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检索条件"任意字段=5th International Conference on Machine Learning and Computer Application, ICMLCA 2024"

共 2889 条记录，以下是11-20 订阅

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Research on Pavement Distress Detection in UAV Images Based on Deep learning 5

Research on Pavement Distress Detection in UAV Images Based ...

引用

5th international conference on machine learning and computer application, icmlca 2024

作者： Shuai, Hongbo Ma, Lianxia Zhang, Yinbo Guo, Dudu He, Rong School of Intelligent Manufacturing Modern Industry Xinjiang University Urumqi China Xinjiang Communications Construction Group Co. LTD. Urumqi China School of Transportation Engineering Xinjiang University Urumqi China

ISBN: (纸本)9798331530334

With the rapid development of road transportation, pavement damage detection becomes very important. In this study, a pavement damage detection method based on UAV images is proposed, using an improved YOLOv5 algorithm, combined with Local window attention, to improve the accuracy and efficiency of detection. the experimental results show that compared with the original YOLOv5, the improved model has a 7.8%increase in the mean accuracy (mAP), which proves its effectiveness in pavement damage detection. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

来源：评论

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Few-Shot Deep learning-Based Detection Method for Sonar Pulse Signals 5

Few-Shot Deep Learning-Based Detection Method for Sonar Puls...

引用

5th international conference on machine learning and computer application, icmlca 2024

作者： Zhou, Leilei Liu, Zhen Zhang, Jing Zhao, Degang Zhang, Zongtang PLA Naval Submarine Academy Shandong Qingdao China IFLYTEK AnHui HeFei China

ISBN: (纸本)9798331530334

the active sonar systems of different platforms vary in terms of transmission power, transducer size, and operational mode, leading to distinctive sonar pulse parameters. these pulse parameters serve as essential features for identifying platform types. Detecting and estimating the sonar pulse parameters emitted by targets is crucial for accurate target recognition. this paper proposes a detection and parameter estimation method for subsequent pulses based on the concept of few-shot learning. By utilizing the first few labeled pulses or pulse trains, we adopt a U-Net-based embedded learning framework for frame-level pulse detection and parameter estimation. Experimental results demonstrate that the proposed method achieves a high detection rate even under conditions of low signal-to-noise ratio and interference. © 2024 IEEE.

关键词： Sonar

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CAE-YOLOV8: Occlusion Object Detection Based on Improved YOLOv8 5

CAE-YOLOV8: Occlusion Object Detection Based on Improved YOL...

引用

5th international conference on machine learning and computer application, icmlca 2024

作者： Liu, Ruiyu Zhang, Hanlin Liu, Zhe Chen, Dan Maynooth international college Fuzhou university Fuzhou China College of Electrical Engineering and Automation Fuzhou University Fuzhou China

ISBN: (纸本)9798331530334

Occlusion recognition plays a vital role in smart manufacturing and industrial automation. this paper proposes an enhanced YOLOv8-based model designed to accurately recognize and locate occluded objects in industrial environments, assisting robots in precise grasping tasks. the model integrates the AKConv module and the Effciou loss function, which improve feature extraction and bounding box regression, especially in complex and highly occluded scenarios. Experimental results confirm that the modified YOLOv8s_Effciou model achieves a balanced performance across different levels of occlusion, showing notable robustness in challenging environments with high occlusion. © 2024 IEEE.

关键词： Object detection

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Research on Daylight Inside Buildings Based on machine learning 5

Research on Daylight Inside Buildings Based on Machine Learn...

引用

5th international conference on machine learning and computer application, icmlca 2024

作者： Chen, Chaofan Chen, Xiao College of Information Engineering Shanghai Maritime University Shanghai China

ISBN: (纸本)9798331530334

Daylight assessment ensures the quality of indoor environments and is crucial for health and thermal comfort. Traditional daylight simulation tools require significant computational resources and time. In the foreseeable future, more efficient and robust machine learning models should be used to develop more convenient programs. In this study, a machine learning model was developed to predict indoor illuminance in buildings, further considering the real-world feature of window transmittance for different building layouts. A method for locating planar vertices was also developed, improving accuracy. All data used for training and prediction were generated by the simulation software Honeybee. the results indicate that introducing more real-world features enhances the model's generalization capability, and the planar vertex location method can replace the uniform ray location method to achieve more accurate predictions. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

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Model Level Contrastive Federated learning with Differential Privacy 5

Model Level Contrastive Federated Learning with Differential...

