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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Agarwal, Parag Nidhya, M.S. Agarwal, Trapty Teetthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350383348

This paper investigates the effect of switch studying on clinical photo segmentation performance. Switch learning entails the usage of a pre-trained model as the basis for a new technique for a comparable project. By leveraging pre-educated models, the manner of schooling a version to perform a project can be made greener. This paper evaluates the effect of transfer getting to know on medical photograph segmentation performance in terms of accuracy and speed of schooling. Moreover, the paper compares the overall performance of transfer getting to know and non-switch gaining knowledge of tactics for segmenting the tumors in MRI and CT scans. Effects from the experiments display that transfer learning outperforms non-transfer mastering approaches in the challenge of scientific image segmentation. Further, the paper offers insights into the VGG16 and U-internet architectures and indicates feasible guidelines for in addition research. © 2024 IEEE.

关键词： architectures investigates learning performance segmentation

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Progressive Motion Boosting for Video Frame Interpolation

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IEEE Transactions on Multimedia 2023年 25卷 8076-8090页

作者： Xiao, Jing Xu, Kangmin Hu, Mengshun Liao, Liang Wang, Zheng Lin, Chia-Wen Wang, Mi Satoh, Shin'Ichi The School of Computer Science Wuhan University Wuhan430072 China The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The Department of Electrical Engineering the Institute of Communications Engineering National Tsing Hua University Hsinchu30013 Taiwan The State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan430072 China The Digital Content and Media Sciences Research Division National Institute of Informatics Tokyo101-8430 Japan

Video frame interpolation has made great progress in estimating advanced optical flow and synthesizing in-between frames sequentially. However, frame interpolation involving various resolutions and motions remains challenging due to limited or fixed pre-trained networks. Inspired by the success of the coarse-to-fine scheme for video frame interpolation, i.e., gradually interpolating frames of different resolutions, we propose a progressive boosting network (ProBoost-Net) based on a multi-scale framework to achieve flexible recurrent scales and then gradually optimize optical flow estimation and frame interpolation. Specifically, we designed a dense motion boosting (DMB) module to transfer features close to real motion to the decoded features from the later scales, which provides complementary information to refine the motion further. Furthermore, to ensure the accuracy of the estimated motion features at each scale, we propose a motion adaptive fusion (MAF) module that adaptively deals with motions with different receptive fields according to the motion conditions. Thanks to the framework’s flexible recurrent scales, we can customize the number of scales and make trade-offs between computation and quality depending on the application scenario. Extensive experiments with various datasets demonstrated the superiority of our proposed method over state-of-the-art approaches in various scenarios. © 2023 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Motion estimation

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VANET-Sec: A Framework to Secure Vehicular Ad-Hoc Networks Using a Permissioned Blockchain

VANET-Sec: A Framework to Secure Vehicular Ad-Hoc Networks U...

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2023 International Symposium on Networks, computers and Communications, ISNCC 2023

作者： Kacem, Thabet School of Engineering and Applied Sciences Department of Computer Science and Information Technology University of the District of Columbia WashingtonDC20008 United States

ISBN: (纸本)9798350335590

In recent years, Vehicular Ad-hoc Networks (VANET) has revolutionized the intelligent vehicle systems thanks to the services that it offers such as traffic information, road transportation emergency services and road traffic warnings, which has the potential of increasing the safety and improving the driving experience. VANET operates by sharing information from vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I) and vehicle-to-everything (V2X) using communication standards such as Dedicated Short Range Communication (DSRC). However, several security and privacy concerns threaten its operation as they pose serious threats to vehicles. In order to tackle this problem, classic security protocols would face serious challenges due to lack of scalability. Even emerging solutions that use blockchains face the challenge of having elaborate cryptographic key exchange scheme. In this paper, we propose a permissioned blockchain-based framework to secure VANETs by allowing only authenticated stakeholders to add or view blocks on the blockchain. Another key contribution of our approach is that we propose a key exchange in which key stakeholders interact by the means of a smart contract. We also discuss the resilience of our approach against common VANET attacks. Finally, we validate our findings with a performance evaluation of the transaction latency of the sent location updates by the vehicles in an urban mobility scenario, which shows potential success of our solution. © 2023 IEEE.

