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检索条件"任意字段=6th International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 2812 条记录，以下是111-120 订阅

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Deep Reinforcement learning for Exact Combinatorial Optimization: learning to Branch 26

Deep Reinforcement Learning for Exact Combinatorial Optimiza...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Zhang, Tianyu Banitalebi-Dehkordi, Amin Zhang, Yong Univ Alberta Edmonton AB Canada Huawei Technol Canada Co Ltd Vancouver BC Canada

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Branch-and-bound is a systematic enumerative method for combinatorial optimization, where the performance highly relies on the variable selection strategy. State-of-theart handcrafted heuristic strategies suffer from relatively slow inference time for each selection, while the current machine learning methods require a significant amount of labeled data. We propose a new approach for solving the data labeling and inference latency issues in combinatorial optimization based on the use of the reinforcement learning (RL) paradigm. We use imitation learning to bootstrap an RL agent and then use Proximal Policy Optimization (PPO) to further explore global optimal actions. then, a value network is used to run Monte-Carlo tree search (MCTS) to enhance the policy network. We evaluate the performance of our method on four different categories of combinatorial optimization problems and show that our approach performs strongly compared to the state-of-the-art machine learning and heuristics based methods.

关键词： Deep learning Systematics Monte Carlo methods NP-hard problem Input variables Reinforcement learning pattern recognition

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2023 6th international conference on Signal Processing and Information Security, ICSPIS 2023

2023 6th International Conference on Signal Processing and I...

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6th international conference on Signal Processing and Information Security, ICSPIS 2023

ISBN: (纸本)9798350329599

the proceedings contain 44 papers. the topics discussed include: blockchain-enabled DNS: enhancing security and mitigating attacks in domain name systems;deep learning-based human tracking, face mask and social distance monitoring, systems using YOLOv5;IMU-based human activity recognition using machine learning and deep learning models;enhancing brain tumor aid diagnosis with augmented reality rendering-based 6 DoF object pose estimation;secure key exchange scheme and blockchain-oriented data protection in the internet of vehicles;an intelligent edge-deployable indoor air quality monitoring and activity recognition approach;DiffT: a novel approach for privacy preserving data analytics;using data integration platform for effective location-aware service development platform;hybrid whale-mud-ring optimization for precise color skin cancer image segmentation;and an extensive evaluation of plant disease detection using diverse machine learning approaches.

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Predictive Maintenance of mining Haul Trucks Via machine learning 6

Predictive Maintenance of Mining Haul Trucks Via Machine Lea...

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6th international conference on Robotics and Computer Vision, ICRCV 2024

作者： Madahana, Milka C. I. Seopana, Kabelo Ekoru, John E. D. School of Mining Engineering University of the Witwatersrand Wits2050 South Africa School of Electrical and Information Engineering University of the Witwatersrand Wits2050 South Africa

ISBN: (纸本)9798331527426

mining operations, on a global scale, experience challenges in ensuring high availability and utilization of their haul trucks for optimum production. Most of the shortfalls in availability are due to mechanical breakdowns of haul trucks. the main objective of this research work is to develop an early monitoring and warning system using predictive analytics for haul truck maintenance by applying an artificial intelligence-based approach. the predictive maintenance method relies on data for the prediction of the likelihood of equipment failure. this study employs Multinomial Logistic Regression and K-Nearest Neighbours on a dataset obtained from a haul truck. It is observed that multinomial logistic regression generally performs better than K-Nearest Neighbours in predicting haul truck failure. To improve this work in future, other artificial intelligence algorithms such as Artificial Neural Network can be used for prediction. © 2024 IEEE.

关键词： Predictive maintenance

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Predicting Endotracheal Suctioning Activities Based On Skeletal Coordinates: A Novel Approach Combining Handcrafted Feature Engineering and Ensemble learning

Predicting Endotracheal Suctioning Activities Based On Skele...

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2024 IEEE international conference on Signal Processing, Information, Communication and Systems, SPICSCON 2024

作者： Yousuf, Shadid Noshin, Fabliha Department of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh Department of Biomedical Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh

ISBN: (纸本)9798331510213

Endotracheal Suctioning (ETS) is a critical procedure typically carried out to patients with artificial airways in intensive care units. It is an extremely delicate procedure requiring adequate expertise to minimize the risk of complication and ensure patient safety. Although numerous studies have applied machine learning techniques for the purpose of nurse-care activity recognition, little works focus specifically on endotracheal suctioning activity recognition. Utilizing a model proficient in categorizing endotracheal suctioning activities can serve as a valuable tool for evaluating nursing proficiency. In this paper we use skeletal coordinate data obtained from the 6th ABC Challenge (Activity recognition of Nurse Training Activity) and apply machine learning techniques to predict activities associated with ETS. Following some data pre-processing steps including data smoothing, we extracted joint angles, velocity and several time-domain features and performed data augmentation. We split the data into training and test set and finally, we employed an ensemble model for classifying the ETS activities. the ensemble model yielded superior results than conventional machine learning approaches with an accuracy of 68% and a macro average f1-score of 71%. © 2024 IEEE.

