The worldwide gaming peripheral market is expanding significantly due to the increasing popularity of online games, and it is predicted that this would increase demand for gaming peripherals. Brand recognition is just the start of the process because many sectors are vying to stand out and wrest mindshare away from rivals. In this paper, we presented a tool named BrandTrend, which enables automated insight discovery for game trending, gaming influencers, and gaming product promotion. The data used in this tool is gathered from social media platforms to analyse gaming contents to match gaming content creators with gaming peripheral brands to promote their brand products via social media. Utilizing data analysis and incorporating evidence from data to build predictions and develop strategies can unambiguously address the issue of distinguish oneself from other rivals and get recognition.

The act of trading in the financial markets from a discretionary standpoint comes with a vast number of pitfalls that lead to participants achieving poor returns on their investments. With trading being a psychologically intense activity, the paper presents development of trading algorithms that will not only eliminate the psychological barriers to trading but do so in a way that ensures that significant returns on investments are made, with these returns being evaluated by testing the strategies on past historical price data of various assets. Findings noted that the algorithms performances varied depending on the market circumstances with certain strategies only being applicable to either strong or weak market conditions. The implication of these findings opens the door to new discussions since the algorithms developed resided outside of the traditional high frequency trading model which are the most prominent trading applications found on the markets. This unconventional algorithmic approach to the markets verifies a way of obtaining significant returns without the requisite of having low latency, thus enabling one to compete with the more sophisticated algorithms developed and used by the major financial institutions without the need for human intervention or any additional resources.

COVID-19's impacts have spread widely in all directions such as economy, people's lifestyles and well-being. Though existing studies have highlighted such an impact, it remains unclear how the current COVID-19 situation has affected the retrenchment, vaccination and global happiness. In this paper, we present an automated tool enables the public to view various insight. In particular, we integrate and analyze the data from various data sources and show how the COVID19 has impacted Singapore and globally. We employ the regression models to identify the correlation between Human Development Index, Stringency Index, Gross Domestic Product per Capita, Total Deaths from COVID-19, and Total Cases of COVID-19; the rate of vaccination and vaccine hesitancy; and the factors to positively correlate to the global happiness. The insight provided adds values to better fight against the COVID-19 pandemic and future global crisis.

This paper presents the design and development of the Java Starter, an ontology-based tutoring system meant to support the teaching of the Java programming language to beginners. The developed prototype is capturing the domain knowledge in the form of an ontology, developed using the Protégé ontology editor. The reasoner used for inference tasks is HermiT 1.4.3.456. The system has been tested at every level of implementation, using JUnit for the Java component, Selenium for the user interface, and the HermiT reasoner for the ontology.

The objective of this paper is to develop a system for predicting diabetes and related conditions in patients using Machine Learning techniques with high degree of accuracy so patients can be treated at an early stage, which could provide a life-saving impact. A Backpropagation Neural Network (BPNN) with 50 nodes in hidden layer and K-Nearest Neighbour (KNN) were created to predict diabetes in patients. A Long Short-Term Memory (LSTM) network and Recurrent Neural Network (RNN) with 100 nodes in hidden layer were created to predict blood glucose levels and generate early warning signs for short-term diabetes complications such as hypoglycaemia, hyperglycaemia and pre-diabetic. The BPNN model achieved the best performance for predicting diabetes with an average classification accuracy of 76.7% and was compared with KNN model which achieved an average classification accuracy of 74.0%. While LSTM model achieved the best performance for predicting blood glucose levels with an average classification accuracy of 90.0%, 88.8% sensitivity, 88.0% specificity, 93.0% positive predictive value and 81.3% negative predictive value, and was compared with RNN model which achieved an average classification accuracy of 84.1%. Obtaining highly accurate predictions on future readings shows potential for the system to be used by healthcare care personnel to determine the right form of treatment at an early stage so patients can be treated in advance. The developed system is at its early stages with two fully working tools and shows promise for further development to increase its effectiveness and performance for complete professional use.