关键词： Blockchain

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Al-Biruni Based Optimization of Rainfall Forecasting in Ethiopia

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computer systems science & engineering 2023年第6期45卷 2885-2899页

作者： El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Fadwa Alrowais Mostafa Abotaleb Abdelhameed Ibrahim Doaa Sami Khafaga Department of Communications and Elecronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science F aculty of Computer and Infomation SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of System Progamming South Ual State UniversityChelyabinsk454080Russia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt

Rainfall plays a significant role in managing the water level in the *** unpredictable amount of rainfall due to the climate change can cause either overflow or dry in the *** individuals,especially those in the agricultural sector,rely on rain *** rainfall is challenging because of the changing nature of the *** area of Jimma in southwest Oromia,Ethiopia is the subject of this research,which aims to develop a rainfall forecasting *** estimate Jimma's daily rainfall,we propose a novel approach based on optimizing the parameters of long short-term memory(LSTM)using Al-Biruni earth radius(BER)optimization algorithm for boosting the fore-casting accuracy.N ash-Sutcliffe model eficiency(NSE),mean square error(MSE),root MSE(RMSE),mean absolute error(MAE),and R2 were all used in the conducted experiments to assess the proposed approach,with final scores of(0.61),(430.81),(19.12),and(11.09),***,we compared the proposed model to current machine-learning regression models;such as non-optimized LSTM,bidirectional LSTM(BiLSTM),gated recurrent unit(GRU),and convolutional LSTM(ConvLSTM).It was found that the proposed approach achieved the lowest RMSE of(19.12).In addition,the experimental results show that the proposed model has R-with a value outperforming the other models,which confirms the superiority of the proposed *** the other hand,a statistical analysis is performed to measure the significance and stability of the proposed approach and the recorded results proved the expected perfomance.

关键词： Rainfall prediction long short-term memory Al-Biruni earth radius algorithm meta-heuristic optimization

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A Comprehensive Study of Convolutional Neural Networks for Medical Image Segmentation 5th

A Comprehensive Study of Convolutional Neural Networks for M...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Nautiyal, Kapish Augustine, Jacob Savita Rengarajan, A. Department of Electronic & Communication Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India

ISBN: (纸本)9789819780426

It affords an overview latest, the modern-day inside the field of modern-day clinical picture segmentation, after which highlights the gain of trendy CNNs in it. The paper then describes today's CNNs’ fundamentals, architectures, and education techniques. It similarly affords one-of-a-kind CNN fashions used for clinical image segmentation. It presents a comprehensive analysis of new research addressing modern-day CNNs’ utility for this assignment. In the end, the paper affords steering for destiny research guidelines in scientific photo segmentation, the use of CNNs, and an outlook for the ability of today’s this era. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Design and Analysis of an Actuator with High Power Density Permanent Magnet motor for Bipedal Robots

Design and Analysis of an Actuator with High Power Density P...

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2024 International Conference on Electrical Machines, ICEM 2024

作者： He, Tianran Leng, Xiaokun Sun, Yangyu Wu, Yucong School of Electronics and Information Engineering Tongji University Shanghai China School of Computer Science and Technology Harbin Institute of Technology Harbin China University of Sheffield Department of Electrical and Electronic Engineering Sheffield United Kingdom Southern University of Science and Technology Department of Mechanical and Energy Engineering Shenzhen China

ISBN: (纸本)9798350370607

Recently, bipedal robots that utilize electric rotary actuators have attracted much attention since they can walk on any kind of path or terrain and can jump or step over obstacles. In addition, bipedal robots are designed to pick up and put down various types of heavy objects for industrial applications. To achieve the specified functions, the electric rotary actuators, as the core driving unit of bipedal robots, need to possess characteristics of high power density and large output torque. This paper presents the design of an electric rotary actuator based on an outer rotor permanent magnet motor for bipedal robots. The proposed electric rotary actuator includes a 36-slot/42-pole permanent magnet motor, a 10:1 planetary gearbox, and an integrated gear shaft. The electromagnetic performances and overload capability of the proposed 36-slot/42-pole outer rotor permanent magnet motor are analyzed by the finite element method. Finally, a prototype of the proposed electric rotary actuator is tested by a customed high-torque testing bench. The experimental results are in good agreement with the finite element analysis results. © 2024 IEEE.

关键词： Permanent magnets

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A Nature-Inspired AI Framework for Accurate Glaucoma Diagnosis

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computer Modeling in engineering & sciences 2025年第4期143卷 539-567页

作者： Jahanzaib Latif Ahsan Wajahat Alishba Tahir Anas Bilal Mohammed Zakariah Abeer Alnuaim School of Software Northwestern Polytechnical UniversityXi’an710072China Shifa College of Medicine Shifa Tameer-e-millat UniversityIslamabad44000Pakistan College of Information Science and Technology Hainan Normal UniversityHaikou571158China Department of Computer Science and Engineering College of Applied Studies and Community ServiceKing Saud UniversityRiyadh11495Saudi Arabia

