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检索条件"任意字段=2022 International Conference on Machine Learning and Data Engineering, ICMLDE 2022"

共 33935 条记录，以下是831-840 订阅

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An Improved Deep Reinforcement learning for Robot Navigation 3

An Improved Deep Reinforcement Learning for Robot Navigation

引用

3rd international conference on machine learning and Computer Application, ICMLCA 2022

作者： Zheng, Qifeng Huang, Xiaogang Dong, Chen Liu, Yuting Cheng, Dong College of Computer and Data Science Fuzhou University Fuzhou China

ISBN: (纸本)9781510664814

Collision-free navigation is an important research direction for multi-robot systems, in which the two core problems are navigating to the target point and avoiding other robots. Many researchers use deep reinforcement learning as navigation strategy to realize multi-robot collision avoidance navigation. However, most of them use raw sensor information or global state information of the agent as the neural network input, which is not conducive to extending the navigation strategy to a larger space. This paper proposes an improved deep reinforcement learning navigation strategy, which enables robots to learn navigation and collision avoidance strategies more accurately. This strategy converts the interactive environment state from the global coordinate representation to the relative vector representation, and attenuates the influence of the rear irrelevant agents on the collision avoidance strategy. Experimental results show that the proposed method outperforms existing learning-based methods in three indicators: success rate, additional time to reach the target, and model convergence speed. © 2023 SPIE.

关键词： Multipurpose robots

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Evaluating Uber Customers' Perception Through machine learning Techniques: A Case Study in Ecuador

Evaluating Uber Customers' Perception Through Machine Learni...

引用

international conference on Computational Science and Computational Intelligence (CSCI)

作者： Becerra-Salas, Maria Roa, Henry N. Ponliticia Univ Catolica Ecuador Fac Ingn Quito Ecuador

ISBN: (纸本)9798350361513;9798350372304

This study analyzes the perception of Uber users through Twitter, currently known as X, using the CRISP-DIM methodology in Python. We collected data from the last twelve years to accomplish this study. The data set is divided into training and testing, processing them using natural language processing and classifying them as neutral, positive, and hostile. Classification algorithms such as Logistic Regression, Support Vector machines (SVM), and Naive Bayes are applied, with SVM being the most effective in predicting user sentiments. This approach leverages Twitter accessibility and data analytics to understand the public perception of Uber.

关键词： Sentiment analysis Artificial intelligence machine learning Twitter (X) CRISP-DM

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Introduction of Optimization Algorithm for Adaptive Gradient 5

Introduction of Optimization Algorithm for Adaptive Gradient

引用

5th international conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Lamine, Mouna Kim, Sang-Chul Kookmin Univ Sch Comp Sci Seoul South Korea

ISBN: (纸本)9781665456456

machine learning is facing rapid development due to the almost unlimited amount of data available, and it is widely applied in diverse areas. Optimization is one of the core components in the machine learning which attracted more researcher's attention to it. In recent years there has been a great deal of work on improving optimisation methods in machine learning. In this paper we will introduce the Adaptive Gradient(Adapg), a new extension in the adaptive learning family Optimization algorithm.

关键词： machine learning Optimization Methods Gradient Decent Adaptive Algorithms AdaDelta Adam Adapg

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GUI Base Prediction of Heart Stroke Stages by Finding the Accuracy using machine learning Algorithm

GUI Base Prediction of Heart Stroke Stages by Finding the Ac...

引用

2022 international conference on data Science, Agents and Artificial Intelligence, ICDSAAI 2022

作者： Gnanavel, V.K. Sukanya, R. Mercy, H. Jayalakshmi, P. Veeramalai, S. Asan Memorial College of Engineering and Technology Department of Computer Science and Engineering Chennai India Jerusalem College of Engineering Department of Computer Science and Engineering Chennai India SRM Institute of Science and Technology Department of Computer Science and Engineering India Chennai Institute of Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350333848

Several types of predictive techniques have been used in clinical decision-making, such as identifying a disease or diagnosis, assessing a patient's prognosis, and providing treatment recommendations. Unfortunately, these techniques are not able to capture the underlying knowledge because they are not able to mimic the complexity of medical issue *** address this issue, a hospital dataset was used to develop a machine learning model that can perform predictive analysis on heart stroke. The symptoms of atrial fibrillation, which are known to be a major risk factor for stroke, are included in this dataset. The machine learning model was able to capture various pieces of information, such as multivariate analysis, variable identification, and univariate *** work was devised with the intent of developing a machine learning model that can perform predictive analysis on heart stroke in patients. It can then classify the data collected by the model and provide a prediction for the severity of the condition. To achieve this, we proposed a method that combines the capabilities of machine learning and supervised ***, the efficiency of GUI based on the suggested machine learning algorithm technique can be compared to quality standards when equated with other models available for health care system data. © 2022 IEEE.

