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Secure Channel Estimation Using Norm Estimation Model for 5G Next Generation Wireless Networks

computers, Materials & Continua 2025年第1期82卷 1151-1169页

作者： Khalil Ullah Song Jian Muhammad Naeem Ul Hassan Suliman Khan Mohammad Babar Arshad Ahmad Shafiq Ahmad Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650500China Department of Computer Science Abbottabad University of Science and TechnologyHavelianAbbottabad22500Pakistan Department of Computing and Electronics Engineering Middle East CollegeMuscat124Oman Department of CS&IT Pak-Austria Fachhochschule Institute of Applied Sciences and Technology MangHaripur22621Pakistan Industrial Engineering Department College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

The emergence of next generation networks(NextG),including 5G and beyond,is reshaping the technological landscape of cellular and mobile *** networks are sufficiently scaled to interconnect billions of users and *** in academia and industry are focusing on technological advancements to achieve highspeed transmission,cell planning,and latency reduction to facilitate emerging applications such as virtual reality,the metaverse,smart cities,smart health,and autonomous *** continuously improves its network functionality to support these *** input multiple output(MIMO)technology offers spectral efficiency,dependability,and overall performance in *** article proposes a secure channel estimation technique in MIMO topology using a norm-estimation model to provide comprehensive insights into protecting NextG network components against adversarial *** technique aims to create long-lasting and secure NextG networks using this extended *** viability of MIMO applications and modern AI-driven methodologies to combat cybersecurity threats are explored in this ***,the proposed model demonstrates high performance in terms of reliability and accuracy,with a 20%reduction in the MalOut-RealOut-Diff metric compared to existing state-of-the-art techniques.

关键词： Next generation networks massive mimo communication network artificial intelligence 5G adversarial attacks channel estimation information security

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All-optically linearized silicon modulator with ultrahigh SFDR of 131 dB·Hz^(6/7)

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Photonics Research 2025年第2期13卷 433-441页

作者： QIANG ZHANG QIKAI HUANG PENGHUI XIA YAN LI XINGYI JIANG SHUYUE ZHANG SHENGYU FANG JIANYI YANG HUI YU Zhejiang Lab Hangzhou 311100China Institute of Integrated Microelectronic Systems College of Information Science and Electronic EngineeringZhejiang UniversityHangzhou 310027China Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China

Integrated high-linearity modulators are crucial for high dynamic-range microwave photonic(MWP)*** linearization schemes usually involve the fine tuning of radio-frequency(RF)power distribution,which is rather inconvenient for practical applications and can hardly be implemented on the integrated photonics *** this paper,we propose an elegant scheme to linearize a silicon-based modulator in which the active tuning of RF power is *** device consists of two carrier-depletion-based Mach-Zehnder modulators(MZMs),which are connected in series by a 1×2 thermal optical switch(OS).The OS is used to adjust the ratio between the modulation depths of the two *** a proper ratio,the complementary third-order intermodulation distortion(IMD3)of the two sub-MZMs can effectively cancel each other *** measured spurious-free dynamic ranges for IMD3 are 131,127,118,110,and 109 d B·Hz^(6∕7)at frequencies of 1,10,20,30,and 40 GHz,respectively,which represent the highest linearities ever reached by the integrated modulator chips on all available material platforms.

关键词： microwave photonic systems ultrahigh SFDR all optical linearization silicon modulator active tuning rf power high linearity modulators third order intermodulation distortion integrated photonics

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Invertibility of Discrete-Time Linear systems with Sparse Inputs 63

Invertibility of Discrete-Time Linear Systems with Sparse In...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Poe, Kyle Mallada, Enrique Vidal, Rene University of Pennsylvania Applied Mathematics and Computational Science Group PA19104 United States Johns Hopkins University Mallada Is with the Department of Electrical and Computer Engineering MD21218 United States University of Pennsylvania Radiology Computer and Information Science Statistics and Data Science Departments of Electrical and Systems Engineering PA19104 United States

