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

共 33936 条记录，以下是1201-1210 订阅

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machine learning Techniques for Cardiac Risk Analysis

Machine Learning Techniques for Cardiac Risk Analysis

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

作者： Medasani, Hari Kumar Kottamasu, Sai Anila Doppalapudi, Sriram Koneru, Suvarnavani Computer Science and Engineering Vr Siddhartha Vijayawada India

ISBN: (纸本)9781665478847

One of the most difficult tasks in medicine is predicting cardiac disease. Heart disease is becoming more common at an alarming rate, and being able to predict such diseases in advance is crucial and important. Because this is a difficult diagnosis to do, it must be completed correctly and swiftly. We developed a machine learning technique to predict whether or not a patient will be diagnosed with heart disease based on their medical history. Physical characteristics, clinical laboratory test results and Symptoms can be examined and used for predicting cardiac disease in electronic medicalrecords. For pre dic ting car di ac related dis eas e, algorithms such as Random Forest, Decision Tree, Logistic Regression were used Among these, logistic regression produces the most accurate results. © 2022 IEEE.

关键词： machine learning

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Can 5th Generation Local Training Methods Support Client Sampling? Yes! 26

Can 5th Generation Local Training Methods Support Client Sam...

引用

26th international conference on Artificial Intelligence and Statistics (AISTATS)

作者： Grudzien, Michal Malinovsky, Grigory Richtarik, Peter KAUST Optimizat & Machine Learning Lab Thuwal Saudi Arabia KAUST Thuwal Saudi Arabia Univ Oxford Oxford England

The celebrated FedAvg algorithm of McMahan et al. (2017) is based on three components: client sampling (CS), data sampling (DS) and local training (LT). While the first two are reasonably well understood, the third component, whose role is to reduce the number of communication rounds needed to train the model, resisted all attempts at a satisfactory theoretical explanation. Malinovsky et al. (2022) identified four distinct generations of LT methods based on the quality of the provided theoretical communication complexity guarantees. Despite a lot of progress in this area, none of the existing works were able to show that it is theoretically better to employ multiple local gradient-type steps (i.e., to engage in LT) than to rely on a single local gradient-type step only in the important heterogeneous data regime. In a recent breakthrough embodied in their ProxSkip method and its theoretical analysis, Mishchenko et al. (2022) showed that LT indeed leads to provable communication acceleration for arbitrarily heterogeneous data, thus jump-starting the 5(th) generation of LT methods. However, while these latest generation LT methods are compatible with DS, none of them support CS. We resolve this open problem in the affirmative. In order to do so, we had to base our algorithmic development on new algorithmic and theoretical foundations.

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APPFed: A Hybrid Privacy-Preserving Framework for Federated learning over Sensitive data 2

APPFed: A Hybrid Privacy-Preserving Framework for Federated ...

引用

2nd international conference on machine learning and Intelligent Systems engineering, MLISE 2022

作者： Yao, Ruichu Tang, Kunsheng Fan, Bingbing South China Normal University School Of Computer Guangdong China

ISBN: (数字)9781665492461

ISBN: (纸本)9781665492461

In the era of Big data, data silos have become a pressing problem due to the difficulty of secure data sharing. Federated learning provides a favorable solution by allowing data holders to collaborate in training a model without sharing local data. However, several existing inference attacks have led to the fact that a pure federated learning methodology is incapable of providing sufficient privacy protection. We propose an APPFed algorithm that combines differential privacy and homomorphic encryption based on federated learning, where exists an evaluation module that enables the privacy budget parameters to be adaptive according to different needs during the training. Trained with our proposed APPFed algorithm, the models are enabled to prevent inference attacks without drastic accuracy depletion. To verify the effectiveness of our proposed algorithm, we use the APPFed algorithm to train a set of sensitive data containing face images. The experimental results show that our approach can enhance privacy protection while balancing model accuracy. © 2022 IEEE.

