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

共 34017 条记录，以下是1171-1180 订阅

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Enhancing Convolutional Models for Indoor Radio Mapping via Ray Marching

Enhancing Convolutional Models for Indoor Radio Mapping via ...

引用

2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wu, Mengfan Skocaj, Marco Boban, Mate Munich Research Center Huawei Technologies Germany School of Electrical Engineering and Computer Science TU Berlin Germany

ISBN: (纸本)9798350368741

In this paper, we present an enhanced convolutional model for indoor radio map generation, focusing on the integration of a novel ray-marching feature. We describe our machine learning pipeline developed for the ICASSP 2025 Signal Processing Grand Challenge, specifically the First Indoor Pathloss Radio Map Prediction Challenge. Our method incorporates a ray-marching feature that, combined with a UNet architecture enhanced by dilated convolution layers, significantly improves indoor pathloss prediction accuracy. Our approach achieved a 3rd-place ranking in the challenge with a weighted RMSE of 10.33 on the test dataset. © 2025 IEEE.

关键词： feature engineering machine learning radio maps Wireless communications

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Deep Residual SVM: A Hybrid learning Approach to obtain High Discriminative Feature for Land Use and Land Cover Classification

Deep Residual SVM: A Hybrid Learning Approach to obtain High...

引用

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

作者： Kumari, Neha Minz, Sonajharia School of Computer and Systems Sciences JNU New Delhi India

Classification of multispectral images is impacted by challenges such as inadequate training samples, limited ground truth, and complex spatiotemporal dependencies. The accuracy of classifiers due to the lack of training samples is also compounded by the class imbalance problem in spatial analytics. This paper proposes a novel idea of exploiting the deep residual networks trained on millions of images for high discriminative feature representations. These derived representations are classified using bias-corrected Adam optimizer-based Support Vector machine classifiers. The intuition behind this approach is the significance of representations in the last layers of the pre-trained network that contributes to the classification accuracy. These encapsulated representations from the pre-trained network can outperform traditional feature representations. Residual networks solved the vanishing gradients problem in deep learning and improved classification accuracy. Hence, this work explores the combined potential of the representation power of deep residual pre-trained networks and the classification ability of Support Vector machines. The model implementation experiments have been conducted using two publicly available benchmark datasets, Eurosat and UC Merced for Land Use and Land Cover Classification. The proposed model DRSVM (Deep Residual Support Vector machines) demonstrated higher efficiency in terms of computational parameters and time complexity than conventional convolutional neural networks, pre-trained networks, and Support Vector machines with comparable accuracy. data augmentation techniques are used further for enhancing the performance of the model. Conventional supervised deep learning algorithms like CNN overfit in dense layers in case of inadequate training data. This hybrid approach alleviates overfitting issues by eliminating dense layers and replacing the Softmax function with Support Vector machines for classification. © 2022 Elsevier B.V.. All righ

关键词： Support vector machines

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Damage evaluation of concrete beams using forced vibration testing and machine learning 11th

Damage evaluation of concrete beams using forced vibration t...

引用

11th international conference on Bridge Maintenance, Safety and Management, IABMAS 2022

作者： Fujisaku, Yusuke Naito, Hideki Japan, Sendai Inaba, Kohko Department of Civil and Environmental Engineering Graduate School of Engineering Tohoku University Japan Railway Technical Research Institute Tokyo Japan

ISBN: (纸本)9781032345314

A decrease in skilled infrastructure inspectors and the cost of maintenance are big issues in Japan. Thus, an effective, automated inspection system is much needed. In this study, local vibration testing, a nondestructive testing method, coupled with machine learning, was utilized. Local vibration testing has the benefit of detecting damage that exists in deep layers because the waves travel through-thickness of concrete members. Using a portable vibrator, local vibration testing was conducted on reinforced concrete (RC) beams to detect the location of damages within. The RC beam specimens had two types of damages: flexural cracks and shear cracks. Results showed that the damaged conditions' frequency responses varied from that of the intact condition. Additionally, high abnormality degrees were detected around the crack region. This implies that the location of damages can be identified from the data produced by local vibration testing. Though this method alone yields results, introducing automation would further increase the diagnostic efficiency. Therefore, machine learning was introduced. A non-supervised learning method was applied. Using this method, the damages were identified with a very high accuracy rate. This indicates that the combination of local vibration testing and non-supervised learning can produce an effective, automated inspection system for the integrity diagnosis of RC beams. © 2022 the Author(s).

