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A Case Study on Data Protection for a Cloud- and AI-based Homecare Medical Device

A Case Study on Data Protection for a Cloud- and AI-based Ho...

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2022 International Health Data Workshop, HEDA 2022

作者： Bende, Philipp Vovk, Olga Caraveo, David Pechmann, Ludwig Leucker, Martin University of Lübeck Institute for Software Engineering and Programming Languages Lübeck Germany Tallinn University of Technology School of Information Technologies Department of Health Technologies Tallinn Estonia UniTransferKlinik Lübeck GmbH Lübeck Germany

To improve the treatment of many diseases, continuous monitoring of the patient at home with the ability of doctors to interact with individual cases demands an increasing number of medical devices connected to the cloud. To support the doctor’s duties, such devices may benefit from AI-based diagnosis routines. In order for such devices to be approved and placed on the market, they need to comply with various legal, regulatory, economic, and social requirements. An integral part of these requirements is the protection of the patients’ data. In this paper, based on a current use case, we describe a workflow on how to identify risks and address their mitigations. To this end, we recall the relevant legal, regulatory, economic, and social data protection requirements. We pursue our findings on a Homecare OCT device that is intended to be used by elderly patients on a daily basis, by taking images of their eyes and sending them for further analysis to a cloud- and AI-based system. The patient’s ophthalmologist gets notified for further dedicated treatment depending on the result. To perform the risk management, we describe (i) the architecture of the homecare system, (ii) analyze its data flow, (iii) discuss several vectors of attack, and (iv) propose ways to mitigate the risks. © 2022 Copyright for this paper by its authors.

关键词： Risk management

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Classification Algorithms with a Complex Structure in Pattern Recognition

Classification Algorithms with a Complex Structure in Patter...

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Akram Kh. Nishanov Oybek Allamov Askar T. Rakhmanov Janar Yusupova Ortik B. Ruzibaev Department of Systematic and Practical Programming Technical Sciences Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan Department of Software Engineering Urgench Branch of Tashkent University of Information Technology named after Muhammad al Khwarizmi Urgench Uzbekistan Department of systematic and practical programming Tashkent university of information technologies named after muhammad al-Khwarizmi Tashkent Uzbekistan Faculty of Software Engineering Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan

ISBN: (数字)9798350389234

ISBN: (纸本)9798350353051

The article considers in the intellectual processing of information, moving from a large unit of measurement to a smaller, more important unit of measurement, selecting sets of informative symbols and classifying symbols, developing modern recognition systems that help specialists in the field, as well as evaluating the states of objects to be classified, developing a classification algorithm for multi-class cases of educational samples. The development of a multi-class algorithm for symbol classification serves to develop the theory of symbol recognition. It deals with the construction of a decision rule construction algorithm. Its main purpose is to determine which of the given classes the recognized object belongs to. The complexity or simplicity of the proposed algorithm depends on the number of classes participating in the study sample. If the number of classes is two, then the formulation of decision rules is simple. That is why the classification algorithms proposed by most authors are solved for cases where the number of classes is three or more, the decisive rule in the matter of classification of objects is solved by making two classes. The construction of a multi-class decision rule for classification, which takes into account all classes at the same time, is a complex and urgent issue. In the article, in the classification of objects, a multi-level complex structured decision rule was developed based on special inequalities, and two-, three- and multi-class classification algorithms were developed.

关键词： Training Productivity Measurement units Atmospheric measurements Symbols Particle measurements Classification algorithms

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Forecasting and modeling on average rainwater and vapor pressure in Chelyabinsk Russia using deep Learning models 6

Forecasting and modeling on average rainwater and vapor pres...

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6th Smart Cities Symposium, SCS 2022

作者： Abotaleb, Mostafa Makarovskikh, Tatiana Subhi, Alhumaima Ali Alkattan, Hussein Adebayo, Adelaja Oluwaseun Department of System Programming South Ural State University Chelyabinsk454080 Russia Electronic and Computer Center University of Diyala BaqubahMJJ2+R9G Iraq Information Communication and Technology Department Lagos State University Lagos102101 Nigeria

