Pinned Repositories
Google-Code-Jam-2021
Secured a Global Rank of 839 and an All India Rank of 138 in Google Code Jam 2021. This repository contains my solutions and approach for the problem statement for that year.
Synapse-AI-Cough-Analytics
Synapse’s fifth feature is a cough analytics analyzer that aims to collect audio of coughs and apply AI algorithms to analyze the composition of the coughs and the potential underlying health effects that could cause them. This is especially important because the analyzer can evaluate the severity of a particular cough (and thus a health condition) and can also help doctors better understand what type of ailment a user has, whether it be Bronchitis, COVID-19, or some other form of respiratory disease. As such, this feature is crucial during the current pandemic. For this feature, we developed a cough-audio monitor application system that can collect audio data streams in a continuous manner via Smartphone embedded microphone sensor and apply a set of AI algorithms to analyze them. These AI algorithms have been trained from a compilation of Institutional & Open-source datasets we generated. This Cough Analyzer assesses forced cough recordings from a user using internal audio microphone sensors to evaluate an illness on the basis of cough identification and classification following a mathematical model eliminating the additional sound layers (by Feature Extraction method), allowing cough sounds to be reconstructed from the feature set while degerating other sounds like background speech automatically. This application reliably collects sound data and uploads them securely to a remote server for subsequent analysis, increasing the accuracy of our model.
Synapse-AI-Heart-Rate-Monitor
Synapse’s third feature is an intelligent heart rate monitor which integrates sensors with the capability to monitor the blood pressure along with force sensors. This feature is uniquely important because it will be one of the first applications that can be used by patients to remotely monitor their heart rate, preventing cardiac arrests, blood clots and heart failure. Our unique AI algorithms and app have been designed to extend the oscillometric principle, which is used by most automated cuff devices to measure blood pressure, to a cuff-less, accessible BP monitoring feature within our app. This feature will allow for patients to observe their heart rate and assess their blood pressure. Our feature operates on the basis of our user pressing their finger against the Smartphone embedded camera sensor & inertial sensor (force sensor); this is an effective approach because the external pressure of the underlying artery (transverse palmar arch artery) increases as the phone measures the applied pressure, resulting in accurate measurements of the variable-amplitude blood volume oscillations that constitute blood pressure measurements. The phone camera, embedded with photoplethysmography (PPG) biosensors and force transducers, serves as the sensor to measure the blood volume oscillations and applied pressure. From there, Synapse’s Blood Pressure monitoring system computes systolic and diastolic BP from the measurements and provides visual feedback to guide the patients in understanding their results.
Synapse-AI-IoT-CGM-Continuous-Glucose-Monitory-System
Synapse AI presents a high intensity, human-centric design and implementation of a CGM (Continuous Glucose Monitor) system for mobile crowdsourcing disease research and care. This CGM utilizes Internet of Things (IoT) capabilities for monitoring patients remotely and, thus, warning them about potentially perilous situations. The proposed CGM system makes use of Smartphone camera sensors to collect blood glucose concentration values from remote patients and then sends them either to a remote cloud server (where algorithm is stored, here the data is processed to produce a prognosis using previously stored values) to store information and execute rule based decisions (e.g., to warn a doctor when the patient’s blood glucose level is above or below a specific threshold) or to distributed fog computing nodes (e.g. Raspberry Pi). Fog Computing transfers the cloud computational and communication capabilities close to the sensor nodes in order to minimize latency, to distribute computational and storage resources, to enhance mobility and location awareness, and to ease network scalability while providing connectivity among devices in different physical environments. Moreover, in order to exchange reliable, trustworthy and cybersecure data with different potential stakeholders (e.g., patients, medical scientists, doctors and caretakers) of the healthcare ecosystem, the system includes the deployment of a decentralized storage system that receives, processes and stores the collected data in remote cloud server which is essentially a front-end that provides a web interface to remote users in order to allow them to access the stored information in a user-friendly way. This server also runs a back-end service that is responsible for sending notifications to remote users through SMS or instant messaging services.
