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共 264 条记录，以下是41-50 订阅

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Optimizing Machine Learning Algorithms to Enhance Blood Donation for Liver Patients: A Case Study 7

Optimizing Machine Learning Algorithms to Enhance Blood Dona...

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7th IET Smart Cities Symposium, SCS 2023

作者： Mijwil, Maad M. El-Kenawy, El-Sayed M. Bala, Indu Aljanabi, Mohammad Abotaleb, Mostafa Ibrahim, Abdelhameed Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt School of Electrical and Electronics Engineering Lovely Professional University Punjab India Department of Computer Aliraqia University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

ISBN: (纸本)9781839539831

Blood is vital for transporting oxygen, nutrients, and hormones to all body parts as it circulates through arteries and veins. It removes carbon dioxide, regulates body temperature, and maintains the body's immune system. Individuals constantly need blood and its derivatives to save their lives and improve their health through medical treatments and surgical operations. Liver diseases are one of the diseases that affects the health of individuals and requires blood to continue living. These diseases cause significant damage to people's health, and early diagnosis plays a crucial role in saving lives. In this paper, machine learning algorithms (support vector machine and random forest) are involved in detecting liver diseases and determining whether donors are suitable to donate blood from blood values. This paper is applied research that found that the performance measures of the random forest algorithm achieved excellent performance in identifying suitable people to donate blood. © The Institution of Engineering & Technology 2023.

关键词： Support vector machines

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Sensing Metric-Based Hybrid Underlay/Overlay Transmission Framework for CR-IoT Networks for Smart City Applications 7

Sensing Metric-Based Hybrid Underlay/Overlay Transmission Fr...

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7th IET Smart Cities Symposium, SCS 2023

作者： Bala, Indu Mijwil, Maad M. Abotaleb, Mostafa El-Kenawy, El-Sayed M. Ibrahim, Abdelhameed School of Electrical and Electronics Engineering Lovely Professional University Punjab India Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

ISBN: (纸本)9781839539831

With the increasing number of IoT devices, there is a growing need for bandwidth to support their communication. Unfortunately, there is a shortage of available bandwidth due to preallocated bands for various services. To address this issue, Cognitive Internet of Things (CR-IoT) enables devices to optimize their efficiency and enhance their communication capabilities by intelligently accessing available bandwidth. This is achieved through the use of soft sensing metrics, where devices continuously monitor the RF environment and transmit data opportunistically in overlay mode if a free channel is detected, or in underlay mode if not. In this paper, a soft sensing metric based hybrid transmission framework is proposed for CR-IoT devices to meet the data rate requirement for the smart city applications. The efficacy of this approach is demonstrated through simulation results. © The Institution of Engineering & Technology 2023.

关键词： Smart city

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Improvement, application and verification of a new multivariate forecasting model for real industry related issues 31st

Improvement, application and verification of a new multivari...

引用

31st European Safety and Reliability Conference, ESREL 2021

作者： Krini, Abderrahim Börcsök, Josef Engineering of Remanufacturing and Quality Robert Bosch GmbH Schwäbisch Gmünd Germany Department of Computer Architecture and System Programming University Kassel Germany

ISBN: (纸本)9789811820168

With this work, a research contribution in the field of reliability theory has been made, with which a realistic prognosis of reliability parameters of technical systems can be carried out. The motivation to deal with this topic resulted from the realization that the prognosis quality of established prognosis models must be optimized. An early and realistic prognosis of reliability parameters contributes to the success of a concern, mainly through the early implementation of quality measures. The work focuses on the development of a new multivariate prognosis model, which uses multivariate stress parameters as reference variables. Its application enables the prediction of reliability parameters for electronic control units. The predicted reliability parameters can be specified as stress-dependent (bivariate/multivariate) or time-dependent variables. While univariate reference quantities usually use the time dwell time of a technical system, the prognosis model newly presented here can process multivariate reference quantities. During the time in the field, technical systems are not only exposed to different usage behavior, but also to other stresses and influences that make a not inconsiderable contribution to failure. The use of time in the field as a univariate reference variable does not allow for this differentiated consideration and does not take into account relevant information in the reliability analysis. All existing prediction models have in common that only univariate reference parameters can be processed. For a fully comprehensive reliability analysis, all stress variables that lead to a failure must be considered. This is not sufficiently possible with a simple univariate approach. With the new approaches, it is now possible for the first time to consider different stress variables, their changes and their effects on the technical system under investigation in a field data analysis. The presented approach for the multivariate prognosis model considers i

关键词： Forecasting

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Design of a Novel Safe Approach to Monitor Drivers While Operating Their Cars

Design of a Novel Safe Approach to Monitor Drivers While Ope...

