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AZore's Projects

2019-microsoft-iot-hackathon icon 2019-microsoft-iot-hackathon

🥇 1st Place (tied) | Bump.IT - Pothole detection and mapping. Using data science methods of analysis, mobile phone's telemetry, computer vision, and, deployed through Azure.

data-analysis-and-prediction-on-crimes-in-montgomery-county icon data-analysis-and-prediction-on-crimes-in-montgomery-county

Our study aims to find spatial and temporal criminal hotspots using a set of real-world datasets of crimes. We intend to perform extensive exploratory data analysis that will help us understand the factors affecting the crimes such as crime locations, timeshifts, response time etc., Based on the preliminary analysis and feature selection, we intend to predict the average response time for a given crime and also predict crime rate based on temporal data. Finally, we provided an analysis/ suggestions by combining our findings that would help the officials to make decisions accordingly in order to improve the safety of Montgomery County.

dynamic-traffic-assignment-node-based-with-ctm icon dynamic-traffic-assignment-node-based-with-ctm

This repository contains code related to a research paper I've been working on titled "Dynamic traffic assignment with a node-based cell transmission model satisfying the link-level first-in-first-out principle".

leetcode-master icon leetcode-master

《代码随想录》LeetCode 刷题攻略:200道经典题目刷题顺序,共60w字的详细图解,视频难点剖析,50余张思维导图,支持C++,Java,Python,Go,JavaScript等多语言版本,从此算法学习不再迷茫!🔥🔥 来看看,你会发现相见恨晚!🚀

ns3-cacc icon ns3-cacc

CACC implementation in ns3 with 802.11p

pestim icon pestim

Population estimation using aggregated mobile phone data

traffic-accidents icon traffic-accidents

In this project, we analyze the effects of various factors that correlate to traffic fatalities in the United States. Logistic regression is used, with the y variable being Fatality Rate (coded 0 for Survived, 1 for Fatality).

traffic-congestion-prediction icon traffic-congestion-prediction

Proposed Proof of Concept to solve Traffic Congestion and Prediction Problem for Smart Cities by using LSTM(for future prediction) + CNN(To detect traffic density) + using tweets and combine all three to produce a traffic congestion factor and suggest routes based on that using Google API at any time in future.

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