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2024 International Annual Conference on Complex Systems and Intelligent science, CSIS-IAC 2024

作者： Chen, Jingjing Li, Genghui Lin, Xi Zhang, Qingfu City University of Hong Kong Department of Computer Science Hong Kong The City University of Hong Kong Shenzhen Research Institute Shenzhen China Shenzhen University College of Computer Science and Software Engineering Shenzhen China

ISBN: (纸本)9798331504755

The decomposition-based multi-objective evolutionary algorithm (MOEA/D) is an efficient framework for solving multi-objective optimization problems. It decomposes a multi-objective optimization problem into a set of single-objective optimization subproblems and solves them collaboratively. However, most MOEA/D variants use heuristic operators to generate new solutions, which might lead to inefficient search performance. Direction estimation is a powerful derivative-free technique for tackling black-box optimization problems. By iteratively estimating valid search directions, this method can efficiently search for the optimal solution in a principled way;yet it suffers from high computational cost since it requires a number of function evaluations to approximate the true gradient. In this work, we leverage the neighborhood structure of MOEA/D and interactions among different subproblems to estimate a promising descent direction. We demonstrate that sharing and exploiting valuable information among neighboring subproblems can significantly reduce the overall computational budget for direction estimation in MOEA/D (MOEA/D-DirE). Experimental results demonstrate that the proposed MOEAD-DirE algorithm outperforms many multi-objective evolutionary algorithms on various multi-objective optimization problems. © 2024 IEEE.

关键词： Optimization algorithms

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LDE-UNet: A Novel Model for Rapid COVID-19 Diagnosis via CT Image Segmentation

LDE-UNet: A Novel Model for Rapid COVID-19 Diagnosis via CT ...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Wang, QiSong Wu, JiaRou Wang, YingZhuo Qu, HuiLi Nie, Shanling Yang, Hai East China University of Science and Technology Department of Computer Science and Engineering Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China The University of Sydney Faculty of Engineering Australia

ISBN: (纸本)9798350337488

The COVID-19 pandemic has had a profound impact on human society. It has highlighted the need for faster diagnostic methods. Research has shown that combining semantic segmentation with traditional medical approaches can significantly accelerate the process. To address this, leveraging COVID-19 CT images, our team designs a revolutionary semantic segmentation model called Level of Detail Enhancement U-Net (LDE-UNet), which shows the lesion area on CT images. By introducing the LDE block, the model has the unique advantage of overcoming the loss of data details during the downsampling process by emphasizing and transmitting details at the same level. Our SOTA model outperforms the second-best model by at least 0.7% in the most critical indicator precision. Compared with other models, LDE-UNet's strong reliability determines its ability to be used in the medical field to accelerate the localization and division of lesion areas on CT images by professional doctors, thus completing patient diagnosis faster. In addition, we also propose a standardized method for processing medical images. © 2023 IEEE.

关键词： COVID-19 Downsample LDE-UNet Semantic segmentation

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Accelerating Blockchain-Enabled Federated Learning with Clustered Clients

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IEEE Transactions on Big Data 2024年 1-14页

作者： Cui, Laizhong Li, Yinghao Zhou, Yipeng Qu, Youyang Liu, Jiangchuan College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computing FSE Macquarie University Macquare Park NSW Australia Data61 CSIRO VIC Australia School of Computing Science Simon Fraser University Burnaby BC Canada

With the rapid development of big data, Federated learning (FL) has found numerous applications, enabling machine learning (ML) on edge devices while preserving privacy. However, FL still faces crucial challenges, such as single point of failure and poisoning attacks, which motivate the integration of blockchain-enabled FL (BeFL). Beyond that, the efficiency issue still limits the further application of BeFL. To address these issues, we propose a novel decentralized framework: Accelerating Blockchain-Enabled Federated Learning with Clustered Clients (ABFLCC), who utilize actual training time for clustering clients to achieve hierarchical FL and solve the single point of failure problem through blockchain. Additionally, the framework clusters edge devices considering their actual training times, which allows for synchronous FL within clusters and asynchronous FL across clusters simultaneously. This approach guarantees that devices with a similar training time have a consistent global model version, improving the stability of the converging process, while the asynchronous learning between clusters enhances the efficiency of convergence. The proposed framework is evaluated through simulations on three real-world public datasets, demonstrating a training efficiency improvement of 30% to 70% in terms of convergence time compared to existing BeFL systems. IEEE

关键词： Big data

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Identifying Underrepresented Driving Scenarios in Training Datasets with SafeML Robustness Monitoring for Autonomous Driving Vehicles 8

Identifying Underrepresented Driving Scenarios in Training D...

