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A Reputation System for distributed intelligent Blockchain Decision-Making 5

A Reputation System for Distributed Intelligent Blockchain D...

引用

5th international conference on Blockchain computing and Applications, BCCA 2023

作者： Rawlins, Charles Jagannathan, S. Nadendla, Venkata Sriram Siddhardh Electrical and Computer Engineering Missouri University of Science and Technology RollaMO United States Computer Science Missouri University of Science and Technology RollaMO United States

ISBN: (纸本)9798350339239

Discerning ground-truth amidst misinformation is a nontrivial task in decentralized networks. While trivial for traditional blockchain networks using Proof of Work (PoW) consensus, it requires immense computational resources at the cost of network scalability and therefore has compatibility challenges with limited devices like Internet of things (IoT) networks. Moving towards a lightweight alternative-a novel scheme is proposed for provable identification of reputable nodes for making intelligent decisions using distributed machine-learning optimization, called Weight-Based Reputation (WBR). A distributed learning task identifies nodes that are willing to cooperate as a means for securing ground-truth, and is provably resistant to data poisoning in theory and experimentation. A novel method combining WBR with a classifier creates lightweight and robust blockchain decision-making. the results of this two-step approach is a novel method for predicting various attacks that result transaction log, such as fraud or the double-spend. A Bayesian game against adversary manipulation formulation and experimental results show superior performance and robustness to the state-of-the-art. Resource consumption compared against blockchain protocols is also provided. Future work will explore WBR in distributed consensus as a complete alternative to blockchain PoW. © 2023 IEEE.

关键词： Internet of things

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Collaborative Hyper-Heuristic Ant Colony Algorithm for Solving Multi-objective Fuzzy Low-Carbon distributed Permutation Flow-Shop and Two-Echelon Vehicle Transportation Integrated Scheduling Problem 20th

Collaborative Hyper-Heuristic Ant Colony Algorithm for Solvi...

引用

20th international conference on intelligent computing (ICIC)

作者： Ma, Jian-hua Hu, Rong Guo, Ning Qian, Bin Kunming Univ Sci & Technol Sch Informat Engn & Automat Kunming 650500 Yunnan Peoples R China

ISBN: (纸本)9789819755776;9789819755783

this paper proposed a collaborative hyper-heuristic ant colony algorithm (CHACA) for solving a kind of multi-objective fuzzy low-carbon distributed permutation flow-shop and two-echelon vehicle transportation integrated scheduling problem (MDF-TVISP), which adopt triangular fuzzy numbers (TFNs) to represent job processing times and vehicle travel times. the optimization objectives of MDF-TVISP are to minimize the total fuzzy production and transportation costs and fuzzy carbon emissions. CHACA integrated collaborative ant colony (CACA) and hyper-heuristic algorithm (HHA). We employ eight pre-designed heuristic operations in the low-level heuristics (LLHs) to enhance the algorithm's local search capability, while utilizing a novel CACA to improve the performance of high-level strategy (HLS). the interaction between LLHs and HLS greatly improves the search performance of the CHACA. Finally, simulation experiments and algorithm comparisons validate the effectiveness of CHACA.

关键词： distributed Permutation Flow-Shop Two-echelon Integrated Scheduling Triangular Fuzzy Numbers Hyper-heuristic Algorithm

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MECaaS: Mobile Edge computing as a Services Based on Blockchain 17th

MECaaS: Mobile Edge Computing as a Services Based on Blockch...

引用

17th Annual conference of China Electrotechnical Society, CES 2022

作者： Li, Bo Lu, Jiangang Zhao, Ruifeng Hu, Rong Zhou, Huafeng Pang, Xueyue Xu, Yihang Electric Power Dispatching and Control Center of Guangdong Power Grid Co. Ltd. Guangzhou China China Southern Power Grid Power Dispatching and Control Center Guangzhou China China Energy Engineering Group Guangdong Electric Power Design Institute Co. Ltd. Guangzhou China State Key Laboratory of Power Transmission Equipment & System Security and New Technology Chongqing University Chongqing China

ISBN: (纸本)9789819904501

As a new generation of computing paradigm for Internet of things (IoT), edge computing has been proved to be promising in solving the problems of insufficient centralized computing power, bandwidth congestion and transmission delay by distributing traditional cloud resources down to edges. While so, massive of computing resources have not been utilized due to the physical heterogeneity and allocation issues. Addressing on the two major challenges, in this paper, we propose a blockchain-based edge computing solution (MECaaS), which integrates smart contract, component interface, distributed allocation algorithm and other elements as a service. Comparing with the existing blockchain-edge computing solutions, we focus more on technical implementation than just the theoretical framework, by distributing this service to inspire devices’ self-potential in interaction, perception and decision, in further forms a distributed intelligent system. the service is deployed in a heterogeneous IoT scenario, and proved to be efficient for edge computing. Also, experimental results show that distributed resource allocation strategies perform better than centralized ways to some extent. © 2023, Beijing Paike Culture Commu. Co., Ltd.