关键词： Decision making

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Tiny Neural Deep Clustering: An Unsupervised Approach for Continual machine learning on the Edge

Tiny Neural Deep Clustering: An Unsupervised Approach for Co...

引用

international conference on Applications in Electronics Pervading Industry, Environment and Society, APPLEPIES 2023

作者： Poletti, Giovanni Albanese, Andrea Nardello, Matteo Brunelli, Davide University of Trento Department of Industrial Engineering Via Sommarive 9 Trento38123 Italy

ISBN: (纸本)9783031481208

Traditional tiny machine learning systems are widely employed because of their limited energy consumption, fast execution, and easy deployment. However, such systems have limited access to labelled data and need periodic maintenance due to the evolution of data distribution (i.e., context drift). Continual machine learning algorithms can enable continuous learning on embedded systems by updating their parameters, addressing context drift, and allowing neural networks to learn new categories over time. However, the availability of labelled data is scarce, limiting such algorithms in supervised settings. This paper overcomes this limitation with an alternative approach which combines supervised deep learning with unsupervised clustering to enable unsupervised continual machine learning on the edge. Tiny Neural Deep Clustering (TinyNDC) is deployed in an OpenMV Cam H7 Plus and tested with the MNIST dataset reaching a classification accuracy of 92.3% and a frame rate of 44 FPS. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Energy utilization

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Robust training databases for supervised learning algorithms in structural health monitoring applications 30

Robust training databases for supervised learning algorithms...

引用

30th international conference on Noise and Vibration engineering, ISMA 2022 and 9th international conference on Uncertainty in Structural Dynamics, USD 2022

作者： Battu, R.S. Agathos, K. Smith, C. Papatheou, E. University of Exeter College of Engineering Mathematics and Physical Sciences North Park Road ExeterEX4 4QF United Kingdom

ISBN: (纸本)9789082893151

machine learning algorithms are progressively used in structural health monitoring (SHM) applications. However, damage identification in a supervised learning context is challenging due to insufficient training data for various damage states of the structure. It may be feasible to acquire unhealthy sensor data for low-cost structures, but not for high-value complex structures, such as aircraft. In this work, numerical modelling is employed to simulate hard to attain damage scenarios that are in turn used to create training databases for damage identification through machine learning. In order to take into account modelling uncertainty, a comprehensive set of models representing different damaged states with varying parameters is created. Clustering techniques are then used to explore robust features between the model and the physical structure which are finally used to train artificial neural networks. The framework is validated by an experimental case study of damage detection, in the form of loosened bolts on a laboratory tested structure. © 2022 Proceedings of ISMA 2022 - international conference on Noise and Vibration engineering and USD 2022 - international conference on Uncertainty in Structural Dynamics. All rights reserved.

关键词： Structural health monitoring

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Comparison of Individualized and Group-based machine learning Approaches to Predict Rate of Perceived Exertion of Professional Football Players 16

Comparison of Individualized and Group-based Machine Learnin...

引用

16th international conference on Human System Interaction (HSI)

作者： Diouron, Iwen Harispe, Sebastien Imoussaten, Abdelhak Chabbi, Massiwa Duhart, Maelia Joffroy, Antoine Texier, Lucas Escudier, Guilhem Dray, Gerard Perrey, Stephane Univ Montpellier EuroMov Digital Hlth Mot IMT Mines Ales Montpellier Herault Sports Club Montpellier France Univ Montpellier EuroMov Digital Hlth Mot IMT Mines Ales Ales France IMT Mines Ales Ales France Montpellier Herault Sports Club Montpellier France Univ Montpellier EuroMov Digital Hlth Mot IMT Mines Ales Montpellier France

ISBN: (纸本)9798350362923;9798350362916

Monitoring fatigue in sport is critical to achieve elite performance and may benefit from machine learning techniques that are liable to predict changes in fatigue state. In this paper we present and compare different machine learning models to predict the Rate of Perceived Exertion (RPE) of training or game sessions for professional football (soccer) players. We compare different approaches to train predictive models in a supervised setting (regression) with a focus on individualized and group-based approaches, i.e. training a specific model for each player or predefined groups of players (full team or clusters defined using unsupervised learning). Both player-informed and player-agnostic models are compared in the group-based approach, i.e. providing or not player id as feature during training and inference. Compared models have been trained on real data collected during a full season of professional football players, and using among others, anthropometric, running activity, heart rate and weather data. The best results are obtained using a player-informed team-based approach with a Random Forest regressor (0.793 MAE, 1.033 RMSE). Results obtained are competitive with the best reported in the literature for this predictive task in elite Football players.