ISBN: (纸本)9781839538544

Machine learning technique is full-fledged as a boosting sector to develop modeling and forecasting of complex time series observations in the present environment. This study made an attempt to inspect the future performance of rainfall data and vapor in Chelyabinsk by using a machine learning technique. The data series is divided into a training set (60%) and a test set (40%) for model developing a validation purpose. We further developed deep learning models such as, LSTM, BILSTM, GRU and compared on the basis of ME, RMSE, MAE, MPE, MAPE, ACF1 on the training data set. For testing data set, we compared these deep learning models based on RMSE. LSTM model acts as a superior machine learning model over BILSTM and GRU in this data series. Forecasting performance of these three models significantly at par. This finding may be significant to build a strong literature of the Chelyabinsk weather's forecast, which can be helpful for policy makers and researchers. Also, we strongly believe that, this work could be used as a literature adaptation of machine learning technique for complex time series over statistical models. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Statistical tests

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Design of Intelligent Decision Support System With Ensembled Machine Learning to Predict Students' Performance

Design of Intelligent Decision Support System With Ensembled...

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2024 International Conference on Communication, Computer Sciences and Engineering, IC3SE 2024

作者： Deepan, S. Rajendran, Dineshkumar Dhaliwal, Navdeep Praveena, K. Lourens, Melanie Anandhi, R.J. School Of Cs & Ai Sr University Telangana Warangal India School Of Computing And Innovative Technologies British University Computer Games Design And Programming Viet Nam Lovely Professional University Phagwara India Institute Of Aeronautical Engineering Dundigal Hyderabad India Durban University Of Technology Faculty Of Management Sciences South Africa New Horizon College Of Engineering Department Of Information Science Engineering Bangalore India

ISBN: (纸本)9798350366846

Evaluating student performance is important for universities and institutions in the current student education landscape because it helps them create models that work better for students. The automation of various features related to fundamental student traits and behaviours that manage massive amounts of data efficiently processes these. To handle student records that included information about students' behaviour and how it related to their academic performance, the companies employed models of classification with mining concepts. Additionally, the quality of result classification can be substantially improved by using learning analytics and Educational Data Mining (EDM). The educational establishments are making an effort to lower the low student performance. To address this issue, numerous methods for assessing student performance have been devised, allowing the relevant faculties to intervene and enhance the final product. Three classes - Low Performance Student, Average Student, and Smart Student - were created using the K-Mean Clustering methodology for classifying student records. Features including grade point, number of deficits, student attendance, medium of education, and board of education are taken into account when classifying the data. In this case, the WEKA tool is also utilized for implementing the model and outcome assessments. © 2024 IEEE.

关键词： Decision support systems

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Evolutionary Algorithm for Calculating the Optimal Temperature Regime of the Catalytic Process

Evolutionary Algorithm for Calculating the Optimal Temperatu...

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International Conference on Control Systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Evgenia Antipina Svetlana Mustafina Andrey Antipin Department of Scientific Research Ufa University of Science and Technology Ufa Russia Faculty of Mathematics and Information Technologies Ufa University of Science and Technology Ufa Russia Department of Applied Informatics and Programming Sterlitamak Branch of Ufa University of Science and Technology Sterlitamak Russia

The paper formulates an algorithm for searching the optimal temperature regime of the catalytic process in an ideal mixing reactor. The problem of optimal control of the catalytic process is formulated in a general form. To find its numerical solution, an algorithm based on the method of differential evolution is described. The algorithm allows to obtain an approximate solution of the optimization problem in a time acceptable from the practical point of view. A computational experiment for the dimerization process of alphamethylstyrene is carried out. The optimal temperature profile for achieving the maximum yield of reaction products – linear dimers – is obtained.

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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

A Method for Forecasting the Demand for Pharmaceutical Produ...

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International Conference on Intelligent and Fuzzy Systems, INFUS 2020

作者： Oglu, Alekperov Ramiz Balashirin Kizi, Isgandarova Ilhama Tarlan Information Technology and Programming Azerimed LLC BakuAZ1147 Azerbaijan Department of Computer Engineering Odlar Yurdu University BakuAZ1072 Azerbaijan

ISBN: (纸本)9783030511555

This article discusses the use of fuzzy logic and a neural network to predict the demand for pharmaceutical products in a distributed network, in conditions of insufficient information, a large assortment and the influence of risk factors. A comprehensive approach to solving forecasting problems is proposed using: the theory of fuzzy logic - when forecasting emerging and unmet needs and a neural network - if there is a lot of retrospective information about the actual sale of drugs and drugs. Using this approach to solve the problems of forecasting demand allows you to get statistics and experience. The general algorithm, mathematical interpretation and examples of forecasting the demand for pharmaceutical products in the face of uncertainty of information are given, and the general structure of the system for forecasting the demand for drugs is described. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Uncertainty analysis

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Estimation of Potential Locations of Trade Objects on the Basis of Fuzzy Set Theory

Estimation of Potential Locations of Trade Objects on the Ba...

