The world of research require researcher, academia and lecturers to share knowledge among them. With the invention of social media, knowledge sharing process has been more effective and easy. Previously, there were numerous researches done to investigate the effect of social media utilization for public used. There were also study that aimed to study social media effects in educatioanal sector but those study were centered around student's perspective. Less consideration is given towards academia's perspective. Therefore, this study is directed to explore other niche area on knowledge sharing environment where it will focused on the effects of social media on knowledge sharing among academia. Initially, literature review analysis was done to discover the potential factors that encourage academia to engage in social media. Ability to facilitate communication, idea generation and group establishment are the most cited reasons. Not only that, this paper will highlight the significance of performing this study. In conclusion, there is no doubt that social media do enhance and upgrading the knowledge sharing process thus assisting academia in their scholarly work.

Many studies have been performed to measure successful knowledge sharing in general. However, limited study has been done to assess successful knowledge sharing through social media. Hence, in this paper intend to discuss our approach to assess   knowledge sharing among personal social media user. In order to achieve our objective, we proposed to integrate Analytic Hierarchy Process (AHP) and Markov Chain (MC) technique to investigate the pattern of the shared knowledge through social media. Markov Chain will be used to model the knowledge sharing success through expert opinion and stochastic process. We anticipate the outcome of the assessment in a form of a final matrix showing the probability of successful knowledge sharing through social media. The elements in each row of the Markov Chain transition matrix will be calculated using Analytic Hierarchy Process.  The assessment tool produce from our research is expected to benefit policy maker or internet user in order to enhance their knowledge sharing strategy in social media application.

This paper aims to study the influence of social media on knowledge sharing among academia. Previously, many researches have been done to explore the importance emergence of social media for public use, but there are still limited studies on how this technological advancement affects the academia. For this study, Facebook is chosen as one of the online social networking tools as the medium of knowledge sharing. To begin with, this study is started with the identification of factors that encourage the academia to share their knowledge through social media. These factors are then categorized based on Theory of Planned Behavior (TPB). After this knowledge has successfully shared, the level of successful knowledge sharing through Facebook is modeled using Fuzzy Logic. Fuzzy inputs for this study are the number of like, comment and share. Findings from this study indeed showed that there are many reasons encouraging academia to utilize social media for their work. Besides, this paper contributes new knowledge to fuzzy logic application as it is the first known research in measuring Facebook engagement for knowledge sharing purposes. In conclusion although there exist some barriers and limitations with the use of social media, academia are showing a positive shift in the application of these tools for work.

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Coronavirus 2019 (COVID-19) pandemic in Malaysia is a part of the ongoing worldwide pandemic. The emergence of COVID-19 has led to high demand for intensive care services worldwide. However, the severity of COVID-19 patients that need intensive care unit (ICU) treatments requires details investigation. This study aims to predict the number of ICU cases due to COVID-19 disease in Malaysia. The prediction was done based on the data related to new, recovered, and treated cases which were collected from the website of the Ministry of Health Malaysia started from April until August 2020. Artificial Neural Networks Multilayers Perceptron Backpropagation (ANN-MLP-BPP) model was developed for predicting ICU cases based on the usage of the real set of data. The ANN-MLP-BPP model was validated by splitting the data into 80% for training and 20% for testing. The results show that with the increase in the number of undertreated cases, the number of predicted ICU will also be increased. The predicted ICU admission is almost equivalent to a 1 percent increment of the number of cases undertreated. These findings may help the frontline physicians in planning and handling the facilities management during the COVID-19 pandemic situation in the future.