ABSTRACT. Harmony Search Algorithm (HSA) is a relatively new nature-inspired algorithm. It evolves solutions in the problem search space by mimicking the musical improvisation process in seeking agreeable harmony measured by aesthetic standards. The Nurse Rostering Problem (NRP) is a well-known NP-hard scheduling problem that aims at allocating the required workload to the available staff nurses at healthcare organizations to meet the operational requirements and a range of preferences. This work investigates research issues of the parameter settings in HSA and application of HSA to effectively solve complex NRPs. Due to the well-known fact that most NRPs algorithms are highly problem (or even instance) dependent, the performance of our proposed HSA is evaluated on two sets of very different nurse rostering problems. The first set represents a real world dataset obtained from a large hospital in Malaysia. Experimental results show that our proposed HSA produces better quality rosters for all considered instances than a genetic algorithm (implemented herein). The second is a set of well-known benchmark NRPs which are widely used by researchers in the literature. The proposed HSA obtains good results (and new lower bound for a few instances) when compared to the current state of the art of meta-heuristic algorithms in recent literature.

Dipper throated optimization (DTO) algorithm is a novel with a very efficient metaheuristic inspired by the dipper throated bird. DTO has its unique hunting technique by performing rapid bowing movements. To show the efficiency of the proposed algorithm, DTO is tested and compared to the algorithms of Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Grey Wolf Optimizer (GWO), and Genetic Algorithm (GA) based on the seven unimodal benchmark functions. Then, ANOVA and Wilcoxon rank-sum tests are performed to confirm the effectiveness of the DTO compared to other optimization techniques. Additionally, to demonstrate the proposed algorithm's suitability for solving complex realworld issues, DTO is used to solve the feature selection problem. The strategy of using DTOs as feature selection is evaluated using commonly used data sets from the University of California at Irvine (UCI) repository. The findings indicate that the DTO outperforms all other algorithms in addressing feature selection issues, demonstrating the proposed algorithm's capabilities to solve complex real-world situations.

A specific phobia is a common anxiety-related disorder that can be treated efficiently using different therapies including exposure therapy or cognitive therapy. One of the most famous methods to treat a specific phobia is exposure therapy. Exposure therapy involves exposing the target patient to the anxiety source or its context without the intention to cause any danger. One promising track of research lies in VR exposure therapy (VRET) and/or AR exposure therapy (ARET), where gradual exposure to a negative stimulus is used to reduce anxiety. In order to review existing works in this field, a systematic search was completed using the following databases: PubMed, ProQuest, Scopus, Web of Science, and Google Scholar. All studies that present VRET and/or ARET solutions were selected. By reviewing the article, each author then applied the inclusion and exclusion criteria, and 18 articles were selected. This systematic review aims to investigate the previous studies that used either VR and/or AR to treat any type of specific phobia in the last five years. The results demonstrated a positive outcome of virtual reality exposure treatment in the treatment of most phobias. In contrast, some of these treatments did not work for a few specific phobias in which the standard procedures were more effective. Besides, the study will also discuss the best of both technologies to treat a specific phobia. Furthermore, this review will present the limitations and future enhancements in this field.