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IEEE International Conference on Intelligent Data Acquisition and Advanced Computing systems

作者： Olga Pavlova Tetiana Hovorushchenko Artem Boyarchuk System Programming Department of Khmelnytsky National University Computer Engineering & System Programming Department of Khmelnytsky National University Lecturer of Computer Systems Networks and Cybersecurity Department of National Aerospace University "Kharkiv Aviation Institute"

ISBN: (纸本)9781728140704

The paper deals with the development of the method of activity and the realization of the intelligent agent (IA) for semantic analysis of the natural language software requirements. This IA performs the analysis of the requirements, determines the number and percentage of missing indicators for determining the metrics of complexity and quality of software, displays which indicators are missing for each metric, and also forms the real ontology of the domain "Software engineering" (part "Quality and complexity of software. Metric analysis"). During an experiment, which is conducted with the help of the realized IA, the semantic analysis of requirements for the Internet of Things (IoT)-system was performed. Such analysis provides an increase of the volume and ensures of the sufficiency of metric information in the software requirements, which, in turn, provides an improvement in the quality of the software of IoT-system.

关键词： Natural language software requirements Semantic analysis Intelligent agent (IA) Ontology-based intelligent agent (OBIA) Internet of things (IoT)-system software requirements Intelligent agents Semantics Metronidazole activity INFORMATION ASSURANCE Metric computer software

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Data Poisoning: Issues, Challenges, and Needs 7

Data Poisoning: Issues, Challenges, and Needs

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7th IET Smart Cities Symposium, SCS 2023

作者： Aljanabi, Mohammad Omran, Alaa Hamza Mijwil, Maad M. Abotaleb, Mostafa El-Kenawy, El-Sayed M. Mohammed, Sahar Yousif Ibrahim, Abdelhameed Department of computer College of Education Aliarqai university Baghdad Iraq University of information technology and communications Baghdad Iraq 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 Science Department Computer Science &Information Technology College Anbar University Anbar Iraq Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

ISBN: (纸本)9781839539831

Data poisoning attacks, where adversaries manipulate training data to degrade model performance, are an emerging threat as machine learning becomes widely deployed in sensitive applications. This paper provides a comprehensive overview of data poisoning including attack techniques, adversary incentives, impacts on security and reliability, detection methods, defenses, and key research gaps. We examine label flipping, instance injection, backdoors, and other attack categories that enable malicious outcomes ranging from IP theft to accidents in autonomous systems. Promising detection approaches include statistical tests, robust learning, and forensics. However, significant challenges remain in translating academic defenses like adversarial training and sanitization into practical tools ready for operational use. With safety and trustworthiness at stake, more research on benchmarking evaluations, adaptive attacks, fundamental tradeoffs, and real-world deployment of defenses is urgently needed. Understanding vulnerabilities and developing resilient machine learning pipelines will only grow in importance as data integrity is fundamental to developing safe artificial intelligence. © The Institution of engineering & Technology 2023.

关键词： Machine learning

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Network Intrusion Detection Based on Feature Selection and Hybrid Metaheuristic Optimization

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computers, Materials & Continua 2023年第2期74卷 2677-2693页

作者： Reem Alkanhel El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Manal Abdullah Alohali Mostafa Abotaleb Doaa Sami Khafaga Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Information Systems Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of System Programming South Ural State UniversityChelyabinsk454080Russia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Applications of internet-of-things(IoT)are increasingly being used in many facets of our daily life,which results in an enormous volume of *** computing and fog computing,two of the most common technologies used in IoT applications,have led to major security *** are on the rise as a result of the usage of these technologies since present security measures are *** artificial intelligence(AI)based security solutions,such as intrusion detection systems(IDS),have been proposed in recent *** technologies that require data preprocessing and machine learning algorithm-performance augmentation require the use of feature selection(FS)techniques to increase classification accuracy by minimizing the number of features *** the other hand,metaheuristic optimization algorithms have been widely used in feature selection in recent *** this paper,we proposed a hybrid optimization algorithm for feature selection in *** proposed algorithm is based on grey wolf(GW),and dipper throated optimization(DTO)algorithms and is referred to as *** proposed algorithm has a better balance between the exploration and exploitation steps of the optimization process and thus could achieve better *** the employed IoT-IDS dataset,the performance of the proposed GWDTO algorithm was assessed using a set of evaluation metrics and compared to other optimization approaches in 2678 CMC,2023,vol.74,no.2 the literature to validate its *** addition,a statistical analysis is performed to assess the stability and effectiveness of the proposed *** results confirmed the superiority of the proposed approach in boosting the classification accuracy of the intrusion in IoT-based networks.

