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检索条件"任意字段=2022 International Conference on Machine Learning and Data Engineering, ICMLDE 2022"

共 34017 条记录，以下是1281-1290 订阅

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A machine learning Fusion Algorithm for Identifying Rainfall Enhancement Potential Areas 5

A Machine Learning Fusion Algorithm for Identifying Rainfall...

引用

5th international conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2023

作者： Zheng, Xucheng Li, Hui Xin, Yue Jin, Yuchen Lin, Hongjin Inner Mongolia Institute of Meteorological Sciences Hohhot China Inner Mongolia Weather Modification Center Hohhot China

ISBN: (纸本)9781839539848

Using the 2005-2020 stratified precipitation cloud system aircraft operation detection data, ground observation data and ERA5 reanalysis data in Inner Mongolia, we use a variety of machine learning algorithms to discuss the applicability of machine learning methods in the prediction of rainfall enhancement potential areas, and build a multi-machine learning fusion model. The results show that the machine learning algorithm can effectively identify seeding areas and non-seeding areas of artificial rain. The fusion model constructed by combining the prediction results of CatBoost, extra tree and random forest algorithms has obtained the optimal prediction effect, and its TS score on the test set is higher than that of any algorithm alone. The probability of detection, false negative rate and false alarm rate of the fusion model are 84.21%, 4% and 15.78%, respectively, and the TS score is 0.8136. © The Institution of engineering & Technology 2023.

关键词： Rain

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Research on Movie Box Office Prediction Method Based on Blending Model Fusion 24

Research on Movie Box Office Prediction Method Based on Blen...

引用

16th international conference on machine learning and Computing (ICMLC)

作者： Zheng, Dequan Sun, Yunjie Yu, Feng Harbin Univ Commerce Sch Comp & Informat Engn Harbin Peoples R China

ISBN: (纸本)9798400709234

As living standards improve, movies have become an essential commodity and consumer product, playing a pivotal role in people's lives by providing entertainment and enjoyment. Therefore, predicting the movie box office is of great significance. Through studying existing movie box office prediction models, it is found that there are problems such as insufficient selection of box office influencing factors and traditional prediction models. Therefore, this paper mainly selects three models: multiple linear regression, BP neural network, and convolutional neural network, and combines them using Blending model fusion to predict movie box office. Experimental results show that the selected factors and constructed models can improve the prediction effect of box office to a certain extent, which can provide meaningful box office prediction references for movie investors and producers and have certain theoretical and practical significance.

关键词： Prediction Movie box office Blending model fusion Deep learning

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IntelEye: An Intelligent Tool for the Detection of Stressful State based on Eye Gaze data while Watching Video

IntelEye: An Intelligent Tool for the Detection of Stressful...

引用

2022 international conference on machine learning and data engineering, icmlde 2022

作者： Jyotsna, C. Amudha, J. Ram, Amritanshu Nollo, Giandomenico Dept. of Computer Science and Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India HCG Cancer Center Bangalore India Department of Industrial Engineering University of Trento Trento Italy

Technology to monitor mental health is gaining popularity as it helps to improve the cognitive and behavioral performance of an individual. Considering the growing need to monitor mental health, there is subsequent research in continuous and real-time monitoring technologies that can increase the quality of life by reducing the cost of health care. Eye tracking technology has played a significant role in monitoring a person's mental health. An intelligent system can apply several computational procedures to extract meaningful information from the massive physiological data obtained from eye tracking. The proposed model IntelEye is a tool to detect the stressful states of an individual while watching calm and stressful videos. The eye gaze measures based on pupil diameter, fixation, and blink were used for detecting stressful conditions. The data was collected from hospital employees, and the K Nearest Neighbor algorithm could successfully recognize the stressful states and the corresponding gaze location during stressful situations. IntelEye is not only identifying the stressful states but also has the novelty of identifying the scene and gaze location, making them stressful while watching the video. © 2023 The Authors. Published by Elsevier B.V.

关键词： Eye tracking

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DIDI: Diffusion-Guided Diversity for Offline Behavioral Generation 41

DIDI: Diffusion-Guided Diversity for Offline Behavioral Gene...

