58 skills found · Page 1 of 2
iGio90 / DwarfFull featured multi arch/os debugger built on top of PyQt5 and frida
kabeor / Capstone Engine DocumentationOfficial Capstone Disassembly Engine API documentation
quangnh89 / OllyCapstoneThis is a plugin for OllyDbg 1.10 to replace the old disasm engine by Capstone disassembly/disassembler framework.
zer0mem / Cccapstonec++ bindings for capstone disasembly framework (http://www.capstone-engine.org/ - https://github.com/aquynh/capstone)
ETeissonniere / EliDecodeThe tool to decode obfuscated shellcodes using the unicorn and capstone engine
supratim94336 / DataEngineeringCapstoneProject😈Complete End to End ETL Pipeline with Spark, Airflow, & AWS
psas / Liquid Propellant EngineRepo for PSAS' liquid propellant engine (LPE) designs (starting with 2015 liquid engine ME capstone and continuing into LV4)
Modingwa / Data Engineering Capstone ProjectUdacity Data Engineering Nanodegree Capstone Project
arkup / JuniEmuEmulator interface for ARM 32-bit
HungNguyenDev1511 / Capstone Project Data EngineerNo description available
danieldiamond / Data Engineering CapstoneData Engineering Capstone Project: ETL Pipelines and Data Warehouse Development
zaas2 / StudyPE 2026Modern PE/ARM/.NET analyzer and editor rebuilt with Qt 5.15
suifei / Asm2hex2The new version is refactored using C++ (cpp), while the original version was developed using Golang. A cross-platform GUI tool for converting between Assembly and Machine Code (Hex), powered by Keystone Engine and Capstone Engine.
archie-cm / IBM Data Engineering Capstone ProjectBusiness challenge that requires building a data platform for retailer data analytics.
Dax89 / LuaCapstoneCapstone Engine bindings for Lua
taylor-ortiz / Dataexpert Data Engineering CapstoneNo description available
LoveNui / DataEngineering Capstone ProjectNo description available
zydeco / Capstone SwiftSwift bindings for Capstone Engine
infomindgithub / Machine Learning Engineer Nanodegree Capstone PROJECT ANALYSIS*****PROJECT SPECIFICATION: Machine Learning Capstone Analysis Project***** This capstone project involves machine learning modeling and analysis of clinical, demographic, and brain related derived anatomic measures from human MRI (magnetic resonance imaging) tests (http://www.oasis-brains.org/). The objectives of these measurements are to diagnose the level of Dementia in the individuals and the probability that these individuals may have Alzheimer's Disease (AD). In published studies, Machine Learning has been applied to Alzheimer’s/Dementia identification from MRI scans and related data in the academic papers/theses in References 10 and 11 listed in the References Section below. Recently, a close relative of mine had to undergo a sequence of MRI tests for cognition difficulties.The motivation for choosing this topic for the Capstone project arose from the desire to understand and analyze potential for Dementia and AD from MRI related data. Cognitive testing, clinical assessments and demographic data related to these MRI tests are used in this project. This Capstone project does not use the MRI "imaging" data and does not focus on AD, focusses only on Dementia. *****Conclusions, Justification, and Reflections***** [Student adequately summarizes the end-to-end problem solution and discusses one or two particular aspects of the project they found interesting or difficult.] The formulation of OASIS data (Ref 1 and 2) in terms of a dementia classification problem based on demographic and clinical data only (and without directly using the MRI image data), is a simplification that has major advantages and appeal. This means the trained model can classify whether an individual has dementia or not with about 87% accuracy, without having to wait for radiological interpretation of MRI scans. This can provide an early alert for intervention and initiation of treatment for those with onset of dementia. The assumption that the combined cross-sectional and longitudinal datasets would lead to dementia label classification of acceptable accuracy came out to be true. The method required careful data cleaning and data preparation work, converting it to a binary classification problem, as outlined in this notebook. At the outset it was not clear which algorithm(s) would be more appropriate for the binary and multi-label classification problem. The approach of spot checking the algorithms early for accuracy led to the determination of a smaller set of algorithms with higher accuracy (e.g. Gadient Boosting and Random Forest) for a deeper dive examination, e.g. use of a k-fold cross-validation approach in classifying the CDR label. The neural network benchmark model accuracy of 78% for binary classification was exceeded by the classification accuracy of the main output of this study, the trained Gradient Boosting and Random Forest classification models. This builds confidence in the latter model for further training with new data and further classification use for new patients.
richardudell / Predicting Urban DevelopmentBeginner's tutorial for working with the Google Earth Engine Python API in Jupyter Notebooks. The tutorial walks through a GEE workflow of measuring urban development surrounding Denver International Airport. Future work will involve development of a linear regression model to predict urban development surrounding DIA. This repository serves as a capstone project submission for Earth Data Analytics Professional Certificate Final Project.
