Due to high demand, many cloud data centers have been developed across the world, consuming a large amount of energy. Making cloud datacenters energy efficient has become essential. Energy consumption of data centers can be minimized by designing energy-efficient hardware, software, and infrastructure. In this paper, we aim at giving an overview of software techniques, affected stakeholders, performance features, datasets, and tools used to make cloud data centers energy-efficient. To achieve this goal, we conducted a systematic mapping study using five online databases. After applying inclusion/exclusion and quality criteria, we selected 58 publications for further analysis. Our results indicate that all publications are solution and validation type of publications. We did not find publications containing evaluations in industry. We found that workload scheduling is the most frequently proposed technique used to improve cloud datacenters' energy efficiency. We found that not considering violations of service level agreements mostly affects end-users of cloud data centers. When analyzing how suggested solutions are validated, we identified the need to develop a standardized set of performance measures to benchmark software techniques proposed to make cloud data centers greener.

Glaucoma is one of the most dangerous causes of blindness that results in permanent blindness within a few years if left untreated. It is very hard to diagnose particularly in early stages. Using ophthalmological images, vasculature of blood vessels is most valuable factor for detecting glaucoma. It can be segmented by image processing techniques which help in early diagnosis. In this research the vasculature found within the optic disc is segmented, then used to calculate its ratio in ISNT quadrants. On the basis of ISNT rule we find out that ratio of blood vessels in each and evaluates the results whether blood vessels are being nasalized i.e. they are violating or obeying ISNT rule. The proposed methodology is examined on 50 images collected from different image databases which are FAU, DMED and MESSIDOR to testify nasalization of vessels in retinal images.

Music genres are the simplest and effect descriptors for searching music libraries stores or catalogues. The paper compares the results of two automatic music genres classification systems implemented by using two different yet simple classifiers (K-Nearest Neighbor and Naïve Bayes). First a 10-12 second sample is selected and features are extracted from it, and then based on those features results of both classifiers are represented in the form of accuracy table and confusion matrix. An experiment carried out on test 60 taken from middle of a song represents the true essence of its genre as compared to the samples taken from beginning and ending of a song. The novel techniques have achieved an accuracy of 91% and 78% by using Naïve Bayes and KNN classifiers respectively.