Analysis of Woman Fiqh Jihad Based on Selected Malaysian Woman Extremism Cases

Human being has experienced several series of pandemics throughout life‘s history. The serious impacts of the pandemic are experiencing towards different sectors include threatening global food security. The consequences of the COVID-19 outbreak have affected people's productivity in daily life. The disruptive effect gives a major strike on the food supply chain which is one of the most vital areas of the economy. Prior to the outbreak of Covid 19, this paper will be discussing the factors and effects of the pandemic on global food security. Due to the income decline, it leads to difficulty accessing healthy and nutritious food. Implementation of Movement Control Order (MCO) has affected the agriculture productivity sector as well. At the early stage of the pandemic, panic buying becomes a new trend among the consumers to ensure the food supply keeps available. The declination of purchasing power is another effect, particularly from the low-income community. The risk of virus infection is reported to be high among the food supply chain workers due to negligence towards Standard Operating Procedure (SOP). Lastly, this pandemic also affected mostly community dietary routine patterns. To write this paper, the researchers are genuinely depending on the secondary sources from recent academic journals and trusted websites to vast up the discussion. It is hoped that this paper provides good ideas for other researchers to vast up another scope of discussion in the future.

Hybrid learning method based on feature clustering and scoring for enhanced COVID-19 breath analysis by an electronic nose.

Breath pattern analysis based on an electronic nose (e-nose), which is a noninvasive, fast, and low-cost method, has been continuously used for detecting human diseases, including the coronavirus disease 2019 (COVID-19). Nevertheless, having big data with several available features is not always beneficial because only a few of them will be relevant and useful to distinguish different breath samples (i.e., positive and negative COVID-19 samples). In this study, we develop a hybrid machine learning-based algorithm combining hierarchical agglomerative clustering analysis and permutation feature importance method to improve the data analysis of a portable e-nose for COVID-19 detection (GeNose C19). Utilizing this learning approach, we can obtain an effective and optimum feature combination, enabling the reduction by half of the number of employed sensors without downgrading the classification model performance. Based on the cross-validation test results on the training data, the hybrid algorithm can result in accuracy, sensitivity, and specificity values of (86 ± 3)%, (88 ± 6)%, and (84 ± 6)%, respectively. Meanwhile, for the testing data, a value of 87% is obtained for all the three metrics. These results exhibit the feasibility of using this hybrid filter-wrapper feature-selection method to pave the way for optimizing the GeNose C19 performance.