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This paper aims to improve one of the recently proposed metaheuristic approaches known as Lévy flight distribution (LFD) algorithm by adopting a well-known simplex search algorithm named Nelder-Mead (NM) method. Three new strategies were utilized to demonstrate the improved capability of the original LFD algorithm. In the first strategy, NM was run twice as much the number of iterations of LFD after the latter completes its task. In the second strategy, NM was applied after each iterations of LFD instead of waiting for the completion of the latter. Lastly, in the third strategy, NM was applied after each iterations of LFD and run for the total number of current iterations of the latter algorithm. Well-known unimodal and multimodal benchmark functions were adopted, and statistical analysis was performed for performance evaluation. Further assessment was carried out through a nonparametric statistical test. The obtained results have shown the proposed versions of LFD algorithm provide significant performance improvement in general. In addition, the efficiency of the third strategy was found to be better for NM modified LFD algorithm which has greater balance between global and local search stages and can be used as an effective tool for function optimization.

Central government budget expenditures, which are very important for parliamentary systems, are respectively in Turkey; It goes through the stages of preparation, approval, implementation and control. Stability is essential for these public expenditures, which will take place in the following fiscal year. Otherwise, budget deficit occurs as a result of public expenditures being more than public revenues. Central government budget expenditures are divided into two as economic and functional classification. The parameters included in the economic and functional classification are presented as the sum of the expenses under them. The aim of this study is to examine the cluster analysis results of Central government budget expenditures per capita in Turkey with Self-Organizing Maps, which is an artificial neural network model and also a clustering analysis method.

Dear Readers, We have completed our book thanks to our friends who do valuable studies in the light of science. The concept of health, which continues to be important in every age, has once again revealed its importance with the covid-19 epidemic that has affected the world for the last few years and the earthquake disaster that our country has experienced deeply. We, the healthcare professionals who believe in the continuity of education, think that societies equipped with knowledge and using intelligence and evidence-based knowledge will pass the health exams with much less injury and loss. For this reason, the aim of the book for us is to shed some light on future studies and to illuminate the darkness by warning its readers through the known information and unknowns in it. We hope that our presented book will be easy to understand and will open new horizons for all humanity as well as supporting scientists from faculties of medicine, dentistry, pharmacy and veterinary medicine. I would like to thank the scientists working in the health sciences and our team of authors who supported our book. Dr. Enes Karaman