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检索条件"任意字段=International Conference on Methods and Models in Automation and Robotics"

共 24666 条记录，以下是371-380 订阅

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LINK: Adaptive Modality Interaction for Audio-Visual Video Parsing

LINK: Adaptive Modality Interaction for Audio-Visual Video P...

引用

international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Langyu Wang Bingke Zhu Yingying Chen Jinqiao Wang School of Logistics Engineering Shanghai Maritime University China Foundation Model Research Center Institute of Automation Chinese Academy of Sciences China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Audio-visual video parsing focuses on classifying videos through weak labels while identifying events as either visible, audible, or both, alongside their respective temporal boundaries. Many methods ignore that different modalities often lack alignment, thereby introducing extra noise during modal interaction. In this work, we introduce a Learning Interaction method for Non-aligned Knowledge (LINK), designed to equilibrate the contributions of distinct modalities by dynamically adjusting their input during event prediction. Additionally, we leverage the semantic information of pseudo-labels as a priori knowledge to mitigate noise from other modalities. Our experimental findings demonstrate that our model outperforms existing methods on the LLP dataset.

关键词： Training Visualization Adaptation models Semantics Noise Feature extraction Acoustics Speech processing

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Anesthesia Level Prediction Using ML

Anesthesia Level Prediction Using ML

引用

Advancements in Electrical, Electronics, Communication, Computing and automation (ICAECA), international conference on

作者： Alagumeenaakshi M Shruthi G T Vikasini S Sai Prasath S Electronics and Communication Engineering Kumaraguru College of Technology Coimbatore Tamil Nadu India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

Contribution of Anesthesia in modern medicine and surgery is critical, offering significant benefits to both patients and medical practitioners. According to a survey, the inaccuracy in predicting anesthesia levels has led to a 47 percent increase in the mortal rate among Clinical patients. This system proposes a solution to anesthesia management by integrating sensors and machine learning using Raspberry Pi. Machine learning algorithms such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Artificial Neural Network (ANN), Gradient Boosting Machine (GBM) are used, and the best features that assist in anesthesia level prediction are selected from various feature selection methods. On evaluating the performances of various algorithms, KNN algorithm provides 97% accuracy, and the best features are found to be heart rate (BPM), blood oxygen saturation (SpO2), body temperature and blood pressure using feature selection methods. The proposed system is designed based on the KNN algorithm and it continuously monitors the vital signs acquired from the feature selection methods using MAX30100, DHT11, and PPG sensor. The prototype system designed decides the anesthesia level for the patient based on the vital parameters measured using sensor technology and the vitals and patients' history are displayed in a GUI. The proposed system signifies an advancement in automated anesthesia management, prominently projecting the potential of integrating ML models and sensor technology in effective implementation of medical procedures.

关键词： Support vector machines Machine learning algorithms Artificial neural networks Nearest neighbor methods Anesthesia Prediction algorithms Feature extraction Sensor systems Sensors Monitoring

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Transforming Classification with Federated Learning on Blockchain: A Unique Model Integration Approach

Transforming Classification with Federated Learning on Block...

引用

international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhihao Hao Bob Zhang Haisheng Li School of Computer Science and Artificial Intelligence Beijing Technology and Business University Beijing China Dept. of Computer and Information Science Faculty of Science and Technology University of Macau Taipa Macau Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation University of Macau Taipa Macau

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The need for robust machine learning models is particularly evident in the realm of biological pattern recognition. Traditional centralized methods often struggle, as they frequently depend on large datasets that are challenging to gather due to stringent data privacy regulations. To address these limitations while maintaining classification accuracy, we propose an innovative approach that unifies models with diverse architectures within a federated learning framework built on a blockchain network. This decentralized and trustworthy system fosters effective collaboration among various models. Furthermore, we have implemented a weight distribution mechanism designed to maximize the individual strengths of each model. By leveraging blockchains inherent transparency and auditability, this approach also ensures secure and traceable data exchanges among participants. Additionally, the adaptability of the framework allows it to be extended to other domains where privacy-preserving data sharing is critical. Our experimental results showcase that the proposed methodology significantly enhances performance in classification tasks compared to existing alternatives.

