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检索条件"任意字段=7th International Conference on Machine Learning and Soft Computing, ICMLSC 2023"

共 787 条记录，以下是11-20 订阅

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Deep learning Approach for Arrhythmia Detection Using STFT-Based Spectrogram 15th

Deep Learning Approach for Arrhythmia Detection Using STFT-B...

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15th international conference on soft computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired computing, NaBIC 2023

作者： Chopra, Shreya Sambyal, Nitigya Bajaj, Anu Thapar Institute of Engineering and Technology Patiala India

ISBN: (纸本)9783031810794

Arrhythmia is a medical condition where a patient experiences an irregular heartbeat. the presence of arrhythmia indicates a much more serious heart condition, which, if not treated, may worsen and even lead to death. Hence, there is a requirement for a system that can detect the presence of such a condition. the patient’s ECG recordings were first converted into a spectrogram using short-time Fourier transform. these resultant spectrogram images were given as input to VGGNet models for arrhythmia classification. the VGG16 model exhibited 98.87% accuracy, 99.18% precision, 99.14% recall, and 97.94% specificity. On the other hand, VGG19 achieved 98.83% accuracy, 99.18% precision, 99.18% recall, and 97.95% specificity. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electrocardiography

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WINS - CVMLH 2023 - Joint Proceedings of the Workshop on Intelligent Systems, WINS 2023 and the Workshop on Computer Vision and machine learning for Healthcare, CVMLH 2023, Co-located with CODD 100 - 7th international conference on computing Sciences, ICCS 2023

WINS - CVMLH 2023 - Joint Proceedings of the Workshop on Int...

引用

2023 Joint of the Workshop on Intelligent Systems, WINS 2023 and the Workshop on Computer Vision and machine learning for Healthcare, CVMLH 2023

the proceedings contain 10 papers. the topics discussed include: comparative analysis on enhanced image compression algorithms;military detection during close combat situations and border monitoring using adaptive customized convolutional neural network;machine learning-based rainfall prediction for various regions;methodology for the registration of images and their inclusion in medica image management systems, based on the DICOM protocol;mobile application success prediction using machine learning;a secure mobile application for speech to text conversion using artificial intelligence techniques;a comparative study of heart disease prediction using machine learning;method for teaching medical imaging diagnosis, applying virtual reality techniques;heart disease prediction using machine learning method – a review article;and web system for the management of clinical laboratory test, applied to health facilities in rural areas.

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Interior Noise Optimization of Powertrain Induced Vibrations for an Electric Vehicle Using machine learning Methods

Interior Noise Optimization of Powertrain Induced Vibrations...

引用

7. internationale ATZ-Fachtagung Fahrzeugakustik - 7th international ATZ conference on Vehicle Acoustics, 2023

作者： Fiedler, Uwe Visser, René Kreissig, Volker CDH-AG Stuttgart Germany Mercedes-Benz AG Sindelfingen Germany

ISBN: (纸本)9783658465544

Numerical analysis plays a significant role in the development of passenger comfort in modern vehicles. Customer perception of quality and comfort is largely influenced by its vibro-acoustic properties. To develop well engineered and robust vehicles that meet such customer requirements, many vibro-acoustic aspects and loading conditions need to be accounted for. this is only feasible when numerical analysis techniques, like the Finite Element Method, are applied over all design stages including the early concept phase. As vibro-acoustic behaviour is typically of global nature, meaning vibrations of different components mutually interact throughout the whole system, complex full-vehicle analyses accounting for all components as well as the interior air cavity are required. Moreover, with the rise of electric vehicle development, the rotational motor speeds and hence the excitation frequencies of interest increase dramatically compared to conventional combustion engine based vehicles. Such large-scale simulations challenge even today’s high-performance computing hardware and software to its limits. For this reason, efficient numerical reduction schemes of the underlying systems of equations are essential to avoid excessive evaluation times and costs, which becomes especially relevant when optimization approaches are considered. the presented paper illustrates the structure-borne acoustic analysis and optimization procedure of an electric full vehicle model subject to high frequency motor load cases. To reduce simulation times, the Frequency Response Function-Substructure reduction technique is applied to model components that are not modified in the successive optimization procedure enabling a very efficient reuse of precomputed data in each iteration cycle. As the focus in this work is on modifications in the powertrain, sub-systems like trimmed body and chassis are viable to be reduced. A new machine learning based optimization approach is applied that combines the

关键词： Convergence of numerical methods

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Improved Multilayer Perceptron Neural Networks Weights and Biases Based on the Grasshopper optimization Algorithm to Predict Student Performance on Ambient learning 23

Improved Multilayer Perceptron Neural Networks Weights and B...

