A global consumption of energy is primarily met by the renewable and non-renewable energy production resources. It is necessary to understand the pattern of global energy consumption in past to refine the overall energy policy for an upcoming demand of the energy market. The consumption of energy and its insights are helpful for grid management and forecasting. This paper presents the consumption of renewable and non-renewable energy resources by different nations and presents the analysis of the impact of COVID19 pandemic over the consumption of Energy. From the detailed analysis in this study, it is evident that all countries are shifting their interest to use renewable sources of energy generation. The global consumption of energy was constantly increasing up to 4% each year for three decades (1990 to 2020). However, during COVID-19 outbreak, energy consumption shows a downward trend in 2020 to -4%, which is twice lower than the decrement of energy consumption observed 2008-2009 economic crisis. The COVID-19 pandemic has seriously affected energy consumption of all countries in the world.

Skin cancer is among common and rapidly increasing human malignancies, which can be diagnosed visually. The diagnosis begins with preliminary medical screening and by dermoscopic examination, histopathological examination, and proceeding to the biopsy. This screening and diagnosis can be automated using machine learning tools and techniques. Artificial neural networks are helping a lot in medical diagnosis applications. In this research, skin images are classified into 7 different classes of skin cancer using deep learning methodology, then analyzed the results w.r.t to their respective precision, recall, support, and accuracy to find its practical applicability. This model is efficient in comparison to the detection of skin cancer with human eyes. Human eyes detection can be 79% accurate at most. Thus, having a scientific method of diagnosis can help the doctors and practitioners to accurately identify the cancer and its type. The model provides 80% accuracy on average for all 7 types of skin diseases, thus being more reliable than human eye examination. It will help the doctors to diagnose the skin diseases more confidently. The model has only 2 misclassified predictions for Basal cell carcinoma and Vascular lesions. However, Actinic keratosis diagnosis is most accurately predicted.