In the field of pharmaceutical research, identifying promising pharmaceutical compounds is a critical challenge. The observance of Lipinski's Rule of Five (RO5) is a fundamental criterion, but evaluating many compounds manually requires significant resources and time. However, the integration of computational techniques in drug discovery in its early stages has significantly transformed the pharmaceutical industry, enabling further efficient screening and selection of possible drug candidates. Therefore, this study explores RO5 using algorithms of Machine Learning (ML), offering a comprehensive method to predict the druggability of pharmaceutical compounds. The study developed, evaluated, and validated the performance metrics of multiple supervised machine learning models. The best model was used to build an application that can predict and classify potential drug candidates. The findings revealed promising capabilities across all models for drug classification. Among all the explored models, Random Forest (RF), Extreme Gradient Boost (XGBoost), and Decision Tree (DT) classifiers demonstrated exceptional performance, achieving near-perfect accuracy of 99.94%, 99.81% and 99.87% respectively. This highlights the robustness of ensemble learning methods in classifying compounds based on RO5 adherence. The comparative analysis of these models underscores the importance of considering balanced accuracy, precision, F1-score, recall, and Receiver Operating Characteristics-Area Under the Curve (ROC-AUC) score, interpretability, and computational efficiency when choosing between ML algorithms in drug discovery. The DrugCheckMaster application was subsequently developed using the most predictive model and is now available on Render (https://capstone-project-dc7w.onrender.com/).

This research study aims to examine the possibilities of Hyperledger Fabric (HLF) in the healthcare sector. The study addresses the gap in the knowledge base through developing customization techniques to enable the simplicity and efficacy of Electronic Medical Records (EMR) adoption for healthcare industry applications. The focus of this research explores methods of using blockchain technology that prioritise users. The investigation of several concepts used in developing web applications has been determined. The study identified that an open-source project, known as Hyperledger Fabric, can be utilised to construct a novel method of storing EMRs. The framework provides a test network that can be customised to satisfy the need of several projects, including storing medical records. This research additionally outlines the difficulties encountered and problems that need to be resolved before Hyperledger Fabric can be successfully implemented in healthcare systems. Considering all types of blockchains available, the needs are met by Hyperledger Fabric, which offers a distributed and secure environment for healthcare systems. Blockchain has the potential to transform healthcare by putting the patient at the centre of the system and enhancing health data protection and interoperability. Also, by using grant and revoke access mechanisms, patients have complete control over their medical information as well as authorized doctors who are allowed to view records. This functionality is made possible by the chaincode defined in the blockchain platform. The research study has both practitioner and research implications for the development of secure blockchain-based EMRs.

The postulation of rationality pervading neoclassical economics, having found to be lacking in veracity, necessitates an update of Homo economicus in neoclassical economics. An empirical formulation of economics predicated on an evolutionary framework of cognitive development is widely akcnowledged to expand the application and utility of economic models and this study identifies the key research areas associated with the contemporisation of neoclassical economics and reviews the extant body of knowledge on the status of the endeavour. A co-occurring keyword procedure is used to perform a bibliometric analysis to evaluate the extent to which the body of knowledge has progressed with updating the conception of Homo economicus in neoclassical economics. Data obtained from the Scopus and Web of Science databases are subjected to treatment by the procedure which ultimately reflects the relevance and quantity of research output related to the objective of updating neoclassical economics. The results show that, apart from the area of cognitive biases, the research contribution from the identified focus areas are insufficient with 14 of the 18 fields contributing only 12% of the research ouput related to the updating of neoclassical economics. The key insight of this study is that while the failures of neoclassical economics have been well documented in the literature, the effort to improve economic predictions to better approach reality by updating the characterisation of Homo economicus with evidence based findings relating to human behaviour is still immature. It is recommended that the computational and analytical tools endowed by the digital revolution be employed to close the research gap enabling the creation of debiasing strategies and acucrate economic predictions.

Sustainability is a crucial concept in agriculture and agricultural production. Since there is an intense competitiveness and risk in the sector, technical advancements are essential for improved development and sustainability. Small farms require cost effective solutions to match the standards of bigger producers, which essentially means best yields on crops, both in terms of quantity and quality. It is crucial to consider water crisis, climate change, and quality farm care when designing new solutions for agriculture. This study proposes the automation of farm irrigation systems based on a supervised machine learning model (SVM, Logistic Regression) that is cost-effective and precise response to farming demands. In order to show the practicality of this proposed new technology, an application was developed to represent the model results. The application created was for the user to be able to use the model to predict and control the valves in an irrigation system. This work is a potential solution for small farms in a country like South Africa, where sustainable farming is taking rise.