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Metaheuristic Optimization of Time Series Models for Predicting Networks Traffic

computers, Materials & Continua 2023年第4期75卷 427-442页

作者： Reem Alkanhel El-Sayed M.El-kenawy D.L.Elsheweikh Abdelaziz A.Abdelhamid Abdelhameed Ibrahim 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 Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Science Faculty of Specific EducationMansoura UniversityEgypt 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 Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia

Traffic prediction of wireless networks attracted many researchersand practitioners during the past decades. However, wireless traffic frequentlyexhibits strong nonlinearities and complicated patterns, which makes it challengingto be predicted accurately. Many of the existing approaches forpredicting wireless network traffic are unable to produce accurate predictionsbecause they lack the ability to describe the dynamic spatial-temporalcorrelations of wireless network traffic data. In this paper, we proposed anovel meta-heuristic optimization approach based on fitness grey wolf anddipper throated optimization algorithms for boosting the prediction accuracyof traffic volume. The proposed algorithm is employed to optimize the hyperparametersof long short-term memory (LSTM) network as an efficient timeseries modeling approach which is widely used in sequence prediction *** prove the superiority of the proposed algorithm, four other optimizationalgorithms were employed to optimize LSTM, and the results were *** evaluation results confirmed the effectiveness of the proposed approachin predicting the traffic of wireless networks accurately. On the other hand,a statistical analysis is performed to emphasize the stability of the proposedapproach.

关键词： Network traffic soft computing LSTM metaheuristic optimization

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Deep Learning-Based Data-Assisted Channel Estimation and Detection

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in communications and Networking 2025年 3卷 534-551页

作者： Hashempoor, Hamidreza Choi, Wan Seoul National University Department of Electrical and Computer Engineering Seoul08826 Korea Republic of Institute of New Media and Communications Seoul National University Seoul08826 Korea Republic of

We introduce a novel structure empowered by deep learning models, accompanied by a thorough training methodology, for enhancing channel estimation and data detection in multiple input multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. Central to our approach is the incorporation of a Denoising Block, which comprises three meticulously designed deep neural networks (DNNs) tasked with accurately extracting noiseless embeddings from the received signal. Alongside, we develop the Correctness Classifier, a classification algorithm adept at distinguishing correctly detected data by leveraging the denoised received signal. By selectively utilizing these identified data symbols as additional pilot signals, we augment the available pilot signals for channel estimation. Our Denoising Block also enables direct data detection, rendering the system well-suited for low-latency applications. To enable model training, we propose a hybrid likelihood objective of the detected symbols. We analytically derive the gradients with respect to the hybrid likelihood, enabling us to successfully complete the training phase. When compared to other conventional methods, experiments and simulations show that the proposed data-aided channel estimator significantly lowers the mean-squared-error (MSE) of the estimation and thus improves data detection performance. Github repository link is https://***/Hamidreza-Hashempoor/5g-dataaided-channel-estimate. © 2023 CCBY.

关键词： Mean square error

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Utilizing FTP Procedures to Improve Sustainable Service Using IEEE 802.11 Technologies 1

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8th Future Technologies Conference, FTC 2023

作者： Mohd Ali, Ali Hassan, Mohammad R. Communications and Computer Engineering Department Faculty of Engineering Al-Ahliyya Amman University Amman19328 Jordan

ISBN: (数字)9783031474514

ISBN: (纸本)9783031474507

In this research paper, we delve into the optimal use of IEEE technology and network architecture for FTP services. Our contribution lies in the proposal of a novel framework and algorithm design that analyzes network performance to determine the most efficient network setup based on available technologies. Our algorithm takes into account various factors, including the spatial distribution and number of nodes, to select networks that guarantee the highest quality services and overall network performance. Furthermore, our research maintains a record of each application's service quality metric, which facilitates the accurate identification and categorization of the most effective technologies in terms of overall performance. We use this information to enhance performance further and build a computational algorithm model. Our empirical findings confirm the effectiveness of our proposed algorithm in achieving optimal network performance. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Network performance

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Human Activity Recognition Using Feedforward Backpropagation Neural Network

Human Activity Recognition Using Feedforward Backpropagation...

