-In this paper, several ensemble cancer survivability predictive models are presented and tested based on three variants of AdaBoost algorithm. In the models we used Random Forest, Radial Basis Function Network and Neural Network algorithms as base learners while AdaBoostM1, Real AdaBoost and MultiBoostAB were used as ensemble techniques and ten other classifiers as standalone models. There has been major research in ensemble modelling in statistics, medicine, technology and artificial intelligence in the last three decades. This might be because of the effectiveness and reliability of the technique in medical diagnosis and incident predictions compare with the standalone classifiers.We used Wisconsin breast cancer dataset in training and testing the models. The performances of the ensemble and standalone models were evaluated using Accuracy, RMSE and confusion matrix predictive parameters. The result shows that despite the complexity of the ensemble models and the required training time, the models did not outperform most of the standalone classifiers.

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-This paper presents the empirical comparison of boosting implementation by reweighting and resampling methods. The goal of this paper is to determine which of the two methods performs better. In the study, we used four algorithms namely: Decision Stump, Neural Network, Random Forest and Support Vector Machine as base classifiers and AdaBoost as a technique to develop various ensemble models. We applied 10-fold cross validation method in measuring and evaluating the performance metrics of the models. The results show that in both methods the average of the correctly classified and incorrectly classified are relatively the same. However, average values of the RMSE in both methods are insignificantly different. The results further show that the two methods are independent of the datasets and the base classier used. Additionally, we found that the complexity of the chosen ensemble technique and boosting method does not necessarily lead to better performance.

The opportunities offered by digital technology are enormous. The global social and economic system is being reconfigured at an incredible rate. Connectivity is increasingly reshaping our world and redefining the way we interact with our environment. The rise of digital technologies is transforming almost every aspect of modern life. More and more of our interactions are mediated by machines. Along with the rapid evolution comes the risks, threats, and vulnerabilities in the system for those who plan to exploit it. In this chapter, firstly, the authors explore the role of 5G, big data, the internet of things (IoT), artificial intelligence (AI), autonomous vehicles (AV), and cloud computing play in the context of smart societies; secondly, they analyse how the synergy between these technologies will be used by governments and other stakeholders around the world to improve the safety of citizens albeit increasingly relinquishing privacy rights and encouraging mass surveillance at the expense of liberty.

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