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Smart Healthcare Systems: IoT and AI for Heart Disease Prediction

Smart Healthcare Systems: IoT and AI for Heart Disease Predi...

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Electronics and Sustainable Communication Systems (ICESC), 2020 international conference on

作者： Vishnu K Thomas Sebastian Binu P K Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Fast food consumption and changes in lifestyle are associated with an increase in heart related problems. This study looks at how cloud computing, the Internet of Things and machine learning can be combined for CVD risk prediction and real-time monitoring. The IoT-ML-Cloud paradigm aims to increment the precision of Cardio Vascular Disease prediction by merging these technologies. The system comprises three main modules: Prediction, data collection and User engagement. Utilizing Arduino and ESP32 modules. An Android application facilitates patient data input and real-time monitoring and predictions using ML models deployed on the Microsoft Azure cloud. Bluetooth technology utilised to facilitates real-time data transfer to the cloud. This paper proposes Bi-LSTM algorithm with an accuracy of 96.55% for the heart disease prediction. Along with highlighting significant discoveries from numerous ML algorithms in the diagnosis of Cardio Vascular Disease, it also highlights the significance of cloud computing and data mining for large-scale medical data analysis.

关键词： Heart Cloud computing Machine learning algorithms Predictive models Prediction algorithms Real-time systems Hardware data models Internet of Things Medical diagnostic imaging

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Multi-bit Collection and Hierarchical Classification Algorithm of Computer Application Fundamentals based Teaching data under the Background of Hybrid Clustering Algorithm 3

Multi-bit Collection and Hierarchical Classification Algorit...

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3rd international conference on Smart Electronics and Communication, ICOSEC 2022

作者： Shao, Dongmei Xi'an Siyuan University Xi'an710038 China

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

The principle and defects of general static background extraction algorithms are analyzed, a novel background extraction method based on improved hybrid clustering algorithm is proposed, and the concept of classification analysis is given. This paper analyzes the behavior of learning online courses. Classification, and sorting out and summarizing the current situation of learning behavior data collection and mining analysis methods respectively, to provide a theoretical basis for improving the online course learning behavior data collection model. Learning analysis technology is based on massive data, using the results of data mining, targeting the online learning environment and students' online learning, exploring information from the data and analysis models generated by learners, and making predictions and suggestions for learning. © 2022 IEEE.

关键词： data mining

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Research on Mining Different Brand Customer models Based on Correlation Coefficient and Bayesian Discrimination Methods

Research on Mining Different Brand Customer Models Based on ...

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

作者： Liu Decheng He Hongjing Zhao Yishu Zhang Fan China Automotive Technology and Research Center Co. Ltd. Tianjin China China Auto Information Technology (Tianjin) Co. Ltd. Tianjin China

ISBN: (数字)9798331527662

ISBN: (纸本)9798331527679

To identify potential customers for various car brands, this paper proposes a comprehensive method based on correlation coefficient and Bayes discriminant to solve related problems. First, this research uses random forests treatment groups 1 and 2 outliers, based on more than 1000 copies of survey data as the training base for the model; Secondly, the significant analysis is applied for calculating the significant factors for different brands; Next, we need to use the Bayesian model to train the constant term and coefficient vectors. Finally, we need to use the test set data to test the model. The results show that the prediction accuracy for different brands is above 90%.

关键词： Correlation coefficient Training Surveys Analytical models Computational modeling Information processing Forestry data models Vectors Bayes methods

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Toulouse: Learning Join Order Optimization Policies for Rule-based data Engines

Toulouse: Learning Join Order Optimization Policies for Rule...

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2023 Workshops of the EDBT/ICDT Joint conference, EDBT/ICDT-WS 2023

作者： Karvelas, Antonios Foufoulas, Yannis Simitsis, Alkis Ioannidis, Yannis University of Athens Athens Greece Athena Research Center Athens Greece

In recent times, several research works have explored the idea of leveraging machine learning techniques to improve or even replace core components of traditional database architectures, such as the query optimizer and selectivity and cardinality cost estimators. These efforts often rely on existing, cost-based optimizers and cost models to avoid a cold-start, and build on top of the optimizer's decisions. In this paper, we investigate whether learning could also be beneficial in rule-based optimizers for known and unknown workloads alike. As a proof of concept, we use MonetDB, an open-source, column-store analytics data engine, and explore whether a learning model based on Graph Neural Networks that is trained on a cost-based engine, such as PostgreSQL, could improve MonetDB optimizer's decisions. Our experimental results reveal deficiencies in MonetDB's query execution plans, especially for queries with long chains of join operators, and potential opportunities in exploiting learning techniques. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： Query processing

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Research on Efficient Perception and Reliable Transmission of Marine Environmental data

Research on Efficient Perception and Reliable Transmission o...

