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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Algriree, Waleed Hassan, Nawar Banwan Ali, Huda Hamdan Sulaiman, Nasri bin Alghrairi, Mokhalad Sabbar, Bayan Mahdi Mutashar, Saad University Putra Malaysia Faculty of Engineering Department of Wireless Communications and Networks Engineering Selangor Malaysia Imam Alkadhim University College Department of Computer Techniques Engineering Baghdad Iraq University Putra Malaysia Faculty of Engineering Department of Electrical and Electronic Engineering Selangor Malaysia Al-Mustaqbal University Medical Instrumentation Techniques Engineering Department Babylon Iraq

ISBN: (纸本)9798350395914

With the exponential growth of wireless communications, there is a great need for innovative telecommunication services throughout the frequency ranges where the present scarcity of spectrum might be addressed. As a result, to enhance the use of spectrum, a Fifth generation -MIMO CR system of communication that utilizes the spectrum sense (SS) methodology was developed. Based on the two-stage method, this suggested SS algorithm uses a two-stage approach: first, it filters the traffic signal using the Cosine law, and then it applies the Welch algorithm to enable segmentation. In addition, wave forms for filter bank multi-carrier (FBMC), universal filtering multi-carrier (UFMC), filtered-orthogonal multiplexing with frequency division (F-OFDM), as well as non-orthogonal multiple access (NOMA) are included here (GFDM). Evaluations of the computational difficulty and other operational factors, including SNR, antenna count, power, as well as signal span, were also conducted. A remarkable accomplishment was observed for the parameters with SNR values less than 0 dB, minimal difficulty, global identification percentage > 99% ,global false alarm a likelihood © 2024 IEEE.

关键词： Signal to noise ratio

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Comparative E-Voting Security Evaluation: Multi-Modal Authentication Approaches 6

Comparative E-Voting Security Evaluation: Multi-Modal Authen...

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6th International Conference on Blockchain Computing and Applications, BCCA 2024

作者： Ahmadieh, Elsi El Madhoun, Nour Lebanese University Faculty of Technology Department Communications and Computer Networks Engineering Beirut Lebanon LISITE Laboratory ISEP 10 Rue de Vanves Issy-les-Moulineaux92130 France Sorbonne Université CNRS LIP6 4 place Jussieu Paris75005 France

ISBN: (纸本)9798350351538

The decentralized, distributed ledger, a fundamental aspect of blockchain technology, plays a pivotal role in various sectors, including electronic voting (e-voting) systems. When considering e-voting, the significance of blockchain technology in boosting the security, transparency, and integrity of the entire process is evident. This ranges from the initial steps of user registration and authentication to the final stages of voting and result validation. Unlike conventional voting systems, which are plagued by issues such as fraud, tampering, and a lack of transparency in the result tallying and viewing processes, not to mention the delays, blockchain-enabled e-voting mitigates these challenges within government frameworks. It ensures a secure, tamper-proof, and transparent procedure, where each vote is recorded as an immutable transaction, thereby enhancing the process’s reliability. This paper explores the integration of voice recognition and fingerprinting using blockchain technology for enhancing the security of e-voting systems, comparing alternative authentication methods, and emphasizing the need for a multi-modal approach to achieve better security and reliability of the voting systems. © 2024 IEEE.

关键词： Voting machines

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Malware Detection Based on Machine Learning Methods, Analysis, and Tools

Malware Detection Based on Machine Learning Methods, Analysi...

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International Conference on communications and Networking, ComNet

作者： Almaha Almuqren Mounir Frikha Abdullah Albuali Mohammed Amin Almaiah dept. Department of Computer Networks and Communications King Faisal University Al Hofuf Saudi Arabia dept. Department of Computer Science King Abdullah the II IT School The University of Jordan Amman Jordan

