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检索条件"任意字段=5th International Conference on Applied Machine Learning and Data Analytics, AMLDA 2022"

共 261 条记录，以下是61-70 订阅

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Research of Heart Disease Prediction Based on machine learning 5

Research of Heart Disease Prediction Based on Machine Learni...

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5th international conference on Advanced Electronic Materials, Computers and Software Engineering, AEMCSE 2022

作者： Ouyang, Shuge University of Minnesota Twin City MinneapolisMN55455 United States

ISBN: (数字)9781665484749

ISBN: (纸本)9781665484749

the use of massive clinical data in the medical field for supporting medical decision support is an inevitable development trend. Medical decision support is based on a variety of data sources accumulated and acquired in real-time in the clinic, and various machine learning algorithms are used to achieve classification of patient disease types or prediction of disease risks. this paper assists in performing cardiac disease prediction starting from different heart disease types (coronary heart disease) and data sets, summarizing the currently adopted machine learning diagnosis and prediction methods, highlighting the characteristics and differences of these methods, and analyzing the challenges and future developments. the results show that machine learning techniques have a wide range of applications in cardiac diseases. However, each machine learning method can only be applied to a specific scope due to the non-uniformity of medical data. At the end of the article, the prediction of heart disease is summarized. © 2022 IEEE.

关键词： Forecasting

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data analytics IN FARMING: RICE PRICE PREDICTION IN ANDHRA PRADESH 5

DATA ANALYTICS IN FARMING: RICE PRICE PREDICTION IN ANDHRA P...

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5th international conference on Multimedia, Signal Processing and Communication Technologies (IMPACT)

作者： Reddy, Pundru Chandra Shaker Suryanarayana, G. Prakash, L. N. C. K. Yadala, Sucharitha REVA Univ Sch Comp & Informat Technol Bengalure India Vardhaman Coll Engn Dept Comp Sci & Engn Hyderabad India CVR Coll Engn Dept Comp Sci & Engn Hyderabad India CMR Inst Technol Dept Comp Sci & Engn Hyderabad India

ISBN: (纸本)9781665476478

the variances regarding prices of horticultural commodities adversely affect the nations GDP. the ranchers are genuinely and monetarily influenced as their long stretches of difficult work go to no end. Forecast of the prices might help the farming production network in settling on vital choices in limiting and dealing with the danger in value variances. By the way of outcome of the diminution of crop yield making due to uneven atmosphere circumstances and global warming. Rice is a significant yield developed in India, with Andhra Pradesh (AP) state being second as far as creation. At present ranchers in AP are moving from rice development to different harvests on account of worth variances and environmental variation. In this research, the costs of rice in AP remain anticipated utilizing time-series using data analytics like machine learning (ML) models. the prototypes SARIMA, Holt-Winter's Periodic technique, and Long Short-Term Memory (LSTM) Neural Networks (NN)be situated and utilized, and their recital was assessed dependent on the RMSE esteem on the rice database whose costs from 2001 to 2020. the conclusions demonstrate that the recommended process, LSTM neural network method is actual expectant and finest method which is suitable for the data.

关键词： Rice price prediction machine learning SARIMA LSTM

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Generating Causal Hypotheses for Explaining Black-Box Industrial Processes 5

Generating Causal Hypotheses for Explaining Black-Box Indust...

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IEEE 5th international conference on Industrial Cyber-Physical Systems (ICPS)

作者： Balzereit, Kaja Diedrich, Alexander Kubus, Daniel Ginster, Jonas Bunte, Andreas Fraunhofer Ctr Machine Learning Fraunhofer IOSB Ind Automat Branch IOSBINA Lemgo Germany Fraunhofer IOSBINA Fraunhofer Ctr Machine Learning Lemgo Germany ContiTech AG Hannover NH Germany

ISBN: (纸本)9781665497701

Industrial processes nowadays are complex networks of various modules. Due to their complexity, the processes are black-boxes, i.e. dependencies between input and output products of the process are mainly unknown. Getting insights into the process is challenging, since the extent of the data collected is usually to large to allow for a direct interpretation. Causal dependencies, i.e. the effect of an input variable on an output variable, allow for explaining what is happening inside the process. However, it is well known, that the identification of causal dependencies cannot be done purely data-driven but always requires at least a minimum of expert knowledge. In this article, a data-driven approach for the generation of causal hypotheses, i.e. dependencies in the data that might indicate a causal dependency, is presented. the approach is based on correlating input and output data of the black-box approach and quantifying the found correlation using regression. the approach is applied to an industrial compounding process and it is shown, that significant causal hypotheses can be found. these hypotheses are later validated by process experts.

