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IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)

作者： Tao Huang Xiang Chen Li Peng School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan Hunan China Hunan Key Laboratory for Service computing and Novel Software Technology

The rapid development of single-cell RNA sequencing (scRNA-seq) technology has enabled researchers to explore gene expression differences at the level of individual cells, revealing more refined cell types and states. However, due to the low expression and high noise of scRNA-seq data, feature selection has become particularly important in the analysis of single-cell data. Here, we introduce the Entropy Stepwise Regression (ESR) method for feature selection. This method utilizes the correlation between genes and the entropy values of each feature to filter out genes that are conducive to downstream analysis. In mouse kidney samples, we compared the performance of three methods in terms of Adjusted Rand Index and achieved good results. This indicates that the method can improve the accuracy of downstream analysis.

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Lead-cnn: lightweight enhanced dimension reduction convolutional neural network for brain tumor classification

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International Journal of Machine Learning and Cybernetics 2025年 1-20页

作者： Khan, Saif Ur Rehman Asif, Sohaib Bilal, Omair Rehman, Hafeez Ur School of Computer Science and Engineering Central South University 932 Lushan S Rd Yuelu District Hunan Changsha China School of Computing and Data Sciences Oryx Universal College with Liverpool John Moores University Doha Qatar Department of Computer Science National University of Computer and Emerging Sciences Islamabad Pakistan Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial Intelligence Taizhou Campus of Zhejiang Cancer Hospital Taizhou Cancer Hospital Taizhou317502 China Chinese Academy of Sciences Taizhou317502 China Chinese Academy of Sciences Hangzhou310022 China

In many nations, brain tumors are among the most serious medical conditions affecting both adults and children. Timely and accurate diagnosis is crucial, as the life of a patient may be shortened if brain tumors are not identified promptly. When brain tumors are diagnosed accurately and promptly, patients can receive the appropriate treatment. One of the challenging aspects of dealing with brain tumors is their diversity, making it difficult to categorize them accurately due to their varying characteristics. Deep learning (DL) models have emerged as a valuable tool in classifying brain tumors into distinct groups. However, these models face challenges related to accuracy and computational cost, necessitating advancements in brain image classification. Our primary aim is to develop a lightweight convolutional neural network (CNN) architecture that offers high efficiency, we incorporate a modified dimension reduction block with CNN to detect brain cancers with minimal computational resources while maintaining a high level of accuracy. In this research, we introduce a novel lightweight network called LEAD-CNN, designed specifically for the accurate and reliable identification of brain cancers from magnetic resonance images (MRI). Our approach begins by evaluating the effectiveness of several pre-trained DL models, including ResNet-101, VGG-19, MobileNet-V1, and DenseNet-201, which have been recommended for the classification of medical images. Subsequently, we propose a dimension reduction base CNN architecture with a lightweight design aimed at achieving a low false-negative rate in tumor image classification for patients. To assess the performance of the proposed model, we utilize a benchmark brain MRI dataset (Kaggle) comprising 7023 images, encompassing three different types of brain tumors long with normal brain images. Our experimental results are demonstrating an overall classification accuracy of 98.70%, with an average recall, F1-score, and precision of 98.60%

关键词： Convolutional neural networks

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Towards a Trustworthy Fog-based Mobile Crowdsensing in Internet-of-vehicles

Towards a Trustworthy Fog-based Mobile Crowdsensing in Inter...

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IEEE International Conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Sunday Oyinlola Ogundoyin Ismaila Adeniyi Kamil Isaac Adewale Ojedokun John Oluwaseun Babalola Vincent Omollo Nyangaresi Electrical and Electronic Engineering Programme Bowen University Iwo Nigeria Department of Electrical and Electronic Engineering University of Ibadan Ibadan Nigeria Department of Computer Science and Software Engineering Jaramogi Oginga Odinga University of Science and Technology Bando Kenya

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

To reduce communication latency and improve vehicular communication, it is crucial to integrate a fog-based mobile crowdsensing (FBMCS) system with the rapidly evolving Internet of Vehicles (IoV). A four-tier FBMCS architecture is proposed for IoV environments, partitioned into smart vehicles, vehicular fog, fog gateway, and cloud services. An extensive experimental simulation is conducted using iFogSim2 to evaluate the impact of trust, fog mobility, and fog-to-fog (F2F) collaboration on the system performance. The results show a reduction in communication latency and better network resource usage when vehicular fogs move at a maximum speed of 10m/s, with the trust management system contributing an insignificant increase in communication latency and network usage. Moreover, the fusion of these three mechanisms marginally increases energy consumption by approximately 0.33%, while the speed of vehicular fogs is limited to 10m/s. The findings demonstrate the potential of the proposed architecture to improve the overall performance of IoV services.

