One of the emerging areas is knowledge co-creation, and more specifically, in an educational context, curriculum co-creation. The ability to solicit feedback, reactions, suggestions, and improvements with regards to courses and educational programs from students is an interesting and promising one, but a concept which requires that existing norms and practices be put aside to allow students to share in the evaluation, revision, and design process, together with instructors and administrators. This paper examines the foundations of curriculum co-creation, including its educational theory foundations, critical variables and considerations, and a review of recent programs and initiatives. A framework of the key curriculum co-creation variables is presented, followed by suggestions for future research.

Context-aware recommender systems (CARSs) are attracting more and more attention from both the academic community and from industry. Users' contextual situations (e.g., location, time, companion, etc.) which can influence their ratings on items, are taken into consideration. Therefore, more accurate and personalized recommendations can be generated. The integration of contextual information in recommender systems to better model users' preferences under different contextual situations is a key research topic. In this paper, the authors propose a new method for representing contextual situations in recommender systems based on the influence of contextual conditions on ratings using Pearson Correlation Coefficient. The authors show the effectiveness of the proposed method compared to state-of-art methods by experiments on three different datasets widely used in CARSs research community.

The present study is about the outbreak of novel Corona disease (COVID-19). The study proposes the mathematical modelling of COVID-19 disease as quadratic growth. Correlation coefficient associated with the characteristic of the modelling trend has been obtained by least square method using matlab function. Analysis of variance show the model proposed is significant. The study gave the information about the transmission of the disease and the prevention observed by the medical practitioner. The graphical representation has shown the outcome of the spread of the disease in India. This disease can be of respiratory type, pneumonia type, or asymptotic carrier type. The simulation result obtained is in accordance to the quadratic model fitted and can be used for the prediction of the growth. Results obtained on the basis of R2 were of approximately 97% in agreement. The only possibilities to avoid the spread of the disease is to monitor self-isolation, maintain cleanliness, improve immunity, and be protected.

Research in disease diagnosis is a challenging task due to inconsistent, class imbalance, conflicting, and the high dimensionality of medical data sets. The excellent features of each data set play an important role in improving performance of classifiers that may follow either iterative or non-iterative approaches. In the present study, a comparative study is carried out to show the performance of iterative and non-iterative classifiers in combination with genetic algorithm (GA)-based feature selection approach over some widely used medical data sets. The experiment assists to identify the clinical data sets for which feature reduction is necessary for improving performance of classifiers. For iterative approaches, two popular classifiers, namely C4.5 and RIPPER, are chosen, whereas k-NN and naïve Bayes are taken as non-iterative learners. Fourteen real-world medical domain data sets are selected from the University of California, Irvine (UCI Repository) for conducting experiment over the learners.

In this work, rather than relating different areas of knowledge, the authors make use of logic, which is intrinsic to mathematical models. They discuss one of the fallacies that hides behind the analytic hierarchy process (AHP). In particular, the analysis of the consistency index, sometimes referred as the Saaty consistency or simply the consistency, will be discussed. The AHP is based on three fundamental axioms: reciprocity, homogeneity, and synthesis. However, it gives a great relevance to consistency, which is managed like a fourth axiom. On each of these four axioms, some comments will be made. However, this work will focus mainly on discussing the importance of the consistency index, to which, undoubtedly, a large number of essays and many hours of work have been devoted. In trying to answer the question, the calculation of consistency is really important. The aim of the paper is to raise a discussion of one of the fallacies that hides behind the analytic hierarchy process and present a simple and direct way to handle Saaty consistency.

Emphasis on knowledge and information is one of the challenges of the 21st century to differentiate the intelligent business enterprises. Enterprises have to develop their organization in order to capture, manage, and use information in a context of continually changing technology. Indeed, knowledge and information are completely distributed in the information network of the company. In addition, knowledge is, by nature, heterogeneous, since it is provided from different information sources like the software, the technical report, the meeting statements, etc. The authors present in this article the architecture of a multi-agent system, which allows the capitalization of the distributed and heterogeneous knowledge. They then present how the agents help business experts to design ontologies in detailing this problem and how the agents extract knowledge from different user databases by using a semantic approach.

Decision-making processes are the utmost important to steer the organizational change whenever business process workarounds are attempted during operational times. However, to decide the non-compliant situations, e.g., bypasses, social resistance, or collusion; the business manager demands contextualized and correct interpretations of the existing business process redesign options to cope with workarounds. This article explores the need to aid the decision-making process with a full constructional perspective to optimize the business processes redesign. So, the Markov decision process is combined with the body of knowledge of business processes, in specific, the concepts of designing enterprise-wide business transactions. This methodology supports the management initiatives with more knowledge about the value of business processes redesign. A classical chain of Order-to-Cash business processes (the order, the production, the distribution and the selling of goods) illustrate the benefits of this quantitative approach. Results obtained for business processes redesign in reaction to workarounds are reported. The analysis results show that this approach can anticipate the sub-optimal solutions before taking actions and highlights the impact of discount factors in the final obtained value. The contribution of this novel conceptual integration to the business processes community is the forecast of value function of business transaction redesign options when facing non-compliant workarounds. From related literature, business processes compliance usually comprises offline computation and the redesign is only considered in the forthcoming business processes instances. This article is innovative in the sense that it anticipates the value impact of a redesign, allowing more effective decisions to be taken.