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检索条件"任意字段=2019 International Conference on Applied Machine Learning and Data Science, ICAMLDS 2019"

共 11127 条记录，以下是51-60 订阅

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Understanding Stroke Risk Profiles in Middle-Aged Adults: A Genetic Algorithm-Based Feature Selection Aproach

Understanding Stroke Risk Profiles in Middle-Aged Adults: A ...

引用

18th international Joint conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2025

作者： Gonçalves, Ligia Ferreira de Carvalho Fiorini, Caio Davi Rabelo Franca, Daniel Rocha Noronha, Marta Dias Moreira Song, Mark Alan Junho Galvez, Luis Enrique Zárate Data Science and Artificial Intelligence Pontifícia Universidade Católica de Minas Gerais Rua Claudio Manuel Belo Horizonte Brazil Computer Science Pontifícia Universidade Católica de Minas Gerais Rua Claudio Manuel Belo Horizonte Brazil Institute of Exact Sciences and Computer Science Pontifícia Universidade Católica de Minas Gerais Rua Claudio Manuel Belo Horizonte Brazil

data mining and machine learning techniques have been widely used in the knowledge extraction process of medical databases, one highlight being their use to improve diagnostic systems. Decision trees are supervised black box machine learning models that, although simple, are easy to interpret. In this work, we propose the use of these techniques to describe the profile of middle-aged adults (40-59) diagnosed with stroke, a disease that in Brazil was one of the main causes of death in previous years. The genetic algorithm was applied to extract the best characteristics so that the Decision Tree algorithm could then be used in the database provided by the 2019 National Health Survey to obtain the most comprehensive rules and identify the most relevant attributes for describing the profile of these individuals. The conclusions indicate that the rules generated for middle-aged adults are mainly about routine habits, such as work or salt consumption. © 2025 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Adversarial machine learning

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Adversarial learning of Summary Statistics for Approximate Bayesian Computation 24

Adversarial Learning of Summary Statistics for Approximate B...

引用

8th international conference on Advances in Artificial Intelligence, ICAAI 2024

作者： Adel, Tameem Cambridge United Kingdom

ISBN: (纸本)9798400718014

Estimating influential parameters in real-world applications can be challenging, especially in problems where the model likelihood is intractable. To perform inference, the machine learning model should be able to understand the underlying phenomena and conditions which have led to the observations in hand. Estimating the posterior distribution of the influential parameters given observations (the inverse problem) can therefore be onerous, especially when the likelihood is intractable. The Approximate Bayesian Computation (ABC) paradigm is the standard statistical methodology to address such likelihood-free problems. In a nutshell, ABC methods establish a proxy for the likelihood based on data simulations. Two main bottlenecks of any ABC technique are the comparisons between observed and simulated datasets, and the manner via which data must be summarized in order to make the eventual data comparisons optimally fit for the inference purpose. We propose an ABC algorithm that learns how to perform data summarization in a data-driven manner, which is not based on pre-selecting summary statistics a priori. The proposed ABC algorithm is based on a neural network which is trained via adversarial learning. We show how the proposed ABC algorithm can infer the posterior distributions of parameters with high fidelity, and with low bias and variance. We apply the algorithm to two different applications and demonstrate its efficacy and robustness with respect to parameter inference. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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machine learning Based Cardiovascular Disease Prediction 3

Machine Learning Based Cardiovascular Disease Prediction

引用

3rd international conference on Advancement in Electronics and Communication Engineering, AECE 2023

作者： Saraswathi, P. Prathiksaa, J.G. Prabha, M. Jhoshitha, N.A. It Velammal College of Engineering and Technology Madurai India

ISBN: (纸本)9798350330724

Heart disease is an illness that affects the heart and manifests itself in a variety of ways, including blood vessel issues, irregular heartbeats, and diseases of the heart muscle or valves. In recent years, researchers have found that machine learning can help to detect heart problems by analyzing digital electrocardiogram (ECG) data. By using this technology, doctors can detect heart issues earlier and save lives. The process involves analyzing large sets of medical data, which includes information about patients' medical history. Logistic Regression, Support Vector machine (SVM), and Random Forest are most widely utilized machine learning techniques to perform classification. Also, it predicts whether a patient has heart ailments or not. In this research, Decision Tree Classifiers are modeled along with the mentioned techniques which predict the heart related issues. A performance measure like accuracy of each technique is analyzed and the best one is identified to guess the heart disease. © 2023 IEEE.

关键词： Electrocardiograms

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Survey and Analysis on machine learning Approaches for Exploring Aspects of Covid-19 3rd

Survey and Analysis on Machine Learning Approaches for Explo...

