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33rd International Conference on computer Theory and Applications, ICCTA 2023

作者： El Awadia, Osama A. Salem, Sameh A. Helwan University Faculty of Engineering Computer & Systems Engineering Dept. Cairo Egypt NTRA Egypt

ISBN: (纸本)9798350309577

In the ever-evolving cybersecurity landscape, detecting unseen, zero-day attacks is both urgent and paramount. These sophisticated attacks often lack precedent, posing a challenge to conventional machine learning techniques that rely on prior knowledge and training data. This paper endeavors to detect zero-day and unseen cyber attacks using zero-shot machine learning technique, which holds the promise of identifying these attacks without any prior exposure. This work explores the effectiveness of unsupervised learning in zero-day attack detection. The experimental results demonstrate that autoencoders can identify anomalies in data, which are typically associated with zero-day attacks. When compared with other unsupervised and semi-supervised learning methods, the proposed autoencoder algorithm outperforms its competitors and achieves an accuracy of 99.9%, shedding light on its relative effectiveness in zero-day attack detection. © 2023 IEEE.

关键词： Semi-supervised learning

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Enhancing Parking Online Reservation with a Recommendation System based on User Preferences: A hybrid Approach

Enhancing Parking Online Reservation with a Recommendation S...

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ACS/IEEE International Conference on computer systems and Applications

作者： Serine Guellab Imane Benkhelifa Dept. of Computer Science USTHB University Algiers Algeria Dept. of Computer Systems’Therories and Engineering CERIST Research Center Algiers Algeria

In response to the pressing challenges in parking online reservation platforms, the primary issue this paper addresses is the need for a user-centric parking reservation experience. To tackle this problem, the study aims to develop a recommendation system that enhances user satisfaction and streamlines the parking reservation *** provide personalized parking recommendations, a hybrid multimodal recommendation system is designed, grounded in distance-based recommendation and content-based filtering, and taking into account user preferences and feedback, history behavior and proximity to preferred tourist attractions and points of *** leveraging a rich dataset comprising 1804 parking items, results indicate a notable improvement and more user-centric user experience, as the system suggests parking lots in line with user preferences and points of interest. User feedback mechanisms are seamlessly integrated, facilitating continuous adaptation and refinement based on user convenience and past *** work shows significant potential in enhancing user satisfaction and streamlining the user experience in parking online reservation systems.

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Analysis of an Urban Air Taxi Inner Loop Controller with Noisy IMUs in an Urban Airflow Environment

Analysis of an Urban Air Taxi Inner Loop Controller with Noi...

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IEEE Conference on Aerospace

作者： Tariq Maksoud Fidel Khouli Mohamed Atia Dept. of Mechanical and Aerospace Engineering Carleton University Ottawa Ontario Dept. of Computer and Systems Engineering Carleton University Ottawa Ontario

Interest in operating commercial Urban Air Taxis (UAT) around the world has been growing rapidly over the last few years. One of the many challenges in designing aircraft suitable for operating in a turbulent urban airflow environment is to design a robust inner loop flight controller. This study investigates the effect of filtered Angular Random Walk (ARW) error found in Inertial Measurement Units (IMU) on the inner loop flight controller's ability to maintain stable, wings level, horizontal flight, while not causing noticeable discomfort to passengers and respecting the limits of authority of the aircraft's control surfaces in a representative urban airflow environment. The performance of two controller architectures were investigated: classical Proportional, Integral, Derivative (PID) control scheme as well as Linear Active Disturbance Rejection Control (LADRC) control scheme. The conclusion of this study provides recommendations on a minimum threshold of IMU sensor grades and general considersations that would be useful to the controller designer. The findings are demonstrated by observing the vertical acceleration, $n_{z}$ , angular rate setpoint tracking performance, and control surface deflections.

关键词： Robust control PI control Filtering Sensor fusion Stability analysis Noise measurement PD control

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Artificial Intelligence (AI) for Improving Performance at the Cutting Edge of Medical Imaging

Artificial Intelligence (AI) for Improving Performance at th...

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Novel Intelligent and Leading Emerging Sciences Conference (NILES)

作者： Samar M. Nour Dept. Computer and Systems Engineering Faculty of Engineering Badr University in Cairo(BUC) Cairo Egypt

Artificial intelligence (AI) has achieved great strides in recent years, with applications in a variety of areas of study, including healthcare. Consequently, the integration of artificial intelligence (AI) and medical imaging has ushered in a new era in healthcare diagnosis and therapy. Artificial intelligence (AI) has shown impressive potential in enhancing accuracy, efficiency, and diagnostic performance across a range of medical imaging modalities by using the power of deep learning (DL), machine learning (ML), and computer vision. In this paper, we are trying to investigate the connection between artificial intelligence (AI) and medical imaging, concentrating on how AI-driven strategies are improving performance at the cutting edge of medical imaging technologies through the proposed architecture model. Furthermore, the paper also explores the limitations and opportunities that result from incorporating artificial intelligence (AI) into the use of medical imaging. The potential for artificial intelligence (AI) to transform image-guided therapies and its implications for personalized medicine are investigated.

