Ethnic minority women's empowerment in agriculture in the central region of Viet Nam.

Gender inequality and women's empowerment are two closely related issues. While the gender inequality index has been assessed by different studies, that of women's empowerment remained limited. In the present work, we attempted to evaluate the women's empowerment index by comparing it with the male partner's empowerment index in the same household. We used the Women's Empowerment in Agriculture Index (WEAI) as a framework for reference. A questionnaire was designed to interview 300 people including both men and women in the same ethnic minority household in central Vietnam. The difference in the empowerment level between men and women was assessed through five-component empowerment indicators: agricultural participation, resource ownership, financial control, social organizations participation, and time usage. The results showed that up to 70% of women were disempowered compared to only 15% of men. The binary logistic model revealed the age at first marriage, the level of children's education, education level, distance to the nearest urban area, and the number of children were associated with women's empowerment; whereas age, income, and the level of gender awareness did not show any correlation.

An effective deep residual network based class attention layer with bidirectional LSTM for diagnosis and classification of COVID-19.

In recent days, COVID-19 pandemic has affected several people's lives globally and necessitates a massive number of screening tests to detect the existence of the coronavirus. At the same time, the rise of deep learning (DL) concepts helps to effectively develop a COVID-19 diagnosis model to attain maximum detection rate with minimum computation time. This paper presents a new Residual Network (ResNet) based Class Attention Layer with Bidirectional LSTM called RCAL-BiLSTM for COVID-19 Diagnosis. The proposed RCAL-BiLSTM model involves a series of processes namely bilateral filtering (BF) based preprocessing, RCAL-BiLSTM based feature extraction, and softmax (SM) based classification. Once the BF technique produces the preprocessed image, RCAL-BiLSTM based feature extraction process takes place using three modules, namely ResNet based feature extraction, CAL, and Bi-LSTM modules. Finally, the SM layer is applied to categorize the feature vectors into corresponding feature maps. The experimental validation of the presented RCAL-BiLSTM model is tested against Chest-X-Ray dataset and the results are determined under several aspects. The experimental outcome pointed out the superior nature of the RCAL-BiLSTM model by attaining maximum sensitivity of 93.28%, specificity of 94.61%, precision of 94.90%, accuracy of 94.88%, F-score of 93.10% and kappa value of 91.40%.