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Innovative Machine Learning Approaches to Cardiovascular Disease Prediction

Innovative Machine Learning Approaches to Cardiovascular Dis...

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intelligent computing and Sustainable Innovations in Technology (IC-SIT), international conference on

作者： Mithila Mehedi Nourin Nishat Janifer Nahar Muniroopesh Raasetti A S M Shoaib Basharat Ali Md Amran Hossen Pabel Department of Electrical and Computer Engineering Rajshahi University of Engineering and Technology Rajshahi Bangladesh Department of Management Information Systems Lamar University Texas USA Department of Finance Louisiana State University USA Department of Electrical Engineering Lamar University Texas USA Business Analytics and Insights Wright State University Dayton Ohio

ISBN: (数字)9798350369175

ISBN: (纸本)9798350369182

Cardiovascular diseases (CVDs) are a principal factor in health issues and death throughout the world, introducing extensive challenges to healthcare systems and impacting countless lives. This paper explores the application of various machine learning models to predict and monitor CVDs, leveraging data from wearable sensors and smart health devices. We analysed models featuring Logistic Regression, Support Vector Machines (SVM), Decision Trees, Random Forests, Gradient Boosting, KNearest Neighbors, and Voting Classifiers. Our in-depth examination revealed that most models achieved remarkable accuracy, precision, recall, and F1-scores with the Voting Classifier. These models’ high accuracy and straightforward implementation make them suitable for real-world clinical settings, where computational resources may be limited. By enabling early detection and continuous monitoring, these machine learning approaches can personalize healthcare, improve patient outcomes, and reduce healthcare costs. The integration of these advanced technologies promises to enhance cardiovascular disease management, providing timely interventions and better health monitoring, ultimately contributing to reduced incidence and severity of CVDs.

关键词： Support vector machines Logistic regression Accuracy Computational modeling Medical services Predictive models Boosting Cardiovascular diseases Monitoring Random forests

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Exploiting Satellite data for Solar Performance Modeling

Exploiting Satellite Data for Solar Performance Modeling

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IEEE international conference on smart Grid Communications (smartGridComm)

作者： Akansha Singh Bansal David Irwin University of Massachusetts Amherst

ISBN: (数字)9781728161273

ISBN: (纸本)9781728163598

Developing accurate solar performance models, which infer solar power output in real time based on the current environmental conditions, are an important prerequisite for many advanced energy analytics. Recent work has developed sophisticated data-driven techniques that generate customized models for complex rooftop solar sites by combining well-known physical models with both system and public weather station data. However, inferring solar generation from public weather station data has two drawbacks: not all solar sites are near a public weather station, and public weather data generally quantifies cloud cover-the most significant weather metric that affects solar-using highly coarse and imprecise *** this paper, we develop and evaluate solar performance models that use satellite-based estimates of downward shortwave (solar) radiation (DSR) at the Earth's surface, which NOAA began publicly releasing after the launch of the GOES-R geostationary satellites in 2017. Unlike public weather data, DSR estimates are available for every 0.5km 2 area. As we show, the accuracy of solar performance modeling using satellite data and public weather station data depends on the cloud conditions, with DSR-based modeling being more accurate under clear skies and station-based modeling being more accurate under overcast skies. Surprisingly, our results show that, overall, pure satellite-based modeling yields similar accuracy as pure station-based modeling, although the relationship is a function of conditions and the local climate. We also show that a hybrid approach that combines the best of both approaches can also modestly improve accuracy.

