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Technological barriers to creating regional resilience in digital platform-based firms: Compound of performance sensitivity analysis and birch algorithm

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THUNDERBIRD INTERNATIONAL BUSINESS REVIEW 2024年第2期66卷 135-149页

作者： Salamzadeh, Aidin Dana, Leo-Paul Ebrahimi, Pejman Hadizadeh, Morteza Mortazavi, Samira GECC Innovat & Entrepreneurship Res Lab London England ICD Business Sch Paris Paris France Hungarian Univ Agr & Life Sci MATE Doctoral Sch Econ & Reg Sci Godollo Hungary GECC Innovat & Entrepreneurship Res Lab 18 King William St London EC4N 7BP England

Entrepreneurial ventures face various problems contributing to the regional resilience of their districts. Among such firms are digital platform-based businesses that could have exponential impacts-indeed, if they succeed in overcoming the barriers. Thus, this study aims to identify the major technological barriers to creating regional resilience in Iran's innovation districts. This study uses the analytical hierarchy process method and an unsupervised machine learning algorithm, as well as the Delphi technique. Online panel surveys are conducted to collect data from experts in the field of online social platforms. The findings show that digital literacy barriers and cultural barriers are the criteria with the uppermost and lowermost importance. Performance sensitivity analysis illustrates that considering the C1 and C4 criteria, the Digikala platform is more resistant to technological barriers compared to other platforms. Furthermore, based on C2 and C3 criteria, it can be stated that the Snapp platform reveals better resistance than the other platforms. The importance-performance matrix also shows that all platforms need more attention in terms of performance. Besides, the results of the birch analysis show that the present model's accuracy is 88%. Meanwhile, outputs show that experts are divided into four groups or four different thoughts.

关键词： analytical hierarchy process (AHP) birch algorithm digital platform innovation regions performance sensitivity analysis resilience technological barriers unsupervised learning

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Fast Node Clustering Based on an Improved birch algorithm for Data Collection Towards Software-Defined Underwater Acoustic Sensor Networks

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IEEE SENSORS JOURNAL 2021年第22期21卷 25480-25488页

作者： Lin, Chuan Han, Guangjie Wang, Tingting Bi, Yuanguo Du, Jiaxin Zhang, Bin Northeastern Univ Software Coll Shenyang 110819 Peoples R China Hohai Univ Dept Informat & Commun Syst Changzhou 213022 Peoples R China Macau Univ Sci & Technol Fac Informat Technol Taipa Macao Peoples R China Northeastern Univ Sch Comp Sci & Engn Shenyang 110819 Peoples R China Dalian Univ Technol Sch Software Dalian 116024 Peoples R China

In this paper, we take an in-depth study on the data collection scheduling issue in underwater acoustic sensor networks (UASNs). To address this issue, we propose the use of software-defined networking (SDN) technique, based on which the paradigm of software-defined underwater acoustic sensor networks (SD-UASNs) is proposed. With SD-UASNs, we propose an efficient data collection scheme on account of node clustering. We take the data scale into account, propose a fast node clustering approach based on an improved birch algorithm including node pre-clustering and node re-clustering. In particular, for the node pre-clustering procedure, we improve the cluster feature tree of the birch algorithm and limit the nodes in each cluster to be within an optimal distance. Based on the proposed pre-clustering approach, we propose a greedy data collection scheme that can heuristically seek optimal solutions when the compressed sampling is taken into account. Evaluation results demonstrate that our scheme performs better in network efficiency than some popular schemes.

关键词： Data collection Sensors Routing Network architecture Oceans Monitoring Energy consumption Underwater acoustic sensor networks software-defined networking birch algorithm compressed sampling data collection

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birch algorithm and data management in financial enterprises based on dynamic panel GMM test

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第2期22卷 S4231-S4237页

作者： Xiao, He Hongkong Baptist Univ Beijing Normal Univ Dept Business & Management United Int Coll Zhuhai 519080 Guangdong Peoples R China

A risk management strategy to inspect birch algorithm and data in financial enterprises based on dynamic panel GMM was proposed to increase effectiveness of risk management for financial enterprises. Firstly, description method of dynamic panel GMM was offered to establish primary model of partners. Secondly, design and realization of birch algorithm on parallelization of Spark was researched on focus and Spark transformation operation of much time consumption in the process of parallelization was obtained through analysis of theoretical performance;meanwhile, shuffle and disk accessing frequency can be reduced to achieve performance optimization according to directed acyclic graph DAG of parallelized birch algorithm. Finally, the proposed algorithm was applied to risk management of financial enterprises.

