Using mobile applications in e-government for the purpose of health protection is a new idea during COVID-19 epidemic. Hence, the goal of this study is to examine the various factors that influence the use of SANAD App As a health protection tool. The factors were adopted from well-established models like UTAUT, TAM, and extended PBT. Using survey data from 442 SANAD App from Jordan, the model was empirically validated using AMOS 20 confirmatory factor analysis, structural equation modeling (SEM) and machine learning (ML) methods were performed to assess the study hypotheses. The ML methods used are ANN, SMO, the bagging reduced error pruning tree (RepTree), and random forest. The results suggested several key findings: the respondents’ performance expectancy, effort expectancy, social influence, facilitating conditions, perceived risk, trust, and perceived service quality of this digital technology were significant antecedents for their attitude to using it. The strength of these relationships is affected by the moderating variables, including age, gender, educational level, and internet experience on behavioral intention. Yet, perceived risk did not have a significant effect on attitude towards SANAD App The study adds to literature by empirically testing and theorizing the effects of SANAD App on public health protection.

Probabilistic broadcast has been widely used as a flooding optimization mechanism to alleviate the effect of broadcast storm problem (BSP)

Problem statement: One of the major issues in current reactive routing protocols for Mobile Ad Hoc Networks (MANETs) is the high bandwidth and power consumptions during the routing process. In this study, we proposed and evaluated the performance of an efficient LocationBased Power Conservation (LBPC) scheme for MANETs. Approach: In this scheme, the transmitting node utilized the location-information of the first-hop neighbors to adjust its radio transmission range according to one of the following criteria: Farthest first-hop neighbor, average distance of the first-hop neighbors and a random distance between the nearest and the farthest first-hop neighbors. Results: A number of simulation were carried-out to evaluate the power conservation ratio that can be achieved for two route discovery algorithms, namely, pure flooding and Location-Aided Routing Scheme 1 (LAR-1) algorithms. Conclusion: The simulation results demonstrated that the scheme can provide power conservation ratios between 10-50% without adding any extra overheads or complexity to the routing algorithm.

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There has been a vast collection of multimedia resources on the net. This has opened an opening for researchers to explore and advance the science in the field of research in storing, handling, and retrieving digital videos. Video classification and segmentation are fundamental steps for efficient accessing; retrieving, browsing and compressing large amount of video data. The basic operation video analysis is to design a system that can accurately and automatically segments video material into shots and scenes. This paper presents a detailed video segmentation technique based on pervious researches which lacks performance and since some of the videos is stored in a compressed form using the Normalized Information Distance (NID) which approximates the value of a theoretical distance between objects using the Kolmogrov Complexity Theory. This technique produced a better result in reference to performance, high recall of 95.5% and a precision of 89.7%.