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3rd international conference on machine Vision and Augmented Intelligence, MAI 2023

作者： Pawar, Sumathi Pandit, Karuna GeethaLaxmi Thinakaran, Rajermani Karnataka Nitte Udupi Karkala India Information Science and Engineering Canara Engineering College Bantwal Benjanapadavu India Data Science and Information Technology INTI International University Negeri Sembilan Nilai Malaysia

ISBN: (纸本)9789819743582

Wuhan, China was the place where COVID-19 was found in December 2019 and proved to be the worst pandemic in the world. This virus is highly contagious and spreads through direct contact and droplets. More than 210 countries suffered from this Virus and witnessed the highest number of fatality rate. This paper focuses on an analysis of machine learning approaches adapted by researchers to study, predict, and recommend related to the spread, recovery, and forecasting of the advent of next mutated virus. It is found in the survey that the results of the experiment, demonstrated accuracy in predicting Covid-19 infections. Researchers worked on various data Mining techniques which are implemented along with artificial intelligence techniques and results are analyzed and proved. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： COVID-19

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Dynamic machine learning Algorithms for Multiple Malware Detection 5

Dynamic Machine Learning Algorithms for Multiple Malware Det...

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5th international conference on Information Technology, applied Mathematics and Statistics, ICITAMS 2023

作者： Fadhil, Zahraa Najah Bhaya, Wesam Sameer. University Of Babylon College Of Information Technology Hilla Iraq

ISBN: (纸本)9798350335118

Vast volumes of data are readily available everywhere today. As a result, it is crucial to evaluate this data to draw out some relevant information and create algorithms based on that analysis. Algorithms are developed using machine learning (ML), which considers past connections and data trends. ML is utilized in various industries, including marketing, virus detection, and information retrieval. Using different machine learning methods, including Feed-Forward Neural Network (FFNN) and Support Vector machine (SVM), this proposed system offers an analysis method for classifying malware into families. The proposal is a weight-based dynamic selection classification system. The study uses the DDoS 2019 dataset and the Microsoft Malware Classification Competition (BIG 2015) publicly accessible Kaggle database. The FFNN approach has a success percentage of 99.99%, but the SVM technology only manages 99.52%, according to the DDoS 2019 dataset. The Big 2015 dataset reveals that the FFNN had a success rate of 96.89%, whereas the SVM only showed an accuracy of 93.20 %. © 2023 IEEE.

关键词： Support vector machines

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learning to Continually Learn with the Bayesian Principle 41

Learning to Continually Learn with the Bayesian Principle

引用

41st international conference on machine learning, ICML 2024

作者： Lee, Soochan Jeon, Hyeonseong Son, Jaehyeon Kim, Gunhee Department of Computer Science and Engineering Seoul National University Seoul Korea Republic of

In the present era of deep learning, continual learning research is mainly focused on mitigating forgetting when training a neural network with stochastic gradient descent on a non-stationary stream of data. On the other hand, in the more classical literature of statistical machine learning, many models have sequential Bayesian update rules that yield the same learning outcome as the batch training, i.e., they are completely immune to catastrophic forgetting. However, they are often overly simple to model complex real-world data. In this work, we adopt the meta-learning paradigm to combine the strong representational power of neural networks and simple statistical models' robustness to forgetting. In our novel meta-continual learning framework, continual learning takes place only in statistical models via ideal sequential Bayesian update rules, while neural networks are meta-learned to bridge the raw data and the statistical models. Since the neural networks remain fixed during continual learning, they are protected from catastrophic forgetting. This approach not only achieves significantly improved performance but also exhibits excellent scalability. Since our approach is domain-agnostic and model-agnostic, it can be applied to a wide range of problems and easily integrated with existing model architectures. Copyright 2024 by the author(s)

关键词： Adversarial machine learning

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Transferable Deep Reinforce learning With Limited Target data 21

Transferable Deep Reinforce Learning With Limited Target Dat...

引用

21st international Computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Xiaoqin, Liang Xingyu, Chen Zheng, Wang Wenyi, Xu Shijiao, Han School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China Chengdu Aircraft Design and Research Institude Chengdu610091 China School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9798331519254

While there has been significant progress in the application of transfer learning within reinforcement learning, most existing research primarily focuses on online reinforcement learning, with few studies addressing the optimization of offline reinforcement learning. This paper proposes a novel transferable deep reinforcement learning method designed to enhance limited offline samples in the target domain by utilizing online interactive samples from a similar source domain. The method is versatile and can be integrated with any offline reinforcement learning algorithm. To validate its effectiveness, extensive evaluations were conducted across multiple Mujoco environments and D4RL datasets. The results indicate that by employing the proposed method, the algorithm's performance can approach that of using 100% of the target domain data, even when only 10% of the data is available. © 2024 IEEE.

