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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

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Blood Pressure Morphology as a Fingerprint of Cardiovascular Health: A machine learning Based Approach 4th

Blood Pressure Morphology as a Fingerprint of Cardiovascular...

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4th international conference on Applied Informatics (ICAI)

作者： Ipar, Eugenia Aguirre, Nicolas A. Cymberknop, Leandro J. Armentano, Ricardo L. Univ Tecnol Nacl Bioengn Res & Dev Grp GIBIO Buenos Aires DF Argentina Univ Technol Troyes Lab Modelisat & Surete Syst Troyes France

ISBN: (纸本)9783030896546;9783030896539

Daily exercise and a healthful diet allows the cardiovascular (CV) system to age slower. However, unhealthy life habits and diseases impact negatively on this condition. In consequence, an individual's "chronological age" (CHA) may differ from the "arterial age" (AA). there is proven evidence of correlation between arterial stiffness (AS), highly associated with AA, and central blood pressure (BP) waveform, which combined with other CV properties, define a group of characteristic parameters to assess CV Health (CVH). Such assessment, obtained by means of noninvasive and simple arterial measurements is extremely useful for the prevention of CV diseases. machine learning (ML) techniques allow the development of predicting models, but usually a large amount of data is required, often unavailable and/or not fully complete. One-dimensional (1D) CV models become an interesting alternative to overcome this issue, since CV system simulations can be performed. OBJECTIVE: A ML model was trained/validated with data from healthy and heterogeneous population (n = 4374), using parameters derived from arterial pressure waveforms (APW) in order to assess CVH. MEthODS: Sixteen features were extracted from carotid and radial APW. Such were used for prediction of cardiac output (CO), systemic vascular resistance (SVR), systolic BP (SBPc), diastolic BP (DBPc), carotid-femoral pulse wave velocity (PWVcf), aortic augmentation index (Ala) and CHA as a surrogate of AA. RESULTS: the normalized-RMSEs for CO, SVR, SBPc, DBPc, PWVcf, Ala and CHA using the best-performing model were 5.4%, 2.2%, 0.1%, 0.06%, 0.6%, 1.2% and 5.5%, respectively. CONCLUSION: the acquired results showed that the ML models obtained from measurements in the assessed locations provide an acceptable performance on estimating CVH parameters. Further in-vivo studies are required to validate the application of the outcome.

关键词： One-dimensional modelling Pulse wave analysis Cardiovascular health machine learning Risk prediction

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4th international conference on Smart Trends for Computing and Communications, SmartCom 2020

4th International Conference on Smart Trends for Computing a...

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4th international conference on Smart Trends for Computing and Communications, SmartCom 2020

ISBN: (纸本)9789811552236

the proceedings contain 50 papers. the special focus in this conference is on Smart Trends for Computing and Communications. the topics include: Selective Sensor Control via Cauchy Schwarz Divergence;step-Factor Resampling Technique for Imbalanced Sequence Data Classification;rice Disease Detection Based on Image Processing Technique;electromagnetic Radiation from Cell Phones Used in Dhaka City;expressing Opinions application: A Case Study at Rajamangala University of Technology, Isan, Khon Kaen Campus;toward Clarifying Human Information Processing by Analyzing Big Data: Comparing Response Time to Questionnaires Included Heteronym Word for Problem Solving Between Visually and Auditory Types;mango Species Detection from Raw Leaves Using Image Processing System;frequency Reconfigurable Planner Antennas for Wireless applications: A Review;Room Temperature Pt-Modified WO3/p–Si Film Gas Sensor for Detection of Methanol;statistical Approach to Detect Alzheimer’s Disease and Autism Spectrum-Related Neurological Disorder Using machine learning;graph Degree Linkage Clustering for Identify Student’s Performance on Kompetisi Sains Madrasah in Indonesia;stepwise Iterative Maximum Likelihood Clustering Based on Kompetisi Sains Madrasah’ Scores for Identifying Quality of Junior High School Grading Distribution;Detecting the Width of Pap Smear Cytoplasm Image Based on GLCM Feature;design of a Novel Wideband Dipole Antenna for mm-Wave Frequencies;forecasting Sea Surface Temperature in Java Sea Using Generalized Regression Neural Networks;Natural Disaster Predictions of Whirlwinds in Cilacap Region of Central Java Using Adaptive Neighborhood Modified Backpropagation (ANMBP);Designing Memristor-Based Timing Circuits and Performance Comparison with CMOS Counterparts;identify Elementary Student Distribution Based on Kompetisi Sains Madrasah Data Using Probabilistic Distance Clustering.

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Analyzing Truck Driver's Behavior on the Road Using YOLO v4 Tiny Algorithm 22

Analyzing Truck Driver's Behavior on the Road Using YOLO v4 ...

