Blood transfusion is an essential medical process that saves millions of lives annually, yet its success critically depends on accurate donor–recipient compatibility assessment. Conventional blood matching systems predominantly rely on ABO and RhD blood group typing, neglecting extended antigen profiles and patient transfusion histories. This limitation elevates the risk of transfusion reactions, particularly in patients requiring repeated transfusions, such as those with thalassemia, sickle cell disease, or cancer. This paper presents a browser-based medical system that employs machine learning algorithms—Random Forest and Naïve Bayes—to predict donor–recipient compatibility using key clinical parameters including ABO/RhD blood group, crossmatch test results, transfusion history, disease type, and extended antigen data. The system supports role-based hospital workflows for administrators, receptionists, laboratory technicians, and patients. Developed with Python, Flask, MySQL, scikit-learn, and Bootstrap, the proposed system demonstrates improved prediction accuracy, reduced manual errors, and enhanced transfusion safety over traditional methods.
Rising levels of carbon emissions have emerged as a key factor to climate change requiring smart mechanisms of monitoring and mitigation. In this paper, CarbonIQ, a machine learning-based, generative AI-based, and IoT-based data collection integrated carbon footprint prediction a…
Chronic Kidney Disease (CKD) is a serious, progressive, and widely known medical condition that afflicts millions around the globe and often is not diagnosed until it has reached its later stages. Healthcare systems face obstacles in the timely diagnosis of CKD, due to the gradua…
Predictive analytics has emerged as a vital component of contemporary educational data analysis, enabling higher education institutions to move from reactive evaluation to proactive academic planning. The increasing availability of digital academic records—such as attendance, int…
Microplastic contamination in coastal ecosystems has emerged as a critical environmental issue with significant ecological, economic, and public health consequences. Conventional monitoring approaches rely heavily on field sampling and laboratory-based analysis, which are time-co…
Outfit compatibility prediction has been studied extensively for Western clothing using the Maryland Polyvore benchmark dataset, with state-of-the-art models such as OutfitTransformer achieving AUC scores of 0.92 (Sarkar et al., 2023). However, no published work addresses this pr…
In recent years, there has been a dramatic expansion of digital communication, online document sharing, and therefore the importance of a highly secure document verification system has risen greatly. Existing systems require manual effort, slow verification process and are suscep…