OBJECTIVES: This research proposes a novel approach for determining the slimming effect of a herbal composition as a natural medicine for weight loss.
METHODS: To build an effective prediction model, a modern hybrid approach, merging adaptivenetwork- based fuzzy inference system and particle swarm optimization (ANFIS-PSO) was constructed for prediction of changes in anthropometric indices including waist circumference, waist to hip ratio, thigh circumference and mid-upper arm circumference, on female athletes after consumption of caraway extract during ninety days clinical trial.
RESULTS: The outcomes showed that caraway extract intake was effective on lowering all anthropometric indices in female athletes after ninety days trial. The results of analysis by ANFIS-PSO was more accurate compared to SPSS. Also, the efficiency of the proposed approach was confirmed using the existing data.
CONCLUSION: It is concluded that a development in predictive accuracy and simplification capability could be attained by hybrid adaptive neuro-fuzzy techniques as modern approaches in detecting changes in body characteristics. These developed techniques could be more useful and valid than other conventional analytical methods for clinical applications.
AIMS: This paper presents a novel local clustering technique, namely, β-hill climbing, to solve the problem of the text document clustering through modeling the β-hill climbing technique for partitioning the similar documents into the same cluster.
METHODS: The β parameter is the primary innovation in β-hill climbing technique. It has been introduced in order to perform a balance between local and global search. Local search methods are successfully applied to solve the problem of the text document clustering such as; k-medoid and kmean techniques.
RESULTS: Experiments were conducted on eight benchmark standard text datasets with different characteristics taken from the Laboratory of Computational Intelligence (LABIC). The results proved that the proposed β-hill climbing achieved better results in comparison with the original hill climbing technique in solving the text clustering problem.
CONCLUSION: The performance of the text clustering is useful by adding the β operator to the hill climbing.