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A Robust Emergency Messages Routing Scheme for Urban VANETs

computers, Materials & Continua 2022年第8期72卷 2617-2632页

作者： Mumtaz Ali Shah Farrukh Zeeshan Khan Ghulam Abbas Department of Computer Science University of Engineering and TechnologyTaxilaPakistan Telecommunications and Networking Research Center GIK Institute of Engineering Sciences and TechnologyTopi23640Pakistan

Vehicular ad-hoc networks(VANETs)play an essential role in enhancing transport infrastructure by making vehicles intelligent and proficient in preventing traffic ***-based greedy protocols pick the next route vehicle for transmitting emergency messages(EMs)depending upon the present location of adjacent vehicles towards sink vehicles by using an optimal uni-directional road traffic ***,such protocols suffer performance degradation by ignoring the moving directions of vehicles in bi-directional road traffic where topological changes happen *** to the high number of vehicles,it is essential to broadcast EMs to all vehicles to prevent traffic delays and collisions.A cluster-based EM transmitting technique is proposed in this *** urban VANETs,this paper pioneers the clustering of bi-directional road traffic for robust and efficient routing of *** this regard,this paper introduces a routing protocol,namely,the bi-directional urban routing protocol(BURP).In addition to the paths and relative locations of vehicles,BURP takes account of the distance parameter by using the Hamming distance function to determine the direction ofmotion of vehicles and communicates EMs through the cluster head(CH).Amodified k-medoids algorithm is presented for the clustering of bi-directional road traffic.A median method is presented for selecting CH to ensure the longrunning of a *** results show that BURP provides enhanced throughput,a maximized packet delivery ratio,low energy consumption,and network delay relative to eminent routing protocols.

关键词： Vehicular ad hoc networks bi-directional road traffic routing protocol emergency message broadcasting direction-based greedy forwarding

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Adaptive VQVAE: a learning-based image compression framework with vector quantization 23

Adaptive VQVAE: a learning-based image compression framework...

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11th International Conference on computer and Communications Management, ICCCM 2023

作者： Chen, Ke-Hong Lin, Yih-Chuan Department of Computer Science and Information Engineering National Formosa University Taiwan Smart Machinery and Intelligent Manufacturing Research Center Department of Computer Science and Information Engineering National Formosa University Taiwan

ISBN: (纸本)9798400707735

With the promising effectiveness of learned image compression, a substantial amount of research has been devoted to this field, following the publication of Ballé's paper. However, there are still some issues to be concerned before it becomes truly practical tools. The JPEG AI standard proposed by the JPEG team advocates that all machine vision tasks should be uniformly expressed under the same specifications of learned image format. While the JPEG AI standard is still under development, vector quantization (VQ) which is an important signal compression technique presents advantages in this context. This paper focuses on the research of vector quantization for image compression, and proposes an improved method called Adaptive VQVAE (Vector-Quantized Variational Autoencoder) to compactly represent the latent space of convolutional neural network. By transmitting an additional 1% of auxiliary information, the original performance of VQ is enhanced by approximately 1 dB PSNR for encoding the information from the latent space in an autoencoder architecture. In addition to the rate-distortion performance, the performance of vector quantized feature maps is also evaluated in terms of accuracy of computer vision tasks with the compressed format. The implementation results of Adaptive VQVAE have been uploaded to GitHub and are available for research purposes and comparison. © 2023 ACM.

关键词： Vector quantization

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Sustainable Solutions for Emerging Issues in Wireless Technology

Sustainable Solutions for Emerging Issues in Wireless Techno...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Al-Dmour, Nidal A. Alnawayseh, Saif. E. A. Ihsan, Anaum Irfan, Muhammad Kousar, Shagufta Ali, Dilawer Abbas, Tahir College of Engineering Department of Computer Engineering Mutah University Jordan College of Engineering Department of Electrical Engineering Mutah University Al-Karak Jordan Lahore Pakistan Multan Pakistan Applied Science Research Center Applied Science Private University Amman Jordan The TIMES Institute Department of Computer Science Multan60000 Pakistan

ISBN: (纸本)9798350369106

The rapid evolution of wireless technology has transformed communication, offering unprecedented accessibility and convenience. However, significant challenges like spectrum scarcity, security vulnerabilities, and environmental concerns must be addressed for sustainable growth. This paper provides an overview of these issues, including the impact of regulatory barriers and increased data demand on network infrastructure. It also suggests measures to support the development of effective wireless communication policies, ensuring sustainable progress as technologies advance towards 5G, MIMO, and IoT. The goal is to identify improvements for more robust wireless communication systems. © 2024 IEEE.

