The purpose of this study is to look into the components that affect the behavioral intention of Jordanians to use Virtual Reality Technology in the Learning Environment (VRTLE). For the educational and entertainment industries, virtual reality presents both obstacles and opportunities. The goal of this study is to determine the factors that influence VRTLE acceptance and to propose how such technology may be integrated into the educational setting of students and universities. A total of 60 students from private universities took part in the research. Among the participants, 63.3 were females and 36.7 were males. The modified UTAUT model used in this study only looked at the effects of four independent variables and two external factors, namely, acceptability (ACC) and usability (USA). Acceptability and usability have a substantial impact on student acceptance of VRTLE, according to the findings. Furthermore, as moderators, there is a major effect on both Awareness and Experience. This paper contributes significantly to the UTUAT model. It also tackles educational concerns about the transition to a virtual reality learning environment. As a result, it would be useful to look into other factors as well. As a result, a comprehensive empirical study should be designed to allow for the assessment of the effects of other variables as well.

Kinematic motion detection aims to determine a person’s actions based on activity data. Human kinematic motion detection has many valuable applications in health care, such as health monitoring, preventing obesity, virtual reality, daily life monitoring, assisting workers during industry manufacturing, caring for the elderly. Computer vision-based activity recognition is challenging due to problems such as partial occlusion, background clutter, appearance, lighting, viewpoint, and changes in scale. Our research aims to detect human kinematic motions such as walking or running using smartphones’ sensor data within a high-performance framework. An existing dataset based on smartphones’ gyroscope and accelerometer sensor values is utilized for the experiments in our study. Sensor exploratory data analysis was conducted in order to identify valuable patterns and insights from sensor values. The six hyperparameters, tunned artificial indigence-based machine learning, and deep learning techniques were applied for comparison. Extensive experimentation showed that the ensemble learning-based novel ERD (ensemble random forest decision tree) method outperformed other state-of-the-art studies with high-performance accuracy scores. The proposed ERD method combines the random forest and decision tree models, which achieved a 99% classification accuracy score. The proposed method was successfully validated with the k-fold cross-validation approach.

Recent breakthroughs in cryptographic technology are being thoroughly scrutinized due to their emphasis on innovative approaches to design, implementation, and attacks. Lightweight cryptography (LWC) is a technological advancement that utilizes a cryptographic algorithm capable of being adjusted to function effectively in various constrained environments. This study provides an in-depth analysis of elliptic curve cryptography (ECC), which is a type of asymmetric cryptographic method known as LWC. This cryptographic approach operates over elliptic curves and has two applications: key exchange and digital signature authentication. Next, we will implement asymmetric cryptographic algorithms and evaluate their efficiency. Elliptic curve elgamal algorithms are implemented for encryption and decryption of data. Elliptic curve Diffie-Hellman key exchange is used for sharing keys. Experimental results have shown that ECC needs small size keys to provide similar security. ECC takes less time in key generation, encryption and decryption of plain text. Time taken by ECC to generate a 2,048 bit long key is 1,653 milliseconds in comparison to 4,258 millisecond taken by Rivest-Shamir-Adleman (RSA) technique.

This chapter looks at how controller and processor responsibilities are defined and allocated within the confines of today's security platforms. It scrutinizes the role definition of security solutions involving managed services, cloud providers, and AI-based analytics that are deployed by organizations. The chapter discusses a few legal aspects such as who has the decision-making authority, who determines the purposes, and who has control over the data processing operations. It focuses heavily on contract clarity, proper documentation, and governance mechanisms that set out the obligations relating to data protection, security measures, incident response, and handling of data subject rights. By unifying legal interpretation with the reality of security platform architectures, the chapter offers a roadmap for unambiguously defining responsibilities, lessening uncertainty, and ensuring accountability and compliance in multi-party security ecosystems.