引用

5th international conference on machine learning and computer application, icmlca 2024

作者： Liu, Peihao Xu, Yongliang Wang, Meiqing Cao, Peng School of Mathematics and Statistics Fuzhou University Fuzhou350108 China

ISBN: (纸本)9798331530334

Federated learning, as a typical paradigm of collaborative learning, effectively mediates the contradictions between model training and data privacy. However, the data heterogeneity and privacy leakage problems undermine the availability of federated learning. Existing studies focus solely on one of the problems. In this work, we manage to handle these two challenges simultaneously, by utilizing contrastive learning and differential privacy in local training, the federated learning system can stay safe and robust. the contrastive loss item pushes the local model towards the global model and a random noise generated by differential privacy is added to the model to protect the sensitive information hidden in the models. Extensive experiment results demonstrate that our method can exceed the baseline around 1%2% in term of test accuracy. © 2024 IEEE.

关键词： Federated learning

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Zero-Shot Low-Light-Level Image Enhancement Algorithm Based on Atmospheric Scattering Model 5

Zero-Shot Low-Light-Level Image Enhancement Algorithm Based ...

引用

5th international conference on machine learning and computer application, icmlca 2024

作者： Shi, Shijie Zhou, Dake Shao, Yiming College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9798331530334

Low-light image enhancement remains a pivotal area of focus within the realm of image processing. While current learning-based enhancement techniques have shown advancements in managing noise or color distortion, they often fail to strike an optimal balance between these two aspects and are constrained by the availability and quality of training data. In this research, we introduce a novel zero-shot learning method for low-light image enhancement, named ZRASM, which leverages the Atmospheric Scattering Model (ASM) and integrates a denoising network grounded in the Noise2Noise theory. ZRASM is capable of noise reduction and color restoration without the need for external training data, operating solely from a single input image. Our experimental results indicate that ZRASM surpasses several state-of-the-art models in terms of image enhancement performance. © 2024 IEEE.

关键词： Zero-shot learning

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Performance Evaluation of Multimodal Image Generation Algorithm Based on Deep learning 5

Performance Evaluation of Multimodal Image Generation Algori...

引用

5th international conference on machine learning and computer application, icmlca 2024

作者： Chen, Zexiang Guizhou University College of Computer Science and Technology Guiyang550025 China

ISBN: (纸本)9798331530334

With the rapid development of deep learning technology, multimodal image generation (MMIG) has become an important research direction in the field of computer vision. In practical applications, the quality of generated images and the consistency between modalities play a crucial role in the effectiveness of the generated model. the problems of poor structural similarity index and peak signal-to-noise ratio in traditional MMIG methods limit their application in complex scenes. this article aims to propose an improved MMIG algorithm based on Generative Adversarial Networks (GANs) to enhance the quality of generated images and consistency between modalities. this study adopts the framework of generative adversarial networks and designs a multimodal generator and discriminator to achieve joint modeling of text and image data. the generator integrates information from different modalities and uses a new loss function to strengthen the relationships between modalities, thereby generating more realistic and diverse images. the experimental results showed that the structural similarity index and peak signal-to-noise ratio of the generated images were significantly improved, with a maximum value of 0.8 for the former and an average value of 41.37 dB for the latter. this study provides an effective solution for MMIG. © 2024 IEEE.

关键词： Generative adversarial networks

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machine learning in Tennis Match Analysis: Predicting Score Point Victor and Momentum Shift 5

Machine Learning in Tennis Match Analysis: Predicting Score ...