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proceedings of the 2022 6th international conference on computer Science and Artificial Intelligence

作者： Felizardo Calimag Reyes Jr. Gerald Tomelden Cayabyab Paula Jean Castro Mendoza College of Information Technology Education Technological Institute of the Philippines Philippines

ISBN: (纸本)9781450397773

the Philippine logistics industry is always in need of competent and qualified truck drivers to meet the increasing demands of the country's economic operations. thus, this research paper investigates the driving competency of truck drivers driving behaviour. the industry plays a critical part in the country's economy, and any trucking accident creates a significant problem for the parties involved that causes a decline in productivity and loss of revenue. this research paper discusses the creation of a system which utilizes machine learning, data analytics, and internet-of-things for easy monitoring and assessment of the truck driver's driving behaviour. In collaboration with the Department of Science and Technology and the Technological Institute of the Philippines, the project aims to integrate various technologies and use it to assess the roadworthiness of truck drivers and use the YOLO v4 algorithm to train the model. It provides all the necessary details covering the statistics needed to assess the truck driver that aligns with the standards set required by the Land Transportation Office (LTO) administration. the Agile methodology was used throughout the development as there are constant changes being made which adds flexibility and puts fewer risks for the system development. Based on the model compiled and the results of the iteration on Yolov4-tiny the classes average precision has a minimum value of 75.45% and a maximum value of 99.89%. the results showed a high accuracy performance of the model created in information retrieval and object detection.

关键词： Truck Drivers Rest APIs IoT Ant Design AWS MERN Stack Web Development Web application

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Implementation of training using electronic learning materials based on web 2.0 Online Services 4

Implementation of training using electronic learning materia...

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4th All-Russian Scientific and Practical conference with international Participation "Distance learning Technologies", DLT 2019

作者： Gerasimova, Elena K. North Caucasus Federal University Stavropol355017 Russia

the development of global economics and a present job market impose new requirements on the pedagogical staff. In general, these requirements concern the professional sphere which is connected with the development of competencies in information and communication technologies meeting the demand of our time. that means the innovative activity of the educator is to be connected not only with the use of stationary computer applications in educational practice, but also integrate the full scope of the global Network. the space of the Internet has a broad spectrum of educational portals, websites, blogs, the methodical communities, which provide access to different electronic educational resources. the organization of network interaction of participants of the educational process is the cornerstone of electronic training with applying of remote technologies. the great popularity is gained by using social network services in the educational process which can serve as web designers for the creation of training materials by the educator and student result representation of participation in projects, individual works, and other types of activity. this article is devoted to the applying of network technologies, in particular, online services based on Web 2.0, in master degree student training who get the pedagogical education in biology. the model of online services application is developed for the creation of electronic training materials in work with students of a magistracy as future educators. © 2021 CEUR-WS. All rights reserved.

关键词： Students

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application of laser absorption spectroscopy and machine learning for component analysis of multicomponent gas media 4

Application of laser absorption spectroscopy and machine lea...

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4th international conference on Terahertz and Microwave Radiation: Generation, Detection, and applications

作者： Prischepa, V. V. Skiba, V. E. Vrazhnov, D. A. Markelov, A. A. Tomsk State Univ Tomsk Russia Inst Strength Phys & Mat Sci SB RAS Tomsk Russia

the regression model was applied to solve the problem of restoration of the concentration of components in a gas mixture using infrared absorption spectra. A solution to the problem of determining the concentrations o... 详细信息

ISBN: (数字)9781510639904

ISBN: (纸本)9781510639904

关键词： absorption spectroscopy machine learning component analysis artificial neural network concentration

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4M: massively multimodal masked modeling 23

4M: massively multimodal masked modeling

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proceedings of the 37th international conference on Neural Information Processing Systems

作者： David Mizrahi Roman Bachmann Oğuzhan Fatih Kar Teresa Yeo Mingfei Gao Afshin Dehghan Amir Zamir Swiss Federal Institute of Technology Lausanne (EPFL) and Apple Swiss Federal Institute of Technology Lausanne (EPFL) Apple

Current machine learning models for vision are often highly specialized and limited to a single modality and task. In contrast, recent large language models exhibit a wide range of capabilities, hinting at a possibility for similarly versatile models in computer vision. In this paper, we take a step in this direction and propose a multimodal training scheme called 4M. It consists of training a single unified Transformer encoder-decoder using a masked modeling objective across a wide range of input/output modalities - including text, images, geometric, and seman tic modalities, as well as neural network feature maps. 4M achieves scalability by unifying the representation space of all modalities through mapping them into dis crete tokens and performing multimodal masked modeling on a small randomized subset of tokens.4M leads to models that exhibit several key capabilities: (1) they can perform a diverse set of vision tasks out of the box, (2) they excel when fine-tuned for unseen downstream tasks or new input modalities, and (3) they can function as a generative model that can be conditioned on arbitrary modalities, enabling a wide variety of expressive multimodal editing capabilities with remarkable *** experimental analyses, we demonstrate the potential of 4M for training versatile and scalable foundation models for vision tasks, setting the stage for further exploration in multimodal learning for vision and other domains.

