A proposed hybrid approaches are incorporated in Electric Vehicle (EV) fast charging station (FCS) using (RES). Hybrid approach is improved by Adaptive Hybrid Particle Swarm Optimization (AHPSO) named as AHWPSO, moreover the proposed work Grey Wolf Optimization (GWO) is assist with adaptive hybridize PSO algorithm. Therefore, an overall pricing cost should be reduced maximum Electric Vehicle Charging Station (EVCS) with minimal installation. This simulation work is verified an adaptive time varying weightage parameters to increase the AHWPSO particle diversity factor. Proposed algorithm is incorporated with improve the novelty, and compared the results are recent version of PSO used for EVCS. Its increase the charging ability, energy loss minimization, voltage deviation reduction, and cost minimization. A distribution micro-grid capacity and demand are tested. Similarly, low to peak period energy variations are controlled by proposed algorithm with reduced capacitor bank. Overall control algorithm code is executed buy MATLAB/Simulink platform, the performance of this work listed, and compare to the existing approaches with achievement of maximum efficiency.
For large data, data mining methods were used on a Hadoop-based distributed infrastructure, using map reduction paradigm approaches for rapid data processing. Though data mining approaches are established methodologies, the Apriori algorithm provides a specific strategy for increasing data processing performance in big data analytics by applying map reduction. Apriori property is used to increase the efficiency of level-wise creation of frequent itemsets by minimizing the search area. A frequent itemset’s subsets must also be frequent (Apriori property). If an itemset is rarely, then all of its supersets are infrequent as well. We refined the apriori approach by varying the degree of order in locating frequent item sets in large clusters using map reduction programming. Fixed Pass Combined Counting (FPC) and Dynamic Pass Combined Counting (DPC) is a classical algorithm which are used for data processing from the huge datasets but their accuracy is not up to the mark. In this article, updated Apriori algorithms such as multiplied-fixed-pass combined counting (MFPC) and average time-based dynamic combined counting (ATDFC) are used to successfully achieve data processing speed. The proposed approaches are based on traditional Apriori core notions in data mining and will be used in the map-reduce multi-pass phase by ignoring pruning in some passes. The optimized-MFPC and optimized-ATDFC map-reduce framework model algorithms were also presented. The results of the experiments reveal that MFPC and ATDFC are more efficient in terms of execution time than previously outmoded approaches such as Fixed Pass Combined Counting (FPC) and Dynamic Pass Combined Counting (DPC). In a Hadoop multi-node cluster, this paradigm accelerates data processing on big data sets. Previous techniques were stated in terms of reducing execution time by 60–80% through the use of several passes. Because of the omitted trimming operation in data pre-processing, our proposed new approaches will save up to 84–90% of that time.
Improving data processing in big data is a delicate procedure in our current digital era due to the massive amounts of data created by humans and machines in daily life. Handling this data, creating a repository for storage, and retrieving photos from internet platforms is a difficult issue for businesses and industries. Currently, clusters have been constructed for many types of data, such as text, documents, audio, and video files, but the extraction time and accuracy during data processing remain stressful. Hadoop Distributed File System (HDFS) is a system that provides a large storage area in big data for managing large datasets, although the accuracy level is not as high as desired. Furthermore, query optimization was used to produce low latency and high throughput outcomes. To address these concerns, this study proposes a novel technique for query optimization termed the Enhanced Salp Swarm Algorithm (ESSA) in conjunction with the Modified K-Means Algorithm (MKM) for cluster construction. The process is separated into two stages: data collection and organization, followed by data extraction from the repository. Finally, numerous experiments with assessments were carried out, and the outcomes were compared. This strategy provides a more efficient method for enhancing data processing speed in a big data environment while maintaining an accuracy level of 98% while processing large amounts of data.
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