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EEA - Electrotehnica, Electronica, Automatica 2024年第4期72卷 65-71页

作者： Tibermacine, Ahmed Guettala, Walid Tibermacine, Imad Eddine Faculty of Exact Sciences Natural And Life Sciences University Mohamed Khider of Biskra Algeria Computer Science Department University Mohamed Khider of Biskra Algeria Department of Computer Automation and Management Engineering Sapienza University of Rome Italy

Text detection in natural images is a crucial task for extracting and recognizing valuable information, but it comes with significant challenges. Traditional image processing methods often rely on synthetic features, which may not be suitable for handling the diverse range of unstructured text scenarios encountered in real-world environments. To overcome this challenge, deep learning techniques have emerged as a powerful tool for adaptively learning and extracting features to digitize text. In this paper, we introduce a novel approach based on the YOLOv5 architecture for text detection. Our method leverages the strengths of YOLOv5, including its efficient and lightweight architecture, multi-scale prediction capability, and advanced data augmentation techniques. By employing these features, our proposed model can accurately identify regions in an image that are more likely to contain text. Through extensive experimentation on popular benchmark datasets, we have evaluated the performance of our proposed model. The results have demonstrated that our method outperforms state-of-the-art approaches in terms of both detection efficiency and accuracy. This indicates the effectiveness of our approach in handling the challenges posed by text detection in natural images. © Editura ELECTRA 2024. All rights reserved.

关键词： Deep Learning Natural scene image Text detection Yolov5

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Optimized Autonomous Computing with Trusted Resilient Data Management for IoT Emerging Networks

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IEEE Sensors Journal 2025年第11期25卷 20778-20785页

作者： Shafique, Ayesha Cheng, Benmao Zhao, Bo Alwakid, Ghadah Naif Haq, Muhammad Inaam Ul Laboratory of Intelligent IOT Technology and Applications in Universities China Jouf University College of Computer and Information Sciences Department of Computer Science Al Jowf Sakaka72341 Saudi Arabia Quanzhou University of Information Engineering School of Computer Science and Software Engineering Quanzhou362000 China

The innovative city network integrates numerous computational and physical components to develop real-time systems. These systems can capture sensor data and distribute it to end stations. Most solutions have been presented based on the centralized computing paradigm, which effectively and systematically increases data flow;however, distributed wireless technologies and heterogeneous network services continue to raise significant research problems. These challenges lower the optimization criteria and reflect communication structure around the network edges. In this research, we proposed a sustainable development for smart networks using efficient big data management with collaborative decisions for network devices. It differs from most existing work in the mentioned aspect. It applies computational lightweight intelligence for forwarding collected data using mobile collectors and reduces the congestion flow between devices on the limited bandwidth of wireless links. Moreover, the energy load is efficiently managed with edge-driven methods, and the incorporation of trusted devices ensures the integrity of the smart network. It also tackles potential communication threats smartly. Based on the experiments conducted in Network Simulator (NS-3), the proposed model enhances the efficacy of smart networks for performance metrics with reliability and effective management of resources in Internet of Things (IoT) network. © 2025 IEEE.

关键词： Mathematical models Computational modeling Intelligent sensors Data aggregation Internet of Things Security Routing Load modeling Silicon Data models

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Artificial Bee Colony with Cuckoo Search for Solving Service Composition

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Intelligent Automation & Soft Computing 2023年第3期35卷 3385-3402页

作者： Fadl Dahan Abdulelah Alwabel Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computer Sciences Faculty of Computing and Information Technology AlturbahTaiz UniversityTaiz9674Yemen Department of Computer Sciences College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia