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2024 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2024

作者： Debs, Fadi Al Jurdy, Makram El Fares, Elias Haidar, Gaby Abou Owayjan, Michel Achkar, Roger Department of Computer and Communications Engineering American University of Science and Technology Beirut Lebanon Department of Mechatronics Engineering American University of Science and Technology Beirut Lebanon

ISBN: (纸本)9798331533137

This paper presents a feedforward backpropagation neural network designed to recognize human activities using IoT sensor data. Human activity recognition based on mobile phones and wearable sensor data has garnered significant attention due to its wide range of applications in healthcare, smart environments, and beyond. Previous studies in this field have utilized various sensors, such as accelerometers, gyroscopes, and orientation sensors, among others, to categorize human activities using modern wearable technologies. The proposed system employs a Human Activity Recognition framework, which collects real-time sensor data and analyzes human movements using deep learning techniques. The model achieves an accuracy of approximately 96% in identifying human motions based on the sensor data. ©2024 IEEE.

关键词： Feedforward neural networks

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Al-Biruni Earth Radius Optimization for COVID-19 Forecasting

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computer Systems Science & engineering 2023年第7期46卷 883-896页

作者： El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Mostafa Abotaleb Tatiana Makarovskikh Amal H.Alharbi Doaa Sami Khafaga Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of System Programming South Ural State University454080ChelyabinskRussia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Several instances of pneumonia with no clear etiology were recorded in Wuhan,China,on December 31,*** world health organization(WHO)called it COVID-19 that stands for“Coronavirus Disease 2019,”which is the second version of the previously known severe acute respiratory syndrome(SARS)Coronavirus and identified in short as(SARSCoV-2).There have been regular restrictions to avoid the infection spread in all countries,including Saudi *** prediction of new cases of infections is crucial for authorities to get ready for early handling of the virus ***:Analysis and forecasting of epidemic patterns in new SARSCoV-2 positive patients are presented in this research using metaheuristic optimization and long short-term memory(LSTM).The optimization method employed for optimizing the parameters of LSTM is Al-Biruni Earth Radius(BER)***:To evaluate the effectiveness of the proposed methodology,a dataset is collected based on the recorded cases in Saudi Arabia between March 7^(th),2020 and July 13^(th),*** addition,six regression models were included in the conducted experiments to show the effectiveness and superiority of the proposed *** achieved results show that the proposed approach could reduce the mean square error(MSE),mean absolute error(MAE),and R^(2)by 5.92%,3.66%,and 39.44%,respectively,when compared with the six base *** the other hand,a statistical analysis is performed to measure the significance of the proposed ***:The achieved results confirm the effectiveness,superiority,and significance of the proposed approach in predicting the infection cases of COVID-19.

关键词： COVID-19 prediction meta-heuristic optimization LSTM Al-Biruni earth radius algorithm

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Proposed 3D chaos-based medical image cryptosystem for secure cloud-IoMT eHealth communication services

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 1-28页

作者： El-Shafai, Walid Khallaf, Fatma El-Rabaie, El-Sayed M. El-Samie, Fathi E. Abd Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electrical Engineering Faculty of Engineering Ahram Canadian University Giza 6th October City Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh84428 Saudi Arabia

Medical image encryption is a mandatory process in various healthcare, Internet of Medical Things (IoMT) and cloud services. This paper provides a robust cryptosystem based on a 3D chaotic map for the medical image encryption in secure IoMT and cloud services. The proposed encryption method depends on a 3D chaotic map. With this map, a nonlinear ciphering process is implemented for pixel value diffusion and position permutation. Five operations are conducted in the suggested cryptosystem on the medical images to be transmitted in order to attain a high security level. These operations are 3D chaos generation, chaos histogram equalization, row rotation, column rotation, and XOR operation. To validate the suggested cryptosystem, different medical images with different characteristics are tested. Moreover, the suggested cryptosystem and more recent state-of-the-art security systems are discussed and analyzed in a comparative study. Different assessment parameters are adopted to assess the suggested cryptosystem, which reveals high security performance. The simulation outcomes show that the suggested cryptosystem is trustworthy. Furthermore, it offers high robustness levels and recommended security levels for utilization in healthcare, IoMT and cloud service applications. It is observed that the amount of average entropy obtained by the proposed 3D chaos cryptosystem is 7.95 which is very close to its best value of 8, and the average NPCR value is 99.62% which is also much close to its supreme value of 99.60%. In addition, all other results scored for the test evaluation parameters on the suggested cryptosystem are superior to those of the traditional security systems. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.

