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检索条件"任意字段=3rd International Conference on Machine Learning and Computer Application, ICMLCA 2022"

共 1897 条记录，以下是391-400 订阅

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An Effective machine learning-based Segmentation and Feature Extraction Technique for Muscular-Disorder 3

An Effective Machine Learning-based Segmentation and Feature...

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3rd international conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Dhanyal, Somashekhar S. Nandyal, Suvarna BLDEA's V. P. Dr. P. G. Halakatti College of Engineering and Technology Department of Artificial Intelligence and Machine Learning Karnataka Vijayapura586103 India Poojya Doddappa Appa College of Engineering Department of CSE India

ISBN: (纸本)9798350322842

application of technology for the classification of sports and exercise, Yogasan identification is now common practice for numerous people in modern society especially when dealing with muscular disorders. Over the past three decades, research has focused on motivating and difficult problems related to the usage of computer vision for target recognition and identification. The skeleton parts of the head, neck, arms, hands, legs, and feet are designed and stored as a vector in this research to identify the human yoga asana pose. These skeleton elements are also responsible for head movements, hand postures, and standing postures. Every single interaction between the skeleton joints makes a pose, and every feature is described as a set of moveable joints. The weighted skeletonization-based image segmentation is proposed for the enhanced Yogasan recognition from the selected keyframes, the most relevant keyframes are selected using the Euclidean distance measure. The performance of the proposed method is analyzed by considering the features as well as the training percentage. The accuracy, precision, and sensitivity based on the features of the multi-SVM classifier are 89.020 %, 89.720 %, and 90.855 %, and the training percentage using the multi-SVM classifier is 94.268 %, 93.297 %, and 97.375 %. © 2023 IEEE.

关键词： Support vector machines

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Bird's Nest Detection Algorithm for Transmission Lines Based on Deep learning 3

Bird's Nest Detection Algorithm for Transmission Lines Based...

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3rd international conference on computer Vision, Image and Deep learning and international conference on computer Engineering and applications, CVIDL and ICCEA 2022

作者： Ge, Zhao Li, Hongwen Yang, Rui Liu, Haifeng Pei, Shaotong Jia, Zhihui Ma, Ziru State Grid Xiong'an New Area Electric Power Supply Company Baoding China North China Electric Power University Hebei Provincial Key Laboratory of Power Transmission Equipment Security Baoding China

ISBN: (纸本)9781665459112

The bird's nest on the power transmission tower may cause the occurrence of bird flash accidents and threaten the safe and reliable operation of the power grid. The realization of the independent identification and positioning of the bird's nest has always been a research hotspot. With the gradual deepening of the application of UAV inspection, higher requirements are put forward for the accuracy and speed of the bird's nest recognition algorithm. This paper proposes a bird's nest defect recognition method based on YOLOv5, which is composed of backbone network, FPN and YOLO head. After multiple rounds of training on the construction of the bird's nest defect database and model of the transmission line, the independent identification and positioning of the bird's nest has been realized. The results show that the recognition rate of the YOLOv5 model for the bird's nest can reach 83.4%, and the FPS can reach 85.32. The recognition algorithm based on YOLOv5 proposed in this paper can meet the real-time and accuracy requirements of UAV inspection for target detection. © 2022 IEEE.

关键词： Electric lines

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A Study of Various Word Embeddings in Deep learning 3

A Study of Various Word Embeddings in Deep Learning

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3rd international conference for Emerging Technology, INCET 2022

作者： Shah, Parth Shah, Smit Joshi, Saurav K.J. Somaiya College of Engineering Computer Engineering Mumbai India

ISBN: (纸本)9781665494991

With the number of users and reviews rising on the internet, and the liberty was given to post anything on it, it is still a major issue to identify the sentiment of the reviews. The traditional machine learning models are taking a lot of computational time to get desired results. The advancement in the field of deep learning has left people wondering whether it would provide better results for such applications. Word Embeddings play a huge role in the classification of text, as they represent similar meaning documents with equivalent representation. The result of the deep learning model trained on a particular set of data depends upon the sort of word embedding used. In this work, we intend to present a study of various embeddings like GloVe, BERT, Word2vec, and Fast-text using a proposed model. The research was conducted on the IMDB movie review dataset and the result displayed that GloVe embeddings produced the best result with an accuracy of 87.936 % on our proposed model. © 2022 IEEE.

