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Adding energy-saving products to your house can benefit the economy, the environment and your living comfort. However, these products are very costly, and many people cannot afford them using their own savings. There exist several options for funding these projects, but people do not take advantage of such due to lack of information and the common negative view on using external funding. Psychological objections on taking loans include future time perspective, perception of short time rewards and connotation of loans itself. This paper presents a serious game aimed at changing people's mindset on taking loans to retrofit energy into their homes; Supreme Green Time Machine is a tycoon game in which you can acquire energy-saving products for your home. A main mechanic in the game is the opportunity to take loans to fund the purchase of these upgrades. Combined with other underlying mechanics, such as the time progress and social feedback, the game targets the different psychological objections to long term loans for home retrofitting. From a preliminary evaluation, we conclude that Supreme Green Time Machine effectively succeeds in making players more positive towards using loans to retrofit their homes.

Brain Tumour is an abnormal cell formation inside the brain. They are mainly classified as benign and malignant tumours. Magnetic Resonance Imaging (MRI) is used for effective diagnosis of brain tumour. An automated method for detection and classification of brain tumour is preferred as analysis of MRI manually is a difficult task for medical experts. The proposed method uses Adaptive Regularized Kernel based Fuzzy C-Means Clustering (ARKFCM) for segmentation. A combination of Support Vector Machine (SVM) and Artificial Neural Network (ANN) is proposed for detection and classification of brain tumour based on the extracted features. A dataset of 94 images is considered for validation of the proposed method which resulted in an accuracy of 91.4%, Sensitivity of 98%, Specificity of 78% and Bit Error Rate (BER) of 0.12. Comparison of the proposed method is carried out with other conventional methods and the results are tabulated.

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