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.

Road traffic accidents in Saudi Arabia have become a serious issue because many of these accidents lead to deaths, injuries, and financial losses. Human lives are often lost in road accidents due to the delay in accident detection by medical assistance. In fact, the accident’s location and the driver’s personal information are considered critical information that plays a vital role in preserving human life. Additionally, previous studies have found a limitation in the encryption of sensitive data; in fact, a leak of private information is thought to be one of the challenges that restrict the use of IoT devices. To resolve this problem, this research presents an intelligent security framework, and an Internet-of-Things-based system is proposed for immediate accident detection. Thus, this system requires the highest level of security and privacy to maintain the driver’s privacy. Moreover, the design science research methodology was followed to design and evaluate the artifacts. Thus, the study’s research resulted in the ability to design a secure and effective IoT-based system to detect and report a car accident instantly. In addition, the message is encrypted using Elliptic Curve Integrated Encryption and sent through Message Queuing Telemetry Transport over GSM. The study’s overall results show the flexibility with which the proposed artifact can be used for other purposes related to the IoT security framework to send and encrypt critical information.

Virtual Reality (VR) has made considerable gains in the consumer and professional markets. As VR has progressed as a technology, its overall usefulness for educational purposes has grown. On the other hand, the educational field struggles to keep up with the latest innovations, changing affordances, and pedagogical applications due to the rapid evolution of technology. Therefore, many have elaborated on the potential of virtual reality (VR) in learning. This research proposes a novel techniques customized curriculum for medical students and recommendations for their learning process based on deep learning techniques. Here the data has been collected based on the pre-historic performance of the student and their current requirement and these data have been created as a dataset. Then this has been processed for analysis based on CAD system integrated with deep learning techniques for creating a customized curriculum. Initially this data has been processed and analysed to remove missing and invalid data. Then these data were classified for creation of the curriculum using a gradient decision tree integrated with naïve Bayes. From this, the customized curriculum has been generated. Based on this customized curriculum, the learning approach recommendation has been carried out using the fuzzy rules integrated knowledge-based recommendation system. The experimental results of the proposed technique have been carried out with an accuracy of 98%, specificity of 82%, F-1 score of 79%, information overload of 75%, and precision of 81%.

Smart cities provide ease in lifestyle to their community members with the help of Information and Communication Technology (ICT). It provides better water, waste and energy management, enhances the security and safety of its citizens and offers better health facilities. Most of these applications are based on IoT-based sensor networks, that are deployed in different areas of applications according to their demand. Due to limited processing capabilities, sensor nodes cannot process multiple tasks simultaneously and need to offload some of their tasks to remotely placed cloud servers, which may cause delays. To reduce the delay, computing nodes are placed in different vicinitys acting as fog-computing nodes are used, to execute the offloaded tasks. It has been observed that the offloaded tasks are not uniformly received by fog computing nodes and some fog nodes may receive more tasks as some may receive less number of tasks. This may cause an increase in overall task execution time. Furthermore, these tasks comprise different priority levels and must be executed before their deadline. In this work, an Efficient Offloaded Task Execution for Fog enabled Smart cities (EOTE − FSC) is proposed. EOTE − FSC proposes a load balancing mechanism by modifying the greedy algorithm to efficiently distribute the offloaded tasks to its attached fog nodes to reduce the overall task execution time. This results in the successful execution of most of the tasks within their deadline. In addition, EOTE − FSC modifies the task sequencing with a deadline algorithm for the fog node to optimally execute the offloaded tasks in such a way that most of the high-priority tasks are entertained. The load balancing results of EOTE − FSC are compared with state-of-the-art well-known Round Robin, Greedy, Round Robin with longest job first, and Round Robin with shortest job first algorithms. However, fog computing results of EOTE − FSC are compared with the First Come First Serve algorithm. The results show that the EOTE − FSC effectively offloaded the tasks on fog nodes and the maximum load on the fog computing nodes is reduced up to 29%, 27.3%, 23%, and 24.4% as compared to Round Robin, Greedy, Round Robin with LJF and Round Robin with SJF algorithms respectively. However, task execution in the proposed EOTE − FSC executes a maximum number of offloaded high-priority tasks as compared to the FCFS algorithm within the same computing capacity of fog nodes.

In the last decade, the need for smart-space design has been on the rise. Various data collected from Internet-of-Things (IoT) and sensors are used to optimize the operation of smart spaces, which, in urban areas, are evolving into smart cities. How can smart spaces provide value to citizens? There is a need to develop smart services that leverage emerging technologies while taking an inclusive and empowering approach to the inhabitants. To address this need, we present a framework for designing smart spaces and we use a bottom-up (inclusive) approach to instantiate a smart kiosk (SK). The SK prototype provides a practical approach for transforming a traditional building into a smart space utilizing IoT and artificial intelligence technologies. The design science research (DSR) methodology was followed for designing and evaluating the prototype. An iterative process that involves occupant feedback and brainstorming sessions coupled with a literature review was carried out to identify the issues and services related to a smart building. The SK prototype implements three intelligent services that were prioritized by the citizens of the building. The results show that the SK has a high usage and acceptance rate and it can transform a lobby into a highly engaged and smart building space. The prototyping process suggests important factors to ideate and assess smart services and shows that small-scale projects can be successful to enable smart buildings. The framework provides a theoretical contribution while the design and development process assists practitioners in identifying and developing intelligent services based on IoT technology.