关键词： Adversarial machine learning

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data Representation by Joint Hypergraph Embedding and Sparse Coding (Extended Abstract) 39

Data Representation by Joint Hypergraph Embedding and Sparse...

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39th IEEE international conference on data Engineering, ICDE 2023

作者： Zhong, Guo Pun, Chi-Man Guangdong University of Foreign Studies School of Information Science and Technology Guangzhou China University of Macau Department of Computer and Information Science China

ISBN: (纸本)9798350322279

Matrix factorization (MF), a popular unsupervised learning technique for data representation, has been widely applied in data mining and machine learning. According to different application scenarios, one can impose different constraints on the factorization to find the desired basis, which captures high-level semantics for the given data, and learns the compact representation corresponding to the basis. We note that almost all previous work on MF in data mining has ignored to find such a basis, which can carry high-order semantics in the data. In this work, we propose a novel MF framework called Joint Hypergraph Embedding and Sparse Coding, in which the obtained basis captures high-order semantic information in data. Experimental results on data clustering demonstrate that the proposed method consistently outperforms the other state-of-the-art matrix factorization methods. © 2023 IEEE.

关键词： Matrix factorization

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An Artificial Intelligence Approach for Analyzing Students' Academic Performance Using machine learning Algorithms 4

An Artificial Intelligence Approach for Analyzing Students' ...

引用

4th international conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Turan, Ahmet Serdar Koksoy, Bedirhan Pasaoglu, Ali Istanbul Rumeli University Computer Engineering Engineering and Natural Science Istanbul Turkey

ISBN: (纸本)9798331523923

This paper investigates the effect of midterm and short quizzes on the success scores at the end of the course through machine learning methods. data from 3,427 students in the Faculty of Engineering and Natural sciences of Istanbul Rumeli University, recorded from the year 2019 to 2024, were analyzed, and of that, 1,547 records were qualified for machine learning after applying specific filtering criteria. Due to insufficient data, 6,300 synthetic records were generated using the Generative Adversarial Networks (GAN) method. To ensure high-quality data generation and stable training, a Wasserstein GAN with Gradient Penalty (WGAN-GP) was implemented, enhancing the synthetic dataset's diversity and reliability. In this paper, the algorithms of Random Forest (RF), AdaBoost, Linear Regression (LR), K-Nearest Neighbors (KNN), and Support Vector machines (SVM) were used for the classification and prediction of course success. Among these, the most accurate performance, quantified for example by R2 score, Mean Absolute Error and Mean Squared Error, was given by RF, with a probability of being correct at 83.81%. The results indicate that midterm exams have a significantly stronger influence on predicting course success, while short quizzes play an important role in maintaining consistent student engagement and reinforcing foundational concepts. Future studies will aim for data diverseness by adding more features and comparing new methods, such as Artificial Neural Networks with hyperparameter optimization. © 2025 IEEE.

关键词： Generative adversarial networks

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Analyzing COVID-19 sentiments on Twitter: A machine learning approach 4th

Analyzing COVID-19 sentiments on Twitter: A machine learning...

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4th international conference on Computational Methods in science and Technology, ICCMST 2024

作者： Aishwarya, V. Sanmuga Priyan, K. Vyshnavi, R. Azhagiri, M. Sathya Priya, S. SRM Institute of Science and Technology Chennai India

ISBN: (纸本)9781032911571

Throughout the outbreak, COVID-19 has been a hot issue on Twitter, providing a forum for people to voice their ideas and perspectives. Unfortunately, there isn’t a thorough rundown of sentiment analysis in Twitter discussions around COVID-19. By analyzing sentiment analysis utilizing COVID-19 Twitter data from both a machine learning and a behavioral and social science standpoint, this article fills this gap. Forty papers examining public attitudes were found in a study of the literature conducted between October 2019 and January 2022. Higher accuracy is shown by ensemble models, especially those that use BERT and RoBERTa. These models do not provide exact outcomes, despite their theoretical promise. Based on the COVIDSENTI dataset, the research emphasizes the need for government action on social media to counter false information. It highlights that, despite the continual examination of datasets, proactive public health actions are crucial in lowering unfavorable sentiments during a pandemic. © 2025 the Author(s).

关键词： Tweets

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