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8th International Conference on System Reliability and Safety, ICSRS 2024

作者： Matthias, Bergler Ramin, Tavakoli-Kolagari Kristina, Lundqvist Nuremberg Institute of Technology Department for Computer Science Nuremberg Germany Mälardalens University Dependable Software Engineering Västerås Sweden

ISBN: (纸本)9798350354508

This study investigates the evaluation of the representativeness of datasets in autonomous driving systems using statistical distance metrics. Eight different datasets, created in the Carla simulation environment, cover various driving conditions, including normal and impaired lighting scenarios, alternative routes, and challenging conditions such as power outages. The datasets were designed to represent similar routes under different conditions. Various distance metrics- Wasserstein, Kuiper, Anderson-Darling, Chernoff, DTS, and CVM-were applied to measure pairwise dataset distances. We anticipated that the dataset for a given route under ideal conditions would exhibit a large distance measure (of any of the listed distance measures) compared to the same route under impaired conditions (e.g., a power failure at the streetlights). However, we were particularly interested in whether a measurable jump at a (potential threshold) value could be recognized even with a smaller drop in dataset condition quality. The results of the study show that a normalization of these distance measures enables precise divergence comparisons and the determination of meaningful threshold values. This in turn means that normalized deviation measures can effectively identify deviations in real time, hence contributing to the development and monitoring of more reliable autonomous driving models. © 2024 IEEE.

关键词： Street lighting

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ResMHA-Net:Enhancing Glioma Segmentation and Survival Prediction Using a Novel Deep Learning Framework

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computers, Materials & Continua 2024年第10期81卷 885-909页

作者： Novsheena Rasool Javaid Iqbal Bhat Najib Ben Aoun Abdullah Alharthi Niyaz Ahmad Wani Vikram Chopra Muhammad Shahid Anwar Department of Computer Science School of Engineering and TechnologyIslamic University of Science and TechnologyAwantipora192122India Faculty of Computing and Technology Al-Baha UniversityAlaqiq65779-7738Saudi Arabia Research Groups in Intelligent Machines National School of Engineers of SfaxUniversity of SfaxSfax3038Tunisia Department of Electrical Engineering College of EngineeringKing Khalid UniversityAbha61421Saudi Arabia Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyPatiala147004Punjab Department of Electrical and Instrumentation Engineering Thapar Institute of Engineering and TechnologyPatiala147004Punjab Department of AI and Software Gachon UniversitySeongnam-si13120Republic of Korea

Gliomas are aggressive brain tumors known for their heterogeneity,unclear borders,and diverse locations on Magnetic Resonance Imaging(MRI)*** factors present significant challenges for MRI-based segmentation,a crucial step for effective treatment planning and monitoring of glioma *** study proposes a novel deep learning framework,ResNet Multi-Head Attention U-Net(ResMHA-Net),to address these challenges and enhance glioma segmentation ***-Net leverages the strengths of both residual blocks from the ResNet architecture and multi-head attention *** powerful combination empowers the network to prioritize informative regions within the 3D MRI data and capture long-range *** doing so,ResMHANet effectively segments intricate glioma sub-regions and reduces the impact of uncertain tumor *** rigorously trained and validated ResMHA-Net on the BraTS 2018,2019,2020 and 2021 ***,ResMHA-Net achieved superior segmentation accuracy on the BraTS 2021 dataset compared to the previous years,demonstrating its remarkable adaptability and robustness across diverse ***,we collected the predicted masks obtained from three datasets to enhance survival prediction,effectively augmenting the dataset *** features were then extracted from these predicted masks and,along with clinical data,were used to train a novel ensemble learning-based machine learning model for survival *** model employs a voting mechanism aggregating predictions from multiple models,leading to significant improvements over existing *** ensemble approach capitalizes on the strengths of various models,resulting in more accurate and reliable predictions for patient ***,we achieved an impressive accuracy of 73%for overall survival(OS)prediction.

