Application of machine learning approach on halal meat authentication principle, challenges, and prospects: A review.

Meat is a source of essential amino acids that are necessary for human growth and development, meat can come from dead, alive, Halal, or non-Halal animal species which are intentionally or economically (adulteration) sold to consumers. Sharia has prohibited the consumption of pork by Muslims. Because of the activities of adulterators in recent times, consumers are aware of what they eat. In the past, several methods were employed for the authentication of Halal meat, but numerous drawbacks are attached to this method such as lack of flexibility, limited application, time,consumption and low level of accuracy and sensitivity. Machine Learning (ML) is the concept of learning through the development and application of algorithms from given data and making predictions or decisions without being explicitly programmed. The techniques compared with traditional methods in Halal meat authentication are fast, flexible, scaled, automated, less expensive, high accuracy and sensitivity. Some of the ML approaches used in Halal meat authentication have proven a high percentage of accuracy in meat authenticity while other approaches show no evidence of Halal meat authentication for now. The paper critically highlighted some of the principles, challenges, successes, and prospects of ML approaches in the authentication of Halal meat.

How social media crisis response and social interaction is helping people recover from Covid-19: an empirical investigation.

Community resilience following a crisis has become essential to avoid panic. In contrast, social media usage has been practical to improve public resilience. However, the impacts of social media crisis response and social interaction have not been fully addressed. Therefore, this study aims to investigate the effects of social media crisis communication on public resilience. The study data were collected through an online medium, and the final responses consist of 393 observations, mainly of Malaysians who have experienced Covid-19 isolation, quarantine, or lockdown. The assessments of the reflective measurement models based on path analysis in PLS-SEM are reliable and valid. The Cronbach's alpha, rho_A, composite reliability, and discriminant validity revealed acceptable values. PLS prediction algorithm was run to assess the model's predictive power, and the findings show that the predictive relevance is satisfactory. Furthermore, the IPMA was applied to evaluate the model's usefulness, which compares the level of the variables from the performance scale mean value against the importance level. The result shows that all the variables are useful and reveal good performance. Thus, crisis management and communication activities should pay more attention to these variables for effective social media crisis communication. Thus, the study offers theoretical and practical implications in the field of social media-based crisis communication and crisis informatics.