PurposeThe purpose of the paper is to evaluate the performance and efficiency of the five most used search engines, i.e. Google, Yahoo!, Live, Ask, and AOL, in retrieving internet resources at specific points of time using a large number of complex queries.Design/methodology/approachIn order to examine the performance of the five search engines, five sets of experiments were conducted using 50 complex queries within two different time frames. The data were evaluated using Excel and SPSS software.FindingsThe paper results highlight the fact that different web search engines, which use different technology to find and present web information, yield different first page search results. The overall analysis of the findings of different measures reveals that Google has a significantly higher rate of performance in retrieving web resources as compared with the other four search engines. Yahoo! is the second best in terms of retrieval performance. The other three search engines did not performed satisfactorily compared with Google and Yahoo!Originality/valueThe paper will provide important insight into the effectiveness of major search engines and their ability to retrieve relevant internet resources. This paper has produced key findings that are important for all web search engine users and researchers, and the web industry. The findings will also assist search companies to improve their services.

Spectrum prediction based sensing schemes minimize the overall energy consumption of the sensing module in cognitive radio networks (CRNs) by predicting the status of spectrum before performing actual physical sensing. But, the performance of independent or local prediction models suffer from inaccuracies. Cooperative mode of spectrum prediction is found to be suitable to overcome the issues of local prediction models. In this work, we propose a cooperative spectrum prediction-driven sensing scheme for energy constrained cognitive radio networks to reduce the energy consumption while maintaining the spectral efficiency. The proposed scheme first employs a long short term memory network technique to perform local spectrum prediction, which identifies the status of a channel before actual sensing to improve energy efficiency. Thereafter, a parallel fusion based cooperative spectrum prediction model is applied to minimize the errors induced in local prediction model. Finally, the resultant cooperative prediction model is combined with a spectrum sensing framework to perform sensing operation when the cooperative spectrum prediction results to an indeterminate state in order to enhance the spectral efficiency. Simulation results show the efficacy of the proposed scheme in terms of spectral efficiency and energy efficiency compared to similar schemes from literature.

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Maintenance of immutable vaccination records and provision of accessing the records in order to prove immunity has been the need of the hour. The recent spread of Covid19 and related uncertainty over vaccinations and immunity have made the search for a secure trustable system for reporting vaccination data more essential. Multiple digital, as well as paper-based solutions have been tested but none has been reported successful enough. In this paper, a technique has been proposed to solve the problem by introducing blockchain-based solution to maintain records of vaccination and proof of immunity for individuals. The purpose has been to present a safe and efficient solution to the problem and hence the model proposed is based on concepts of smart contracts and built over Ethereum blockchain. The paper goes on to give a detailed study of the technique based on discussions of its various aspects like design, development and feasibility.

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