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2025 International Conference on Control, automation, and Instrumentation, IC2AI 2025

作者： Nehme, Bechara Al Ahmar, Elie Zalaket, Joseph Electrical Telecommunication and Computer Engineering Department Jounieh Lebanon University Furn El Chebbak Faculty of Engineering Sagesse Baabda Lebanon

ISBN: (纸本)9798331529604

University administrations are always in need for tools to optimize their operation decreasing operational costs and ensuring a good learning experience for students. Advising and course offering are two main tasks that require knowledge in the field and expertise. The tasks need also data and its related analytics. This paper presents an innovative advising tool based on data mining leveraging from electrical power concepts. The tool uses Python and Excel to automate advising sheets and electrical power engineering by applying simultaneity and the factor of utilization to students records and courses to predict future course enrollment with a RMSE=0.75. The research proposed is unique since it helps administration and advisors. With the reduction of resources and disruption of the classical educational system new tools should be elaborated to reduce costs and secure on time graduation of students. © 2025 IEEE.

关键词： Students

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Camouflage Object Segmentation with Attention-Guided Pix2Pix and Boundary Awareness 14

Camouflage Object Segmentation with Attention-Guided Pix2Pix...

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14th International Conference on computer and Knowledge engineering, ICCKE 2024

作者： Sany, Erfan Akbarnezhad Naserizadeh, Fatemeh Sinichi, Parsa Hosseini, Seyyed Abed Faculty of Computer Engineering Quchan University of Technology Quchan Iran Faculty of Electrical and Computer Engineering Malek Ashtar University of Technology Tehran Iran Department of Electrical Engineering Mashhad Branch Islamic Azad University Mashhad Iran

ISBN: (纸本)9798331511272

Segmentation of camouflaged objects presents a significant challenge in computer vision due to their inherent blending with the background environment. This study proposes an innovative method for precise camouflage segmentation using an advanced Pix2Pix architecture. The proposed method leverages the VGG16 network as a backbone for extracting robust features. Subsequently, convolutional block attention modules are integrated to refine these features by focusing attention on spatial and channel dimensions. The generator's decoding pathway employs upsampling stages and skips connections to reconstruct high-resolution segmentation maps effectively. Simultaneously, a discriminator network differentiates between ground truth segmentation maps and the generated ones. To optimize the model's performance, a combination of adversarial, L1, and Soft Dice losses was used during training. Here, the Dice score loss function was employed twice. Firstly, it was used to quantify the disparity between the ground truth mask and the mask predicted by the generator. Secondly, it was applied to assess the difference between the boundary of the ground truth mask and the boundary of the predicted region extracted by morphological techniques. Comprehensive evaluations of the CAMO dataset show the effectiveness of the proposed model. This method obtains a pixel accuracy of 85.4054%, intersection over union of 43.9517, F-measure of 59.3806, and mean absolute error of 0.1307. These results highlight advancements in developing robust computer vision systems to identify and understand camouflage strategies in real-world scenarios. ©2024 IEEE.

关键词： Camouflage

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Detection of Pulmonary Tuberculosis Using Neural Network with Feature Extraction of Gray Level Run-Length Matrix Method on Lung X-Ray Images 24

Detection of Pulmonary Tuberculosis Using Neural Network wit...

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24th International Seminar on Intelligent Technology and Its Applications, ISITIA 2023

作者： Simarmata, Togi Siholmarito Isnanto, R.Rizal Triwiyatno, Aris Diponegoro University Faculty of Engineering Department of Electrical Engineering Semarang Indonesia Diponegoro University Faculty of Engineering Department of Computer Engineering Semarang Indonesia

