In higher education institutions (HEI), particularly in biology and medical education, the use of 3D animation, virtual reality, and simulation offers great potential in terms of enhancing learning and engaging students. Higher education researchers are still investigating virtual reality’s possibilities and outcomes in various fields. This study focuses on the effects of 3D gamification using an Artificial Intelligence integrated Internet of Medical Things (AIoMT) implemented with virtual reality application for biology and medical students to learn about the human brain. Nowadays, both theoretical and practical education frequently incorporate virtual reality and augmented reality. Virtual tours of the human body’s systems are offered to biology students so that they may comprehend such systems’ functions. This study focuses on the use of 3D animation, virtual reality, and simulation in medical education, with a specific focus on the effects of a 3D gamification app using the Internet of Medical Things (AIoMT) on medical professionals’ passion for learning. This study uses the ARCS model and SEM analysis to examine the impact of virtual reality on students’ motivation and learning. The results show that virtual reality positively impacts motivation and the understanding of the concept-to-execution process through practice and simulation-based training. To assess how well students are learning, what they are analyzing, and how well they can understand the objects of analysis, a 3D-simulation-based and user-feedback-based design has been developed using the proposed research methodology. According to this article’s findings, a smartphone app that uses virtual reality can help medical professionals better understand the concept-to-execution process through practice. VR simulation-based training, as well as Biology teachers or medical colleges, can offer high-definition 3D VR models rather than organs in jars to understand the human anatomy and its functions more experientially and effectively.

Immersive technology is one of the emerging trends in education in the twenty-first century, whether that be university training programs, or real-world technical training. However, there has been very little research into the effects and consequences of virtual reality. Various types of eLearning have been used to transmit information in recent years, and especially for medical education, virtual reality plays a vital role in terms of providing effective training; the virtual reality app bridged the gap between traditional learning and practical exposure. This unified reality environment enables users to simulate real-life scenarios and obtain useful information that would otherwise be unavailable. In the real world, it is difficult to grasp. In India’s education sector, virtual reality technology is also being researched at an early stage. The goal of this research paper is to assess and explain the impact of virtual reality simulators on medical students’ desire to learn. In the classroom, the core motivation hypothesis is used to boost motivation. The attention, relevance, confidence, and satisfaction (ARCS) model influenced the interpretation of virtual reality’s impact on student motivation and content update implementation. The study examined the numerous variables of virtual reality simulators and their impact on medical education, using the ARCS model as a factor analysis. According to the study, students wsould learn more and be more motivated if virtual reality simulators were used. Attention, relevance, satisfaction, and confidence indicators were used to develop motivational variables, and the results were significant. We have taken the sample of 607 students’ data for this analysis, through which we have identified the potential of VR made available to students, as well as the faculty, which has the potential to transform medical education. Instructors may be wary of incorporating new technology like VR into their curriculums, but with the support of their students’ learning habits, this may not be a problem. It may help instructors feel more confident, while also enhancing the relationship between faculty, librarians, and students.

The ordered clustered travelling salesman problem is a variation of the usual travelling salesman problem in which a set of vertices (except the starting vertex) of the network is divided into some prespecified clusters. The objective is to find the least cost Hamiltonian tour in which vertices of any cluster are visited contiguously and the clusters are visited in the prespecified order. The problem is NP-hard, and it arises in practical transportation and sequencing problems. This paper develops a hybrid genetic algorithm using sequential constructive crossover, 2-opt search, and a local search for obtaining heuristic solution to the problem. The efficiency of the algorithm has been examined against two existing algorithms for some asymmetric and symmetric TSPLIB instances of various sizes. The computational results show that the proposed algorithm is very effective in terms of solution quality and computational time. Finally, we present solution to some more symmetric TSPLIB instances.

The multiple traveling salesman problem (MTSP) is one of the combinatorial optimization problems, which can be defined as follows: There are a `m' number of salesmen who must travel to `n' number of cities starting with depot and ending at the same depot. Furthermore, the salesmen must travel from one city to another continuously without repeating any city that has been crossed over by the salesmen and considering the shortest path during this travel. The MTSP is a generalization of the travelling salesman problem (TSP), where more than one salesman is used in the solution. The problem is NP-hard, and it has many applications in real life. This research aims to develop genetic algorithms (GAs) that considers six different crossover operators separately in order to find optimal solutions, and then compare GAs using different crossover operators. The efficiency of the algorithms is shown by experimental study on benchmark TSPLIB instances of various sizes. Experimental study shows that GA using sequential constructive crossover (SCX) is the best crossover method among six crossover methods.

We consider the multiple travelling salesman Problem (MTSP) that is one of the generalization of the travelling salesman problem (TSP). For solving this problem genetic algorithms (GAs) based on numerous crossover operators have been described in the literature. Choosing effective crossover operator can give effective GA. Generally, the crossover operators that are developed for the TSP are applied to the MTSP. We propose to develop simple and effective GAs using sequential constructive crossover (SCX), adaptive SCX, greedy SCX, reverse greedy SCX and comprehensive SCX for solving the MTSP. The effectiveness of the crossover operators is demonstrated by comparing among them and with another crossover operator on some instances from TSPLIB of various sizes with different number of salesmen. The experimental study shows the promising results by the crossover operators, especially CSCX, for the MTSP.