No abstract

InformationSupport Systems (ISS) are computer technology/network support systems that interactively support the information processing mechanisms for individuals and groups in life, public, and private organizations, and other entities. With the increasing use of technology in modern times, there is a growing requirement of Information Support Systems for the organizations.Over some decades in the past, organizations have put efforts to be at the forefront of the development and application of computer-based Information Support Systems to collect, analyze and process the data and generate information to support decisions. Various computing paradigms have been employed for the purpose and needs have emerged for enormous infrastructure, unlimited system accessibility, cost effectiveness, increased storage, increased automation, flexibility, system mobility and shift of IT focus. This paper presents a brief evaluation on how Cloud Computing paradigm can be used to meet the increasing demands of the Information Support Systems and how Cloud Computing paradigm can prove to be future solution for such systems.

Background Hepatitis B virus (HBV) infection is highly endemic in Nigeria. The primary objective of this study is to describe the knowledge, self-reported vaccination status, and intention of healthcare workers to receive hepatitis B vaccine at a tertiary referral center in conflict-ravaged northeastern Nigeria. Methods This was cross-sectional analytical study among medical practitioners, nurses, laboratory workers, health attendants, pharmacists, and radiographers working at Federal Medical Center Nguru, Yobe State. Written informed consent was obtained from all study participants. Data were obtained using questionnaires and entered into a Microsoft Excel spreadsheet, cleaned and analyzed using JMP Pro software. Results Of the 182 participants, we found that 151 (82.97%), 81 (44.51%), 85 (46.70%), and 33 (18.13%) had good knowledge of HBV, good knowledge of hepatitis B vaccine, were vaccinated against HBV by the least dose, and had a complete hepatitis B vaccination status, respectively. The lack of availability of the vaccine was the main reason for not receiving the vaccine among the unvaccinated 36/91 (39.56%), followed by not knowing where to access the vaccine 19/91 (20.88%). Conclusion The study highlights the need for strategies to ensure the availability of hepatitis B vaccine in conflict settings and need for vaccinology training given the suboptimal level of awareness and uptake of the hepatitis B vaccine among the healthcare workers.

In the current era of social media, different platforms such as Twitter and Facebook have frequently been used by leaders and the followers of political parties to participate in political events, campaigns, and elections. The acquisition, analysis, and presentation of such content have received considerable attention from opinion-mining researchers. For this purpose, different supervised and unsupervised techniques have been used. However, they have produced less efficient results, which need to be improved by incorporating additional classifiers with the extended data sets. The authors investigate different supervised machine learning classifiers for classifying the political affiliations of users. For this purpose, a data set of political reviews is acquired from Twitter and annotated with different polarity classes. After pre-processing, different machine learning classifiers like K-nearest neighbor, naïve Bayes, support vector machine, extreme gradient boosting, and others, are applied. Experimental results illustrate that support vector machine and extreme gradient boosting have shown promising results for predicting political affiliations. | Research study motivationDifferent techniques, including supervised and unsupervised methods, have been applied to predict election results [4].This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.