关键词： Energy consumption Crowdsensing System performance Collaboration Logic gates Delays Reliability Vehicle dynamics Trust management Internet of Vehicles

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DDR: Exploiting Deep Degradation Response as Flexible Image Descriptor

arXiv

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arXiv 2024年

作者： Wu, Juncheng Ni, Zhangkai Wang, Hanli Yang, Wenhan Zhou, Yuyin Wang, Shiqi School of Computer Science and Technology Tongji University China Pengcheng Laboratory China Department of Computer Science and Engineering University of California Santa Cruz United States Department of Computer Science City University of Hong Kong Hong Kong

Image deep features extracted by pre-trained networks are known to contain rich and informative representations. In this paper, we present Deep Degradation Response (DDR), a method to quantify changes in image deep features under varying degradation conditions. Specifically, our approach facilitates flexible and adaptive degradation, enabling the controlled synthesis of image degradation through text-driven prompts. Extensive evaluations demonstrate the versatility of DDR as an image descriptor, with strong correlations observed with key image attributes such as complexity, colorfulness, sharpness, and overall quality. Moreover, we demonstrate the efficacy of DDR across a spectrum of applications. It excels as a blind image quality assessment metric, outperforming existing methodologies across multiple datasets. Additionally, DDR serves as an effective unsupervised learning objective in image restoration tasks, yielding notable advancements in image deblurring and single-image super-resolution. Our code is available at: https://***/eezkni/DDR Copyright © 2024, The Authors. All rights reserved.

关键词： Image denoising

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Enhancing Real-World Facial Expression Recognition: A Deep Learning Approach based on Attention Mechanisms

Enhancing Real-World Facial Expression Recognition: A Deep L...

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computer science, Electronic Information engineering and Intelligent Control Technology (CEI), IEEE International Conference on

作者： Kai Wu Zhuang Chen Department of Software Engineering Chongqing University of Posts and Telecommunications Chongqing China Department of Computer Science and Engineering Chongqing University of Technology Chongqing China

ISBN: (数字)9798350372052

ISBN: (纸本)9798350372069

Occlusion and pose variation are the biggest interference factors in real-world face expression recognition (FER). Faces are likely to be occluded by items such as sunglasses, ducktails, and scarves, which can significantly change the visual appearance of the face, while varied poses can lead to partial information loss and inaccurate localization. In this paper, we propose a new deep learning network model for facial expression recognition, firstly, an attention mechanism module is added on top of the residual neural network, which includes spatial and channel attention modules for suppressing unimportant feature information and highlight the key information. Second, a regional attention mechanism is added for adaptively extracting feature vectors from localized unobstructed facial regions, which are combined with global feature information to learn more comprehensive and detailed potential key information. The proposed method achieved good results on two facial expression datasets, FER2013 and RAF-DB, obtaining an accuracy of 74.37 and 89.12, respectively.

关键词： Deep learning Location awareness Visualization Face recognition Interference Feature extraction Vectors

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A Lightweight VK-Net Based on Motion Profiles for Hazardous Driving Scenario Identification 23

A Lightweight VK-Net Based on Motion Profiles for Hazardous ...