关键词： Clouds Meteorology Satellites data models Mathematical model Computational modeling Atmospheric modeling

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Enhanced-Blind Approach for Privacy Protection of IoT

Enhanced-Blind Approach for Privacy Protection of IoT

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international conference on computing for Sustainable Global Development (INDIACom)

作者： Adnan Ahmed Abi Sen Fathy Albouraey Eassa Kamal Jambi Nour Mahmoud Bahbouh Sami S. Albouq Abdullah Alshanqiti College of Computing and Information Technology Islamic University Al-Madinah SAUDI ARABIA College of Computing and Information Technology King Abdulaziz University Jeddah SAUDI ARABIA College of Computing and Information Technology Islamic University of Madinah Madinah SAUDI ARABIA

ISBN: (数字)9789380544380

ISBN: (纸本)9781728129587

The Internet of Thing (IoT) refers to a vast number of things (e.g., sensors) that are connected to the internet to share data such as in smart cities, intelligent transportation, and healthcare. However, these data may be subject to privacy breaching attacks that pose considerable challenges in the development and deployment of IoT. Therefore, many solutions have been introduced with the assumption of trusted parties' inclusion (e.g., Service Providers) to increase the level of privacy. For example, the blind approach solution depends on asymmetric keys in which the third parties anonymize the user's identity before forwarding the encrypted query to the service provider without being able to perusal the transferred data. But, what will happen if the anonymizer sends the request to the service provider itself without the user's knowledge, in this case, it will be able to read the answers and hacking the privacy of the user. In our research, we propose an enhanced technique for the blind approach to enable users to detect any unauthorized access to their data and then take action. A case-study will be presented in the paper to prove the effectiveness of the enhanced technique in addressing the drawback of the main Blind approach and thereby improving the level of user's privacy in IoT.

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Monitoring of Prolonged and Asymmetrical Posture to Improve Sitting Behavior

Monitoring of Prolonged and Asymmetrical Posture to Improve ...

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data analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), international conference on

作者： Arif Reza Anwary Michael Vassallo Hamid Bouchachia Swansea University Medical School Swansea University Swansea United Kingdom General (Internal) Medicine The Royal Bournemouth Hospital CoPMRE Bournemouth University Bournemouth United Kingdom Professor in Data Science and Intelligent Systems Bournemouth University Bournemouth United Kingdom

ISBN: (数字)9781728196756

ISBN: (纸本)9781728196763

Prolonged and asymmetrical sitting has been known to cause severe health problems like musculoskeletal pain, low back pain and spinal deformity. These in turn increase the cost of medical care. If sitting posture could be measured in a user friendly manner it will be possible to take steps to prevent deterioration in the patient's health, reducing the cost of medical care. To meet this goal, we develop and implement a low cost portable automatic sitting posture monitoring system. A pressure sensing system is constructed to measure different sitting postures (active, static or prolonged and asymmetry). The pressure sensors are placed in six different ergonomic locations on a chair. Sitting information is acquired from a healthy office worker for 10 days and stored in a cloud server through Internet of Things (IoT). A smart IoT end point device application is designed and developed to provide real time posture visualization, display daily summary score, customize different thresholds, configure personal notifications and set targets for improving sitting behavior. Experimental results show that high asymmetry is observed in the thigh region comparing with the lower back and shoulder regions. The subject spent 58% time with Excellent, 28% time with Good and 14% time with Poor sitting postures. This posture monitoring system is significantly simplified, making it suitable for office, school and home based assessment and support posture improvement strategies.

关键词： Sensors Resistance Monitoring Fabrics Pressure sensors Polyethylene Silver

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An Ethereum-based implementation of English, Dutch and First-price sealed-bid auctions

An Ethereum-based implementation of English, Dutch and First...

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IEEE international conference on intelligent Computer Communication and Processing (ICCP)

作者： Claudia Pop Mirela Prata Marcel Antal Tudor Cioara Ionut Anghel Ioan Salomie Technical University of Cluj-Napoca Cluj-Napoca Romania

ISBN: (数字)9781728190808

ISBN: (纸本)9781728190815

This paper addresses the problem of the traditional online auctions by proposing an Ethereum based implementation for three of the most popular auctions: English, Dutch, First-price sealed-bid (FPSB). Such centralized systems are subjects to malicious attacks, unreliable third-party for payment module or repudiation of bids. We present a set of properties that advantages a blockchain auction, which tackles the drawbacks of a traditional one. Furthermore, we define smart contracts for expressing the auctions based on strict rules regarding the role of the participants and stage delimitation. While English and Dutch auction have a public character, the sealed bids from a FPSB can be questioned. The proposed solution incorporates the Merkle proof algorithm for honest verification of these offers. The experimental results show that a Dutch auction is more suitable for sellers, while an English auction for bidders in terms of transaction fees. Based on the behavior of each type of auction, the solution is exemplified by an English auction in the real-estate field, including a protocol for storing data off-chain.

