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Twitter led a remarkable breakthrough in information sharing on online social media. The eminent technology can propagate a piece of rumor to a large community of people in a short period. The timely detection of rumor tweets in social media curtails panic among the public during critical situations. Traditional machine learning techniques are not capable of categorizing rumor information effectively. To address this problem, the author has proposed a novel neural network approach called veracity detection neural network for identifying the rumor-related Twitter posts’ content in real-time events. This algorithm utilized the convolutional sentence encoder–bi-directional long short-term memory (CSE-BiLSTM) model with pre-trained vectorization methods such as Word2vec, fastText and universal sentence encoder (USE). The hybrid CSE-BiLSTM with USE vectorization technique yields the best results for the performance metrics of accuracy, F1-score, precision and recall. The proposed algorithm achieves 90.56%, 86.18% and 93.89% accuracy values to classify the tweet into rumor or non-rumor for the datasets such as PHEME, newly emerged rumors on Twitter and #gaja, respectively. Finally, a comparative study shows that the proposed neural network model outperformed all other existing rumor text classification systems.

Online Social Networking (OSN) is a platform, which allows users to impart information to other people. Twitter plays an instrumental role in public and political conversations related to the particular crisis events. This research work aims to rank the credibility of the tweets associated with the Natural Disasters and identifies the tweets that spread fake information. The authors propose a Credibility-Analysis-System to rank tweet messages according to their credibility. The authors have compared the performance of tweet credibility with state-of-art of machine learning algorithms such as Naive Bayes classifier, SVM rank algorithm, and Random Forest classifier. The experimentation has been performed on the real-time dataset collected during the Gaja cyclone event. The results present the Random Forest classifier to be the most suitable algorithm for credibility analysis with an accuracy of 87%.

Automatically facial recognition has become more and more important today. But FR still remains a challenge problem in computer vision .so in this paper we proposed a new approach that use Gabor wavelet for facial feature extraction and robust structured sparse representation for classification. Gabor wavelet has beenwidely used in the face recognition task because it's good imitation of human visual. The structured sparse representation based classification (SSRC) is used for classifying the test images robustly. Unlike sparse representation based classification (SRC),the SSRC explicitly takes structure of the dictionary into account for a better classification.