ISBN: (纸本)9798350316339

One of the fundamental problems of interest for discrete-time linear systems is whether its input sequence may be recovered given its output sequence, a.k.a. the left inversion problem. Many conditions on the state space geometry, dynamics, and spectral structure of a system have been used to characterize the well-posedness of this problem, without assumptions on the inputs. However, certain structural assumptions, such as input sparsity, have been shown to translate to practical gains in the performance of inversion algorithms, surpassing classical guarantees. Establishing necessary and sufficient conditions for left invertibility of systems with sparse inputs is therefore a crucial step toward understanding the performance limits of system inversion under structured input assumptions. In this work, we provide the first necessary and sufficient characterizations of left invertibility for linear systems with sparse inputs, echoing classic characterizations for standard linear systems. The key insight in deriving these results is in establishing the existence of two novel geometric invariants unique to the sparseinput setting, the weakly unobservable and strongly reachable subspace arrangements. By means of a concrete example, we demonstrate the utility of these characterizations. We conclude by discussing extensions and applications of this framework to several related problems in sparse control. © 2024 IEEE.

关键词： State space methods

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Metaheuristic Optimization Through Deep Learning Classification of COVID-19 in Chest X-Ray Images

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computers, Materials & Continua 2022年第11期73卷 4193-4210页

作者： Nagwan Abdel Samee El-Sayed M.El-Kenawy Ghada Atteia Mona M.Jamjoom Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Noha E.El-Attar Tarek Gaber Adam Slowik Mahmoud Y.Shams Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura 35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura 35712Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityEgypt School of Science Engineeringand EnvironmentUniversity of SalfordUK Faculty of Computers and Informatics Suez Canal UniversityIsmailia41522Egypt Koszalin University of Technology Poland Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt

As corona virus disease(COVID-19)is still an ongoing global outbreak,countries around the world continue to take precautions and measures to control the spread of the *** of the excessive number of infected patients and the resulting deficiency of testing kits in hospitals,a rapid,reliable,and automatic detection of COVID-19 is in extreme need to curb the number of *** analyzing the COVID-19 chest X-ray images,a novel metaheuristic approach is proposed based on hybrid dipper throated and particle swarm *** lung region was segmented from the original chest X-ray images and augmented using various transformation ***,the augmented images were fed into the VGG19 deep network for feature *** the other hand,a feature selection method is proposed to select the most significant features that can boost the classification ***,the selected features were input into an optimized neural network for *** neural network is optimized using the proposed hybrid *** experimental results showed that the proposed method achieved 99.88%accuracy,outperforming the existing COVID-19 detection *** addition,a deep statistical analysis is performed to study the performance and stability of the proposed *** results confirm the effectiveness and superiority of the proposed approach.

关键词： Covid-19 feature selection dipper throated optimization particle swarm optimization deep learning

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Deep Transfer Learning Driven Oral Cancer Detection and Classification Model

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computers, Materials & Continua 2022年第11期73卷 3905-3920页

作者： Radwa Marzouk Eatedal Alabdulkreem Sami Dhahbi Mohamed K.Nour Mesfer Al Duhayyim Mahmoud Othman Manar Ahmed Hamza Abdelwahed Motwakel Ishfaq Yaseen Mohammed Rizwanullah Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam Bin Abdulaziz UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

Oral cancer is the most commonly occurring‘head and neck cancers’across the *** of the oral cancer cases are diagnosed at later stages due to absence of awareness among *** earlier identification of disease is essential for improved outcomes,Artificial Intelligence(AI)and Machine Learning(ML)models are used in this *** this background,the current study introduces Artificial Intelligence with Deep Transfer Learning driven Oral Cancer detection and Classification Model(AIDTLOCCM).The primary goal of the proposed AIDTL-OCCM model is to diagnose oral cancer using AI and image processing *** proposed AIDTL-OCCM model involves fuzzy-based contrast enhancement approach to perform data *** by,the densely-connected networks(DenseNet-169)model is employed to produce a useful set of deep ***,Chimp Optimization Algorithm(COA)with Autoencoder(AE)model is applied for oral cancer detection and ***,COA is employed to determine optimal parameters involved in AE model.A wide range of experimental analyses was conducted on benchmark datasets and the results were investigated under several *** extensive experimental analysis outcomes established the enhanced performance of AIDTLOCCM model compared to other approaches with a maximum accuracy of 90.08%.