ISBN: (数字)9798331520793

ISBN: (纸本)9798331520809

Malware identification is essential for safeguarding digital systems from cyber attacks, and machine learning techniques are proving to be efficient in this field. This systematic literature re-view thoroughly analyzes research undertaken from 2020 to 2024 on malware detection using machine learning techniques, with a specific emphasis on analysis and tools. This research follows systematic literature review procedures to identify notable developments and trends in machine learning-based malware detection. The examination focuses on research approaches, highlighting the use of supervised, unsupervised, and deep learning algorithms. The research also examines the use of fundamental tools and frameworks for machine-learning-based malware detection. The assessment includes open-source software tools used for data preparation, feature extraction, and model evaluation. The research also explores the practical consequences of using machine learning for detecting malware. The evolution, classification, trends, and possible hazards linked to malicious software, specifically on Android and network platforms, have been the subject of an abundance of research. Nonetheless, a significant research void persists concerning the effectiveness of utilizing machine learning techniques to analyze and detect malware on di-verse platforms. By presenting a methodical examination of diverse malware analysis and detection approaches, this article attempts to bridge this disparity. The article discusses obstacles and constraints faced in machine learning-based malware detection, including dataset imbalances and adversarial assaults. The text explores possible solutions and future research paths to address these difficulties, emphasizing the importance of creating strong and adaptable detection systems. This study is a great resource for academics and practitioners that aim to strengthen cybersecurity measures and effectively address changing cyber threats.

关键词： Systematics Machine learning algorithms Software algorithms Machine learning Market research Feature extraction Malware Hazards Open source software Systematic literature review

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Drone-based AI/IoT Framework for Monitoring,Tracking and Fighting Pandemics

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computers, Materials & Continua 2022年第6期71卷 4677-4699页

作者： Abdelhamied A.Ateya Abeer D.Algarni Andrey Koucheryavy Naglaa.F.Soliman Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Electronics and Communications Engineering Zagazig UniversityZagazigSharqia44519Egypt Department of Telecommunication Networks and Data Transmission the Bonch-Bruevich Saint-Petersburg State University of TelecommunicationsSaint Petersburg193232Russia

Since World Health Organization(WHO)has declared the Coronavirus disease(COVID-19)a global pandemic,the world has *** life’s fields and daily habits have moved to adapt to this new *** to WHO,the probability of such virus pandemics in the future is high,and recommends preparing for worse *** this end,this work provides a framework for monitoring,tracking,and fighting COVID-19 and future *** proposed framework deploys unmanned aerial vehicles(UAVs),e.g.;quadcopter and drone,integrated with artificial intelligence(AI)and Internet of Things(IoT)to monitor and fight *** consists of two main systems;AI/IoT for COVID-19 monitoring and drone-based IoT system for *** two systems are integrated with the IoT paradigm and the developed algorithms are implemented on distributed fog units connected to the IoT network and controlled by software-defined networking(SDN).The proposed work is built based on a thermal camera mounted in a face-shield,or on a helmet that can be used by people during *** detected images,thermal images,are processed by the developed AI algorithm that is built based on the convolutional neural network(CNN).The drone system can be called,by the IoT system connected to the helmet,once infected cases are *** drone is used for sterilizing the area that contains multiple infected *** proposed framework employs a single centralized SDN controller to control the network *** developed system is experimentally evaluated,and the results are *** indicate that the developed framework provides a novel,efficient scheme for monitoring and fighting COVID-19 and other future pandemics.

关键词： Internet of Things convolutional neural network fog computing software-defined networking COVID-19 drone

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Ultrahigh-coherence microcomb generation and low-noise amplification using integrated photonics

Ultrahigh-coherence microcomb generation and low-noise ampli...

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2024 IEEE Photonics Conference, IPC 2024

作者： Zhang, Xuguang Zhou, Zixuan Guo, Yijun Zhuang, Minxue Jin, Warren Shen, Bitao Chen, Yujun Huang, Jiahui Hu, Weiwei Shu, Haowen Bowers, John E. Wang, Xingjun Chang, Lin University of California Santa Barbara Department of Electrical and Computer Engineering Santa BarbaraCA United States Peking University State Key Laboratory of Advanced Optical Communications System and Networks School of Electronics Beijing China Peking University Frontiers Science Center for Nano-Optoelectronics Beijing China

ISBN: (纸本)9798350361957

We demonstrate a high-coherence parallel integrated light source utilizing a self-injection locked microcomb and injection locking amplification. Our strategy exhibits a record high on-chip gain of 60 dB, nearly 0 dB noise factor, which enable more than 30 highly coherent channels in integrated photonics whose linewidths down to 10 Hz, power exceeding 20 dBm per line and a wall plug efficiency of 19%. © 2024 IEEE.

关键词： Photonic integrated circuits

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Malware Detection Based on Machine Learning Methods, Analysis, and Tools 11

Malware Detection Based on Machine Learning Methods, Analysi...

