The aim of this paper is to review machine learning (ML) algorith ms and techniques for hate speech detection in social media (SM). Hate speech problem is normally model as a text classification task. In this study, we examined the basic baseline components of hate speech classification using ML algorithms. There are five basic baseline componentsdata collection and exploration, feature extraction, dimensionality reduction, classifier selection and training, and model evaluation, were reviewed. There have been improvements in ML algorithms that were employed for hate speech detection over time. New datasets and different performance metrics have been proposed in the literature. To keep the researchers informed regarding these trends in the automatic detection of hate speech, it calls for a comprehensive and an updated state-of-the-art. The contributions of this study are two-fold. First to equip the readers with the necessary information on the critical steps involved in hate speech detection using ML algorithms. Secondly, the weaknesses and strengths of each method is critically evaluated to guide researchers in the algorithm choice dilemma. The different variants of ML techniques were reviewed which include classical ML, ensemble approach and deep learning methods. Researchers and professionals alike will benefit immensely from this study.
Cancer is a second foremost life-threatening disease next to cardiovascular diseases. In particular, brain cancer holds the least rate of survival than all other cancer types. The categorization of a brain tumor depends upon the various factors such as texture, shape and location. The medical experts have preferred the appropriate treatment to the patients, based on the accurate identification of tumor type. The process of segmenting the Magnetic Resonance Imaging (MRI) has high complicacy during the analysis of brain tumor, owing to its variable shape, location, size, and texture. The physicians and radiologists can easily detect and categorize the tumors if there exists a system by combining Computer Assisted Diagnosis (CAD) as well as Artificial Intelligence (AI). An approach of automated segmentation has proposed in this paper, which enables the segmentation of tumor out of MRI images, besides enhances the efficiency of segmentation and classification. The initial functions of this approach include preprocessing and segmentation processes for segmenting tumor or tissue of benign and malignant by expanding a range of data and clustering. A modern learning-based approach has suggested in this study, in order to process the automated segmentation in multimodal MRI images to identify brain tumor, hence the clustering algorithm of Bat Algorithm with Fuzzy C-Ordered Means (BAFCOM) has recommended segmenting the tumor. The Bat Algorithm calculates the initial centroids and distance within the pixels in the clustering algorithm of BAFCOM, which also acquires the tumor through determining the distance among tumor Region of Interest (RoI) and non-tumor RoI. Afterwards, the MRI image has analyzed by the Enhanced Capsule Networks (ECN) method to categorize it as normal and brain tumor. Ultimately, the algorithm of ECN has assessed the performance of proposed approach by distinguishing the two categories of the tumor over MRI images, besides the suggested ECN classifier has assessed by the measurement factors of accuracy, precision, recall, and F1-score. In addition, the genetic algorithm has applied to process the automatic tumor stage classification, which in turn classification accuracy enhanced.INDEX TERMS Machine learning, enhanced capsule networks (ECN), brain tumor, bat algorithm with fuzzy c-ordered means (BAFCOM), magnetic resonance imaging (MRI) images.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.