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Securing an IoT Medical System Using AI and a Unidirectional Network Device: Application to a Driver 11

Securing an IoT Medical System Using AI and a Unidirectional...

引用

11th International Conference on Electronics, Communications and Networks, CECNet 2021

作者： El Hajal, Georges Daou, Roy Abi Zeid Ducq, Yves Boercsoek, Josef Univ. Bordeaux IMS Laboratory France Biomedical Technologies Department Lebanese German University Lebanon MART Learning Education and Research Center Chananir Lebanon University of Kassel Institute for Computer Architecture and System Programming Germany

ISBN: (纸本)9781643682402

Security in systems and networks has always been a major issue for IT administrators. When it comes to medical applications, this concern is much more important due to the sensitivity of data and the risks that may be caused due to alteration or falsification of such critical information. The proposed paper presents a solution to assure the best security possible in such an environment. Thus, based on an application that monitors a driver's health while driving his car, a data diode will be implemented in order to assure security of the system by forcing unidirectional flow of network data to the healthcare provider side. Added to that, an AI-based program will be developed to verify the confidentiality, the integrity and the availability of the exchanged data and to check the patient health for abnormalities. Every sub-part of the system has been tested separately and results have shown that falsified data has been filtered out of the received end, e.g. the healthcare provider side. © 2022 The authors and IOS Press.

关键词： Medical applications

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An Approach for Parallel Reading in Multiple Sequence Alignment

An Approach for Parallel Reading in Multiple Sequence Alignm...

引用

2020 International Conference Automatics and Informatics, ICAI 2020

作者： Ko, Soon-Heum Gancheva, Veska Linkoping University National Supercomputing Center Linkoping Sweden Technical University of Sofia Programming and Computer Technologies Department Sofia Bulgaria

ISBN: (纸本)9781728193083

We propose an approach for faster file reading of multiple sequence alignment input through the use of MPI-I/O over a subset of MPI cores. The idea is to allow a subset of MPI cores that perform I/O operations and to broadcast locally on individual neighbors, so the code is less sensitive to the stability of the parallel file system. It is achieved by creating a number of subgroups under a global MPI communicator. The size of each subgroup and the buffer size of each reading operation are tuned through the synthetic benchmark. We verify the performance of our approach by comparing it with the traditional way of "sequential file reading and global broadcast", and apply it to the existing MPI version of multiple sequence alignment software ClustalW. In the production run over 8192 BlueGene/Q cores, the current approach provides 6.8 times acceleration of the existing ClustalW-MPI implementation. © 2020 IEEE.

关键词： bioinformatics ClustalW-MPI MPI-I/O multiple sequence alignment parallel reading

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Information technologies for calculating the effect of wire thickness and insulation material on its heating temperature during operation

Information technologies for calculating the effect of wire ...

引用

Advanced Technology (KhPIWeek), IEEE KhPI Week on

作者： Albert Katunin Oleksii Kolomiitsev Oleg Kulakov Hennadii Heiko Ihor Rudakov Department of fire and technological safety of facilities and technologies National University of Civil Defence of Ukraine Kharkiv Ukraine Computer Engineering and Programming Department National Technical University "Kharkiv Polytechnic Institute" Kharkiv Ukraine Scientific Department on Problems of Civil Protection and Technogenic and Ecological Safety National University of Civil Defence of Ukraine Kharkiv Ukraine

The paper evaluates the influence of wire thickness and insulation material on its heating temperature during operation. The temperature and time characteristics of wire exploitation under conditions of different load currents are analysed. The ranges of the wire temperature increase during operation are determined for wires with insulation made of polyethylene, polyvinyl chloride, enamel, and rubber.

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Artificial Intelligence in Medicine for Chronic Disease Classification Using Machine Learning

Artificial Intelligence in Medicine for Chronic Disease Clas...

引用

International Conference on Application of Information and Communication technologies (AICT)

作者： Mekhriddin Rakhimov Ravshanjon Akhmadjonov Shahzod Javliev Department of Artificial Intelligence Tashkent University of Information Technologies Named After Muhammad Al-Khwarizmi Tashkent Uzbekistan Department of System and Application Programming Tashkent University of Information Technologies Named After Muhammad Al-Khwarizmi Tashkent Uzbekistan Department of Computer System Tashkent University of Information Technologies Named After Muhammad Al-Khwarizmi Tashkent Uzbekistan

ISBN: (纸本)9781665451635

Artificial intelligence (AI) systems in medicine are one of the most important modern trends in global healthcare. Artificial intelligence technologies are fundamentally changing the global healthcare system, making it possible to radically rebuild the system of medical diagnostics while reducing healthcare costs. AI is actively used in research to develop methods for diagnosing coronary heart disease (CHD). There are different types of CHD. Before treating a disease, it is necessary to determine which class of diseases it belongs to. Based on the feature space of the disease, it is possible to classify the type of CHD. Machine learning algorithms can solve this problem. The k-nearest neighbors (KNN) algorithm is a simple, easy-to-implement supervised machine learning algorithm that can be used to solve classification problems. The dataset is the more important part of the supervised machine learning algorithm for training. Gathering data is the most important step in solving any supervised machine learning problem. But choosing more important part from the collected data is one of the tasks to be solved. The main purpose of this study is to select more useful parametric attributes from the dataset to obtain a high F1-score of CHD classification.

