Assessment of Diabetic Foot

Diabetic Foot Complications are the main reason for hospitalization and amputation in people with diabetes. Globally ~435 million people have diabetes, with ~83–148 million of those estimated to develop foot ulcers in their lifetime. It is estimated that 16.8 million YLDs resulted from diabetic foot complications. Once an ulcer has developed, there is an increased risk of wound progression that may lead to amputation (~85% cases). In every 30 seconds, one lower limb amputation in diabetes patients occurs world-wide. The average cost for each amputation is over $70,000. American Podiatric Medical Association says that diabetic foot complications can be prevented by periodical Assessment of foot, which include visual inspection of bare foot; deformities, neurovascular abnormalities of foot and assessment of footwear. Relevant assessment and proactive foot care can reduce the burden of diabetic foot disease which will increase quality of life and reduce health care costs

Diabetic Peripheral Neuropathy

Diabetes mellitus is one of the most common medical disorders often associated with neurological complications. Peripheral neuropathy is the most common neurological complication from diabetes with a prevalence of 10–26% of newly diagnosed adult diabetics. Diabetic neuropathy is a heterogeneous group of conditions that present with sensory and/or motor and/or autonomic dysfunction and affect different parts of the peripheral nervous system. Diabetic neuropathy might present as a polyneuropathy, mononeuropathy, mononeuropathy multiplex, radiculopathy, and/or plexopathy. Diabetic neuropathies may also be associated with foot ulcers and infections in 5–24% of patients, which translate into five out of 1000 of diabetics ending with an amputation. Therefore, it is essential to screen diabetic patients for early recognition and management of diabetic neuropathies

Cross-validation for graph matching based offline signature verification

Biometric is an authentication system that identifies a person depending on his physiological or behavioral traits. Signature verification is a socially accepted biometric method and is widely used for banking transactions. In this paper, we propose Cross-validation for Graph Matching based Offline Signature Verification (CGMOSV) algorithm. Database signatures are pre-processed in which signature extraction method is used to obtain high resolution for smaller normalization box. The dissimilarity measure between two signatures in the database is determined by (i) constructing a bipartite graph G, (ii) obtaining complete matching in G and (iii) finding minimum Euclidean distance by Hungarian method. We use Cross-validation principle to select reference signatures from which an optimum decision threshold value is determined. The given test signature is pre-processed and a test feature is extracted from it, which is then compared with the threshold value to authenticate the test signature. It is observed that our algorithm gives better Equal Error Rate (EER) for skilled forgeries and random forgeries compared to the existing algorithm. © 2008 IEEE

Offline Signature Authentication using cross-validated Graph Matching

The biometric system is used to identify a person depending on his physiological or behavioral characteristics. Signature verification is a commonly accepted biometric method and is widely used for banking transactions. In this paper, we propose Offline Signature Authentication using Cross-validated Graph Matching (OSACGM) algorithm. The signatures are pre-processed in which signature extraction method is used to obtain high resolution for smaller normalization box. The similarity measure between two signatures in the database is determined by (i) constructing a bipartite graph G, (ii) obtaining complete matching in G and (iii) finding minimum Euclidean distance by Hungarian method. An optimum decision threshold value is determined using Cross-validation technique to select reference signatures. The test feature is extracted from the given test signature by pre-processing. Then the test feature is compared with the threshold value to authenticate the test signature. Compared to the existing algorithm, our algorithm gives better Equal Error Rate (EER) for skilled and random forgeries

The actual and predicted daily count of the total IFT and non-IFT calls for ambulance service with percentage difference and effect size expressed in Cliff’s Delta.

The actual and predicted daily count of the total IFT and non-IFT calls for ambulance service with percentage difference and effect size expressed in Cliff’s Delta.</p

Plot of the actual daily count of calls for ambulance services during the entire period covering pre-pandemic to post pandemic phases for total, pregnancy and trauma related in Tamil Nadu.

Plot of the actual daily count of calls for ambulance services during the entire period covering pre-pandemic to post pandemic phases for total, pregnancy and trauma related in Tamil Nadu.</p

Fig 10 -

Plot (Discrete) of the actual and predicted daily count of total calls, pregnancy related calls, trauma-vehicular calls (on Y-axis from Left to Right) in Tamil Nadu during the three waves of COVID-19 from 2020 to 2022 (on X-axis). The vertical bars indicate the periods of three waves. Top Panels pertain to Non-IFT calls and Bottom Panels refer to IFT Calls. The prediction is done using a hybrid model of feedforward network with AR Net.</p

Timeline of various pandemic waves in Tamil Nadu, India.

Timeline of various pandemic waves in Tamil Nadu, India.</p