Financial fraud is a growing problem that poses a significant threat to the banking industry, the government sector, and the public. In response, financial institutions must continuously improve their fraud detection systems. Although preventative and security precautions are implemented to reduce financial fraud, criminals are constantly adapting and devising new ways to evade fraud prevention systems. The classification of transactions as legitimate or fraudulent poses a significant challenge for existing classification models due to highly imbalanced datasets. This research aims to develop rules to detect fraud transactions that do not involve any resampling technique. The effectiveness of the rule-based model (RBM) is assessed using a variety of metrics such as accuracy, specificity, precision, recall, confusion matrix, Matthew’s correlation coefficient (MCC), and receiver operating characteristic (ROC) values. The proposed rule-based model is compared to several existing machine learning models such as random forest (RF), decision tree (DT), multi-layer perceptron (MLP), k-nearest neighbor (KNN), naive Bayes (NB), and logistic regression (LR) using two benchmark datasets. The results of the experiment show that the proposed rule-based model beat the other methods, reaching accuracy and precision of 0.99 and 0.99, respectively.

Cost-effective extension strategies are needed to promote widespread adoption of agricultural technologies in developing countries. Integrated pest management (IPM) practices, for example, can offer economic, health, and environmental benefits but remain largely underused. This study evaluates the current IPM dissemination program implemented by the Bangladesh Department of Agricultural Extension and uses a linear programming model to examine alternative strategies to improve IPM adoption. Results suggest that technology transfer programs may increase their impact by reallocating funding from intensive but costly interpersonal communication methods (i.e., farmer field schools) to less intensive methods (i.e., mass media and field days) that reach broader audiences.

Background: The indigenous people are socially, linguistically, and scientifically diverse. A global trend leads us to the notion that primitive women are somewhat less privileged than non-tribal mothers around the world. This problem also sounds true in the context of Bangladesh. A competent birth attendant at birth is the most important intervention for pregnant women. However, the use of skilled birth attendants has been widely discriminated among tribal and non-tribal women. Objective: This study assesses SBA acceptance and utilization barriers during childbirth among Bangladesh tribal women. Method: A quantitative approach was employed, and the data were collected via questionnaires. Descriptive statistics, Chi-square ( ) χ 2 tests and Binary Logistic Regression were used to analyze the frequency, relationship, and to determine tribal women seeking the services of SBA during childbirth. Results: The results showed that the highest prevalence of delivery (66%) among tribal women occurred at home. 46% of the mothers gave birth to their children with the assistance of a skilled birth attendant, of which 12% of them were at home, and 34% were in the hospital. The remaining 54% of mothers gave birth to their babies with the assistance of a traditional midwife. Results also show that socio-economic conditions, education of women, distance from health care facilities, family planning, husbands’ occupation, and media exposure are the key determinants in pursuing SBA. Conclusion: The study revealed a low prevalence of utilizing skilled birth attendance among tribal women of Bangladesh. Hence, upgrading socio-economic conditions and boosting the education levels of tribal women, ensuring easy access to the media, and launching short-term training to train typical birth attendants are the key recommendations for seeking expert birth attendants.

Background: Safe maternity is a global issue with the ongoing discussion of ways to reduce mortality. This problem is prevalent among women from low-income countries such as Bangladesh. Hence, this study evaluated the factors affecting delivery practices among tribal women in Bangladesh Chittagong Hill Tracts (CHT). Method: This is an analysis of cross-sectional research. To achieve the sample size of 556 married women, a convenient sampling technique was used. In a semi-structured questionnaire, data were collected. Associations were tested using Chi-square ( ) tests, and multivariate regression analysis was applied to elicit results from the data. Result: Result from the present study shows that most deliveries (66%) occur at home. Of this quantity, 50% of births were facilitated by untrained traditional midwives, 12% by trained conventional birth caregivers, and 4% by relatives and neighbors. Wealth index, place of dwelling, women’s educational level, age at first childbirth, range to health care, and profession of the husband have all significantly contributed to the approach to delivery. Conclusion: This study concludes that institutional delivery (34%) is poor among tribal women of Bangladesh. Factors limiting the adoption of institutional delivery by these tribal women should be managed adequately via awareness campaigns and improving the socio-economic status of the tribal people of CHT. Keywords: Delivery care; Institutional delivery; Choice of the birthplace; Skilled birth attendant (SBA); Traditional birth attendants (TBA); Trained birth attendants (TTBA); Home delivery