关键词： Training Adaptation models Federated learning Biological system modeling Collaboration Signal processing Regulation Blockchains Security Speech processing

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LSTM-CNN Ensemble methods for Predicting Malignant Brain Tumors

LSTM-CNN Ensemble Methods for Predicting Malignant Brain Tum...

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automation and Computation (AUTOCOM), international conference on

作者： Ayaan Faiz K Gopalakrishna Ankita Agarwal R Suchithra Pavas Saini Ishan Ayus Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India School of Engineering and Technology Electronics and Communication Engineering JAIN (Deemed to be University) Bangalore Karnataka Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Applications (DCA) Presidency College Bengaluru India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

The LSTM-CNN ensemble method performs well in predicting malignant brain tumors. The model above combines the best of LSTM and CNN to enhance accuracy and consistency in forecasts. An LSTM model is an RNN architecture that allows the collecting and processing of repeatable data, e.g., time series or language text. It is one of the best tools for medical data analysis, such as brain tumor image scans. It will allow the LSTM model to learn patterns and correlations in your data regarding precision when predicting future observations. Meanwhile, the CNN model is best for interpreting high-dimensional data and then amalgamating them to make predictions. It renders it a beneficial tool for analyzing brain tumor imaging scans and predicting features indicative of a malignant nature. It unites the best of abilities in both models and helps to make up for their shortcomings, respectively. It leads to a more reliable and precise prognosis of brain cancers, both for early detection as well as treatment planning. Additionally, employing deep learning methods such as LSTM-CNN ensemble may greatly help enhance brain tumor diagnosis and therapy efficiency.

关键词： Deep learning Brain tumors Computer architecture Predictive models Brain modeling Feature extraction Planning Ensemble learning Long short term memory Medical diagnostic imaging

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Adaptive Embedding Using Contrastive Learning with Median Absolute Deviation and Variance for Recommendation

Adaptive Embedding Using Contrastive Learning with Median Ab...

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Electrical automation and Artificial Intelligence (ICEAAI), international conference on

作者： Qinxin Xu Haofeng Li Wei Zhou Zhiyong Yang School of Big Data and Internet of Things Chongqing Vocational Institute of Engineering Chongqing China Tongji University Shanghai China School of Big Data and Software Engineering Chongqing University Chongqing China

ISBN: (数字)9798331506797

ISBN: (纸本)9798331506803

Recommendation systems have increasingly relied on embedding techniques to capture user preferences and item features. Adaptive temperature techniques have rapidly developed to provide more accurate recommendations. However, these methods often struggle with popularity bias and a lack of adaptability in dynamic environments, which will end in a decline of the robustness and the bias in temperature prediction. To address these challenges, we propose integrating noise-based data augmentation and InfoNCE loss from the XSimGCL framework into the adaptively modulating embedding magnitude model. Our method enhances traditional adaptive temperature adjustment by incorporating statistical measures like median absolute deviation and variance of embedding norms, aiming to create more robust and adaptive embeddings. This approach eliminates the necessity for adapting temperature by loss, instead focusing on incorporating statistical measures and adding noise to enhance robustness. Extensive evaluations on several real-world datasets demonstrate that our approach effectively mitigates popularity bias and significantly improves overall recommendation quality. This suggests that the nuanced integration of contrastive learning with adaptive embedding techniques can lead to more personalized and equitable recommendations.

关键词： Temperature measurement Adaptation models Temperature distribution Accuracy Contrastive learning Data augmentation Data models Robustness Loss measurement Recommender systems

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Student Posture Extraction and Attentiveness Classification Using Deeplabcut and Support Vector Machine

Student Posture Extraction and Attentiveness Classification ...