In recent years,cloud computing has provided a Software As A Service(SaaS)platform where the software can be reused and applied to fulﬁll compli-cated user demands according to speciﬁc Quality of Services(QoS)*** user requirements are formulated as a workﬂow consisting of a set of ***,many services may satisfy the functionality of each task;thus,searching for the composition of the optimal service while maximizing the QoS is formulated as an NP-hard *** work will introduce a hybrid Artiﬁcial Bee Colony(ABC)with a Cuckoo Search(CS)algorithm to untangle service composition *** ABC is a well-known metaheuristic algorithm that can be applied when dealing with different NP-hard problems with an outstanding record of ***,the ABC suffers from a slow convergence ***,the CS is used to overcome the ABC’s limitations by allowing the abandoned bees to enhance their search and override the local *** proposed hybrid algorithm has been tested on 19 datasets and then compared with two standard algorithms(ABC and CS)and three state-of-the-art swarm-based composition *** addition,extensive parameter study experiments were conducted to set up the proposed algorithm’s *** results indicate that the proposed algorithm outperforms the standard algorithms in the three comparison criteria(bestﬁtness value,averageﬁtness value,and average execution time)overall datasets in 30 different ***,the proposed algorithm also exhibits better performance than the state–of–the–art algorithms in the three comparison criteria over 30 different runs.

关键词： Cloud computing web service composition artiﬁcial bee colony cuckoo search

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A Cloud Based Sentiment Analysis through Logistic Regression in AWS Platform

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computer Systems Science & engineering 2023年第4期45卷 857-868页

作者： Mohemmed Sha Department of Software Engineering College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The use of Amazon Web Services is growing rapidly as more users are adopting the *** has various functionalities that can be used by large corporates and individuals as *** analysis is used to build an intelligent system that can study the opinions of the people and help to classify those related *** this research work,sentiment analysis is performed on the AWS Elastic Compute Cloud(EC2)through Twitter *** data is managed to the EC2 by using elastic load *** collected data is subjected to preprocessing approaches to clean the data,and then machine learning-based logistic regression is employed to categorize the sentiments into positive and negative *** accuracy of 94.17%is obtained through the proposed machine learning model which is higher than the other models that are developed using the existing algorithms.

关键词： Sentiment analysis AWS cloud data analytics machine learning

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Enhancing Intrusion Detection by Using Machine Learning 1st

Enhancing Intrusion Detection by Using Machine Learning

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1st International Conference on Intelligent Systems, Blockchain, and Communication Technologies, ISBCom 2024

作者： Khan, Andleeb Nand, Parma Bhushan, Bharat Hameed, Alaa Ali Jamil, Akhtar Department of Computer Science and Engineering Sharda University Greater Noida India Department of Computer Engineering Istinye University Istanbul34396 Turkey Department of Computer Science National University of Computer and Emerging Sciences Islamabad44000 Pakistan

ISBN: (纸本)9783031823763

With the advancing technology, the increase in threats has been exponential. These technologies have led to the production of huge amounts of network traffic data. Therefore, it is of immense importance for the companies to safeguard this sensitive data from any attack by the implementation of robust security techniques as this can cause heavy financial losses to the companies. There is need for the enhancement in the performance of existing Intrusion Detection Systems in order to detect the attacks more diligently. This is done by the integration of Machine Learning in the intrusion detection approaches as they develop models which are data driven and hence detect and prevent cyber-attacks. This study develops an ML based intrusion detection model by using Random Forest, Naïve Bayes and XGBoost classifiers on UNSW_NB15 dataset and pre-processing the dataset by applying One Hot Encoder and thereby giving an accuracy of 99.98%, 74.59% and 66.20% respectively. The performance evaluation indicates that the Random Forest model with an accuracy of 99.98% clearly outperforms the other algorithms. Hence, this framework can improve the system security to a greater extent. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Network intrusion

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Deep Learning-based Techniques for Intrusion Detection Systems 2

Deep Learning-based Techniques for Intrusion Detection Syste...