关键词： Blockchain

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Data Enhancement Strategy Using Reinforcement Learning and Xgboost with BlockChain Security 4

Data Enhancement Strategy Using Reinforcement Learning and X...

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4th international conference on intelligent Technologies, CONIT 2024

作者： Selvapriya, S. Manvizhi, N. Ajay, S. Kavya, V.K. Gilmary, Rosario Ifet College of Engineering Dept. of Information Technology Tamilnadu Villupuram India

ISBN: (纸本)9798350349900

the enormous volumes of data generated by Next Generation Sequencing (NGS) technology have transformed genomics and made efficient data analysis techniques necessary. To create data analytics applications, the extraction, transformation, and loading (ETL) process is necessary. In understanding data features, representation, storage, I/O operations, intermediate findings, in-memory analysis, and total processing time, the data format is crucial, as this study emphasizes. Data conformance is required for effective analysis since different machine learning and data mining algorithms require different types and forms of data. In this paper, we explore data format-centric optimization of the ETL process for NGS data analysis. We investigate how computing efficiency and analytical correctness are affected by the choice of data type. By using distributed computing and parallel processing, we evaluate the effectiveness of several data formats in NGS data analysis processes, such as FASTQ, BAM, and VCF. Our study offers suggestions for developing data analytics applications in the genomics field by clarifying the relationship between data format, ETL, and algorithmic needs. We also look at the trade-offs between resource usage and data format choice. In the end, our study contributes to more accurate and efficient genomics research by addressing the urgent need for well-informed data format options in NGS data analysis. © 2024 IEEE.

关键词： Reinforcement learning

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Enhanced Interactive Ant Colony Algorithm for Solving Multi-objective distributed Flow Shop Production and Time-Dependent Multi-compartment Vehicle Routing Integrated Optimization Problem 20th

Enhanced Interactive Ant Colony Algorithm for Solving Multi-...

引用

20th international conference on intelligent computing (ICIC)

作者： Song, Chao Guo, Ning Qian, Bin Hu, Rong Shang, Qing-Xia Kunming Univ Sci & Technol Sch Informat Engn & Automat Kunming 650500 Yunnan Peoples R China

ISBN: (纸本)9789819755776;9789819755783

In this paper, an enhanced interactive ant colony algorithm (EIACA) is proposed to solve the multi-objective distributed flow shop production and time-dependent multi-compartment vehicle routing integrated optimization problem (MODFSP_TDMCVRIOP) with the objectives of minimizing total carbon emissions and total cost. In the global search phase of EIACA, a random initialization method is first employed to ensure a diverse and dispersed population, generating high-quality initial solutions and initializing the pheromone matrix. Subsequently, three ant colonies are utilized, with the first and second colonies optimizing total carbon emissions and total cost separately, while the third colony concurrently optimizes both objectives to enhance search efficiency. In the local search phase of EIACA, specific local search methods are designed to perform in-depth exploration of discovered high-quality regions. Finally, the effectiveness of EIACA is validated through simulation experiments and algorithm comparisons.

关键词： Multi-ant colony algorithm Multi-objective distributed flow shop production Time-dependent multi-compartment vehicle routing integrated optimization problem

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Research on Internet of things Signals Acquisition and Processing based on Edge Computation 5

Research on Internet of Things Signals Acquisition and Proce...

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5th international conference on Computer Engineering and intelligent Control, ICCEIC 2024

作者： Zhang, Xiaolong Qiu, Wenheng Yang, Huaping Hou, Aohua College of Electrical Engineering and New Energy China Three Gorges University Yichang Hubei443002 China Hubei University of Arts and Sciences Faculty of Automotive and Traffic Engineering Xiangyang Hubei441053 China

ISBN: (纸本)9798331507992

the proliferation of Internet of things (IoT) devices has led to the generation of vast quantities of data, placing considerable strain on conventional cloud computing infrastructure. Edge computing has emerged as a viable solution to address this challenge. In this study, we propose an Internet of things (IoT) signal processing framework that is combined with distributed edge nodes. the objective is to improve data transmission efficiency and real-time performance. Firstly, in consideration of the constrained resources of edge devices, we have devised lightweight preprocessing algorithms, including frequency domain filtering based on Fourier transform and a denoising algorithm based on wavelet transform, with the objective of reducing the noise and redundant information present in the original signal. Subsequently, the adaptive compressed sensing technology was employed to compress the preprocessed signal, which resulted in a notable reduction in the bandwidth requirement for data transmission. Subsequently, in order to fully utilise the computing capabilities of edge nodes, we introduce a multi-task learning collaborative computing model. the model permits the sharing of computing tasks between multiple edge nodes, facilitates data collaboration between edge nodes through federated learning, and reduces computing redundancy and communication overhead. the experimental phase employs the Google Edge TPU Dataset to evaluate the dataset pertinent to the Internet of things. the results indicate that the proposed methodology exhibits a notable enhancement in data processing latency and transmission efficacy in comparison to the conventional approach. © 2024 IEEE.

