28 skills found
sclavijosuero / Wick A11yA Cypress plugin for configurable accessibility analysis supporting WCAG 2.2 (A–AAA). It reports detailed violations in the Cypress log, provides visual feedback in the runner, and generates a severity-based HTML report with per-violation details, fix guidance, and annotated screenshots. Built on axe-core and cypress-axe for comprehensive analysis.
sauravmishra1710 / Heart Failure Condition And Survival AnalysisPerform a survival analysis based on the time-to-event (death event) for the subjects. Compare machine learning models to assess the likelihood of a death by heart failure condition. This can be used to help hospitals in assessing the severity of patients with cardiovascular diseases and heart failure condition.
call518 / LogSentinelAIDeclarative LLM-powered analyzer for security events and all types of logs. Extracts, structures, and visualizes data for Kibana/Elasticsearch.
jahnavik186 / Healthcare AI Assistant For Early Disease ScreeningMachine learning–based healthcare chatbot for symptom analysis, disease prediction, severity scoring, and precaution recommendations.
s4zong / Cybersecurity Threat Severity AnalysisCode for "Analyzing the Perceived Severity of Cybersecurity Threats Reported on Social Media".
Legit-Health / ASCORADOfficial development code of the Automatic Scoring of Atopic Dermatitis (ASCORAD) by Legit.Health 🩺🤖
abebual / Predicting ICU Patient Clinical Deterioration ReportFor this project, I used publicly available Electronic Health Records (EHRs) datasets. The MIT Media Lab for Computational Physiology has developed MIMIC-IIIv1.4 dataset based on 46,520 patients who stayed in critical care units of the Beth Israel Deaconess Medical Center of Boston between 2001 and 2012. MIMIC-IIIv1.4 dataset is freely available to researchers across the world. A formal request should be made directly to www.mimic.physionet.org, to gain access to the data. There is a required course on human research ‘Data or Specimens Only Research’ prior to data access request. I have secured one here -www.citiprogram.org/verify/?kb6607b78-5821-4de5-8cad-daf929f7fbbf-33486907. We built flexible and better performing model using the same 17 variables used in the SAPS II severity prediction model. The question ‘Can we improve the prediction performance of widely used severity scores using a more flexible model?’ is the central question of our project. I used the exact 17 variables used to develop the SAPS II severity prediction algorithm. These are 13 physiological variables, three underlying (chronic) disease variables and one admission variable. The physiological variables includes demographic (age), vital (Glasgow Comma Scale, systolic blood pressure, Oxygenation, Renal, White blood cells count, serum bicarbonate level, blood sodium level, blood potassium level, and blood bilirubin level). The three underlying disease variables includes Acquired Immunodeficiency Syndrome (AIDS), metastatic cancer, and hematologic malignancy. Finally, whether admission was scheduled surgical or unscheduled surgical was included in the model. The dataset has 26 relational tables including patient’s hospital admission, callout information when patient was ready for discharge, caregiver information, electronic charted events including vital signs and any additional information relevant to patient care, patient demographic data, list of services the patient was admitted or transferred under, ICU stay types, diagnoses types, laboratory measurments, microbiology tests and sensitivity, prescription data and billing information. Although I have full access to the MIMIC-IIIv1.4 datasets, I can not share any part of the data publicly. If you are interested to learn more about the data, there is a MIMIC III Demo dataset based on 100 patients https://mimic.physionet.org/gettingstarted/demo/. If you are interested to requesting access to the data - https://mimic.physionet.org/gettingstarted/access/. Linked repositories: Exploratory-Data-Analysis-Clinical-Deterioration, Data-Wrangling-MIMICIII-Database, Clinical-Deterioration-Prediction-Model--Inferential-Statistics, Clinical-Deterioration-Prediction-Model--Ensemble-Algorithms-, Clinical-Deterioration-Prediction-Model--Logistic-Regression, Clinical-Deterioration-Prediction-Model---KNN © 2020 GitHub, Inc.