Glaucoma,a leading cause of blindness,demands early detection for effective *** AI-based diagnostic systems are gaining traction,their performance is often limited by challenges such as varying image backgrounds,pixel intensity inconsistencies,and object size *** address these limitations,we introduce an innovative,nature-inspired machine learning framework combining feature excitation-based dense segmentation networks(FEDS-Net)and an enhanced gray wolf optimization-supported support vectormachine(IGWO-SVM).This dual-stage approach begins with FEDS-Net,which utilizes a fuzzy integral(FI)technique to accurately segment the optic cup(OC)and optic disk(OD)from retinal images,even in the presence of uncertainty and *** the second stage,the IGWO-SVM model optimizes the SVM classification process,leveraging a gray wolf-inspired optimization strategy to fine-tune the kernel function for superior *** testing on three benchmark glaucoma image databases DRIONS-DB,Drishti-GS,and Rim-One-r3 demonstrates the efficacy of our method,achieving classification accuracies of 97.65%,94.88%,and 93.2%,*** results surpass existing state-of-the-art techniques,offering a promising solution for reliable and early glaucoma detection.

关键词： Glaucoma fundus images machine learning nature-inspired algorithm segmentation classification

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A Simplified Method for Optimising Geometrically Shaped Constellations of Higher Dimensionality 49

A Simplified Method for Optimising Geometrically Shaped Cons...

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49th European Conference on Optical Communications, ECOC 2023

作者： Gumus, Kadir Chen, Bin Bradley, Thomas Okonkwo, Chigo High-Capacity Optical Lab Eindhoven University of Technology Netherlands School of Computer Science and Information Engineering Hefei University of Technology China

We introduce a simplified method for calculating the loss function for use in geometric shaping, allowing for the optimisation of high dimensional constellations. We design constellations up to 12D with 4096 points, w... 详细信息

ISBN: (纸本)9781839539954

关键词： Geometry

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Game-Based Service Requests and Channel Selection in Mobile Edge Computing 15

Game-Based Service Requests and Channel Selection in Mobile ...

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15th International Conference on Communication Software and Networks, ICCSN 2023

作者： Yin, Tong Chen, Xin Jiao, Libo Xing, Hua Min, Geyong School of Computer Science Beijing Information Science & Technology University Beijing China College of Engineering Mathematics and Physical Sciences University of Exeter Exeter United Kingdom

ISBN: (纸本)9798350329360

Mobile edge computing (MEC) can provide service requests and resource allocation for user equipments (UEs), effectively reducing service delay for massive Internet-of-Things (IoT). Application providers cache services in the edge servers to reduce service requests delay. However, limited channel resources will affect transmission delay and communication quality when UEs request services from edge servers, thus reducing the user's Quality-of-Experience. In this paper, we investigate the service offloading problem between UEs and base stations (BSs), as well as between BSs and remote data centers (RDCs) in MEC systems with heterogeneous networks, considering channel selection between UEs and BSs. To minimize the service delay of each UE and thus improve user satisfaction, we propose game theory to model the problem as a non-cooperative game of channel selection and then demonstrate the existence of Nash equilibrium (NE). In order to find the ideal channel selection scheme, this paper proposes a best response based on a multi-user service request channel selection (SRCS) algorithm. Simulation results reveal that the proposed channel selection mechanism can improve user satisfaction and verify the effectiveness and convergence of the proposed algorithm. © 2023 IEEE.

关键词： Game theory

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HyperRTV: Neural-Enhanced Adaptive Real-Time Video Streaming Based on Terminal-Edge Collaboration 26

HyperRTV: Neural-Enhanced Adaptive Real-Time Video Streaming...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Chen, Baijun Yin, Daheng Teng, Lifei Dong, Fang Southeast University School of Computer Science and Engineering Nanjing211189 China China Information Consulting & Designing Institute Co Ltd China

ISBN: (纸本)9798350331684

Video has become the primary source of Internet traffic due to the advance of streaming media technology and the surge in user demand for real-time video streaming applications. In this case, neural-enhanced video streaming and mobile edge computing are proposed to improve video content quality on nearby MEC (Mobile Edge Computing) devices and to maintain low interaction delay under limited bandwidth. In order to maximize the architectural advantages brought by MEC, we designed HyperRTV, a terminal-edge collaborative real-time video transmission system with neural-enhanced streaming. HyperRTV consists of three key components: 1) a video super-resolution structure that uses hardware-accelerated DNNs to satisfy latency limits;2) an adaptive bitrate controller that can dynamically adjust the bitrate under various network conditions;3) a heuristic task offloading strategy based on the ant colony algorithm to further meet the heterogeneity and dynamics of the MEC environment. Experiments show that HyperRTV decreases bandwidth consumption by 46% on average and achieves a 48%-65% reduction in latency, consistently maintaining better visual quality and higher stability in unpredictable networks. Our task offloading strategy can reduce the execution and transfer time by 16.1%-19.5% in a MEC environment with multi-task requests. © 2023 IEEE.

关键词： Bandwidth

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