Scopus Research — Sarah Abdulridha Rahman

The primary purpose of this study is to look at the differential quasi-subordination result for meromorphic multivalent analytic functions. We show how higher-order differential quasisubordination discoveries can be applied in a variety of ways in an open unit disk. We provide a new class (Formula Presented). to represent higher-order derivatives of meromorphic multivalent analytic functions associated with the operator. We have some findings for this class. © 2023, Taru Publications. All rights reserved.

We investigate the characteristics of differential subordination for meromorphic multivalent functions and superordination linked to defined multiplier transforms for meromorphic multivalent functions. As a result, we get sandwich outcomes. © 2023, Taru Publications. All rights reserved.

Electronic voting has evolved as a substitute for paper-based balloting in order to reduce redundancies and inconsistencies. In recent years, it has been found that paper-based balloting fails owing to security and privacy concerns, and it has been recommended that electronic balloting be used instead. To guarantee the security of the data, we devised and implemented an efficient hashing utilising SHA-256. The use of the block sealing idea aids in the adjustment of the block chain. The consortium block chain idea is utilised so that the block chain may only be accessed by authorised users/candidates and is maintained by the election commission. The architecture described in this article may give reliable polling technique results. The hashing technique (SHA-256), block generation, information collection, and final result declaration were the approaches used in this study. The block chain technique will be used to carry out all of the operations. The use of block chain in voting systems may improve information security and make maintaining sensitive data easier in the electronic voting process. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Due to the Covid-19 epidemic, there is now a high need for social seclusion. As several studies have shown, it is possible to limit the transmission of Covid-19 by keeping appropriate social distances. Open CV with deep learning is used to detect the distance between individuals to lessen the effect of the coronavirus epidemic, as we describe in this paper. Individuals should be warned to stay a safe distance from one other using a video broadcast, it was suggested. To put the model to the test, we use CCTV camera footage to gather video clips, which we then feed into CNN models that have already been trained. We are attempting to use YOLOv3 algorithm to recognize pedestrians on the road. Video file now transformed top-down perspective distance measurement from the 2D plane once pedestrian recognition is complete. Social distance violation is the visible distance between any two persons less than the intended length. For every person, the distance between them is highlighted in blue or green if it seems to be more than the predicted distance. Our current model is being tested with prerecorded videos of people strolling the street to verify its correctness. According to the finding, the suggested system is capable of detecting various degrees of social distance between different characters given movie. The same approach may be used future to identify social distance violations in real-time applications. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.