ISBN: (纸本)9798350313956

Tuberculosis is a disease caused by Mycobacterium tuberculosis, which attacks the respiratory system. The purpose of the research is to detect bacteria on chest X-ray images. The x-ray images will be processed in the four stages used in this study i.e. pre-processing, segmentation, feature extraction using GLRLM (Gray Level Run Length Matrix) and detection methods using an artificial neural network. The accuracy rate for detecting tuberculosis in this research is 98.8%, with normal lungs at 97.5% and tuberculosis lungs at 100%. The input images in this study were X-ray images of the lungs in patients with positive tuberculosis and normal conditions obtained from Kaggle. The images used are 2099 images which are divided into 2019 training images, 334 tuberculosis training images, and 165 normal training images. The testing is 80 images, which consist of 40 tuberculosis images and 40 normal images. Based on the results of percentage accuracy, it can be said that the system created is very good for detecting tuberculosis using the GLRLM feature extraction method and Artificial Neural Network. © 2023 IEEE.

关键词： Feature extraction

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Deep Learning Based Side-Channel Attack Detection for Mobile Devices Security in 5G Networks

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1012-1026页

作者： Amjed A.Ahmed Mohammad Kamrul Hasan Ali Alqahtani Shayla Islam Bishwajeet Pandey Leila Rzayeva Huda Saleh Abbas Azana Hafizah Mohd Aman Nayef Alqahtani Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Imam Alkadhim University College Department of Computer Techniques EngineeringBaghdad 10066Iraq Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Department of Networks and Communications Engineering College of Computer Science and Information SystemsNajran UniversityNajran 61441Saudi Arabia Institute of Computer Science and Digital Innovation UCSI UniversityKuala Lumpur 56000Malaysia Department of Intelligent Systems and Cyber Security Astana IT UniversityAstana 20000Kazakstan Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinah 42353Saudi Arabia Department of Electrical Engineering College of EngineeringKing Faisal UniversityAl-Hofuf 31982Saudi Arabia

Mobile devices within Fifth Generation(5G)networks,typically equipped with Android systems,serve as a bridge to connect digital gadgets such as global positioning system,mobile devices,and wireless routers,which are vital in facilitating end-user communication ***,the security of Android systems has been challenged by the sensitive data involved,leading to vulnerabilities in mobile devices used in 5G *** vulnerabilities expose mobile devices to cyber-attacks,primarily resulting from security ***-permission apps in Android can exploit these channels to access sensitive information,including user identities,login credentials,and geolocation *** such attack leverages"zero-permission"sensors like accelerometers and gyroscopes,enabling attackers to gather information about the smartphone's *** underscores the importance of fortifying mobile devices against potential future *** research focuses on a new recurrent neural network prediction model,which has proved highly effective for detecting side-channel attacks in mobile devices in 5G *** conducted state-of-the-art comparative studies to validate our experimental *** results demonstrate that even a small amount of training data can accurately recognize 37.5％of previously unseen user-typed ***,our tap detection mechanism achieves a 92％accuracy rate,a crucial factor for text *** findings have significant practical implications,as they reinforce mobile device security in 5G networks,enhancing user privacy,and data protection.

关键词： Fifth Generation(5G)networks smartphone information leakage Side-Channel Attack(SCA) deep learning

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Revised Correction Factors in the Specht's Model for Calculation of the Transformer Inrush Current

Revised Correction Factors in the Specht's Model for Calcula...

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2024 International Conference on electrical Machines, ICEM 2024

作者： Žarko, Damir Hanic, Zlatko University of Zagreb Faculty of Electrical Engineering and Computing Department of Electrical Machines Drives and Automation Unska 3 Zagreb10000 Croatia

ISBN: (纸本)9798350370607

In this study, correction factors are introduced for Specht's analytical model, which is used to estimate the inrush current peaks of transformers and their decay rates. The factors are derived for a set of five transformers using a least squares approach to minimize the discrepancies between the inrush current values from the Specht's model and the transient 2D finite element (FE) simulations. The 2D FE model uses an equivalent permeability of air and copper to mimic the flux linkage vs. current characteristics of the 3D FE model and effectively accounts for the effects of air core inductance at extreme saturation. The study considers the winding connections YNyn and YNd under the assumption that the connection to the grid is made on both the high voltage and the low voltage side. © 2024 IEEE.