关键词： causality black box explainability

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Application of data-driven feature extraction methods in biometrics 5

Application of data-driven feature extraction methods in bio...

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5th international conference on Computer Information Science and Artificial Intelligence, CISAI 2022

作者： Li, Huixing Xue, Yan Zeng, Xiancai Henan Key Laboratory of Industrial Microbial Resources and Fermentation Technology Nanyang Institute of Technology Henan Nanyang473004 China School of Biological and Chemical Engineering Nanyang Institute of Technology Henan Nanyang473004 China

ISBN: (纸本)9781510662520

With the continuous development of research in the field of machine learning, especially the progress in deep learning and the continuous improvement of arithmetic power such as image processors, the recognition technology using biometric big data has gained wide attention and has been well applied in many fields such as human-witness matching, intelligent monitoring and epidemic prevention and control. the development trend of big data biometric identification technology is analyzed, the types of biometric features and the development and application of big data-driven biometric identification technology are summarized, and the future development trend of big data biometric identification technology is discussed. © 2023 SPIE.

关键词： Image enhancement

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Satellite Orbit Prediction using a machine learning Approach 5

Satellite Orbit Prediction using a Machine Learning Approach

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Joint 5th international conference on applied Informatics Workshops, ICAIW 2022: 3rd international Workshop on applied Artificial Intelligence, WAAI 2022, 4th international Workshop on applied Informatics for Economy, Society, and Development, AIESD 2022, 5th international Workshop on data Engineering and analytics, WDEA 2022, 1st international Workshop on Intelligent Transportation Systems and Smart Mobility Technology, WITS 2022, 2nd international Workshop on Knowledge Management and Information Technologies, WKMIT 2022 and 1st international Workshop on Systems Modeling, WSSC 2022

作者： Jadala, Giridhar Meedinti, Gowri Namratha Delhibabu, Radhakrishnan Vellore Institute of Technology Vellore India

the most cutting-edge approaches for orbit prediction right now use physics-based models, which call for exceedingly precise information about the object's trajectory, its operating environment, and its intended course of action. We don't actually have access to this information. Our knowledge of space weather and air density is limited, trajectories are only seldom obtained from noisy ground-based radar systems, and satellite operators are reluctant to disclose their maneuvering intentions. Current orbit predictions that are only based on physics-based models may not achieve the required accuracy for collision avoidance and have already resulted in satellite collisions due to the lack of information regarding the state of the space environment and resident space objects' (RSOs') body characteristics. Two line element sets(TLE) made accessible to the public lack any related error or correctness information. the majority of TLE prediction techniques used today fit polynomials, which cannot capture periodic properties. this paper has presented a methodology for orbital prediction using curve fitting and LSTM on historical orbital data. the proposed machine learning approaches are used with various TLE parameters where the LSTM model is trained to learn through large amounts of historical TLE data. the fitted data is synthesized and then compared with the SGP4 predictions. the two proposed methods focus on reducing prediction errors. the results of the study demonstrate that the proposed machine learning approaches can improve orbital prediction accuracy with good performance in most cases. We go into further optimization and the computing needs for using all-on-all conjunction analysis on the whole TLE collection and visualize when and where conjunction may occur, both currently and in the near future. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： Curve fitting

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machine learning applied to Star-Galaxy-QSO Classification and data Visualization 5

Machine Learning Applied to Star-Galaxy-QSO Classification a...