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

作者： Zihang Gao Nannan Zhao Wenzhou Vocational College of Science and Technology Wenzhou China

ISBN: (数字)9798331527662

ISBN: (纸本)9798331527679

The research analyzes the needs of efficient data perception and reliable transmission from the perspective of marine environmental monitoring. At the same time, we integrate wireless sensor networks and Mesh networks, and build multi-sensor heterogeneous wireless ad hoc networks to solve technical problems such as efficient data perception and reliable transmission in complex marine environments. We closely focus on key technical contents such as multi-sensor marine environment detection models, environment-sensitive wireless sensor network MAC protocols, cross-layer and geographical location-assisted routing protocols. The research results have great application value and social benefits for marine economic development and marine environmental protection. The proposed models, protocols and solutions also have strong academic significance for promoting the development of wireless sensor networks.

关键词： Wireless sensor networks Cross layer design Computer network reliability Biological system modeling Predictive models Routing protocols data models Sensors Reliability Protection

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Combining multi-spectral data with statistical and deep-learning models for improved exoplanet detection in direct imaging at high contrast 31

Combining multi-spectral data with statistical and deep-lear...

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31st European Signal processing conference, EUSIPCO 2023

作者： Flasseur, Olivier Bodrito, Théo Mairal, Julien Ponce, Jean Langlois, Maud Lagrange, Anne-Marie Centre de Recherche Astrophysique de Lyon CNRS Univ. de Lyon Univ. Claude Bernard Lyon 1 ENS de Lyon France Département d'Informatique de l'École Normale Supérieure ENS-PSL CNRS Inria France Univ. Grenoble Alpes Inria CNRS Grenoble INP LJK France Courant Institute of Mathematical Sciences Center for Data Science New York Univ United States Laboratoire d'Études Spatiales et d'Instrumentation en Astrophysique Obs. de Paris Univ. PSL Sorbonne Univ. Univ. Diderot France Univ. Grenoble Alpes Institut de Planétologie et d'Astrophysique de Grenoble France

ISBN: (纸本)9789464593600

Exoplanet detection by direct imaging is a difficult task: the faint signals from the objects of interest are buried under a spatially structured nuisance component induced by the host star. The exoplanet signals can only be identified when combining several observations with dedicated detection algorithms. In contrast to most of existing methods, we propose to learn a model of the spatial, temporal and spectral characteristics of the nuisance, directly from the observations. In a pre-processing step, a statistical model of their correlations is built locally, and the data are centered and whitened to improve both their stationarity and signal-to-noise ratio (SNR). A convolutional neural network (CNN) is then trained in a supervised fashion to detect the residual signature of synthetic sources in the preprocessed images. Our method leads to a better trade-off between precision and recall than standard approaches in the field. It also outperforms a state-of-the-art algorithm based solely on a statistical framework. Besides, the exploitation of the spectral diversity improves the performance compared to a similar model built solely from spatio-temporal data. © 2023 European Signal processing conference, EUSIPCO. All rights reserved.

关键词： Matched filters

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Personalized Stress Detection System Using Physiological data from Wearable Sensors 10th

Personalized Stress Detection System Using Physiological Dat...

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10th international conference on Future data and Security Engineering, FDSE 2023

作者： Pham, Vinh Lee, Yongho Chung, Tai-Myoung Department of Computer Science and Engineering Sungkyunkwan University Suwon Korea Republic of Hippo T &C Inc. Suwon Korea Republic of

ISBN: (纸本)9789819982950

Thanks to advancements in wearable technologies and biosensors, the accurate collection of physiological data is now more available and convenient than ever before. Thus, it has become more practical to use artificial intelligence technology to analyze physiological data collected from wearable elements. This could be beneficial to a large number of patients suffering from psychiatric disorders who experience high levels of pressure and stress in the modern era, as it allows for timely identification and intervention. Federated learning is a state-of-the-art machine learning paradigm that helps solve the impracticality of collecting personal physiological data, in a privacy respecting and distributed manner, for implementing artificial intelligence-powered healthcare applications. However, healthcare solutions based on federated learning still face accuracy problems as while physiological data varies from person to person, the averaging strategy of general federated learning algorithms cannot converge into an effective one-for-all solution model. To address this issue, we propose a stress detection system that integrates personalized federated learning algorithms. This system tailors the stress detection mechanism to each individual’s physiological data, thus improving the accuracy and overall performance of the entire system. To evaluate our proposed system, we conducted extensive simulations using a physiological dataset of drivers who were stressed while driving on crowded streets. The experiment results demonstrated that our proposed system improves stress detection accuracy by more than 5% compared to a typical federated learning system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Learning algorithms

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Movie Recommendation Algorithm Based on Sentiment Analysis and LDA 8

Movie Recommendation Algorithm Based on Sentiment Analysis a...