引用

5th international conference on machine learning and computer application, icmlca 2024

作者： Hu, Jinming Yang, Xiaohua Huang, Zixuan Xie, Jinqi School of Computer and Communication Engineering Changsha University of Science & Technology Changsha China School of Mathematics and Computer Science Shanxi Normal University Taiyuan China

ISBN: (纸本)9798331530334

In tennis and other competitive sports, momentum plays a pivotal role, where actions like consecutive scoring can enhance a player's momentum and increase his chances of winning the match. Quantifying momentum and predicting match outcomes in real-time are critical for both scientific training and strategic decision-making in the sports industry. However, traditional forecasting approaches often overlook the cumulative nature of time-series data and lack quantitative analysis of the dynamic properties influencing match outcomes. In this study, we compare seven machine learning models across two key tasks: predicting the winner of the next point and identifying the critical moment of momentum transition. We introduce a novel momentum formula to quantify the win/loss trends throughout a match. Our experiments using data from the 2023 Wimbledon Championships, demonstrate up to 70% accuracy in predicting point winners using logistic regression (LR) and up to 97% accuracy in detecting momentum shifts via a light gradient boosting machine (LGBM). these findings underscore the significance of momentum in tennis and offer valuable insights for performance optimization and competitive strategy development. © 2024 IEEE.

关键词： Tennis

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Perturbation-Based SMOTE for Multi-Class Imbalanced Classification 5

Perturbation-Based SMOTE for Multi-Class Imbalanced Classifi...

引用

5th international conference on machine learning and computer application, icmlca 2024

作者： Zheng, Suyang Zhou, Kai Chen, Chouyong School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China School of Management Hangzhou Dianzi University Hangzhou China

ISBN: (纸本)9798331530334

Multi-class imbalanced classification is a challenge in machine learning. the classical synthetic minority oversampling technique (SMOTE) alleviates this challenge by balancing the class distribution. However, it tends to generate many noisy examples and synthesize some invalid examples. To address these issues, we propose a perturbation-based SMOTE method for multi-class imbalanced classification, which focuses on example perception in boundary regions. Different from the existing methods of randomly select anchor examples, we first consider the global class distribution, and select the examples that are difficult to classify near class boundaries as anchor examples. this ensures that synthetic examples are within class boundaries, avoiding invalid interpolation. then, the nearest neighbors are used to perturb the synthetic examples in the feature space, which avoids overlap between examples and increases the diversity of minority classes. Finally, iterative-partitioning filter is introduced to delete the existing noisy examples. Experiments with real datasets demonstrate that our proposed method achieves competitive performance. © 2024 IEEE.

关键词： Adversarial machine learning

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Evaluating the Effectiveness of Feature Selection and Explainable AI in Predicting Acute Myocardial Infarction Using machine learning Models 5

Evaluating the Effectiveness of Feature Selection and Explai...

引用

5th international conference on machine learning and computer application, icmlca 2024

作者： Wang, Yong Shen, Jiexuan Li, Shi Li, Xinglin Nneji, Grace Ugochi Monday, Happy Nkanta Chengdu University of Technology Chengdu610059 China HACE SOFTTECH Intelligent Computing Lab Lagos102241 Nigeria

ISBN: (纸本)9798331530334

Acute myocardial Infarctions (MIs), commonly known as heart attacks, are critical medical emergencies that often lead to severe complications, including post-myocardial infarction angina (PMIA). Early and accurate prediction of PMIA is crucial for timely intervention and improved patient outcomes. this study focuses on predicting PMIA using machine learning classifiers on the UCI myocardial infarction complication datasets, which includes 1700 patient samples with 111 clinical features. the dataset is balanced using Borderline Synthetic Minority Oversampling (Borderline-SMOTE) and standardized. Twelve classifiers are employed and the Shapley Additive exPlanations (SHAP) are used to interpret the best-performing models, revealing the decision-making processes. the SVM classifier achieved the highest cross-validated accuracy of 98.55%, demonstrating superior predictive capability. SHAP values provided insights into the importance of each feature, enhancing model interpretability and transparency. this approach highlights the significance of feature selection and explain ability in building trustworthy AI models for healthcare. the findings contribute to improved diagnosis and management of PMIA, underscoring the ethical adoption of AI in clinical settings. © 2024 IEEE.

关键词： Decision making

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