Multidimensional Parity Algorithms to Escalate the Security of Intelligent Mobile Models in Education

Virtually enhancing the real environment with augmented reality (AR) has lots of potential but is still in the early stages of research. The definition of appropriate user interfaces (UIs) is complicated by the absence of standards and the growing complexity of interaction opportunities. Several educators have discussed the advantages of XR for students as well as the use of AR and VR in the laboratory. Utilizing AR and VR to create immersive learning experiences is challenging since it takes time and effort to construct instructional AR and VR tools, apps, or educational settings. Because of this, even though these new technologies are said to help today’s students, their implementation in education may be postponed or stopped. In this research, the usage of XR technologies in education has been investigated through the examination of websites, technical papers, reports, and mobile app stores. This research study proposes a collision detection algorithm (CDA) utilizing machine learning. In order to aid in the identification of the meeting of two objects in the virtual environment, the collision detection method is employed in applications that support augmented reality and simulated reality technologies. In this study, mean, standard deviation and error parameters were utilized to analyze competitions that were related to augmented reality and virtual reality.

Section: Implementation and Experimental Resultsmentioning

confidence: 99%

Expanding an Education-based Collision Detection System Created on Virtual Reality and Augmented Reality

Sharma,

Misra,

Kumar

et al. 2023

“…The implementation of active, practice-based teaching methods is a crucial aspect of a comprehensive educational system. It cannot be recognized without electronic media [4]. This model will also include an attention mechanism to improve accuracy and efficiency.…”

Section: Fig 2 Wireless Virtual Learning Environmentmentioning

confidence: 99%

Analyzing the Deep Learning-Based Mobile Environment in Educational Institutions

Hassan,

Pathan,

Kotamjani

et al. 2024

More efficient instructional models that encourage students to engage more actively in their education are essential in today’s world. How information can be found and exchanged, as well as how explanations are delivered, have been shaped by technology. The outcomes demonstrate that mobile learning (m-learning) has enhanced collaboration among instructors and learners, provided immediate feedback, increased student participation and engagement, allowed for authentic learning and evaluation, and supported learning communities in higher education institutions. There was also a change in the teachers’ instructional methods. Students will benefit from simple learning activities, convenient communication, and coaching provided through mobile devices. This study recommends using a learning-based convolutional neural network (CNN) technique to improve students’ English-speaking fluency. In addition, communication between teachers and children in remote areas becomes easier through an interactive system, making both sides more accessible. As a result, it is advisable that educational institutions continually develop innovative teaching techniques that bridge face-to-face and formal-informal learning in the educational setting. The article discusses the background of mobile learning and how it might enhance e-learning as a whole. The m-learning approach is described in this paper as a future version of e-learning. In comparison to the next generation of educational systems, it will be extensively offered and simple to operate for any individual interested in learning. The article also discusses the benefits and potential drawbacks of mobile learning in current educational institutions.

“…Sub-assembly, which is one level higher, makes up the second branching. From the second limbs, a diagnosis is selected for a sample investigation [15]. While a patient has an invisible sensor device with an attached cell phone, their physiological indices can be gathered, examined, and computed.…”

Section: Data-driven Mobile Health Decisionsmentioning

confidence: 99%

Current Knowledge and Future Possibilities of Medical Digital Technologies based on Mobile Health

Salameh,

Abu-AlSondos,

Abu

et al. 2023

Medical digital technologies have the potential to improve public access to healthcare by enhancing its flexibility. The availability of complementary details about health, ailments, complications, and the most recent advancements in biomedical research are all included. Diagnostic and medical services are now becoming more available and accessible, especially in low-income countries. Regarding digital health technology, there are still a lot of challenges that need to be overcome, such as dependability, safety, testing, and ethical concerns. We propose that mobile technology should enhance rather than replace the psychiatrist-patient connection in the existing environment of inadequate regulatory oversight and scientific research to lessen possible clinical and moral harm to patients at risk. We identify potential areas of moral conflict between consumer-driven mobile businesses and healthcare practice and create a decision tree model for putting ethical safeguards in place. Informed consent, confidentiality, and shared treatment expectations are the main areas of attention for this paradigm when it comes to risk management in the therapeutic partnership. To comprehend the utility, capacity, and limitations, this study covers recent breakthroughs in mobile technologies, their applications, and a comparative analysis of their performance metrics. Understanding the weaknesses of current technologies can facilitate the creation of new frameworks with increased performance capabilities and superior service quality.