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Rapid pattern recognition of Electric Submersible Pump Ammeter Card Based on Artificial Neural Network 28th

Rapid Pattern Recognition of Electric Submersible Pump Ammet...

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28th international conference on Computational and Experimental Engineering and Sciences, ICCES 2022

作者： Wang, Biao Han, Guoqing Lu, Xin Tan, Shuai Key Laboratory of Petroleum Engineering China University of Petroleum – Beijing Beijing102249 China

ISBN: (纸本)9783031020964

Ammeter card diagnosis is a typical diagnostic method for ESP working conditions. the traditional ammeter card pattern recognition method needs to be completed manually, which contains certain technical barriers and induces subjective errors. As a machine learning algorithm, artificial neural network (ANN) can make up for these errors. For the purpose of realizing fast, accurate and objective pattern recognition. We use the relationships between the current characteristics of the collected ammeter card data after pre-processing and the actual working conditions, establishes the ANN models. Using the established ANN models to diagnose the working condition, which has the advantages that the traditional ammeter card manual identification is incomparable. In this paper, the ANN model is established and then used for the pattern recognition by the method mentioned above. By extracting the ammeter card data not involved in the training stage, the diagnostic model of the working condition is verified, and reached high accuracy, which proved the feasibility and reliability of the diagnosis of the working condition using the ANN model in quickly identifying the ammeter cards. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Neural networks

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Students' Experiences on Covid-19: An Automatic Classification Using machine learning-Based Sentiment Analysis 4

Students' Experiences on Covid-19: An Automatic Classificati...

引用

4th international conference on Expert Clouds and Applications, ICOECA 2024

作者： Ursulum, Daniel T. Alipio, Manny S. Callitong, Grecilia A. Masuli, Freddie P. Pacris, Segundo D. Padirayon, Lourdes M. College of Information and Computing Sciences Cagayan State University - Sanchez Mira Sanchez Mira Philippines

ISBN: (纸本)9798350385793

the COVID-19 epidemic that struck the world in 2020 caused severe disruptions to the economy and society. the digitization of instruction has brought about a significant change in the realm of higher education. With this, Cagayan State University - Sanchez Mira introduces innovative techniques to combat the negative effects of pandemic to students' educational, social, family, personal and health journey. the main purpose of this study is to classify the experiences of students of CSU-Sanchez Mira during COVID-19 outbreak. In this experiment, machine learning was done using RapidMiner. the first level of categorization involved labeling statements as having good or negative experiences. the second level of segmentation entailed grouping experiences into categories based on how they related to personal, family, education, social, and health experiences. Statements that are unfavorable outnumber those that are favorable. Since the focus of this study focuses on the viewpoints of students, the bulk of statements were on experiences related to school. the f-score and accuracy increase when the aforementioned traits are added to the classifier, indicating that the features enhance the classifier. the majority of ratings were average, according to the results, which may have been a result of noise and insufficient data. Nevertheless, every accuracy score that was reported during the test was more than 60%, indicating a relatively accurate classification. the Naive Bayes approach has the most accurate classification outcomes. the 80.65% accuracy of Naive Bayes classification throughout the experiment is promising, aligning well with recent outcomes in opinion mining research in recent years. © 2024 IEEE.

关键词： automatic classification feature selection machine learning sentiment analysis

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Web Services Recommendation system using machine learning Algorithms 4

Web Services Recommendation system using Machine Learning Al...

引用

4th international conference for Emerging Technology, INCET 2023

作者： Bhopale, Shrikant Dnyandeo Sahu, Alok Pandyaji, Kishor K. Kalinga University Raipur India PVPIT Budhgaon India

ISBN: (纸本)9798350335750

the ability to forecast and propose the most relevant digital things to the end users is a key use of data analytics. Product, book, movie, news, and other types of recommendation systems are only a few examples. Recommending websites is a particularly difficult task in the field of service computing. In today's information-rich online environment, web sites are suggested based on the aggregated hit rate at which they were accessed by various people. It's possible this won't be the best fit for the user's query. thus, sophisticated recommendation engines are created, which utilise machine learning algorithms to foresee the user's interests. Hence, recommendation process-related web page customization has emerged. To accomplish customization, it is necessary to do research centred on the user's navigational habits and access pathways as they have been used before. this information is used to build a profile of the user, which is then used to provide more tailored recommendations. Innovative algorithms are being developed to enhance the effectiveness of recommender systems. Collaborative filtering, Content-based approach, Association rule mining, Clustering, Classification, Hybrid systems, Evolutionary and Swarm intelligence based algorithms, and so on are all examples of the types of algorithms used by pre-existing recommendation models. Cold start problem, popularity bias, sparsity, time consumption, inability to handle dynamic web sites, and lack of personalisation is only some of the issues with current models. © 2023 IEEE.

关键词： Recommender systems

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Comprehensive Teacher Teaching Posture Evaluation Model: Based on OpenPose Posture recognition in the Context of Digitalization and Intelligence Integration 4

Comprehensive Teacher Teaching Posture Evaluation Model: Bas...