关键词： Glioma MRI segmentation multihead attention survival prediction deep learning

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Time-enhanced Dynamic Graph Network for Next POI Recommendation

Time-enhanced Dynamic Graph Network for Next POI Recommendat...

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2024 International Symposium on Internet of Things and Smart Cities, ISITSC 2024

作者： Liu, Zhao Liu, Wei Chen, Junyang School of Artificial Intelligence Sun Yat-sen University Zhuhai China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9798331505516

Predicting the next Point-of-Interest (POI) is crucial for location-based services. In this paper, we propose the Time-enhanced Sequence Prediction Model (TSPM) to improve the accuracy of next POI recommendations by incorporating temporal information and dynamic graph *** approach utilizes a Time-enhanced Sequence-based Dynamic Graph (TSDG) that captures both temporal transitions of POIs and sequential dependencies in user behavior. By embedding temporal information directly into the graph structure, TSPM effectively models user movements. We further enhance POI embeddings using knowledge graph techniques and Eigenmap to preserve the topological properties of the *** proposed model integrates these enriched embeddings into a Time-aware Recurrent Neural Network (TiRNN) to capture the influence of past check-ins across different time intervals. Experiments on real-world datasets demonstrate that TSPM significantly outperforms existing methods in prediction accuracy. © 2024 IEEE.

关键词： Graph embeddings

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Metaheuristics Based Node Localization Approach for Real-Time Clustered Wireless Networks

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computer Systems science & engineering 2023年第1期44卷 1-17页

作者： R.Bhaskaran P.S.Sujith Kumar G.Shanthi L.Raja Gyanendra Prasad Joshi Woong Cho Department of Information Technology PSNA College of Engineering and TechnologyDindigul624622India Department of Computer Science and Engineering MEA Engineering CollegeMallapuram679325KeralaIndia Department of Electronics and Communication Engineering Rajalakshmi Institute of TechnologyKuthambakkamChennai600124India Department of Electronics and Communication Engineering Sri Eshwar College of EngineeringCoimbatore641202India Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Software Convergence Daegu Catholic UniversityGyeongsan38430Korea

In recent times,real time wireless networks have found their applicability in several practical applications such as smart city,healthcare,surveillance,environmental monitoring,*** the same time,proper localization of nodes in real time wireless networks helps to improve the overall functioning of *** study presents an Improved Metaheuristics based Energy Efficient Clustering with Node Localization(IM-EECNL)approach for real-time wireless *** proposed IM-EECNL technique involves two major processes namely node localization and ***,Chaotic Water Strider Algorithm based Node Localization(CWSANL)technique to determine the unknown position of the ***,an Oppositional Archimedes Optimization Algorithm based Clustering(OAOAC)technique is applied to accomplish energy efficiency in the ***,the OAOAC technique derives afitness function comprising residual energy,distance to cluster heads(CHs),distance to base station(BS),and *** performance validation of the IM-EECNL technique is carried out under several aspects such as localization and energy efficiency.A wide ranging comparative outcomes analysis highlighted the improved performance of the IM-EECNL approach on the recent approaches with the maximum packet delivery ratio(PDR)of 0.985.

关键词： Wireless networks real time applications clustering node localization energy efficiency metaheuristics

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3D Reconstruction From Traditional Methods to Deep Learning 10

3D Reconstruction From Traditional Methods to Deep Learning

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10th IEEE International Conference on Cyber Security and Cloud Computing and 9th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Yang, Lan Yang, Chaoyi Xie, Rui Liu, Jingnian Zhang, Huan Tan, Wenjin QuanZhou University of Information Engineering Software College QuanZhou362000 China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China

ISBN: (纸本)9798350312461

Vision is one of the important pathways for human perception of external information, with over 80% of perception being acquired through vision. How to enable computers to possess efficient and flexible visual systems similar to humans has always been a hot topic in the field of computer science. One of the main goals of computer vision research is to reconstruct the geometric structure of 3D objects visible on the visible surfaces from 2D photos. Recently, this technology has become mature enough and its applications range from autonomous driving, virtual reality, cultural heritage preservation and restoration, among others, with significant research value. In this paper, we summarize the key technical issues in 3D reconstruction from existing technologies, first by summarizing traditional methods of 3D reconstruction, then analyzing commonly used deep learning methods for 3D reconstruction and their application scenarios in different fields. Finally, we conclude and provide an outlook on future development directions. © 2023 IEEE.

