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Big Data Analytics in Healthcare——A Systematic Literature Review and Roadmap for Practical Implementation

IEEE/CAA Journal of Automatica Sinica 2021年第1期8卷 1-22页

作者： Sohail Imran Tariq Mahmood Ahsan Morshed Timos Sellis the Faculty of Computer Science Karachi Institute of Economics and TechnologyKarachi 75190Pakistan the Faculty of Computer Science Institute of Business AdministrationKarachi 75270Pakistan the School of Engineering and Technology CQ UniversityMelbourne 3000Australia the Data Science Research Institute Swinburne University of TechnologyHawthorn 3122Australia

The advent of healthcare information management systems(HIMSs)continues to produce large volumes of healthcare data for patient care and compliance and regulatory requirements at a global *** of this big data allows for boundless potential outcomes for discovering *** data analytics(BDA)in healthcare can,for instance,help determine causes of diseases,generate effective diagnoses,enhance Qo S guarantees by increasing efficiency of the healthcare delivery and effectiveness and viability of treatments,generate accurate predictions of readmissions,enhance clinical care,and pinpoint opportunities for cost ***,BDA implementations in any domain are generally complicated and resource-intensive with a high failure rate and no roadmap or success strategies to guide the *** this paper,we present a comprehensive roadmap to derive insights from BDA in the healthcare(patient care)domain,based on the results of a systematic literature *** initially determine big data characteristics for healthcare and then review BDA applications to healthcare in academic research focusing particularly on No SQL *** also identify the limitations and challenges of these applications and justify the potential of No SQL databases to address these challenges and further enhance BDA healthcare *** then propose and describe a state-of-the-art BDA architecture called Med-BDA for healthcare domain which solves all current BDA challenges and is based on the latest zeta big data *** also present success strategies to ensure the working of Med-BDA along with outlining the major benefits of BDA applications to ***,we compare our work with other related literature reviews across twelve hallmark features to justify the novelty and importance of our *** aforementioned contributions of our work are collectively unique and clearly present a roadmap for clinical administrators,practitioners and professionals to successf

关键词： Big data analytics(BDA) big data architecture healthcare NoSQL data stores patient care roadmap systematic literature review

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Streamlining Software Release Process and Resource Management for Microservice-based Architecture on multi-cloud 25

Streamlining Software Release Process and Resource Managemen...

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25th International Multi Topic Conference, INMIC 2023

作者： Herath, Isuru Pathum Jayawardena, Samuditha Fadhil, Ahamed Kodagoda, Nuwan Arachchillage, Udara Srimath S.Samaratunge Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science Sri Lanka

ISBN: (纸本)9798350317701

The software development process is more flexible with the concept of containerization in the microservice platform. This research is on three key components to resolve problems faced by the developers and DevOps teams in the IT industry. First, the development phase expects a fully automated software release process from to the deployment phase and then optimize processes tailored to Docker, and Kubernetes, in microservice-based applications. Then streamline the process and leverage the container orchestration technologies to monitor the main aspect of the development lifecycle through the multi-cloud deployment on demand of the growth of day-today releases on multi-regions. A centralized monitoring platform is developed to monitor the deployed applications and that provides comprehensive visibility regarding performance and health of microservices. At the stage of scalarization in microservices, Vertical Pod Autoscaling (VPA) and Horizontal Pod Autoscaling (HPA) are available approaches for resource allocation, and they require measuring the minimum and maximum resource limits. As a result, an intelligent resource allocation system is proposed using a combination of Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) algorithms to cater to dynamic resource allocation, optimizing scalability, and improving cost-efficiency. This research aims to achieve practical insights into the IT industry's automated deployment, managing, scaling, and monitoring of microservice-based applications through the mentioned components. © 2023 IEEE.

