Parkinson's disease (PD) is a neurodegenerative disorder, which is responsible for the deterioration of motor function due to loss of dopamine-producing brain cells i.e. neurons. Tremors, stiffness, slowness in movements, shaking, and impaired balance are some of the primary symptoms of PD. In this paper, two neural network based models namely, VGFR Spectrogram Detector and Voice Impairment Classifier have been introduced, which aim to help doctors and people in diagnosing disease at an early stage. An extensive empirical evaluation of CNNs (Convolutional Neural Networks) has been implemented on large-scale image classification of gait signals converted to spectrogram images and deep dense ANNs (Artificial Neural Networks) on the voice recordings, to predict the disease. The experimental results indicate that the proposed models outperformed the existing state of the arts in terms of accuracy. The classification accuracy on VGFR Spectrogram Detector is recorded as 88.1% while Voice Impairment Classifier has shown 89.15% accuracy.

With the advancement of wireless communication, internet of things, and big data, high performance data analytics tools and algorithms are required. Data clustering, a promising analytic technique is widely used to solve the IoT and big data based problems, since it does not require labeled datasets. Recently, meta-heuristic algorithm have been efficiently used to solve various clustering problems. However, to handle big data sets produced from IoT devices, these algorithm fail to respond within desired time due to high computation cost. This paper presents a new meta-heuristic based clustering method to solve the big data problems by leveraging the strength of MapReduce. The proposed methods leverages the searching potential of military dog squad to find the optimal centroids and MapReduce architecture to handle the big data sets. The optimization efficacy the proposed method is validated against 17 benchmark functions and the results are compared with 5 other recent algorithms namely, bat, particle swarm optimization, artificial bee colony, multiverse optimization, and whale optimization algorithm. Further, the parallel version of the proposed method is introduced using MapReduce (MR-MDBO) for clustering the big datasets produced from industrial IoT. Moreover, the performance of MR-MDBO is studied on 2 benchmark UCI datasets and 3 real IoT based datasets produced from industry. The F-measure, and computation time of the MR-MDBO is compared with the 5 other state-of-the-art methods. The experimental results witness that the proposed MR-MDBO based clustering outperforms the other considered algorithms in terms of clustering accuracy and computation times.