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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

共 4166 条记录，以下是121-130 订阅

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application of an Improved YOLOv9 algorithm for stem node detection in High-Resolution global sugarcane images 4

Application of an Improved YOLOv9 algorithm for stem node de...

引用

4th international conference on computer Vision, application, and Algorithm, CVAA 2024

作者： Chen, Weiqi Xie, Zhongjian Chen, Xinwei Zhang, Yaya Wan, Zhuxuan College of Electronic Information Guangxi Minzu University Nanning China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

this study aims to address the problem of poor stem node detection and recognition accuracy in high-resolution global sugarcane images, which is crucial for accurately identifying sugarcane stem node locations during automated sugarcane cutting. Since the sugarcane stem node occupies a very small area of the entire image, only about 0.2%, the feature blurring problem caused by scaling the original image to fit the network input size requirements often results in the loss of important information. In order to overcome this problem, this study conducts an in-depth study on the problem of small target feature loss caused by high-resolution images and proposes an improved algorithm STMFF-YOLOv9 (small target multiple feature fusion YOLOv9) based on YOLOv9-c. the algorithm uses a multi-scale stitching strategy to enhance the feature acquisition of small objects in image data training, designs an FDS (fusion down sampling) module with an improved feature fusion network downsampling process to reduce the loss of small target features, and implements a SE channel attention mechanism to recalibrate the fused channel features, thereby strengthening the focus on useful features. Experimental results showed that STMFF-YOLOv9 significantly improves the detection accuracy of sugarcane stem nodes in global images. Compared to YOLOv9-c, it achieves an 8.3% increase in precision, a 13.1% increase in recall rate, a 10.3% increase in mAP0.5, and a 9% increase in mAP0.5:0.95. Compared to other models, STMFF-YOLOv9 also demonstrates superior detection performance, effectively proving its capability to detect global sugarcane stem nodes. © 2025 SPIE.

关键词： Image enhancement

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Vehicle Information Monitoring and Identification on Highway Bridges Using an Array of Fiber Bragg Grating Sensors and Deep learning Algorithms 4

Vehicle Information Monitoring and Identification on Highway...

引用

4th international Computational Imaging conference, CITA 2024

作者： Duan, Zhenhui Zhang, Faxiang Jiang, Shaodong Sun, Zhihui Liu, Zhaoying Shen, Yongxu Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9781510688834

Traditional traffic management methods are increasingly inadequate in coping with the growing pressure on traffic systems. Consequently, there is a need for a more intelligent and efficient approach to monitoring and identifying vehicle information. Current vehicle monitoring methods often suffer from environmental interference and limited coverage.. To address these challenges, we propose a novel method for monitoring and identifying vehicle information on bridges using an array of Fiber Bragg Grating (FBG) strain sensors, combined with a hybrid deep learning algorithm based on Discrete Wavelet Transform (DWT) and Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM). Strain data were collected from a continuous hollow slab beam on a section of a highway bridge. Vehicles were classified and labeled using on-site video data to create a dataset, which was subsequently used to train and test the proposed CNN-LSTM model. the results demonstrate that the proposed method can accurately identify and classify the lane position, vehicle type, and vehicle speed category, achieving detection accuracies of 94.0%, 93.11%, and 90.67%, respectively. the DWT-CNN-LSTM method, utilizing the FBG strain sensor array, leverages existing strain monitoring platforms, supplements current traffic monitoring systems, and offers high efficiency and cost-effectiveness. this approach provides valuable data references for management and statistical analysis. © 2025 SPIE.

关键词： Discrete wavelet transforms

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Hands Free Mouse Using Facial Expression for Physically Disabled People

Hands Free Mouse Using Facial Expression for Physically Disa...

引用

Sentiment Analysis and Deep learning (ICSADL), international conference on

作者： Rushabhkumar Jain Murahari Naga Bhavana Aditya Kale Suvan Rastogi Payel thakur Department of Computer Engineering Pillai College of Engineering New Panvel

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

this research study explores the development of a Hands-Free Mouse system designed to empower physically disabled individuals through facial expression recognition. In the domain of assistive technology, where the focus is on enhancing accessibility, we delve into the novel application of facial expression recognition techniques. Commonly employed techniques in this domain include computer vision and machine learning algorithms, with diverse approaches such as deep neural networks and feature-based methods.

关键词： Performance evaluation Human computer interaction Hands Sentiment analysis machine learning algorithms Face recognition Scalability Lighting People with disabilities Reliability

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Predicting Stroke through Retinal Graphs and Multimodal Self-supervised learning

Predicting Stroke Through Retinal Graphs and Multimodal Sel...

