A Decision Support Framework for Automated Screening of Diabetic Retinopathy

The early signs of diabetic retinopathy (DR) are depicted by microaneurysms among other signs. A prompt diagnosis when the disease is at the early stage can help prevent irreversible damages to the diabetic eye. In this paper, we propose a decision support system (DSS) for automated screening of early signs of diabetic retinopathy. Classification schemes for deducing the presence or absence of DR are developed and tested. The detection rule is based on binary-hypothesis testing problem which simplifies the problem to yes/no decisions. An analysis of the performance of the Bayes optimality criteria applied to DR is also presented. The proposed DSS is evaluated on the real-world data. The results suggest that by biasing the classifier towards DR detection, it is possible to make the classifier achieve good sensitivity

Study on the qualitative assessment of in-vessel food waste compost by indexing method

The consumption of different food-based goods produces a considerable amount of waste that needs to be conserved in an eco-friendly manner. A study was carried out on food waste compost made from the in-vessel compost process for use in agriculture and its marketability for its fertility and contamination potential. Food waste samples were collected from the canteen and hostels of GITAM University,  Visakhapatnam (Andhra Pradesh), India and were transferred to a 125Kg in-vessel food waste composter (Molten Mind F125) and allowed to digest for 24 hrs followed by curing for seven days. After curing, the samples were characterized for nutrient content for fertility index (FI) and heavy metal contamination for clean index (CI). The compost quality index was derived from FI and CI to assess its suitability for agriculture. The pH of the food waste compost sample was reported as 8.4 and the C/N ratio was 28, which was higher than the standard ratio (15-20). The other physicochemical characteristics were analyzed using the standard methods and the concentration of metals was analyzed using Inductively Coupled Plasma Mass Spectrometry ( ICPMS). From the analysis, it was evident that heavy metal concentrations were well within the permissible limits. Further, the compost was characterized to know the fertility index (FI) and contamination index (CI) and its suitability to the soil. FI value was reported as more than 3.1 and CI value more than 4, which indicated that compost was best in quality, having high-value potential and low heavy-metal content, which will be suitable for high-value crops such as organic farming.

Securing Organizational Knowledge Using Automated Annotation

Conference proceeding from the First NSF/ NSA/ AFRL workshop on Secure Knowledge Management, Buffalo, NY, September 23-24, 2004

A decision support framework for automated screening of diabetic retinopathy

The early signs of diabetic retinopathy (DR) are depicted by microaneurysms among other signs. A prompt diagnosis when the disease is at the early stage can help prevent irreversible damages to the diabetic eye. In this paper, we propose a decision support system (DSS) for automated screening of early signs of diabetic retinopathy. Classification schemes for deducing the presence or absence of DR are developed and tested. The detection rule is based on binary-hypothesis testing problem which simplifies the problem to yes/no decisions. An analysis of the performance of the Bayes optimality criteria applied to DR is also presented. The proposed DSS is evaluated on the real-world data. The results suggest that by biasing the classifier towards DR detection, it is possible to make the classifier achieve good sensitivity

Energy-Aware, Collaborative Tracking with Ad-Hoc Wireless Sensor Networks

Abstract An energy aware, collaborative tracking algorithm is proposed for ad-hoc wireless sensor networks consisting of randomly distributed low-end sensors and a high-end data gathering node which is geometrically located at the center of each cluster. The collaborative tracking algorithm is implemented distributively by passing sensing and computation operations from one cluster to another. The network lifetime is maximized by choosing active sensors according to an energy-based cost function. Simulation results for both single and multiple simultaneously active sensors have also been preformed. Performance is evaluated based on both tracking error and energy consumption of the whole network. 1