关键词： Embeddings

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Examine Bitcoin Price Predictability with machine learning Algorithms 3

Examine Bitcoin Price Predictability with Machine Learning A...

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3rd international conference on Intelligent Computing and Human-computer Interaction, ICHCI 2022

作者： Shi, Jingwen University of New South Wales Sydney2052 Australia

ISBN: (纸本)9781510661301

As the most-traded digital asset, Bitcoin receives a tremendous increase in investment interests. The primary purpose of this paper is to examine and compare two commonly applied machine learning algorithms on their capability and feasibility in Bitcoin price prediction. The regression models of Extreme Gradient Boosting and Long Short-Term Memory are selected as the investigated objects in this paper. The experiments are evaluated by considering the extra impacts of sample dimensions and time-series frequency, along with the trade-offs between prediction accuracy (measured by residual error) and computational efficiency (measured by computing time). Long Short-Term Memory, as a more convoluted deep learning method, achieves better accuracy when a daily dataset with limited input features is used. However, its predictability has considerably decreased and thus become less efficient than XGBoost, when more peripheral information and higher frequency data points (trading price every 15 minutes) are available. Besides algorithmic complexity, Long Short-Term Memory also takes much longer computing time than Extreme Gradient Boosting, making it a less applicable model to use when dealing with large sample sizes. © 2023 SPIE.

关键词： Parallel processing systems

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Research and implementation of fingerprint erasure technology for videos based on Google MediaPipe 3

Research and implementation of fingerprint erasure technolog...

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3rd international conference on computer Vision and Data Mining, ICCVDM 2022

作者： Zhang, Tao Liu, Hongying Nanjing University of Science & Technology Zijin College Wenlan Rd. 89 Nanjing210033 China

ISBN: (纸本)9781510661363

Fingerprints are used in many fields as important biological data of people. As one of people's important personal privacy, fingerprints are also prone to leakage. Images and videos posted on social media are invitations to this problem. Research in the direction of protection of fingerprint privacy is nearly vacant. Therefore, this paper presents a technical implementation for locating and erasure human fingerprints for videos. This implementation adopted Google's open-source MediaPipe machine learning framework to identify the hand in the frame and obtain crucial landmark information. The fingerprint core range and finger state are deduced from landmark points' positional relationship and distance. This method can efficiently erase as many as 88% of frames with one hand presenting. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Palmprint recognition

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On the Development of Mobile application Breathing Analyzer to Detect Breathing Abnormalities 3

On the Development of Mobile Application Breathing Analyzer ...

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3rd international conference on Intelligent Data Science Technologies and applications, IDSTA 2022

作者： Hall, Dylan Gladdney, Anthony Labriny, Yasimine Alqudah, Yazan A University of West Florida Electrical and Computer Engineering PensacolaFL32514 United States

ISBN: (数字)9781665499606

ISBN: (纸本)9781665499606

This work presents a solution to monitor breathing patterns to detect any signs of abnormalities and ensure properly ventilating pulmonary system. The solution includes the ability to track and detect coughing. The system can be used by individuals to monitor breathing or athletes to monitor performance while exercising. The solution utilizes machine learning algorithms implemented through Edge Impulse to classify and analyze breathing patterns. It also features a user mobile application to record and transmit data and receive the classification results. © 2022 IEEE.

关键词： machine learning

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Personalized Federated learning with Data Heterogeneity Constraints 3

Personalized Federated Learning with Data Heterogeneity Cons...

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3rd international conference on computer Science and Management Technology, ICCSMT 2022

作者： Lin, Junke Wu, Xiangjun Henan University School of Computer and Information Engineering Kaifeng China Henan University School of Software Kaifeng China

ISBN: (纸本)9798350320404

Federated learning, a new distributed, edge-device based machine learning paradigm that does not require the collection of raw data from the client and has a significant privacy protection effect. However, since the data distribution of each client is Noniid, it will lead to the degradation of the model performance. We propose a new personalization federated learning approach to alleviate this problem that uses base layer and personalization layer, In addition, we add a restriction that we use the degree of Noniid (DoN) to constrain the personalization layer training. The larger the DoN, the more we allow the local model personalization layer to deviate from the global model personalization layer. Further, we have experimentally demonstrated that our method is superior to the federated averaging algorithm. © 2022 IEEE.

