Riddhiman Das

Riddhiman Das

Miami-Fort Lauderdale Area
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Builder. Curious thinker and gritty doer. Techno-Optimist.

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Publications

  • Using Transfer Learning and BPDFHE to Improve Ocular Image Recognition Accuracy

    Riddhiman Das

    We used image enhancement algorithms along with transfer learning to fine-tune a deep convolutional neural network to perform ocular image recognition. To enhance the input images, we used a novel color image histogram equalization technique called Brightness Preserving Dynamic Fuzzy Histogram Equalization, which showed significant accuracy improvements: on the test data, using AlexNet, the ROC Area Under the Curve (AUC) increased to over 0.99, Equal Error Rate (EER) decreased 4-fold and…

    We used image enhancement algorithms along with transfer learning to fine-tune a deep convolutional neural network to perform ocular image recognition. To enhance the input images, we used a novel color image histogram equalization technique called Brightness Preserving Dynamic Fuzzy Histogram Equalization, which showed significant accuracy improvements: on the test data, using AlexNet, the ROC Area Under the Curve (AUC) increased to over 0.99, Equal Error Rate (EER) decreased 4-fold and dropped below 4%, and decidability (a measure of class separability) increased from 1.89 to 4.17.

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  • Apps Gone Rogue: Maintaining Personal Privacy in an Epidemic

    Ramesh Raskar, Isabel Schunemann, Rachel Barbar, Kristen Vilcans, Jim Gray, Praneeth Vepakomma, Suraj Kapa, Andrea Nuzzo, Rajiv Gupta, Alex Berke, Dazza Greenwood, Christian Keegan, Shriank Kanaparti, Robson Beaudry, David Stansbury, Beatriz Botero Arcila

    Containment, the key strategy in quickly halting an epidemic, requires rapid identification and quarantine of the infected individuals, determination of whom they have had close contact with in the previous days and weeks, and decontamination of locations the infected individual has visited. Achieving containment demands accurate and timely collection of the infected individual's location and contact history. Traditionally, this process is labor intensive, susceptible to memory errors, and…

    Containment, the key strategy in quickly halting an epidemic, requires rapid identification and quarantine of the infected individuals, determination of whom they have had close contact with in the previous days and weeks, and decontamination of locations the infected individual has visited. Achieving containment demands accurate and timely collection of the infected individual's location and contact history. Traditionally, this process is labor intensive, susceptible to memory errors, and fraught with privacy concerns. With the recent almost ubiquitous availability of smart phones, many people carry a tool which can be utilized to quickly identify an infected individual's contacts during an epidemic, such as the current 2019 novel Coronavirus crisis. Unfortunately, the very same first-generation contact tracing tools have been used to expand mass surveillance, limit individual freedoms and expose the most private details about individuals. We seek to outline the different technological approaches to mobile-phone based contact-tracing to date and elaborate on the opportunities and the risks that these technologies pose to individuals and societies. We describe advanced security enhancing approaches that can mitigate these risks and describe trade-offs one must make when developing and deploying any mass contact-tracing technology. With this paper, our aim is to continue to grow the conversation regarding contact-tracing for epidemic and pandemic containment and discuss opportunities to advance this space.

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