Taxonomy of Artist and Art Works Using Hybrid TF-IDF Fuzzy C-Means Clustering

The Indonesian government has just promulgated a Law on the promotion of culture (Law No.5 2017). The Government, through the Tourism Office, determines the method of classification of cultural data using the Taxonomy method. The purpose of this study is taxonomy or mapping of big data art activists in the city of Malang, East Java, Indonesia based on the expertise of each person, so that it will facilitate the search for data for reference decision making. This research tests the calculation based on word linguistics and multi tagging from the data that the artist fills in online instruments. This study proposes the TF-IDF Fuzzy C-Means hybrid as a method of resolving these problems. TF-IDF is used as a feature extraction while Fuzzy C-Means as a clustering method. To find out the performance of the proposed method, this study uses the Variant cluster (V) technique. Based on the research analysis, the level value of V = 0.0000163 is getting smaller. This shows that all cluster variants are getting better

An innovative metaheuristic strategy for solar energy management through a neural networks framework

Proper management of solar energy as an effective renewable source is of high importance toward sustainable energy harvesting. This paper offers a novel sophisticated method for predicting solar irradiance (SIr) from environmental conditions. To this end, an efficient metaheuristic technique, namely electromagnetic field optimization (EFO), is employed for optimizing a neural network. This algorithm quickly mines a publicly available dataset for nonlinearly tuning the network parameters. To suggest an optimal configuration, five influential parameters of the EFO are optimized by an extensive trial and error practice. Analyzing the results showed that the proposed model can learn the SIr pattern and predict it for unseen conditions with high accuracy. Furthermore, it provided about 10% and 16% higher accuracy compared to two benchmark optimizers, namely shuffled complex evolution and shuffled frog leaping algorithm. Hence, the EFO-supervised neural network can be a promising tool for the early prediction of SIr in practice. The findings of this research may shed light on the use of advanced intelligent models for efficient energy development

Fuzzy Clustering-Based Adaptive Regression for Drifting Data Streams

© 1993-2012 IEEE. Current models and algorithms have been increasingly required to learn in a nonstationary environment because the phenomenon of concept drift (or pattern shift) may occur, that is, the assumption that data are identically distributed may be invalid in data streams. Once the data pattern changes, a well-trained model built on the previous, now obsolete data cannot provide an accurate prediction for future data. To obtain reliable prediction, it is important to understand the existing patterns in the data stream and to know which pattern the current examples belong to during the modeling process. However, it is ambiguous to classify an example to a certain pattern in many real-world cases. In this paper, we propose a novel adaptive regression approach, called FUZZ-CARE, to dynamically recognize, train, and store patterns, and assign the membership degree of the upcoming examples belonging to these patterns. Membership degrees are presented by the membership matrix obtained from a kernel fuzzy c-means clustering, which is synchronously trained and adapted with regression parameters. Rather than designing a complicated procedure to continuously chase the newest pattern, which is a common approach in the literature, FUZZ-CARE abstracts useful past information to help predict newly arrived examples. It thus effectively avoids the risk of insufficient training due to the lack of new data and improves prediction accuracy. Experiments on six synthetic datasets and 21 real-world datasets validate the high accuracy and robustness of our approach