SudiptaSingh / Q Learning Based Smart CabProblem Statement A smart city needs smart mobility, and to achieve this objective, the travel should be made convenient through sustainable transport solutions. Transportation system all over the world is facing unprecedented challenges in the current scenario of increased population, urbanization and motorization. Farewell to all difficulties as reinforcement learning along with deep learning can now make it simpler for consumers. In this paper we have applied reinforcement learning techniques for a self-driving agent in a simplified world to aid it in effectively reaching its destinations in the allotted time. We have first investigated the environment, the agent operates in, by constructing a very basic driving implementation. Once the agent is successful at operating within the environment, we can then identify each possible state the agent can be in when considering such things as traffic lights and oncoming traffic at each intersection. With states identified, we can implement a Q-Learning algorithm for the self-driving agent to guide the agent towards its destination within the allotted time. Finally, we can improve upon the Q-Learning algorithm to find the best configuration of learning and exploration factors to ensure the self-driving agent is reaching its destinations with consistently positive results. Our aim is also to find optimum values of parameters of the fitting function alpha, gamma and epsilon, so that the agent can work in an optimized way with the most optimum parameter values. Hence, a comparative analysis has also been conducted. Methodology used The solution to the smart cab objective is deep reinforcement learning in a simulated environment. The smart cab operates in an ideal, grid-like city (similar to New York City), with roads going in the North-South and East-West directions. Other vehicles will certainly be present on the road, but there will be no pedestrians to be concerned with. At each intersection there is a traffic light that either allows traffic in the North-South direction or the East-West direction. We have assumed that the smart cab is assigned a route plan based on the passengers' starting location and destination. The route is split at each intersection into waypoints, and the smart cab, at any instant, is at some intersection in the world. Therefore, the next waypoint to the destination, assuming the destination has not already been reached, is one intersection away in one direction (North, South, East, or West). The smart cab has only an egocentric view of the intersection it is at: It can determine the state of the traffic light for its direction of movement, and whether there is a vehicle at the intersection for each of the oncoming directions. For each action, the smart cab may either stay idle at the intersection, or drive to the next intersection to the left, right, or ahead of it. Finally, each trip has a time to reach the destination which decreases for each action taken (the passengers want to get there quickly). If the allotted time becomes zero before reaching the destination, the trip has failed. The smart cab will receive positive or negative rewards based on the action it has taken. Expectedly, the smart cab will receive a small positive reward when making a good action, and a varying amount of negative reward dependent on the severity of the traffic violation it would have committed. Based on the rewards and penalties the smart cab receives, the self-driving agent implementation should learn an optimal policy for driving on the city roads while obeying traffic rules, avoiding accidents, and reaching passengers' destinations in the allotted time. Environment: The smartcab operates in an ideal, grid-like city (similar to New York City), with roads going in the North-South and East-West directions. Other vehicles will certainly be present on the road, but there will be no pedestrians to be concerned with. At each intersection there is a traffic light that either allows traffic in the North-South direction or the East-West direction. U.S. Right-of-Way rules apply: On a green light, a left turn is permitted if there is no oncoming traffic making a right turn or coming straight through the intersection. On a red light, a right turn is permitted if no oncoming traffic is approaching from your left through the intersection. To understand how to correctly yield to oncoming traffic when turning left.
hyhmrright / Brooks LintAI code reviews grounded in 10 classic engineering books — decay risk diagnostics with book citations, severity labels, and 4 analysis modes
Deepnarayan70 / Road Accident Severity Prediction MLThis project focuses on analyzing road traffic accident data and predicting casualty severity using machine learning techniques. It includes data preprocessing, exploratory data analysis, correlation analysis, classification models, and decision tree–based interpretation to identify key factors influencing accident severity.
neda77aa / FTCThis repo holds the code for: {Transformer-based Spatio-temporal Analysis for Automatic Classification of Aortic Stenosis Severity from B-mode Ultrasound Cine Series}.
KavindaDulhan / Eye Diagrams Of PAM SignallingEye diagrams are used for visual analysis of the severity of inter symbol interference (ISI), accuracy of sampling timing extraction and noise immunity. In this report, eye diagrams are used to analyze the robustness of binary phase shift keying (BPSK) and 4-PAM (pulse amplitude modulation) digital communication systems. In task 1, a random bit sequence is generated and those bits are mapped to BPSK symbols. Impulse train of BPSK symbols was convoluted with a pulse shaping filter. Three pulses were used as the impulse response of the pulse shaping filter. They were sinc pulse, raised-cosine pulse with α = 0:5 and raised-cosine pulse with α = 1 where α is the roll-off factor. The impulse responses are shown in below Eye diagrams for those three transmit sequences were generated and the systems were compared as follows. After the task 1, additive white Gaussian noise (AWGN) is added to the transmit signals with specified bit energy to noise ratio. Finally, in task 3, 4-PAM system is used.. Then the eye diagrams are generated for the received signals with the presence of AWGN. Same pulse shaping filters as in task 1 are used.
LeeJMJM / UM GearEccDatasetA comprehensive gear eccentricity dataset with multiple fault severity levels: Description, characteristics analysis, and fault diagnosis applications
bipashant / RubionSecurity scanner for Ruby gems and NPM packages. Finds vulnerabilities and outdated dependencies with release dates, version analysis, and severity indicators.
Sumit0x00 / GoInspectGoInspect identifies website technologies and checks for known vulnerabilities using the NVD API, providing CVE IDs, descriptions, and severity levels for enhanced security analysis and vulnerability assessment.
uzairshah32 / Coursera CapstoneCar Accident Severity Analysis - Seattle Washington (Machine Learning Application)
Aurindom / Car Accident Severity ANALYSISCar Accident Severity deals with finding the best ML technique to analyse and predict car accidents causing severity.
mikoontz / Local Structure Wpb SeverityAnalysis of drone imagery to characterize forest structure and severity of a tree killing insect
FarhaKousar1601 / Road Safety Analysis DashBoardThis project is a comprehensive data analysis and visualization dashboard created using Power BI It explores road accident data to provide valuable insights into accident patterns based on severity, weather, time, road types, vehicle types, and more.
lnferreira / Global Fss Analysis ForecastingCode used in the paper "Global Fire Season Severity Analysis and Forecasting"