关键词： Nursing

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Opinion mining System for Influence Detection Using machine learning to Secure Business Reputation 6th

Opinion Mining System for Influence Detection Using Machine ...

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6th international conference on Soft Computing and data mining (SCDM)

作者： Ismail, Shahrinaz Khant, Kyi Lin Asia Pacific Univ Technol & Innovat APU Kuala Lumpur Malaysia Huazhong Univ Sci & Technol HUST Wuhan Hubei Peoples R China

ISBN: (纸本)9783031669644;9783031669651

the use of social media becomes a must for almost all businesses, including micro and small enterprises. Despite the benefits of social media marketing, the feedbacks and responses retrieved from audience or customers via posts and comments in social media could be a challenge. Comments received could be negative in nature, which could affect the businesses in a negative way. the negative influence could jeopardize the business reputation, especially if the comments are highly negative and go viral. In sustaining their businesses, the micro and small business owners should not be left behind in using the latest analysis method in securing their business reputation. this paper proposes a simple website that makes use of the back-end sentiment analysis, to detect negative influence on their products and businesses. the results are presented in the form of process flow and interface design of the proposed website, made simple for the target users.

关键词： Opinion mining Sentiment Analysis machine learning Influence Detection Securing Reputation

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Leveraging Semi-Supervised learning to Enhance data mining for Image Classification under Limited Labeled data 4

Leveraging Semi-Supervised Learning to Enhance Data Mining f...

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4th international conference on Electronic Information Engineering and Computer Communication, EIECC 2024

作者： Shen, Aoran Dai, Minghao Hu, Jiacheng Liang, Yingbin Wang, Shiru Du, Junliang University of Michigan Ann Arbor United States Columbia University New York United States Tulane University New Orleans United States Northeastern University Seattle United States Dartmouth College Hanover United States Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798331534622

In the 21st-century information age, with the development of big data technology, effectively extracting valuable information from massive data has become a key issue. Traditional data mining methods are inadequate when faced with large-scale, high-dimensional and complex data. Especially when labeled data is scarce, their performance is greatly limited. this study optimizes data mining algorithms by introducing semi-supervised learning methods, aiming to improve the algorithm's ability to utilize unlabeled data, thereby achieving more accurate data analysis and pattern recognition under limited labeled data conditions. Specifically, we adopt a self-training method and combine it with a convolutional neural network (CNN) for image feature extraction and classification, and continuously improve the model prediction performance through an iterative process. the experimental results demonstrate that the proposed method significantly outperforms traditional machine learning techniques such as Support Vector machine (SVM), XGBoost, and Multi-Layer Perceptron (MLP) on the CIFAR-10 image classification dataset. Notable improvements were observed in key performance metrics, including accuracy, recall, and F1 score. Furthermore, the robustness and noise-resistance capabilities of the semi-supervised CNN model were validated through experiments under varying noise levels, confirming its practical applicability in real-world scenarios. © 2024 IEEE.

关键词： Self-supervised learning

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Research on Motion Capture and Digital Protection of Shadow Puppetry Based on Deep learning and machine Vision 6

Research on Motion Capture and Digital Protection of Shadow ...

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6th international conference on Artificial Intelligence and Computer Applications, ICAICA 2024

作者： Lan, Yunfei School of Humanities and Arts Hunan University of Communications Engineering Hunan Hengyang China

ISBN: (纸本)9798350377255

this paper proposes a shadow puppetry motion capture and digital protection system based on deep learning and machine vision technology. the system uses a high-precision camera to collect the motion data of shadow puppet performances, combines deep learning algorithms for feature extraction and motion recognition, and accurately captures the complex shadow puppet movement trajectory. According to the characteristics of shadow puppetry movements, a CNN model is designed to efficiently extract motion features, and LSTM is used for timing prediction to improve the accuracy and fluency of motion capture. this paper takes a typical performance clip of shadow puppetry in a certain area as the research object, and verifies the effectiveness of the system through experiments. Simulation results show that the accuracy of motion capture of the system reaches 96.8%, and the timing prediction error is controlled at 2.3%, which is better than the existing methods. At the same time, this paper performs three-dimensional modeling and digital storage of the captured data, realizes the dynamic display and virtual reproduction of shadow puppetry performances, and provides a new technical approach for the protection of cultural heritage. Research shows that the combination of deep learning and machine vision technology can not only improve the accuracy of shadow puppetry motion capture, but also open up new ideas for the digital inheritance and promotion of traditional culture. © 2024 IEEE.