关键词： 5G Accessibility Communication IOT Network Infrastructure Security Wireless technology

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Novelty of Different Distance Approach for Multi-Criteria Decision-Making Challenges Using q-Rung Vague Sets

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computer Modeling in engineering & sciences 2024年第6期139卷 3353-3385页

作者： Murugan Palanikumar Nasreen Kausar Dragan Pamucar Seifedine Kadry Chomyong Kim Yunyoung Nam Saveetha School of Engineering Saveetha Institute of Medical and Technical SciencesChennai602105India Department of Mathematics Faculty of Arts and ScienceYildiz Technical UniversityEsenlerIstanbul34220Turkey Department of Operations Research and Statistics Faculty of Organizational SciencesUniversity of BelgradeBelgrade11000Serbia College of Engineering Yuan Ze UniversityTaoyuan City320315TaiwanChina Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman346United Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblos1102-2801Lebanon ICT Convergence Research Center Soonchunhyang UniversityAsan31538South Korea Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538South Korea

In this article,multiple attribute decision-making problems are solved using the vague normal set(VNS).It is possible to generalize the vague set(VS)and q-rung fuzzy set(FS)into the q-rung vague set(VS).A log q-rung normal vague weighted averaging(log q-rung NVWA),a log q-rung normal vague weighted geometric(log q-rung NVWG),a log generalized q-rung normal vague weighted averaging(log Gq-rung NVWA),and a log generalized q-rungnormal vagueweightedgeometric(logGq-rungNVWG)operator are discussed in this *** is provided of the scoring function,accuracy function and operational laws of the log q-rung *** algorithms underlying these functions are also described.A numerical example is provided to extend the Euclidean distance and the Humming ***,idempotency,boundedness,commutativity,and monotonicity of the log q-rung VS are examined as they facilitate recognizing the optimal alternative more quickly and help clarify *** chose five anemia patients with four types of symptoms including seizures,emotional shock or hysteria,brain cause,and high fever,who had either retrograde amnesia,anterograde amnesia,transient global amnesia,post-traumatic amnesia,or infantile *** numbers q are used to express the results of the *** demonstrate the effectiveness and accuracy of the models we are investigating,we compare several existing models with those that have been developed.

关键词： Vague set aggregating operators euclidean distance hamming distance decision making

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Motion-Based Convolutional Neural Networks for Super-Resolution from Compressed Videos 13

Motion-Based Convolutional Neural Networks for Super-Resolut...

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13th IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2023

作者： Chang, Han-Yuan Hsieh, Hsiang-Yu Lin, Yih-Chuan National Formosa University Department of Computer Science and Information Engineering Taiwan Smart Machinery and Intelligent Manufacturing Research Center National Formosa University Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350324150

In this paper, an improved super-resolution method is proposed for compressed videos, which integrates the multi-image super-resolution (MISR) method into standard video decoders such as H.26x and AV1 to employ the reference frame (RF) and motion vector (MV) information from video decoder to gain better super-resolution efficiency and performance. The proposed motion vector-based multi-image super-resolution (MVSR) is trained by the convolutional neural networks (CNN) model, which consists of a series of convolution layers and is learned to identify the relationship of image features between the ground truth high-resolution (HR) and super-resolution (SR), and generates the SR frames that could improve the quality of the final output frame. The experimental results have shown that the proposed MVSR is capable of enhancing the SR quality over 1dB in terms of peak-to-noise ratio (PSNR) when compared to other similar approaches. © 2023 IEEE.