引用

international conference on Computer and automation Engineering, ICCAE

作者： Froilan Imarx A. Ami Analyn N. Yumang Edward Jay M. Quinto School of Electrical Electronics and Computer Engineering Mapúa University Manila Philippines School of Medical and Health Sciences Mapúa University Manila Philippines

ISBN: (数字)9798331533816

ISBN: (纸本)9798331533823

This study proposes a computer vision system to classify student attentiveness in physical classrooms using upperbody posture analysis. Leveraging the DeepLabCut framework for pose estimation and a Support Vector Machine (SVM) classifier, the system processes video data captured via a Raspberry Pi 5 and an OBSBot Tail Air camera to categorize attentiveness into three levels: fully attentive, partially attentive, and nonattentive. Unlike existing methods focused on online learning environments, this approach targets physical classroom settings, addressing gaps in engagement monitoring. The model was trained on annotated posture data from volunteers, with body part coordinates normalized relative to the thorax for consistency. Evaluation using a confusion matrix revealed an accuracy of $\mathbf{7 0. 8 5 \%}$ , highlighting challenges in distinguishing subtle posture variations between attentiveness levels. The system provides instructors with actionable insights to adapt teaching strategies, promoting personalized learning. This work demonstrates the feasibility of integrating low-cost hardware and machine learning for classroom engagement analytics, though future studies could benefit from larger datasets and enhanced classification models.

关键词： Support vector machines Computer vision Pose estimation Education Machine learning Tail Thorax Hardware Data models Monitoring

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Flood Early Warning and Probability Prediction Based on Multiple Machine Learning models

Flood Early Warning and Probability Prediction Based on Mult...

引用

Digital Analysis and Processing, Intelligent Computation (DAPIC), international conference on

作者： Zhengsheng Zhan Ruiyan Zhu Changyue Li School of Automation Science and Engineering South China University of Technology Guangzhou China School of Mathematics and Statistics Huaiyin Normal University Huai'an China College of Science Shenyang Aerospace University Shenyang China

ISBN: (数字)9798331536169

ISBN: (纸本)9798331536176

This paper presents a study on flood early warning and probability prediction using a multi-model machine learning approach. The research involves the classification of data into three risk categories-low, medium, and high-using the K-nearest neighbors (KNN) clustering method. Subsequently, principal component analysis (PCA) was employed to analyze the importance of various indicators in flood early warning, resulting in the calculation of weight values for each indicator. Following this, evaluation models based on random forests and support vector machines (SVM) were established. Furthermore, utilizing 12 indicators as dependent variables, prediction models were developed based on the backpropagation (BP) neural network, and LightGBM. Comparative analysis of the error data and R2 values revealed that the BP neural network demonstrated the best performance with an R2 of 0.862. Subsequently, reducing the indicators to 5, adjusting indicator weights and model parameters, and retraining the prediction models resulted in an improved R2 of 0.873. The distribution of data conformed to a normal distribution, indicating the enhanced capability of the prediction model.

关键词： Support vector machines Neural networks Evaluation models Linear regression Predictive models Nearest neighbor methods Data models Floods Random forests Principal component analysis

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Reliability Evaluation Method of Communication Network Applicable to New Distribution System Under Extreme Disasters

Reliability Evaluation Method of Communication Network Appli...

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Electrical automation and Artificial Intelligence (ICEAAI), international conference on

作者： Linfeng Chen Bingsen Xia Cui Li Zhaozheng Zhou Puyang Zhang State Grid Fujian Economic Research Institute Fuzhou China School of Electrical and Electronic Engineering North China Electric Power University Beijing China

ISBN: (数字)9798331506797

ISBN: (纸本)9798331506803

The communication network of the new Distribution System with the characteristics of “double-high” integrates a large number of heterogeneous communication technologies. Its anti-destruction characteristics for extreme disasters are different from those of the ring-shaped optical fiber network. Most of the traditional network reliability evaluation methods are used to evaluate the power optical fiber backbone network and are difficult to be directly applied to the new Distribution System. This paper proposes a reliability evaluation method of the communication network applicable to the new Distribution System under extreme disasters. Firstly, considering the correlation characteristics of the power grid and the communication network, a dual-network coupling model adapted to lO-kv lines is constructed to provide a basis for subsequent analysis. Secondly, based on the historical data, the wind speed probability distribution function of the power grid nodes under typhoon disasters is generated, and at the same time, the anti-destruction models of the power grid nodes and the communication nodes are constructed. Finally, according to the cascading failure mechanism, the disaster impact process is analyzed to achieve an objective evaluation of the reliability of the communication network of the distribution system under extreme disasters, hoping to provide scientific guidance for the construction of subsequent disaster-tolerance planning schemes.

