This research implements an intelligent, bilingual pregnancy health monitoring system for expectant mothers. A significant problem commonly experienced by expectant mothers in rural areas in Nigeria is the unavailability of a decent antenatal system and a shortage of experienced medical personnel and equipment. The proposed system comprises IoT sensors, including Electrocardiogram (ECG), body temperature, and heart rate sensors, connected to an ESP32 microcontroller for data acquisition and transmission. A predictive system built using Random Forest and Support Vector Machine (SVM) classifiers categorises pregnancy risk into low, medium, and high. A Flask-based web application for real-time data visualization and diagnosis was developed to display the collected data and visually represent the risk level diagnosis. The performances of the predictive models, Random Forest and Support Vector Machine (SVM), were evaluated using accuracy, precision, recall, and F1-score. Random Forest achieved an accuracy surpassing SVMs by a margin of 5.28%. Random Forest and SVM precision were then compared and there was an improvement of 6.49%. 
In addition, Random Forest had a higher recall than SVM by 6.58%, and also had a performance increase of 6.49% on F1-score as compared to SVM. The comparative analysis shows that the Random Forest model works better than SVM in all the main measures. In this project, the Random Forest model was better than the SVM because it uses ensemble learning to manage the non-linear relationship, imbalance data and noise better to achieve superior accuracy, recall, and the F1 Scores. It was also more reliable in categorizing risks in pregnancy, as it was interpretable, which was also strong and guaranteed the timely and suitable intervention of health care.

Rice isn't just essential to global food security; disease outbreaks and poor soil health management stymie its growth. Often, traditional rice farmers lack the knowledge or resources to diagnose plant diseases and monitor soil conditions in real time. Existing solutions typically focus on either soil health monitoring or disease detection, but not both, leaving farmers unable to respond to the identified threat. This project will mitigate this by combining real-time monitoring, disease classification, and a recommendation system into a single solution. Poor disease detection and inadequate health examinations of soils commonly result in decreased rice productivity. The proposed research is also focused on the development of an intelligent system equipped with IoT sensors to monitor soil parameters such as moisture, nitrogen concentration (NPK), and temperature in real time, and a machine-learning-based system capable of classifying 15 different rice diseases. The system also includes a recommendation engine that provides actionable recommendations for treating an illness, making it a complete soil and crop health management tool. The system is based on a transfer learning model (MobileNetV2) that classifies rice illnesses using image classification. The model was trained on 22,688 images of rice diseases, achieving a detection accuracy of 96.42%. The system was also highly accurate for monitoring soil health, with minimum standard deviations of 0.20% and 0.22 for soil moisture and nitrogen levels, respectively. The results obtained reflect the effectiveness of the developed system in enhancing the farming process by enabling farmers to identify diseases at early stages and improve soil conditions. Lastly, the methodology enhances rice production, reduces crop losses, and helps achieve global food security.

The systems related to safety are becoming more and more important and are dependent on complex data both in terms of volume and variety. This is especially of importance in applications demanding data analysis, intensive maintenance and focuses on the potential threats due to possible data errors, such as railway signaling, traffic management etc. Errors in analysis of data could result in loss of many lives and financial loss such as the cases of Annabella container ship- Baltic Sea accident (United Kingdom Merchant Shipping, Regulations 2005 – Regulation 5). Despite these potential errors in data leading to accidents or mishaps, this part of the system has been ignored; this study focuses on the integrity of data in safety critical applications. It did so by developing a method for building metadata through a data chain, mining this metadata and representing it in such a way that a consumer of the data can judge the integrity of the data and factor this into the decision-making aspect of their response. This research proposes a design, implementation and evaluation of a safety data model that helps to ensure integrity of data use for data analysis and decision making to prevent loss of lives and properties. Modern and sophisticated ETL software tools including Microsoft SQL Server 2012 Data Tools and Microsoft SQL Server Management Studio were explored. The data were extracted from Safety Related Condition Reports (SRCRs) dataset and used data mining techniques to transform and filter unsafe and hazardous data from the extracted data and stored the safe data into the Data Warehouses (DWs). The prototype was able to load data into designated DWs. The success of the developed model proved that the prototype was able to extract all datasets, transform and load data into the DWs and moved extracted files to archive folder within 7.406 seconds.