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11th IEEE International Conference on communications and Networking, ComNet 2024

作者： Almuqren, Almaha Frikha, Mounir Albuali, Abdullah Amin Almaiah, Mohammed King Faisal University Dept. Department of Computer Networks and Communications Al Hofuf Saudi Arabia King Abdullah the Ii It School The University of Jordan Dept. Department of Computer Science Amman11942 Jordan

ISBN: (纸本)9798331520793

关键词： Android malware

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Digital Object Architecture for IoT networks

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Intelligent Automation & Soft Computing 2023年第1期35卷 97-110页

作者： Mahmood Al-Bahri Abdelhamied Ateya Ammar Muthanna Abeer D.Algarni Naglaa F.Soliman Department of Computing and IT Sohar UniversitySohar311Oman Department of Electronics and Communications Engineering Zagazig UniversityZagazig44519SharqiaEgypt Telecommunication Networks and Data Transmission St.Petersburg State University of TelecommunicationSt.Petersburg193232Russia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

The Internet of Things(IoT)is a recent technology,which implies the union of objects,“things”,into a single worldwide *** promising paradigm faces many design challenges associated with the dramatic increase in the number of *** identification is one of these challenges that becomes complicated with the increase of network *** this,there is still no universally accepted method of identifying things that would satisfy all requirements of the existing IoT devices and *** this regard,one of the most important problems is choosing an identification system for all IoT devices connected to the public communication *** unique soft-ware and hardware solutions are used as a unique global identifier;however,such solutions have many *** article proposes a novel solution,based on the Digital Object Architecture(DOA),that meets the requirements of identifying devices and applications of the *** work analyzes the benefits of using the DOA as an identification platform in modern telecommunication *** propose a model of an identification system based on the architecture of digital objects,which differs from the well-known *** proposed model ensures an acceptable quality of service(QoS)in the common architecture of the existing public communication networks.A novel interaction architecture is developed by introducing a Middle Handle Register(MHR)between the global register,i.e.,Global Handle Register(GHR),and local register,i.e.,Local Handle Register(LHR).The aspects of the network interaction and the compatibility of IoT end-devices with the integrated DOA identifiers in heterogeneous communication networks are *** developed model is simulated for a wide-area network with allocated registers,and the results are introduced and discussed.

关键词： Internet of things identification digital object architecture handle system security

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Convolutional Neural Network Based Deep Neural Network Model for Electrocardiogram Records Classification 7

Convolutional Neural Network Based Deep Neural Network Model...

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7th International Conference on Future networks and Distributed Systems, ICFNDS 2023

作者： Khudhur, Ahmed Mahmood Alwan, Mohammed Hasan Al Omairi, Qayssar Mahmood, Omar Abdulkareem Hammadi, Yousif I Muthanna, Mohammed Saleh Ali Aziz, Ahmed Muthanna, Ammar Information Technology Department College Of Computer Science And Information Technology Kirkuk University Iraq Department Of Communications Engineering College Of Engineering University Of Diyala Baqubah32001 Iraq Department Of Computer Engineering College Of Engineering University Of Diyala Iraq Bilad Alrafidain University College Diyala32001 Iraq Institute Of Computer Technologies And Information Security Southern Federal University Taganrog344006 Russia Department Of Computer Science Faculty Of Computer And Artificial Intelligence Benha University Egypt Department Of International Business Management Tashkent State University Of Economics Tashkent Uzbekistan Department Of Telecommunication Networks And Data Transmission The Bonch Bruevich Saint Petersburg State University Of Telecommunications Russia Department Of Applied Probability And Informatics Peoples Friendship University Of Russia Russia

ISBN: (纸本)9798400709036

Cardiovascular diseases, also known as CVDs, currently rank as the primary incidence of mortality. The present approach for identifying illnesses involves the analysis of the Electrocardiogram (ECG), an electronic diagnostic device utilized to capture the rhythm of the heart. Regrettably, the process of seeking out specialists to conduct analysis on a substantial volume of electrocardiogram (ECG) data results in a significant depletion of medical capabilities. Hence, the utilization of deep learning algorithms for the identification of ECG characteristics has progressively gained prominence. Nevertheless, there exist certain limitations associated with these conventional approaches, which necessitate the need for manual characteristic identification, intricate models, and extensive training duration. This study presents a novel and effective approach for categorizing the five different types of heartbeats in the MIT-BIH Arrhythmia database. The proposed method involves the utilization of a 16-layer deep one-dimensional convolutional neural network, which exhibits both robustness and efficiency in its classification performance. Thus, the designed model is consisting of five-groups, first to fourth groups are the main feature extraction and mapping blocks, while the fifth group is fully connected and classification layers. The findings indicate that the model presented in this study has superior performance in terms of accuracy, precision, and F1-score. The proper classification of medical cases has a significant impact on the conservation of medical resources, hence positively influencing clinical practice. Additionally, the model training phase is characterized by its efficiency, since it does not significantly deplete resources. © 2023 ACM.