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41st international conference on machine learning, ICML 2024

作者： Liu, Jinxin Guo, Xinghong Zhuang, Zifeng Wang, Donglin School of Engineering Westlake University Hangzhou China Zhejiang University Hangzhou China Institute of Advanced Technology Westlake Institute for Advanced Study Hangzhou China

In this paper, we propose a novel approach called DIffusion-guided DIversity (DIDI) for offline behavioral generation. The goal of DIDI is to learn a diverse set of skills from a mixture of label-free offline data. We achieve this by leveraging diffusion probabilistic models as priors to guide the learning process and regularize the policy. By optimizing a joint objective that incorporates diversity and diffusion-guided regularization, we encourage the emergence of diverse behaviors while maintaining the similarity to the offline data. Experimental results in four decision-making domains (Push, Kitchen, Humanoid, and D4RL tasks) show that DIDI is effective in discovering diverse and discriminative skills. We also introduce skill stitching and skill interpolation, which highlight the generalist nature of the learned skill space. Further, by incorporating an extrinsic reward function, DIDI enables reward-guided behavior generation, facilitating the learning of diverse and optimal behaviors from suboptimal data. Copyright 2024 by the author(s)

关键词： Contrastive learning

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Comparison of machine learning Algorithms for Propagation Path Loss Prediction Using Ray Tracing data 15

Comparison of Machine Learning Algorithms for Propagation Pa...

引用

15th international conference on Information and Communication Technology Convergence, ICTC 2024

作者： Yoon, Chaewon Kim, Junseok Kim, Hyuk-Je Kim, Chung Sup Choi, Sung Woong Lee, Il-Kyoo Kongju National University Dept of electrical electronics & Control Engineering Korea Republic of Radio Research Division ETRI Daejeon Korea Republic of

ISBN: (纸本)9798350364637

In this paper, we compared several regression algorithms through machine learning approach to predict path loss in an outdoor environment using only the location information of Tx and Rx. We constructed an outdoor environment with intersections, buildings and collected path loss data through Ray-tracing. After training by adjusting hyperparameters, two algorithms with low Mean Squared Error (MSE) values were compared in detail by statistically comparing the values of Mean Absolute Error (MAE), MSE, Root Mean Square Error (RMSE), and R2 using K-fold cross validation. The results showed that the K-Nearest Neighbor (K-NN) algorithm had the best prediction performance for path loss. The prediction model also showed faster prediction time compared to the estimation time of Ray tracing. © 2024 IEEE.

关键词： Prediction models

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Prediction of Cardio Vascular Disease by Deep learning and machine learning-A Combined data Science Approach 1

Prediction of Cardio Vascular Disease by Deep Learning and M...

引用

1st international conference on Computer, Power and Communications, ICCPC 2022

作者： Romalt, A. Ann Kumar, R. Mathusoothana S. Stella Mary's College of Engineering Department of Computer Science and Engineering Tamil Nadu 629202 India Noorul Islam Centre for Higher Education Department of Informatin Technology Tamil Nadu 629180 India

ISBN: (纸本)9798350397840

machine learning is the process in which the computer system can automatically learn from the data. It's an application of Artificial Intelligence without being explicitly programmed. Making use of machine learning and AI an efficient algorithm for prediction of Cardio Vascular Disease (CVD) can be done. By combing Deep learning method to the ML model, a high-level abstract feature with improved performance can be obtained. Heart disease or Cardio Vascular disease is the predominant disease all over the world. Accurate prediction of heart disease is a challenging task. The accuracy of prediction can be improved by applying machine learning algorithms. Results with high accuracy can be produced by combining a machine learning model with Statistical concepts. The main objective of this research is to predict high accurate CVD by applying Deep learning model combined with machine learning algorithm. By recognizing the symptoms of the disease, people can get prompt treatment on time. © 2022 IEEE.

关键词： Forecasting

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Simulating Federated Transfer learning for Lung Segmentation using Modified UNet Model

Simulating Federated Transfer Learning for Lung Segmentation...

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2022 international conference on machine learning and data engineering, icmlde 2022

作者： Ambesange, Sateesh Annappa, B. Koolagudi, Shashidhar G. Computer Science Engineering Dept National Institute of Technology Karnataka Surathkal Karnataka state Mangalore575025 India

Lung segmentation helps doctors in analyzing and diagnosing lung diseases effectively. Covid -19 pandemic highlighted the need for such artificial intelligence (AI) model to segment Lung X-ray images and diagnose patient covid conditions, in a short time, which was not possible due to huge number of patient influx at hospitals with the limited radiologist to diagnose based on test report in short time. AI models developed to assist doctors to diagnose faster, faces another challenge of data privacy. Such AI Models, for better performance, need huge data collected from multiple hospitals/diagnostic centres across the globe into single place to train the AI models. Federated learning (FL) framework, using transfer learning approach addresses these concerns as FL framework doesn't need data to be shared to outside hospital ecosystem, as AI model get trained on local system and AI model get trained on distributed data. FL with Transfer learning doesn't need the parallel training of the model at all participants nodes like other FL. Paper simulates Federated Transfer learning for Image segmentation using transfer learning technique with few participating nodes and each nodes having different size dataset. The proposed method also leverages other healthcare data available at local system to train the proposed model to overcome lack of more data. Paper uses pre-trained weights of U-net Segmentation Model trained for MRI image segmentation to lung segmentation model. Paper demonstrates using such similar healthcare data available at local system helps improving the performance of the model. The paper uses Explainable AI approach to explain the result. Using above three techniques, Lung segmentation AI model gets near perfect segmentation accuracy. © 2023 The Authors. Published by Elsevier B.V.

