FIRMACA-Fuzzy intelligent recommendation model using ant clustering algorithm for social networking

Abstract

Error rate is a challenging parameter to be met for most of the recommendation systems where there is a high probability of item movement among the data clusters. In social networking applications, it is often necessary to produce apt user recommendations where ant-based clustering techniques provide best optimal solutions to the clustering problems. However, the existing ant clustering algorithms lack in efficient local search procedures. Also, they are in need of intelligent fuzzy rule improvements for effective cluster partitioning. Therefore, a new model named Fuzzy Intelligent Recommendation Model using Ant Clustering Algorithm (FIRMACA) is proposed in this paper where a combination of biased exploration and fuzzy rules is used for ant clustering. Experimental analysis shows improvement in recommendation metrics-precision, recall, normalized discounted cumulative gain (NDCG) (≤ 5%) and significant reduction (≤ 1%) in misclassification error rate (MER) compared to contemporary ACO-based algorithms. The selected clusters are optimized globally and locally to pull out the best possible redefined clusters.

DFPC: Dynamic Fuzzy-based Primary User Aware clustering for Cognitive Radio Wireless Sensor Networks

“…Although optimization methods have demonstrated their effectiveness in WSNs [13][14][15][16][17], adapting such approaches to CR-WSNs could potentially lead to an increase in the computational overhead associated with the clustering process. In contrast, compared to optimization-focused techniques, fuzzy-based solutions [18][19][20], offer a more lightweight and efficient alternative for addressing clustering challenges in CR-WSNs. These unique challenges serve as the driving force to propose an innovative clustering solution and provide a robust and efficient method explicitly tailored to the distinctive characteristics and requirements of CR-WSNs.…”