关键词： Long short-term memory

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Designing of an Autonomous System for Electric Vehicle 3

Designing of an Autonomous System for Electric Vehicle

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3rd International Conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2023

作者： Mridul, Towfiq Mahmud Sagor, Abidur Rahman Mridha, Faisal Ahmad Bhuiyan, Md. Mosaraf Hosen Hasan, Md. Jahid Rahman, Md. Abdur American International University-Bangladesh Department of Electrical and Electronics Engineering Dhaka Bangladesh Independent University Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh American International University-Bangladesh Faculty of Engineering Dhaka Bangladesh

ISBN: (纸本)9798350346435

The creation of an autonomous vehicle has two major goals: to minimize the amount of labor that must be performed manually and to provide the automobile with superior intelligence. In order to turn a standard automobile into an autonomous vehicle that can drive itself around a predetermined map (in this case, a university campus), several modifications need to be made. The outcomes of this project will also contribute to the creation of a more technologically advanced campus atmosphere. Additional responsibilities include taking control of the horn system and being in charge of the headlights while also receiving the response signal from the station. In order to guarantee that everything functions correctly, we utilized components such as the Raspberry Pi, light sensors, motors, Arduino Uno, ultrasonic sensors, buzzers, and cameras. A temporary model is built to ensure that this project is successful right from the start. The training model trains the deep learning approach using images captured by a camera in manual mode, which enables the vehicle to operate in autonomous mode using the multilayered neural network that was taught. The final prototype utilizes image processing methods to track lanes. A few image processing technologies, such as intelligent edge detection, provide autonomous movement capabilities. These algorithms were built and tested on the vehicle. The system operated without a hitch, and the data gathered was within reasonable bounds of expectations. As well as demonstrating how the autonomous system of an electric car works. © 2023 IEEE.

关键词： Cameras

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Intelligent Real-Time Hypoglycemia Prediction for Type 1 Diabetes

Intelligent Real-Time Hypoglycemia Prediction for Type 1 Dia...

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Intelligent Methods, Systems, and Applications (IMSA)

作者： Nevien A. Mahdy Ahmed F. Elnokrashy D. A. Hammad Wael A. Mohamed Electrical Engineering Department Benha Faculty of Engineering Benha University Benha Egypt Computer Science Department Faculty of Information Technology and Computer Science Nile University Giza Egypt Basic Engineering Sciences Department Benha Faculty of Engineering Benha University Benha Egypt

ISBN: (数字)9798350362633

ISBN: (纸本)9798350362640

Hypoglycemia in Type 1 Diabetes (T1D) refers to a condition where blood glucose (BG) levels drop to abnormally low levels, typically below 70 mg/dL. This can occur when there is an excessive amount of insulin relative to the blood glucose level, leading to an imbalance that can be dangerous and potentially life-threatening if not promptly treated. The availability of large amounts of data from continuous glucose monitoring (CGM), insulin doses, carbohydrate intake, and additional vital signs, together with deep learning (DL) techniques, has revolutionized algorithmic approaches for BG prediction in T1D, achieving superior performance. In our study, we employed a Long Short-Term Memory (LSTM) neural network architecture to predict hypoglycemia events in patients with T1D. For the training and testing, we utilized the OhioT1DM (2018) dataset. In addition, real-time data collected from an individual patient for the evaluation. This patient utilized the CGM FreeStyle Libre (FSL) system, along with a smartwatch to monitor step count. The LSTM model exhibited performance demonstrating exceptional levels of sensitivity, specificity, and accuracy scores of 97.09%, 94.17%, and 95.63%, respectively, when assessed using the Ohio test dataset. Our research provides strong evidence supporting the system's efficacy in managing hypoglycemia events in individuals diagnosed with T1D.

关键词： Training Wearable Health Monitoring Systems Real-time systems Data models Glucose Diabetes Insulin

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Explainable Robust Smart Meter Data Clustering for Improved Energy Management

Explainable Robust Smart Meter Data Clustering for Improved ...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Al-Bazzaz, Hussein Azam, Muhammad Amayri, Manar Bouguila, Nizar Concordia University Faculty of Gina Cody School of Engineering and Computer Science MontrealQCH3G 1M8 Canada

