Fake news causes a huge impact on the reader’s mind, therefore it has become a major concern. Identifying fake news or differentiating between fake and authentic news is quite challenging. The trend of fake news in Pakistan has grown a lot in the last decade. This research aims to develop the first comprehensive fake news detection dataset for Pakistani news by using multiple fact-checked news APIs. This research also evaluates the developed dataset by using multiple state-of-the-art artificial intelligence techniques. Five machine learning techniques namely Naive Bayes, KNN, Logistic Regression, SVM, and Decision Trees are used. While two deep learning techniques CNN and LSTM are used with GloVe and BERT embeddings. The performance of all the applied models and embeddings is compared based on precision, F1-score, accuracy, and recall. The results show that LSTM initialized with GloVe embeddings has performed best. The research also analyzes the misclassified samples by comparing such samples with human judgements.

The need of automated information extraction increases with the increase in biomedical text. Named entity recognition is one of the core tasks in automatic information extraction. Performing named entity recognition tasks are quite challenging in biomedical field. These challenges include limited availability of annotated datasets and misclassification of entities having multiple meanings. Many Neural Network and deep learning-based models are developed for overcoming these challenges and for increasing the performance of named entity recognition tasks. This paper compares different models based on neural network architecture. The performance of these models is compared on JNLPBA dataset. The results show that Long short-term memory - conditional random field model with Wiki PubMed-PMC embeddings has outperformed other models by achieving highest precision and F1-score. CollaboNet model achieves the highest recall. Further analysis is needed to explore and compare the tools for performing named entity tasks in biomedical field.

The growth of fake news in multiple fields such as in the political or health sector has become a great concern as it possess huge impact on the reader's mind. Identifying the fake news or differentiating between fake and authentic news is quite challenging. The focus of this research is to identify fake news by applying different artificial intelligence techniques along with different embeddings and to assess the performance of all the applied models. The performance of these models and the embeddings is compared based on precision, accuracy, Fl-score and recall. For machine learning techniques SVM, KNN, Naive Bayes, Logistic Regression and Decision Trees are used, while for deep learning techniques CNN and LSTM are used with GloVe and BERT embeddings. Multiple experiments using these techniques are performed on the LIAR and Fake-or-Real dataset. Naïve Bayes has shown the best results from machine learning techniques on both datasets. While in deep learning techniques, LSTM with GloVe has shown the best results on the LIAR dataset and CNN with BERT has shown the best performance on the Fake-or-Real dataset. Overall GloVe word embeddings performed well on the LIAR dataset while BERT sentence embeddings have shown good performance on the Fake-or-Real dataset.