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Extreme Process learning machine Based on Improved Shuffled Leapfrog Algorithm and Its application 20

Extreme Process Learning Machine Based on Improved Shuffled ...

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4th international conference on computer Science and application Engineering, CSAE 2020

作者： Zhang, Qiang Wang, Yanqi School of Computer and Information Technology Northeast Petroleum University Daqing China

ISBN: (纸本)9781450377720

In this paper, an extreme learning machine model with process input is constructed. the improved shuffled leapfrog algorithm based on three evolutionary strategies autonomous selection is proposed to optimize the number of hidden nodes and network weight of extreme learning machine model. the evolutionary behavior of the worst individuals is determined by calculating the immediate value, the future value and the comprehensive reward of each evolutionary strategy during each iteration. the experimental results show that, compared with five improved shuffled leapfrog algorithms, the proposed algorithm has the best optimization effect in six high-dimensional function optimization and oil well fault diagnosis. It has good practical application value. © 2020 ACM.

关键词： Failure analysis

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Electromagnetic data completion and prediction method based on tensor train 22

Electromagnetic data completion and prediction method based ...

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proceedings of the 4th international conference on Advanced Information Science and System

作者： Shuli Ma Liting Sun Yufei Niu Han Liu Huiqian Du Feihuang Chu Shengliang Fang School of Space Information Space Engineering University China Beijing Institute of computer technology and Application China Graduate School Space Engineering University China School of Information and Electronics Beijing Institute of Technology China

ISBN: (纸本)9781450397933

In residential environment, electromagnetic power density exceeding a certain value will affect people's livelihood and health. In the monitoring of electromagnetic environmental quality of residential buildings, the grid method is generally used to measure the data value of electromagnetic radiation sources, and the visualization technology is used to display the data of electromagnetic radiation sources in the region. In this paper, we use the method of randomly deploying sensor nodes to sample grid electromagnetic data, which greatly saves the deployment cost of sensor nodes. However, it will lead to data loss and pulse noise interference. Giving that the general electromagnetic data visualization diagram are local smoothing and sparse in transformation domain, we propose to use the tensor form of electromagnetic data to completion/restoration or predict the area grid that cannot be monitored based on the completion theory. the prediction model based on tensor train and algorithm are given. Experimental results show that the method can make the data smoother visually and within a certain accuracy.

关键词： data enhancement

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4th international conference on computer, Communication and Signal Processing, ICCCSP 2020

4th International Conference on Computer, Communication and ...

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4th international conference on computer, Communication and Signal Processing, ICCCSP 2020

ISBN: (纸本)9781728165097

the proceedings contain 54 papers. the topics discussed include: GAN based data augmentation for enhanced tumor classification;exploration of big data analytics in healthcare analytics;cryptocurrency wallet: a review;analysis on various approaches of complexity reduction for intra prediction modes in high efficiency video coding;application of random forests for air quality estimation in India by adopting terrain features;secure agritech farming using staging level blockchaining and transaction access control using micro QR code;an exploratory of hybrid techniques on deep learning for image classification;data compression algorithm for audio and image using feature extraction;third vision for women using deep learning techniques;and rainy image enhancement using modified multistage Gaussian filter and weighted median guided filter.

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Diagnosis of Crime Rate against Women using k-fold Cross Validation through machine learning 4

Diagnosis of Crime Rate against Women using k-fold Cross Val...

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4th international conference on Computing Methodologies and Communication, ICCMC 2020

作者： Tamilarasi, P. Rani, R.Uma Sri Sarada College for Women Autonomous Department of Computer Science Salem India

ISBN: (纸本)9781728148892

Crime against women has become a very big problem of our nation. Many countries are trying to control this offence continuously and its prevention is an essential task. In recent years crimes are significantly increasing against women. Currently the Indian government show interest to address this problem and give more importance to develop our society. Every year a huge amount of data collection is generated on the basis of the crime reporting. this data can be very useful for assessing and predicting crime, and can help us to some degree stop the crime. Data analysis is a process of examining, cleansing, transformation and modelling data with the goal of establish useful information, reporting conclusion and sustaining decision-making. Feature Scaling is one of the most important techniques to standardize the independent features to place the data in a fixed range. It is performed at the time of data pre-processing. K-fold cross-validation is a re-sampling method used for calculating machine learning models on a small sample of data. It is a common strategy since it is easy to understand and usually results in a model deftness calculation that is less biased or less negative than other approaches, such as a simple train or test divide. machine learning plays a large part in data processing. this paper introduces six different types of machine learning algorithms such as KNN and decision trees, Naïve Bayes, Linear Regression CART (Classification and Regression Tree) and SVM using similar characteristics on crime data. those algorithms are tested for accuracy. the main objective of this research is to evaluate the efficacy and application of the machine learning algorithms in data analytics. © 2020 IEEE.

关键词： Crime

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