关键词： Sensitive data

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Are We Training with The Right data? Evaluating Collective Confidence in Training data using Dempster Shafer Theory 44

Are We Training with The Right Data? Evaluating Collective C...

引用

44th ACM/IEEE international conference on Software engineering - New Ideas and Emerging Results (ICSE-NIER)

作者： Dey, Sangeeta Lee, Seok-Won Ajou Univ Suwon Gyeonggi Do South Korea

ISBN: (纸本)9781665495967

The latest trend of incorporating various data-centric machine learning (ML) models in software-intensive systems has posed new challenges in the quality assurance practice of software engineering, especially in a high-risk environment. ML experts are now focusing on explaining ML models to assure the safe behavior of ML-based systems. However, not enough attention has been paid to explain the inherent uncertainty of the training data. The current practice of ML-based system engineering lacks transparency in the systematic fitness assessment process of the training data before engaging in the rigorous ML model training. We propose a method of assessing the collective confidence in the quality of a training dataset by using Dempster Shafer theory and its modified combination rule (Yager's rule). With the example of training datasets for pedestrian detection of autonomous vehicles, we demonstrate how the proposed approach can be used by the stakeholders with diverse expertise to combine their beliefs in the quality arguments and evidences about the data. Our results open up a scope of future research on data requirements engineering that can facilitate evidence-based data assurance for ML-based safety-critical systems.

关键词： data uncertainty safety machine learning Dempster Shafer theory

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Sign Language Digit Detection with MediaPipe and machine learning Algorithm 12

Sign Language Digit Detection with MediaPipe and Machine Lea...

引用

12th IEEE international conference on Control System, Computing and engineering, ICCSCE 2022

作者： Salim, Safyzan Jamil, Muhammad Mahadi Abdul Ambar, Radzi Roslan, Rozaini Kamardan, Muhammad Ghazali Research Group Universiti Tun Hussein Onn Malaysia Faculty of Electric and Electronic Engineering Batu Pahat Johor 86400 Malaysia Universiti Tun Hussein Onn Malaysia Faculty of Applied Sciences and Technology Department of Mathematics and Statistics Muar Johor 84600 Malaysia

ISBN: (数字)9781665483391

ISBN: (纸本)9781665483391

A major challenge when developing machine learning (ML) sign language recognition using wearable is how to efficiently translate the gestures based on the acquired sensors data. Conventional method utilizes data fusion based on the obtained sensors' information by producing mapping/lookup table for creating classification model of gestures corresponding sensor value. Although this method is effective, it increases programming complexity. Therefore, emerging technology that can improve the simplicity and provide accuracy of gestures' data processing is needed. This work experiments the artificial intelligence approach of the development of American Sign Language (ASL) detection using MediaPipe, a ready-to-use cross-platform machine learning framework for computer vision works and Google Teachable machine a free web tool of machine learning model creation. © 2022 IEEE.

关键词： machine learning

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machine learning and Trust Based AODV Routing Protocol to Mitigate Flooding and Blackhole Attacks in MANET

Machine Learning and Trust Based AODV Routing Protocol to Mi...

引用

2022 international conference on machine learning and data engineering, icmlde 2022

作者： Shafi, Shaik Mounika, S. Velliangiri, S. Department of ECE B v Raju Institute of Technology Telangana Narsapur India Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur Campus Chennai India

