Purpose This study aims to determine direct and indirect ways of strengthening consumer’s halal buying behaviour. For this, the researchers explore the role of religiosity and consumers’ personal norms on consumers’ attitudes and halal buying behaviour. The study also reconnoiters the mediating role of consumer attitudes. Design/methodology/approach With a structured questionnaire, a survey was conducted to collect data on consumer attitudes, personal norms and halal buying behaviour. Finally, 229 valid questioners were retained for data analysis. The structural equation modelling technique was used for data analysis using SmartPLS 3.0 software. Findings The result of this study suggests that consumers’ attitude towards halal purchase depends on consumers’ personal norms and religiosity. Further, the role of consumer attitudes and religiosity on the halal buying behaviour of consumers is significant. However, the personal norm is not a significant predictor of halal buying behaviour. Consumer attitudes mediate the relationships between personal norms and halal buying behaviour, as well as religiosity and halal buying behaviour. Research limitations/implications The findings of the present study indicate that consumers’ personal norms and religiosity are the important determinants of consumer attitude and behaviour towards halal purchase. Marketers of halal products and services should focus on strengthening consumers’ attitudes and religiosity to influence consumer behaviour towards halal purchase. Originality/value In light of recent research studies on the halal purchase, the present research finds the essential predictors of consumers’ halal purchase attitude and behaviour. The study also reveals that consumer attitude is an important role in strengthening halal buying behaviour, as it has both direct and indirect impact halal buying behaviour.

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The disease may be an explicit status that negatively affects human health. Cardiopathy is one of the common deadly diseases that is attributed to unhealthy human habits compared to alternative diseases. With the help of machine learning (ML) algorithms, heart disease can be noticed in a short time as well as at a low cost. This study adopted four machine learning models, such as random forest (RF), decision tree (DT), AdaBoost (AB), and K-nearest neighbor (KNN), to detect heart disease. A generalized algorithm was constructed to analyze the strength of the relevant factors that contribute to heart disease prediction. The models were evaluated using the datasets Cleveland, Hungary, Switzerland, and Long Beach (CHSLB), and all were collected from Kaggle. Based on the CHSLB dataset, RF, DT, AB, and KNN models predicted an accuracy of 99.03%, 96.10%, 100%, and 100%, respectively. In the case of a single (Cleveland) dataset, only two models, namely RF and KNN, show good accuracy of 93.437% and 97.83%, respectively. Finally, the study used Streamlit, an internet-based cloud hosting platform, to develop a computer-aided smart system for disease prediction. It is expected that the proposed tool together with the ML algorithm will play a key role in diagnosing heart diseases in a very convenient manner. Above all, the study has made a substantial contribution to the computation of strength scores with significant predictors in the prognosis of heart disease.

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Magnetic resonance imaging (MRI) offers visual representations of the interior of a body for clinical analysis and medical intervention. The MRI process is subjected to a variety of image processing and machine learning approaches to identify, diagnose, and classify brain diseases as well as detect abnormalities. In this paper, we propose an improved classification method for distinguishing cancerous and noncancerous tumors from brain MRI images by using Log Polar Transformation (LPT) and convolutional neural networks (CNN). The LPT has been applied for feature extraction of rotation and scaling of distorted images, while the integration of CNN introduces a machine learning approach for the tumor classification of distorted images. The dataset was formed with images of seven different brain diseases, and the training set was formed by applying CNN with the extracted features. The proposed method is then evaluated in comparison to state-of-the-art algorithms, showing a definite improvement of the former. The obtained results show that the machine learning approach offers better classification with a success rate of about 96% in both plain brain MR images and rotation- and scale-invariant brain MR images. This work also successfully classified T-1 and T-2 weighted images of neoplastic and degenerative brain diseases. The obtained accuracy is perfected by several kernel procedures, while the combined performance of the two wavelet transformations and a strong dataset make our method robust and efficient. Since no earlier study on machine learning approaches with rotated and scaled brain MRI has come to our attention, it is expected that our proposed method introduces a new paradigm in this research field.

This study focuses on perusing how inter-personal relationship (IPRs) and inter-organizational relationships (IORs) interacts in the supply chain integration (SCI). Previous studies on supply chain integrations focuses more on inter-organizational relationships and ignoring inter-personal relationships. In this study an exploratory multiple case studies in Malaysia is used. We realize that in the early stage of supply chain integration, inter-personal relationships are identified as a precursor to building Inter-organizational relationships. During the operational stage, the two levels of relationships continuously interact with each other, until the end of the entire life-cycle of the dyad, inter-personal relationships helps in the emergence and growth pf IORs while the latter often uses these ties to negotiate for resource acquisition.

Bangladesh garments industry plays a significant role for Bangla-desh economy. It helps millions of workers in Bangladesh. As difficulties to trade among nations have failed due to improved shipping systems, technology transfer and government cooperation , the industry has seen a rapid increase in globalization and struggle. The all Bangladesh apparels mills association and individuals needs to enhance the quality of its goods. However, the promises in the RMG can be comprehended only if the challenges in some areas like-organizations, compliances, workforces supply , dealers' performances, raw resources, political stability are tackled. In order to minimize the issues we need a proper supply chain using Blockchain.