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Mixed RF/FSO Communication Systems With Rate Splitting

IEEE Journal on Selected Areas in Communications 2025年第5期43卷 1621-1634页

作者： Zhang, Yiran Xiao, Zhichen Yang, Liang Bithas, Petros S. Hasna, Mazen O. Karagiannidis, George K. Hunan University College of Computer Science and Electronic Engineering Changsha410082 China National and Kapodistrian University of Athens Department of Digital Industry Technologies Athens157 72 Greece Qatar University Department of Electrical Engineering Doha2713 Qatar Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Thessaloniki541 24 Greece Artificial Intelligence and Cyber Systems Research Center Beirut0379-7751 Lebanon

In this paper, rate splitting (RS) has been applied in a mixed dual-hop radio frequency/free space optical (RF/FSO) relay system. In particular, assuming fixed-gain amplify-and-forward (AF) and decode-and-forward (DF) relay protocols, the performance of both heterodyne detection and intensity-modulated direct detection (IM-DD) has been analytically evaluated. In this framework, it is assumed that the RF channel suffers from Rayleigh fading, while the FSO channel is affected by the atmospheric turbulence-induced fading with Málaga (M) distribution and pointing errors. For the considered system, we derive expressions for the outage probability, the average bit error rate (BER), and the average channel capacity. In addition, the asymptotic performance of the system at high signal-to-noise ratios regime is analyzed, special cases are discussed, and useful insights have been extracted. Finally, numerical results are presented to verify the accuracy of the analytical and asymptotic expressions. © 1983-2012 IEEE.

关键词： Heterodyne detection

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Design of a garbage classification system based on deep transfer learning

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Journal of Ambient Intelligence and Humanized Computing 2025年第1期16卷 225-232页

作者： Zucai, Tang Luping, Wang Miaoyan, Qu Aitong, Sheng Nianwang, Huai Department of Automation University of Shenyang Aerospace Liaoning Shenyang China Engineering Training Center University of Shenyang Aerospace Liaoning Shenyang China Department of computer science and technology University of Shenyang Aerospace Liaoning Shenyang China

In light of the present challenges related to the insufficient autonomous identification and classification of garbage, along with the management and movement of garbage bins, a novel intelligent garbage classification system utilizing deep learning is suggested. This system is designed to autonomously identify types of garbage and to finalize the mobile garbage classification system for packaging. An analysis of the three-dimensional structure of the garbage bin and a dynamic examination of the motion system of the three-wheeled chassis are performed. The Raspberry Pi 3B and STM32 microcontrollers are employed for management, while the Inception v3 network structure algorithm is carefully adjusted and enhanced to facilitate garbage image recognition and classification. The outcomes of experimental research demonstrate that the fine-tuned and optimized Inception v3 network structure algorithm effectively facilitates the identification of garbage image recognition, and the model demonstrates superior performance in recognizing and categorizing images. This investigation offers a reliable approach for enhancing the efficiency and accuracy of garbage classification, thereby contributing to environmental protection and sustainable development. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Transfer learning

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Healthcare management in Bigdata with Blockchain and IOT integrated System 1

Healthcare management in Bigdata with Blockchain and IOT int...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Kaliyahan, Shalini Bhatnagar, Prachi Gupta, Shilpi Jain, Archana Malik, Suraj Gochhait, Saikat Sharma, Aditi Pandey, Mahima Shankar IIMT University Department of Computer Science and Application Uttar Pradesh Meerut India Meerut Institute of Technology Department of Computer Science and Engineering Uttar Pradesh Meerut India IIMT University Department of Computer Science and Engineering Uttar Pradesh Meerut India Constituent of Symbiosis International Deemed University Symbiosis Institute of Digital and Telecom Management Pune India Galgotia College of Engineering and Technology Department of Computer Science and Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350356816

In 21st decade, healthcare is one of the biggest challenges, and the current scenario very flexible to adapt the effective execution technology solutions like internet of things (IoT). Through this a patient may access the treatment remotely and rapidly. Most suitable and effective applications need complex data of personal care, and reliability among the entire participated component or parties. Blockchain technology has been captured the industry domain with its computation and trusted model among participated parties. This paper presents a data storage decision framework for managing blockchain storage using on-chain and off-chain methods. There is a need for making balanced decisions between the very expensive on-chain storage methods and the less secure off-chain storage, towards optimizing blockchain storage costs. © 2024 IEEE.