关键词： Feature selection grey wolf optimization dipper throated optimization intrusion detection internet-of-things(IoT)

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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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Advanced IOT Traffic Light Control system 5

Advanced IOT Traffic Light Control System

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5th International Conference on Advances in Computational Tools for engineering Applications, ACTEA 2023

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

ISBN: (纸本)9798350309959

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. © 2023 IEEE.

关键词： Traffic congestion

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AbotalebNet: A Novel Deep Learning Architecture for Time Series Forecasting with Convolutional and LSTM Layers Enhanced by Multi-Head Attention 2

AbotalebNet: A Novel Deep Learning Architecture for Time Ser...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Abotaleb, Mostafa Makarovskikh, Tatiana Mijwil, Maad M. Bala, Indu Hiran, Kamal Kant Doshi, Ruchi South Ural State University Dep. of System Programming Chelyabinsk454080 Russia Baghdad College of Economic Sciences University Computer Techniques Engineering Department Baghdad Iraq Lovely Professional University Electrical and Electronics Engineering Punjab India Sir Padampat Singhania University Dep. of Computer Science Udaipur India Universidad Azteca Dep. of Computer Science and Engineering Chalco Mexico

ISBN: (纸本)9798350384277

This paper introduces AbotalebNet, a novel deep learning architecture optimized for time series forecasting, with a particular focus on the complexities of COVID-19 data. AbotalebNet's architecture is mathematically represented as:\begin{equation*}\hat Y = {\text{Attention}}(\operatorname{LSTM} ({\text{CNN}}(X)))\tag{1}\end{equation*}Here, X represents the input time series data, and Ŷ is the forecasted output. AbotalebNet integrates the feature extraction capabilities of Convolutional Neural Networks (CNNs) with the sequential data processing power of Long Short-Term Memory (LSTM) networks, further enhanced by a Multi-Head Attention *** address overfitting, a key challenge in deep learning, the model incorporates regularization strategies, dropout mechanisms, and batch normalization:\begin{equation*}\mathop {\min }\limits_\Theta ( Loss (X,\hat Y;\Theta ) + \lambda R(\Theta ))\tag{2}\end{equation*}In this equation, Θ denotes the model parameters, R(Θ) represents regularization terms, and λ is the regularization coefficient. These additions aid in preventing the model from overfitting to the training data, ensuring robust performance on unseen *** evaluation on COVID-19 time series data demonstrates AbotalebNet's enhanced predictive accuracy over traditional models, solidifying its potential for advanced non-linear time series analysis. © 2024 IEEE.

关键词： Convolutional neural networks

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Universal mitigation of NBTI-induced aging by design randomization

Universal mitigation of NBTI-induced aging by design randomi...

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East-West Design & Test Symposium (EWDTS)

作者： Maksim Jenihhin Alexander Kamkin Zainalabedin Navabi Somayeh Sadeghi-Kohan Department of Computer Engineering Tallinn University of Technology ESTONIA Institute for System Programming of the Russian Academy of Sciences (ISP RAS) RUSSIA Department of Electrical and Computer Engineering University of Tehran IRAN

ISBN: (纸本)9781509006946

In this paper we propose to think out of the box and discuss an approach for universal mitigation of Negative Bias Temperature Instability (NBTI) induced aging untied from the limitations of its modelling. The cost-effective approach exploits a simple property of a randomized design, i.e., the equalized signal probability and switching activity at gate inputs. The techniques considered for structural design randomization involve both the hardware architecture and embedded software layers. Ultimately, the proposed approach aims at extending the reliable lifetime of nanoelectronic systems.

关键词： Negative bias temperature instability Thermal variables control Logic gates Delays Registers Aging Degradation

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Design and Implementation of a QoL Improvement system for Visually Impaired Patients 5

Design and Implementation of a QoL Improvement System for Vi...