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International Conference on Advances in Biomedical Engineering (ICABME)

作者： Aseel Elhawawsha Carla Zeine Roy Abi Zeid Daou Dominik Kalinowski Joseph Khattar Samer Telawi Josef Boercsoek Department of Computer Architecture and System Programming University of Kassel Kassel Germany Mechatronics Eng. Department Saculty of Engineering Université La Sagesse Beirut Lebanon

Road accidents have been known to be one of the leading death causes around the world for a long time. Thus, cars and all kinds of road vehicles form a huge source of danger, and they relate to multiple high risks. There are many factors that can contribute to road traffic accidents such as the health condition of the driver if a medical emergency event happened while driving, drowsiness, or mind-wandering. The proposed system can monitor the behavior of the driver while driving as well as his/her health status. Moreover, it analyzes the monitored data, alerts the driver in a timely manner, and assists him/her in adjusting the driving behavior. Different dangerous and risky situations which can be faced during driving can be captured and avoided by using such a system which leads at the end to increase road traffic safety and reduce vehicle collisions. Notifications are developed in cases of drowsiness, loss of focus, or other concerns that may emerge while driving. However, when the driver's ability to operate the vehicle safely is hampered by a health concern, the implemented system is only able at this stage to alarm the driver itself and the surrounding cars as a first level of alarms.

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Epistemic Multiagent Reasoning with Collaborative Robots 37

Epistemic Multiagent Reasoning with Collaborative Robots

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37th Italian Conference on Computational Logic, CILC 2022

作者： Soldà, Davide Fabiano, Francesco Dovier, Agostino Constraint and Logic Programming Lab University of Udine Udine33100 Italy Embedding System Unit of Fondazione Bruno Kessler Trento38123 Italy Department of Mathematical Physical and Computer Sciences University of Parma Parma43124 Italy Department of Mathematics Computer Science and Physics University of Udine Udine33100 Italy

Over the last few years, the fields of Artificial Intelligence, Robotics and IoT have gained a lot of attention. This increasing interest has brought, among other things, to the development of autonomous multi-agent systems where robotic entities may interact with each other. As for all the other autonomous settings, also these systems require arbitration. Our work tries to address this problem by presenting a framework that embeds both a classical and a multi-agent epistemic (epistemic, for brevity) planner in a robotic control architecture. The idea is to combine the (i) classical and the (ii) epistemic solvers to model efficiently the interaction with: the (i) physical world and the (ii) information flows, respectively. In particular, the presented architecture starts by planning on the "epistemic level" refining then single-agent world-altering actions thanks to the classical planner. To further optimize the solving process, we also introduce the concept of macros in epistemic planning. Macros, in fact, have been successfully employed in classical planning as they allow for opportune aggregations of actions that may lead to a reduction of plans' length. Finally, the overall framework is exemplified and validated with two Franka Emika manipulators. This allowed us to empirically justify how the combination of the two planning approaches (classical and epistemic), and the introduction of macros, reduce the computational time required by the orchestrating phase. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

关键词： Robot programming

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Concept of an Optical Distance and Speed Sensor Using Novel Offsetless Spatial Frequency Filters in the Area of Functional Safety

Concept of an Optical Distance and Speed Sensor Using Novel ...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Frank Wasinski Werner Bonath Ubbo Ricklefs Josef Börcsök Michael Schwarz Eike Hahn Dept. of Electrical Engineering and Information Technology THM University of Applied Sciences Giessen Germany ICAS Institute for Computer Architecture and System Programming University of Kassel Kassel Germany

Optical speed sensors based on the spatial frequency filter method are a proven technology that offer high measurement accuracy over a wide speed range. Newly developed variants also enable measurements at very low speeds and increase the robustness of the method [1]. This makes the sensor very suitable for use in the area of functional safety. A measurement chain up to safety integrity level 3 (SIL3) should be feasible via a combination with a safety system on a chip (SoC) for evaluating the measurement data and an extension of the sensor module to include safety-related elements [2], [3]. By integrating the sensor at IC level, a compact overall solution is to be made possible. After the first successful tests with the safety SoC, further examinations of the measuring method with regard to functional safety are now necessary.