关键词： Cloud computing architecture

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Comparative Analysis of Movie Recommendation Systems Using Filtering Techniques on IMDB and Rotten Tomatoes 15

Comparative Analysis of Movie Recommendation Systems Using F...

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15th International Conference on Information, Intelligence, Systems and Applications, IISA 2024

作者： Majhi, Santosh Kumar Sahani, Preetipadma Pradhan, Rosy Acharya, Biswaranjan Grivokostopoulou, Foteini Kanavos, Andreas Gerogiannis, Vassilis C. Department of Computer Science and Information Technology Bilaspur India Veer Surendra Sai University of Technology Department of Computer Science and Engineering Odisha Burla India Veer Surendra Sai University of Technology Department of Electrical Engineering Odisha Burla India Marwadi University Department of Computer Engineering Ai Rajkot India Computer Technology Institute and Press 'Diophantus' Patras Greece Ionian University Department of Informatics Corfu Greece University of Thessaly Department of Digital Systems Larissa Greece

ISBN: (纸本)9798350368833

Recommendation systems are pivotal in industries like entertainment, where competitiveness is intense. This study enhances movie recommendations by utilizing rich datasets from IMDB and Rotten Tomatoes, incorporating movie ratings, user profiles, audience scores, and critics' reviews. These elements were subjected to sentiment analysis to refine the data quality. We explored three filtering techniques: content-based, which analyzes movie features;collaborative, which processes user behavior patterns;and hybrid, combining both to leverage their strengths. The effectiveness of these methods was assessed using Mean Average Precision at k (MAP-k) and Root Mean Square Error (RMSE), with our results showing notable improvements in recommendation accuracy. For instance, the hybrid method achieved a MAP ©k of 0.271 and an RMSE of 0.634, indicating enhanced precision and reliability of the recommendations. These findings suggest significant potential for improving user satisfaction and retention in digital platforms, demonstrating the benefit of integrating diverse data sources and analytical techniques in recommendation systems.

关键词： Collaborative filtering

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Prediction of Geopolymer Concrete Compressive Strength Using Convolutional Neural Networks

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computer Modeling in engineering & sciences 2024年第5期139卷 1455-1486页

作者： Kolli Ramujee Pooja Sadula Golla Madhu Sandeep Kautish Abdulaziz S.Almazyad Guojiang Xiong Ali Wagdy Mohamed Department of Civil Engineering VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Information Technology VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Computer Science LBEF Campus(Asia Pacific University of Technology&InnovationMalaysia)Kathmandu44600Nepal Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmmanJordan

Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental *** attributes as a non-toxic,low-carbon,and economical substitute for conventional cement concrete,coupled with its elevated compressive strength and reduced shrinkage properties,position it as a pivotal material for diverse applications spanning from architectural structures to transportation *** this context,this study sets out the task of using machine learning(ML)algorithms to increase the accuracy and interpretability of predicting the compressive strength of geopolymer concrete in the civil engineering *** achieve this goal,a new approach using convolutional neural networks(CNNs)has been *** study focuses on creating a comprehensive dataset consisting of compositional and strength parameters of 162 geopolymer concrete mixes,all containing Class F fly *** selection of optimal input parameters is guided by two distinct *** first criterion leverages insights garnered from previous research on the influence of individual features on compressive *** second criterion scrutinizes the impact of these features within the model’s predictive *** to enhancing the CNN model’s performance is the meticulous determination of the optimal *** a systematic trial-and-error process,the study ascertains the ideal number of epochs for data division and the optimal value of k for k-fold cross-validation—a technique vital to the model’s *** model’s predictive prowess is rigorously assessed via a suite of performance metrics and comprehensive score ***,the model’s adaptability is gauged by integrating a secondary dataset into its predictive framework,facilitating a comparative evaluation against conventional prediction *** unravel the intricacies of the CNN model’s learning trajectory,a loss plot is deployed to elucidate its learning rat

关键词： Class F fly ash compressive strength geopolymer concrete prediction deep learning convolutional neural network

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Adaptive finite-time command-filtered backstepping sliding mode control for stabilization of a disturbed rotary-inverted-pendulum with experimental validation