关键词： Heart Training Machine learning algorithms Machine learning Market research Classification algorithms Information and communication technology

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Improving Predictions of Long Sequences by Hyperparameter Tuning

Improving Predictions of Long Sequences by Hyperparameter Tu...

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computer Science (COMSCI), International Scientific Conference on

作者： Kiril Andreev Koparanov Krasin Krasimirov Georgiev Vasil Aleksandrov Shterev Daniela Veleva Minkovska Department of Programming and Computer Technologies Faculty of Computer Systems and Technologies Technical University of Sofia 8 Kliment Ohridski blvd Sofia Bulgaria Department of Air Transport Faculty of Transport Technical University of Sofia 8 Kliment Ohridski blvd Sofia Bulgaria Department of Communication Networks Faculty of Telecommunications Technical University of Sofia 8 Kliment Ohridski blvd Sofia Bulgaria

ISBN: (数字)9781665497770

ISBN: (纸本)9781665497787

The problem of forecasting long sequences is important in many different domains. Proper selection of the hyperparameters when a machine learning approach is applied could make the difference between adequate and inadequate model. Several algorithms for automatic hyperparameters tuning were evaluated and compared with baseline selection. As a result, recommendations have been made. Some of the intuitive assumptions for the baseline model proved to be wrong.

关键词： Industries Machine learning algorithms Computational modeling Time series analysis Diversity reception Machine learning Prediction algorithms

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Analysis of Hardware and Software Tools for Implementation of Cognitive Radio Networks

Analysis of Hardware and Software Tools for Implementation o...

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International Conference on Information Science and Communications technologies (ICISCT)

作者： Ernazar Reypnazarov Halimjon Khujamatov Doston Khasanov Zamira Allamuratova Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan Dean of the Computer Enjineering Faculty Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan Department of Data Communication Networks and Systems Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan Department of Information-Computer Technologies and Programming Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan

This paper analyzes of the hardware and software tools for implementing a cognitive radio network. In particular, software-configurable radio modules, which are considered the main element of the cognitive network, were studied according to their main characteristics, among which the widely used ones were comparatively analyzed. As a result of the analysis, the HackRF One device was selected as the most preferred option for cognitive network research, and its working principles, structure, and characteristics were revealed in detail. It also provides information about the GNU Radio software environment and its capabilities, which are used to operate the HackRF One device.

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Comparative Study for Classification Methods to Predict and Detect Epilepsy Seizure 5

Comparative Study for Classification Methods to Predict and ...

引用

5th IEEE Middle East and Africa Conference on Biomedical Engineering, MECBME 2020

作者： El Halabi, Nashaat Abi Zeid Daou, Roy Achkar, Roger Hayek, Ali Boercsoek, Josef American University of Science and Technology Department of Computer and Communications Engineering Faculty of Engineering Beirut Lebanon Faculty of Public Health Biomedical Technologies Department Jounieh Lebanon American University of Science and Technology Department of Computer and Communications Engineering Faculty of Engineering Beirut Lebanon Computer Science University of Kassel Department of Computer Architecture and System Programming Faculty of Electrical Engineering Germany Computer Science University of Kassel Department of Computer Architecture and System Programming Faculty of Electrical Engineering Germany

ISBN: (纸本)9781728123585

Epilepsy is a medical problem that tackles lots of patients. It limits the life activity of such patients due to the seizures that occur anytime and anywhere. Thus, creating a monitoring system that could make their life easier and allow them to perform daily life activities as safe as possible has been studied. This paper is a continuity for a project where a system monitors epileptic patients using a set of wearable sensors connected to several processing units, the main sensors are Electroencephalogram (EEG) for the electrical activity of the brain, electrocardiogram (ECG) for the electrical activity of the heart, and accelerometers to monitor the posture of the patient, features extracted from EEG and ECG signals are used for prediction/detection of seizures. Thus, the objective of this paper is to present a comparative study in order to choose the appropriate classification method for seizure prediction and detection. Five classification methods were studied: Decision Trees, Discriminant Analysis, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Ensemble Learning. For each of the classification methods, the accuracy, the specificity, the sensitivity, the precision, the false detection rate and the Fmeasure were studied and compared. The results for seizure prediction system show that the highest accuracy, specificity and F-Measure, and lowest false detection rate were achieved with Bagged Trees Ensemble Learning, the highest sensitivity and precision were achieved with Quadratic Support Vector Machine. However, for seizure detection system, the highest accuracy and F-Measure were achieved with Linear Support Vector, the highest sensitivity and specificity were achieved with Fine Gaussian Support Vector Machine, and the highest precision and lowest false detection rate were achieved with Subspace Discriminant Ensemble Learning. © 2020 IEEE.