关键词： Frequency response

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machine learning Methods for Temperature Prediction of Autonomous Underwater Vehicles' Battery Pack

Machine Learning Methods for Temperature Prediction of Auton...

引用

international conference on Autonomous Unmanned Systems (ICAUS)

作者： Li, Bo Wang, Mou Mao, Zhaoyong Song, Baowei Tian, Wenlong Sun, Qixuan Wang, Wenxin Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Peoples R China AVIC Xian Flight Automat Control Res Inst Xian 710065 Peoples R China

ISBN: (纸本)9789819904815;9789819904792;9789819904785

Battery pack layout is of great significance to enhance the thermal behavior of autonomous underwater vehicles(AUVs). Because battery pack layout is a high-dimensional and nonlinear problem, there is few research on this topic at present. In order to more accurately predict the maximum temperature (MT) and temperature difference (TD) for different battery pack layouts, two machine learning surrogate models were proposed in this paper, including support vector machine (SVM) and the feed-forward fully-connected neural network (FFN). Tens of thousands of battery pack layout scheme databases were obtained through the finite element method. Then, the machine learning based methods were used to predict the MT and TD of the battery pack. The simulation results of this paper showed that both FFN and SVM have low mean absolute percentage error (MAPE) and mean square error (MSE), which means FFN and SVM can accurately predict the temperature. Meanwhile, it can be found that SVM has more advantage in small-scale problem. The methods in this paper can provide guidance for temperature prediction of AUV's battery pack layout.

关键词： Autonomous underwater vehicle Battery pack layout Temperature prediction Support vector machine Feed-forward fully-connected neural network

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Real Time Botnet Detection System Based on machine learning Algorithms 2

Real Time Botnet Detection System Based on Machine Learning ...

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2022 2nd conference on High Performance Computing and Communication engineering, HPCCE 2022

作者： Fang, Wenhao St. George’s International School Chem. de Saint-Georges 19 Montreux1815 Switzerland

ISBN: (纸本)9781510663374

Studies towards botnet activity detection methods have become a major focus over the years since many concerns have been raised due to botnets. The detection against Domain Name System (DNS) queries, which bots widely used for their connection with the Command&Control (C&C) server, was considered the main method of botnet prevention. However, this method does not apply to Domain Generation Algorithm (DGA)-based botnets, which dynamically generate a large number of Non-existent domains, combined with fast flux. The approach of using machine learning in which the models are trained to study the characteristics of DGA domains is thought to be more effective than the methods that rely solely on a set of fixed rules. For such reasons, this paper will explore a novel method of botnet detection using machine learning. The effectiveness of this method is to be assessed in a system we designed for botnet detection, fed with truth marked dataset. The results indicate a high performance of our method with 95.3% in accuracy and a 5.4% false positive rate (FPR). © 2023 SPIE.

关键词： Botnet

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UNSUPERVISED machine learning: A WELL PLANNING TOOL FOR THE FUTURE 41

UNSUPERVISED MACHINE LEARNING: A WELL PLANNING TOOL FOR THE ...

引用

ASME 41st international conference on Ocean, Offshore and Arctic engineering (OMAE)

作者： Batruny, Peter Robinson, Tim PETRONAS Kuala Lumpur Malaysia Exebenus Stavanger Norway

ISBN: (纸本)9780791885956

In recent years, the industry has sought insights from abundant data generated by drilling operations. One of the key focus areas is the rate of penetration (ROP) which impacts costs directly, and emissions indirectly. Previous work has succeeded in predicting and optimizing ROP, however was limited to specific fields and small-scale applications. This limitation stems from unobserved information between different fields or operations that often impacts model usability. This paper provides a new way of well planning by leveraging the power of unsupervised machine learning to deliver higher drilling efficiency, lower costs, and less uncertainty. Unsupervised machine learning techniques are used to infer information about drilling operations from real-time data that is not directly measured. Certain well types seem to be separable in low dimensions, based on qualitative interpretation of clusters visualized in 2D, and according to analyzing cluster membership based on which wells the data came from, and by associating common characteristics of wells within clusters using external information. This work introduces a novel approach to collect, separate, and extract value from data that is otherwise unused. data synthesized by Variational Autoencoders could be used for enhanced well planning, sold as a standalone product, or shared between industry players with reduced privacy concerns, increasing the wealth of data available.

