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A data Mining Method Based on Natural Language processing and Machine Learning

A Data Mining Method Based on Natural Language Processing an...

引用

Advances in Electrical Engineering and Computer Applications( AEECA), 2020 IEEE international conference on

作者： Zhihang Tang Rong Lin Zikai Zhou Zipei Tang School of Computer and Communication Hunan Institute of Engineering Xiangtan China School of Finance and Information Ningbo University of Finance and Economics Ningbo China

The data source and data collection method are described, and the results of initial data are presented. And, the method of word segmentation and stop words removal in the preprocessing process is used to clean the data to obtain the data text before clustering. Then, the application scenarios and main characteristics of K-means algorithm are introduced. The algorithm process and calculation methods of the traditional K means algorithm and the improved dichotomous K-means algorithm are introduced in two sections, finally, the experimental text clustering effects of the two algorithms are tested through experiments, and the experimental results are briefly analyzed. It is found that the improved dichotomous K means clustering algorithm has more stable clustering results, and can get more accurate keywords.

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data Collection in Advanced Metering Infrastructure Using UAVs 4

Data Collection in Advanced Metering Infrastructure Using UA...

引用

4th international conference on Communications, Signal processing, and their Applications, ICCSPA 2020

作者： Shahin, Haiam Shaaban, Mostafa F. Ismail, Mahmoud H. Mourad, Hebat-Allah Khattab, Ahmed Department of Electronics and Communications Engineering Faculty of Engineering Cairo University Giza12613 Egypt Department of Electrical Engineering American University of Sharjah PO Box 26666 Sharjah United Arab Emirates

ISBN: (纸本)9781728165356

The use of unmanned aerial vehicles (UAVs) in the collection of data from wireless devices, sensor nodes, and Internet of Things (loT) devices has recently received significant attention. In this paper, we study the data collection process from a set of smart electric meters enabled by UAVs. Our objective is to minimize the total annual cost of the electric utility by jointly optimizing the number of UAVs, their power source sizing (represented by the UAVs' batteries), the selected starting point of each UAV as well as the data collection trip planning. The problem is formulated as a mixed-integer nonlinear programming (MINLP) problem, which is generally difficult to be optimally solved. In order to find a solution, the problem has been decoupled into two sub-problems where a candidate UAV and a number of buildings are first grouped into trips via genetic algorithms (GA), and then the optimum trip path is found using a travelling salesman problem (TSP) branch and bound algorithm in the second. © 2020 IEEE

关键词： data acquisition

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Fabrication and Optimization of a Four-Bit Absolute Comparator

Fabrication and Optimization of a Four-Bit Absolute Comparat...

引用

Computers, Information processing and Advanced Education (CIPAE), 2020 international conference on

作者： Hongyu Deng Maynooth International Engineering College Fuzhou University Fujian China

ISBN: (数字)9798331527662

ISBN: (纸本)9798331527679

With the development of artificial intelligence, AI large models are becoming more and more popular. This is followed by an increasing demand for graphics card computing power. There are many areas in the graphics card, such as image and video processing, 3D rendering. These areas need to process a large amount of data, in order to reduce the computing power consumed by the graphics card. People can use the comparator for edge detection and threshold processing or noise removal, filtering waveform and so on. The purpose of this paper is to make a four-bit absolute comparator. In this paper, a four-bit absolute value comparator is made by circuit principle and truth table and the circuit is simplified by formula simplification method to make the logic circuit more concise. And through MATLAB simulation test, we successfully realized the function. Finally, through the critical path, changing the size of the logic gates and Vdd input increases the delay to some extent but corresponds to a significant reduction in energy loss. After optimization, the energy loss is reduced by 48.13% to 38.40 J at 1.5 times delay.

关键词： Graphics Fabrication Energy loss Three-dimensional displays Logic circuits Logic gates Rendering (computer graphics) Delays Artificial intelligence Optimization

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Selective Word Substitution for Contextualized data Augmentation 27th

Selective Word Substitution for Contextualized Data Augmenta...

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27th international conference on Applications of Natural Language to Information Systems (NLDB)

作者： Pantelidou, Kyriaki Chatzakou, Despoina Tsikrika, Theodora Vrochidis, Stefanos Kompatsiaris, Ioannis Ctr Res & Technol Hellas Informat Technol Inst Thessaloniki Greece

ISBN: (纸本)9783031084737;9783031084720

The often observed unavailability of large amounts of training data typically required by deep learning models to perform well in the context of NLP tasks has given rise to the exploration of data augmentation techniques. Originally, such techniques mainly focused on rule-based methods (e.g. random insertion/deletion of words) or synonym replacement with the help of lexicons. More recently, model-based techniques which involve the use of non-contextual (e.g. Word2Vec, GloVe) or contextual (e.g. BERT) embeddings seem to be gaining ground as a more effective way of word replacement. For BERT, in particular, which has been employed successfully in various NLP tasks, data augmentation is typically performed by applying a masking approach where an arbitrary number of word positions is selected to replace words with others of the same meaning. Considering that the words selected for substitution are bound to affect the final outcome, this work examines different ways of selecting the words to be replaced by emphasizing different parts of a sentence, namely specific parts of speech or words that carry more sentiment information. Our goal is to study the effect of selecting the words to be substituted during data augmentation on the final performance of a classification model. Evaluation experiments performed for binary classification tasks on two benchmark datasets indicate improvements in the effectiveness against state-of-the-art baselines.

