan Tibet Univ Lhasa Xizang Peoples R China Guangdong Prov Clin Res Ctr Ocular Dis Guangzhou Guangdong Peoples R China ShenZhen Technol Univ Coll Big Data & Internet Shenzhen Guangdong Peoples R China

ISBN: (纸本)9798400711848

Deep learning has achieved groundbreaking progress in the field of image recognition, yet existing networks have not fully explored the potential connections between network architecture and fundus image features. To address this issue, this paper proposes the Hierarchical Multi-Scale Synergistic Fusion Network (HMSFNet), aimed at resolving the problems of feature collapse in CNNs and the performance limitations of ensemble learning fusion mechanisms. HMSFNet is based on a dual-branch integrated architecture that analyzes key features in fundus images from multiple dimensions and embeds a feature synergy stability module to promote mutual learning between the two branches. Additionally, this study designs a novel and more efficient attention mechanism focused on multi-scale feature recognition of lesion areas. the study constructed a dataset comprising 11,495 high-quality fundus images. In benchmark tests, HMSFNet achieved optimal performance in diagnosing atrophic lesions (A), tractional lesions (T), and neovascular lesions (N), with accuracies of 94.9%, 95.82%, and 99.4%, respectively, significantly outperforming existing models. this achievement not only demonstrates the effectiveness of HMSFNet in diagnosing pathological myopic maculopathy (ATN) but also provides a powerful auxiliary tool for clinical applications.

关键词： Deep learning Multi-modal fusion Attention mechanism Pathologic myopia Medical Image Analysis

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Assessment and Prediction of Corrosion Rate of Marine Railway Bridges Based on Ridge Regression Model 4

Assessment and Prediction of Corrosion Rate of Marine Railwa...

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4th IEEE international conference on Pattern Recognition and machine learning, PRML 2023

作者： Wang, Huan Chen, Zhengbin Rail Transit Branch of Sinohydro 7th Engineering Bureau Co. LTD Chengdu China

ISBN: (纸本)9798350324303

this investigation employs ridge regression analysis to assess and forecast the corrosion rate of maritime railway bridges. We have gathered and organized pertinent data, devising a regression model encompassing multiple attributes, intending to scrutinize seawater's influence on railway bridge corrosion. the model's performance is appraised using cross-validation techniques, and the significance of each point is measured via ridge regression coefficients. Experimental findings demonstrate that the ridge regression model excels in predicting the corrosion rate of maritime railway bridges, exhibiting minimal discrepancies between predicted and actual values. Upon analysis of the model's attributes, we discern that salinity and sulfide content in seawater are critical determinants of railway bridge corrosion rates. the findings of this study provide valuable reference points for marine engineering and railway bridge construction while offering novel insights into the application of ridge regression models for predicting and analyzing maritime environmental issues. © 2023 IEEE.

关键词： Corrosion rate

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