关键词： Cryptography

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Reinforcement Learning to Improve QoS and Minimizing Delay in IoT

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Intelligent Automation & Soft Computing 2023年第5期36卷 1603-1612页

作者： Mahendrakumar Subramaniam V.Vedanarayanan Azath Mubarakali S.Sathiya Priya Department of Electronics and Communications Engineering Velalar College of Engineering and TechnologyErodeTamil NaduIndia Department of Electronics and Communication Engineering Sathyabama Institute of Science and TechnologyChennaiTamil NaduIndia College of Computer Science King Khalid UniversityAbhaSaudi Arabia Department of Electronics and Communications

Machine Learning concepts have raised executions in all knowledge domains,including the Internet of Thing(IoT)and several business *** of Service(QoS)has become an important problem in IoT surrounding since there is a vast explosion of connecting sensors,information and ***-sor data gathering is an efﬁcient solution to collect information from spatially dis-seminated IoT *** Learning Mechanism to improve the QoS(RLMQ)and use a Mobile Sink(MS)to minimize the delay in the wireless IoT s proposed in this ***,we use machine learning concepts like Rein-forcement Learning(RL)to improve the QoS and energy efﬁciency in the Wire-less Sensor Network(WSN).The MS collects the data from the Cluster Head(CH),and the RL incentive values select *** incentives value is computed by the QoS parameters such as minimum energy utilization,minimum bandwidth utilization,minimum hop count,and minimum time *** MS is used to col-lect the data from CH,thus minimizing the network *** sleep and awake scheduling is used for minimizing the CH dead in the *** work is simu-lated,and the results show that the RLMQ scheme performs better than the base-line *** prove that RLMQ increased the residual energy,throughput and minimized the network delay in the WSN.

关键词： Wireless sensor network quality of service parameters Internet of things reinforcement learning mobile sink

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An Efficient Intrusion Detection Framework for Industrial Internet of Things Security

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computer Systems Science & engineering 2023年第7期46卷 819-834页

作者： Samah Alshathri Ayman El-Sayed Walid El-Shafai Ezz El-Din Hemdan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

Recently,the Internet of Things(IoT)has been used in various applications such as manufacturing,transportation,agriculture,and healthcare that can enhance efficiency and productivity via an intelligent management console *** the increased use of Industrial IoT(IIoT)applications,the risk of brutal cyber-attacks also *** leads researchers worldwide to work on developing effective Intrusion Detection Systems(IDS)for IoT infrastructure against any malicious ***,this paper provides effective IDS to detect and classify unpredicted and unpredictable severe attacks in contradiction to the IoT infrastructure.A comprehensive evaluation examined on a new available benchmark TON_IoT dataset is *** data-driven IoT/IIoT dataset incorporates a label feature indicating classes of normal and attack-targeting IoT/IIoT ***,this data involves IoT/IIoT services-based telemetry data that involves operating systems logs and IoT-based traffic networks collected from a realistic medium-scale IoT *** is to classify and recognize the intrusion activity and provide the intrusion detection objectives in IoT environments in an efficient ***,several machine learning algorithms such as Logistic Regression(LR),Linear Discriminant Analysis(LDA),K-Nearest Neighbors(KNN),Gaussian Naive Bayes(NB),Classification and Regression Tree(CART),Random Forest(RF),and AdaBoost(AB)are used for the detection intent on thirteen different intrusion *** performance metrics like accuracy,precision,recall,and F1-score are used to estimate the proposed *** experimental results show that the CART surpasses the other algorithms with the highest accuracy values like 0.97,1.00,0.99,0.99,1.00,1.00,and 1.00 for effectively detecting the intrusion activities on the IoT/IIoT infrastructure on most of the employed *** addition,the proposed work accomplishes high performance compared to other recent rela

关键词： Attacks intrusion detection machine learning deep learning industrial IoT TON_IoT dataset

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Exploring Impact of Prioritizing Intra-Singer Acoustic Variations on Singer Embedding Extractor Construction for Singer Verification 27

Exploring Impact of Prioritizing Intra-Singer Acoustic Varia...

