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Special mathematical functions are an integral part of Fractional Calculus, one of them is the Airy function. But it’s a gruelling task for the processor as well as system that is constructed around the function when it comes to evaluating the special mathematical functions on an ordinary Central Processing Unit (CPU). The Parallel processing capabilities of a Graphics processing Unit (GPU) hence is used. In this paper GPU is used to get a speedup in time required, with respect to CPU time for evaluating the Airy function on its real domain. The objective of this paper is to provide a platform for computing the special functions which will accelerate the time required for obtaining the result and thus comparing the performance of numerical solution of Airy function using CPU and GPU.

Parkinson’s disease is a chronic neurodegenerative condition that demonstrate the progressive loss of the ability to correlate movements mainly occurs in the elderly. For the purpose of monitoring tremors in Parkinson’s disease, a system has to be designed and developed. For coordination of movements, people with Parkinson’s, deprive of a chemical called dopamine which behaves as the messenger between the brain parts and the nervous system .Detecting Parkinson’s disease is a very arduous task as there is no evidence currently present to do this. Therefore, the main intention of our work is the designing of a system for recognizing Parkinson’s disease at an initial stage. An Android application is being designed that allows the status of PD patients to be assessed based on the tests found on the Unified Parkinson’s Disease Rating Scale approved by the Movement Disorders Society (MDS-UPDRS).

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Automatically identifying what types of the bird is present in the sound recording using the monitor reading. To distinguishing automatic birds based on their sound patterns.This is useful in the field of ornithology for studying bird species and their behavior based on their sound. Proposed method will be used to distinguish birds automatically using different sound processing methods and mechanical learning methods based on their chirping patterns. We propose a sequential model for audio features within a short interval of time. The model will be used Mel Frequency Cepstral Coefficients to extract features from the audio files and presented it in the model. The proposed work classifies the data set containing three species of bird, and outperform support vector machines.