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检索条件"任意字段=14th International Conference on Machine Learning and Computing, ICMLC 2022"

共 2133 条记录，以下是1-10 订阅

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2022 14th international conference on machine learning and computing, icmlc 2022

2022 14th International Conference on Machine Learning and C...

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14th international conference on machine learning and computing, icmlc 2022

ISBN: (纸本)9781450395700

the proceedings contain 84 papers. the topics discussed include: characterizing phenotypes for mental health disorders with wrapper‐typed feature selection techniques: comparison of RF‐RFE and fuzzy forest;chaos prediction of power systems by using deep learning;a highly efficient, confidential, and continuous federated learning backdoor attack strategy;a transformer‐based deep learning model for evaluation of accessibility of image descriptions;single stock trading with deep reinforcement learning: a comparative study;self‐supervised domain adaptation model based on contrastive learning;pseudo reward and action importance classification for sparse reward problem;a machine learning‐based approach for cardiovascular diseases prediction;DeepGCNMIL: multi‐head attention guided multi‐instance learning approach for whole‐slide images survival analysis using graph convolutional networks;and optimize the NOx emission concentration of circulation fluidized bed boiler based on on‐line learning neural network and modified TLBO algorithm.

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14th international conference on Frontier computing, FC 2024

14th International Conference on Frontier Computing, FC 2024

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14th international conference on Frontier computing, FC 2024

ISBN: (纸本)9789819623945

the proceedings contain 104 papers. the special focus in this conference is on Frontier computing. the topics include: Multimedia Digital AI Painting System Based on machine Vision Algorithm;intelligent Decision Optimization of Big Data in the Digital Economy Under Deep Reinforcement learning;development of Virtual Maintenance Training Platform for Electric Motors;design of Automatic Driving and Parking System for New Energy Vehicles Based on Artificial Intelligence;application of machine learning Algorithms to Mental Health Data Processing for College Students;on the Translation Strategy of "Intangible Cultural Heritage" Based on Visual Multimodality;research on the Improvement of Human-machine Collaborative machine Translation Model Based on Internet;research on Subway Station Space Design from the Perspective of Regional Culture;application of Intelligent Algorithm in Image Recognition Under the Vision of Artificial Intelligence;risk Assessment Model of Electricity Customer Data Based on Dynamic Grid Generation Technology;an Evaluation Model of Equestrian Athletes’ Sense of Speed Based on Inertial Sensor Network;emotion Analysis of Low Resource Domain Based on Transfer learning and Meta-learning Methods;digital Technology Integrated with Artificial Intelligence Digital Product Research and Development;research on Artificial Neural Network Prediction of the Strength of Metal-Based Carbon Fiber Reinforced Polymer Layer;Intelligent Recommendation Algorithm for New Media Content Based on AI Technology;design of Daily Life Ability Training System Based on Intelligent Evaluation Algorithm;business Model for Developing Power Load Resources;practice of Artificial Intelligence Technology in Enterprise Human Resource Management;medical Service Management Based on Modern Information Technology;tetris Game System Based on Java.

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14th international conference on Frontier computing, FC 2024

14th International Conference on Frontier Computing, FC 2024

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14th international conference on Frontier computing, FC 2024

ISBN: (纸本)9789819627974

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14th international conference on Frontier computing, FC 2024

14th International Conference on Frontier Computing, FC 2024

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14th international conference on Frontier computing, FC 2024

ISBN: (纸本)9789819623907

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14th international conference on Frontier computing, FC 2024

14th International Conference on Frontier Computing, FC 2024

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14th international conference on Frontier computing, FC 2024

ISBN: (纸本)9789819627936

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Sailing through Data: Automated Data Annotation Systems for machine learning Applications in Maritime Navigation 24

Sailing Through Data: Automated Data Annotation Systems for ...

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14th international conference on the Internet of things, IoT 2024

作者： Vibild, Patrick Dominique Hernandez, Jon Kim, Daeyoung School of Computing KAIST Daejeon Korea Republic of

ISBN: (纸本)9798400712852

the maritime industry plays a crucial role in global trade and transport, necessitating effective collision avoidance, navigation security, and reduction in human errors and costs. this paper presents an automated data annotation pipeline for detection and distance estimation using monocular camera systems in maritime applications. Our system integrates AIS data with ship detections from a pre-trained deep learning model, creating comprehensive RGB/IR data with annotations of ship positions, distances, speeds, categories, and sizes. We use sensor data from our developed embedded solution, AISail, and its post-processing techniques to generate this data. We evaluate our system’s performance against manually annotated images, demonstrating its potential to enhance maritime navigation and safety by accurately estimating ship positions and speeds. © 2024 Copyright held by the owner/author(s).