ISBN: (纸本)9798350337020

The widespread deployment of smart meters in residential settings has led to a wealth of high-resolution electrical power consumption data, providing the opportunity to discover valuable insights into energy consumption patterns. Mixture models are essential for revealing hidden patterns in data, enabling accurate insights and informed decision-making across diverse applications. In this paper, we introduce the mixture of mixtures of bounded asymmetric generalized Gaussian and Uniform distributions (BAGGUMM) and investigate its potential for characterizing residential energy users, thereby enhancing energy management applications, including demand response and energy efficiency programs. We investigate the potential for improved clustering efficacy by incorporating an inner mixture containing the Uniform distribution to enhance robustness against outliers. Additionally, we integrate a decision tree algorithm for model explainability to define pattern boundaries using if-then statements. We validate our proposed model using three real-life datasets. Additionally, The performance of BAGGUMM is compared against several state-of-the-art mixture models. © 2023 IEEE.

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Attitudes of Slovenian Upper Secondary School Students towards Online Education

Attitudes of Slovenian Upper Secondary School Students towar...

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Proceedings of the International Convention MIPRO

作者： Andrej Šorgo Mihaela Merdaus Faculty of Natural Sciences and Mathematics Faculty of Electrical Engineering and Computer Science University of Maribor Maribor Slovenia

ISBN: (数字)9798350382501

ISBN: (纸本)9798350382518

Online teaching and learning is seen by many as a way to not only improve the learning outcomes of traditional courses, but also to give participants the opportunity to learn anytime, anywhere. During the COVID-19 pandemic, when educational institutions were forced to offer online courses to students, a critical reflection on the sometimes overly optimistic predictions about the possibilities of such education was possible. Based on the assumption that knowledge and skills acquired in the classroom only form competences in connection with attitudes, we were interested in the attitudes of Slovenian upper secondary school students in connection with online teaching. We were able to collect data from 1081 students from different secondary schools. The main findings were that the majority of students, regardless of their gender: (1) would prefer to fulfil their duties at school rather than learning online; (2) do not find working in online environments challenging; and (3) do not think that this work is more productive or efficient compared to face-to-face teaching.

关键词： COVID-19 Surveys Distance learning Pandemics Education Video conferencing Reflection

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A Multiresolution Chain Coding Scheme for Different Resolution Ratios

A Multiresolution Chain Coding Scheme for Different Resoluti...

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International Conference on Mathematics and computers in sciences and in Industry (MCSI)

作者： Krista Rizman Žalik Faculty of Electrical Engineering and Computer Science Faculty of Natural Sciences and Mathematics University of Maribor Maribor Slovenia

ISBN: (数字)9798350377224

ISBN: (纸本)9798350377231

A multiresolution chain coding scheme for contours based on Freeman chain codes in four direction is proposed. It progressively refines the grid size and encodes by the proposed R11 chain codes. Encoding and decoding processes are performed using a new hash function. The algorithm generates a multiresolution representation of a contour for the resolution ratio n:1, for any positive whole number n. The new R11 code is at least 12,5% better than the concatenation of F4 chain codes for various resolutions. The multiresolution generation of a multi-scale representation allows the algorithm to perform well within time, storage, or transmission constraints.

关键词： Industries Hash functions Codes Symbols Data visualization Encoding Mathematics Blockchains Decoding Huffman coding

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Refining Nonparametric Mixture Models with Explainability for Smart Building Applications

Refining Nonparametric Mixture Models with Explainability fo...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Al-Bazzaz, Hussein Saravanakumar, Kumar Prabhakaran Amayri, Manar Bouguila, Nizar School of Engineering and Computer Science Concordia University Faculty of Gina Cody MontrealQCH3G 1M8 Canada

