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Iraqi Journal for computer science and Mathematics 2024年第3期5卷 501-512页

作者： Ahmed, Bilal Hamdan, Hazlina Muhammed, Abdullah Husin, Nor Azura Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang43400 UPM Malaysia Department of Communication Technology and Networks Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang43400 UPM Malaysia

Berth Allocation Problem (BAP) is a renowned difficult combinatorial optimization problem that plays a crucial role in maritime transportation systems. BAP is categorized as non-deterministic polynomial-time hard (NP-hard) problems, that is very tough to resolve for optimality within an acceptable timeframe. Many metaheuristic algorithms have been suggested to tackle this problem, and yet, most of these algorithms have some drawbacks such as they have a weak ability to explore the solution space (they struggle escaping from local minima) and they face the difficulties to operate on different datasets. Consequently, the need to either enhance the existing algorithms or utilize a new algorithm is still necessary. Harmony Search Algorithm (HSA) is one of the recent population-based optimization methods which inspired by modern-nature. HSA has confirmed its ability to tackle various difficult combinatorial optimization problems like vehicle routing, exam timetabling to name a few. However, as far as we are concerned, it has never been applied to tackle the BAP problem. The primary objective of this article is to examine the effectiveness of HSA in solving BAP by identifying suitable values for the parameters of the HSA and then applying HSA to tackle BAP. Therefore, in this article, the basic HSA is proposed to tackle the BAP. The suggested HSA is tested on BAP benchmark (I3 dataset) and compared the results with other latest algorithms found in the literature. The trial outcomes evidenced that the HSA is promising, competitive, and that it has surpassed some other algorithms that have solved the same dataset, and the results were very near to the best-known results. Experimental results also prove the suitability and applicability of HSA in tackling the BAP. © 2024 College of Education, Al-Iraqia University. All rights reserved.

关键词： Combinatorial optimization

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Decomposition-based learning in drone-assisted wireless-powered mobile edge computing networks

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Digital communications and networks 2024年第6期10卷 1769-1781页

作者： Xiaoyi Zhou Liang Huang Tong Ye Weiqiang Sun State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Jiao Tong UniversityShanghai 200240China College of Computer Science and Technology Zhejiang University of TechnologyHangzhou 310058China

This paper investigates the multi-Unmanned Aerial Vehicle(UAV)-assisted wireless-powered Mobile Edge Computing(MEC)system,where UAVs provide computation and powering services to mobile *** aim to maximize the number of completed computation tasks by jointly optimizing the offloading decisions of all terminals and the trajectory planning of all *** action space of the system is extremely large and grows exponentially with the number of *** this case,single-agent learning will require an overlarge neural network,resulting in insufficient ***,the offloading decisions and trajectory planning are two subproblems performed by different executants,providing an opportunity for *** thus adopt the idea of decomposition and propose a 2-Tiered Multi-agent Soft Actor-Critic(2T-MSAC)algorithm,decomposing a single neural network into multiple small-scale *** the first tier,a single agent is used for offloading decisions,and an online pretrained model based on imitation learning is specially designed to accelerate the training process of this *** the second tier,UAVs utilize multiple agents to plan their *** agent exerts its influence on the parameter update of other agents through actions and rewards,thereby achieving joint *** results demonstrate that the proposed algorithm can be applied to scenarios with various location distributions of terminals,outperforming existing benchmarks that perform well only in specific *** particular,2T-MSAC increases the number of completed tasks by 45.5%in the scenario with uneven terminal ***,the pretrained model based on imitation learning reduces the convergence time of 2T-MSAC by 58.2%.

关键词： Mobile-edge computing Multi-agent reinforcement learning Offloading decision Trajectory planning Unmanned aerial vehicle Wireless power transfer

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Unsupervised Concept Drift Detection Based on Parallel Activations of Neural Network 1st

Unsupervised Concept Drift Detection Based on Parallel Acti...

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1st International Workshop on Discovering Drift Phenomena in Evolving Landscapes, DELTA 2024

作者： Komorniczak, Joanna Ksieniewicz, Pawel Department of Systems and Computer Networks Wroclaw University of Science and Technology Wroclaw Poland

ISBN: (纸本)9783031823459

Practical applications of artificial intelligence increasingly often have to deal with the streaming properties of real data, which, considering the time factor, are subject to phenomena such as periodicity and more or less chaotic degeneration – resulting directly in the concept drifts. The modern concept drift detectors almost always assume immediate access to labels, which, due to their cost, limited availability, and possible delay, has been shown to be unrealistic. This work proposes an unsupervised Parallel Activations Drift Detector, utilizing the outputs of an untrained neural network, presenting its key design elements, intuitions about processing properties, and a pool of computer experiments demonstrating its competitiveness with state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Data streams

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Changing Lineup Classifier Ensemble for Drifting Imbalanced Data Streams 24

Changing Lineup Classifier Ensemble for Drifting Imbalanced ...