关键词： machine learning Fatigue Prediction RPE

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Preliminary results to predict tuberculosis outcomes applying traditional and automated machine learning models

Preliminary results to predict tuberculosis outcomes applyin...

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2022 international conference on ENTERprise Information Systems, CENTERIS 2022 - international conference on Project MANagement, ProjMAN 2022 and international conference on Health and Social Care Information Systems and Technologies, HCist 2022

作者： De Andrade Mioto, Ana Clara Mozini, Mariana Tavares Segamarchi, Renan Barbieri Soares, Giovane Thomazini Martins, Pedro Emilio Andrade Cassao, Victor Ferrassini, Luís Gustavo Barichello Miyoshi, Newton Shydeo Brandao Alves, Domingos De Oliveira, Lariza Laura Bioengineering Postgraduate Program University of Sao Paulo Sao Carlos Brazil Ribeirao Preto Medical School University of Sao Paulo Ribeirao Preto Brazil University Center Barao de Mauá Ribeirao Preto Brazil

Tuberculosis (TB) remains one of the most lethal infectious diseases in the world and, despite being preventable and curable, kills 4.500 people daily, according to the World Health Organization (WHO). Brazil, being a country heavily affected by TB, works to improve social intervention programs, since the decrease in the patients vulnerability seems to have a positive effect for the cure of TB. The Brazilian public health system records data on TB treatment that can guide actions and interventions. In this context, machine learning (ML) algorithms have been used successfully to analyze health and medicine (H&M) datasets. An emerging area of ML called Automated machine learning (Auto-ML) was tested in this analysis to predict the following TB results: good and bad outcomes. Our results indicate that it is possible to build reasonable ML models with the available data. © 2022 The Authors. Published by ELSEVIER B.V.

关键词： machine learning

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Evaluation of Stock Prices Prediction Using Recent machine learning Algorithms

Evaluation of Stock Prices Prediction Using Recent Machine L...

引用

international conference on Advances and Applications of Artificial Intelligence and machine learning, ICAAAIML 2022

作者： Kesharwani, Harshit Mallick, Tamoshree Nakarmi, Aakash Raj, Gaurav School of Engineering and Technology Sharda University Greater Noida India

ISBN: (纸本)9789819959730

In this current era, stock market plays a very vital role in the economic situation of the nation. And so, the prediction of the stock values is very crucial. With the growth in technology and huge data, there is also requirement of the most efficient modern solution as the conventional methods of data analysis will not fit the huge data of today’s world. Using machine learning, it is possible. By the precise data analysis and use of the correct predictive model with efficient algorithm, an absolute prediction of price is possible with outstanding accuracy. In this research work, a model of machine learning is presented for forecasting the prices of stock entities. The Gradient boosted Regressor model with a test score of 98.28% stands out as the most accurate model among the four models proposed in this paper. There was a comparative analysis between the four regression and classification models. For the dataset, remote data access reader was used to analyze the last 5 years stock market data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Forecasting

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Prediction of Risk in Cardiovascular Disease using machine learning Algorithms

Prediction of Risk in Cardiovascular Disease using Machine L...

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2022 international conference on Sustainable Computing and data Communication Systems, ICSCDS 2022

作者： Kurian, Nami Susan Renji, Kathu Sara Sajithra, S. Yuvasree, R. Jenefer, F. Anne Swetha, G. Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Chennai India V.S.B Engineering College Department of Biomedical Engineering Karur India R.M.K Engineering College Department of Computer Science and Engineering Tamilnadu India Panimalar Engineering College Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9781665478847

Prediction of heart disease is one of the most complex tasks in medical field and prior detection of heart disease become an area of research to save patient lives. During the pandemic period, the number of cardiac arrest cases at home has drastically increased due to inaccurate predictions and delay in seeking medical attention. The health care industry works on processing of huge data and the solution for this is machine learning. data science process large amount of data to make intelligent health care decisions thereby avoids risk and alert the patients. In this paper, a comparative analysis of different machine learning classifiers based on dataset to predict the chance of heart disease with minimal attributes. The ML algorithms used for HD prediction are K- Nearest Neighbour, Gradient Boosting Classifier, Support Vector machine, Naive Bayes, Logistic Regression and Random Forest algorithm. The paper also finds the correlation between different attributes and hence using them efficiently for prediction of heart attack. © 2022 IEEE.

关键词： Forecasting

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