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2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2024

作者： Rizwani, Nafay Jamil, Akhtar Hameed, Alaa Ali National University of Computer and Emerging Sciences Department of Computer Science Islamabad Pakistan Istinye University Department of Computer Engineering Istanbul Turkey

ISBN: (纸本)9798331529529

In the evolving landscape of cybersecurity, effective Intrusion Detection Systems (IDS) are essential for protecting against increasingly sophisticated cyber threats. This study explores the efficacy of various neural network models in detecting network intrusions, utilizing the widely recognized UNSW NB15 dataset. Our research encompasses a comparative analysis of Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRU), Artificial Neural Networks (ANN), and DenseNet. Each model was meticulously tailored and evaluated for binary (attack or not) and multiclass (type of attack) classification tasks. In addition, to address the challenge of UNSW-NB 15 dataset, we implemented strategic data preprocessing techniques, including scaling and over/under sampling techniques, aiming to enhance model accuracy and generalizability. The models' performances were rigorously assessed using key metrics for binary and multiclass classification. These metrics provided a comprehensive understanding of each model's strengths and weaknesses in the context of IDS. Furthermore, our research highlights the impact of various data preprocessing strategies, establishing benchmarks for future advancements in deep learning-based IDS. © 2024 IEEE.

关键词： Network intrusion

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Comprehensive analysis of multiple machine learning techniques for rock slope failure prediction

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Journal of Rock Mechanics and Geotechnical engineering 2024年第11期16卷 4386-4398页

作者： Arsalan Mahmoodzadeh Abed Alanazi Adil Hussein Mohammed Hawkar Hashim Ibrahim Abdullah Alqahtani Shtwai Alsubai Ahmed Babeker Elhag Department of Civil Engineering University of HalabjaHalabjaKurdistan Region46018Iraq Department of Computer Science College of Computer Engineering and Sciences in Al-KharjPrince Sattam bin Abdulaziz UniversityP.O.Box 151Al-Kharj11942Saudi Arabia Department of Communication and Computer Engineering Faculty of EngineeringCihan University-ErbilKurdistan RegionIraq Department of Civil Engineering College of EngineeringSalahaddin University-Erbil44002ErbilKurdistan RegionIraq Software Engineering Department College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityP.O.Box 151Al-Kharj11942Saudi Arabia Department of Civil Engineering College of EngineeringKing Khalid UniversityAbha61413Saudi Arabia

In this study,twelve machine learning(ML)techniques are used to accurately estimate the safety factor of rock slopes(SFRS).The dataset used for developing these models consists of 344 rock slopes from various open-pit mines around Iran,evenly distributed between the training(80%)and testing(20%)*** models are evaluated for accuracy using Janbu's limit equilibrium method(LEM)and commercial tool GeoStudio *** assessment metrics show that the random forest model is the most accurate in estimating the SFRS(MSE=0.0182,R2=0.8319)and shows high agreement with the results from the LEM *** results from the long-short-term memory(LSTM)model are the least accurate(MSE=0.037,R2=0.6618)of all the models ***,only the null space support vector regression(NuSVR)model performs accurately compared to the practice mode by altering the value of one parameter while maintaining the other parameters *** is suggested that this model would be the best one to use to calculate the SFRS.A graphical user interface for the proposed models is developed to further assist in the calculation of the SFRS for engineering *** this study,we attempt to bridge the gap between modern slope stability evaluation techniques and more conventional analysis methods.

关键词： Rock slope stability Open-pit mines Machine learning(ML) Limit equilibrium method(LEM)

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Resilient Distributed Optimization for Multi-Agent Cyberphysical Systems

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IEEE Transactions on Automatic Control 2025年第6期70卷 3952-3967页

作者： Yemini, Michal Nedic, Angelia Goldsmith, Andrea J. Gil, Stephanie Bar-Ilan University Faculty of Engineering Israel Arizona State University School of Electrical Computer and Energy Engineering TempeAZ85281 United States Princeton University Department of Electrical and Computer Engineering PrincetonNJ08544 United States Harvard University Computer Science Department School of Engineering and Applied Sciences CambridgeMA02139 United States

