New prospects for health care monitoring is engendered by the augmentation of Internet of Things (IoT) and Big data, as well as the ubiquitous nature of tiny wearable bio sensors. Many confront have yet to be addressed to create a dependable and flexible system for health care monitoring. In this paper Big data analytics on IoT based Health care system is proposed. IoT based health care monitoring system comprises of “Internet of health sensor things”. These things produce huge volumes of data that could not be handled by the physician. The Physicians important concern is that they need to make critical decisions about their patient's health from these huge volumes of health information. He/she has to segregate the information about one particular patient from the flood of health care information arriving from the massive number of patients. Intel Galileo Gen 2 is acting as IoT agent in this paper and is used to deploy the health information of patients into the Cloud. The Cloud could manage the increasing volume of health data, resourcefully share the information across healthcare systems and provide viability for Big data analytics. Real time alerting of patient health information is an important exercise in Big data which is crucial in the proposed work. The Big health sensor data are analyzed using the Hadoop framework. Since the response time of the proposed system is less, it is suitable for real time alerting.

Detection of eye pathologies from the database of iris images is taken into deliberation. The images of disease affected and normal eyes are collected from High-Resolution Fundus (HRF) Image Database is analyzed and the influence of ocular diseases on iris using a reliable Fuzzy recognition scheme is proposed here. These eye images are then subjected to various image processing techniques like pre-processing for de-noising using Blind de-convolution, wavelet based feature extraction, Principal Component Analysis (PCA) for dimension reductionality, followed by fuzzy C-means clustering inference scheme to categorize the normal and diseased eyes. It is inferred that the proposed method takes only 2 minutes with an accuracy, specificity and sensitivity varying in the range of 94% to 98% respectively.

This project deals with the monitoring the combustion quality of the power station boilers using Artificial Intelligence for improvement in the combustion quality in the power station boiler. The colour of the flame indicates whether the combustion taking place is complete, partial or incomplete. When complete combustion takes place the flue gases released are within the permissible limits otherwise its level is high which is out of limit. By analyzing the flame color which is captured using infrared camera and displayed on CCTV the quality of combustion is estimated. If combustion is partial or incomplete the flue gases released will create air pollution. So this work includes enhancement in the quality of combustion, saving of energy as well as check on the pollution level. The features are extracted from the flame images such as average intensity, area, brightness and orientation are obtained after preprocessing. Three classes of images corresponding to different burning conditions are taken from continuous video. Further training, testing and validation with the data collected have been carried out and performance of the various intelligent algorithms is presented.

Isolation of human pathogenic bacteria in two edible fishes, Priacanthus hamrur and Megalaspis cordyla at Royapuram waters of Chennai, India

This research work includes a combination of Fisher’s Linear Discriminant (FLD) analysis by combining Radial Basis Function Network (RBF) and Back Propagation Algorithm (BPA) for monitoring the combustion conditions of a coal fired boiler so as to control the air/fuel ratio. For this two dimensional flame images are required which was captured with CCD camera whose features of the images, average intensity, area, brightness and orientation etc., of the flame are extracted after pre-processing the images. The FLD is applied to reduce the n-dimensional feature size to 2 dimensional feature size for faster learning of the RBF. Also three classes of images corresponding to different burning conditions of the flames have been extracted from a continuous video processing. In this the corresponding temperatures, the Carbon monoxide (CO) emissions and other flue gases have been obtained through measurement. Further the training and testing of Parallel architecture of Radial Basis Function and Back Propagation Algorithm (PRBFBPA) with the data collected have been done and the performance of the algorithms is presented.