关键词： Dynamic panel birch algorithm Risk management of financial enterprises Spark frame

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Lbirch: An Improved birch algorithm Based on Link 2018

LBIRCH: An Improved BIRCH Algorithm Based on Link

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10th International Conference on Machine Learning and Computing (ICMLC)

作者： Guo, Dongwei Chen, Jingwen Chen, Yingjie Li, Zhiyu Jilin Univ JLU Coll Comp Sci & Technol Changchun Jilin Peoples R China

ISBN: (纸本)9781450363532

birch algorithm is a hierarchical clustering method which is suitable for clustering very large datasets especially. The traditional birch algorithm uses distance to control the shape of clusters. However, the clustering results effect of the non-spherical dataset is not good, and in some cases the non-spherical clusters are divided into different clusters. In order to break through such limitation, this paper presents an improved birch algorithm based on Link, which draws on the link concept of ROCK algorithm. Experiments show that the improved algorithm can cluster any shape clusters.

关键词： birch algorithm Link Clustering algorithm

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Energy management in HDHEV with dual APUs: Enhancing soft actor-critic using clustered experience replay and multi-dimensional priority sampling

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ENERGY 2025年 319卷

作者： Zhang, Dongfang Sun, Wei Zou, Yuan Zhang, Xudong Beijing Inst Technol Sch Mech Engn Natl Engn Lab Elect Vehicles Beijing 100081 Peoples R China

Traditional experience sampling methods in reinforcement learning often overlook sample diversity, which limits learning effectiveness. This research proposes an Enhanced Soft Actor-Critic (ESAC) algorithm for energy management in Heavy-Duty Hybrid Electric Vehicles equipped with dual Auxiliary Power Units. ESAC addresses the limitations of existing methods by integrating multi-dimensional evaluation metrics and the birch clustering algorithm for online experience sampling. The proposed approach optimizes performance in complex multipower source systems, ensuring diverse sample selection and enhancing learning capacity. Comparative analyses of ESAC against TD3, SAC, and SAC-birch-PER demonstrate that ESAC achieves superior convergence performance, with a nearly 10-episode faster convergence rate than Prioritized Experience Replay. Additionally, ESAC shows significant reductions in fuel consumption-up to 5.32 % compared to the dynamic programming benchmark-outperforming SAC and TD3 by 10.54 % and 8.84 %, respectively. These results highlight that enhancing data diversity and prioritization not only stabilizes learning but also optimizes fuel efficiency in lowspeed, high-torque conditions, thereby providing a robust solution for real-world energy management challenges.

关键词： Heavy-duty hybrid electric vehicles Energy management strategy Soft actor-critic birch algorithm Multi-dimensions priority

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Towards Deep Industrial Transfer Learning: Clustering for Transfer Case Selection 27

Towards Deep Industrial Transfer Learning: Clustering for Tr...

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IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Maschler, Benjamin Knodel, Tim Weyrich, Michael Univ Stuttgart Inst Ind Automat & Software Engn Stuttgart Germany

ISBN: (数字)9781665499965

ISBN: (纸本)9781665499965

Industrial transfer learning increases the adaptability of deep learning algorithms towards heterogenous and dynamic industrial use cases without high manual efforts. The appropriate selection of what to transfer can vastly improve a transfer's results. In this paper, a transfer case selection based upon clustering is presented. Founded on a survey of clustering algorithms, the birch algorithm is selected for this purpose. It is evaluated on an industrial time series dataset from a discrete manufacturing scenario. Results underline the approaches' applicability caused by its results' reproducibility and practical indifference to sequence, size and dimensionality of (sub)datasets to be clustered sequentially.

