We describe a system for the automated diagnosis of diabetic retinopathy and glaucoma using fundus and optical coherence tomography (OCT) images. Automatic screening will help the doctors to quickly identify the condition of the patient in a more accurate way. The macular abnormalities caused due to diabetic retinopathy can be detected by applying morphological operations, filters and thresholds on the fundus images of the patient. Early detection of glaucoma is done by estimating the Retinal Nerve Fiber Layer (RNFL) thickness from the OCT images of the patient. The RNFL thickness estimation involves the use of active contours based deformable snake algorithm for segmentation of the anterior and posterior boundaries of the retinal nerve fiber layer. The algorithm was tested on a set of 89 fundus images of which 85 were found to have at least mild retinopathy and OCT images of 31 patients out of which 13 were found to be glaucomatous. The accuracy for optical disk detection is found to be 97.75%. The proposed system therefore is accurate, reliable and robust and can be realized.

It is always important to monitor the quality of air that we inhale to protect ourselves from the respiratory diseases. In this paper, we present different regression models to forecast air quality index (AQI) in particular areas of interest. Support vector regression (SVR) and linear models like multiple linear regression consisting of gradient descent, stochastic gradient descent, mini-batch gradient descent were implemented. In these models, the air quality index (AQI) is dependent on pollutant concentrations of NO₂, CO, O₃, PM_2.5, PM₁₀ and SO₂. Among these models, support vector regression (SVR) exhibited high performance in terms of investigated measures of quality.

With the growing internet technology, science and requirement of concealing defense research work, there is a need for highly secured information exchange, which is the essence of steganography. Classic secret information could be undermined or even faked thereby, creating a menace to the core of secrecy. Steganography camouflages the hidden information into an unsuspicious digital file i.e. image, video or audio, thereby covering the existence of it. Using modified LSB substitution and readjustment procedure the mean square error has been reduced. Pixel indicator is used for increasing the embedding capacity of secret data. The security of secret data embedded into a cover file can be increased using random walk inside the file. In this paper we have used knight's tour for random walk, by not affecting the image quality. For high security while embedding, we randomize the three planes of RGB cover image using row vector, divide the image into four pixel blocks and then use Pixel value differencing (PVD) to embed data adaptively followed by knight's tour to select next block for embedding. This method gives highly secured and high capacity steganography