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检索条件"任意字段=International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2022"

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Characteristics of Pulse Waveform in Chronic Atrial Fibrillation: Simultaneous Comparison with ECG

Characteristics of Pulse Waveform in Chronic Atrial Fibrilla...

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international Symposium on Soft computing and intelligent systems (SCIS)

作者： Emi Yuda Yuki Ogata Yoshimi Shoji Makoto Yoshizawa Norihiro Sugita Junichiro Hayano Center for Data-driven Science and Artificial Intelligence Tohoku University Sendai Japan Makabe Hospital Itoku-kai Medical Corporation Higashi-matsushima Japan Center for Promotion of Innovation Strategy Tohoku University Sendai Japan Cyberscience Center Tohoku University Sendai Japan Heartbeat Science Lab Co.Ltd Nagoya Citu University Sendai Japan

Early detection and treatment are critical for patients with atrial fibrillation (AF), which is known to increase the risk of stroke and total mortality. In recent years, it has become possible to detect AF using electrocardiography and heart rate sensing. However, detection from pulse waveforms remains a difficult technology. Recent studies have shown that orthostatic hypotension (OH) increases the risk of developing AF. In this study, we focused on the effect of orthostasis on the autonomic nervous system and visualized the pulse wave characteristics of patients with chronic AF during orthostasis by simultaneous measurement of electrocardiogram (ECG) and photoplethysmogram (PPG).

关键词： Heart rate Visualization Pulse measurements Autonomic nervous system Atrial fibrillation Electrocardiography Stroke (medical condition)

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international conference on intelligent and Fuzzy systems, INFUS 2020

International Conference on Intelligent and Fuzzy Systems, I...

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international conference on intelligent and Fuzzy systems, INFUS 2020

ISBN: (纸本)9783030511555

The proceedings contain 197 papers. The special focus in this conference is on intelligent and Fuzzy systems. The topics include: Basket Patterns in Turkey: A Clustering of FMCG Baskets Using Consumer Panel data;modeling Urban Traffic by Means of Traffic Density data: Istanbul Case;realising Newspaper Sales by Using Statistic Methods;extensions of Fuzzy Sets in Big data Applications: A Literature Review;a Big data Semantic driven Context Aware Recommendation Method;Blended Environment of Naive Bayes and Support Vector Machine (SVM) for Designing Simulation Based E-Learning Respiratory System;interCriteria Analysis of Public Health data in Bulgaria;a Case Study on Vehicle Battery Manufacturing Using Fuzzy Analysis of Variance;new Suggestion for Fuzzy Random Variable and Its Statistical Simulation;fuzzy Centrality Analysis for Smart City Trajectories;improving Customer Experience for an Internet Service Provider: A Neural Networks Approach;journey Segmentation of Turkish Tobacco Users Using Sequence Clustering Techniques;segmentation of Larynx Histopathology Images via Convolutional Neural Networks;Glioma Brain Tumor Grade Classification from MRI Using Convolutional Neural Networks Designed by Modified FA;dynamic System Control Using Z-number Based Fuzzy Neural Network;comprehensive Research of the Commodity Market: Theoretical, Methodological and Modern Approaches Using Neural Networks with Fuzzy Logic;the Maximum Lexicographic Contraflow Finding in a Fuzzy Dynamic Network;An Enhanced Particle Swarm Optimization with Levy Flight for RBF Neural Network in Typical Karst Area, South China;a Method for Forecasting the Demand for Pharmaceutical Products in a Distributed Pharmacy Network Based on an Integrated Approach Using Fuzzy Logic and Neural Networks.

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A Novel Approach for Big data Monetization as a Service 1st

A Novel Approach for Big Data Monetization as a Service

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1st international conference of Advanced computing and Informatics, ICACIN 2020

作者： Faroukhi, Abou Zakaria El Alaoui, Imane Gahi, Youssef Amine, Aouatif Laboratoire Génie Des Systèmes Ecole Nationale Des Sciences Appliquées Ibn Tofail University Kenitra Morocco Laboratoire Des Systèmes de Télécommunications Et Ingénierie de La Décision Ibn Tofail University Kenitra Morocco

ISBN: (纸本)9789811560477

As a result of the digital transformation that has led by different production sectors, the organizations were flooded by vast, various, and complex data, called Big data. To manage their data assets, the adoption of the Big data value chain (BDVC) was suitable for a value realization as well as a data-intensive decision-making. The manipulation and exploitation of these data gave rise to the concept of Big data monetization. The marriage between the BDVC and data monetization remains an own and recent approach. This combination allows the organization's process to become entirely data-driven and expand the scope of its exchanges. Besides, advanced deployment models such as cloud computing will enable this combination to become more resilient by providing large amounts of computational and networking capabilities. Furthermore, it allows sharing data between different BDVCs to build an expandable ecosystem. In this contribution, we review the literature regarding the association between BDVC, data monetization, and cloud computing. Then, we propose a Big data monetization-driven value chain model that relies on cloud computing and analytical capabilities. It allows monetizing both data and insight as a service under a collaborative concept. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Ecosystems

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Discovering the effects of learning analytics dashboard on students' behavioral patterns using differential sequence mining 26

Discovering the effects of learning analytics dashboard on s...