关键词： Collaborative computing Edge Computation Internet of things Signal Processing

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An Enhanced Feature Selection Approach Using Capuchin Search Algorithm for high-dimensional Biological Data Classification 17

An Enhanced Feature Selection Approach Using Capuchin Search...

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17th international conference on INnovations in intelligent SysTems and Applications, INISTA 2023

作者： Jaber, Iyad Hassouneh, Yousef Khemaja, Maha Birzeit University Department of Computer Science Birzeit Palestine ISITCom Hammam Sousse University of Sousse Higher Institute of Applied Science and Technology of Sousse Prince Research Lab Tunisia

ISBN: (纸本)9798350338904

Meta-heuristic algorithms were effectively utilized in solving a wide array of problems in business, science, data mining, and finance. they are population-based approaches that develop novel optimal solutions based on the natural behaviors of creatures. A meta-heuristic method implied in reducing the number of present features and improving classification accuracy is the wrapper feature selection approach which was specifically proposed in this paper. this approach was based on the Capuchin Search Algorithm (CapSA) [1], which is mainly inspired by the functional behavior of Capuchin monkeys in food-searching actions through leaping, swinging, and climbing, all implemented in solving constrained and global optimization problems. this paper aims to enhance the CapSA algorithm to search in a binary search space rather than a continuous one. the proposed improvements on the algorithm can efficiently outperform other present algorithms with fewer selected features, less computational time, and high solution accuracy. the enhanced algorithm, named (EbCapSA) was tested afterwards on several biological benchmark datasets and then followed by a broad comparative study with four other algorithms, including the Gray Wolf Optimizer (GWO) [2], Particle Swarm Optimization (PSO) [3], the Firefly Algorithm (FA) [4], the Whale Optimization Algorithm (WOA) [5] and the original CapSA Algorithm. Overall results rendered a superiority of the proposed approach and clear outperformance based on the quality of the final solution in searching for the most favorable feature subsets. © 2023 IEEE.

关键词： Feature Selection

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13th international conference on Frontier computing, FC 2023

13th International Conference on Frontier Computing, FC 2023

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13th international conference on Frontier computing, FC 2023

ISBN: (纸本)9789819994151

the proceedings contain 180 papers. the special focus in this conference is on Frontier computing. the topics include: Optimization of English Machine Translation Model Based on Neural Network;exploration on Text Detection Optimization Algorithm Based on Neural Network technology;CO2 Emission Prediction of Vehicle Fuel Consumption Based on EMD-LSTM;application of Data Fusion Algorithms in the Data Processing of intelligent Greenhouses;intelligent Film and Television Communication Optical technology Based on Network New Media;design of Heterogeneous Database Encryption and Decryption System Based on Data Mining;research and Implementation of Music Recommendation System Based on Particle Swarm Algorithm;design of High-Speed Multi-channel Data Transmission System Based on Single-Chip Microcomputer;application of distributed Database System in Financial Management;Research on the Development of Japanese MOOC System Based on BP Neural Algorithm;research on Default Risk Prediction of Listed Companies’ Green Credit Based on Deep Learning Algorithm;improvement of Cloud Platform Utilization Based on Evaluation and Optimization of computing Resource Runtime Surplus Capacity;design and Implementation of Operation and Maintenance Monitoring Platform Based on Data Mining Algorithm;civil Aviation Logistics Transportation Route Optimization Algorithm Based on A+ Algorithm;design of Interactive Japanese Translation System Based on Feature Extraction Algorithm;a Preliminary Study on Low-Carbon City Planning Methods Supported by the Cluster Optimization Algorithm;object Detection Algorithms in Embedded Systems and their Applications;Application of Computer VR technology in Digital Media System Design;discuss the Technical Application of Cloud Data Center and Cloud Management Platform;Design of ARM-Based Program Automatic Shelling Data Acquisition System.

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13th international conference on Frontier computing, FC 2023

13th International Conference on Frontier Computing, FC 2023

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13th international conference on Frontier computing, FC 2023

ISBN: (纸本)9789819995370

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13th international conference on Frontier computing, FC 2023

13th International Conference on Frontier Computing, FC 2023

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13th international conference on Frontier computing, FC 2023

ISBN: (纸本)9789819993413

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