引用

Workshop on Longitudinal Disease Tracking and Modeling with Medical Images and Data, LDTM 2024, 5th international Workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, 1st Workshop on machine learning for Multimodal/-sensor Healthcare Data, ML4MHD2024 and Workshop on Multimodal learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th international conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2024

作者： Huang, Yuqing Wittmann, Bastian Demler, Olga Menze, Bjoern Davoudi, Neda Department of Computer Science ETH Zürich Zürich Switzerland ETH AI Center ETH Zürich Zürich Switzerland Department of Quantitative Biomedicine University of Zurich Zürich Switzerland Harvard Medical School BostonMA United States

ISBN: (纸本)9783031845246

Early identification of stroke is crucial for intervention, requiring reliable models. We proposed an efficient retinal image representation together with clinical information to capture a comprehensive overview of cardiovascular health, leveraging large multimodal datasets for new medical insights. Our approach is one of the first contrastive frameworks that integrates graph and tabular data, using vessel graphs derived from retinal images for efficient representation. this method, combined with multimodal contrastive learning, significantly enhances stroke prediction accuracy by integrating data from multiple sources and using contrastive learning for transfer learning. the self-supervised learning techniques employed allow the model to learn effectively from unlabeled data, reducing the dependency on large annotated datasets. Our framework showed an AUROC improvement of 3.78% from supervised to self-supervised approaches. Additionally, the graph-level representation approach achieved superior performance to image encoders while significantly reducing pre-training and fine-tuning runtimes. these findings indicate that retinal images are a cost-effective method for improving cardiovascular disease predictions and pave the way for future research into retinal and cerebral vessel connections and the use of graph-based retinal vessel representations. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Self-supervised learning

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Research on the Optimization of Translation theory and Practice Based on Deep learning 24

Research on the Optimization of Translation Theory and Pract...

引用

proceedings of the 2024 4th international conference on Big Data, Artificial Intelligence and Risk Management

作者： Huijie Xu Guangxi Minzu University Nanning Guangxi China

ISBN: (纸本)9798400709753

To enhance the accuracy and fluency of machine translation, this paper analyzes the application of deep learning techniques in natural language processing, with a particular focus on machine translation. It adopts multilayer neural network models, such as the encoder-decoder architecture. through experimental validation, deep learning models, especially transformer models, demonstrate advantages in handling complex language patterns and improving translation quality. the results show that optimized deep learning models can effectively enhance the naturalness and semantic accuracy of translations, providing strong support for cross-language communication.

关键词： deep learning

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machine learning Based Critical Velocity Predictions in Slurry Flows

Machine Learning Based Critical Velocity Predictions in Slur...

引用

AI in Cybersecurity (ICAIC), international conference on

作者： Ravi Chandra Mathi Aparna Aravelli Dept. of Computer and Information Sciences Florida International University Miami FL USA Department of Mechanical Engineering North Carolina A&T State University Greensboro NC USA

ISBN: (数字)9798331518882

ISBN: (纸本)9798331518899

this paper examines the application of machine learning models for the prediction of critical velocity in Newtonian and non-Newtonian slurry flows. Experimental data and sensor measurements from established literature are used to train and test the models. Data cleaning, preprocessing, descriptive statistics, comparative analysis, and error evaluation have been conducted to make the models reliable. Several multi regression models have been trained and evaluated using original and synthetic data sets to improve generalization. Results look promising in their predictive ability, where different performances are realized for diverse slurry types and experimental conditions.

关键词： Analytical models Fluids Computational modeling machine learning Predictive models Data augmentation Data models Acoustics Slurries Synthetic data

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Research on the application of machine learning and automated financial decision-making models in accounting computerization systems 25

Research on the application of machine learning and automate...

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proceedings of the 2025 4th international conference on Big Data, Information and computer Network

作者： Jiaxin Zhang Lian Duan Lin Deng Wei Zhou Quanzhou economic and trade vocational and technical college Quanzhou Fujian China Jiujiang Vocation University Jiujiang Jiangxi China Jiangxi Ganbo Taxation Firm Jiujiang Jiangxi China Quanzhou economic and trade vocational and technical college Quanzhou FuJian China

ISBN: (纸本)9798400712425

computerized accounting systems are very popular in today's society. Now almost all enterprises, including individual businesses, are using this system. However, with the progress of the times, some computerized accounting systems are slightly old . this paper mainly adds machine learning functions to traditional computerized accounting systems, so that enterprises can make better decisions . Based on field research and data analysis, this paper selects a variety of machine learning algorithms such as random forests, linear regression, and neural networks, and combines them with the original computerized accounting system to better help enterprises make decisions . through empirical analysis, the random forest model performs well in multiple indicators such as accuracy, mean square error, and AUCROC. It can effectively identify high-risk financial decisions and provide enterprises with robust financial planning solutions. this paper shows that machine learning has great development prospects in automated financial decision-making models, but it is still in a stage to be developed. If it can be used in other fields, it can solve problems of different types of enterprises.