关键词： Support vector machines

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Research of Energy Detection Method in Spectrum Sensing in Cognitive Radio Networks

Research of Energy Detection Method in Spectrum Sensing in C...

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International Conference on Information Science and Communications technologies (ICISCT)

作者： Ernazar Reypnazarov Halimjon Khujamatov Doston Khasanov Zamira Allamuratova Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan Computer Enjineering Faculty Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan Department of Data Communication Networks and Systems Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan Information-Computer Technologies and Programming Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan

This paper presents the threshold value determination model, its implementation algorithm, and the characteristics of the threshold value depending on the probability of false signal detection, developed for the purpose of researching the method of energy determination in spectrum sensing in cognitive radio networks. Also, the main methods of single-band spectrum sensing in cognitive radio networks were initially studied in the article, and the main spectrum detection algorithms were comparatively analyzed.

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Promising Developments in Cyber Physical System Tools for Background Monitoring of Reserve Landscape 11

Promising Developments in Cyber Physical System Tools for Ba...

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11th International Conference on Advanced computer Information technologies, ACIT 2021

作者： Nykolaychuk, Yaroslav Grynchychyn, Taras Pitukh, Ihor Petrashchuk, Yaroslav Nykolaychuk, Lyubov Hryha, Volodymyr West Ukrainian National University Department of Specialized Computer Systems Ternopil Ukraine Nadvirna Vocational College by National Transport University Department of Programming and Information Technologies Nadvirna Ukraine Gorgany Nature Reserve Nadvirna Ukraine Ivano-Frankivsk National Technical University of Oil and Gas Department of Social Communication and Law Ivano-Frankivsk Ukraine Vasyl Stefanyk Precarpathian National University Department of Computer Engineering and Electronics Ivano-Frankivsk Ukraine

Experience in designing and building cyber physical interactive distributed monitoring systems for industrial facilities and reserve landscapes is analyzed. Advantages of the existing interactive and dialogue computerized systems, replicated by many companies, are systematized and their functional limitations are determined. The legal and system requirements for the implementation of this class of information system components, which can be applied in strict Nature Reserve, are clarified. It is proposed to expand the functionality and increase the reliability of the ring-star system for background monitoring of nature reserves based on binary-manipulated optical signals. A sensor system for measuring the height of snow cover along the monitoring route is developed using a relay sensor network and quadcopters. © 2021 IEEE.

关键词： Cyber Physical System

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Sequential Series-Based Prediction Model in Adaptive Cloud Resource Allocation for Data Processing and Security

Sequential Series-Based Prediction Model in Adaptive Cloud R...

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IEEE International Conference on Dependable Systems, Services and technologies (DESSERT)

作者： Inna Petrovska Heorhii Kuchuk Nina Kuchuk Oleksandr Mozhaiev Maxim Pochebut Yurii Onishchenko National Technical University “Kharkiv Polytechnic Institute” Kharkiv Ukraine Department of Computer Engineering and Programming National Technical University “Kharkiv Polytechnic Institute” Kharkiv Ukraine Department of “Cyber Security and DATA Technologies” Kharkiv National University of Internal Affairs Kharkiv Ukraine Academic Board SET University Kiev Ukraine Department № 4 Kharkiv National University of Internal Affairs Kharkiv Ukraine

A developed adaptive forecasting model for cloud resource allocation is presented. It employs principal component analysis on a sequence of virtual machine (VM) requests. Requests are processed to detect anomalies, and adaptive predictions are computed using EEMD-ARIMA or EEMD-RT-ARIMA methods. The choice between EEMD-ARIMA and EEMD-RT-ARIMA methods is determined by comparing the execution time values ${\mathrm {R}}_{\mathrm {{i}}}$ (sequential series test) with the threshold value ${\mathrm {R}}_{\mathrm {{t d}}}$. If ${\mathrm {R}}_{\mathrm {{i}}} \gt {\mathrm {Rtd}}$, EEMD-ARIMA is used; if ${\mathrm {R}}_{\mathrm {{i}}} \leq {\mathrm {R}}_{\mathrm {{t d}}}$, EEMD-RT-ARIMA is applied. This adaptive approach enables the selection of a prediction method based on data characteristics and resource demands. To optimize the selection of the ${\mathrm {R}}_{\mathrm {{t d}}}$ threshold, the impact on accuracy and time costs is examined. A quartile method is utilized to detect dynamic spikes, and cubic spline interpolation is employed to smooth data. EEMDRT-ARIMA-based forecasting enhances accuracy through preprocessing of dynamic spikes and adaptive method selection. Calculations of time costs indicate that this method reduces forecasting time by 1.5 times by extracting core component sequences.

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