关键词： machine vision

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Face recognition Technology Powered By Artificial Intelligence For Enhanced Security Measures 6

Face Recognition Technology Powered By Artificial Intelligen...

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6th Parul international conference on Engineering and Technology, PICET 2024

作者： Dhakal, Sushil Parvez, Anjum Kathuria, Samta Chanti, Yerrolla Law College Dehradun Uttaranchal University Dehradun India LCD Division of Research and Innovation Uttaranchal University Dehradun India School of Computer Science and Artificial Intelligence SR University Warangal India

ISBN: (纸本)9798350369748

this study provides a general overview of face recognition technology's incorporation with artificial intelligence, which has emerged as an important component to enhance a nation’s security in respect of internal as well as external threats. machine learning and deep learning, which are subsets of artificial intelligence, can be crucial in respect of facial recognition by employing their algorithms and network architecture due to their accuracy and usability. the main objective of this study is to explore face recognition technology with the integration of artificial intelligence, which can be employed to secure various areas of human life, including homes, banking, education, and employment. this study, with the help of tables and a graphical chart, helps to comprehend recent technological developments in the related field and provides a way forward for addressing ethical implications and gray areas in the field of face recognition technology. Furthermore, this study, along with machine learning algorithms and deep learning network architecture, provides integration of transfer learning and data augmentation for better results in face recognition. this study outlines future research directions for addressing ethical and accuracy issues arising from the limitations of current algorithms. © 2024 IEEE.

关键词： Adversarial machine learning

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Cross-sensor Maritime Target recognition Algorithm Based on Heterogeneous Sensor Shared Representation learning 6

Cross-sensor Maritime Target Recognition Algorithm Based on ...

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6th IEEE international conference on pattern recognition and Artificial Intelligence, PRAI 2023

作者： Liu, Yihai Jiangsu Automation Research Institute First Research Department Lianyungang China

ISBN: (纸本)9798350325485

We investigate a cross-sensor recognition algorithm for heterogeneous sensors based on shared representation learning to address the difficulty of non-cooperative object recognition in C4ISR systems due to modal barriers, imbalanced data distribution, and few-shot labeled samples. First, deep multiset canonical correlation analysis is used to learn the shared space among heterogeneous sensors by only unlabeled spatiotemporal correlation data from sensors. Second, scattered labeled data from multiple sensors are concentrated in shared space, which removes the modality barrier of each sensor and produces big data samples for training the classification network. Finally, the classification model is trained and directly used to classify the new observations for each sensor in the shared space, which constitutes a general-purpose cross-sensor target recognition. Experiments based on target recording data show that the proposed algorithm can achieve 60-78% accuracy in cross-sensor recognition for radar, ESM, optical, and sonar sensors, indicating a significant engineering value. © 2023 IEEE.

关键词： Object recognition

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6th international conference on Software Testing, machine learning and Complex Process Analysis, TMPA 2021

6th International conference on Software Testing, Machine Le...

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6th international conference on Software Testing, machine learning and Complex Process Analysis, TMPA 2021

ISBN: (纸本)9783031504228

the proceedings contain 18 papers. the special focus in this conference is on Software Testing, machine learning and Complex Process Analysis. the topics include: Link Graph and data-Driven Graphs as Complex Networks: Comparative Study;An Approach to Creating a Synthetic Financial Transactions dataset Based on an NDA-Protected dataset;optic Flow Approximated by a Homogeneous, three-Dimensional Point Renewal Process;fair Mutual Exclusion for N Processes;data Stream Processing in Reconciliation Testing: Industrial Experience;Detection of Flying Objects Using the YOLOv4 Convolutional Neural Network;modern Experiment Management Systems Architecture for Scientific Big data;an Approach to Modules Similarity Definition Based on System Trace Analysis;process mining Algorithm for Online Intrusion Detection System;bayesian Optimization with Time-Decaying Jitter for Hyperparameter Tuning of Neural Networks;investigation of the Capabilities of Artificial Neural Networks in the Problem of Classifying Objects with Dynamic Features;analysis of Hardware-Implemented U-Net–Like Convolutional Neural Networks;early Detection of Tasks with Uncommonly Long Run Duration in Post-trade Systems;Unpaired Image-To-Image Translation Using Transformer-Based CycleGAN;model-Based Testing Approach for Financial Technology Platforms: An Industrial Implementation;searching for Deviations in Trading Systems: Combining Control-Flow and data Perspectives.

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