Bionic Eye

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This document describes a bionic eye robot that can read colors and measure distances from colored objects. It was built to help colorblind people by determining distances to colored objects in dark places. The creators first designed a prototype virtually using Lego Digital Designer, then built a real robot and tested it, finding that the device works perfectly.

BIONIC EYE
This is a robot used to read colours and measure distance from the coloured
objects. The device reads the colour while being close to it. Moving in dark
places, it is able to determine how far from a coloured object there is a particular
person. We have built our robot for colour blind people and programmed it in
such a way to make it easier for them to function in the society. Firstly, in a
three-dimensional program - Lego Digital Designer we built a prototype of our
device, designing it virtually. Next, we created a real robot and finally we tested
it. The device works perfectly. Good job!
BIONIC EYE
18 stycznia 2016
16:47
Ogólne Strona 1

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An Approach to Detecting Writing Styles Based on Clustering Techniques

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An Approach to Detecting Writing Styles Based on Clustering Techniques Authors: -Devkinandan Jagtap -Shweta Ambekar -Harshit Singh -Nakul Sharma (Assistant Professor) Institution: VIIT Pune, India Abstract: This paper proposes a system to differentiate between human-generated and AI-generated texts using stylometric analysis. The system analyzes text files and classifies writing styles by employing various clustering algorithms, such as k-means, k-means++, hierarchical, and DBSCAN. The effectiveness of these algorithms is measured using silhouette scores. The system successfully identifies distinct writing styles within documents, demonstrating its potential for plagiarism detection. Introduction: Stylometry, the study of linguistic and structural features in texts, is used for tasks like plagiarism detection, genre separation, and author verification. This paper leverages stylometric analysis to identify different writing styles and improve plagiarism detection methods. Methodology: The system includes data collection, preprocessing, feature extraction, dimensional reduction, machine learning models for clustering, and performance comparison using silhouette scores. Feature extraction focuses on lexical features, vocabulary richness, and readability scores. The study uses a small dataset of texts from various authors and employs algorithms like k-means, k-means++, hierarchical clustering, and DBSCAN for clustering. Results: Experiments show that the system effectively identifies writing styles, with silhouette scores indicating reasonable to strong clustering when k=2. As the number of clusters increases, the silhouette scores decrease, indicating a drop in accuracy. K-means and k-means++ perform similarly, while hierarchical clustering is less optimized. Conclusion and Future Work: The system works well for distinguishing writing styles with two clusters but becomes less accurate as the number of clusters increases. Future research could focus on adding more parameters and optimizing the methodology to improve accuracy with higher cluster values. This system can enhance existing plagiarism detection tools, especially in academic settings.

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An Approach to Detecting Writing Styles Based on Clustering Techniques

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