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24th IEEE International Conference on Data Mining Workshops, ICDMW 2024

作者： Wegier, Weronika Maczynski, Maciej Wozniak, Michal Wroclaw University of Science and Technology Department of Systems and Computer Networks Wroclaw Poland

ISBN: (纸本)9798331530631

Currently, most data requires stream processing. This kind of processing causes new challenges in classifier learning method, such as data difficulties for example data imbalance since we cannot observe the entire set of data simultaneously or concept drift, which is the possibility of changes in the probabilistic characteristics of the processed data. These challenges were addressed by proposing a new learning algorithm that maintains consistency with the current data distribution by training a classifier ensemble with the changing lineup and applying dedicated mechanisms to account for the imbalance of the incoming data stream. The proposed IMB-WAE (Imbalance Weighted Aging Ensemble) method has been evaluated on the basis of exhaustive computer experiments conducted on a large number of real and synthetic data streams assessing the impact of its parameters on classification quality and confirming its quality compared to the state-of-the-art algorithm. © 2024 IEEE.

关键词： classifier ensemble learning concept drift imbalanced data nonstationary data streams

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Non-stationary Imbalanced Data Stream Classification with Weighted One-Class Ensemble 24

Non-stationary Imbalanced Data Stream Classification with We...

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24th IEEE International Conference on Data Mining Workshops, ICDMW 2024

作者： Klikowski, Jakub Wozniak, Michal Wroclaw University of Science and Technology Department of Systems and Computer Networks Wroclaw Poland

ISBN: (纸本)9798331530631

The presented study focuses on imbalanced data stream classification that requires fast processing of incoming data without memorizing it. An additional difficulty is the imbalanced data distribution, i.e., significant disparities among objects from different classes, which can change over time. We have proposed a novel classifier for a non-stationary and imbalanced data stream to solve the task mentioned above One-Class Support Vector Machine Weighted Ensemble (OCWE). OCWE, on the one hand, uses a pool of binary classifiers to deal with the disparity between classes. On the other hand, the ability of the classifier ensemble to follow the changes in data distributions is provided by updating its lineup based on currently incoming training examples. The performance of the proposed method has been evaluated through extensive experimental studies on synthetic and real data streams. The results prove that OCWE can outperform state-of-the-art classifiers for the considered task. © 2024 IEEE.

关键词： Data streams

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A deep decentralized privacy-preservation framework for online social networks

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Blockchain(Research and Applications) 2024年第4期5卷 163-174页

作者： Samuel Akwasi Frimpong Mu Han Emmanuel Kwame Effah Joseph Kwame Adjei Isaac Hanson Percy Brown School of Computer Science and Communication Engineering Jiangsu UniversityZhenjiang212013China Department of Computer Engineering Ghana Communication Technology UniversityAccraPMB 100Ghana Department of Electrical&Electronic Engineering Ghana Communication Technology UniversityAccraPMB 100Ghana Department of Computer Science and Information Systems Ashesi UniversityAccra3042Ghana Department of Telecommunication Engineering Ghana Communication Technology UniversityAccraPMB 100Ghana

This paper addresses the critical challenge of privacy in Online Social networks(OSNs),where centralized designs compromise user *** propose a novel privacy-preservation framework that integrates blockchain technology with deep learning to overcome these *** methodology employs a two-tier architecture:the first tier uses an elitism-enhanced Particle Swarm Optimization and Gravitational Search Algorithm(ePSOGSA)for optimizing feature selection,while the second tier employs an enhanced Non-symmetric Deep Autoencoder(e-NDAE)for anomaly ***,a blockchain network secures users’data via smart contracts,ensuring robust data *** tested on the NSL-KDD dataset,our framework achieves 98.79%accuracy,a 10%false alarm rate,and a 98.99%detection rate,surpassing existing *** integration of blockchain and deep learning not only enhances privacy protection in OSNs but also offers a scalable model for other applications requiring robust security measures.

关键词： Preprocessing Privacy-preservation Blockchain Deep learning Online social network

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Combined QoS Analysis of Smart Distribution Grids with Constraint Satisfaction Problem and Network Calculus 14

Combined QoS Analysis of Smart Distribution Grids with Const...