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Optimizing Random Forest Classifier for Detection of Alzheimer's Using OASIS

Optimizing Random Forest Classifier for Detection of Alzheim...

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Silchar Subsection Conference (SILCON), IEEE

作者： Karri Sai Sanjana Reddy Kishor Kumar Reddy C. Srinath Doss Kari Lippert Dept. of Computer Science and Engineering Stanley College of Engineering and Technology for Women Hyderabad India Faculty of Engineering and Technology Botho University Botswana Dept. of Systems Engineering University of South Alabama Alabama USA

ISBN: (数字)9798331540821

ISBN: (纸本)9798331540838

Alzheimer's disease (AD) is a progressive brain disorder impacting behavior, memory, and cognition, with over a million cases reported annually in India. The risk significantly increases beyond age 65. Early diagnosis and treatment can result in better recovery. We propose a predictive model using the Random Forest algorithm and the OASIS dataset for early AD diagnosis, leveraging MRI data, clinical notes, genetic markers, and cognitive test results. Our model was evaluated against several others, including Decision Tree, AdaBoost, SVM, and Logistic Regression. With a 97.3% accuracy and a 2.7% error rate, our Random Forest Classifier o utperformed t he others, demonstrating superior predictive power for early AD diagnosis and potentially improving patient care.

关键词： Training Accuracy Transfer learning Predictive models Prediction algorithms Genetics Classification algorithms Alzheimer's disease Random forests Medical diagnostic imaging

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Cyberattack Classification in Smart Grid Distribution Substations using a Novel Ensemble Bagging Learning Technique

Cyberattack Classification in Smart Grid Distribution Substa...

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Canadian Conference on Electrical and computer engineering (CCECE)

作者： Victor O. Ijeh Walid G. Morsi Computer and Software Engineering Dept. Smart Grid & Electric Vehicles Research Laboratory Electrical Ontario Tech University Oshawa Canada

ISBN: (数字)9798350371628

ISBN: (纸本)9798350371635

Smart grid incorporates the communication networking that enables the exchange of information among the monitoring and controlling devices. Such incorporation of the communication networking into the electricity grid infrastructure poses the risk of cyberattacks that target the critical assets within such an infrastructure. Most of the existing research focuses on the detection of such cyberattacks but without identifying the type of the attacks. This can result in overlooked threats and misdirected the necessary countermeasures. Recognizing the attack’s type is essential for timely responses and strategic planning against future threats, thereby enhancing the resilience of the smart grid. In this paper, a Fine Tree Bagging-based Ensemble Learning (FTBE) technique is proposed to detect and classify the different types of cyberattacks and power quality disturbances. The salient features of the attacks’ types are highlighted, which helps in identifying the types of the attack following the detection process.

关键词： Training Accuracy Substation automation Uncertainty Scalability Smart grids Ensemble learning

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A Semantic Mapping Framework for Service Robots 25

A Semantic Mapping Framework for Service Robots

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40th Annual ACM Symposium on Applied Computing, SAC 2025

作者： Frota, Lucyanno Rocha, Rui P. Dept. of Electrical and Computer Engineering University of Coimbra Coimbra Portugal Institute of Systems and Robotics University of Coimbra Coimbra Portugal

ISBN: (纸本)9798400706295

Service robots are undergoing a massification process similar to what happened with personal computers and cell phones a few decades ago. Their ubiquitous coexistence and interaction with humans requires that their representation models of the workspace go beyond metric information used for safe navigation. They are also required to assign semantic meaning to objects and places, i.e. to build semantic maps, in order to understand scenes and engage in human-like interactions. This paper proposes the Semantic MAPping (SMAP) framework to provide a service robot operating in human populated environments with a semantic mapping layer on top of a metric SLAM layer. SMAP is modular, expandable, and efficient enough to run locally on the robot. It has been implemented in Robot Operating System 2 (ROS2) using modular Docker containers. Preliminary experiments with a Pioneer 3-DX mobile robot having a system on module Nvidia Jetson AGX Xavier demonstrated its potential for future service robotics applications. Copyright © 2025 held by the owner/author(s).