The latest advancements in wireless communication promotes the researchers to concentrate more in the expansion of Mobile Ad hoc Networks (MANETs), where nodes communicate each other to provide the demanded real time infotainment services. However, due to wireless connectivity and decentralized architecture, constructing secure routing is still a challenging issue in MANETs. Therefore, in this paper, A machine learning and Trust Based AODV Routing Protocol (ML-AODV) to mitigate Flooding and Blackhole Attacks in MANET is proposed. Primarily, cooperative intermediate nodes in the network are selected through trust estimation at each node to avoid (flooding attack) unnecessary transmission of huge number of routing packets to nonexistent destinations. Towards this, the node distinct metrics like Hop Count (HC), Residual Energy (RE), and Link Expiration Time (LET) are considered for trust estimation. The nodes with highest trust value are chosen as trusted relay forwarders to find the optimal paths with reduced packet drop rate (Blackhole attack). Further, finds best optimal path to eliminate the energy disparity and delay for packet transmission using machine learning based Artificial Neural Network (ANN) with Support Vector machine (SVM) classifier. Here, SVM is used to identify the intruder in the selected route to improve efficiency and intrusion detection accuracy. The performance of the proposed ML-AODV is carried out using NS-2 and compared over existing routing schemes under attack. Based on the simulation results, the proposed ML based AODV exhibits improved throughput to 4% and reliability to 44% over existing approaches. In addition, delay, routing overhead, packet loss ratio are reduced to 12%, 15%, and 10% respectively. © 2023 The Authors. Published by Elsevier B.V.

关键词： Mobile ad hoc networks

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A Multi-modal CBIR Framework with Image Segregation using Autoencoders and Deep learning-based Pseudo-labeling

A Multi-modal CBIR Framework with Image Segregation using Au...

引用

2022 international conference on machine learning and data engineering, icmlde 2022

作者： John, Manu Mathew, Terry Jacob Bindu, V.R. School of Computer Sciences Mahatma Gandhi University Kerala Kottayam India MACFAST Tiruvalla India

Various modulated techniques of Content-Based Image Retrieval (CBIR) using deep learning provide better search outputs even though they are computationally challenging. These methods can be enhanced further, if the search key can be tagged effectively and directed towards target images. In this paper, we have developed a new multilevel aggregation technique along with autoencoders, to be implemented on image features for precise feature selection and accurate search output. Locally significant datasets and generic ones are dealt separately for obtaining better hit rates along with the process of query expansion. The concept of pseudo-labelling by deep learning is introduced to classify images into positive and negative classes, where positive class consists of the images that are similar to the query image. The feature spaces of query images are compared with the images in the search pool based on their assigned weights. The target images are finally ranked and selected based on an adaptive threshold level. The methodology for this enhanced CBIR technique is tested on public datasets and its results are collated with recent approaches proposed in the literature. The proposed method has attained significant improvements in precision, recall and computation and thereby the use of images from connected devices has been proven to be significant. Given the importance of more accurate image retrieval in CBIR, this method can be experimented for other similar applications as well. © 2023 The Authors. Published by Elsevier B.V.

关键词： Deep learning

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Stock Selection by machine learning in Thailand Stock Market (SET) 8

Stock Selection by Machine Learning in Thailand Stock Market...

引用

8th international conference on Information Technology, InCIT 2024

作者： Thong-Ou, Paruch Nadee, Winai Thammasat University Thammasat Business School Bangkok Thailand

ISBN: (纸本)9798350366303

This research studies and compares the use of different machine learning tools - K-means clustering, Hierarchical clustering, Affinity propagation clustering, and the Random Forest model - with the Low-Correlation Strategy for selecting stocks to create a portfolio on the Stock Exchange of Thailand (SET). Each stock is given equal weight and evaluated by the Sharpe ratio. The data used for clustering and training to predict stock returns includes adjusted closing prices and six financial ratios: Return on Total Assets (ROA), Return on Equity (ROE), Net Profit Margin, Earnings Per Share (EPS), Dividend Yield, and Debt to Equity Ratio (D/E Ratio). This data spans from January 1, 2020, to December 31, 2022, for the in-sample period, and the adjusted closing stock price data from January 1, 2023, to December 31, 2023, for the in-sample forecasts. During the in-sample period, it was found that using machine learning to select stocks, both Unsupervised learning and Supervised learning created portfolios with higher Sharpe ratios than the Low-Correlation Strategy. On the other hand, when evaluating the Sharpe ratio for the in-sample forecast period using 2023 data, the Sharpe ratio of portfolios using Unsupervised learning was negative for all portfolios. The Sharpe ratio of the Random Forest portfolio get rapidly decreased. In contrast, the Sharpe ratio of the Low-Correlation Strategy portfolio increased to 0.61. In conclusion, using machine learning, both Supervised and Unsupervised learning, led to higher Sharpe ratios than the Low-Correlation Strategy during the in-sample period. However, in the in-sample forecast period, the portfolios from both types of machines learning had lower Sharpe ratios than those from the Low-Correlation Strategy. However, the Random Forest Model of this study still has high errors due to data and economic factors. © 2024 IEEE.