关键词： Image segmentation

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Safety Gear Compliance Detection Using data Augmentation-Assisted Transfer learning in Construction Work Environment

Safety Gear Compliance Detection Using Data Augmentation-Ass...

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2022 IEEE international conference on Electronics, Computing and Communication Technologies, CONECCT 2022

作者： Reyes, Ryan C. Sevilla, Rovenson V. Zapanta, Godofredo S. Merin, Jovencio V. Maaliw, Renato R. Ferrer Santiago, Al Technological University of the Philippines Department of Electrical Engineering Manila Philippines Pamantasan Ng Lungsod Ng Pasig College of Engineering Pasig Philippines University Lucban College of Engineering Southern Luzon State Quezon Philippines Eulogio "Amang" Rodriguez Insitute of Science and Technology Computer Science Manila Philippines

ISBN: (数字)9781665497817

ISBN: (纸本)9781665497817

The study provides a practical solution to the concern of detecting safety gear compliance in construction. This is imperative given that safety in the construction work environment is one of the greatest global concerns, and advancements in deep learning algorithms, especially in the area of machine learning and database management, enable the possibility to address this challenge in construction. This study developed a framework to recognize construction personnel's safety compliance with PPE, which is designed to be implemented into an organization's operational procedure. The Convolutional Neural Network model was constructed by employing machine learning to a basic version of the YOLOv3 deep learning model for the study. On the testing data, the detection method generated an F1 score of 0.9299, with a mean precision-recall rate of 92.99 %. The purpose of this study is to testify to the viability and applicability of machine vision-based methodologies for automated safety-related compliance processes on construction sites. © 2022 IEEE.

关键词： Object recognition

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Case machine learning Experience into Modelling and Simulation to Improve the Efficiency of Construction engineering Process Management 2

Case Machine Learning Experience into Modelling and Simulati...

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2nd international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2022

作者： Alanya-Beltran, Joel Kanwer, Budesh Buddhi, Dharam Jeelani, Syed Hamim Valderrama-Zapata, Carlos Jaiswal, Sushma Universidad Tecnológica del Perú Electronic Department Peru Science and Engineering Poornima Institute of Engineering and Technology Department of Computer Rajasthan Jaipur India Uit Uttaranchal University Uttarakhand Dehradun India Engineering Koneru Lakshmaiah Education Foundation. Deemed to Be University Department of Civil Andhra Pradesh Vaddeswaram522502 India Universidad San Ignacio de Loyola C.G. Bilaspur India

ISBN: (数字)9781665437899

ISBN: (纸本)9781665437899

The research work has highlighted that the main purpose of this research work is to identify the role of ML (machine learning) in simulation modelling and its effect on construction project engineering. In this research, it has been clearly identified that ML achieves the advantages such as risk identification, quality enhancement, and reduction of financial burden. These critical aspects are integrated into the engineering of the construction projects enabled by ML. The present research also shed light on the machine learning technologies for the completion of a construction project with much ease. Lastly, the research consists of some of the data collection methods by which the researcher could appropriately gather the research data and information for the smooth completion of the construction project. © 2022 IEEE.

关键词： machine learning

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A Study of Deep learning Techniques for Predicting Breast Cancer Types

A Study of Deep Learning Techniques for Predicting Breast Ca...

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international conference on Intelligent Systems and Sustainable Computing, ICISSC 2022

作者： Ashwini, P. Suguna, N. Vadivelan, N. Research Scholar Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Chidambaram India Associate Professor Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Chidambaram India Professor Department of Computer Science and Engineering Teegala Krishna Reddy Engineering College Hyderabad India

ISBN: (纸本)9789819947164

Breast cancer is today’s deadly health issue which causes high mortality in woman worldwide. The preliminary detection and classification may help for proper treatment of the same. Understanding the causes of this cancer using traditional machine learning techniques misread feature extraction are leading to a complication. Traditional machine learning models are best suited for feature-based raw data. The novel deep learning techniques have been introduced for diagnosis of breast abnormalities using various imaging modalities like mammogram, magnetic resonance imaging (MRI) and ultrasound with high accuracy. This survey focuses on challenges of classical machine learning models and supervene efficient prediction models using novel deep learning techniques. This review presents comparison analysis of traditional machine learning and deep learning models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Magnetic resonance imaging

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