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Indian Premier League (IPL) is one of the more popular cricket world tournaments, and its financial is increasing each season, its viewership has increased markedly and the betting market for IPL is growing significantly every year. With cricket being a very dynamic game, bettors and bookies are incentivised to bet on the match results because it is a game that changes ball-by-ball. This paper investigates machine learning technology to deal with the problem of predicting cricket match results based on historical match data of the IPL. Influential features of the dataset have been identified using filter-based methods including Correlation-based Feature Selection, Information Gain (IG), ReliefF and Wrapper. More importantly, machine learning techniques including Naïve Bayes, Random Forest, K-Nearest Neighbour (KNN) and Model Trees (classification via regression) have been adopted to generate predictive models from distinctive feature sets derived by the filter-based methods. Two featured subsets were formulated, one based on home team advantage and other based on Toss decision. Selected machine learning techniques were applied on both feature sets to determine a predictive model. Experimental tests show that tree-based models particularly Random Forest performed better in terms of accuracy, precision and recall metrics when compared to probabilistic and statistical models. However, on the Toss featured subset, none of the considered machine learning algorithms performed well in producing accurate predictive models.

Bitcoin is among the highest rated digital crypto-currency in financial investment markets. This technology relies on a backbone of distributed data architecture and peer-to-peer networking model called Blockchain. Unlike the current digital economy, which is governed centrally by financial institution or governments, Blockchain is fully autonomous without any third-party involvement. The exorbitant success of Bitcoin has attracted investors, scholars as well as organizations to peek into this lucrative technology for the possible application to other areas apart from crypto-currency. Blockchain can adopt Smart Contracts, which are digitally enabled contracts that can be executed and enforced fully or partially using pre-defined notions. The aim of this research is to investigate the synergy between Smart Contract and Blockchain to propose a digital framework for an academic paper publication model that has the capability to automate the entire process and challenge the existing system. It can also bring together all the stakeholders under the same system. The proposed model can further hold the stakeholders accountable for breach of contracts and/or reward them for executing the successes of terms pre-configured in the Smart Contract. The proposed model, called Digital Smart Publication or DSP (as referred in the document), is highly secure and ensures balance in distributing rewards to the involved stakeholders while keeping data integrity and security as paramount features.