关键词： Unsupervised machine learning Drilling Operations Artificial Intelligence

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Supervised machine learning approaches for medical data classification 2

Supervised machine learning approaches for medical data clas...

引用

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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SINO-CT-FUSION-NET: A LIGHTWEIGHT DEEP learning FRAMEWORK FOR DETECTION AND CLASSIFICATION OF INTRACRANIAL HEMORRHAGES 31

SINO-CT-FUSION-NET: A LIGHTWEIGHT DEEP LEARNING FRAMEWORK FO...

引用

2024 international conference on Image Processing

作者： Sindhura, Chitimireddy Yalavarthy, Phaneendra K. Gorthi, Subrahmanyam Indian Inst Technol Tirupati Dept Elect Engn Chindepalle Andhra Pradesh India Indian Inst Sci Dept Computat & Data Sci Bengaluru India

ISBN: (纸本)9798350349405;9798350349399

Deep learning has significantly improved medical diagnostics with its ability to learn the underlying complex patterns. A sinogram contains a sequence of X-ray projections of the patient into a lower dimensional space from different viewing angles, and a CT image is obtained as a result of applying reconstruction algorithms on the sinograms acquired by the scanner. While CT images are commonly used for automated diagnosis, recent developments have demonstrated that sinogram-based approaches can provide results on par with CT-based methods. This work leverages from the advantages of both approaches through the fusion of features learned from both those images. This paper presents a new lightweight deep learning model to detect and classify Intracranial Hemorrhages (ICH) through the fusion of high-level features learned from both sinogram and CT images. The proposed method is trained and evaluated on the publicly available RSNA ICH dataset. Furthermore, we analyze its multi-label classification capability in categorizing hemorrhages into five types. The proposed fusion model outperformed both CT-based and sinogram-based methods in general, it is particularly useful when there is less annotated training data and limited computational resources. The code and data can be found at https://***/sindhura234/SinoCTFusionNet

关键词： CT Sinogram Deep learning machine learning Computer Aided Diagnosis Intracranial Hemorrhage

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Prediction of Disc Cutter Replacement of Tunnel Boring machine Using Denoising Auto Encoder 31st

Prediction of Disc Cutter Replacement of Tunnel Boring Machi...

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31st international conference on Flexible Automation and Intelligent Manufacturing

作者： Liu, Yang Huang, Shuaiwen Wang, Di Zhu, Guoli Zhang, Dailin Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

ISBN: (纸本)9783031176289;9783031176296

While a tunnel boring machine (TBM) is working, rocks are crushed into pieces by disc cutters which often fail during construction. To replace disabled cutters timely, the condition of the cutters needs to be checked regularly. However, this is time-consuming and uneconomical. In this paper, a denoising auto encoder (DAE) model is proposed to judge whether TBM disc cutters need to be replaced. First of all, the field data related to dis cutter status are selected as inputs. Then, the cutter conditions can be learned automatically base on a DAE model. Test result on a water transport tunnel shows that the proposed model can obtain an average accuracy of 99.7% and an average F1 score of 99.4% on field data prediction. Compared with other machine learning and deep learning models, proposed method reduces the need of manual data denoising and feature extraction.

关键词： Tunnel boring machine Disc cutter Deep learning Denoising auto encoder

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An Investigation of the Bibliometric Landscape of the Many Facets of machine learning from the Years 2000 to 2022

An Investigation of the Bibliometric Landscape of the Many F...

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2022 international conference on Computational Modelling, Simulation and Optimization, ICCMSO 2022

作者： Sharma, Neeru Chadha, Raman Chandigarh University UIE Punjab India

ISBN: (纸本)9798350332889

machine learning as an emerging and applicable learning plays a vital role in the world of Computer Science. machinelearning has attracted much attention in the last decade. With the development of machine learning, huge researchers and publications opted for this as the path. This paper is about the bibliometric analysis that is deployed from the year 2000 to 2022. The main three fundamental factors that guide the progress of machine learning are data, computing, and algorithmic advances. Mainly there are four different types of machine learning which accomplish Supervised ML, Unsupervised ML, Semi-Supervised ML, and Reinforcement machine learning which is used in different areas and applications. This paper is mainly focused on what are the major areas where machine learning has used and what are the minimal area where there is still a requirement of using machine learning is to be used. © 2022 IEEE.

关键词： Reinforcement

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