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26th international conference on Knowledge-Based and intelligent Information and Engineering systems, KES 2022

作者： Akçapinar, Gökhan Hasnine, Mohammad Nehal Department of Computer Education and Instructional Technology Hacettepe University Ankara06800 Turkey Reseach Center for Computing and Multimedia Studies Hosei University 3-7-2 Kajinocho Tokyo184-8584 Japan

Interventions based on learning analytics have a very important place in closing the learning analytics loop. However, data-driven studies that test the effects of learning analytics-based interventions on students' online learning behaviors are very limited. In this study, the effect of the student-facing learning analytics dashboard (LAD) on the learning behavior of students in the online learning environment was investigated by using the differential pattern mining method. In a completely remote course, the learning behaviors of the participants before the introduction of the dashboard were compared with the learning behaviors they exhibited after the dashboard was introduced. In this way, it has become possible to analyze the behavior changes after the dashboard intervention. Wilcoxon signed-rank test was used to test whether these behavioral changes were statistically significant or not. According to the Wilcoxon signed-rank test results, while there is no significant difference in terms of students' assignment and quiz interactions, it is seen that there is a statistically significant increase in terms of students' forum-related activities such as reading other students' posts, starting a new discussion, and replying others' posts. Students' SCORM interactions (e.g, launch, complete) were also increased after engaging with the LAD. In addition, it was found that the overall interaction of students in the online learning environment increased by 57% when the LAD was used. © 2022 The Authors. Published by Elsevier B.V.

关键词： Students

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TSPM-CNN based power system transient stability assessment using a new data extraction method

TSPM-CNN based power system transient stability assessment u...

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12th IET international conference on advances in Power System Control, Operation and Management (APSCOM 2022)

作者： J. Zhou S. Bu Department of Electrical Engineering Hong Kong Polytechnic University Hong Kong China

The increasing deployment of Wide Area Measurement systems (WAMS) and Phasor Measurement Units (PMUs) in power grids has significantly improved the situational awareness capability of the system. Together with the increasing computing power of computers, data-driven Transient Stability Assessment (TSA) has become a very popular tool for system operator. In order to improve the accuracy of data-driven transient stability prediction, a new TSPM-CNN TSA method based on a new data extraction method is proposed in this paper. The method makes full use of the excellent feature self-extraction capability of convolutional neural network (CNN) in order to effectively extract the time-series information of individual features and the correlation information of different features, and effectively solve the problems of insufficient datasets and loss of time-series information in traditional data-driven TSA methods. Furthermore, this paper proposes a TSA method based on a combination of time-series prediction model (TSPM) and CNN. The method uses the features of the TSPM to predict the subsequent data of the PMU of the power system and combines it with CNN to predict the transient stability status of the power systems. The effectiveness and accuracy of the method have been successfully verified by simulation experiments in this paper.

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Hybridization of AES and RSA Algorithm in File Encryption Using Parallel computing 1st

Hybridization of AES and RSA Algorithm in File Encryption Us...

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1st international conference on Advanced Communication and intelligent systems, ICACIS 2022

作者： Parikh, Sakshi Jhanwar, Raghav Singh, Apoorv School of Information Technology and Engineering VIT Vellore India School of Electronic Science and Engineering VIT Vellore India

ISBN: (纸本)9783031250873

As technology advances there is an increase in the amount of data transmitted over the network. One of the biggest challenges facing is safety. Cryptography is the study of computational methods related to information security, including privacy, integrity of data, business validation, and source verification, which provides security. In this chapter, we propose a hybrid cryptographic solution using the AES and RSA algorithm as well as the OpenMP shared memory method for memory. The proposed hybrid algorithms aim at better security and efficiency of data. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cryptography

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Strategic Thinking on High-Quality Development of Digital Economy in Yangtze River Delta Region of China in the Era of Big data

Strategic Thinking on High-Quality Development of Digital Ec...

