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7th International Conference on Vocational Education and Electrical engineering, ICVEE 2024

作者： Abood, Layla Hattim Abood, May H. Action, Dalal Hammood University of Technology Department of Control and Systems Engineering Baghdad Iraq Al-Iraqia University Department of Computer Engineering Baghdad Iraq Mtu Edu Department of Computer Engineering Baghdad Iraq

ISBN: (纸本)9798331505103

Electric Vehicles (EVs) become very important issue and gained attention due to many reasons like its economic price, saving environment and more reliable. In this study, controlling speed for EV is utilized by tracking the desired speed using Fractional Order Proportional Derivative (FOPD) controller with a smart tuning algorithm named Sunflower Optimization (SFO) algorithm to satisfy robust and efficient controlled speed. The numerical analysis is obtained using Matlab program in two steps, controlling the system to reach the desired value in best and stable response then apply two undesired signals (disturbance signals) to test system robustness and finally compare the suggested controller with different conventional controllers(Normal PD controller-Optimal PD controller). © 2024 IEEE.

关键词： MATLAB

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On Detecting Malicious Code Injection by Monitoring Multi-Level Container Activities 14

On Detecting Malicious Code Injection by Monitoring Multi-Le...

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14th International Conference on Cloud Computing and Services Science, CLOSER 2024

作者： Bhuiyan, Md. Olid Hasan Das, Souvik Majumder, Shafayat Hossain Majumdar, Suryadipta Hossain, Md. Shohrab Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Bangladesh Concordia Institute for Information Systems Engineering Concordia University Canada

ISBN: (纸本)9789897587016

In recent years, cloud-native applications have been widely hosted and managed in containerized environments due to their unique benefits, such as being lightweight, portable, and cost-efficient. Their growing popularity makes them a common subject of cyberthreats, as evidenced by recent attacks. Many of those attacks take place due to malicious code injection to breach systems and steal sensitive data from a containerized environment. However, existing solutions fail to classify malicious code injection attacks that impact multiple levels (e.g., application and orchestrator). In this paper, we fill in this gap and propose a multi-level monitoring-based approach where we monitor container activities at both the system call level and the container orchestrator (e.g., Kubernetes) level. Specifically, our approach can distinguish between the expected and unexpected behavior of a container from various system call characteristics (e.g., sequence, frequency, etc.) along with the activities through event logs at the orchestrator level to detect malicious code injection attacks. We implement our approach for Kubernetes, a major container orchestrator, and evaluate it against various attack paths outlined by the Cloud Native Computing Foundation (CNCF), an open-source foundation for cloud native computing. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Network security

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Detection of Cyberbullying in Arabic using Machine Learning and ChatGPT 5

Detection of Cyberbullying in Arabic using Machine Learning ...

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5th International IEEE Novel Intelligent and Leading Emerging Sciences Conference, NILES 2023

作者： Sadek, Ahmed Khalil, Mahmoud I. Salama, Cherif Ain Shams University Computer and Systems Engineering Department Cairo Egypt Ain Shams University American University in Cairo Computer and Systems Engineering Department Cairo Egypt

ISBN: (纸本)9798350381030

Cyberbullying is one of the complicated problems in Arabic communities. Cyberbullying detection is the main key to control racism and hate speech over the Internet especially on social media. Detection of cyberbullying in Arabic is challenging due to the variety of expressions and different Arabic dialects. Many researches discussed this topic using only one dialect and achieved good results. In this paper, we consolidated datasets with multiple Arabic dialects from different social media platforms. Text pre-processing enhanced the performance of the used models. We classified the comments in the dataset using Naive Bayes model, AraBERT model and ChatGPT for comparison purposes. The experiments showed that AraBERT outperformed the other classifiers for our multi-dialect dataset showing its potential for effectively monitoring Arabic content over the Internet. © 2023 IEEE.

关键词： Sentiment analysis

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RT-YOSO: Revisiting YOSO for Real-time Panoptic Segmentation 5

RT-YOSO: Revisiting YOSO for Real-time Panoptic Segmentation

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5th International IEEE Novel Intelligent and Leading Emerging Sciences Conference, NILES 2023

作者： Ammar, Abdallah Khalil, Mahmoud I. Salama, Cherif Ain Shams University Computer and Systems Engineering Department Cairo Egypt Ain Shams University American University in Cairo Computer and Systems Engineering Department Cairo Egypt

ISBN: (纸本)9798350381030

Panoptic segmentation, a crucial computer vision task for scene understanding, simultaneously combines semantic segmentation and instance segmentation to classify pixels and identify object instances in images. Despite significant progress in this field, real-time performance with deep learning methods has yet to reach its full potential. In this paper, we present a novel adaptation of the "You Only Segment Once"(YOSO) architecture, designed for real-time panoptic segmentation. Our primary contribution involves substituting YOSO's original ResNet50 backbone with the time-efficient STDC networks. Our "Real-time-YOSO"(RT-YOSO) model significantly improves real-time performance, demonstrating its compatibility with robotic systems and autonomous driving scenarios. Thorough experiments on the Cityscapes dataset confirm the robustness and accuracy of our model, achieving 59.2% and 51.2% PQ with corresponding frame rates of 18.5 and 27.8 FPS, respectively. This establishes the best accuracy-speed trade-off by maintaining comparable accuracy while delivering an average of 50% higher FPS. The source code is publicly available at https://***/AbdallahReda/RT-YOSO © 2023 IEEE.