关键词： Deep learning

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RemoteBlock: A Scalable Blockchain Storage Framework for Ethereum 4

RemoteBlock: A Scalable Blockchain Storage Framework for Eth...

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4th IEEE International Conference on Power, Electronics and computer Applications, ICPECA 2024

作者： Xia, Ding Yao, Peiyuan Liang, Junyuan Chen, Wuhui School of Computer Science Sun Yat-sen University Guangzhou China School of Software Engineering Sun Yat-sen University Zhuhai China

ISBN: (纸本)9798350359589

This paper addresses the continuously increasing storage demands challenge faced by blockchain networks, with a particular focus on Ethereum. We propose a novel framework that divides the network into consensus nodes, which inherit Ethereum characteristics, and storage nodes responsible for storing Merkle Patricia Trie (MPT) nodes. This design aims to reduce the storage load on individual nodes by distributing MPT nodes based on their key values. Our approach maintains network security and data integrity while easing the storage burden through a distributed storage mechanism. Key to our work is the dynamic adjustment of storage load across an expandable network of storage nodes. We validate our framework through practical experiments, involving modifications to the go-ethereum source code and testing with authentic Ethereum block data. The results confirm that our work not only mitigates storage issues but also enhances synchronization efficiency. © 2024 IEEE.

关键词： blockchain distributed system ethereum merkle patricia trie storage scalability

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Dynamic Topology Management In Ad-Hoc Networks For Improved Performance 2

Dynamic Topology Management In Ad-Hoc Networks For Improved ...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Anakath, A.S. Kannadasan, R. Margarat, G. Simi Pandian, A. Pasumpon Antony Sibiya Varghese, V. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology VIT University Tamil Nadu Vellore India Department of Computer Science and Engineering New Prince Shri Bhavani College of Engineering and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering CARE College of Engineering Trichy India Department of Artificial Intelligence and Data Science GRT Institute of Engineering and Technology Tamil Nadu Tiruttani India

ISBN: (纸本)9798350383867

Optimization in ad hoc networks is a highly specialized task because the structure of the network is loosely formed, and each node is independently responsible for its operation. In order to obtain a better result from this solely formed networks this report offer extensive Dynamic Topology Management (DTM) method. Dynamic routing protocols lager lines, dividend balancing, Life-saving administration, guarantee of reliability, cross-layer design, security aspects and the on-time monitoring and discovery of topology changes are the main objectives and steps to be accomplished. Real-time Location monitoring and forest structure discovery are made possible by employing adaptive routing protocols, namely Dynamic Source Routing(DSR) and Ad-Hoc On-Demand Distance Vector(AODV). Also, the algorithms are capable of making advance adjustments to routing routes and resource allocations drawn from the mobility patterns forecasting which is vital. Load balancing methods, which distribute traffic in a network evenly, enable the system to run efficiently and avoid problems caused by overloading. Algorithms that are energy-efficient and make the best use of energy resources having their topology's optimized are introduced to the streamlined energy distribution systems. These algorithms are focused on the node's energy level. Through fault tolerance techniques which recognize and handle failures in nodes and partitioned networks, connectedness activity is a continuous one. Through QoS perception, the network topology might automatically be tailored to conform to the given quality criteria, thus not restricting the choice for differing applications. The proposed could have defenses against adversary nodes and their potential DDoS attacks which will enhance network protection. Yet another advantage of an architectural style that promotes cross-layer collaboration is enhanced topology management which is the goal around which the functions of various levels of the protocol stack revolve

关键词： Decision making

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