引用

4th international conference on machine learning and Computer Application, ICMLCA 2023

作者： Yu, Zengyi Jin, Zhou Xue, Qinda Liu, Yingchun College of Educational Science and Technology Zhejiang University of Technology Hangzhou China

ISBN: (纸本)9798400709449

In the contemporary landscape, the assessment of teaching postures among educators traditionally relies on manual techniques, such as micro-teaching sessions and peer evaluations. However, these methods are often characterized by their inefficiency. Consequently, there is a pressing demand for automated approaches to evaluate and intervene in teaching postures. In the age of data-driven intelligence, this paper introduces the development of an intelligent teaching posture evaluation model. this model is constructed upon the foundation of the OpenPose posture recognition framework, which transforms unstructured video data into structured, analyzable data. Subsequent analysis of this structured data yields valuable metrics for the assessment of teaching postures. Ultimately, through the application of a fuzzy comprehensive evaluation method, this model enables quantitative assessments of teaching postures for both pre-service and in-service educators. the experimental results demonstrate that the evaluations generated by this model closely align with traditional manual assessments. this finding holds substantial implications for enhancing the efficiency and precision of teacher skill evaluations. Additionally, it offers innovative insights and methodologies for supporting educators in improving their teaching postures. © 2023 ACM.

关键词： Deep learning

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machine learning-Based Research on Reserve Prediction of Natural-Gas-Hydrates 4

Machine Learning-Based Research on Reserve Prediction of Nat...

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4th international conference on Artificial Intelligence and Computer Engineering, ICAICE 2023

作者： Ye, Sumin Mai, Shuxian Niu, Wenbo Yang, Tao Ye, Lei Zhang, Kun Tang, Huimin Li, Chenglong Peng, Boyang Yu, Qiannan College of Energy and Power Engineering Guangdong University of Petrochemical Technology China Co. Ltd. China Heilongjiang Key Laboratory of Reservoir Physics and Fluid Mechanics in Porous Medium China Geological Research Institute No. 4 Oil Production Plant of Daqing Oilfield Limited Company China

ISBN: (纸本)9798400708831

Currently, there are challenges in reserve prediction of natural-gas-hydrates, including difficulties in obtaining parameters, limited calculation accuracy, and complex processes. machine learning is a data-driven predictive method with strong generalization capabilities, capable of abstracting and inducing knowledge and rules from basic data witch has made significant advancements in various fields and has been preliminarily applied in fields of earth sciences and energy exploration and development. Various machine learning algorithms for determining the parameters of gas hydrate reserves were introduced in this paper. the support vector machine algorithm was employed to calculate effective area and thickness, while neural network algorithm was introduced to calculate porosity, saturation, and decomposition coefficient for the determination of parameters of natural-gas-hydrates reserves in Shenhu area of China's offshore, and then the feasibility and accuracy of the method was verified. Compared to evaluation results based on trial mining or numerical simulation, the machine learning-based reserve prediction method demonstrates high predictive accuracy, clear processes, and simple structure, making it highly valuable for future research on storage and transportation of natural-gas-hydrates and provides foundation for future industrial applications. © 2023 ACM.

关键词： Forecasting

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Transfering RGB-D Models to RGB by Multi-View Embedding Space learning for Object Detection 4

Transfering RGB-D Models to RGB by Multi-View Embedding Spac...

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4th international conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2022

作者： Liu, Jinde Zhang, Zhang University of Chinese Academy of Sciences Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665463997

In this paper, we propose a new framework for detecting objects in RGB images captured by conventional cameras by leveraging a set of labeled RGB-D data. We formulate this problem into a new multi-view learning problem, where we have the depth information in the training set that is not available in the testing set. To solve this problem, we use a three-view CCA learning method which combines visual features, depth features, and label information together to get the projection matrices. Sufficient experiments are done to demonstrate the effectiveness of our method, and improvement measures in the future are also proposed. © 2022 IEEE.

关键词： Object detection

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1st international Virtual conference on Visual pattern Extraction and recognition for Cultural Heritage Understanding (VIPERC 2022) 1

1st International Virtual Conference on Visual Pattern Extra...

引用

1st international Virtual conference on Visual pattern Extraction and recognition for Cultural Heritage Understanding, VIPERC 2022

作者： Amelio, Alessia Montelpare, Sergio Ursino, Domenico InGeo University "G. d’Annunzio" Chieti-Pescara Viale Pindaro 42 Pescara65127 Italy DII Marche Polytechnic University Via Brecce Bianche 12 Ancona60131 Italy

this document introduces the 1st international Virtual conference on Visual pattern Extraction and recognition for Cultural Heritage Understanding (VIPERC 2022), a premier forum for presenting the state-of-the-art, new research, ongoing work, academic and project reports in advanced statistics and machine learning, 3D modelling and simulation, knowledge representation, intelligent systems, information retrieval and software engineering, for visual pattern extraction, analysis and recognition to preserve the cultural heritage. © 2022 Copyright for this paper by its authors.