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7th international conference on machine learning and soft computing, icmlsc 2023

作者： Michira, Mercy K. Rimiru, Richard M. Mwangi, Waweru R. School of Computing and Information Technology Jomo Kenyatta University of Agriculture and Technology Kenya School of Computing and Information Jomo Kenyatta University of Agriculture and Technology Kenya

ISBN: (纸本)9781450398633

the classification accuracy of a multi-layer Perceptron Neural Networks depends on the selection of its parameters such the connection weights and biases. Generating an optimal value of these parameters requires a suitable algorithm to train the multilayer perceptron neural networks. this paper presents swam based Grasshopper optimization algorithm that optimizes the connection weights and biases of Multilayer Perceptron Neural Network. Grasshopper optimization algorithm is a swarm-based metaheuristic algorithm applied for accurate learning of Multilayer Perceptron Neural Networks. the proposed Multilayer Layer Perceptron Neural Networks based on the Grasshopper Optimization Algorithm was validated using a Genetic algorithm and Backpropagation algorithm this algorithm has proved to perform satisfactorily performance by escaping local optimal and its fast convergence. © 2023 Owner/Author.

关键词： Multilayer neural networks

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LDRC: Long-tail Distantly Supervised Relation Extraction via Contrastive learning 23

LDRC: Long-tail Distantly Supervised Relation Extraction via...

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7th international conference on machine learning and soft computing, icmlsc 2023

作者： Li, Tingwei Wang, Zhi Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781450398633

Long-tail problem is one of the major challenges in distantly supervised relation extraction. Some recent works on the long-tail problem attempt to transfer knowledge from data-rich and semantically similar head classes to data-poor tail classes using a relation hierarchical tree. these methods, however, are based on the assumption that long-tail and head relations have a strong correlation, which does not always hold true, and the model's ability to learn long-tail relations is essentially not improved. In this paper, a novel joint learning framework that combines relation extraction and contrastive learning is proposed, allowing the model to directly learn the subtle differences between different categories to improve long-tail relation extraction. Experimental results show that our proposed model outperforms the current state-of-the-art (SOTA) model on various mainstream datasets. © 2023 ACM.

关键词： Extraction

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Prediction Interval Construction for Multivariate Point Forecasts Using Deep learning 7

Prediction Interval Construction for Multivariate Point Fore...

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7th international conference on soft computing and machine Intelligence (ISCMI)

作者： Mathonsi, thabang van Zyl, Terence L. Univ Witwatersrand Sch Comp Sci & Appl Math Johannesburg South Africa Univ Johannesburg Inst Intelligent Syst Johannesburg South Africa

ISBN: (纸本)9781728175591

It has been demonstrated that deep learning can in certain instances outperform traditional statistical methods at forecasting. this outperformance, however, does not address the challenge of quantifying forecast uncertainty (prediction intervals). Artificial neural networks often do not have probability distributions linked to their point forecasts, which complicates the construction of prediction intervals. In this paper, we explore computational methods of artificially deriving said probability distributions and constructing prediction intervals. the point forecasts, and the associated constructed prediction intervals are compared to those produced by means of the oft-preferred traditional statistical counterparts. Our finding is deep learning outperforms (or al the very least is competitive to) the former. We focus on three deep learning architectures, namely, cascaded neural networks, reservoir computing and long short-term memory recurrent neural networks.

关键词： Deep learning Forecasting Multivariate time series Prediction intervals

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Research on License Plate Recognition Based on Deep learning in Complex Scenarios 23

Research on License Plate Recognition Based on Deep Learning...

