Rabia Batool

TL;DR: The proposed methodology has performed better than the existing system in terms of tweets classification and sentiment analysis and the increase in information gain has enabled the proposed system to better summarize the twitter data for user sentiments regarding a keyword from a particular category.

TL;DR: An integrated framework which bridges the gap between lexicon-based and machine learning approaches to achieve better accuracy and scalability is proposed and a novel genetic algorithm (GA)-based feature reduction technique is proposed to solve the scalability issue that arises as the feature-set grows.

TL;DR: The Smart Clinical Decision Support System, a system that takes input from diverse modalities, such as sensors, user profile information, social media, clinical knowledge bases, and medical experts to generate standards-based personalized recommendations, and generates recommendations based on physicians’ guidelines provided at the hospital during patient follow-ups.

TL;DR: The experimental outcomes on real-life data and feedback from the law enforcement officers suggest that the proposed chat log mining framework meets the need for law enforcement agencies and is very effective for crime investigation.

TL;DR: This paper introduces some unfolded challenges in the field of ontology evolution, which must be tackled to complete the process automatically and proposes some possible solutions to achieve this goal.