关键词： learning systems

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Ensemble machine learning Algorithms for Predicting Cardiovascular Disease 3rd

Ensemble Machine Learning Algorithms for Predicting Cardiova...

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3rd international conference on Innovations in Computational Intelligence and computer Vision, ICICV 2022

作者： Deneshkumar, V. Jithu, G. Jebitha, R. Department of Statistics Manonmaniam Sundaranar University Abishekapatti Tamil Nadu Tirunelveli627012 India

ISBN: (纸本)9789819926015

Cardiovascular disease has evolved into one of the most dangerous diseases affecting human life. During the recent decade, it has emerged as one of the top causes of death among individuals all over the world. A correct identification of heart disease on time is more helpful for preventing the patients from high risk. Interpreting the complex interactions in medical data is difficult for medical practitioners. machine learning algorithms extensively utilized for extremely beneficial in accurately diagnosing cardiac disease in a short period of time. The main objective of this paper is to introduce several heart disease prediction models utilizing an ensemble of machine learning algorithms to identify the best characteristics and also compared the performance of ensemble machine learning methods based on classification and regression models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Diseases

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CIBDA 2022 - 3rd international conference on computer Information and Big Data applications

CIBDA 2022 - 3rd International Conference on Computer Inform...

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2022 3rd international conference on computer Information and Big Data applications, CIBDA 2022

ISBN: (纸本)9783800758760

The proceedings contain 244 papers. The topics discussed include: a proposed Siamese convolutional neural network for fingerprint recognition;research of early warning of corporate financial crisis based on machine learning neural network;corporate influence analysis by integrating social network models;research named entity recognition based on transfer learning;classification of complex network based on improved hierarchical model;handwritten character recognition based on convolution neural network models;back propagation neural network based stroke prediction;design of a network storage system based on peer-to-peer network with supernode;hybrid Weibo tags and topic mining for user similarity model;stability analysis of power grid based on generative adversarial network;convolutional neural network for English character recognition;build a precision marketing prediction model for intelligent recommendation technology;application of text mining technology in power data prediction;research on used car transaction price and transaction cycle based on model fusion;and underwater wireless sensor networks trustworthiness evaluation using Monte Carlo simulation.

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Fetal Health Classification using AI from Cardiotocography Features 3

Fetal Health Classification using AI from Cardiotocography F...

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3rd international conference on Innovative Mechanisms for Industry applications, ICIMIA 2023

作者： Jyostna, G. Goyal, Himanshu Rai Soudagar, Manzoore Elahi M. Parikh, Swapnil Patil, Harshal Alaskar, Kamal Potti Sriramulu Chalavadi Mallikarjuna Rao College of Engineering & Technology Department of CSE-DS Andhra Pradesh Vijayawada520 012 India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of VLSI Microelectronics Tamil Nadu Chennai602105 India Parul Institute of Engineering and Technology Department of Computer Science and Engineering Parul University Post Limda Gujarat Waghodia391760 India Computer Science and Engineering Symbiosis Institute of Technology Maharashtra Pune412 115 India Department of Computer Application Institute of Management Maharashtra Kolhapur416003 India

ISBN: (纸本)9798350343632

Maternal health issues during pregnancy are a major global concern because they result in fetal death, which is more frequent in rising and poor countries. Cardiotocography (CTG) is the major non-invasive and cost-effective method for monitoring fetal health. CTG delivers critical parameters such as Fetal Heart Rate (FHR) and Uterine Contraction (UC), with a total of 21 features for accurate monitoring. These characteristics assist obstetricians in categorizing fetal health as normal, suspect, or pathological. However, differences in CTG interpretation among obstetricians frequently result in incorrect procedures. This work presents a unique way to detect deficient fetal states using a hybrid machine learning (ML) model that combines Support Vector machine (SVM) and Random Forest (RF). The UCI repository dataset is pre-processed, which includes balancing, outlier reduction, and feature selection. The hybrid model's performance is then compared to that of classic ML models such as SVM, K-Nearest Neighbour (KNN), RF, and Decision Tree (DT). The hybrid model outperforms the other models in simulation, attaining the greatest accuracy rate of 98.83%. This novel ML-based technique has the potential to improve fetal health detection while reducing the influence of human CTG interpretation discrepancies across healthcare providers. © 2023 IEEE.

关键词： Cardiotocography Correlation Features Fetal Hybrid machine learning Outliers UCI Repository

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