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The implications of blockchain-coordinated information sharing within a supply chain: A simulation study

Blockchain(Research and Applications) 2023年第1期4卷 21-35页

作者： Aaliya Sarfaraz ripon k.chakrabortty Daryl L.Essam School of Engineering and Information Technology University of New South WalesCanberra2600Australia

The profitability of a supply chain(SC)is proportional to the stability of all its stakeholders as well as their consistent information sharing with an effective and efficient communication *** inefficiencies,such as the bullwhip effect(BWE)and product unavailability,may be caused by a lack of coordination in an *** importance of sharing consumer demand has been quantified by comprehensive studies under the assumption that all SC participants will access the same ***,only a few studies have studied the effect of minimal coordination or limited visibility of information while considering their effect on the overall efficiency of an *** work primarily leverages blockchain technology(BCT)to create a simulation *** do this,an SC BWE-based model is initially *** that,a blockchain-based robust information sharing system is ***,information sharing is challenging,and SC stakeholders may not really trust each other and hence be reluctant to share sensitive *** that,this paper propose an improved proof-ofauthority(PoA)consensus algorithm that will increase trust in a decentralized SC *** experiments are carried out to demonstrate the effectiveness of our approach,and the simulation results clearly demonstrate the effectiveness of information sharing in a supply chain via blockchain,as well as that trust between partners tends to increase overall SC efficiency and reduce BWE.

关键词： Blockchain Supply chain collaboration Bullwhip effect Lead time Consensus protocol Trust

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An Improved Jellyfish Algorithm for Multilevel Thresholding of Magnetic Resonance Brain Image Segmentations

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Computers, Materials & Continua 2021年第9期68卷 2961-2977页

作者： Mohamed Abdel-Basset Reda Mohamed Mohamed Abouhawwash ripon k.chakrabortty Michael J.Ryan Yunyoung Nam Faculty of Computers and Informatics Zagazig UniversityZagazig44519Egypt Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA Capability Systems Centre School of Engineering and ITUNSW CanberraAustralia Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea

Image segmentation is vital when analyzing medical images,especially magnetic resonance(MR)images of the ***,several image segmentation techniques based on multilevel thresholding have been proposed for medical image segmentation;however,the algorithms become trapped in local minima and have low convergence speeds,particularly as the number of threshold levels ***,in this paper,we develop a new multilevel thresholding image segmentation technique based on the jellyfish search algorithm(JSA)(an optimizer).We modify the JSA to prevent descents into local minima,and we accelerate convergence toward optimal *** improvement is achieved by applying two novel strategies:Rankingbased updating and an adaptive ***-based updating is used to replace undesirable solutions with other solutions generated by a novel updating scheme that improves the qualities of the removed *** develop a new adaptive strategy to exploit the ability of the JSA to find a best-so-far solution;we allow a small amount of exploration to avoid descents into local *** two strategies are integrated with the JSA to produce an improved JSA(IJSA)that optimally thresholds brain MR *** compare the performances of the IJSA and JSA,seven brain MR images were segmented at threshold levels of 3,4,5,6,7,8,10,15,20,25,and *** was compared with several other recent image segmentation algorithms,including the improved and standard marine predator algorithms,the modified salp and standard salp swarm algorithms,the equilibrium optimizer,and the standard JSA in terms of fitness,the Structured Similarity Index Metric(SSIM),the peak signal-to-noise ratio(PSNR),the standard deviation(SD),and the Features Similarity Index Metric(FSIM).The experimental outcomes and the Wilcoxon rank-sum test demonstrate the superiority of the proposed algorithm in terms of the FSIM,the PSNR,the objective values,and the SD;in terms of the SSIM,IJSA was competitive with

关键词： Magnetic resonance imaging brain image segmentation artificial jellyfish search algorithm ranking method local minima Otsu method

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Medical Feature Selection Approach Based on Generalized Normal Distribution Algorithm

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Computers, Materials & Continua 2021年第12期69卷 2883-2901页

作者： Mohamed Abdel-Basset Reda Mohamed ripon k.chakrabortty Michael J.Ryan Yunyoung Nam Mohamed Abouhawwash Faculty of Computers and Informatics Zagazig UniversityZagazig44519Egypt Capability Systems Centre School of Engineering and ITUNSWCanberraAustralia Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast Lansing48824MIUSA

This paper proposes a new pre-processing technique to separate the most effective features from those that might deteriorate the performance of the machine learning classifiers in terms of computational costs and classification accuracy because of their irrelevance,redundancy,or less information;this pre-processing process is often known as feature *** technique is based on adopting a new optimization algorithm known as generalized normal distribution optimization(GNDO)supported by the conversion of the normal distribution to a binary one using the arctangent transfer function to convert the continuous values into binary ***,a novel restarting strategy(RS)is proposed to preserve the diversity among the solutions within the population by identifying the solutions that exceed a specific distance from the best-so-far and replace them with the others created using an effective updating *** strategy is integrated with GNDO to propose another binary variant having a high ability to preserve the diversity of the solutions for avoiding becoming stuck in local minima and accelerating convergence,namely improved GNDO(IGNDO).The proposed GNDO and IGNDO algorithms are extensively compared with seven state-of-the-art algorithms to verify their performance on thirteen medical instances taken from the UCI *** is shown to be superior in terms of fitness value and classification accuracy and competitive with the others in terms of the selected *** the principal goal in solving the FS problem is to find the appropriate subset of features that maximize classification accuracy,IGNDO is considered the best.

关键词： Generalized normal distribution optimization feature selection transfer function novel restarting strategy UCI repository

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