关键词： Electrocardiograms

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Control of Master-Slave DC Microgrid with Optical Fiber Communication Link 15

Control of Master-Slave DC Microgrid with Optical Fiber Comm...

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15th International Renewable Energy Congress, IREC 2025

作者： Alfergani, Asma Shaltami, Farhat M. Alfaitor, Khalid Ateea Khalil, Ashraf Mohamed, Faisal University of Benghazi Electrical and Electronics Engineering Department Benghazi Libya College of Electrical and Electronics Technology - Benghazi Benghazi Libya University of Benghazi Faculty of Information Technology Department of Computer Networks and Communications Benghazi Libya Dtu Engineering Technology Technical University of Denmark Kongens LyngbyDK-2750 Denmark Authority of Natural Science Research and Technology Tripoli Libya

ISBN: (纸本)9798331516772

In this paper, a model for the master-slave DC Microgrid over a fiber optic link is proposed. The delay margin is calculated according to Lyapunov-Krasovskii functional using the LMI toolbox in Matlab with the binary iteration algorithm. The optical fiber link is implemented using optisystem simulator. The optical fiber link is set to transmit the digitalized reference current from master controller to the slave controller. The link tested at 100Mbps, 1Gbps and 2.5Gbps data rate values and several optical fiber lengths and wavelength of 1550nm and 1400nm. The results show that the performance of the link is sufficient at received power more than -30dBm for all data rate values at 1550nm. While at 1400nm, the attenuation rate is very high. Therefore, the link must be 10km to achieve acceptable performance. This wavelength is used in this simulation to avoid the C band and O band, which used for telecommunication purposes. © 2025 IEEE.

关键词： MATLAB

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Energy-Efficient Distributed Learning for NOMA-Based Unmanned Aerial Agent-assisted MEC networks

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IEEE Internet of Things Journal 2025年

作者： Consul, Prakhar Budhiraja, Ishan Garg, Deepak Kumar, Neeraj Rodrigues, Joel J. P. C. Almuhaideb, Abdullah IILM University School of Computer Science and Engineering Department of Computing and Security Greater Noida India Bennett University School of Computer Science Engineering and Technology Greater Noida India SR University School of Artificial Intelligence and Computer Science Telangana Warangal India Thapar Institute of Engineering and Technology Department of Computer Science and Engineering Punjab Patiala India Imam Abdulrahman Bin Faisal University Department of Networks and Communications College of Computer Science and Information Technology Saudi Arabia PI Teresina Brazil

The Internet of Things (IoT) has become a revolutionary concept that connects various devices and systems to enable smooth communication and data exchange. In this vast network, unmanned aerial agents (UAA)-assisted mobile edge computing (MEC) communication plays a crucial role in facilitating direct interaction between edge devices. This aspect of IoT goes beyond traditional interactions between humans and machines. It creates a dynamic environment where devices collaborate autonomously, share information, and perform tasks. UAA-assisted MEC network offers several benefits such as supports short range communication, reduced delay, improved scalability, and enhanced energy efficiency. Furthermore, for the purpose of enhancing the widespread interconnection and exceptionally dependable minimal delay in the fifth generation (5G) and beyond network, the utilization of non-orthogonal multiple access (NOMA) can be considered. Within this context, the impact of federated learning (FL) on NOMA-based UAV-assisted MEC network in wirelesspowered communication networks is examined. Initially, the transmitters extract energy from the radio frequency signals emitted by the MEC server. Subsequently, the transmitters utilize NOMA to establish communication with the receivers by utilizing the stored harvested energy. The formulation of a stochastic optimization problem is proposed with the aim of improving energy consumption (EC) and minimizing delay. Results indicate that the proposed scheme exhibit superior accuracy compared to baseline schemes, achieving an accuracy 98.37% after 59 communication rounds. The FL is employed to attain the objective and accelerate the local training data across the UAA-assisted MEC network. © 2025 IEEE.

关键词： Resource allocation

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