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Nowadays, Google Forms is becoming a cutting-edge tool for gathering research data in the educational domain. Several researchers are using real-time web applications to collect the responses of respondents. Demographic and geographic features are the most important in the researcher's study. Identifying students' demographics (gender, age-group, course, institution, or university) and geographic features (locality and country) is a challenging problem in machine learning. We proposed a novel predictive algorithm, Student Demographic Identification (SDI), to identify a student's demographic features (age-group, course) with the highest accuracy. SDI has been tested on primary reliable samples. SDI has also been compared with the traditional machine algorithms Random Forest (RF), and Logistic Regression (LR), and Radial Support Vector Machine (R–SVM). The proposed algorithm significantly improved the performance metrics such as accuracy, F1-score, precision, recall, and Matthews Correlation Coefficient (MCC) of these classifiers. We also proposed significant features to identify students' age-group, course, and gender. SDI has identified the student's age group with an accuracy of 96% and the course with an accuracy of 97%. Gradient Boosting (GB) has improved the accuracy of LR, R-SVM, and RF to predict the student's gender. Also, the RF algorithm with the support of GB attained the highest accuracy of 98% to identify the gender of the students. All three classifiers have also identified the student's locality and institution with an identical accuracy of 99%. Our proposed SDI algorithm may be useful for real-time survey applications to predict students' demographic features. [ABSTRACT FROM AUTHOR]

As the backbone of every blockchain application, the consensus protocol is impacted by numerous risks, namely resource requirements and energy consumption, which limit the usage of blockchain. Applications such as IoT/IIoT cannot use these high-cost consensus methods due to limited resources. Therefore, we introduce Delegated Proof of Accessibility (DPoAC), a new consensus technique that employs secret sharing, PoS with random selection, and an interplanetary file system (IPFS).DPoAC is decomposed into two stages. During the initial stage, a secret is generated by a randomly chosen super node and divided into n shares. These shares are encrypted and stored in different n nodes on the IPFS network. The nodes will compete to access these shareholders to reconstruct the secret. The winning node will be awarded block generation rights. PoS with random selection is used in the second stage to compute the appropriate hash value and construct a block with valid transactions. In this novel approach, a node with few computational resources and small stakes can still obtain block generation rights by providing access to secret shares and reconstructing the secret, making the system reasonably fair. We qualitatively analyze and compare our scheme based on performance parameters against existing mainstream consensus protocols in the context of IoT/IIoT networks. [ABSTRACT FROM AUTHOR]

We study the local convergence analysis of a fifth order method and its multi-step version in Banach spaces. The hypotheses used are based on the first Fréchet-derivative only. The new approach provides a computable radius of convergence, error bounds on the distances involved, and estimates on the uniqueness of the solution. Such estimates are not provided in the approaches using Taylor expansions of higher order derivatives, which may not exist or may be very expensive or impossible to compute. Numerical examples are provided to validate the theoretical results. Convergence domains of the methods are also checked through complex geometry shown by drawing basins of attraction. The boundaries of the basins show fractal-like shapes through which the basins are symmetric. [ABSTRACT FROM AUTHOR]