关键词： Contrastive learning

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Pseudo Computed Tomography estimation from brain MRI using anatomic signature and joint dictionary learning

Pseudo Computed Tomography estimation from brain MRI using a...

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2022 international conference on machine learning and data engineering, icmlde 2022

作者： Sreeja, S. Mubarak, D. Muhammad Noorul Department of Computer Science University of Kerala Karyavattom campus Kerala Trivandrum695581 India Head of Department Department of Computer Science University of Kerala Karyavattom campus Kerala Trivandrum695581 India

Most research on using pseudo-computed tomography (pCT) on brain-imaging techniques relies on in-house methods. As performance as a whole increase, they pay particular attention when using MRI imaging. Methodologies for predicting CT values from MRI data are needed in radiation treatment (RT). Although the employment of dictionary-learning-based approaches for defining picture patches has not been considered, it has been found that Deep learning (DL) offers increased opportunities in the medical domains. The stages of this paper CT estimation from MRI using Anatomic Signature and Joint Dictionary learning (ASJDL) are as follows: a) data gathering from the RIRE image data and b) image pre-processing to remove anomalies. c) Using Gabor Filters for feature extraction to extract significant features d) The choice of anatomic signature traits is used to identify strong, illuminating characteristics that classify and identify objects. e) Development of the pCT through shared dictionary learning. Using cutting-edge techniques like Accelerated Simplified Swarm Optimization (ASSO), Particle Swarm Optimization (PSO), Simplified Swarm Optimization (SSO), SLA12, Intensity-based, Fast-patch based, and Coupled dictionary over measures like peak S/N ratio, MAE, normalized cross-correlation, SSIM, Accuracy, and Computation time, the evaluation of both feature selection and classification methods is compared. With values of 23.825.08 (PSNR), 83.327.05 (MAE(HU)), 0.920.03 (NCC), and 0.860.03 (SSIM), the suggested framework (ASJDL) performs better than all other methods. © 2022 Elsevier B.V.. All rights reserved.

关键词： Magnetic resonance imaging

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Performance Comparison of Various Supervised learning Algorithms for Credit Card Fraud Detection 2nd

Performance Comparison of Various Supervised Learning Algori...

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2nd international conference on Computational Intelligence in machine learning, ICCIML 2022

作者： Tekkali, Chandana Gouri Natarajan, Karthika Guruteja Reddy, Thota VIT-AP University Andhra Pradesh Amaravati India

ISBN: (纸本)9789819979530

The aim of this research work is to classify credit card fraudulent transactions. Nowadays, online transactions have become a necessary part of our lives. Credit card fraud has skyrocketed. In fact, it is one of the most prevalent menaces of the banking, financial services, and insurance (BFSI) sector. In the era of digitalization, there are so many frauds happening. Credit card fraud is the most common thing happening nowadays;thus, the need to identify the same is necessary. In this work, firstly, collected the unstructured data related to this research from a frequently used site, i.e., Kaggle. Secondly, implemented different machine learning classification algorithms (supervised) like linear regression (LR), decision trees (DT), artificial neural networks (ANN), and gradient boosting (GB). Among all machine learning (ML) algorithms, DT outperforms the other algorithms with accuracy. The performance of these specified algorithms was marked by their comparative analysis with some performance metrics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Decision trees

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