关键词： Distribution transformers

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Fuzzy clustering for electric field characterization and its application to thunderstorm interpretability

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Digital Communications and Networks 2025年第2期11卷 299-307页

作者： Xu Yang Hongyan Xing Xinyuan Ji Wei Xu Witold Pedrycz School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing 210044China Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 2R3Canada Systems Research Institute Polish Academy of Sciences00-901 WarsawPoland Department of Computer Engineering Faculty of Engineering and Natural SciencesIstinye University34010 IstanbulTurkey

Changes in the Atmospheric Electric Field Signal(AEFS)are highly correlated with weather changes,especially with thunderstorm ***,little attention has been paid to the ambiguous weather information implicit in AEFS *** this paper,a Fuzzy C-Means(FCM)clustering method is used for the first time to develop an innovative approach to characterize the weather attributes carried by ***,a time series dataset is created in the time domain using AEFS *** AEFS-based weather is evaluated according to the time-series Membership Degree(MD)changes obtained by inputting this dataset into the ***,thunderstorm intensities are reflected by the change in distance from a thunderstorm cloud point charge to an AEF ***,a matching relationship is established between the normalized distance and the thunderstorm dominant MD in the space ***,the rationality and reliability of the proposed method are verified by combining radar charts and expert *** results confirm that this method accurately characterizes the weather attributes and changes in the AEFS,and a negative distance-MD correlation is obtained for the first *** detection of thunderstorm activity by AEF from the perspective of fuzzy set technology provides a meaningful guidance for interpretable thunderstorms.

关键词： Atmospheric electric field(AEF) Thunderstorm Fuzzy C-means(FCM) Attribute

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Battery Monitoring System on IoT-Based Smart Micro Grid 4

Battery Monitoring System on IoT-Based Smart Micro Grid

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4th International Conference on Electronic and electrical engineering and Intelligent System, ICE3IS 2024

作者： Iksan, Nur Widodo, Djoko Adi Djuniadi, Djuniadi Putri, Riana Defi Mahadji Suryanto, Agus Udayanti, Erika Devi Universitas Negeri Semarang Faculty of Engineering Department of Electrical Engineering Indonesia Universitas Dian Nuswantoro Faculty of Computer Science Department of Information System Indonesia

ISBN: (纸本)9798350378368

The use of batteries as an energy storage medium has a very important role in the installation of renewable energy power plants, such as photovoltaics to overcome intermittency in photovoltaics and to maintain stable power quality. In this study, the installation of solar plants that was used as the research object was a smart micro grid that integrates photovoltaic electrical energy sources with the Utility electricity network for residential scale. The battery monitoring system is used to manage the performance of existing batteries on the electricity grid through analysis of the battery's electrical values such as current, voltage, power and energy, so that it can function optimally in storing electrical energy and supplying electricity to users. Internet of things (IoT) technology is used to provide easy transfer of data obtained through the process of data acquisition to data visualization provided to be accessed by users in real time. This research aims to develop an IoT-based battery monitoring system. The development carried out focuses on the data acquisition process, the data transfer process using IoT technology, the process of analyzing and visualizing data on the IoT cloud and remote access to users. © 2024 IEEE.

关键词： Battery IoT Monitoring Smart Micro Grid

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Digital Twin-Based Automated Fault Diagnosis in Industrial IoT Applications

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computers, Materials & Continua 2023年第4期75卷 183-196页

作者： Samah Alshathri Ezz El-Din Hemdan Walid El-Shafai Amged Sayed Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Security Engineering LabPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Electrical Energy Engineering College of Engineering&TechnologyArab Academy for Science Technology&Maritime TransportSmart Village CampusEgypt