ISBN: (纸本)9798350337020

Nonparametric mixture models are a powerful and flexible approach to data clustering;they account for uncertainty by using Bayesian inference to obtain a posterior distribution over the model's parameters and a better fit to the data. Additionally, nonparametric mixture models can adaptively adjust the number of components to fit the data. Incorporating asymmetric generalized Gaussian distribution (AGGD) within the mixture framework extends the capabilities of the widely used Gaussian mixture model (GMM) by adding parameters that control the shape and the skewness of the per-component distribution, which enables the model to capture diverse and complex data patterns. Furthermore, we incorporate explainability within our proposed infinite asymmetric generalized Gaussian mixture model (IAGGMM) to provide interpretable insights into the clustering results, enhancing the model's practicality and transparency. This integration facilitates a deeper understanding of the underlying data structures and the rationale behind the model's decisions, fostering trust and promoting the adoption of our approach in various real-world scenarios. In this study, we explore the application of occupancy estimation for optimizing energy efficiency and facility management in smart buildings. Our approach demonstrates superior performance in modelling complex and asymmetric data distributions, resulting in improved accuracy and adaptability for occupancy level estimation. Therefore, we achieve the optimal trade-off between model complexity and accuracy. © 2023 IEEE.

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Development Perspectives in Modern Education 30

Development Perspectives in Modern Education

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30th Telecommunications Forum, TELFOR 2022

作者： Lacrama, Dan L. Naaji, Antoanela Pintea, Florentina A. Alexa, Florin University of Timisoara Computers and Applied Computer Science Faculty 'Tibiscus' Timisoara Romania Western University of Arad Faculty of Economics Computer Science and Engineering 'Vasile Goldis' Arad Romania University of Timisoara Electronics and Telecomunications Faculty 'Politehnica' Timisoara Romania

ISBN: (数字)9781665472739

ISBN: (纸本)9781665472739

This paper debates the changes in modern education caused by the accelerated development of computers & telecommunication. This evolution was enhanced by the Covid-19 pandemic and the modifications in students' preferences about learning. Three main directions of development are presented and compared. A practical implementation of a learning site is given as an illustrative example. © 2022 IEEE.

关键词： E-learning

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Technical Job Distribution at BSD SHARP Service Center Using Combination of Naïve Bayes and K-Nearest Neighbour

Technical Job Distribution at BSD SHARP Service Center Using...

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2022 IEEE Symposium on Wireless Technology and Applications, ISWTA 2022

作者： Pebrianti, Dwi Ariawan, Angga Bayuaji, Luhur Mahdiana, Deni Rusdah, Rusdah Universitas Budi Luhur Magister of Computer Science Faculty of Information Technology Jakarta Indonesia International Islamic University Malaysia Faculty of Engineering Dept. of Mechanical Engineering Gombak Malaysia Universitas MNC Faculty of Computer Science Department of Computer and Media Jakarta Indonesia Universitas Budi Luhur Faculty of Information Technology Department of Information System Jakarta Indonesia

ISBN: (数字)9781665484824

ISBN: (纸本)9781665484824

Works distribution is a routine carried out every day by the head of the branch in the SHARP Service Center. The accuracy of the labor division is very important to get customer satisfaction. Inappropriate work distribution can increase complaints from customers. Currently, works distribution in SHARP Service Center is carried out manually, where the works received on the selected system is then shared through the document provided. Time taken for this process is about 1.42 minutes on average for each damage reports. Speed of Service also depends on the Head of Department's expertise and experience. In this study, an automatic system based on Machine Learning will be designed for the technicians work distribution by using a combination of k Nearest Neighbor (k-NN) and Naïve Bayes. Naïve Bayes algorithm is used to improve the feature extraction accuracy by considering the feature below the average (α). Meanwhile, k-NN algorithm is used to classify the experimental data. From the study, it is found that the best of k value for k-NN algorithm is 15. It is known that a high number of accuracy values, the labor distribution can be more accurate. The validation of the proposed method is conducted by using a confusion matrix with a composition of 80% training data and 20% test data. The single Classifier test with the Naïve Bayes algorithm produces the highest accuracy value of 72.7%, while using k-NN algorithm is 81.5%. With a combination of Naive Bayes and k-NN algorithms, the accuracy value is increasing to 86%. This result shows that the proposed method improves the accuracy by 13.3% on single Naive Bayes algorithms and 4% on a single k-NN algorithm. The results obtained show that in the manual process, the average time per job is 1.42 minutes, while by using the proposed method, the average processing time is around 0.03 seconds per job. An increase of 2480 times faster is found and confirmed during the implementation of the proposed method. © 2022 IEEE.

关键词： Customer satisfaction

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