关键词： Contextual data augmentation Word substitution Binary classification

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Federated Reservoir Computing Neural Networks

Federated Reservoir Computing Neural Networks

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international Joint conference on Neural Networks (IJCNN)

作者： Bacciu, Davide Di Sarli, Daniele Faraji, Pouria Gallicchio, Claudio Micheli, Alessio Univ Pisa Dept Comp Sci Pisa Italy

ISBN: (纸本)9780738133669

A critical aspect in Federated Learning is the aggregation strategy for the combination of multiple models, trained on the edge, into a single model that incorporates all the knowledge in the federation. Common Federated Learning approaches for Recurrent Neural Networks (RNNs) do not provide guarantees on the predictive performance of the aggregated model. In this paper we show how the use of Echo State Networks (ESNs), which are efficient state-of-the-art RNN models for time-series processing, enables a form of federation that is optimal in the sense that it produces models mathematically equivalent to the corresponding centralized model. Furthermore, the proposed method is compliant with privacy constraints. The proposed method, which we denote as Incremental Federated Learning, is experimentally evaluated against an averaging strategy on two datasets for human state and activity recognition.

关键词： Training Recurrent neural networks Computational modeling Training data Predictive models Reservoirs Collaborative work

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Big data Platform for Oil and Gas Production Based on Apache Spark 1st

Big Data Platform for Oil and Gas Production Based on Apache...

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1st international conference on Industrial IoT, Big data and Supply Chain (IIoTBDSC)

作者： Qing, Peng Li, Yi Luo, Shuqin Xu, Zhuoqun Tsinghua Univ Shenzhen 518000 GD Peoples R China

ISBN: (纸本)9789813361430;9789813361416;9789813361409

For promoting the construction of intelligent oil field and applying big data technology to the daily production activities of oil field, this paper proposes a data mining platform based on Apache Spark multi-functional oil and gas production big data. This platform combines real-time and historical data processing-related framework and machine learning framework, designs massive oil and gas production data processing for different oil fields, supports real-time and offline processing data mining and other functions, supports the prediction of some data using related machine learning algorithms, supports the cleaning of large amounts of data, is used to train various deep learning models and will provide corresponding interfaces and permissions to relevant oil fields. Docker container is used to build the training environment of deep learning and machine learning, and the Kubernetes framework is used to complete the scheduling function. Struts, Spring and Hibernate relatively classic background processing framework is adopted at the Web framework level of the platform, different functions are decomposed into independent functional modules to reduce coupling, and agile development is used to improve the extended performance of the whole big data platform. This platform allows our data analysts to focus on data analysis and model training, without spending a lot of time and effort to deal with data problems, while the related calculation results are returned to other oil companies in an interface way or provide relevant operation authority to the field operation and maintenance personnel to access the data platform and data analysis.

关键词： Big data data mining Hadoop Machine learning Spark Web

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Machine Learning-Based Classification of Mental Health State Using the DASS-21 Profile

Machine Learning-Based Classification of Mental Health State...

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Biomedical Engineering international conference (BMEiCON)

作者： Khanita Duangchaemkarn Patiphan Khammarew Supavadee Aramvith Department of Pharmacy Practice School of Pharmaceutical Sciences University of Phayao Phayao Thailand Pharmacy research extern under the Doctor of Pharmacy Program School of Pharmaceutical Sciences University of Phayao Phayao Thailand Multimedia Data Analytic and Processing Research Unit Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Bangkok Thailand

ISBN: (数字)9798331505431

ISBN: (纸本)9798331505448

This study aimed to determine the most effective machine learning model for classifying emotional states of depression, anxiety, and stress based on the DASS-21 profile. Six machine learning models were developed and tested using 2,602 records from the University of Phayao Counselling Center, collected between 23 July and 16 August 2020. The results demonstrate that the deep learning model provided the highest accuracy and precision, exceeding 90% for all three emotional categories, with an AUC greater than 99%. These findings introduce an innovative approach for classifying emotional states of depression, anxiety, and stress using the DASS-21 profile as a benchmark. Incorporating additional features into the model could further enhance its utility in clinical decision-making, improving the accuracy of mental health screening and diagnosis.

关键词： Deep learning Employee welfare Adaptation models Accuracy Anxiety disorders Decision making Mental health Depression Reliability engineering data models

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An Adaptive Median Filtering Denoising Algorithm for "Pepper & Salt" Noised Image 13

An Adaptive Median Filtering Denoising Algorithm for "Pepper...