Synapse-AI-Pulse-Oximeter
Synapse’s second feature involves a smart, automated Pulse Oximeter that accurately and remotely measures heart rate (HR) and blood oxygen saturation (Spo2 level) of patients. This feature is really important because it helps patients prevent blood clots, sudden cardiac arrests, and heart failure. The Pulse Oximeter utilizes camera sensors embedded into all smartphones as well as with photoplethysmography (PPG) biosensors that accurately measure heart rate (HR) and blood oxygen saturation (Spo2 level) of patients. As a patient places their index finger over the rear camera sensor system, the PPG sensor measures the distension of arteries and arterioles in the subcutaneous tissue (thanks to blood flow from each cardiac cycle). As blood flows through the vessels, the pulse pressure is detected by illuminating the skin with the light from two inorganic light-emitting diodes-OLED (embedded in a majority of camera sensors). The amount of light transmitted, absorbed, or reflected to a photodiode is measured and processed by Synapse’s AI, which contains an algorithm that detects and interprets the PPG signal to determine HR and Spo2 values. Finally, additional AI framework within the app then accesses and displays individual biosensor readings on the Smartphone screen.
Synapse-AI-Thermometer-Skin-Temperature-Sensor
Synapse’s second feature involves a smart, automated Pulse Oximeter that accurately and remotely measures heart rate (HR) and blood oxygen saturation (Spo2 level) of patients. This feature is really important because it helps patients prevent blood clots, sudden cardiac arrests, and heart failure. The Pulse Oximeter utilizes camera sensors embedded into all smartphones as well as with photoplethysmography (PPG) biosensors that accurately measure heart rate (HR) and blood oxygen saturation (Spo2 level) of patients. As a patient places their index finger over the rear camera sensor system, the PPG sensor measures the distension of arteries and arterioles in the subcutaneous tissue (thanks to blood flow from each cardiac cycle). As blood flows through the vessels, the pulse pressure is detected by illuminating the skin with the light from two inorganic light-emitting diodes-OLED (embedded in a majority of camera sensors). The amount of light transmitted, absorbed, or reflected to a photodiode is measured and processed by Synapse’s AI, which contains an algorithm that detects and interprets the PPG signal to determine HR and Spo2 values. Finally, additional AI framework within the app then accesses and displays individual biosensor readings on the Smartphone screen.
U2Net-Image-Segmentation-ML-Model
One of the most important operations in Computer Vision is Segmentation. Image segmentation is the task of clustering parts of an image together that belong to the same object class. This process is also called pixel-level classification. Here, it involves partitioning images (or video frames) into multiple complex segments or objects in order to produce optimum augmented images utilizing the U2Net and Rembg Image Proceesing Models with OpenVINO toolkit.
VacTrack-AI-Vaccine-Tracking-System
VacTrack (Vaccine Tracking System) is a smart, web-based AI information technology system to order and manage vaccine distribution among state, local, territorial health departments and health care providers using Satellite geolocation, GPRS to monitor, evaluate & store demographical physiology of the target population in order to enhance Vaccine Allocation, Monitoring, Tracking, and Distribution Systems.
VacTrack-REQ-System
The VacTrack REQ platform is the interface through which state, local, and territorial health departments and distribution teams can request vaccine shipments and VacPack teams. The REQ platform also integrates the related manufacturing, supply chain, allocation, state and territory planning, delivery and administration of both vaccine products and ancillary kits.
Volumetric-MRI-Validation-of-Additional-Brain-Structures
Semantic segmentation in computer vision enables precise brain tumor diagnosis, differentiating tumors from surrounding brain regions. It empowers healthcare with micro-level insights for enhanced patient care and diagnostics.
Phirat-Passi's Repositories
Phirat-Passi/Synapse-AI-Heart-Rate-Monitor
Synapse’s third feature is an intelligent heart rate monitor which integrates sensors with the capability to monitor the blood pressure along with force sensors. This feature is uniquely important because it will be one of the first applications that can be used by patients to remotely monitor their heart rate, preventing cardiac arrests, blood clots and heart failure. Our unique AI algorithms and app have been designed to extend the oscillometric principle, which is used by most automated cuff devices to measure blood pressure, to a cuff-less, accessible BP monitoring feature within our app. This feature will allow for patients to observe their heart rate and assess their blood pressure. Our feature operates on the basis of our user pressing their finger against the Smartphone embedded camera sensor & inertial sensor (force sensor); this is an effective approach because the external pressure of the underlying artery (transverse palmar arch artery) increases as the phone measures the applied pressure, resulting in accurate measurements of the variable-amplitude blood volume oscillations that constitute blood pressure measurements. The phone camera, embedded with photoplethysmography (PPG) biosensors and force transducers, serves as the sensor to measure the blood volume oscillations and applied pressure. From there, Synapse’s Blood Pressure monitoring system computes systolic and diastolic BP from the measurements and provides visual feedback to guide the patients in understanding their results.
