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This study aims to examine the moderator effect of religiosity on the relationship between halal brand awareness and habit towards purchasing decisions of halal products.

The quantitative method is used in this study. Descriptive and statistical (multiple and moderated regression) analyses are employed to test the hypothesis according to the research model. The data is collected using a cross-sectional design from 197 respondents consisting of business owners in North Sumatera, Indonesia.

It is found that both halal brand awareness and habit have a positive and significant effect on the purchasing decision of halal products. Meanwhile, religiosity significantly acts as a moderating variable in the relationship between awareness and purchasing decisions, as well as habit and purchasing decisions.

This study revealed the important factor of religiosity as a moderating factor in purchase decisions of halal products. The government may need to collaborate with Islamic educational institutions to raise awareness of the halal concept and product awareness. It is assumed that individuals who know about the Islamic religion will have a higher degree of awareness of halal products compared to individuals with limited knowledge of Islam; thus, providers of Islamic education play a crucial role in raising the level of awareness of halal products. Schools may serve as catalysts for the dissemination of knowledge of halal products.

Developing halal product markets can be done by enhancing the religiosity level of consumers, one of them through attending formal or informal religious classes.

Diagnosing brain tumors is a process that demands a significant amount of time and is heavily dependent on the proficiency and accumulated knowledge of radiologists. Over the traditional methods, deep learning approaches have gained popularity in automating the diagnosis of brain tumors, offering the potential for more accurate and efficient results. Notably, attention-based models have emerged as an advanced, dynamically refining and amplifying model feature to further elevate diagnostic capabilities. However, the specific impact of using channel, spatial or combined attention methods of the convolutional block attention module (CBAM) for brain tumor classification has not been fully investigated.

To selectively emphasize relevant features while suppressing noise, ResNet50 coupled with the CBAM (ResNet50-CBAM) was used for the classification of brain tumors in this research.

The ResNet50-CBAM outperformed existing deep learning classification methods like convolutional neural network (CNN), ResNet-CBAM achieved a superior performance of 99.43%, 99.01%, 98.7% and 99.25% in accuracy, recall, precision and AUC, respectively, when compared to the existing classification methods using the same dataset.

Since ResNet-CBAM fusion can capture the spatial context while enhancing feature representation, it can be integrated into the brain classification software platforms for physicians toward enhanced clinical decision-making and improved brain tumor classification.

This research has not been published anywhere else.