关键词： Couplings Adaptation models Tropical cyclones Disasters Wind speed Ultraviolet sources Reliability engineering Power grids Probability distribution Communication networks

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Utilizing Machine Learning in Hand Exoskeleton Rehabilitation System for Partial Paralysis 5th

Utilizing Machine Learning in Hand Exoskeleton Rehabilitatio...

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5th international conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2024

作者： Gangadharan, S.M.P. Singh, Jagendra Garg, Neha Srivastava, Satyajee Rhaif Alsahlanee, Abbas Thajeel Elangovan, Muniyandy Liverpool John Moores University Liverpool United Kingdom School of Computer Science Engineering & Technology Bennett University Greater Noida India Ambala India Development and Continuous Education Center University of Thi-Qar Thi-Qar Iraq Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (纸本)9789819788644

This research looked into whether machine learning algorithms into a hand exoskeleton rehabilitation system benefited persons with partial paralysis. Five people participated in the study by training with the exoskeleton for a period of one month. This was a mechanism for training ML models via electromyography (EMG), including Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Decision Trees (DT), to make adaptive adjustments in exoskeleton parameter values. All participants saw their flexion and extension angles improve considerably as they went through this month-long regimen of exercises. In terms of accuracy, performance evaluation indicators precision, recall, and F1 score reflect the ML models’ success at predicting outcomes of rehabilitation. An ANN model with precision of 0.85, recall of 0.82, F1 score of 0.83 and an overall accuracy of 0.86 also demonstrated strong performance in guiding personalized interventions, particularly through feedback. Similarly, SVM and DT models performed well but with differing degrees of precision, recall, F1 score, and accuracy. On a level of individual accuracy, confusion matrices confirm the classification results of each ML mode. These results point the way for ML methods as a way of designing more effective rehabilitation strategies, greatly improving motor function for people with partial paralysis. With data from EMG sensors in real-time, this proposed exoskeleton-based rehabilitation provides a way to improve both range and functionality. Many future efforts in research are striving to continue refining the system and proving its efficacy in different clinical circumstances, in the end to improve the lives of those with paralysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Exoskeleton (robotics)

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Research on Model Matching and Anomaly Detection Method of Distribution Network Equipment Based on OCR and Image Recognition

Research on Model Matching and Anomaly Detection Method of D...

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Electrical automation and Artificial Intelligence (ICEAAI), international conference on

作者： Juan Bai Zhenjun Xie Chang Li Shulei Feng Yu Zou Qinzhou Power Supply Bureau of Guangxi Power Grid Co. Ltd Qinzhou Guangxi

ISBN: (数字)9798331506797

ISBN: (纸本)9798331506803

The acceptance work of distribution network engineering lines is an important link to ensure the quality of distribution network engineering lines and effectively improve the safe and stable operation level of the distribution network. However, traditional on-site verification methods for distribution network engineering suffer from time-consuming, labor-intensive, and error prone issues, making it difficult to efficiently identify potential hazards such as construction process defects and equipment model mismatches, which pose a threat to the safe operation of the distribution network. Therefore, this article proposes a model matching and anomaly detection method for distribution network equipment based on OCR and image recognition technology. Firstly, key text information on equipment nameplates and identification plates is automatically extracted through OCR technology; Secondly, by combining image recognition models to analyze the appearance characteristics of the equipment, precise matching and anomaly detection of the equipment model can be achieved. This method integrates image processing, deep learning, and database matching techniques, which can effectively improve the efficiency and accuracy of line engineering quality acceptance. The experimental results show that this method has good robustness and practicality in various complex scenarios, providing important technical support for the intelligent acceptance and management of distribution network engineering.

关键词： Deep learning Analytical models Image recognition Optical character recognition Distribution networks Information retrieval Robustness Hazards Anomaly detection Optimization

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