In order to use our Yosnalab shopping services, you need to read carefully about our terms and conditions. If You disagree any part of terms then you cannot access our service.
I agree to pay the rental rate for the period we used the product and in transit and further agrees to promptly return the products at the end of the rental period in the same condition as received.
I will give alert to Yosnalab if my contact information changes.
I also agree to pay for any damages to, or loss of, the rented merchandise occurring during their time of possession or because of loss and or damage of the products. Upon return and inspection if any and all repairs necessary and or accessories missing that were itemized will be charged at our current rates and billed to you.
A full day rental is charged, even for a partial day use. There are absolutely no refunds for early returns.
The products can be used upto 4 weeks only, Cost will be calculated as per day of usage. We are not responsible for the damage of materials or other liability of any kind resulting from the use or malfunction of the equipment. We will not return the initial deposit of money until the product is returned back.
I am responsible for keeping track of my due dates. I understand that any notices sent out by Yosnalab are a courtesy only and failure to receive them does not excuse me from any charges.
A copy of both sides of their valid institution identification card need to be present. Depositing the money can be done only in the form of cash, not through credit/debit cards.
If you have questions or suggestions, please contact us.
This project is performed to detect text and image-based CyberBullying in social network sites like Instagram. To implement the Convolutional Neural Network (CNN) for the detection of bullying image and Bag of Words (BOW) model for the detection of bullying words list. This project describes the process of building a cyberbullying intervention interface driven by a machine-learning-based text-classification service. We make two main contributions. First, we show that cyberbullying can be identified in real-time before it takes place, with available machine learning and natural language processing tools, in particular, CNN. Second, we present a mechanism that provides individuals with early feedback about how other people would feel about wording choices in their messages before they are sent out. This interface not only gives a chance for the user to revise the text but also provides a system-level flagging/intervention in a situation related to cyberbullying.
Why: Problem statement
CyberBullying is an increasingly important and serious social problem, which can negatively affect individuals. It is defined as the phenomenon of using the internet, cell phones, and other electronic devices to willfully hurt or harass others. Due to the growth of social media platforms like Instagram, CyberBullying is becoming more and more prevalent.
How: Solution description
To reduce the cybercrime, we are going to detect the bullying images and the text from various social media using deep learning techniques.
We have collected some images from various social media like Facebook, Twitter, and Instagram.
The data pre-processing is an important phase in representing data in feature space to the classifiers. Social network data are noisy, thus pre-processing has been applied to improve the quality of the data collected.
This module is used for extracting the data required from processed data. The part of speech for every word in the conversation is obtained. We used OCR (Optical character recognition) to extract text from the images.
Bag of words:
The Bag of words (BOW) model is a baseline text feature wherein the given text is represented as a multiset of its words, disregarding grammar and word order. The multiplicity of words are maintained and stored as a word frequency vector. Finally, we create a word vector, where each component represents a word in the dictionary which we have generated and its value corresponds to its frequency.
A Naive Bayes classifier is a simple probabilistic classifier based on applying Bayes theorem (from Bayesian statistics) with strong (naive) independence assumptions. We trained the bullying and non-bullying text taken from social media using naive Bayes classifier. We got the testing accuracy of 98.2%.
Convolutional Neural Networks:
Convolutional neural networks are used primarily to classify images. They are algorithms that can identify faces, individuals, street signs, tumors, platypuses and many other aspects of visual data. It will extract image features using a pre-trained Convolutional Neural Network (CNN) which is the benchmark standard for image classification. Here, we trained some bullying and non-bullying images taken from social media. We got the testing accuracy of 97.6%.
How is it different from competition
We got more accuracy for the bullying and non bullying images when compared to previous models.
Who are your customers
People who are in the crime department and the cops can use this project.
Project Phases and Schedule
Phase 1: Data collection
Phase 2: Data cleaning and feature extraction
Phase 3: Training using Naive Bayes
Phase 4: Implementation of CNN
Anaconda tool with Python 3.7 version
Installation of required libraries
Leave a Comment
Are you Interested in this project?
Do you need help with a similar project? We can guide you. Please Click the Contact Us button.