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JVC/Journal of Vibration and Control 2023年第5-6期29卷 1431-1446页

作者： Mofid, Omid Alattas, Khalid A Mobayen, Saleh Vu, Mai The Bouteraa, Yassine Future Technology Research Center National Yunlin University of Science and Technology Douliu Taiwan Department of Computer Science and Artificial Intelligence College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia School of Intelligent Mechatronics Engineering Sejong University Seoul Korea Republic of Department of Computer Engineering College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj Saudi Arabia University of Sfax Sfax3038 Tunisia

In this paper, the finite-time stabilization of the disturbed and uncertain rotary-inverted-pendulum system is studied based on the adaptive backstepping sliding mode control procedure. For this purpose, first of all, the dynamical equation of the rotary-inverted-pendulum system is obtained in the state-space form in the existence of external disturbances and model uncertainties with unknown bound. Afterward, a novel command filter is defined to enhance the control strategy by consideration of a virtual control input. Therefore, the differential signal is replaced by the output of the command filter to reduce the complicated computing in the control process. Hence, the finite-time convergence of the sliding surface to the origin is attested by using the backstepping sliding mode control scheme according to the Lyapunov theory. Besides, the unknown upper bound of the exterior perturbation and uncertainty is approximated providing the adaptive control technique. Finally, simulations and experimental results are done to demonstrate the impression and proficiency of the suggested method. © The Author(s) 2022.

关键词： Inverted pendulum

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Radiative cooling for passive thermal management towards sustainable carbon neutrality

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National science Review 2023年第1期10卷 87-97页

作者： Jun Liang Jiawei Wu Jun Guo Huagen Li Xianjun Zhou Sheng Liang Cheng-Wei Qiu Guangming Tao Wuhan National Laboratory for Optoelectronics and Optics Valley Laboratory Huazhong University of Science and Technology Department of Electrical and Computer Engineering National University of Singapore MOE Key Laboratory of Thermo-Fluid Science and Engineering School of Energy and Power Engineering Xi'an Jiaotong University Key Laboratory of Education Ministry on Luminescence and Optical Information Technology National Physical Experiment Teaching Demonstration CenterDepartment of PhysicsSchool of Physical Science and Engineering Beijing Jiaotong University State Key Laboratory of Material Processing and Die &Mould Technology School of Materials Science and Engineering Huazhong University of Science and Technology

Photonic structures at the wavelength scale offer innovative energy solutions for a wide range of applications,from high-efficiency photovoltaics to passive cooling,thus reshaping the global energy *** cooling based on structural and material design presents new opportunities for sustainable carbon neutrality as a zero-energy,ecologically friendly cooling *** this review,in addition to introducing the fundamentals of the basic theory of radiative cooling technology,typical radiative cooling materials alongside their cooling effects over recent years are summarized and the current research status of radiative cooling materials is outlined and ***,technical challenges and potential advancements for radiative cooling are forecast with an outline of future application scenarios and development *** the future,radiative cooling is expected to make a significant contribution to global energy saving and emission reduction.

关键词： photonic structures radiative cooling energy saving and emission reduction carbon neutrality

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A cloud service composition method using a fuzzy-based particle swarm optimization algorithm

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Multimedia Tools and Applications 2024年第19期83卷 56275-56302页

作者： Nazif, Habibeh Nassr, Mohammad Al-Khafaji, Hamza Mohammed Ridha Jafari Navimipour, Nima Unal, Mehmet Department of Mathematics Payame Noor University Tehran Iran Communication Technology Engineering Department Tartous University Tartus Syria Dpartment of Mathematics and Natural Sciences Gulf University for Science and Technology Mishref Campus Mubarak Al-Abdullah Kuwait Biomedical Engineering Department College of Engineering and Technologies Al-Mustaqbal University Babil Hillah51001 Iraq Department of Computer Engineering Faculty of Engineering and Natural Sciences Kadir Has University Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou64002 Taiwan Department of Computer Engineering Nisantasi University Istanbul Turkey