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Gao, Zhen Xu, Jingning Zheng, Jiang Yu Fan, Hongfei Yu, Rongjie Zong, Jiaqi Li, Xinyi School of Software Engineering Tongji University Shanghai China Indiana University-Purdue University Department of Computer Science Indianapolis United States Jiangsu China Tongji University Key Laboratory of Road and Traffic Engineering of the Ministry of Education Shanghai China College of Transportation Engineering Tongji University Shanghai China

ISBN: (纸本)9781665494571

Hazardous driving scenarios are critical for the development and testing of highly autonomous vehicles. Recent studies have proposed new approaches for extracting hazardous scenarios from driving videos, which greatly improve the detection accuracy compared with traditional approaches that extract information from structured kinematic data. However, direct processing on driving videos is costly, and the videos need to be uploaded and processed in a centralized manner. In this study, we propose a novel approach for reducing the cost of video analysis while maintaining high accuracy in hazardous scenario identification. Firstly, the videos are significantly compressed into motion profile images that contain motion trajectories of objects. Secondly, a lightweight CNN with attention mechanism has been designed to extract risky trajectory features of the conflicting traffic objects. Furthermore, data augmentation has been applied to increase the generalizability of the model. The proposed approach achieves significantly lightweight network size, and meanwhile, experimental results have demonstrated that the approach achieves high accuracy with sensitivity 0.95, specificity 0.85 and AUC 0.95, which outperforms state-of-the-art hazardous scenario identification approaches. The efficient execution of identification implies that it is suitable to be distributed and deployed at each vehicle, and consequently, videos could be processed via edge computing, which would improve the diversity of hazardous scenarios and the efficiency of scenario collection for further research work. © 2021 IEEE.

关键词： Sensitivity Image coding Smart cities Kinematics Feature extraction Trajectory Data mining

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Strengthened Initialization of Adaptive Cross-Generation Differential Evolution

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computer Modeling in engineering & sciences 2022年第3期130卷 1495-1516页

作者： Wei Wan Gaige Wang Junyu Dong Department of Computer Science of Technology Ocean University of ChinaQingdao266100China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchun130012China Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis Guangxi University for NationalitiesNanning530006China

Adaptive Cross-Generation Differential Evolution(ACGDE)is a recently-introduced algorithm for solving multiobjective problems with remarkable performance compared to other evolutionary algorithms(EAs).However,its convergence and diversity are not satisfactory compared with the latest *** order to adapt to the current environment,ACGDE requires improvements in many aspects,such as its initialization and mutant *** this paper,an enhanced version is proposed,namely *** incorporates a strengthened initialization strategy and optimized parameters in contrast to its *** improvements make the direction of crossgeneration mutation more clearly and the ability of searching more *** experiments show that the new algorithm has better diversity and improves convergence to a certain *** the same time,SIACGDE outperforms other state-of-the-art algorithms on four metrics of 24 test problems.

关键词： Differential Evolution(DE) multi-objective optimization(MO) opposition-based learning parameter adaptation

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Dynamic traffic congestion pricing and electric vehicle charging management system for the internet of vehicles in smart cities

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Digital Communications and Networks 2021年第4期7卷 492-504页

作者： Nyothiri Aung Weidong Zhang Kashif Sultan Sahraoui Dhelim Yibo Ai National Center for Materials Service Safely University of Science and Technology BeijingBeijing100083China Department of Software Engineering Bahria University Islamabad CampusIslamabadPakistan School of Computer and Communication Engineering University of Science and Technology Beijing Beijing 100083 China

The integration of the Internet of Vehicles(IoV)in future smart cities could help solve many traffic-related challenges,such as reducing traffic congestion and traffic *** congestion pricing and electric vehicle charging policies have been introduced in recent ***,the majority of these schemes emphasize penalizing the vehicles that opt to take the congested roads or charge in the crowded charging station and do not reward the vehicles that cooperate with the traffic management *** this paper,we propose a novel dynamic traffic congestion pricing and electric vehicle charging management system for the internet of vehicles in an urban smart city *** proposed system rewards the drivers that opt to take alternative congested-free ways and congested-free charging *** propose a token management system that serves as a virtual currency,where the vehicles earn these tokens if they take alternative non-congested ways and charging stations and use the tokens to pay for the charging *** proposed system is designed for Vehicular Ad-hoc Networks(VANETs)in the context of a smart city environment without the need to set up any expensive toll collection *** large-scale traffic simulation in different smart city scenarios,it is proved that the system can reduce the traffic congestion and the total charging time at the charging stations.