引用

27th Conference on the Oriental COCOSDA International Committee for the Co-Ordination and Standardisation of Speech Databases and Assessment Techniques, O-COCOSDA 2024

作者： Toma, Sayaka Ariga, Tomoki Higuchil, Yosuke Hayasaka, Ichiju Shigyo, Rie Ogawa, Tetsuji Waseda University Department of Communications and Computer Engineering Tokyo Japan DAIICHIKOSHO CO. LTD. Tokyo Japan

ISBN: (纸本)9798331506032

We explored the significance of prioritizing acoustic variations within singers while training a singer embedding ex-tractor for singer verification. Neural networks effective for speaker verification, like ECAPA-TDNN, may lead to in-creased false rejections as the number of identifiable speakers grows. Therefore, it is crucial to enhance the variation in training data per speaker. Singing voice, with its diverse ex-pressions and singing techniques, is believed to exhibit more intra-speaker variability compared to spoken voice. This study aimed to investigate the impact of intra-singer acous-tic variations on training singer embeddings. Experiments conducted with a self-constructed Japanese singing voice corpus revealed the following findings: 1) the importance of using multiple songs per singer, 2) the limited significance of increasing the total number of identifiable singers, and 3) the detrimental effects of discrepancies in singing technique between enrollment and verification data. © 2024 IEEE.

关键词： Network embeddings

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Secure speaker identification in open and closed environments modeled with symmetric comb filters

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Multimedia Tools and Applications 2024年 1-43页

作者： Shafik, Amira Monir, Mohamad El-Shafai, Walid Khalaf, Ashraf A. M. Nassar, M.M. El-Fishawy, Adel S. El-Din, M. A. Zein Dessouky, Moawad I. El-Rabaie, El-Sayed M. Abd El-Samie, Fathi E. Department Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Laboratory Department of Computer Science Prince Sultan University Riyadh11586 Saudi Arabia Electrical Engineering Department Faculty of Engineering Minia University Minia61519 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Speech is a fundamental means of human interaction. Speaker Identification (SI) plays a crucial role in various applications, such as authentication systems, forensic investigation, and personal voice assistance. However, achieving robust and secure SI in both open and closed environments remains challenging. To address this issue, researchers have explored new techniques that enable computers to better understand and interact with humans. Smart systems leverage Artificial Neural Networks (ANNs) to mimic the human brain in identifying speakers. However, speech signals often suffer from interference, leading to signal degradation. The performance of a Speaker Identification System (SIS) is influenced by various environmental factors, such as noise and reverberation in open and closed environments, respectively. This research paper is concerned with the investigation of SI using Mel-Frequency Cepstral Coefficients (MFCCs) and polynomial coefficients, with an ANN serving as the classifier. To tackle the challenges posed by environmental interference, we propose a novel approach that depends on symmetric comb filters for modeling. In closed environments, we study the effect of reverberation on speech signals, as it occurs due to multiple reflections. To address this issue, we model the reverberation effect with comb filters. We explore different domains, including time, Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Discrete Sine Transform (DST) domains for feature extraction to determine the best combination for SI in case of reverberation environments. Simulation results reveal that DWT outperforms other transforms, leading to a recognition rate of 93.75% at a Signal-to-Noise Ratio (SNR) of 15 dB. Additionally, we investigate the concept of cancelable SI to ensure user privacy, while maintaining high recognition rates. Our simulation results show a recognition rate of 97.5% at 0 dB using features extracted from speech signals and their DCTs. Fo

关键词： Speech enhancement

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