This work focuses on the problem of distributed optimization in multi-agent cyberphysical systems, where a legitimate agent's iterates are influenced both by the values it receives from potentially malicious neighboring agents, and by its own self-serving target function. We develop a new algorithmic and analytical framework to achieve resilience for the class of problems where stochastic values of trust between agents exist and can be exploited. In this case, we show that convergence to the true global optimal point can be recovered, both in mean and almost surely, even in the presence of malicious agents. Furthermore, we provide expected convergence rate guarantees in the form of upper bounds on the expected squared distance to the optimal value. Finally, numerical results are presented that validate our analytical convergence guarantees even when the malicious agents compose the majority of agents in the network and where existing methods fail to converge to the optimal nominal points. © 1963-2012 IEEE.

关键词： Multi agent systems

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A Review of Blockchain based Decentralised Authentication Solutions and their improvement through Metamask 2

A Review of Blockchain based Decentralised Authentication So...

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2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2024

作者： Khan, Andleeb Nand, Parma Bhushan, Bharat Hameed, Alaa Ali Jamil, Akhtar Sharda School of Engineering & Technology Department of Computer Science & Engineering Greater Noida India Istinye University Department of Computer Engineering Istanbul Turkey National University of Computer and Emerging Sciences Department of Computer Science Islamabad Pakistan

ISBN: (纸本)9798331529529

With the advancing technology, it becomes difficult to cope up with novel trends and configurations. Similarly, it is difficult to secure the systems against each emerging threat. With this the loopholes in conventional forms of system security based on centralized format are now visible. Hence there are explorations in various alternatives of centralized system authentication and authorization. Blockchain technology has appeared as a means of securing the systems as it works on decentralization thereby by passing the loopholes that exist in centralized authentication. Hence, this study lays down the recent advancements in the field of blockchain authorization and authentication techniques. It further supplements the study with an explanation of MetaMask extension which connects the user to the blockchain network thereby enabling decentralization in system security. This study further proposes the future potentials of MetaMask and blockchain integration. © 2024 IEEE.

关键词： Authentication Protocol

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Voting Classifier and Metaheuristic Optimization for Network Intrusion Detection

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computers, Materials & Continua 2023年第2期74卷 3183-3198页

作者： Doaa Sami Khafaga Faten Khalid Karim Abdelaziz A.Abdelhamid El-Sayed M.El-kenawy Hend K.Alkahtani Nima Khodadadi Mohammed Hadwan Abdelhameed Ibrahim Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt The Department of Civil and Environmental Engineering Florida International UniversityMiamiFLUSA Department of Information Technology College of ComputerQassim UniversityBuraydah51452Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt

Managing physical objects in the network’s periphery is made possible by the Internet of Things(IoT),revolutionizing human *** attacks and unauthorized access are possible with these IoT devices,which exchange data to enable remote *** attacks are often detected using intrusion detection methodologies,although these systems’effectiveness and accuracy are *** paper proposes a new voting classifier composed of an ensemble of machine learning models trained and optimized using metaheuristic *** employed metaheuristic optimizer is a new version of the whale optimization algorithm(WOA),which is guided by the dipper throated optimizer(DTO)to improve the exploration process of the traditionalWOA *** proposed voting classifier categorizes the network intrusions robustly and *** assess the proposed approach,a dataset created from IoT devices is employed to record the efficiency of the proposed algorithm for binary attack *** dataset records are balanced using the locality-sensitive hashing(LSH)and Synthetic Minority Oversampling Technique(SMOTE).The evaluation of the achieved results is performed in terms of statistical analysis and visual plots to prove the proposed approach’s effectiveness,stability,and *** achieved results confirmed the superiority of the proposed algorithm for the task of network intrusion detection.

关键词： Voting classifier whale optimization algorithm dipper throated optimization intrusion detection internet-of-things

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