A cloud computing based Big-Bang Big-Crunch fuzzy logic multi classifier system for Soccer video scenes classification

2016 ◽  
Vol 8 (4) ◽  
pp. 307-323
Author(s):  
Song Wei ◽  
Hani Hagras ◽  
Daniyal Alghazzawi
Keyword(s):  
Cloud Computing ◽  
Fuzzy Logic ◽  
Big Bang ◽  
Classifier System ◽  
Soccer Video ◽  
Big Crunch

scholarly journals Performance evaluation of an adopted model based on big-bang big-crunch and artificial neural network for cloud applications

2021 ◽  
Vol 48 (4) ◽  
Author(s):  
Pradeep Singh Rawat ◽  
◽  
Robin Singh Bhadoria ◽  
Punit Gupta ◽  
G. P. Saroha ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Cloud Computing ◽  
Big Bang ◽  
Ann Model ◽  
Computing Paradigm ◽  
Big Crunch ◽  
Artificial Neural ◽  
Cloud Applications ◽  
The Way

High-performance computing is changing the way we compute. In the past decade, the cloud computing paradigm has changed the way we compute, communicate, and technology. Cover real-world problems. There are still many complex challenges in the cloud computing paradigm. Improving effective planning strategies is a complex problem in the service-oriented computing paradigm.In this article, our research focuses on improving task scheduler strategies to improve the performance of cloud applications. The proposed model is inspired by an artificial neural network-based system and astrology base scheduler Big-Bang Big-Crunch. The results show that the proposed strategy based on BBBC and neural network is superior to the method based on astrology (BigBang BigCrunch costaware), genetic cost and many other existing methods.The proposed BB-BC-ANN model is validated using standard workload file (San Diego Supercomputer Center (SDSC) Blue Horizon logs). The results show that the proposed BB-BC-ANN model performs better than some of the existing approaches using performance indicators like total completion time (ms), average start time (ms), average finish time(ms), scheduling time(ms), and total execution time(ms).

scholarly journals A Big-Bang Big-Crunch Optimized General Type-2 Fuzzy Logic Approach for Multi-Criteria Group Decision Making

2013 ◽  
Vol 3 (2) ◽  
pp. 117-132 ◽  
Author(s):  
Syibrah Naim ◽  
Hani Hagras
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Group Decision Making ◽  
General Type ◽  
Group Decision ◽  
Big Bang ◽  
Decision Makers ◽  
Big Crunch ◽  
Interval Type

Abstract Multi-Criteria Group Decision Making (MCGDM) aims to find a unique agreement from a number of decision makers/users by evaluating the uncertainty in judgments. In this paper, we present a General Type-2 Fuzzy Logic based approach for MCGDM (GFLMCGDM). The proposed system aims to handle the high levels of uncertainties which exist due to the varying Decision Makers’ (DMs) judgments and the vagueness of the appraisal. In order to find the optimal parameters of the general type-2 fuzzy sets, we employed the Big Bang-Big Crunch (BB-BC) optimization. The aggregation operation in the proposed method aggregates the various DMs opinions which allow handling the disagreements of DMs’ opinions into a unique approval. We present results from an application for the selection of reading lighting level in an intelligent environment. We carried out various experiments in the intelligent apartment (iSpace) located at the University of Essex. We found that the proposed GFL-MCGDM effectively handle the uncertainties between the various decision makers which resulted in producing outputs which better agreed with the users’ decision compared to type 1 and interval type 2 fuzzy based systems.

A Big Bang-Big Crunch Type-2 Fuzzy Logic System for Explainable Predictive Maintenance

2021 ◽  
Author(s):  
Shreyas J Upasane ◽  
Hani Hagras ◽  
Mohammad Hossein Anisi ◽  
Stuart Savill ◽  
Ian Taylor ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Big Bang ◽  
Fuzzy Logic System ◽  
Predictive Maintenance ◽  
Big Crunch ◽  
Logic System

Learning-Based Task Scheduling Using Big Bang Big Crunch for Cloud Computing Environment

2020 ◽  
Vol 13 (2) ◽  
pp. 137-146 ◽  
Author(s):  
Pradeep Singh Rawat ◽  
Priti Dimri ◽  
Punit Gupta
Keyword(s):  
Genetic Algorithm ◽  
Resource Allocation ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Low Cost ◽  
Big Bang ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Big Crunch ◽  
Resource Cost

: Cloud Computing is a growing industry for secure and low cost pay per use resources. Efficient resource allocation is the challenging issue in cloud computing environment. Many task scheduling algorithms used to improve the performance of system. It includes ant colony, genetic algorithm and Round Robin improve the performance but these are not cost efficient at the same time. : Scheduling issue and resource cost resolve using improved meta-heuristic approaches. In this work, a cost aware algorithm improved using Big-Bang Big-Crunch based task mapping is proposed which reduces the execution time and cost paid for the resources at the time of execution. The cost aware meta-heuristic technique used. Results show that the proposed algorithm provides better cost efficiency than the existing genetic algorithm. The proposed Big-Bang Big-Crunch based resource allocation technique evaluated against the Genetic approach. Results: Performance is measured using an optimization criteria tasks completion time and resource operational cost in the duration of execution. The population size and user requests measures the performance of the proposed model. : The simulation shows that the proposed cost and time aware technique outperforms using performance measurement parameters (average finish time, resource cost).

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