In recent years, Digital Twin (DT) has gained significant interestfrom academia and industry due to the advanced in information technology,communication systems, Artificial Intelligence (AI), Cloud Computing (CC),and Industrial Internet of Things (IIoT). The main concept of the DT isto provide a comprehensive tangible, and operational explanation of anyelement, asset, or system. However, it is an extremely dynamic taxonomydeveloping in complexity during the life cycle that produces a massive amountof engendered data and information. Likewise, with the development of AI,digital twins can be redefined and could be a crucial approach to aid theInternet of Things (IoT)-based DT applications for transferring the data andvalue onto the Internet with better decision-making. Therefore, this paperintroduces an efficient DT-based fault diagnosis model based on machinelearning (ML) tools. In this framework, the DT model of the machine isconstructed by creating the simulation model. In the proposed framework,the Genetic algorithm (GA) is used for the optimization task to improvethe classification accuracy. Furthermore, we evaluate the proposed faultdiagnosis framework using performance metrics such as precision, accuracy,F-measure, and recall. The proposed framework is comprehensively examinedusing the triplex pump fault diagnosis. The experimental results demonstratedthat the hybrid GA-ML method gives outstanding results compared to MLmethods like LogisticRegression (LR), Na飗e Bayes (NB), and SupportVectorMachine (SVM). The suggested framework achieves the highest accuracyof 95% for the employed hybrid GA-SVM. The proposed framework willeffectively help industrial operators make an appropriate decision concerningthe fault analysis for IIoT applications in the context of Industry 4.0.

关键词： Automated fault diagnosis control system ML AI CC IIoT digital twins genetic algorithm GA-ML technique

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YOLOatt-Med: YOLO-Based Attention Mechanism for Medical Image Classification 14

YOLOatt-Med: YOLO-Based Attention Mechanism for Medical Imag...

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14th International Conference on computer and Knowledge engineering, ICCKE 2024

作者： Naserizadeh, Fatemeh Sany, Erfan Akbarnezhad Sinichi, Parsa Hosseini, Seyyed Abed Faculty of Electrical and Computer Engineering Malek Ashtar University of Technology Tehran Iran Faculty of Computer Engineering Quchan University of Technology Quchan Iran Department of Electrical Engineering Mashhad Branch Islamic Azad University Mashhad Iran

ISBN: (纸本)9798331511272

Early and accurate diagnosis of respiratory diseases can significantly reduce mortality rates. This study presents a novel approach for classifying respiratory diseases from medical images using computer vision. The approach combines the YOLOv8 object detection model with CBAM to boost feature extraction and enhance classification precision. The effectiveness of the method was tested on two datasets: chest computed tomography scans for lung cancer detection and chest X-rays for pneumonia diagnosis. Results indicate that the CBAMYOLOv8 model surpasses current leading methods, underscoring the method's efficiency in emphasizing essential image features for precise disease classification. ©2024 IEEE.

关键词： Lung cancer

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An Improved ECG Segmentation Method Based on Adaptive Hermite Functions 31

An Improved ECG Segmentation Method Based on Adaptive Hermit...

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31st International Conference on electrical engineering, ICEE 2023

作者： Arabameri, Abazar Klidbary, Sajad Haghzad University of Zanjan Faculty of Engineering Department of Electrical Engineering Zanjan Iran University of Zanjan Faculty of Engineering Department of Computer Engineering Zanjan Iran

ISBN: (纸本)9798350312560

Electrocardiograms (ECGs), which are recordings of the electrical activity of the heart with electrodes placed on the body, have many clinical applications. Each phase of the heart's mechanical function is represented by one of the P, QRS, and T waves in the ECG. The change in the morphological characteristics of these waves compared to the normal state can be used to diagnose cardiac functional abnormalities. Therefore, developing algorithms that can automatically extract these waves from the ECG signal with high accuracy is of high diagnostic importance. In this paper, an adaptive method is presented to estimate the shape of each heartbeat individually. The proposed algorithm is based on the improvement of previous studies. It tries to get a more accurate estimate of ECG waves by including the ST segment model in the ECG signal model. Simulation results on experimental data show that the proposed method outperforms previous similar algorithms. © 2023 IEEE.

关键词： Electrocardiograms

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