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8th international conference on Information Technology and Quantitative Management (ITQM) - Developing Global Digital Economy after COVID-19

作者： Zhang, Yilin Zhang, Lingling Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

Traditional recommendation algorithms have problems such as data sparseness and not paying attention to the diversity of recommendation results. In this paper, we use LDA to extract topics of comments about movies, and identify the emotional tendencies related to topics. As a result, we enrich user interest model and product feature model based on emotional tendencies to improve content-based recommendation algorithms. Most of prior work on applying sentiment classification to recommendation systems only consider the use of sentiment dictionaries to judge polarity, and adopt pattern matching methods to identify features. This paper uses BERT to train sentiment classification models and uses LDA to extract topics. The algorithm is run on the movie review database crawled from Douban, and the experimental result showed that the diversity of recommendation lists had been significantly improved. (C) 2021 The Authors. Published by Elsevier B.V.

关键词： Sentiment Analysis Topic Extraction Content-based Recommendation Movie Reviews

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data Migration from Relational to NoSQL database: Review and Comparative Study

Data Migration from Relational to NoSQL Database: Review a...

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international conference on Advanced Intelligent Systems for Sustainable Development, AI2SD 2022

作者： Saadouni, Chaimae El Bouchti, Karim Reda, Oussama Mohamed Ziti, Soumia Department of Computer Science Intelligent Processing Systems and Security Team Mohammed V University Rabat Morocco Algorithms Networks Intelligent Systems and Software Engineering Research Team Faculty of Sciences Mohammed V University Rabat Morocco

ISBN: (纸本)9783031263835

New storage requirements, analysis, and visualization of Big data, which includes structured, semi-structured, and unstructured data, have caused the developers in the past decade to begin preferring Big data databases, such as NoSQL, that comes to overcome the boundaries of relational databases such as unlimited scalability, high performance, data modeling, data distribution, and continuous availability. Due to those benefits ensured by this new technology, quite recently, considerable attention has been paid to data migration from relational database to NoSQL database. So, in this context, the purpose of this paper is to provide a comparative study of some recent approaches proposed by various researchers to migrate data from relational to NoSQL databases, as well as discussing and analyzing the results of those proposed techniques. This research allowed us to gain a basic understanding of the contributions and approaches that have already been made, as well as the benefits and limits of each proposal. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Big data

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Influence of COVID-19 in Tourism: A Sentiment Analysis of Twitter data

Influence of COVID-19 in Tourism: A Sentiment Analysis of Tw...

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Sustainable Expert Systems (ICSES), international conference on

作者： T. Kumaravel C. Nallusamy P. Natesan K.S. Nagul A.M. Naveen A. Sakthisundaram Department of Computer Science and Engineering Kongu Engineering College Erode Tamil Nadu India Dept of information Technology K S Rangasamy College of Technology Namakkal TamilNadu India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

This study investigates the economic impacts of the COVID-19 pandemic on Thailand's tourism-driven economy by analyzing English-language tweets related to tourism from July to December 2020. Utilizing advanced machine learning models-Logistic Regression, Decision Tree Classifier, XGBoost Classifier, and the Pelican Optimization Algorithm-we aimed to understand the sentiments and intentions expressed in these tweets. The research analysis revealed high predictive accuracy, with Logistic Regression achieving 98%, Decision Tree Classifier 99%, XGBoost Classifier 96%, and Pelican Optimization Algorithm 94.5%. These results highlight the importance of addressing concerns related to COVID-19 and political tensions to support the recovery of Thailand's tourism sector. The research also highlights the critical role of information and communications technology (ICT) in managing and revitalizing the tourism industry, especially in the context of reduced consumer spending and travel restrictions. Social media platforms, particularly Twitter, were used to gauge public sentiment and inform recovery strategies. The proposed methodology included extensive data collection, cleaning, and natural language processing, followed by the application of various machine learning algorithms to analyze and interpret sentiment data. The findings offer actionable insights for enhancing tourism marketing strategies and improving the sector's resilience amid ongoing pandemic challenges.

关键词： COVID-19 Machine learning algorithms Social networking (online) Pandemics Tourism industry Blogs Prediction algorithms Information and communication technology Classification algorithms Optimization

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