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international conference on Machine Learning and Big data Analytics for IoT Security and Privacy, SPIoT 2020

作者： Zhang, Ruitong Department of Economics Shanghai University Shanghai China

ISBN: (纸本)9783030627423

As the world has entered the era of big data, the digital economy, as an important part of the development of the real economy and innovation-driven development, has become a key factor in China’s implementation of major national strategies. In this paper, the Yangtze River Delta region, which ranks the top among Chinese three major urban agglomerations, is taken as the research object. At the same time, this paper combines with the experience of high-quality development of digital economy of other countries, the key elements of the development of big data industry are analyzed and summarized, and a package of enlightening suggestions for the development of big data industry are given. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Big data

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data-driven analysis of human-machine systems – a data logger and possible use cases for field studies with cordless power tools 3rd

Data-driven analysis of human-machine systems – a data logg...

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3rd international conference on Human Interaction and Emerging Technologies: Future Applications, IHIET 2020

作者： Dörr, Matthias Peters, Julian Matthiesen, Sven IPEK - Institut für Produktentwicklung Kaiserstr. 10 Karlsruhe76131 Germany

ISBN: (纸本)9783030553067

For the optimization of human-machine systems, it is necessary to gather knowledge about human-machine interaction. For power tools this knowledge is often built up in laboratory and field studies. A problem that often arises is the high amount of effort required for the attachment of measurement equipment, which makes it difficult to conduct large studies, especially in the field. The effort could be reduced by using a data logger, but it is not clear which measurement variables and record frequencies are relevant. In this paper requirements for a data logger to capture the human-machine interaction are presented. Furthermore, a realization of a data logger and four suitable studies are presented. This allows large field studies to be carried out much more easily, making it possible to optimize power tools on the basis of big data collected in the field. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Machine learning

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Comparative Analysis of Recommendation systems based on Medical data Sets

Comparative Analysis of Recommendation Systems based on Medi...

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Ravi Kumar Barwal Neeraj Raheja Department of Computer Science and Engineering Maharishi Markandeshwar Engineering College Maharishi Markandeshwar (Deemed to be University) Ambala Haryana India

In today’s world, a number of most common diseases such as DM (Diabetes Mellitus) for which not assured medicine has searched yet, the capable method for these types of patients for the survival is to take care and proper diet. Fasting Blood Sugar is one of the vital parameters of diabetes mellitus. In the health care system, numerous data of the patients have analyzed that help to obtain insights and assist the estimate of the diseases. Hence, there is a need for an intelligent scheme for estimation of the health situation by analyzing the standard of living, health check reports, and communal activities. However, the recommendation system is becoming the main framework for health care applications. Moreover, fitness intelligent schemes have become a necessary tool in the decision-making approach in health areas. Conversely, people use an online system for understanding the physical condition. Therefore, health recommendation schemes have estimated better results in diagnosis, medical insurance, medical-based management techniques, and other medicine based on the health profile of the patient. This paper provides comparative analysis of various recommendation methods such as MLP, RNN, LSTM, SVM, LR, and NB. These recommendation methods are used for the recommended diseases with Medical data Sets.

关键词： Deep learning Support vector machines Recurrent neural networks Error analysis Diabetes Medical diagnostic imaging Recommender systems

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international conference on Computational Methods and data Engineering, ICMDE 2020

International Conference on Computational Methods and Data E...

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international conference on Computational Methods and data Engineering, ICMDE 2020

ISBN: (纸本)9789811579066

The proceedings contain 50 papers. The special focus in this conference is on Computational Methods and data Engineering. The topics include: Energy-efficient schemes in underwater wireless sensor network: A review;information hiding techniques for cryptography and steganography;geospatial knowledge management-fresh fuel for banking and economic growth?;affect recognition using brain signals: A survey;"memorize, reproduce, and forget" inclination;students’ perspectives: A study of selected universities in ghana;neural network and pixel position shuffling-based digital image watermarking;hybrid optimized image steganography with cryptography;txtlineseg: Text line segmentation of unconstrained printed text in devanagari script;intelligent strategies for cloud computing risk management and testing;effective survey on handwriting character recognition;preface;Software quality optimization of coupling and cohesion metric for CBSD model;enhancement in braille systems—a survey;a critical review on use of data mining technique for prediction of road accidents;rio olympics 2016 on twitter: A descriptive analysis;a survey on vehicle to vehicle communication;obd-ii and big data: A powerful combination to solve the issues of automobile care;obu (on-board unit) wireless devices in vanet(s) for effective communication—a review;chinese postman problem: A petri net based approach;weather dataset analysis using apache pig;analysis of learner’s behavior using latent dirichlet allocation in online learning environment;an overview of recent developments in convolutional neural network (cnn) based face detector;detecting wsn attacks through hmac and sch formation;a review of artificial intelligence techniques for requirement engineering;data-driven model for state of health estimation of lithium-ion battery;smell-o-vision device.

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