There is a growing interest in university academic staff selection since the quality of staff has a direct influence on any organization's effectiveness. The process of selecting suitable academic staff for employment is complex and involves taking multiple criteria into consideration for good decision making. Analytic Hierarchy Process (AHP) is a Multi-Criteria Decision Making (MCDM) model for dealing with decision making problems affected by several conflicting factors. It is useful for selecting the best among alternatives based on certain criteria. However, academic staff selection also contains uncertainties which pose another problem, since the AHP lacks the ability to deal with imprecise and subjective judgment in its pair-wise comparison process. This problem can be overcome by the use of AHP model with fuzzy logic, called Fuzzy AHP model, where triangular fuzzy numbers (TFNs) and linguistic variables are used to achieve better accuracy and consistency in the decision makers' (DM) judgment. A system architecture is developed for problem solving using this model. This paper uses Chang's synthetic extent analysis with TFNs to improve human experts' decision making when recruiting by generating a range of values to incorporate DMs' uncertainty, instead of a crisp value. Numerical example using three alternative candidates based on these criteria: work experience, academic background, and individual skill is presented. The result indicates that the alternative with the highest normalized weight is the most suitable candidate to be selected for employment. This work could be very useful to university establishment and to any other organization that may be interested in fair and efficient recruitment exercise.

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Securing organizational data is an important information management issue that continues to pose significant challenges for organizations especially in developing countries. Different organizations have made several failed attempts to develop authentic solution that will centrally create and manage document referential, place documents in their respective categories and apply retention on the documents during end-oflive, in a seamless manner. There have been several complaints from users on issues bordering on missing documents, premature destruction of important documents, organization's top-secret documents been found in wrong hands and placement of documents in wrong categories as a result of no well-defined organizational policies. We developed an enhanced online document referential, classification and retention system, which combated these challenges. We used object-oriented analysis and design methodology (OOADM) in our approach. Microsoft ASP.Net and Python technologies were used for this implementation. Automated document referential, classification and retention system provides a platform for easy creation of document referential, placement of documents in respective document categories and automatic application of retention on documents at the end of their lifespan. From the results, the overall accuracy of the classification model is 88% which indicates that most predictions made by the model are correct. The specificity of the individual classes ranges between ninetyeight (98) and one hundred (100) percent, whereas their precision, recall and f1-scores are above 0.5 which is good for prediction. This work could be beneficial to both small, medium and large-sized organizations.

Mental fog, also known as confusion, is one of the main reasons for poor performance in the learning process or any type of daily task that involves and requires thinking. Detecting confusion in the human mind is a real time paradigm that appears to be more difficult and important tasks that can be applied to online education, driver fatigue detection, etc. The Random Forest model achieve a better performance compared to other machine learning approaches and shows a great robustness evaluated by cross validation. We can predict if a student is confused about 100% accuracy. In addition, we found that the most important characteristic for detecting brain confusion is the beta 2 and gamma 1 wave of the Electroencephalography (EEG) signal. Our results suggest that machine learning is a potentially powerful tool for modeling and understanding brain activity. This work could be beneficial to individuals, to Ministry of Health, patients with brain diseases and to any other organization that deals on human state of mind in terms of performance.

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