关键词： birch algorithm Clustering Deep Learning Industrial Transfer Learning Transfer Case Selection

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Large-Scale Data Clustering algorithm Based on Quantum Immune Regulation Network

Large-Scale Data Clustering Algorithm Based on Quantum Immun...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Li, Yangyang Bai, Xiaoyu Hou, Xiaoju Jiao, Licheng Xidian Univ Int Res Ctr Intelligent Percept & ComputatMinist Joint Int Res Lab Intelligent Percept & Computat Key Lab Intelligent Percept & Image Understanding Xian 710071 Shaanxi Peoples R China

ISBN: (纸本)9781538627266

Clustering is one of the unsupervised machine learning methods in data mining. It organizes a collection of data into different groups. With the rapid development of information technology, the scale of data is increasingly large, and the typeof data tends to diversify, the traditional clustering algorithms have been unable to effectively solve the existing problems. In this paper, a large-scale data clustering algorithm based on quantum immune regulation network is proposed by improving the artificial immune network clustering algorithm. In order to improve the performanceof artificial immune network, the quantum immune mechanism is introduced, which avoids the local optimal solutions and premature phenomenon. It can adaptively obtain the natural death threshold and reduce the sensitivity of the algorithm to the parameters by improving the Balanced Iterative Reducing and Clustering using Hierarchies (birch) algorithm. A number of datasets including artificial datasets and real-world datasets are used to evaluate the performance of the proposed algorithm compared with other existing clustering algorithms, such as K-means, FCM, OFCM, IWKO2 and birch. The simulation results demonstrate that the proposed algorithm improves the clustering accuracy and reduces the dependence of the algorithm on the parameters.

关键词： clustering large-scale data immune regulation network quantum immune mechanism birch algorithm

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Large-Scale Data Clustering algorithm Based on Quantum Immune Regulation Network

Large-Scale Data Clustering Algorithm Based on Quantum Immun...

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IEEE Symposium Series on Computational Intelligence

作者： Yangyang Li Xiaoyu Bai Xiaoju Hou Licheng Jiao Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi'an Shaanxi Province China

Clustering is one of the unsupervised machine learning methods in data mining. It organizes a collection of data into different groups. With the rapid development of information technology, the scale of data is increasingly large, and the type of data tends to diversify, the traditional clustering algorithms have been unable to effectively solve the existing problems. In this paper, a large-scale data clustering algorithm based on quantum immune regulation network is proposed by improving the artificial immune network clustering algorithm. In order to improve the performance of artificial immune network, the quantum immune mechanism is introduced, which avoids the local optimal solutions and premature phenomenon. It can adaptively obtain the natural death threshold and reduce the sensitivity of the algorithm to the parameters by improving the Balanced Iterative Reducing and Clustering using Hierarchies (birch) algorithm. A number of datasets including artificial datasets and real-world datasets are used to evaluate the performance of the proposed algorithm compared with other existing clustering algorithms, such as K-means, FCM, OFCM, IWKO2 and birch. The simulation results demonstrate that the proposed algorithm improves the clustering accuracy and reduces the dependence of the algorithm on the parameters.

关键词： Clustering Large-scale data Immune regulation network Quantum immune mechanism birch algorithm

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Density Clustering Based SVM and Its Application to Polyadenylation Signals

Density Clustering Based SVM and Its Application to Polyaden...

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第三届最优化与系统生物学国际研讨会

作者： Yuanhai Shao Yining Feng Jing Chen Naiyang Deng College of Science China Agricultural University

Support vector machines(SVM) have been promising methods for classification analysis due to their solid mathematical ***-based SVMs are used to solve large samples classification problems and reduce the computational *** this paper,we present a density clustering based SVM(DCB-SVM) method to predict polyadenylation signal(PAS) in human DNA and mRNA *** decrease the original data scale by using the density restricted hierarchical *** strategy leads to solving smaller sized problems,making DCB-SVM work faster than standard *** to the results of the PAS experiment,the proposed method is not only fast,but also shows better improvement in sensitivity than the SVM.

关键词： Support vector machines Polyadenylation signals birch algorithm

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