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7th international conference on machine learning and soft computing, icmlsc 2023

作者： Tang, Yinqing Yu, Benguo Liu, Fengning Wang, Anran Hainan Medical University China

ISBN: (纸本)9781450398633

the license plate angle is unfixed, the vehicle position is ununiform, and the picture is not sufficiently illuminated which leads to the decrease of license plate recognition accuracy. In order to improve the accuracy of license plate recognition, a deep learning-based license plate recognition method is proposed. For license plate location problem, YOLOv3 algorithm is used. the algorithm is more capable of recognizing small targets and is suitable for license plate location recognition that requires precise positioning. For the problem of license plate character recognition, the CNN plus multitask recognition method is proposed for recognition. the experimental results show that the accuracy of the license plate recognition method proposed in this paper reaches 96%, and the intelligent license plate recognition is realized. © 2023 ACM.

关键词： License plates (automobile)

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A Multiscale Approach to Detect the Semantics of Locomotion without Positioning Information 7

A Multiscale Approach to Detect the Semantics of Locomotion ...

引用

7th international conference on soft computing and machine Intelligence (ISCMI)

作者： Qu, Chen Yang, Jinxin Si, Guangwen Zhao, Yufei Zhao, Yiren Yang, Wen-Chi NeuHelium Co Ltd Comp Vis Lab Shanghai 200090 Peoples R China

ISBN: (纸本)9781728175591

the detection of locomotion patterns and trajectory semantics has long relied on the positioning information. however, positioning information can be unavailable in specific scenarios. In addition, the position values are sensitive to rotation and hence a bias can exist during the training process in deep learning models. this paper introduces an alternative model that classifies trajectories based on accelerometers, instead of positioning systems. We built up a convolutional neural network that inputs the degree of velocity and turning angles at several time scales and converts this information into a semantic class. We examined the model in a simulated environment and also a benchmark task. this model has exhibited a competitive performance even compared with those models based on positioning information.

关键词： locomotion semantics trajectory classification acceleration sensors rotational invariance deep learning

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Comparative Analysis of Catastrophic Forgetting in Metric learning 7

Comparative Analysis of Catastrophic Forgetting in Metric Le...

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7th international conference on soft computing and machine Intelligence (ISCMI)

作者： Huo, Jiahao van Zyl, Terence L. Univ Witwatersrand Sch Comp Sci & Appl Math Johannesburg South Africa Univ Johannesburg Inst Intelligent Syst Johannesburg South Africa

ISBN: (纸本)9781728175591

Catastrophic forgetting in neural networks during incremental learning remains a challenging problem. Previous research investigated the catastrophic forgetting in fully connected networks with some earlier work exploring activation functions and learning algorithms. Applications of neural networks have been extended to include similarity and metric learning. It is of significant interest to understand how metric learning loss functions would be affected by catastrophic forgetting. Our research investigates catastrophic forgetting for four well-known metric-based loss functions during incremental class learning. the loss functions are angular, contrastive, center, and triplet loss. Our results show that the rate of forgetting is different across loss functions on multiple datasets. Triplet loss was least affected followed by contrastive, center, and angular loss. Center and angular loss produce better embeddings on difficult tasks when trained on all available training data, however. they are the least robust to forgetting during incremental class learning. We argue that triplet loss provides the ideal middle ground for future improvements.

关键词： Catastrophic forgetting incremental learning metric learning convolutional neural network (CNN)

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A Traffic Sign Recognition Method Based on YOLOv5 Deep learning Algorithm 23

A Traffic Sign Recognition Method Based on YOLOv5 Deep Learn...

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7th international conference on machine learning and soft computing, icmlsc 2023

作者： Tang, Yinqing Yu, Benguo Wang, Anran Liu, Fengning Hainan Medical University China

ISBN: (纸本)9781450398633

Aiming at the problems of low accuracy and slow recognition efficiency of the traditional traffic sign recognition algorithm in complex environment, a deep learning traffic sign recognition method based on YOLOv5 is proposed. Firstly, the Chinese traffic sign data set TT100K is randomly divided into training set and test set. Convolutional neural network YOLOv4 and convolutional neural network YOLOv5 are used to train respectively on the training set, so as to build the prediction model of traffic signs. then the trained model is validated on the test set. through the evaluation of the experimental, it is found that compared with YOLOv4 model, YOLOv5 model has higher recognition accuracy and faster recognition speed. © 2023 ACM.

关键词： Traffic signs

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