关键词： Blockchain smart contracts Usability Reliability Privacy Peer-to-peer computing Fingerprint recognition

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Machine learning Implementation in IoT based intelligent System for Agriculture

Machine learning Implementation in IoT based Intelligent Sys...

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Emerging Technology (INCET), international conference for

作者： K. N. Bhanu H. J. Jasmine H. S. Mahadevaswamy Mysuru Amrita Vishwa Vidyapeetham Mysore India

ISBN: (数字)9781728162218

ISBN: (纸本)9781728162225

Innovation in Internet of Things (IoT) has acquired changes in everyday life. Agriculture plays a major role in most of the countries and the need for this sector is to become "smart". A primary inference is the absence of knowledge with respect to soil. There are many types of soil present and each kind of soil has various qualities. A thorough knowledge about soil conditions give rise to various information of soil that can be handled to obtain better yields of crop. Machine learning is a trending technology and it helps in the agricultural area to build the exactness and gives solutions for the crop yield problem. Machine learning (ML) is combined with enormous informational advancements and superior figuring to make new chances to unwind, measure, and understand the data intensive prediction in agricultural environments. This paper conducts a thorough study of various concepts of machine learning for IoT based smart Agriculture system.

关键词： Agriculture Cloud computing Wireless sensor networks Temperature sensors Machine learning Internet of Things

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A smart Factory Prediction Method Combining Big data Experience Feedback and Deep Learning

A Smart Factory Prediction Method Combining Big Data Experie...

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Artificial Intelligence and Computer Engineering (ICAICE), international conference on

作者： Xiangquan Yin Jiankai Zuo Xue Huang Zeyuan Liu Guilu Sang Shenyang Aerospace University Shenyang Tongji University Shanghai School of Electronic Information Technology University of Science and Technology Liaoning Anshan

ISBN: (数字)9781728191461

ISBN: (纸本)9781728191478

In recent years, with the further development of deep learning and high-performance computing technologies, more intelligent algorithms have been applied to the actual production processes. The main research content of this paper is the prediction of element yield in the steel industry. First of all, considering that there is a lot of noise in the original data, it will affect the prediction accuracy, so this paper uses the "3-σ" principle and wavelet threshold denoising to preprocess the original data. Subsequently, this article starts from the effect of influencing factors on the yield, and uses a convolutional neural network (CNN) to predict the yield. By analyzing the prediction results, it is found that the prediction effect of CNN for some samples is not good. In order to optimize the model, this article starts from the two perspectives of the model and the algorithm. On the one hand, the LSTM neural network is introduced to take the historical data information of the yield into account, on the other hand, the CNN network and the LSTM are integrated through the Adaboost method. The advantages of the network are combined to establish an integrated learning algorithm of CNNLSTM-Adaboost, which further improves the prediction accuracy of the model. By simulating the model, a high-precision prediction result is obtained.

关键词： Deep learning Absorption Steel industry Predictive models Prediction algorithms Steel smart manufacturing

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A Self-stabilizing Control Plane for Fog Ecosystems

A Self-stabilizing Control Plane for Fog Ecosystems

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IEEE international conference on Utility and Cloud computing (UCC)

作者： Zacharias Georgiou Chryssis Georgiou George Pallis Elad M. Schiller Demetris Trihinas University of Cyprus Computer Science and Engineering Chalmers University of Technology University of Nicosia