关键词： Oral cancer detection image classification artificial intelligence machine learning deep learning

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Cat Swarm with Fuzzy Cognitive Maps for Automated Soil Classification

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computer systems science & Engineering 2023年第2期44卷 1419-1432页

作者： Ashit Kumar Dutta Yasser Albagory Manal Al Faraj Majed Alsanea Abdul Rahaman Wahab Sait Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Kingdom of Saudi Arabia Department of Computing Arabeast CollegesRiyadh11583Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl AhsaHofuf31982Kingdom of Saudi Arabia

Accurate soil prediction is a vital parameter involved to decide appro-priate crop,which is commonly carried out by the *** an auto-mated soil prediction tool helps to considerably improve the efﬁcacy of the *** the same time,fuzzy logic(FL)approaches can be used for the design of predictive models,particularly,Fuzzy Cognitive Maps(FCMs)have involved the concept of uncertainty representation and cognitive *** other words,the FCM is an integration of the recurrent neural network(RNN)and FL involved in the knowledge engineering *** this aspect,this paper introduces effective fuzzy cognitive maps with cat swarm optimization for automated soil classiﬁca-tion(FCMCSO-ASC)*** goal of the FCMCSO-ASC technique is to identify and categorize seven different types of *** accomplish this,the FCMCSO-ASC technique incorporates local diagonal extrema pattern(LDEP)as a feature extractor for producing a collection of feature *** addition,the FCMCSO model is applied for soil classiﬁcation and the weight values of the FCM model are optimally adjusted by the use of CSO *** exam-ining the enhanced soil classiﬁcation outcomes of the FCMCSO-ASC technique,a series of simulations were carried out on benchmark dataset and the experimen-tal outcomes reported the enhanced performance of the FCMCSO-ASC technique over the recent techniques with maximum accuracy of 96.84%.

关键词： Soil classiﬁcation intelligent models fuzzy cognitive maps cat swarm optimization fuzzy logic

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Artificial Intelligence Enabled Apple Leaf Disease Classification for Precision Agriculture

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computers, Materials & Continua 2022年第3期70卷 6223-6238页

作者： Fahd N.Al-Wesabi Amani Abdulrahman Albraikan Anwer Mustafa Hilal Majdy M.Eltahir Manar Ahmed Hamza Abu Sarwar Zamani Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia Faculty of Computer and IT Sana’a UniversityYemen Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

Precision agriculture enables the recent technological advancements in farming sector to observe,measure,and analyze the requirements of individual fields and *** recent developments of computer vision and artificial intelligence(AI)techniques find a way for effective detection of plants,diseases,weeds,pests,*** the other hand,the detection of plant diseases,particularly apple leaf diseases using AI techniques can improve productivity and reduce crop ***,earlier and precise apple leaf disease detection can minimize the spread of the *** works make use of traditional image processing techniques which cannot assure high detection rate on apple leaf *** this motivation,this paper introduces a novel AI enabled apple leaf disease classification(AIE-ALDC)technique for precision *** proposed AIE-ALDC technique involves orientation based data augmentation and Gaussian filtering based noise removal *** addition,the AIE-ALDC technique includes a Capsule Network(CapsNet)based feature extractor to generate a helpful set of feature ***,water wave optimization(WWO)technique is employed as a hyperparameter optimizer of the CapsNet ***,bidirectional long short term memory(BiLSTM)model is used as a classifier to determine the appropriate class labels of the apple leaf *** design of AIE-ALDC technique incorporating theWWO based CapsNetmodel with BiLSTM classifier shows the novelty of the *** range of experiments was performed to showcase the supremacy of the AIE-ALDC *** experimental results demonstrate the promising performance of the AIEALDC technique over the recent state of art methods.

关键词： Artificial intelligence apple leaf plant disease precision agriculture deep learning data augmentation

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Control and Data Integrity are Important Factors of Data Governance Technology 10

Control and Data Integrity are Important Factors of Data Gov...

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Chandra, Yakob Utama Prabowo, Harjanto Gaol, Ford Lumban Purwandari, Betty School of Information Systems Bina Nusantara University BINUS Graduate Program Doctor of Computer Science Information Systems Department Jakarta11480 Indonesia BINUS Business School Bina Nusantara University Doctor of Research Management Management Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program - Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia Universitas Indonesia Faculty of Computer Science Depok Indonesia

ISBN: (纸本)9798350339543

The proliferation of data has become an inevitable consequence of the digital age as individuals across all domains increasingly rely on data for various purposes. Both companies and governments have a significant need for data. However, the sheer volume and complexity of this data, along with the potential risks associated with mishandling it, necessitate the establishment of a framework known as data governance. The primary objective of this research paper is to examine the factors that contribute to the effective implementation and utilization of Data governance. This study employs qualitative methods and adopts a systematic literature review approach to address the research inquiry: 'What are the important factors that determine the utilization of data governance technology?' Through the systematic literature review, a total of 41 key factors were identified from 20 research literatures that have been found to significantly impact the effective implementation of data governance. There are thirteen primary technological factors that hold significance in the realm of data governance. These factors encompass technology, application and use, storage of data, sharing, archiving, and preservation, big data algorithmic systems, data flow, deletion, diversity of data, mechanisms, size of data, control, and data integrity. © 2023 IEEE.