关键词： Accounting computerization System

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Social Media Analysis for Criminal Behavior Detection: Methods, application and Challenge

Social Media Analysis for Criminal Behavior Detection: Metho...

引用

Sentiment Analysis and Deep learning (ICSADL), international conference on

作者： Prachi Patel Kush Bhushanwar Hemlata Patel Computer Engineering Department PIET Parul University Vadodara Gujarat India Computer Science & Engineering Department PIET Parul University Vadodara Gujarat India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Social media websites have provided rich contextual data but its misuse for criminal activities creates lot of problems. this paper seeks to solve the problem of criminal behavioral analysis on social media platforms using machine learning, deep learning and Natural Language Processing (NLP) methodologies. the goal is to assess the effectiveness of classification algorithms such as SVM, Navier Bayes, Random Forest, Convolutional Neural Network, Recurrent Neural Network, and the Natural Language Processing (NLP) methodology that include sentiment analysis, topic modelling and entity recognition for detecting irregularities. Specifically, this research study discusses about the recent progress in these methods while considering essential problems, including data accessibility, model flexibility, and the topic of ethics.

关键词： Deep learning Support vector machines Ethics Sentiment analysis Analytical models Social networking (online) Computational modeling Scalability Transfer learning Data models

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application of AI and ML in Wound Healing and Skin Regeneration

Application of AI and ML in Wound Healing and Skin Regenerat...

引用

Sentiment Analysis and Deep learning (ICSADL), international conference on

作者： Taniya G. Petkar Praveen Kumar Kirtiksha U. Sarate Department of Computer Science & Medical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Sawangi Wardha Maharashtra India Department of Computer Science and Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Sawangi Wardha Maharashtra India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

By enabling precise, individualized, and effective treatments, the integration of artificial intelligence (AI) and machine learning (ML) into wound and skin healing is revolutionizing healthcare. Artificial intelligence-controlled tools improve wound assessment, anticipate healing, and collaborate with regular examinations using cutting-edge dressings and imaging technologies. AI computations work on the management of express data to enhance treatment systems, while advancements in nanomaterials like marine collagen and therapies employing young microorganisms further accelerate skin recovery. Despite encouraging findings, there are still problems, such as potential overfitting and dataset limitations. In general, machine learning is changing the way that injuries are treated by providing proactive, tailored solutions that aim to understand outcomes and lower the cost associated with clinical outcomes.

关键词： Sentiment analysis Medical treatment Wounds Predictive models Prediction algorithms Skin Real-time systems Sensors Monitoring Overfitting

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Secure Resource Allocation via Constrained Deep Reinforcement learning 6th

Secure Resource Allocation via Constrained Deep Reinforceme...

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6th international conference on machine learning for Cyber Security, ML4CS 2024

作者： Sun, Jianfei Gao, Qiang Wu, Cong Li, Yuxian Wang, Jiacheng Niyato, Dusit School of Computing and Information Systems Singapore Management University Singapore188065 Singapore School of Computing and Artificial Intelligence Southwestern University of Finance and Economics Chengdu611130 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

ISBN: (纸本)9789819645657

the proliferation of Internet of things (IoT) devices and the advent of 6G technologies have introduced computationally intensive tasks that often surpass the processing capabilities of user devices. Efficient and secure resource allocation in serverless multi-cloud edge computing environments is essential for supporting these demands and advancing distributed computing. However, existing solutions frequently struggle with the complexity of multi-cloud infrastructures, robust security integration, and effective application of traditional deep reinforcement learning (DRL) techniques under system constraints. To address these challenges, we present SARMTO, a novel framework that integrates an action-constrained DRL model. SARMTO dynamically balances resource allocation, task offloading, security, and performance by utilizing a Markov decision process formulation, an adaptive security mechanism, and sophisticated optimization techniques. Extensive simulations across varying scenarios-including different task loads, data sizes, and MEC capacities-show that SARMTO consistently outperforms five baseline approaches, achieving up to a 40% reduction in system costs and a 41.5% improvement in energy efficiency over state-of-the-art methods. these enhancements highlight SARMTO ’s potential to revolutionize resource management in intricate distributed computing environments, opening the door to more efficient and secure IoT and edge computing applications. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep reinforcement learning

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