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5th International Conference on Advances in Computational Tools for engineering Applications, ACTEA 2023

作者： Abi Zeid Daou, Roy Beaino, Jad Hawat, Johnny Ibrahim, Ramy Hayek, Ali Boercsoek, Josef Olmedo, Jose Javier Serrano Université la Sagesse Faculty of Engineering Mechatronics Department Beirut Lebanon MART Learning Education and Research Center Chananir Lebanon University of Applied Science Hamm-Lippstadt Department Lippstadt 2 Hardware Engineering Hamm59063 Germany University of Kassel Faculty of Electrical Engineering/Computer Science Department of Computer Architecture and System Programming Germany Centro de Tecnología Biomédica Universidad Politécnica de Madrid Lab of Bioinstrumentation and Nanomedicine Spain

ISBN: (纸本)9798350309959

Visually impaired people encounter several challenges in their mobility and navigation. Their daily activities are obstructed due to their inability to adapt or recognize accurately their surroundings, especially outside their house which they are more familiar with. Thus, it becomes the main reason of accidents, falling off, getting lost in unknown areas, etc. Furthermore, one of the sensory systems that helps the body to process data about the external environment is the visual system. Blind people also lose touch with the outside world, develops poor motor habits, which results in postural problems as a result. This project will assist visually impaired people in their daily life and simplify normal tasks through a system combining two previously designed projects, 'Smart Shoes for Blind and Visually Impaired People', and 'Human posture monitoring device'. The multifunctional system is developed with the goal of securing safe movements for visually impaired people as well as maintaining a good back posture by detecting leaning postures (LP). The purpose of the smart shoe is to identify obstacles and protect the user from unintended accidents. A compatible Android application has been developed to alert the user when there is an obstruction or when he is walking on a wet surface. Voice alarms will be used to acoustically alert the user. If the user collapses, a message with their position will be sent right away to a relative. On the other hand, the smart vest will identify the position of the user's back and alert him to maintain a straight posture through the same application as well. As the system is dealing with human health, some safety measurements would be taken into consideration to implement a safe electrical system in order to reduce error and to increase accuracy. © 2023 IEEE.

关键词： Accidents

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Evaluating the Software Requirements Specifications Using Ontology-Based Intelligent Agent

Evaluating the Software Requirements Specifications Using On...

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International Scientific and Technical Conference on computer Science and Information Technologies

作者： Tetiana Hovorushchenko Olga Pavlova Computer Engineering and System Programming Department Khmelnitsky National University (KhNU) Khmelnitsky Ukraine Informatics department Khmelnitsky Gymnasium No.2 Khmelnitsky Ukraine

The purpose of this study is the development of the method of activity of ontology-based intelligent agent (OBIA) for evaluating the software requirements specifications (SRS). OBIA works on the basis of the developed method, and evaluates the sufficiency of information in the SRS for assessing the non-functional software features - provides the conclusion and the numerical evaluation of the level of sufficiency of information in the SRS for assessment of non-functional features.

关键词： Software Software requirements specification (SRS) Ontology Intelligent agent (IA) Ontology-based intelligent agent (OBIA)

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The relationship between diagnostic coverage and proof test interval for 1002 and 1002D architectures

The relationship between diagnostic coverage and proof test ...

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13th IFAC Symposium on Control in Transportation systems, CTS 2012

作者： Üstoǧlu, Ilker Kaymakçi, Özgür T. Börcsök, Josef Yildiz Technical University Davutpaşa Campus A209 34220 34220 Esenler Istanbul Turkey Yildiz Technical University Davutpaşa Campus A210 34220 34220 Esenler Istanbul Turkey University Kassel Faculty of Electrical Engineering / Computer Science Department of Computer Architecture and System Programming Wilhelmshöher Allee 71 34121 Kassel Germany

ISBN: (纸本)9783902823137

In order to measure the effectiveness of safety protection systems there are several design parameters. Diagnostic coverage factor is one of the most important parameter which influences all architectures. In this short paper the relationship between PFDavg, DC and TI are presented. 1002 and 1002D architectures are considered as examples. © 2012 IFAC.

关键词： Architecture

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