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International Conference on Intelligent and Fuzzy Systems, INFUS 2020

作者： Oglu, Alekperov Ramiz Balashirin Oglu, Salahli Vuqar Mamadali Information Technology and Programming Azerimed LLC BakuAZ1147 Azerbaijan Department of Computer Engineering Odlar Yurdu University BakuAZ1072 Azerbaijan

ISBN: (纸本)9783030511555

In the present article, we have considered the issue of selection of potential locations of trade objects as a multi-factor decision-making in the conditions of uncertainty by applying the theory of fuzzy sets. Examples were given to assess the location options of commercial enterprises and the potential location options of the facilities on these samples were assessed on the basis of a fuzzy extraction method in the notation MATLAB\Fuzzy Inferences Systems. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision making

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Spectral Analysis of Neural Network Long-Term Predictions

Spectral Analysis of Neural Network Long-Term Predictions

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2021 International Conference on High technology for Sustainable Development, HiTech 2021

作者： Koparanov, Kiril Trifonov, Rumen Minkovska, Daniela Georgiev, Krasin Technical University of Sofia Department of Programming and Computer Technologies Sofia Bulgaria Technical University of Sofia Information Technology in Industry Sofia Bulgaria Technical University of Sofia Department of Aeronautics Sofia Bulgaria

ISBN: (纸本)9781665448734

Long-term predictions of time series is a task with multiple applications. Exploiting neural networks for such problem has not been studied enough for financial purposes. An empirical study of specific cases with real financial multivariate time series and data augmentation was performed. The results are compared for different neural network models and two datasets using standard and customized spectral evaluation metrics. © 2021 IEEE.

关键词： Spectrum analysis

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The prediction of students' Academic performances with a classification Model Built using Data Mining Techniques 6

The prediction of students' Academic performances with a cla...

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6th Smart Cities Symposium, SCS 2022

作者： Alkattan, Hussein Abotaleb, Mostafa Subhi, Alhumaima Ali Adelaja, Oluwaseun A. Kadi, Ammar Ibrahim Al-Mahdawi, Hassan K. Department of System Programming South Ural State University Chelyabinsk454080 Russia Electronic and Computer Center University of Diyala BaqubahMJJ2+R9G Iraq Information Communication and Technology Department Lagos State University Lagos102101 Nigeria Department of Food and Biotechnology South Ural State University Chelyabinsk454080 Russia

ISBN: (纸本)9781839538544

In the purview of most educational sectors today, numerous reviews regarding data mining have been the primary focus, with goals of discovering vast knowledge patterns for students' data. This paper focuses on building model that predicted the performance of computer science students in some courses they were examined through classification technique. The model built for the purpose of classification via decision tree classifier which was adopted in WEKA and KNIME analytics platform. The workflow model basically connected nodes to generate the decision trees while J4.8 Decision Tree Classifier programmable outputs were generated for the courses to obtain the confusion matrix and other instances. Bar Charts were plotted to display the students' performance in the courses. The mean absolute errors, RMS error and relative absolute error all read 0% while the kappa value obtained from the analysis ranged between 0.991 - 1.00 which perfectly agrees for most statistical values. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Decision trees

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Creating a Dynamic Online Learning Environment with AI Integration

Creating a Dynamic Online Learning Environment with AI Integ...

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International Computer Sciences and information Technologies (CSIT)

作者： Oleksandra Bulgakova Zarina Viniarska Viacheslav Zosimov P.M Platonov Educational and Scientific Institute of Computer Engineering Automation Robotics and Computer Programming Odesa National University of Technology Odesa Ukraine Department of Applied information systems Taras Shevchenko National University of Kyiv Kyiv Ukraine

ISBN: (数字)9798331542634

ISBN: (纸本)9798331542641

Educational, online learning has become a common practice due to rapid digitalization and recent global events. This study focuses on developing a comprehensive online learning platform that addresses users' needs by integrating advanced AI capabilities for personalized support and adaptive learning. The platform supports user registration and authentication, course and lesson management, and offers interactive features like AI-driven chat support. In this study, we show the importance of creating user-friendly online learning platforms with the implementation of modern AI trends. The development and testing of this platform is ongoing to determine the adaptation of the system to different learning styles.

关键词： Adaptive learning Electronic learning Authentication Passwords Market research Real-time systems Artificial intelligence information technology Standards Testing

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