Detection of Americans’ Behavior toward Islam on Facebook

Social network websites have become a rich place for detecting and analyzing people’s attitudes, perceptions, and feelings towards news, products,  and other real-world issues. Facebook is a popular platform among different age groups and countries and is generally used to convey ideas about certain topics based on likes, comments and sharing. In recent years, one of the most controversial topics were the idea behind Islamophobia and other ideas built in people’s minds about Islam around the world. This research studied the public opinion of American citizens about Islam during the presidency of Donald Trump, as that period was rich in diversity of opinion between his supporters and detractors. In this paper, sentiment analysis was used to analyze American citizens’ behavior towards posts about Islam during Trump’s presidency in various states across the United States. Sentiment analysis was performed on Facebook posts and comments extracted from American news channels from the year 2017. Several machine learning methods were used to detect the polarity in the dataset. The highest classification accuracy among the classifiers used in this research was achieved using a logistic regression classifier, reaching 84%

Enhancing Steganography by Image Segmentation and Multi-level Deep Hiding

In this paper, we present Modify Deep Hiding Extraction Algorithm (MDHEA) that is a steganography algorithm with Multi-Level Steganography (MLS) and color image segmentation. Through experimental results, MDHEA shows improvement in the results of previous works by securing encrypted secret data against attacks. We use segmentation to choose the appropriate segment, pass it on the cover image, calculate the value of the change at the pixel of the segment and select the best segment and its location in the cover image based on the least effect. MDHEA applies multi-level steganography to hide the confidential data in color images to ensure the integrity of the hidden data and obtain the largest volume of hidden data without distorting the image of the stego image. To reduce distortion in the cover image due to hiding a large amount of secret data and obtaining a high-quality stego image after hiding the secret data, we implement the Blue Smoothing Algorithm (BSA) to achieve smoothing the largest possible number of pixels in the image