Time series tools and their usage in the biomedical field; specifically, in neuroscience, has not been previously quantified. This study evaluates the principles and applications of few classical time series tools, such as PCA, Neural Networks, ARMA/ARIMA Models, Markov Models, Hidden Markov Models, Fourier Analysis, Spectral Analysis.

This study quantifies the usage from two perspectives, one, information technology professionals’, other, researchers utilizing these tools for biomedical and neuroscience research.

Computer-aided diagnosis of retinopathy is a research hotspot in the field of medical image classification. Optical coherence tomography (OCT) is widely applied in the diagnosis of ocular diseases.

In this study we present the solution to classify the OCT images using simple baseline 3,5 and 7 Layer deep Convolutional Neural Networks (CNN). It also explores the effect of hyper-parameters such as dropout, image-size, batch normalization, epochs and their relationships with the accuracy, sensitivity and specificity of the models.

In this paper we did a comparative analysis on automated bug-clasification in the software Industry. We worked on characterization and prediction of different software issue types with respect to three parameters: distribution, mean time to repair, and top terms present in various issue types. We have compared several classic and ensemble machine learning classifiers with respect to accuracy and prediction time and provided detailed analysis on the same.