Phirat-Passi/U2Net-Image-Segmentation-ML-Model
One of the most important operations in Computer Vision is Segmentation. Image segmentation is the task of clustering parts of an image together that belong to the same object class. This process is also called pixel-level classification. Here, it involves partitioning images (or video frames) into multiple complex segments or objects in order to produce optimum augmented images utilizing the U2Net and Rembg Image Proceesing Models with OpenVINO toolkit.
Phirat-Passi/Synapse-AI-IoT-CGM-Continuous-Glucose-Monitory-System
Synapse AI presents a high intensity, human-centric design and implementation of a CGM (Continuous Glucose Monitor) system for mobile crowdsourcing disease research and care. This CGM utilizes Internet of Things (IoT) capabilities for monitoring patients remotely and, thus, warning them about potentially perilous situations. The proposed CGM system makes use of Smartphone camera sensors to collect blood glucose concentration values from remote patients and then sends them either to a remote cloud server (where algorithm is stored, here the data is processed to produce a prognosis using previously stored values) to store information and execute rule based decisions (e.g., to warn a doctor when the patient’s blood glucose level is above or below a specific threshold) or to distributed fog computing nodes (e.g. Raspberry Pi). Fog Computing transfers the cloud computational and communication capabilities close to the sensor nodes in order to minimize latency, to distribute computational and storage resources, to enhance mobility and location awareness, and to ease network scalability while providing connectivity among devices in different physical environments. Moreover, in order to exchange reliable, trustworthy and cybersecure data with different potential stakeholders (e.g., patients, medical scientists, doctors and caretakers) of the healthcare ecosystem, the system includes the deployment of a decentralized storage system that receives, processes and stores the collected data in remote cloud server which is essentially a front-end that provides a web interface to remote users in order to allow them to access the stored information in a user-friendly way. This server also runs a back-end service that is responsible for sending notifications to remote users through SMS or instant messaging services.
Phirat-Passi/Synapse-AI-Pulse-Oximeter
Synapse’s second feature involves a smart, automated Pulse Oximeter that accurately and remotely measures heart rate (HR) and blood oxygen saturation (Spo2 level) of patients. This feature is really important because it helps patients prevent blood clots, sudden cardiac arrests, and heart failure. The Pulse Oximeter utilizes camera sensors embedded into all smartphones as well as with photoplethysmography (PPG) biosensors that accurately measure heart rate (HR) and blood oxygen saturation (Spo2 level) of patients. As a patient places their index finger over the rear camera sensor system, the PPG sensor measures the distension of arteries and arterioles in the subcutaneous tissue (thanks to blood flow from each cardiac cycle). As blood flows through the vessels, the pulse pressure is detected by illuminating the skin with the light from two inorganic light-emitting diodes-OLED (embedded in a majority of camera sensors). The amount of light transmitted, absorbed, or reflected to a photodiode is measured and processed by Synapse’s AI, which contains an algorithm that detects and interprets the PPG signal to determine HR and Spo2 values. Finally, additional AI framework within the app then accesses and displays individual biosensor readings on the Smartphone screen.
Phirat-Passi/Volumetric-MRI-Validation-of-Additional-Brain-Structures
Semantic segmentation in computer vision enables precise brain tumor diagnosis, differentiating tumors from surrounding brain regions. It empowers healthcare with micro-level insights for enhanced patient care and diagnostics.
Phirat-Passi/Synapse-AI-Cough-Analytics
Synapse’s fifth feature is a cough analytics analyzer that aims to collect audio of coughs and apply AI algorithms to analyze the composition of the coughs and the potential underlying health effects that could cause them. This is especially important because the analyzer can evaluate the severity of a particular cough (and thus a health condition) and can also help doctors better understand what type of ailment a user has, whether it be Bronchitis, COVID-19, or some other form of respiratory disease. As such, this feature is crucial during the current pandemic. For this feature, we developed a cough-audio monitor application system that can collect audio data streams in a continuous manner via Smartphone embedded microphone sensor and apply a set of AI algorithms to analyze them. These AI algorithms have been trained from a compilation of Institutional & Open-source datasets we generated. This Cough Analyzer assesses forced cough recordings from a user using internal audio microphone sensors to evaluate an illness on the basis of cough identification and classification following a mathematical model eliminating the additional sound layers (by Feature Extraction method), allowing cough sounds to be reconstructed from the feature set while degerating other sounds like background speech automatically. This application reliably collects sound data and uploads them securely to a remote server for subsequent analysis, increasing the accuracy of our model.
