Brain tumor classification: a novel approach integrating GLCM, LBP and composite features.

Identifying and classifying tumors are critical in-patient care and treatment planning within the medical domain. Nevertheless, the conventional approach of manually examining tumor images is characterized by its lengthy duration and subjective nature. In response to this challenge, a novel method is proposed that integrates the capabilities of Gray-Level Co-Occurrence Matrix (GLCM) features and Local Binary Pattern (LBP) features to conduct a quantitative analysis of tumor images (Glioma, Meningioma, Pituitary Tumor).

Sentiment Thesaurus, Synset and Word2Vec Based Improvement in Bigram Model for Classifying Product Reviews.

Classifying product reviews is one of the tasks in Natural Language Processing by which the sentiment of the reviewer towards a product can be identified. This identification is useful for the growth of the business by increasing the number of satisfied customers through product quality improvement. Bigram models are more popular in performing this classification since it considers the occurrence of two words consecutively in the reviews.