Leveraged on the abundant weight data comprised of more than 200 offshore platforms, a smart digitalized analytical tool called i-WEIGHT, an integrated weight control tool consisting of three (3) main modules: centralized multi-discipline weight database module for all offshore platforms, seamlessly linked with Insights dashboard module in providing actionable insights, and weight predictive module supported by Machine Learning (ML) model was developed. This paper discussed the Minimum Viable Product (MVP) Phase 1 development outcome, using a close-loop weight control ecosystem for continuous update of validated weight data in Module 1, and eventually improve & enhance capability of both the EDA and Predictive module. Using a supervised machine learning algorithms, the identified target variables were observed to provide weight prediction between 16% to 38% of Mean Absolute Percentage Error (MAPE), using Extreme Gradient Boosting Regressor (XGBR) algorithm. Top 10 important features were identified for each target variable, which provide insights to the operators on critical data required for topside with identified missing equipment weight data for future i-WEIGHT improvement. Based on more than 200 integrated platform topside data gathered for this study, consolidated insights from the data enabled operators to identify the threat of current data quality and thus bringing forward a promising opportunity to enhance platform weight data management system. Having a centralized and automated platform weights data, this tool has the potential answers for United Nations’ Sustainability Development Goals, in particular Goal 9.4, where the study represents a small but crucial step to upgrade from an existing conventional process into a digitally driven operation, introducing a sustainable ecosystem in offshore structure weight management, thus fostering sustainable growth within the industry.

This research aims to increase people's awareness of fake news on online social networks and help them determine the reliability of information they consume. It investigates methods for detecting fake news sources, authors, and subjects on online social networks. The project uses an open-source online dataset of fake and real news to determine the credibility of news. Various text feature extraction techniques and classification algorithms are reviewed, with the Support Vector Machine (SVM) linear classification algorithm using TF-IDF feature extraction achieving the highest accuracy of 99.36%. Random Forest (RF) and Naive Bayes (NB) had accuracy scores of 98.25% and 94.74%, respectively.