Phirat-Passi/Synapse-AI-Thermometer-Skin-Temperature-Sensor
Synapse’s second feature involves a smart, automated Pulse Oximeter that accurately and remotely measures heart rate (HR) and blood oxygen saturation (Spo2 level) of patients. This feature is really important because it helps patients prevent blood clots, sudden cardiac arrests, and heart failure. The Pulse Oximeter utilizes camera sensors embedded into all smartphones as well as with photoplethysmography (PPG) biosensors that accurately measure heart rate (HR) and blood oxygen saturation (Spo2 level) of patients. As a patient places their index finger over the rear camera sensor system, the PPG sensor measures the distension of arteries and arterioles in the subcutaneous tissue (thanks to blood flow from each cardiac cycle). As blood flows through the vessels, the pulse pressure is detected by illuminating the skin with the light from two inorganic light-emitting diodes-OLED (embedded in a majority of camera sensors). The amount of light transmitted, absorbed, or reflected to a photodiode is measured and processed by Synapse’s AI, which contains an algorithm that detects and interprets the PPG signal to determine HR and Spo2 values. Finally, additional AI framework within the app then accesses and displays individual biosensor readings on the Smartphone screen.
Phirat-Passi/VacTrack-REQ-System
The VacTrack REQ platform is the interface through which state, local, and territorial health departments and distribution teams can request vaccine shipments and VacPack teams. The REQ platform also integrates the related manufacturing, supply chain, allocation, state and territory planning, delivery and administration of both vaccine products and ancillary kits.
Phirat-Passi/BiopsyAI
Phirat-Passi/Google-Code-Jam-2021
Secured a Global Rank of 839 and an All India Rank of 138 in Google Code Jam 2021. This repository contains my solutions and approach for the problem statement for that year.
Phirat-Passi/Phishing-Attack-Detection
This project is our submission for Kavach Hackathon 2023 on Phishing Detection Solution, problem statement ID (KVH-004).
Phirat-Passi/VackTrack-SEE-System-for-Examining-Effectiveness
The VacTrack SEE system integrates the REQ and MMM (Multifaceted Mobile Management) components, allowing providers to monitor their enrollment in public vaccine programs, ordering of vaccines, reporting of inventory, and documenting of vaccine spoilage/wastage. SEE is the source of all data relating to vaccine supply, ordering, tracking, and monitoring in the states of Haiti and serves as a national, confidential, population-based, computerized registry that oversees the rollout & administration of vaccines. By centralizing and encrypting data from each VacPack distribution team within this large, national database, researchers can demarcate specific hotspots of cases of disease to target the movement. of more vaccines to that particular location. Teams can also evaluate the Average Efficacy Rating (AER) of our vaccination system and store relevant healthcare data in their own unique systems.
Phirat-Passi/VacTrack-AI-Vaccine-Tracking-System
VacTrack (Vaccine Tracking System) is a smart, web-based AI information technology system to order and manage vaccine distribution among state, local, territorial health departments and health care providers using Satellite geolocation, GPRS to monitor, evaluate & store demographical physiology of the target population in order to enhance Vaccine Allocation, Monitoring, Tracking, and Distribution Systems.
Phirat-Passi/VacTrack-MMM-Multifaceted-Mobile-Management-System
VacTrack Mobile App to register, document, and report vaccinations for temporary, high-volume sites. Option for state and provider use. Four VAMS modules under development for use by IIS jurisdictions, employers/organizations, clinics, and vaccine recipients. Different data elements required for standard and mass vaccination sites
Phirat-Passi/Career-Prediction
Phirat-Passi/Engine-Rating-Prediction
Phirat-Passi/phiratpassi.github.io
Phirat-Passi/Liver-Segmentation-UNet
Phirat-Passi/Logistic-Regression-Supervised-ML
Sonar (sound navigation and ranging or sonic navigation and ranging) Rock Vs Mine Binary classification using Supervised Machine Learning algorithm - Logistic Regression