关键词： IoV EV VANET Smart city Congestion pricing Congestion avoidance EV charging Traffic optimization

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Ion thruster accelerator grid erosion mechanism under extreme conditions of cylindrical erosion and chamfer erosion

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Chinese Journal of Aeronautics 2023年第6期36卷 128-139页

作者： Guangqing XIA Jiahui LI Chang LU Hai GENG Vladimir A.SAETCHNIKOV Juan LI Yanlong WANG State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentDalian University of TechnologyDalian 116024China Key Laboratory of Advanced Technology for Aerospace Vehicles of Liaoning Province Dalian University of TechnologyDalian 116024China Collaborative Innovation Center of Micro&Nano Satellites of Hebei Province North China Institute of Aerospace EngineeringLangfang 065000China Science and Technology on Vacuum Technology and Physics Laboratory Lanzhou Institute of PhysicsLanzhou 73000China Physics and Aerospace Technology Department Belarusian State UniversityMinsk 220030Belarus

In this paper,the abnormal experimental phenomenon on barrel erosion under extreme working conditions in the ultra-long life experiment(>10000 h)of ion thruster ion optics is studied by the Immersed-Finite-Element Particle-In-Cell Monte-Carlo-Collision(IFE-PIC-MCC)method and the grid erosion evaluation *** transport process of beam ions and Charge Exchange(CEX)ions in the grid system,and the characteristics and mechanisms of the aperture barrel erosion under extreme erosion conditions(*** cylindrical erosion and chamfer erosion)were systematically *** to the advantage of the IFE method for dealing with complex boundaries in structured mesh,the aperture barrel erosion morphology of the accelerator grid is reconstructed accurately based on the experimental *** results show that,with the evolution of working conditions,the mechanism of the aperture barrel erosion changes significantly,which relies heavily on the accelerator grid *** change of the accelerator grid aperture barrel morphology has a significant effect on the behavior of CEX ions,and only affects the local electric field distribution,but has no effect on the upstream plasma *** the erosion progresses,the erosion position moves downstream along the grid aperture axis direction,and the erosion range becomes *** of the erosion phase,the erosion rate of the CEX ions located downstream of the decelerator grid is the *** erosion rate is related to the mean incident energy and angle,and their variation is closely related to the position and trajectory of CEXions.

关键词： Accelerator grid Charge Exchange(CEX)ions Erosion IFE-PIC-MCC Ion propulsion

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BMRMIA:A Platform for Radiologists to Systematically Learn Automated Medical Image Analysis by Three Dimensional Medical Decision Support System

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computer Modeling in engineering & sciences 2022年第5期131卷 851-863页

作者： Yankun Cao Lina Xu Zhi Liu Xiaoyan Xiao Mingyu Wang Qin Li Hongji Xu Geng Yang School of Software Shandong UniversityJinan250101China School of Information Science and Engineering Shandong UniversityQingdao266237China School of Computer Science and Technology Shandong Jianzhu UniversityJinan250012China Department of Nephrology Qilu Hospital of Shandong UniversityJinan250012China State Key Laboratory of ASIC&Systems The School of MicroelectronicsFudan UniversityShanghai200433China Shenzhen Institute of Information Technology Shenzhen518172China

Contribution:This paper designs a learning and training platform that can systematically help radiologists learn automated medical image analysis *** platform can help radiologists master deep learning theories and medical applications such as the three-dimensional medical decision support system,and strengthen the teaching practice of deep learning related courses in hospitals,so as to help doctors better understand deep learning knowledge and improve the efficiency of auxiliary ***:In recent years,deep learning has been widely used in academia,industry,*** increasing number of companies are starting to recruit a large number of professionals in the field of deep *** numbers of colleges and universities also offer courses related to deep learning to help radiologists learn automated medical image analysis *** now,however,there is no practical training platform that can help radiologists learn automated medical image analysis ***:The platform proposes the basic learning,model combat,business application(BMR)concept,including the learning guidance system and the assessment training system,which constitutes a closed-loop learning guidance mode of“learning-assessment-training-learning”.Findings:The survey results show that most of radiologists met their learning expectations by using this *** platform can help radiologists master deep learning techniques quickly,comprehensively and firmly.

关键词： BMR deep learning three dimensional medical decision support system deep learning engineer standard

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