The paper proposes to develop a field programmable gate array (FPGA) based low cost, low power and high speed novel diagnostic system that can detect in absence of the physician the approaching critical condition of a patient at an early stage and is thus suitable for diagnosis of patients in the rural areas of developing countries where availability of physicians and availability of power is really scarce. The diagnostic system could be installed in health care centres of rural areas where patients can register themselves for periodic diagnoses and thereby detect potential health hazards at an early stage. Multiple pathophysiological parameters with different weights are involved in diagnosing a particular disease. A novel variation of particle swarm optimization called as adaptive perceptive particle swarm optimization has been proposed to determine the optimal weights of these pathophysiological parameters for a more accurate diagnosis. The FPGA based smart system has been applied for early detection of renal criticality of patients. For renal diagnosis, body mass index, glucose, urea, creatinine, systolic and diastolic blood pressures have been considered as pathophysiological parameters. The detection of approaching critical condition of a patient by the instrument has also been validated with the standard Cockford Gault Equation to verify whether the patient is really approaching a critical condition or not. Using Bayesian analysis on the population of 80 patients under study an accuracy of up to 97.5% in renal diagnosis has been obtained.
Assessing risk for voluminous legal documents such as request for proposal, contracts is tedious and error prone. We have developed "risk-o-meter", a framework, based on machine learning and natural language processing to review and assess risks of any legal document. Our framework uses Paragraph Vector, an unsupervised model to generate vector representation of text. This enables the framework to learn contextual relations of legal terms and generate sensible context aware embedding. The framework then feeds the vector space into a supervised classification algorithm to predict whether a paragraph belongs to a pre-defined risk category or not. The framework thus extracts risk prone paragraphs. This technique efficiently overcomes the limitations of keyword based search. We have achieved an accuracy of 91% for the risk category having the largest training dataset. This framework will help organizations optimize effort to identify risk from large document base with minimal human intervention and thus will help to have risk mitigated sustainable growth. Its machine learning capability makes it scalable to uncover relevant information from any type of document apart from legal documents, provided the library is pre-populated and rich.
Abstract-The paper describes the development of an FPGA based fuzzy processing system for pulmonary spirometry applications predicting the approaching obstructive or restrictive pulmonary disorder of the patient before criticality actually occurs. The system employs a smart agent that accepts the Peak Expiratory Flow Rate (PEFR), Forced Expiratory Volume in 1 second (FEV1) and Forced Vital Capacity (FVC) data of patients. In order to speed up the computation process, hybrid parallel data processing architectures with dynamic scheduling mechanism have been employed leading to a speed up of approximately 12 times. The processor implemented on the FPGA can perform fuzzy inferencing at a speed of approximately 5.0 MFLIPS. The whole system is realized on Altera Cyclone EP1K6Q240C8 FPGA chip requiring 5,865 logic blocks. The system has been designed to be inexpensive, portable and user friendly for occupational health care applications in developing countries.Using the system, approaching pulmonary disorder of patients has been predicted with an accuracy of 95.83%.
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