关键词： Motion estimation

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Unleashing the Power of Multi-Agent Reinforcement Learning for Algorithmic Trading in the Digital Financial Frontier and Enterprise Information Systems

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computers, Materials & Continua 2024年第8期80卷 3123-3138页

作者： Saket Sarin Sunil K.Singh Sudhakar Kumar Shivam Goyal Brij Bhooshan Gupta Wadee Alhalabi Varsha Arya Chandigarh College of Engineering and Technology Chandigarh160019India Department of Computer Science and Information Engineering Asia UniversityTaichung413Taiwan Symbiosis Centre for Information Technology(SCIT) Symbiosis International UniversityPune411057India Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India Department of Computer Science Immersive Virtual Reality Research GroupKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Business Administration Asia UniversityTaichung413Taiwan Department of Electrical and Computer Engineering Lebanese American UniversityBeirut11022801Lebanon UCRD Chandigarh UniversityChandigarh140413India

In the rapidly evolving landscape of today’s digital economy,Financial Technology(Fintech)emerges as a trans-formative force,propelled by the dynamic synergy between Artificial Intelligence(AI)and Algorithmic *** in-depth investigation delves into the intricacies of merging Multi-Agent Reinforcement Learning(MARL)and Explainable AI(XAI)within Fintech,aiming to refine Algorithmic Trading *** meticulous examination,we uncover the nuanced interactions of AI-driven agents as they collaborate and compete within the financial realm,employing sophisticated deep learning techniques to enhance the clarity and adaptability of trading *** AI-infused Fintech platforms harness collective intelligence to unearth trends,mitigate risks,and provide tailored financial guidance,fostering benefits for individuals and enterprises navigating the digital *** research holds the potential to revolutionize finance,opening doors to fresh avenues for investment and asset management in the digital ***,our statistical evaluation yields encouraging results,with metrics such as Accuracy=0.85,Precision=0.88,and F1 Score=0.86,reaffirming the efficacy of our approach within Fintech and emphasizing its reliability and innovative prowess.

关键词： Neurodynamic Fintech multi-agent reinforcement learning algorithmic trading digital financial frontier

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A secure remote health monitoring model for early disease diagnosis in cloud-based IoT environment

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Personal and Ubiquitous Computing 2023年第3期27卷 697-713页

作者： Akhbarifar, Samira Javadi, Hamid Haj Seyyed Rahmani, Amir Masoud Hosseinzadeh, Mehdi Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Department of Mathematics and Computer Science Shahed University Tehran Iran Institute of Research and Development Duy Tan University Da Nang550000 Viet Nam Mental Health Research Center Psychosocial Health Research Institue Iran University of Medical Sciences Tehran Iran

Internet of Things (IoT) and smart medical devices have improved the healthcare systems by enabling remote monitoring and screening of the patients’ health conditions anywhere and anytime. Due to an unexpected and huge increasing in number of patients during coronavirus (novel COVID-19) pandemic, it is considerably indispensable to monitor patients’ health condition continuously before any serious disorder or infection occur. According to transferring the huge volume of produced sensitive health data of patients who do not want their private medical information to be revealed, dealing with security issues of IoT data as a major concern and a challenging problem has remained yet. Encountering this challenge, in this paper, a remote health monitoring model that applies a lightweight block encryption method for provisioning security for health and medical data in cloud-based IoT environment is presented. In this model, the patients’ health statuses are determined via predicting critical situations through data mining methods for analyzing their biological data sensed by smart medical IoT devices in which a lightweight secure block encryption technique is used to ensure the patients’ sensitive data become protected. Lightweight block encryption methods have a crucial effective influence on this sort of systems due to the restricted resources in IoT platforms. Experimental outcomes show that K-star classification method achieves the best results among RF, MLP, SVM, and J48 classifiers, with accuracy of 95%, precision of 94.5%, recall of 93.5%, and f-score of 93.99%. Therefore, regarding the attained outcomes, the suggested model is successful in achieving an effective remote health monitoring model assisted by secure IoT data in cloud-based IoT platforms. © 2020, Springer-Verlag London Ltd., part of Springer Nature.

