In today’s era most of the YouTuber’s are facing the major problem with electronic spam as troublesome Internet phenomenon. This work proposes a methodology for the detection of spam comments on the video-sharing website - YouTube. YouTube is running its own spam blocking system but continues to fail to block them properly. In this work, we examined several top- performance classification techniques for spam comment screening and proposed a novel methodology. In this work, we have analyzed such comments by applying conventional machine learning algorithms such as Naive Bayes, Random Forest, Support Vector Machine, Logistic regression, Decision Tree and will construct another model utilizing ensemble and hybrid approach. This paper proposed the YouTube spam comments detection framework, examined, and validated by using data collected from the YouTube using Naïve Bayes multinomial, Gradient Boosting, Random Forest and tested in Weka and Python data mining tools.

This study provides an intelligent classification method for distinguishing between abnormal and normal MRI brain images. Medical pictures like MRI, ECG, and CTscan pictures are vital tools for accurately diagnosing human disease. Whenever a lot of MRIs need to be examined, traditional approach of manual tumour analysis, which relies on visual examination by a physician and radiologist, might lead to inaccurate classification. To remove human mistakes, a proposal is made for an intelligent classification system that responds to the essentials of image classification. Brain tumours are one of the primary causes of human mortality. If a tumor is diagnosed appropriately at an early stage, the chances of survival can be improved. The human brain is studied using the MRI method. The acronym MRI stands for magnetic resonance imaging. In this study, classification strategies based on Support Vector Machines (SVM) are proposed and used to brain imaging categorization. In this research, grayscale, symmetry, and texture features are utilised to extract features from MRI images. The fundamental objective of this study is to offer a decent classification result (improved accuracy and lower error rate) to detect MRI brain tumours with help of SVM. Keywords— Brain tumor, Classification, SVM, MRI.

Recently, social media platforms like LinkedIn, Twitter, Facebook, YouTube, etc., are immensely popular, especially in the pandemic era. This is because they provide connection and interaction between people by posting images, comments, or videos.YouTube has become a very popular video-sharing platform, and because of this, it has also attracted several types of spammers or malicious users whose aim is to distribute viruses or promote their videos. Spammers also want to spread phishing, malware, or advertisements in the comment section of the videos. Spam is generally related to unsought content or irrelevant comments with low-grade information. They are usually found as images, texts, or videos, clogging the visualization of interesting content. Users spend a lot of time eradicating spam since it causes a variety of issues that could lead to traffic and financial losses. To filter spam, several techniques have been developed. Automatic comment spam filtering on YouTube is not a simple task even for well-known classification methods since comments are very short and often contain slang, symbols and abbreviations. However, typical machine learning classification algorithms have been confirmed to be fairly effective, but there is still space for improved accuracy with new methods. In this paper, we will evaluate several topperformance classification techniques (such as Naive Bayes, Random Forest, and Support Vector Machine) of Machine Learning classification algorithms to detect such comments as spam or ham with the help of text classification, feature extraction and text pre-processing. It will work after posting the comments. This paper provides a comparative analysis of various ML techniques implemented by various researchers and authors in their work. The dataset would be used with reference to the UCI machine learning repository for future implementations of this related work.