Background: A brain tumor is an abnormal cell proliferation within the brain, and it strongly impacts people. Computer-aided diagnosis to detect brain tumours has been prevalent due to high reliability and accuracy in recent years. Objective: This research work aims to understand various methods in brain tumor detection and the critical research challenges in this domain. So that in future, the researchers can work on these areas to come up with new methods. Method: This manuscript mainly discusses various medical image processing methods to detect brain tumors. Results: The manuscript discusses the efficiency of the existing schemes and the key areas where further improvement is required Conclusion: This manuscript gives an overview of various categories of brain tumors, existing methods to detect brain tumors, the critical challenges in this domain and the medical image dataset available for the study.

Virtual Machine (VM) instance price prediction in cloud computing is an emerging and important research area. VM instance’s price prediction is used for different purposes such as reducing energy consumption, maintaining Service Level Agreement (SLA), and balancing workload at cloud data centers. In this paper, we propose a Seasonal Auto-Regressive Moving Average (SARIMA) based VM instance price prediction. We also investigate two VM instance price prediction models known as Auto Regressive Integrated Moving Average (ARIMA), and Long ShortTerm Memory (LSTM). The experimental results show that the proposed SARIMA (0,1,0) (1,1,0) instance’s price prediction model outperforms the ARIMA and LSTM models with a MAPE percentage of 1.147.