In today's dynamic business landscape, organizations heavily rely on cloud computing to leverage the power of virtualization and resource sharing. Service composition plays a vital role in cloud computing, combining multiple cloud services to fulfill complex user requests. Service composition in cloud computing presents several challenges. These include service heterogeneity, dynamic service availability, QoS (Quality of Service) constraints, and scalability issues. Traditional approaches often struggle to handle these challenges efficiently, leading to suboptimal resource utilization and poor service performance. This work presents a fuzzy-based strategy for composing cloud services to overcome these obstacles. The fact that service composition is NP-hard has prompted the use of a range of metaheuristic algorithms in numerous papers. Therefore, Particle Swarm Optimization (PSO) has been applied in this paper to solve the problem. Implementing a fuzzy-based PSO for service composition requires defining the fuzzy membership functions and rules based on the specific service domain. Once the fuzzy logic components are established, they can be integrated into the PSO algorithm. The simulation results have shown the high efficiency of the proposed method in decreasing the latency, cost, and response time. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Fuzzy logic

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Neural-XGBoost: A Hybrid Approach for Disaster Prediction and Management Using Machine Learning

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IEEE Access 2025年 13卷 86768-86780页

作者： Saleem, Muhammad Asim Javeed, Ashir Benjapolakul, Watit Srisiri, Wattanasak Chaitusaney, Surachai Kaewplung, Pasu Chulalongkorn University Center of Excellence in Artificial Intelligence Machine Learning and Smart Grid Technology Department of Electrical Engineering Faculty of Engineering Bangkok10330 Thailand Blekinge Institute of Technology Department of Computer Science Karlskrona Sweden

Effective disaster prediction is essential for disaster management and mitigation. This study addresses a multi-classification problem and proposes the Neural-XGBoost disaster prediction model (N-XGB), a hybrid model that combines neural networks (NN) for feature extraction with XGBoost for classification. The NN component extracts high-level features, while XGBoost uses gradient-boosted decision trees for accurate predictions, combining the strengths of deep learning and boosting techniques for improved accuracy. The N-XGB model achieves an accuracy of 94.8% and an average F1 score of 0.95 on a real-world dataset that includes wildfires, floods and earthquakes, significantly outperforming baseline models such as random forest, Support vector machine and logistic regression 85% accuracy. The balanced F1 scores for wildfires 0.96, floods 0.93, and earthquakes 0.96 demonstrate the model's robustness in multi-class classification. The Synthetic Minority Oversampling Technique (SMOTE) balances datasets and improves model efficiency and capability. The proposed N-XGB model provides a reliable and accurate solution for predicting disasters and contributes to improving preparedness, resource allocation and risk management strategies. © 2013 IEEE.

关键词： Risk management

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Advanced Cyber-Attack Detection in IoT Networks Using Deep Belief Networks and Gradient Boosting Mechanisms 4

Advanced Cyber-Attack Detection in IoT Networks Using Deep B...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Mohandas, R. Elavarasi, M. Praveen, R.V.S. Chittapragada, Hemanand Nithiya, C. Prabagar, S. Chennai Institute of Technology Department of Electronics and Communication Engineering Chennai India Vels Institute of Science Technology & Advanced Studies Department of Bca - It Pallavaram India LTIMindtree Limited Digital Engineering and Assurance Hyderabad India Madanapalle Institute of Technology & Science Department of Computer Science and Engineering Madanapalle India R.M.K. College of Engineering and Technology Department of Electronics and Communication Engineering Puduvoyal India Alliance School of Advanced Computing Alliance University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350352931

The rapid growth of Internet of Things (IoT) devices has increased security weaknesses, rendering networks vulnerable to advanced cyber-attacks. Traditional detection systems frequently encounter difficulties in real-time anomaly detection and resource limitations, leaving IoT systems exposed. This study presents a hybrid model that combines Deep Belief Networks (DBN) for hierarchical feature extraction with Gradient Boosting (GB) to improve classification accuracy. Driven by the increasing incidence and complexity of cyber threats, the study seeks to rectify significant deficiencies in IoT security by providing a scalable, efficient, and high-performance detection system. Experimental findings confirm the model's efficacy, attaining 98.5% accuracy, an F1-score of 0.97, and a recall of 0.97, markedly surpassing current methodologies. The study addresses the issues of protecting IoT networks, suggests a comprehensive detection methodology, and offers a comparative analysis of performance indicators, laying the groundwork for refined, versatile approaches to counter new threats in IoT ecosystems. © 2024 IEEE.

关键词： Cyber attacks

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