关键词： Data mining

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Far-field super-resolution chemical microscopy

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Light(science & Applications) 2023年第7期12卷 1246-1267页

作者： Mingwei Tang Yubing Han Danchen Jia Qing Yang Ji-Xin Cheng State Key Laboratory of Modern Optical Instrumentation College of Optical Science and EngineeringZhejiang UniversityHangzhou 310027China Intelligent Perception Research Institute Zhejiang LabHangzhou 311100China Department of Biomedical Engineering Department of Electrical and Computer EngineeringPhotonics CenterBoston UniversityBostonMA 02459USA

Far-feld chemical microscopy providing molecular electronic or vibrational fingerprint information opens a new window for the study of three-dimensional biological,material,and chemical *** microscopy provides a nondestructive way of chemical identification without exterior ***,the diffraction limit of optics hindered it from discovering more details under the resolution *** development of super-resolution techniques gives enlightenment to open this door behind far-field chemical ***,we review recent advances that have pushed the boundary of far-field chemical microscopy in terms of spatial *** further highlight applications in biomedical research,material characterization,environmental study,cultural heritage conservation,and integrated chip inspection.

关键词： chemical details optics

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Optimizing Length of Stay Prediction After Intubation: An Advanced Machine Learning Model with Real-time Vital Sign Integration 7

Optimizing Length of Stay Prediction After Intubation: An Ad...

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7th International Conference on Image Information Processing, ICIIP 2023

作者： Sundas, Amit Singh, Gurpreet Badotra, Sumit Verma, Amit Kaur, Balwinder Lovely Professional University Department of Computer Science And Engineering Punjab Phagwara India Bennett University School of Computer Engineering And Technology Greater Noida India University Center For Research And Development Department of Computer Science And Engineering Chandigarh India

ISBN: (纸本)9798350371406

In the critical care unit, bedside monitors track patients' vital signs to help physicians make choices. With increased storage and analysis capability, huge datasets may be processed faster. Lately, physiological data have showed promise for identifying specific consequences and occurrences. At the critical care unit. Our method was aimed to Live bedside vital sign data may be automatically retrieved. Devices that can predict patient mechanical ventilation for more than 4 days in a pediatric critical care unit. 284 ICU patients had their vital signs and clinical history documented continuously from admission. 24 hours of artificial breathing in Boston Children's Hospital's pediatric medical-surgical critical care unit. They trained many machine learning models using subsets. Assess the likelihood of each person staying long. We examined our systems' capacity to anticipate outcomes for unknown persons. Adding vital sign data increased model performance to 90% (area under the curve) from >83% when using just vital sign data. static participant clinical data in electronic health records. For research group representative sample reliability, we employed this approach on 300 individually trained tests employing a variety of sets for training and hold-out validation. Other deep learning methods failed to forecast like ours. Gaining equivalent work outcomes prediction skills. We may be able to help create scalable ICU-based prediction systems that leverage real-time patient monitor data. © 2023 IEEE.

关键词： Decision support systems

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GrCol-PPFL:User-Based Group Collaborative Federated Learning Privacy Protection Framework

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computers, Materials & Continua 2023年第1期74卷 1923-1939页

作者： Jieren Cheng Zhenhao Liu Yiming Shi Ping Luo Victor S.Sheng School of Compute Science and Technology Hainan UniversityHaikou570228China Hainan Blockchain Technology Engineering Research Center Hainan UniversityHaikou570228China Department of Computer Science Texas Tech University TX79409USA

With the increasing number of smart devices and the development of machine learning technology,the value of users’personal data is becoming more and more *** on the premise of protecting users’personal privacy data,federated learning(FL)uses data stored on edge devices to realize training tasks by contributing training model parameters without revealing the original ***,since FL can still leak the user’s original data by exchanging gradient *** existing privacy protection strategy will increase the uplink time due to encryption *** is a huge challenge in terms of *** there are a large number of devices,the privacy protection cost of the system is *** on these issues,we propose a privacy-preserving scheme of user-based group collaborative federated learning(GrCol-PPFL).Our scheme primarily divides participants into several groups and each group communicates in a chained transmission *** groups work in parallel at the same *** server distributes a random parameter with the same dimension as the model parameter for each participant as a mask for the model *** use the public datasets of modified national institute of standards and technology database(MNIST)to test the model *** experimental results show that GrCol-PPFL not only ensures the accuracy of themodel,but also ensures the security of the user’s original data when users collude with each ***,through numerical experiments,we show that by changing the number of groups,we can find the optimal number of groups that reduces the uplink consumption time.

关键词： Federated learning privacy protection uplink consumption time

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