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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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GAIWAP (Gait Analysis to Determine the Improvement of Walking Ability in People with Disabilities)

GAIWAP (Gait Analysis to Determine the Improvement of Walkin...

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Control, Automation, and Instrumentation (IC2AI), International Conference on

作者： Josef Börcsök Michael H. Schwarz Malte Drabesch Christian Kinsch Walter Michael Strobl Mathilde Sengölge Jürgen Breitenbach Dept. of computer architecture and system programming University of Kassel Kassel Germany Dept. für Gesundheitswissenschaften Fakultät für Gesundheit und Medizin Medizin und Forschung Zentrum für Gesundheitswissenschaften und Medizin Universität für Weiterbildung Krems Krems

ISBN: (数字)9798331529604

ISBN: (纸本)9798331529611

The use of complex electronic systems in medical technology applications is constantly increasing. These systems are becoming increasingly important, particularly in the area of analyzing the progress of treatment, as they can be used to identify any deficits that may still exist. Although systems are now available from a number of manufacturers, they are not always applicable to all mobile problems, so approaches with adapted system technology must be used.

关键词： Legged locomotion Automation Three-dimensional displays Accuracy Instruments People with disabilities Cameras Recording Gait recognition

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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...

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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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Design and Implementation of a Smart Human Posture Monitoring Device Using a Wearable Vest 7

Design and Implementation of a Smart Human Posture Monitorin...

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7th International Conference on Advances in Biomedical Engineering, ICABME 2023

作者： Daou, Roy Abi Zeid Geha, Elio Aoun, Youssef Hayek, Ali Boercsoek, Josef Olmedo, Jose Javier Serrano Université la Sagesse Biomedical Engineering Department Furn El Chebbak Beirut Lebanon Mart Learning Education and Research Center Chananir Lebanon Lebanese German University Faculty of Public Health Biomedical Technologies Department Jounieh Lebanon University of Applied Science Hamm-Lippstadt Department Lippstadt 2 Hardware Engineering Marker Allee 76-78 Hamm59063 Germany University of Kassel Faculty of Electrical Engineering /Computer Science Department of Computer Architecture and System Programming Germany Universidad Politecnica de Madrid Lab of Bioinstrumentation and Nanomedicine Centro de Tecnologia Biomedica Spain

ISBN: (纸本)9798350325850

Chronic back pain can present a serious health concern, with symptoms that can significantly affect an individual's well-being, mobility, and overall quality of life over an extended period. While chronic back pain may manifest suddenly in some cases, it often develops gradually and persists for weeks, and in untreated cases, it can linger for years. Hence, the utilization of assistive devices such as wearable posture-monitoring vests can offer valuable assistance and guidance to users. This research paper is dedicated to the development of a system for detecting, diagnosing, and correcting poor posture, specifically leaning posture. The vest is designed to provide users with visual, auditory, and tactile cues to help them address this issue, thereby reducing the risk associated with leaning. Additionally, an integrated electrical box has been designed to consolidate all components directly onto the main board in a secure enclosure. This box also displays the daily count of instances where the user has leaned. This system is characterized by its electrical safety, portability, compactness, comfort, and affordability. A comprehensive analysis of the system's performance has been conducted with a meticulous evaluation of accuracy. Each component of the system has undergone successful testing, and the system as a whole is currently in the testing phase. The results of these tests have indicated a lack of faulty errors and have demonstrated outstanding accuracy and detection rates. Over 100 individuals of varying ages, genders, and BMI categories were involved in testing, with each person wearing the device for an average of six hours. The accuracy rate achieved was 98.85%, with an average of 54.35 instances of poor posture detected per participant. © 2023 IEEE.

关键词： Wearable technology

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Advanced IOT Traffic Light Control system

Advanced IOT Traffic Light Control System

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International Conference on Advances in Computational Tools for Engineering Applications, ACTEA

作者： Martin Abou Hamad Antonio El Hajj Roy Abi Zeid Daou Ali Hayek Gaby Abou Haidar Biomedical Technologies Department Faculty of Public Health Lebanese German University Lebanon MART Learning Education and Research Center Lebanon Department of Computer Architecture and System Programming University of Kassel Kassel Germany Engineering Faculty American University of Science and Technology Zahle Lebanon

For several years, traffic congestion has been a major problem in big cities where the number of cars and different means of transportation has been increasing significantly. The problem of congestion is becoming more and more critical, and if not treated smartly this issue will negatively affect drivers by wasting time and fuel gas while waiting for hours in lanes. This paper presents a new and smart way to mitigate this issue in an affordable cost, minimum processing power, and low power consumption. This concept takes into consideration the majority of the cases that may cause congestion and presents a smart and accurate outputs to ease traffic flow leading to the prediction of the peak hours of traffic congestion for smarter control. A model is designed to study the case of a four lanes crossroad with two traffic lights and two LCD monitors. The strategy in reading data is divided into two parts: real data from sensors and pre collected data from google maps to create a kind of a predicted pattern over a certain time interval. The responsiveness of the system is analyzed thoroughly, and the accuracy of all possible cases is carefully considered and evaluated. Each part of the system was tested alone, and the overall system is still in an ongoing testing phase. The results have shown minimum faulty errors and accepted outputs that can lead to safe traffic control decisions. Finally, integrating more IoT devices and sensors between V2V, V2P, V2I with the help of artificial intelligence will definitely optimize this system with higher accuracy.