关键词： Deep learning

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machine learning Algorithms for Artificial Intelligence Technology Development Online Communication Platform 14th

Machine Learning Algorithms for Artificial Intelligence Tech...

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14th international conference on Frontier computing, FC 2024

作者： Ma, Nancao Liaoning Institute of Science and Engineering Liaoning Jinzhou China

ISBN: (纸本)9789819627936

Artificial intelligence creates an English learning situation that connects virtual and real, guides students to enter deep learning, and is conducive to students’ exploration in practice. Major breakthroughs in artificial intelligence research and applications depend on massive data collection, improvements in computer speed and computing power, and advances in machine learning algorithms. Implementing the teaching concept in the era of artificial intelligence in specific forms of educational practice reflects interactivity and real-time, breaking the barriers of traditional offline education. Based on artificial intelligence technology, the development of an online communication platform for interactive English teaching will help the interactive teaching model to achieve results in English language teaching. this paper studies augmented reality technology and Agent technology, designs the online communication platform hierarchy, and provides solutions for platform development. In the process of practical application, give full play to the leadership of teachers and the subjectivity of students, and improve students’ language knowledge and English language skills. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Towards Adaptive Asynchronous Federated learning for Human Activity Recognition 24

Towards Adaptive Asynchronous Federated Learning for Human A...

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14th international conference on the Internet of things, IoT 2024

作者： Gajanin, Rastko Danilenka, Anastasiya Morichetta, Andrea Nastic, Stefan Distributed Systems Group TU Wien Vienna Austria Faculty of Mathematics and Information Science Warsaw University of Technology Warsaw Poland

ISBN: (纸本)9798400712852

In this work, we tackle the problem of performing multi-label classification in the case of extremely heterogeneous data and with decentralized machine learning. Solving this issue is very important in IoT scenarios, where data coming from various sources, collected by heterogeneous devices, serve the learning of a distributed ML model through Federated learning (FL). Specifically, we focus on the combination of FL applied to Human Activity Recognition (HAR), where the task is to detect which kind of movements or actions individuals perform. In this case, transitioning from centralized learning (CL) to federated learning is non-trivial as HAR displays heterogeneity in action and devices, leading to significant skews in label and feature distributions. We address this scenario by presenting concrete solutions and tools for transitioning from centralized to FL for non-IID scenarios, outlining the main design decisions that need to be taken. Leveraging an open-sourced HAR dataset, we experimentally evaluate the effects that data augmentation, scaling, optimizer, learning rate, and batch size choices have on the performance of resulting machine learning models. Some of our main findings include using SGD-m as an optimizer, global feature scaling across clients, and persistent feature skew in the presence of heterogeneous HAR data. Finally, we provide an open-source extension of the Flower framework that enables asynchronous FL. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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Information Content of machine learning Under Robo-Advisory Inference 14th

Information Content of Machine Learning Under Robo-Advisory ...

引用

14th international conference on Frontier computing, FC 2024

作者： Yeh, Chih-Chuan Department of Finance National Taichung University of Science and Technology No.129 Sec. 3 Sanmin Rd. North Dist Taichung City40401 Taiwan

ISBN: (纸本)9789819623945

this article examines the transformative impact of Robo-Advisors on wealth management through the integration of big data and artificial intelligence (AI) in algorithmic trading strategies. We explore how AI technologies, including machine learning and natural language processing, can analyze large datasets, identify market trends, and optimize investment portfolios. this study compares the performance of various forecasting algorithms within the robo-advisory framework, taking the efficient frontier into consideration as a key metric. the research highlights the potential of AI-driven innovations to improve investment decision-making and shape the future of financial services. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Research on machine learning Prediction Algorithm in Stability of Mountain Rock Slope 14th

Research on Machine Learning Prediction Algorithm in Stabili...

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14th international conference on Frontier computing, FC 2024

作者： Xiang, Juan Zhang, Xuguang Li, Yongde School of Architecture and Civil Engineering Chongqing Metropolitan College of Science and Technology Chongqing China Architectural Design Institute China Construction Fifth Engineering Division Corp Ltd. Changsha China

ISBN: (纸本)9789819627974

In order to predict the slope stability, a method based on machine learning is put forward. By analyzing the slope instability characteristics, six typical slope parameters (pore water pressure, slope height) are selected and analyzed, and the corresponding evaluation data set is established. the slope stability model is established by using machine learning methods such as gradient hoist (GBM) and support vector machine (SVM), and verified by 50% cross-validation. By analyzing the characteristics of slope instability and combining with domestic slope cases, according to the sensitivity of characteristic parameters, the slope protection measures for different sensitive factors are put forward. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Slope stability

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