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https://github.com/yash22222/terrorist-activity-forecasting-and-risk-assessment-system
In an era marked by global security challenges, the "TAFRAS" emerges as a cutting-edge solution to tackle the ever-evolving threat of terrorism. The project is grounded in the urgent need for predictive systems that can anticipate, assess, and mitigate potential terrorist activities.
https://github.com/yash22222/terrorist-activity-forecasting-and-risk-assessment-system
corpora data-vizualisation folium-maps gensim global-terrorism-database lda machine-learning matplotlib networkx nltk nmf numpy pandas python random-forest-classifier seaborn sklearn spacy textblob vader-sentiment-analysis
Last synced: 21 days ago
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In an era marked by global security challenges, the "TAFRAS" emerges as a cutting-edge solution to tackle the ever-evolving threat of terrorism. The project is grounded in the urgent need for predictive systems that can anticipate, assess, and mitigate potential terrorist activities.
- Host: GitHub
- URL: https://github.com/yash22222/terrorist-activity-forecasting-and-risk-assessment-system
- Owner: Yash22222
- Created: 2023-10-02T18:11:31.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-11-03T06:30:12.000Z (about 1 year ago)
- Last Synced: 2024-11-09T23:22:38.401Z (3 months ago)
- Topics: corpora, data-vizualisation, folium-maps, gensim, global-terrorism-database, lda, machine-learning, matplotlib, networkx, nltk, nmf, numpy, pandas, python, random-forest-classifier, seaborn, sklearn, spacy, textblob, vader-sentiment-analysis
- Language: HTML
- Homepage: https://yashashokshirsath.netlify.app/
- Size: 31.6 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Terrorist Activity Forecasting and Risk Assessment System (TAFRAS)
## Table of Contents
- [Introduction](#introduction)
- [Motivation](#motivation)
- [Problem Statement & Objectives](#problem-statement--objectives)
- [Organization of the Report](#organization-of-the-report)
- [Literature Survey](#literature-survey)
- [Survey of Existing System](#survey-of-existing-system)
- [Limitation of Existing System](#limitation-of-existing-system)
- [Mini Project Contribution](#mini-project-contribution)
- [Proposed System](#proposed-system)
- [Introduction](#introduction-1)
- [Architecture/Framework](#architectureframework)
- [Algorithm and Process Design](#algorithm-and-process-design)
- [Details of Hardware & Software](#details-of-hardware--software)
- [Code](#code)
- [Experiment and Results](#experiment-and-results)
- [Conclusion and Future Work](#conclusion-and-future-work)
- [Details of Hardware & Software](#details-of-hardware--software-1)
- [Installation](#installation)
- [Usage](#usage)
- [Contributing](#contributing)
- [License](#license)
## Introduction
Welcome to the **Terrorist Activity Forecasting and Risk Assessment System (TAFRAS)**, a project designed to address the critical issue of predicting and assessing terrorist activities. Our system leverages data analysis and risk assessment techniques to provide insights and predictions in the field of counter-terrorism.
## Motivation
The motivation behind TAFRAS is the increasing global concern about terrorism and the need for proactive measures to mitigate risks and respond effectively to emerging threats. TAFRAS is driven by the goal of making our world a safer place.
## Problem Statement & Objectives
**Problem Statement:** The world faces the continuous challenge of forecasting terrorist activities and assessing associated risks in real time. TAFRAS addresses this challenge by predicting and assessing terrorist incidents.
**Objectives:**
1. Develop a forecasting model for predicting terrorist incidents.
2. Create a risk assessment framework to evaluate the potential severity of these incidents.
3. Provide a user-friendly interface for users to access this information easily.
## Organization of the Report
This report is structured as follows:
- Section 2 presents a **Literature Survey**, including a review of existing systems, their limitations, and our contributions.
- Section 3 delves into the **Proposed System**, explaining our approach, architecture, algorithms, software, and hardware details, the code repository, experimental results, and our conclusions and future work.
## Literature Survey
### Survey of Existing System
In the existing landscape, various systems attempt to address similar issues. We review these systems to understand their strengths and shortcomings.
### Limitation of Existing System
Our analysis of existing systems and research gaps led to the development of TAFRAS, which aims to overcome the limitations of current solutions.
### Mini Project Contribution
TAFRAS significantly contributes to filling the research gap by providing more accurate predictions and risk assessments for counter-terrorism efforts.
## Proposed System
### Introduction
The proposed system, TAFRAS, leverages advanced machine learning, data analysis, and real-time data collection to forecast terrorist activities and assess associated risks.
### Architecture/Framework
TAFRAS is designed with a modular architecture, incorporating multiple components that work seamlessly to provide the desired predictions and assessments.
### Algorithm and Process Design
Our system utilizes cutting-edge algorithms to process large datasets and extract valuable insights for risk assessment.
### Details of Hardware & Software
To run TAFRAS, you'll need basic hardware and software requirements, which are outlined in this section.
### Code
The project's source code is hosted on GitHub, where you can access and contribute to the development.
### Experiment and Results
We present the results of our experiments, including the accuracy of our predictions and risk assessments, in this section.
### Conclusion and Future Work
This section summarizes the key findings and outlines areas for future work and improvements in the TAFRAS system.
## Details of Hardware & Software
### Hardware
- CPU: 2.0 GHz or faster
- RAM: 4 GB or more
- Storage: 20 GB or more
- Internet connection
### Software
- Python 3.x
- Data analysis and machine learning libraries (e.g., NumPy, Pandas, Scikit-Learn)
- Web framework
## Installation
Details on how to install and set up TAFRAS on your local machine can be found in the installation guide in our repository.
## Usage
TAFRAS is designed to be user-friendly. Our user guide explains how to utilize the system effectively.
## License
TAFRAS is distributed under the [](https://opensource.org/licenses/MIT).