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5th international conference on Pattern Recognition and Artificial Intelligence, PRAI 2022

作者： Ji, Xiang Wu, Yadong Zhang, Yuling Zhang, Han Zhang, Weihan Zhang, Wei Li, Yi Jiang, Liyuan Sichuan University of Science and Engineering School of Computer Science and Engineering Yibin China

ISBN: (数字)9781665499163

ISBN: (纸本)9781665499163

With the continuous development of modern astronomical observation methods, the sky survey data obtained through observation has increased exponentially, and machine learning has gradually replaced traditional scientific computing methods with its powerful computing power in the big data environment. In view of the problems of low classification accuracy, poor classification efficiency and few classification types in the current astronomical spectrum classification work, this study added sub-classification to the original classification benchmark and compared the performance of different machine learning algorithms in the classification problem in detail. Visual presentation of data improves the intuitiveness of data. In this study, one hundred thousand LAMOST DR6 spectral data were used for training, and different machine learning algorithms were used to evaluate the performance of various classifiers, and then correlation analysis was used to greatly improve the classification accuracy of specific categories on the basis of the original research, making the classification accuracy of galaxies and QSO is higher than 98%, which proves the validity of this research. Finally, various attributes of the classified celestial objects are displayed through visualization methods. © 2022 IEEE.

关键词： Classification (of information)

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A Big data analytics Framework for analyzing global tourism trends of Indians using Social Media analytics 5

A Big Data Analytics Framework for analyzing global tourism ...

引用

5th international conference on Computational Intelligence and Communication Technologies, CCICT 2022

作者： Ranga, Kamal Kumar Nagpal, C.K. Jc Bose University of Science and Technology Department of Computer Engineering Haryana Faridabad India

ISBN: (数字)9781665472241

ISBN: (纸本)9781665472241

While planning to visit a place it is very difficult to decide any place based on our preferences that may include, availability of veg food or weather a place is family friendly or not. Searching out such place is really a cumbersome task which involves going around good number of websites and social media platforms and reading never ending reviews. To cater this evolving issue an innovative and simple solution is proposed in this paper. Here, data is extracted from world famous and most authentic travel website TripAdvisor, where most of users post their experiences in form of review about the place they have visited in natural language text. the system extracts these reviews and analyzes with the help of machine learning model based on five factors that are of common concern like Veg_food availability, Family_friendliness, other_facilities, destination and hotels around etc., based on user's true experiences. the system generates likeness score on the scale of 0 to 1, for each destination to visit on the basis of these metrics and an overall rating is represented visually for the ease of future user. © 2022 IEEE.

关键词： data analytics

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Comparison of Regressors applied to 3D Motion Marker data of a Historic Building Prototype Subjected to Shake Table Tests 5th

Comparison of Regressors Applied to 3D Motion Marker Data of...

引用

5th international conference on Protection of Historical Constructions-PROHITECH

作者： Palumbo, Domenico Ormando, Chiara Roselli, Ivan ENEA Casaccia Res Ctr Rome Italy

ISBN: (纸本)9783031873188;9783031873164;9783031873157

In the present study the performance of several machine learning regression techniques for the analysis of displacement data acquired in dynamic identification shaking table tests to assess a building's prototype state of damage were compared. the displacement data of a typical Italian historic masonry building prototype were recorded by a passive 3D motion capture system. these recorded displacement data were analyzed by calculating a widely accepted Damage Index (DI) based on the decay of the first modal frequency of the tested structure. To such purpose, a conventional Frequency Response Function (FRF) algorithm for modal analysis was adopted. then, several regressors, commonly used in machine learning applications, were applied to cepstral coefficients extracted from time-series segmentation of the 3D motion markers signals to predict the DI values. To assess the quality of the tested regressors, Coefficient of determination (R-2) and Relative Percent Deviation (RPD) parameters were evaluated. Different lengths of time-series segments were also explored and the results were statistically compared to highlight the differences between regressors. Excellent results were obtained in terms of R-2 (0.98) and RPD (6.87) with a weak dependence on the time partitioning performed on the data with optimal results for 4 or more seconds. Moreover, Stacked Regressor, Decision Tree Bagging Regressor and XGB Regressor were found as the best overall regressors. the results obtained demonstrated that machine learning methods can be effectively utilized to predict the level of damage of historic masonry buildings with very high accuracy.

