Background: Significant work has been conducted in the direction of an intelligent interface development through Human-Computer Interaction (HCI). Various forms of information, such as video, audio, or in the written form, have been proposed either separately or in combination. Methods: This paper proposes an interactive contact solution based on the distinct characteristics of contract distribution and the spatial and temporal consistency to establish a multiple display system. Results: The correspondence between the user’s arm position information and the virtual scene was established by utilizing a virtual 3D interactive rectangular parallelepiped. An estimation technique of the arm motion was designed, in conjunction with the employment of the Fuzzy Predictive Control Mamdani Algorithm (FPCMA) using Robust Tracking (RT) for the user’s arm position and for validating the efficiency and accuracy, Kalman filter algorithm (VEA) was used to test the stability. Conclusion: For RT, using FPCMA is 1.21 for 17fps while 0.83 for 35fps. For the output, the VEA accuracy rate is 0.97.

Background: Augmented reality (AR) technology has altered the retail business while also improving the shopping experience. Several studies have been conducted to investigate the factors that influence the relevance of perceived AR technology values, as well as how these values influence customer enjoyment and the desire to spread good word of mouth (WOM). This study explores how the perceived value of AR affects customer satisfaction, enjoyment, and recommendations. Objective: This study aimed to examine how the usage frequency moderates the associations between human value orientation and perceived AR values, as well as the correlations between valued AR and satisfaction and positive WOM intention. It studies the relationship between perceived AR values and consumer happiness, as well as the influence of human value orientation on perceived AR values (receptivity to change, preservation of resources, selfimprovement, and transcendence of oneself) (playful, social, aesthetic appeal, and usability). Methods: This research extends the human value orientation theory by proposing a novel model for the link between AR value orientation and usage frequency. To obtain data, a survey was distributed to those who have engaged with retail AR features. Results: Contrary to popular belief, the data demonstrate that a certain group of perceived AR values is linked to each value orientation, emphasizing the significance of a certain perceived AR value in affecting customer satisfaction and positive WOM intention. This study shows that usage frequency moderates the associations between human value orientation and perceived AR values, as well as the correlations between valued AR and satisfaction and positive WOM intention. It also shows that consumer value orientation is the most essential aspect in choosing which AR technology advantage they value most. AR technology values also affect consumer satisfaction and word-of-mouth marketing (WOM). Conclusion: The value orientation of an individual influences the value of augmented reality technology, which may vary depending on the frequency of usage. This paper concludes with suggestions for merchants regarding augmented reality technology. Moreover, the precise number of applications that can be made available to customers and retailers is determined by this study, which benefits both retailers and researchers.

This article has been retracted. After a thorough investigation, the listed authors of “Neural network to investigate gaming addiction and its impact on health effects during the COVID-19 Pandemic” committed a serious misconduct of standards of publication ethics, since they submitted a contribution that did not satisfy originality.

Lossy Compression of Hyperspectral Images Using Real-Time Technique

Maximal frequent itemset is the largest frequent itemset in a database which is not covered by other itemsets. All frequent itemsets can be built up from maximal one. Moreover, it is possible to focus on any part of the maximal frequent itemset to supervise Data Mining. Bees' Algorithm is simple, robust and population-based stochastic optimization algorithm which is based on bees' natural foraging habits. It performs a neighborhood search combined with random search. This paper produces a turning on by pairing of maximal frequent itemset and Bees'Algorithm; it presents maximal frequent itemset - oriented Bees'Algorithm to mine maximal frequent itemsets from transactional databases which has been named Mining Maximal Itemsets Bees' Algorithm (MMIBA). The fitness, coding, scout bees' duties, type of harvested information, and termination criteria have been oriented to the problem of maximal frequent itemset mining. MMIBA was applied on real life databases available publicly on the Internet which are chess, Mushroom, Cancer Cells, Census data, and Dense Census. The experiments were accomplished depending on three levels of minimum support threshold, which are twenty five, fifty, and seventy five percentages of the databases' sizes, to validate the efficiency of MMIBA. The level twenty five percentage was depicted to elucidate the ability of MMIBA in mining the MFIs with low values of minimum support, while the level seventy five percentage to validate its ability in high minimum support values.