关键词： Semantic Segmentation

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Single-level Discrete Two Dimensional Wavelet Transform Based Multiscale Deep Learning Framework for Two-Wheeler Helmet Classification

EAI Endorsed Transactions on Industrial Networks and Intelli...

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EAI Endorsed Transactions on Industrial Networks and Intelligent systems 2025年第2期12卷 1-13页

作者： Annadurai, Amrutha Prusty, Manas Ranjan Pandey, Trilok Nath Patra, Subhra Rani School of Computer Science and Engineering Vellore Institute of Technology Chennai India Centre for Cyber Physical Systems Vellore Institute of Technology Chennai India Information Systems and Operations Management University of Texas at Arlington Texas 76019 United States

INTRODUCTION: A robust method is proposed in this paper to detect helmet usage in two-wheeler riders to enhance road safety. OBJECTIVES: This involves a custom made dataset that contains 1000 images captured under diverse real-world scenarios, including variations in helmet size, colour, and lighting conditions. This dataset has two classes namely with helmet and without helmet. METHODS: The proposed helmet classification approach utilizes the Multi-Scale Deep Convolutional Neural Network (CNN) framework cascaded with Long Short-Term Memory (LSTM) network. Initially the Multi-Scale Deep CNN extracts modes by applying Single-level Discrete 2D Wavelet Transform (dwt2) to decompose the original images. In particular, four different modes are used for segmenting a single image namely approximation, horizontal detail, vertical detail and diagonal detail. After feeding the segmented images into a Multi-Scale Deep CNN model, it is cascaded with an LSTM network. RESULTS: The proposed model achieved accuracies of 99.20% and 95.99% using both 5-Fold Cross-Validation (CV) and Hold-out CV methods, respectively. CONCLUSION: This result was better than the CNN-LSTM, dwt2-LSTM and a tailor made CNN model. © 2025 A. Annadurai et al.

关键词： Convolutional neural networks

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Binary Representation of Polar Bear Algorithm for Feature Selection

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computer systems Science & engineering 2022年第11期43卷 767-783页

作者： Amer Mirkhan NumanÇelebi Sakarya University Computer Engineering DepartmentSakaryaTurkey Sakarya University Information Systems Engineering DepartmentSakaryaTurkey

In most of the scientific research feature selection is a challenge for *** all available features is not an option as it usually complicates the research and leads to performance drop when dealing with large *** the other hand,ignoring some features can compromise the data *** the rough set theory presents a good technique to identify the redundant features which can be dismissed without losing any valuable information,however,exploring all possible combinations of features will end with NP-hard *** this research we propose adopting a heuristic algorithm to solve this problem,Polar Bear Optimization PBO is a metaheuristic algorithm provides an effective technique for solving such kind of optimization *** other heuristic algorithms it proposes a dynamic mechanism for birth and death which allows keep investing in promising solutions and keep dismissing hopeless *** evaluate its efficiency,we applied our proposed model on several datasets and measured the quality of the obtained minimal feature set to prove that redundant data was removed without data loss.

关键词： Optimization rough set feature selection heuristic algorithms

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Operational Current Amplifier-Based Quadrature Oscillators Family

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IEEE Access 2025年 13卷 87281-87291页

作者： Alzaher, Hussain A. Alkhalifa, Ibrahim M. Department of Electrical Engineering Dhahran31261 Saudi Arabia Irc for Communication Systems and Sensing Dhahran31261 Saudi Arabia Department of Electrical and Computer Engineering Thuwal23955 Saudi Arabia

This paper investigates the application of the operational current amplifier (OCA) in the design of quadrature sinusoidal oscillators. There are numerous quadrature sinusoidal oscillators based on the three fundamental amplifiers namely the operational amplifier (OpAmp), the operational transconductance amplifier (OTA), and the operational transresistance amplifier (OTRA);however, their counterparts based on the OCA have not been reported. In this paper, several possible quadrature oscillator structures based on the OCA are systematically developed from their voltage mode active-RC counterparts using adjoint network theorem. Consequently, it is shown that the resulted oscillators do not necessarily preserve the attractive features of the voltage mode quadrature oscillators;namely providing explicit quadrature current output signals with equal amplitudes. When attempting to retain these features, it is found that the minimum OCAs needed is three. Analyzing this issue has paved the way for developing a novel OCA based integrator that promotes the invention of a new quadrature oscillator using only two OCAs. Simulation results obtained from a standard 150 nm CMOS process are provided. It is demonstrated that the proposed oscillator consumes approximately 350μ W while working in the 3 MHz range. Measurement results obtained from a prototype implemented using CMOS OCA ICs also confirms the proposed theory. © 2025 IEEE.