关键词： machine learning regression historic buildings

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machine learning Adoption in Blockchain-Based Smart Applications 5

Machine Learning Adoption in Blockchain-Based Smart Applicat...

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5th international conference on Contemporary Computing and Informatics, IC3I 2022

作者： Suthar, Vishal Bansal, Vipul Reddy, Ch.Srinivasa Gonzales, Jose Luis Arias Singh, Devendra Singh, Devesh Pratap Government Polytechnic College Department of Computer Engineering Mandore Rajasthan Jodhpur India Kurukshetra University Department of Information Technology Haryana India Vignan's Institute of Information Technology Department of IT Duvvada India Pontifical Catholic University of Peru Departamento of Business Peru Uttaranchal Institute of Technology Uttaranchal University Uttarakhand Dehradun India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350398267

the development of blockchain technology (BT) in recent years has made it a distinctive, revolutionary, and popular innovation. Information security and confidentiality are prioritised by the decentralised database in BT. Additionally, the consensus process in it ensures the validity and security of the data. However, it brings up fresh security concerns including majority assault and the double expenditures. data analytics using cryptocurrency sensitive data are needed to address the aforementioned problems. these dataset' analytics highlight the value of recently developed techniques such as machine learning (ML). ML uses a reasonable quantity of data to generate accurate predictions. In ML, data exchange and dependability are essential to enhancing the precision of outcomes. Results from the fusion of these two technologies (ML and BT) may be quite exact. In this research, we give a thorough investigation into the use of machine learning (ML) to strengthen the security of BT-based intelligent systems. the assaults on a blockchain-based network may be analysed using a variety of classic machine learning (ML) approaches, including Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Clustering, Bagging, and Support Vector machines (SVM) (LSTM).We also discuss how the two technologies may be used together in a number of advanced areas, including smart urban, the national grid, medicine, and autonomous aerial vehicles (UAVs). the difficulties and concerns facing future research are then examined. Finally, a study based with a thorough analysis is offered. © 2022 IEEE.

关键词： Blockchain

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5th international conference on Electrical Engineering and Control Applications, ICEECA 2022

5th International Conference on Electrical Engineering and C...

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5th international conference on Electrical Engineering and Control Applications, ICEECA 2022

ISBN: (纸本)9789819747757

the proceedings contain 50 papers. the special focus in this conference is on Electrical Engineering and Control Applications. the topics include: Sliding Mode Control Based on Exponential Reaching Law applied on a Five-Phase Permanent Magnet Synchronous Generator Based Wind System;indirect Field Oriented Control and Synergetic Control of Doubly Fed Induction Generator-Comparative Study;Storage Management Algorithm of a PV System;Energy Management of PV/WT/Battery Hybrid Power System Using Flatness-Based Control;Solar Radiation Measurements in the Adrar Region of Algeria: Radiometric Stations, Statistics, and Comparison with MERRA2 data;estimation of Solar Radiation on the Ground from Meteosat Satellite Images and Different Models: Case of the Ghardaïa Region;fuzzy Logic Control Strategy for a Grid-Connected Wind System with a Battery Energy Storage;Impact of Distribution Generation on Transient Stability with PSAT;Ageing Impact on Reliability and Components Importance of an NPP’s Emergency Power Supply Model;Performance Improvement of a DFIG-Based Wind Energy Conversion System Using Fuzzy-MPPT and SVM-DTC;combined machine learning Techniques for Analyzing the Back Contact Influence on the Stability of Perovskite-Based Solar Cells;Test of Advanced Functionalities of Photovoltaic Inverters Integrated in Algerian LV Distribution Network;Simulations of Silicon n-on-p Pixel Detectors for HEP Experiments;A Multiple-Areas Based on PID-Virtual Inertia Control Strategy for Inverters;Characterization of Radiated Electromagnetic Fields Using Equivalent Sources with HGAGWO Method;fault Diagnosis of Photovoltaic Arrays Based on Support Vector machine and t-Distributed Stochastic Neighbor Embedding;studying the Effect of Mn Doping on the Electrical and Optical Properties of ZnO/Ag/ZnO Multilayer.

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