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13th international conference on Graphics and Image processing (ICGIP)

作者： Qin, Fengqing Jiang, Li Xie, Liman Cao, Lilan Zhu, Lihong Li, Chaorong Zhang, Yanhong Wen, Xingdong Yibin Univ Fac Artificial Intelligence & Big Data Yibin 644000 Peoples R China

ISBN: (纸本)9781510650435;9781510650428

There are different degrees of "pepper & salt" noise in most acquired images. To overcome the shortcomings of traditional median filtering algorithm, an adaptive median filtering algorithm is proposed. Firstly, the appropriate filter window size is automatically selected according to the intensity of noise in the image. Secondly, whether each pixel is a noise point is judged. The noise point outputs the median value and the non-noise point outputs the original value. Thirdly, the denoising effect of the two algorithms was compared by simulation experiments, and the influence of different "pepper & salt" noise density on denoising effect was tested. From the perspective of subjective visual effect and objective PSNR value, the denoising effect of adaptive median filtering is better than that of traditional median filtering.

关键词： "Pepper & salt" noise image denoising traditional median filter adaptive median filter

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Machine-Learning-Assisted Well-Log data Quality Control and Preprocessing Lab

Machine-Learning-Assisted Well-Log Data Quality Control and ...

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Abu Dhabi international Petroleum Exhibition and conference 2022, ADIPEC 2022

作者： Gerges, Nader Makarychev, Gennady Barillas, Luisa Ana Maarouf, Alaa Madhavan, Midhun Gore, Sonal Almarzooqi, Lulwa Wlodarczyk, Sylvain Kloucha, Chakib Kada Mustapha, Hussein ADNOC United Arab Emirates Schlumberger

ISBN: (纸本)9781613998724

Log and core data has been collected in the mature supergiant fields of the Middle East over many decades with variable data quality and vintages. These data could be affected by artifacts created by borehole conditions, different logging tools, water- or oil-based mud systems, and different processing parameters. The data are frequently lacking traceability to borehole/experiment conditions required to apply appropriate and consistent corrections for further modeling workflows. As a result, petrophysicists must perform substantial log, core quality control (QC) and editing prior to interpreting logs or using core data. The purpose of this paper is to discuss machine-learning (ML) applications grouped in the solution to automatically QC and process core and log data for hundreds of wells. The goal was to detect poor data and outliers to apply corrections that mimic human-led interpretation for at least 80 to 90% of the data processed. For several data sets and cases, ML was successfully used to various aspects of log editing. (Akkurt et al. 2018;Liang et al. 2019;Mawlod et al. 2019;Singh et al. 2020). However, as a result of our 2020 study, we discovered numerous lessons that are addressed in this work: • Geological and geospatial information are potentially as valuable as well data and cannot be overlooked by any ML algorithm working with well data at the field scale. • ML algorithms are more efficient than human-led data processing and can produce more accurate and consistent outcomes. However, the parameter selection and QC of outcomes require an expert assessment. Thus, ML-based applications cannot be "black boxes" and must include a user-interaction toolbox for efficient workflow control. • To deliver a more efficient solution in terms of performance over large data sets, the full capacity of cloud-based technologies should be utilized, thereby enabling the parallelization of operations while simultaneously constructing and analyzing multiple solution scenarios

关键词： Quality control

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UAV Inspection Algorithm for Power Transmission and Distribution Lines Based on Satellite-ground Fusion Network

UAV Inspection Algorithm for Power Transmission and Distribu...

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Advances in Electrical Engineering and Computer Applications( AEECA), 2020 IEEE international conference on

作者： Chao Huang Jigao Song Lin Zhang Wenwei Chen Liang Han Jiaxu Song Jiaxing Lian Beijing Fibrlink Communication Co. Ltd. Beijing China

ISBN: (纸本)9798350355260

Due to characteristics of unmanned aerial vehicle (UAV) such as simple operation, easy portability and applicability to a variety of operating environments, transmission and distribution line failure UAV inspection is applied in the inspection field. However, mountainous areas and post-disaster areas may not have ground communication network coverage. It directly affects the efficiency and effectiveness of transmission and distribution line UAV inspection. In addition, a set of transmission and distribution line UAV inspection algorithms suitable for mountainous areas is provided, which is an important problem currently faced by transmission and distribution line UAV inspection. Therefore, the paper proposes a satellite-ground fusion network framework based on the consideration of the effect of wind on recognition accuracy and energy usage. This framework can provide efficient and low-cost communication services, and can provide uninterrupted and seamless communication services. Then, the paper proposes a UAV inspection algorithm for power transmission lines based on a satellite-ground fusion network. This method can reduce the effect of wind on UAVs during the inspection process by optimizing the inspection area and the inspection sequence of UAV flight sorties. Finally, the simulation verification results show that the algorithm proposed in the paper has obvious advantages over other algorithms in terms of energy consumption of UAV identification and data processing. It also improves the inspection effect and achieves intelligent inspection of distribution lines.

关键词： Fault diagnosis Energy consumption Power transmission lines Accuracy Trajectory planning Wind speed Power transmission Inspection Autonomous aerial vehicles data processing

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