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14th IEEE International Conference on communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2023

作者： Alshra'a, Abdullah S. Vietz, Matthias Hielscher, Kai-Steffen Oest, Frauke Lehnhoff, Sebastian German, Reinhard Friedrich-Alexander-University Computers Networks and Communication Systems Germany University of Oldenburg Department of Computer Science Germany

ISBN: (纸本)9781665455541

The expansion of renewable energies requires implementing Smart Grid Services (SGSs) in the distribution grid. Analytical verification of the SGSs' Quality of Service (QoS) requirements is crucial for reliable operation. This paper proposes a two-step approach: a constraint satisfaction problem (CSP) considers network infrastructure and resource allocations to find candidates' solutions. Then, the solutions are evaluated using Network Calculus (NC) to calculate delay bounds at the worst-case and compare them to the requirements. If necessary, alternative solutions are generated. The NC framework is adjustable and considers different service priorities by implementing scheduling policies such as strict priority, weighted fair queuing, weighted round robin, and deficit round robin. In the end, the best-performing parameters are derived for a sample network and the results manifest that the NC framework can be used to analyze random communication networks. © 2023 IEEE.

关键词： Quality of service

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Enhancing the efficiency of lung cancer screening: predictive models utilizing deep learning from CT scans

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Neural Computing and Applications 2025年第17期37卷 11459-11477页

作者： Tawfeek, Medhat A. Alrashdi, Ibrahim Alruwaili, Madallah Shaban, Warda M. Talaat, Fatma M. Department of Computer Science Faculty of Computers and Information Menoufia University Shebin Elkom32511 Egypt Department of Computer Science College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Communication and Electronics Engineering Nile Higher Institute for Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa35712 Egypt

Lung cancer is the most lethal form of cancer. This paper introduces a novel framework to discern and classify pulmonary disorders such as pneumonia, tuberculosis, and lung cancer by analyzing conventional X-ray and CT scan images called lung cancer risk prediction (LCRP) model. LCRP has four modules, namely data collection and preprocessing, data augmentation module, image segmentation module, and prediction module. Actually, LCRP employs three deep learning models;sequential model, functional model, and transfer model on publicly available training datasets. Convolutional neural networks (CNNs) have emerged as a highly effective field in machine learning, particularly for image datasets in the field of biomedical applications. The primary goal is to validate these models by comparing their performance with other models in order to determine their effectiveness in addressing challenging datasets. Our research has revealed a noteworthy enhancement in the efficiency of binary and multi-class classification using mask R-CNN image segmentation. During the model training process, a combination of Adam and stochastic gradient descent dual optimizers has been used to improve performance. LCRP have outperformed current pre-trained models by minimizing training parameters, computational costs, and overhead. It introduces 98.5% accuracy, 88.7% specificity, 89% sensitivity, 89.2% precision, and 89.09% F-measure. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Lung cancer

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Accurate Phase Detection for ZigBee Using Artificial Neural Network

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Intelligent Automation & Soft Computing 2023年第6期36卷 2505-2518页

作者： Ali Alqahtani Abdulaziz A.Alsulami Saeed Alahmari Mesfer Alrizq Department of Networks and Communications Engineering College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia

The IEEE802.15.4 standard has been widely used in modern industry due to its several benefits for stability,scalability,and enhancement of wireless mesh *** standard uses a physical layer of binary phase-shift keying(BPSK)modulation and can be operated with two frequency bands,868 and 915 *** frequency noise could interfere with the BPSK signal,which causes distortion to the signal before its arrival at ***,filtering the BPSK signal from noise is essential to ensure carrying the signal from the sen-der to the receiver with less ***,removing signal noise in the BPSK signal is necessary to mitigate its negative sequences and increase its capability in industrial wireless sensor ***,researchers have reported a posi-tive impact of utilizing the Kalmen filter in detecting the modulated signal at the receiver side in different communication systems,including ***-while,artificial neural network(ANN)and machine learning(ML)models outper-formed results for predicting signals for detection and classification *** paper develops a neural network predictive detection method to enhance the performance of BPSK ***,a simulation-based model is used to generate the modulated signal of BPSK in the IEEE802.15.4 wireless personal area network(WPAN)***,Gaussian noise was injected into the BPSK simulation *** reduce the noise of BPSK phase signals,a recurrent neural networks(RNN)model is implemented and integrated at the receiver side to esti-mate the BPSK’s phase *** evaluated our predictive-detection RNN model using mean square error(MSE),correlation coefficient,recall,and F1-score *** result shows that our predictive-detection method is superior to the existing model due to the low MSE and correlation coefficient(R-value)metric for different signal-to-noise(SNR)*** addition,our RNN-based model scored 98.71%and 96.34%based on recall and F1-score,respectively.

关键词： Neural networks RNN BPSK phasedetection WPAN IEEE802.15.4 signal demodulation

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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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