Social network technologies have gained widespread attention in many fields. However, the research on Tibetan Social Network (TSN) is limited to the sentiment analysis of micro-blogs, and few researchers focus on text classification and data mining in TSN. It cannot meet the social needs of the majority of Tibetans and the text information they really care about. In this paper, we investigate and compare different models that we adopted for the classification of Tibetan text. Machine learning models including Naive Bayesian (NB), Random Forest (RF), Support Vector Machine (SVM), fastText and text Convolutional Neural Networks (CNN) are used as classifiers to determine the best approach in Tibetan Social Network. In addition, term frequency-inverse document frequency (TF-IDF) is used to extract hot words and generate the word cloud. The results show that the random forest is significantly better than other machine learning algorithms on Tibetan text classification.
Accurate manual detection of brain tumor by a team of radiologists may be a long and tedious process, and further rely on their skills in the subject. Nowadays various medical imaging modalities are extensively used to minimize the above complexities and enable the patients to live a long and healthy life. This paper mainly focuses on the suspected patients of the brain tumor. A new method for feature extraction has been introduced and the framework for it has been briefed in the following steps. To begin with, a dual segmentation i.e. Fuzzy K-mean and Expectation-Maximization method has been performed consequently. The Ranklet Transformation+ along with Statistical Feature Analysis, named as hybrid feature extraction has been proposed. Further, two classification techniques have been presented by involving Auto-Encoder Neural Network in addition to Support Vector Machine classifier. Here, Auto-Encoder is trained by using extracted feature vectors, and the resultant is subsequently tra
The focal point of this work is to automatically detect metaphor instances in short texts. It is the study of extricating the most optimal features for the task by using a deep learning architecture and carefully hand-crafted contextual features. The first feature set is created using a Convolutional Neural Network (CNN) architecture. Then, three other feature sets are manually hand-crafted using contextual justifications. Next, all of the feature sets are combined. Finally, the combined feature sets undergo the classification process using Support Vector Machine, Logistic Regression, Decision Tree, K-Nearest Neighbour and Discriminatory Analysis. These well-known ma-chine learning classification algorithms are used at the same time for the purpose of comparison. The best algorithm for this task is found to be Support Vector Machine (SVM). The outcome of all the experiments using SVM are good in all the metrics used, with F1-measure of 0.83. Finally, comparison to existing works and pe