关键词： Digital storage

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LayOut Loud: An AI-Powered Augmented Reality and Mobile Application for Room Interior Design and Layout Optimization 4

LayOut Loud: An AI-Powered Augmented Reality and Mobile Appl...

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4th International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2024

作者： Cuevas, John Raphael G. Rili, John Kenneth R. Santillan, Anny Neolicia G. Agustin, Vivien A. Gonzales, Mary Grace N. Centeno, Criselle J. Regala, Richard C. College of Information Systems and Technology Management Pamantasan ng Lungsod ng Maynila Department of Information Technology Manila Philippines College of Information Systems and Technology Management Pamantasan ng Lungsod ng Maynila Department of Computer Science Manila Philippines Pamantasan ng Lungsod ng Maynila Faculty of College of Engineering and Technology Department of Information Technology Manila Philippines College of Information Systems and Technology Management Pamantasan ng Lungsod ng Maynila Department of Information Technology Manila Philippines Pamantasan ng Lungsod ng Maynila College of Engineering and Technology Department of Computer Science Manila Philippines

ISBN: (纸本)9798331506490

LayOut Loud is an AI-powered augmented reality (AR) and mobile application designed to revolutionize room interior design by offering tailored, real-time solutions for layout optimization. The primary objective of this tool is to design AI-powered features that analyze user-provided room dimensions, furniture types, and style preferences. By leveraging advanced algorithms, LayOut Loud generates personalized design suggestions that cater to the individual needs and aesthetic tastes of users, ensuring that each interior design experience is customized and unique. A key goal of the application is to enhance user experience and decision-making through a clustering algorithm. This algorithm categorizes furniture pieces based on their suitability and appearance in living spaces, streamlining the selection process for users. By enabling the visualization of these items in a 3D model, users can make informed decisions about their interior design choices, ensuring that each piece contributes to the overall aesthetic and functional harmony of the space. Additionally, LayOut Loud focuses on improving its AR capabilities by allowing users to capture their room layouts and view virtual furniture arrangements in real time. This feature provides practical, interactive design solutions, empowering users to experiment with different configurations and instantly see how potential changes would appear in their actual living environments. Ultimately, LayOut Loud offers a user-friendly, immersive platform that transforms the way individuals approach interior design. © 2024 IEEE.

关键词： Architectural design

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Optimal Deep Learning-based Cyberattack Detection and Classification Technique on Social Networks

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computers, Materials & Continua 2022年第7期72卷 907-923页

作者： Amani Abdulrahman Albraikan Siwar Ben Haj Hassine Suliman Mohamed Fati Fahd NAl-Wesabi Anwer Mustafa Hilal Abdelwahed Motwakel Manar Ahmed Hamza Mesfer Al Duhayyim Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Science and ArtsKing Khalid UniversityMahayil AsirSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrince Sultan UniversitySaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Computer and Self-Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Cyberbullying(CB)is a distressing online behavior that disturbs mental health *** studies have employed statistical and Machine Learning(ML)techniques for CB *** this motivation,the current paper presents an Optimal Deep Learning-based Cyberbullying Detection and Classification(ODL-CDC)technique for CB detection in social *** proposed ODL-CDC technique involves different processes such as pre-processing,prediction,and hyperparameter *** addition,GloVe approach is employed in the generation of word ***,the pre-processed data is fed into BidirectionalGated Recurrent Neural Network(BiGRNN)model for ***,hyperparameter tuning of BiGRNN model is carried out with the help of Search and Rescue Optimization(SRO)*** order to validate the improved classification performance of ODL-CDC technique,a comprehensive experimental analysis was carried out upon benchmark dataset and the results were inspected under varying aspects.A detailed comparative study portrayed the superiority of the proposed ODL-CDC technique over recent techniques,in terms of performance,with the maximum accuracy of 92.45%.

关键词： Cybersecurity cyberbullying social networks parameter tuning deep learning metaheuristics

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