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检索条件"任意字段=2nd International Conference on Signal Processing and Machine Learning, CONF-SPML 2022"

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conf-spml 2022 - Proceedings of the 2nd international conference on signal processing and machine learning

CONF-SPML 2022 - Proceedings of the 2nd International Confer...

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2nd international conference on signal processing and machine learning, conf-spml 2022

The proceedings contain 16 papers. The topics discussed include: melanoma classification using feature extraction methods and machine learning approaches;strategies to solve the problem of information cocoon â€" research progress of cross-domain recommendation algorithm based on mining the potential interests of users;the development of ray tracing and its future;convolutional neural network and its application in handwritten digit and traffic sign recognition;a two-step rumor detection and classification method using machine learning;lip reading using multi-dilation temporal convolutional network;comprehensive survey on video denoise methods;optimization of CNN and LSTM based application on RC frame and long-span structural health monitoring;comparison of underlying algorithms in recommendation systems;and can PID make the electronic stability program more effective? research on co-simulation of electronic stability program.

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Proceedings of 2019 2nd international conference on signal processing and machine learning, spml 2019

Proceedings of 2019 2nd International Conference on Signal P...

引用

2nd international conference on signal processing and machine learning, spml 2019

ISBN: (纸本)9781450372213

The proceedings contain 19 papers. The topics discussed include: the development and trend of ECG diagnosis assisted by artificial intelligence;learning how to avoiding obstacles for end-to-end driving with conditional imitation learning;multi-scale fusion and channel weighted CNN for acoustic scene classification;multi-source radar data fusion via support vector regression;multi-scale deep convolutional nets with attention model and conditional random fields for semantic image segmentation;discrete sidelobe clutter determination method based on filtering response loss;deep neural network-based scale feature model for BVI detection and principal component extraction;and an attention-enhanced recurrent graph convolutional network for skeleton-based action recognition.

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THE 2nd CLARITY PREDICTION CHALLENGE: A machine learning CHALLENGE FOR HEARING AID INTELLIGIBILITY PREDICTION 49

THE 2ND CLARITY PREDICTION CHALLENGE: A MACHINE LEARNING CHA...

引用

49th IEEE international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Barker, Jon Akeroyd, Michael A. Bailey, Will Cox, Trevor J. Culling, John F. Firth, Jennifer Graetzer, Simone Naylor, Graham Univ Sheffield Dept Comp Sci Sheffield S Yorkshire England Univ Nottingham Sch Med Nottingham England Univ Salford Acoust Res Ctr Salford Lancs England Cardiff Univ Sch Psychol Cardiff Wales

ISBN: (纸本)9798350344868;9798350344851

This paper reports on the design and outcomes of the 2nd Clarity Prediction Challenge (CPC2) for predicting the intelligibility of hearing aid processed signals heard by individuals with a hearing impairment. The challenge was designed to promote new approaches for estimating the intelligibility of hearing aid signals that can be used in future hearing aid algorithm development. It extends an earlier round (CPC1, 2022) in a number of critical directions, including a larger dataset coming from new speech intelligibility listening experiments, a greater degree of variability in the test materials, and a design that requires prediction systems to generalise to unseen algorithms and listeners. This paper provides a full description of the new publicly available CPC2 dataset, the CPC2 challenge design, and the baseline systems. The challenge attracted 12 systems from 9 research teams. The systems are reviewed, their performance is analysed and conclusions are presented, with reference to the progress made since the earlier CPC1 challenge. In particular, it is seen how reference-free, non-intrusive systems based on pre-trained large acoustic models can perform well in this context.

关键词： speech-in-noise speech intelligibility hearing aids hearing loss machine learning

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Use XGBOOST to Predict the Rental Based on Airbnb Open Data 2

Use XGBOOST to Predict the Rental Based on Airbnb Open Data

引用

2nd international conference on signal processing and machine learning, conf-spml 2022

作者： Yao, Suhang School of International College Beijing University of Posts and Telecommunications Beijing100876 China

XGBOOST is a considerably effective method for machine learning, which performs well in all kinds of competition. Using a dataset from Airbnb Open data, the paper examines extent of factors affecting the rental and what is the future trend of rental by applying XGBOOST and other machine learning methods. The various features of data are visualized for further procession, and knowledge transfers between the statistic of hyperparameters of XGBOOST and test error has been cognized, which are illustrates by the merging and collapsing of different hyperparameters. Results show that the data pre-processing significantly influence on the precision of prediction. Also, variation of distinct hyperparameters impacts test error on this dataset in varying degrees, and the most appropriate figure of them has been confirmed by making comparisons recursively. Finally, general ideas of data preprocessing and hyperparameters tuning are proposed. © 2022 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0)

关键词： Data Preprocessing machine learning Prediction on Rental Visualization XGBOOST

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A Two-step Rumor Detection and Classification Method Using machine learning 2

A Two-step Rumor Detection and Classification Method Using M...

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2nd international conference on signal processing and machine learning, conf-spml 2022

作者： Pan, Borui Lyle School of Engineering Southern Methodist University DallasTX United States

The spread of the Internet and mobile devices has made it easier, faster and more widely to disseminate information. But rumors also spread quickly through the Internet, which can have a big impact on people's lives and social stability, especially during the COVID-19 pandemic. Therefore, this paper presents a Two-step model for solving this problem. A novel feature selection method is first proposed to find the suitable features. Then a two-step model is offered to detect and classify COVID-19 rumors simultaneously. Finally, the analysis of the proposed method is described in detail as well. © 2022 CEUR-WS. All rights reserved.

关键词： COVID-19 Multi-dimension Features Rumor Detection and Classification Two-step Model

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Melanoma Classification Using Feature Extraction Methods and machine learning Approaches 2

Melanoma Classification Using Feature Extraction Methods and...

