High-Accuracy Reliability Prediction Approach for Blockchain Services Under BaaS

2020 ◽  
pp. 648-660
Author(s):  
Jianlong Xu ◽  
Zicong Zhuang ◽  
Kun Wang ◽  
Wei Liang
Keyword(s):  
High Accuracy ◽  
Reliability Prediction ◽  
Prediction Approach

A Stochastic Approach to Integrated Vehicle Reliability Prediction

2009 ◽  
Author(s):  
Justin Madsen ◽  
Dan Ghiocel ◽  
David Gorsich ◽  
David Lamb ◽  
Dan Negrut
Keyword(s):  
Integrated Approach ◽  
Stochastic Approach ◽  
Reliability Prediction ◽  
Operational Environment ◽  
Research Project ◽  
Element Analysis ◽  
Ground Vehicle ◽  
Us Army ◽  
University Of Wisconsin ◽  
Prediction Approach

This paper addresses some aspects of an on-going multiyear research project of GP Technologies in collaboration with University of Wisconsin-Madison for US Army TARDEC. The focus of this research project is to enhance the overall vehicle reliability prediction process. A combination of stochastic models for both the vehicle and operational environment are utilized to determine the range of the system dynamic response. These dynamic results are used as inputs into a finite element analysis of stresses on subsystem components. Finally, resulting stresses are used for damage modeling and life and reliability predictions. This paper describes few selected aspects of the new integrated ground vehicle reliability prediction approach. The integrated approach combines the computational stochastic mechanics predictions with available statistical experimental databases for assessing vehicle system reliability. Such an integrated reliability prediction approach represents an essential part of an intelligent virtual prototyping environment for ground vehicle design and testing.

An Adaptive Fuzzy Clustering Technique for Traffic Prediction of Packet-switched Networks

2001 ◽  
Vol 5 (3) ◽  
pp. 180-188
Author(s):  
Yau-Hwang Kuo ◽  
◽  
Mong-Fong Horng ◽  
Jung-Hsien Chiang
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
High Accuracy ◽  
Traffic Prediction ◽  
Prediction Errors ◽  
Traffic Patterns ◽  
Switched Networks ◽  
Real Video ◽  
And Performance ◽  
Prediction Approach

Traffic prediction is significant to QoS design because it assists efficient management of network resources to improve the reliability and performance of the next generation Internet. The unavoidable traffic variation caused by diverse Internet services complicates traffic prediction, particularly in a multi-hop network. To simplify the complicated statistical analysis used in traditional approaches, an adaptive traffic prediction approach featuring robustness, high accuracy and high adaptability is proposed in this paper. The proposed approach bases on a novel fuzzy clustering algorithm to generalize and unveil the hidden structure of traffic patterns. The unveiled structure represents the characteristics of the target traffic. Therefore, it can be referenced to predict traffic in a limited time period by fuzzy matching. To track the variation of target traffic, the proposed approach adopts an incremental and dynamic on-line clustering procedure so that the prediction can maintain high accuracy under traffic variation. To verify the performance of the proposed approach and investigate its properties, the periodical, Poisson and real video traffic patterns have been used to experiment. The experimental results showed an excellent performance of the developed adaptive predictor. The prediction errors, in average, are near 2.2%, 13.6% and 7.62% for periodical, Poisson and real video traffics, respectively.

A Multiple-Model Reliability Prediction Approach for Condition-Based Maintenance

2018 ◽  
Vol 67 (3) ◽  
pp. 1364-1376 ◽  
Author(s):  
Kim Verbert ◽  
Bart De Schutter ◽  
Robert Babuska
Keyword(s):  
Condition Based Maintenance ◽  
Reliability Prediction ◽  
Multiple Model ◽  
Model Reliability ◽  
Prediction Approach

Metric-based software reliability prediction approach and its application

2016 ◽  
Vol 22 (4) ◽  
pp. 1579-1633 ◽  
Author(s):  
Ying Shi ◽  
Ming Li ◽  
Steven Arndt ◽  
Carol Smidts
Keyword(s):  
Software Reliability ◽  
Reliability Prediction ◽  
Software Reliability Prediction ◽  
Prediction Approach

scholarly journals A New Prediction Approach for Preventing Default Customers from Applying Personal Loans Using Machine Learning

2021 ◽  
Vol 10 (12) ◽  
pp. 71-82
Author(s):  
Mohamed H. Khedr ◽  
Nesrine A. Azim ◽  
Ammar M. Ammar
Keyword(s):  
Machine Learning ◽  
Personal Data ◽  
High Accuracy ◽  
Decision Tree Classifier ◽  
Application Form ◽  
Machine Learning Classifiers ◽  
Predictive Method ◽  
Learning Classifiers ◽  
Tree Classifier ◽  
Prediction Approach

In the Egyptian banking industry, loan officers use pure judgment to make personal loan approval decisions. In this paper, we develop a new predictive method for default customers' loans using machine learning. The new predictive method uses the available personal data and historical credit data to evaluate the credit trust-worthiness of customers to obtain loans. We used the ABE dataset for training and testing, as we used 10 features from the application form and i- score report class that could give great help to credit officers for taking the right decision through avoiding customer selection using random techniques. The collected dataset was analysed by using various machine learning classifiers based on important selected features, to obtain high accuracy. We compared the performance of several machine learning classifiers before and after feature selection. We have found that in terms of high accuracy, the most important features are (activity – income – loan) and in terms of better performance the decision tree classifier has surpassed any other machine learning classifier with significant prediction accuracy of almost 94.85%.

scholarly journals ELPKG: A High-Accuracy Link Prediction Approach for Knowledge Graph Completion

Symmetry ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1096 ◽  
Author(s):  
Jiangtao Ma ◽  
Yaqiong Qiao ◽  
Guangwu Hu ◽  
Yanjun Wang ◽  
Chaoqin Zhang ◽  
...  
Keyword(s):  
Link Prediction ◽  
Large Scale ◽  
High Accuracy ◽  
Prediction Algorithm ◽  
Knowledge Graph ◽  
Named Entity ◽  
Knowledge Reasoning ◽  
Novel Approach ◽  
Soft Logic ◽  
Prediction Approach

Link prediction in knowledge graph is the task of utilizing the existing relations to infer new relations so as to build a more complete knowledge graph. The inferred new relations plus original knowledge graph is the symmetry of completion knowledge graph. Previous research on link predication only focuses on path or semantic-based features, which can hardly have a full insight of features between entities and may result in a certain ratio of false inference results. To improve the accuracy of link predication, we propose a novel approach named Entity Link Prediction for Knowledge Graph (ELPKG), which can achieve a high accuracy on large-scale knowledge graphs while keeping desirable efficiency. ELPKG first combines path and semantic-based features together to represent the relationships between entities. Then it adopts a probabilistic soft logic-based reasoning method that effectively solves the problem of non-deterministic knowledge reasoning. Finally, the relation between entities is completed based on the entity link prediction algorithm. Extensive experiments on real dataset show that ELPKG outperforms baseline methods on hits@1, hits@10, and MRR.

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