关键词： Modular robots

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Survey of Machine Learning HIDS Techniques 16

Survey of Machine Learning HIDS Techniques

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16th International Conference on computer engineering and systems, ICCES 2021

作者： Soliman, Khaled Sobh, Mohamed Ali Bahaa-Eldin, Ayman M. Ain Shams University Dept. of Computer and Systems Engineering Cairo Egypt

ISBN: (纸本)9781665408677

The development of cyber-attacks has unprecedented effect on businesses and governments. The recent years have witnessed various number of security breaches against organizations equipped by diverse cybersecurity solutions. These attacks showed the limitations of the current security systems to remediate the advanced threats. This critical challenge poses a vital need to find new techniques to detect the recently developed malwares called Zero-Day Threats. This type of attacks cannot be discovered by signature-based scanning and traditional cybersecurity systems. In addition, they can bypass the network security appliances by concealing malwares in the encrypted channels such as Virtual Private Network (VPN) and Hypertext Transfer Protocol Secure (HTTPS). Therefore, there is a great importance to study the endeavors of developing a Host-based Intrusion Detection System (HIDS) using Artificial Intelligence (AI) techniques to protect computers against sophisticated cyber-attacks. This paper will discuss and compare the recent proposed techniques using Machine Learning at the host level to detect the advanced cybersecurity attacks based on the behavioral impact of these attacks on computers. © 2021 IEEE.

关键词： Cybersecurity

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Multi-Criteria Client Selection and Scheduling with Fairness Guarantee for Federated Learning Service

arXiv

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arXiv 2023年

作者： Zhang, Meiying Zhao, Huan Ebron, Sheldon Xie, Ruitao Yang, Kan Dept. of Computer Science University of Memphis United States College of Computer Science and Software Engineering Shenzhen University China

Federated Learning (FL) enables multiple clients to train machine learning models collaboratively without sharing the raw training data. However, for a given FL task, how to select a group of appropriate clients fairly becomes a challenging problem due to budget restrictions and client heterogeneity. In this paper, we propose a multi-criteria client selection and scheduling scheme with a fairness guarantee, comprising two stages: 1) preliminary client pool selection, and 2) per-round client scheduling. Specifically, we first define a client selection metric informed by several criteria, such as client resources, data quality, and client behaviors. Then, we formulate the initial client pool selection problem into an optimization problem that aims to maximize the overall scores of selected clients within a given budget and propose a greedy algorithm to solve it. To guarantee fairness, we further formulate the per-round client scheduling problem and propose a heuristic algorithm to divide the client pool into several subsets such that every client is selected at least once while guaranteeing that the ‘integrated’ dataset in a subset is close to an independent and identical distribution (iid). Our experimental results show that our scheme can improve the model quality especially when data are non-iid. © 2023, CC BY.

关键词： Heuristic algorithms

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Selection of Beamforming in 5G MIMO scenarios using Machine Learning approach 19

Selection of Beamforming in 5G MIMO scenarios using Machine ...

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19th International Conference on Electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON 2022

作者： Silva, Douglas Henrique Augusto Ribeiro, David Ramirez, Miguel Arjona Lopes Rosa, Renata Chaudhary, Sushank Rodriguez, Demostenes Zegarra Federal University of Lavras Dept. of Computer Science MG Lavras Brazil Dept. of Automation Federal University of Lavras MG Lavras Brazil University of São Paulo Dept. of Electronic Systems Engineering São Paulo Brazil Chulalongkorn University Dept. of Electrical Engineering Bangkok Thailand

ISBN: (纸本)9781665485845

In new generation networks, 5G and 6G networks, intelligent mechanisms based on artificial intelligence algorithms are playing a relevant role in the performance improvement at different network levels. In 5G networks, different techniques are used, such as MIMO systems, and its network infrastructure is utilized by diverse services, for instance, vehicular communications. Thus, the description and the tracing of a communication scenario needs a large volume of data. Because of the difficulty to implement actual 5G networks, there is a lack of datasets containing complete 5G scenarios, the data is not enough or contain imbalanced classes to properly train machine learning (ML) algorithms. In this context, we propose a method to increase the amount of data to improve the machine learning performance of some classification models, specifically Random Forest, Multilayer Perceptron, and k-Nearest Neighbors. In the experimental results of the test phase, considering the inclusion of synthetic data, Random Forest, Multilayer Perceptron and k-Nearest Neighbors reached macro F1 scores of 0.9341, 0.9241 and 0.9456, respectively, which are superior to the results obtained when training with the original data only. © 2022 IEEE.

关键词： MIMO systems

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