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2nd international conference on signal processing and machine learning, conf-spml 2022

作者： He, Sihui Huang, Zheyang Zhong, Xinjie Faculty of Science Western University LondonN6A 37K Canada Faculty of Engineering The University of Sydney SydneyNSW2006 Australia School of Electrical Engineering Zhejiang University Hangzhou30058 China

Due the unbalanced melanoma data and the complexity and resolution of the melanoma image backgrounds, classification of the melanoma regions is very challenging. In this paper, EffNet B5 models with different augmentation methods and machine learning models that use feature vectors from feature extraction method are proposed to solve the classification task. GLCM, LBP, SFTA, and ResNet18 are chosen as the feature extraction methods in this study, while SVM, Random Forest, and XGBoost are chosen to be the machine learning classification models. In this work, the most effective feature extraction algorithm and the classification algorithm with the best AUC scores are determined. © 2022 CEUR-WS. All rights reserved.

关键词： Classification Feature Extraction machine learning Melanoma

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Multi-Agent Reinforcement learning Based on Cross Task Information Sharing 2

Multi-Agent Reinforcement Learning Based on Cross Task Infor...

引用

2nd international conference on signal processing and Intelligent Computing, SPIC 2024

作者： Gao, Yu Zhu, Lizhong Liu, Yunting Yang, Jiaming Harbin First Machinery Group Corporation Harbin China Shenyang Ligong University Shenyang China

ISBN: (纸本)9798350368888

This article selects StarCraft as the experimental environment, which has a complex and vast action and state space, making it difficult for intelligent agents to make optimal decisions. In this article, the sharing of parameter experience values among multiple agents in a single task is used to enhance the collaboration ability between agents and avoid repetitive learning of basic strategies. In addition, after distilling and refining the core strategies of individual tasks, policy interaction is carried out in multiple tasks of the same type. By establishing a joint objective function for multiple tasks and alternating optimization for mutual constraints and updating iterations, information exchange between cross task multi-agent systems is completed, solving the problem of difficulty in obtaining effective data in complex scenarios and improving the learning efficiency of micro unit joint decision-making. This paper also adds qualification tracking to solve the problem of reward delay at the initial stage of model training, and uses course transfer learning to complete incremental joint training. © 2024 IEEE.

关键词： Adversarial machine learning

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Comprehending and Detecting Vulnerabilities using Adversarial machine learning Attacks 2

Comprehending and Detecting Vulnerabilities using Adversaria...

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2nd international conference on Artificial Intelligence and signal processing, AISP 2022

作者： Mehta, Charmee Harniya, Purvi Kamat, Sagar Department of Information Technology K J Somaiya College of Engineering Mumbai India

ISBN: (纸本)9781665442909

In today’s world, machine learning is an emerging technology which is being used extensively in different domains. In order to offer effective solutions in the broad area of computer security with the use of machine learning (ML) models, applications which identify and protect against potential adversarial attacks are employed. In the ever-growing field of adversarial machine learning, attackers with different extents of accessibility to a machine learning model can launch a number of attacks to achieve their goals. Concurrently, ML models and algorithms are quite susceptible to various cybersecurity threats. In this paper, an in-depth survey has been carried out on the impact of cybersecurity in machine learning and the adversarial attacks which can be encountered in a ML based system. © 2022 IEEE.

关键词： Cybersecurity

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Supervised machine learning approaches for medical data classification 2

Supervised machine learning approaches for medical data clas...

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2nd international conference on Artificial Intelligence and signal processing, AISP 2022

作者： Dalai, A.K. Jena, A.K. Ramana, B.V. Maneesha, B. Panda, Nibedan School of Computer Science and Engineering VITAP University India Department of Information Technology Aditya Institute of Technology and Management AP Tekkali532201 India Department of CSE Presidency University Yelahanka Bengaluru560064 India

ISBN: (纸本)9781665442909

Recently there is an emergent curiosity among researchers to apply machine learning algorithms over diversified real world complications to get simpler *** notion behind this briefing is to represent the basic machine learning algorithms and its applicability in current research. Broadly machine learning algorithms falls to the category of either supervised or unsupervised learning technique. In this paper we have discussed supervised machine learning techniques with its simplicity to apply over various problem areas and simultaneously the challenges for such algorithms. Furthermore SVM and Random Forest (RF) are utilised learn, categorise, and compare cancer, liver, diabetes, iris, and heart data in this study. For all considered data sets, the results of SVM and RF are compared. The results are properly analysed in order to develop better prediction learning techniques. © 2022 IEEE.

关键词： Support vector machines

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Comprehensive Survey on Video Denoise Methods 2

Comprehensive Survey on Video Denoise Methods

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2nd international conference on signal processing and machine learning, conf-spml 2022

作者： Bi, Zhanming International College Guangxi University Nanning China

Today we have entered a smart information age on a variety of carriers, especially images and videos as carriers of information are widely used in our lives. There is a strong demand for clearer images and more visual enjoyable videos. However, in modern society, images and videos are inevitably affected by noise, which leads to a deterioration in image quality. We therefore need to reduce noise without losing as much of the image characteristics (edges, corners, resolution) as possible. So far, researchers have proposed a considerable number of classes of noise removal methods, each with its own advantages and disadvantages. In this paper, we present several traditional denoising methods as well as neural network-based denoising methods, supplemented by additional denoising methods based on human vision. We present the main formulations of each algorithm and give experimental results, as well as analyzing the advantages and disadvantages of them. We conclude with ideas for future trends in denoising methods finally. © 2022 CEUR-WS. All rights reserved.

关键词： CNN Denoise Methods HVS Perceptual Quality Video and Image Noise

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