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A Practical Approach for Reliability Prediction of Safety Critical Software Using Multi-Model Ensemble Techniques

A Practical Approach for Reliability Prediction of Safety Cr...

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International Conference on system Reliability and Science (ICSRS)

作者： Aymen Ouertani Ossmane Krini Habil. Josef Börcsök Institute of functional safety & cyber security Baden-Wuerttemberg Cooperative State University Lörrach Germany Computer Architecture und System Programming University of Kassel Kassel Germany

This paper introduces a novel and comprehensive approach for estimating the reliability of safety critical software components in autonomous vehicle motion systems. The proposed approach in this paper presents a combination software reliability model (CSRM) that integrates multiple non-homogeneous Poisson process (NHPP) software reliability growth models (SRGMs) to achieve a reasonable compromise between accuracy, the trade-off between the goodness of fit and the simplicity, and stability. By using machine learning techniques, the CSRM effectively combines the strengths of individual SRGMs while mitigating their weaknesses through suitable evaluation and calibration techniques. The developed CSRM has been successfully applied and validated to facilitate a smoother and more efficient evaluation of reliability targets for software components in autonomous robotic wheelchair (ARW). Based on the validation results, the new CSRM has significantly enhanced the efficiency of the process for evaluating whether the defined reliability goals were being achieved. Furthermore, has facilitated a more accurate assessment of the need for further test executions and better planning of the required verification and validation session. This new approach provides valuable insights into the reliability of the developed software, particularly for software developers lacking extensive experience in identifying and applying appropriate SGRM.

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Artificial Intelligence in Medicine for Chronic Disease Classification Using Machine Learning 16

Artificial Intelligence in Medicine for Chronic Disease Clas...

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16th IEEE International Conference on Application of Information and Communication Technologies, AICT 2022

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

ISBN: (纸本)9781665451628

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. © 2022 IEEE.

关键词： Diseases

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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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IoT Cyberattack Detection Approach Based on Energy Consumption Analysis

IoT Cyberattack Detection Approach Based on Energy Consumpti...

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IEEE International Conference on Dependable systems, Services and Technologies (DESSERT)

作者： Kira Bobrovnikova Oleg Savenko Sergii Lysenko Ivan Hurman Department of Computer Engineering and Information Systems Khmelnitsky National University Khmelnitsky Ukraine Department of Computer Engineering and System Programming Khmelnitsky National University Khmelnitsky Ukraine

ISBN: (纸本)9798350333053

Today, the Internet of Things has been widely used and is becoming an increasingly vital part in various areas of human activity, which helps to effectively solve various real-life tasks and problems. The infrastructure of the Internet of Things includes a lot of heterogeneous devices with limited capacities, which makes it difficult to control them in order to detect cyberattacks. The increase in the number of attacks on the infrastructure of the Internet of Things indicates the relevance of developing new solutions for detecting attacks on the networks of the Internet of Things. One of the possible approaches to identify attacks is to analyze the physical parameters associated with IoT devices that describe the behavior of devices on the network, such as power consumption. Power analysis allows you to detect power-hungry attacks such as crypto mining and denial-of-service attacks. The paper proposes a new method for cyberattack detection in the infrastructure of the Internet of Things that is based on the analysis of energy consumption. In order to improve the accuracy of detecting cyberattacks, the approach also uses the analysis of the actions of the IoT software. The proposed approach makes it possible to detect attacks such as DoS/DDoS with high efficiency with a level of detection up to 99.95%.

关键词： Energy consumption Power demand Software Behavioral sciences Internet of Things Object recognition Data mining

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