关键词： 3D modeling

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Deep learning based Human Activity recognition in Smart Home 4

Deep Learning based Human Activity Recognition in Smart Home

引用

4th IEEE international conference on data Engineering and Communication Systems, ICDECS 2024

作者： Anbazhagan, K. Swamy, G. Janani, R. Farakte, Amarsinh Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Vignana Bharathi Institute of Technology Department of Computer Science and Engineering Andhra Pradesh India Panimalar Engineering College Department of Computer Science and Business Systems Chennai India Sant Gajanan Maharaj College of Engineering Department of Electronics and Telecommunication Engineering Maharashtra India

ISBN: (纸本)9798350393354

Human activity recognition (HAR) in the context of smart homes has attracted considerable interest because to its potential to increase residents' quality of life, safety, and energy efficiency. this study dives deeper into the use of deep learning (DL) algorithms for human activity recognition in a smart home scenario. A realistic approach for distinguishing distinct human behaviors like as walking, sitting, cooking, and sleeping is provided by the combination of artificial intelligence and sensor technology. this study offers FCN-LSTM, a revolutionary technique for HAR in smart homes that combines a Fully Convolutional Network (FCN) architecture with a Long Short-Term Memory (LSTM). the proposed FCN-LSTM model capitalises on the strengths of both architectures, combining the FCN's ability to capture specific information spatially with the LSTM's experience modelling temporal correlations. this hybrid method efficiently captures both spatial and temporal variability inherent in human activities, overcoming the HAR issues. the FCN-LSTM model is trained on a huge dataset acquired from sensors deployed in a smart home setting, which includes a wide range of actions encompassing ordinary daily routines. this method is used to the Wireless Sensor data mining (WISDM) dataset, which was produced from data collected from a large number of people who engaged in six different activities: walking, sitting, downstairs, running, standing, and upstairs. According on the experimental data, the FCN-LSTM model outperforms established approaches and standalone systems. the results show that the FCN-LSTM algorithm performs well in recognizing human actions. the FCN-LSTM algorithm achieves a testing precision of 95.99%, with recall and f-measure of 97.11% and 99.99%, respectively. © 2024 IEEE.

关键词： Long short-term memory

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5E Teaching Model Based on Neural Network data Sampling and Clustering 4

5E Teaching Model Based on Neural Network Data Sampling and ...

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4th international conference on Intelligent Computing and Human-Computer Interaction, ICHCI 2023

作者： Ye, Jingjing Architecture and Design College Nanchang University Nanchang Jiangxi330031 China

ISBN: (纸本)9798350343113

Clustering analysis plays a decisive role in data processing in information exchange system. Select internal connection to make database data. Internal connections for in-group database data should be as large as possible, and internal connections for non-in-group database data should be as small as possible. through the way of teaching-Testing-Analysis and summary, students' scores are taken as the only reference index. this mode only gets the results on the final scores, which can not predict and arrange the knowledge mastery of different types of students, and also causes the uncertainty of professional courses. through the introduction of 5E teaching model, taking into account the basic indicators such as reading rate, it is easy to capture students' interest habits. In this paper, based on the basic indicators of professional courses, data sampling is carried out from the information of students' interest in courses. the indicators of self-mastery ability and learning time are the comprehensive performance of students' thinking ability and the attractiveness of professional courses. then the least square method of clustering analysis is adopted to divide the students into different types according to their interest. Finally, the clustering subdivision of data sampling is achieved. 224 pattern works were selected for experimental analysis. After the experimental test, comparative analysis of the algorithm effect, this algorithm can measure the results of the students' pattern works algorithm recognition, and judge the degree of coincidence of the test situation. Compared with random forest algorithm, genetic algorithm and traditional mode, it is obvious that the test accuracy of this algorithm is more than 97%, while the test accuracy of traditional mode is the lowest-less than 95%. © 2023 IEEE.

关键词： Students

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Balancing Label Imbalance in Federated Environments Using Only Mixup and Artificially-Labeled Noise 4th

Balancing Label Imbalance in Federated Environments Using O...

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4th international conference on pattern recognition and Artificial Intelligence, ICPRAI 2024

作者： Rabbani, Tahseen Sang, Kyle Huang, Furong University of Maryland College ParkMD20742 United States

ISBN: (纸本)9789819787043

Clients in a distributed or federated environment will often hold data skewed towards differing subsets of labels. this scenario, referred to as heterogeneous or non-iid federated learning, has been shown to significantly hinder model training and performance. In this work, we explore the limits of a simple yet effective augmentation strategy for balancing skewed label distributions: filling in underrepresented samples of a particular label class using pseudo-images. While existing algorithms exclusively train on pseudo-images such as mixups of local training data, our augmented client datasets consist of both real and pseudo-images. In further contrast to other literature, we (1) use a DP-Instahide variant to reduce the decodability of our image encodings and (2) as a twist, supplement local data using artificially labeled, training-free ‘natural noise’ generated by an untrained StyleGAN. these noisy images mimic the power spectra patterns present in natural scenes which, together with mixup images, help homogenize label distribution among clients. We demonstrate that small amounts of augmentation via mixups and natural noise markedly improve label-skewed CIFAR-10 and MNIST training. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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