关键词： Circuit oscillations

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Cybersecurity Landscape on Remote State Estimation:A Comprehensive Review

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 851-865页

作者： Jing Zhou Jun Shang Tongwen Chen the Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 1H9Canada the Department of Control Science and Engineering and Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai 200092China

Cyber-physical systems(CPSs)have emerged as an essential area of research in the last decade,providing a new paradigm for the integration of computational and physical units in modern control *** state estimation(RSE)is an indispensable functional module of ***,it has been demonstrated that malicious agents can manipulate data packets transmitted through unreliable channels of RSE,leading to severe estimation performance *** paper aims to present an overview of recent advances in cyber-attacks and defensive countermeasures,with a specific focus on integrity attacks against ***,two representative frameworks for the synthesis of optimal deception attacks with various performance metrics and stealthiness constraints are discussed,which provide a deeper insight into the vulnerabilities of ***,a detailed review of typical attack detection and resilient estimation algorithms is included,illustrating the latest defensive measures safeguarding RSE from ***,some prevalent attacks impairing the confidentiality and data availability of RSE are examined from both attackers'and defenders'***,several challenges and open problems are presented to inspire further exploration and future research in this field.

关键词： Cyber-attacks Kalman filtering remote state estimation unreliable transmission channels

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Application of internet of things in healthcare domain

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Journal of Umm Al-Qura University for engineering and Architecture 2023年第1期14卷 1-12页

作者： Almotairi, Khaled H. Computer Engineering Department Computer and Information Systems College Umm Al-Qura University Makkah 21955 Saudi Arabia

Internet of things (IoT) enables machine-to-machine, human-to-human and machine-to-human interaction. Recent advancement in IoT systems has positively impacted the daily activities of humans, from accessing information to the delivery of service in real-time. This has improved healthcare management and services, especially in medical hospitals, for effective and timely access to diagnostic information and treatment of patients. Several existing research mainly focused on the design of IoT architecture, its challenges, and benefits to human society with minor or without considering applying IoT in the healthcare domain. To bridge this gap, this study investigates the implications of IoT integration in the healthcare management domain. It presents a detailed discussion on IoT utilization to improve the functionalities of hospital management system. It also discusses some potential emerging innovations that aids the development and application of IoT in hospital management systems. Investigations show that healthcare personnel can administer treatments to patients anytime–anywhere. Patients, especially the elderly are administered treatment, as well as monitoring their wellbeing while at home with the support of wearable sensor devices. Also, some of the challenges that impedes the integration of IoT application into healthcare domain includes generation of irrelevant huge amount of data, issues of security and privacy of patient information and high cost of IoT adoption. Furthermore, the future research trends in adoption of IoT to improve healthcare domain includes stroke and epileptic seizure predictions and prosthetic sensors which is used to retrieve relevant data or information to aid the treatment of a patient in real-time. © The Author(s) 2022.

关键词： Artificial intelligence Blockchain Healthcare Hospital Management Internet of things (IoT) Machine learning Security Smart equipment

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Postural Sway Detection using Kolmogorov-Arnold Network as Siamese Model

Postural Sway Detection using Kolmogorov-Arnold Network as S...

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2024 International Symposium on Sensing and Instrumentation in 5G and IoT Era, ISSI 2024

作者： Nehary, Ebrahim A. Rajan, Sreeraman Ando, Bruno Carleton University Systems and Computer Engineering Ottawa Canada University of Catania Electric Electronic and Information Engineering Catania Italy

ISBN: (纸本)9798350378689

Continuous monitoring of postural sway is crucial for safeguarding elderly individuals and patients with neurological conditions, such as Parkinson's disease, as their balance is significantly impacted. This continuous monitoring is achieved using triaxial accelerometer sensors that provide time series signals and are used to train models to detect the postural sway of elderly individuals or patients with neurological conditions. However, the available postural sway dataset has a limited number of samples, and the performance of trained models deteriorates when the accelerometer signal is noisy. A Siamese network Kolmogorov-Arnold Network (Siamese-KAN) is pro-posed to address this issue. This network can be trained with a few samples from each class. Training of the Siamese network is conducted by flattening the bispectrum of each accelerometer channel and then fusing the magnitudes to construct a single input vector. Various similarity functions are employed along with contrastive loss to train the Siamese network. Additionally, a Siamese network with multi-layer perceptron (MLP) is also similarly trained for comparison purposes. Preliminary results show that the Siamese-KAN model achieves better accuracy with clean signals than the Siamese-MLP. However, when the signal is noisy, the Siamese-KAN model outperforms the Siamese-MLP using Euclidean and Manhattan similarity functions, while the Siamese-MLP performs better with Cosine and Bray-Curtis similarity functions. © 2024 IEEE.

关键词： Accelerometers

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