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Research of Neural Network Methods of Visual Defectoscopy of Metal Structures

Research of Neural Network Methods of Visual Defectoscopy of...

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International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)

作者： Alexander Chernikov Elena Smirnova Oleg Zhavoronkov Higher School of Automation and Robotics Peter the Great St. Petersburg Polytechnic University St. Petersburg Russia Department of System Programming Saint Petersburg State University St. Petersburg Russia Computer Vision Systems LLC St. Petersburg Russia

The production of metal pipes is an important component of metallurgy and the entire industry as a whole. Traditional surface quality control is carried out by human inspectors, which is unsatisfactory due to low productivity, reliability and economy. Therefore, the introduction of an automatic visual inspection system into this production allows you to quickly find defects in products and reduce economic losses. To solve this problem, an algorithm based on the neural networks of the YOLOv5 and Unet architectures has been created, which allows finding the bounding frames of defects and their segmentation mask from the image. The created algorithm was applied to two control sites: mandrel defectoscopy and pipe defectoscopy. For mandrel defectoscopy, the accuracy for mAP 0.5 detection is 0.785, for iou_score segmentation – 0.562. For pipe defectoscopy, the accuracy for mAP 0.5 detection is 0.693, for iou_score segmentation - 0.526.

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Investigation of a safety parameter observer for wireless communication

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International Journal of Circuits, systems and Signal Processing 2020年 14卷 1005-1005页

作者： Schwarz, Michael H. Gaus, Larissa Börcsök, Josef Dept. Computer architecture and System programming University Kassel Kassel Germany Dept. Computer architecture and system programming Universität Kassel Kassel Germany

This paper investigates the possibilities to monitor the degree of disturbances of a wireless communication and to use this information to calculate online the necessary safety parameters in order to estimate the probability of failure per hour (PFH) and the safety integrity level (SIL). Depending of the degree of disturbances, addition actions are performed with the intention of keeping the current safety level. A hardware set-up is introduced to get process data and to evaluate the results. © 2020, North Atlantic University Union NAUN. All rights reserved.

关键词： Hamming distance

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Machine Learning Model for Predicting Failures in the Context of Autonomous Vehicles

Machine Learning Model for Predicting Failures in the Contex...

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International Conference on system Reliability and Science (ICSRS)

作者： Abderrahim Krini Aymen Ouertani Habil. Josef Börcsök Engineering of Reliability and Quality Functional Safety Robert Bosch GmbH Schwäbisch Gmünd Germany Institute of Functional Safety & Cyber Security Baden-Wuerttemberg Cooperative State University Lörrach Germany Computer Architecture und System Programming University of Kassel Kassel Germany

In general, failure data is obtained in the automotive industry during the warranty period. If these contain the expression of a service life characteristic for each failure, statements can be made about the reliability and availability of the systems. For this purpose, estimation methods are used to adapt empirical lifetime distributions to theoretical lifetime distributions. By means of the distribution characteristics, a prognosis of the reliability and availability to be expected is also possible beyond the observation time. In addition to the data basis to be investigated, which is available completely in test bench and experimental trials or re-censored in the field, various methods can be used for estimation. In the past, several methods have been used, such as the estimators according to Eckel [1], Kaplan-Meier [2] or the estimators according to Pauli [3]. In general, a field failure is subject to several stresses, which can be described by expressions of several lifetime characteristics. For this application case, which occurs in the automotive industry, the known estimation methods, [1]–[3], cannot be used. In this paper, the necessity of multidimensional estimation methods will be introduced first. Then, a new estimation method for multidimensional metrics is presented. In a further step, mathematical proof is given that the new method can provide realistic results. Finally, two example data sets from bench testing and field are presented. Furthermore, some recommendations for the use of the new method are concretized in this context.

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

Comparative Study for Classification Methods to Predict and ...

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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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Novel Approach of a Smart Medical Mirror system for Medical Applications 5

Novel Approach of a Smart Medical Mirror System for Medical ...

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

作者： Halaby, Shady Khoury, George Zeid Daou, Roy Abi Hayek, Ali Boercsoek, Josef Lebanese German University Biomedical Technologies Department Jounieh Lebanon Lebanese German University Biomedical Technologies Department Jounieh Lebanon Lebanese German University Biomedical Technologies Department Jounieh Lebanon University of Kassel Faculty of Electrical Engineering/Computer Science Department of Computer Architecture and System Programming Germany University of Kassel Faculty of Electrical Engineering/Computer Science Department of Computer Architecture and System Programming Germany

ISBN: (纸本)9781728123585

Humans always face problems when it comes to medical check-up due to lack of time or laziness. However, lots of medical problems and fatal illnesses can be detected at earlier stages if the person is subject to continuous check-up. Hence, the idea of the proposed system is to design a tool enabling the measurement of the basic vital signs with the smallest effort possible from the person's side. Thus, this paper proposes a smart medical mirror system that acts as a normal mirror and also delivers the basic vital signs of the user such as the heart rate, SPO2, body temperature, and Body Mass Index (BMI). The system takes five seconds for initialization and starts the measurements and displays the results. The user will not lose time as this check-up can be performed every morning while the person is wearing his clothes and getting ready to start his daily activities. The system was tested by multiple users having different body statuses. The recorded values showed very small differences with the exact ones. At the end, the system was able to meet the user specifications concerning its portability, sensitivity, accuracy and, most importantly, its low cost. © 2020 IEEE.

关键词： Mirrors

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