ISBN: (数字)9780738123943

ISBN: (纸本)9781665415637

Fog computing is now emerging as the dominating paradigm bridging the compute and connectivity gap between sensing devices and latency-sensitive services. However, as fog deployments scale by accumulating numerous devices interconnected over highly dynamic and volatile network fabrics, the need for self-healing in the presence of failures is more evident. Using the prevailing methodology of self-stabilization, we propose a fault-tolerant framework for control planes that enables fog services to cope and recover from a very broad fault model. Specifically, our model considers network uncertainties, packet drops, node fail-stops and violations of the assumptions according to which the system was designed to operate (e.g., system state corruption). Our self-stabilizing algorithms guarantee automatic recovery within a constant number of communication rounds without the need for external (human) intervention. To showcase the framework's effectiveness, the correctness proof of the self-stabilizing algorithmic process is accompanied by a comprehensive evaluation featuring an open and reproducible testbed utilizing real-world data from the smart vehicle domain. Results show that our framework ensures a fog system recovers from faults in constant time, analytics are computed correctly, while the control plane overhead scales linearly towards the IoT load.

关键词： Cloud computing Fault tolerant systems Fault tolerance Internet of Things Edge computing Ecosystems Circuit faults

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A Scalable Heterogeneous Big data Framework for e-Learning Systems

A Scalable Heterogeneous Big Data Framework for e-Learning S...

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international conference on Advances in Big data, computing and data Communication Systems (icABCD)

作者： David Otoo-Arthur Terence L. van Zyl School of Computer Science and Applied Mathematics University of the Witwatersrand Johannesburg South Africa

ISBN: (数字)9781728167701

ISBN: (纸本)9781728167718

The adoption of e-learning systems in higher education is a remarkable phenomenon that has redefined teaching and learning. Initially, it was proposed to allow people to learn for personal accomplishment without physically attending any traditional university or academic settings. While these systems continue to provide an efficient and flexible approach for teaching and learning, the rapid integration of ICTs and the expansion of data from these systems remain much concern to the education community. In this study, we propose a smart and secure data flow architectural framework for e-learning that uses a rich set of big data tools within a distributed and parallel analysis platform. Reference Model of Open Distributed Processing (RM-ODP) reference model guided the development of this Big data framework for e-learning analytics. Using the RM-ODP model as a benchmark, we classify the educational institution's architecture in terms of existing elements, functions and processes to understand stakeholders' views for the development of the framework. Our approach uses an existing distributed computing environment and offers an adaptable standard framework to improve the data acquisition, storage, processing, analysis, security and virtualization for e-learning systems. We implement a scalable and adaptable big data framework for e-learning (BiDeL) and point out how big data concept could integrate into online learning systems to improve teaching and learning in higher education using Apache Spark as a case. The proposed framework was applied to both batch and streaming dataset of students online activities on moodle LMSs. BiDel framework performance shows improved data integration and data governance. This big data framework and the general view of the current state of the art in “big data” technologies will serve as a guide for the creation of e-learning systems, which create new value from existing but underused data. The acquired value will provide decision-makers in

关键词： Big data Electronic learning data models Analytical models Standards Business

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An Overview of AI-Enabled Remote smart- Home Monitoring System Using LoRa

An Overview of AI-Enabled Remote Smart- Home Monitoring Syst...

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international conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Md. Shahjalal Moh. Khalid Hasan Md. Mainul Islam Md. Morshed Alam Md. Faisal Ahmed Yeong Min Jang Kookmin University Seoul Korea

ISBN: (数字)9781728149851

ISBN: (纸本)9781728149868

With the advancement of communication technology, Internet of Things (IoT) enabled smart home (SH) applications have engrossed substantial attention nowadays. However, a few meter range coverage and higher implementation cost are the main limitations of existing SH systems based on other cellular networks or short distance technologies. In this paper, a long range (LoRa) based SH system is proposed for remote monitoring and maintenance of IoT sensors and devices using artificial intelligence (AI) concept. A brief overview of what tasks LoRa can perform in SH networking are conferred. An AI-based data flow system for IoT server and cloud is also presented in this paper.

关键词： intelligent sensors Sensor systems Artificial intelligence Servers Monitoring Cloud computing

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