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Item A framework for the prediction of earthquakes in western himalayas(UMT, Lahore, 2021) RABIA TEHSEENEarthquake is a natural phenomenon which may cause significant loss of life and infrastructure. Researchers have applied multiple artificial intelligence-based techniques to predict earthquakes, but they have not been able to achieve high accuracies due to the huge size of multidimensional data, communication delays, transmission latency, processing capacity limitations and data privacy issues. In this research, a novel earthquake prediction framework has been proposed that is capable enough to process data using different Artificial Intelligence (AI) approaches. The proposed framework has been equally effective in both centralized and distributed settings. We have initially implemented the proposed framework using Fuzzy Expert System (FES). FES is a traditional benchmark system used in the literature for earthquake prediction. We have achieved 47% earthquake prediction accuracy by using FES. To improve the accuracy in earthquake prediction, the proposed framework has been implemented using different Machine learning (ML) methods and state-of-the-art Federated learning (FL) mechanism. FL has given better performance over already developed ML methods applied to earthquake prediction in terms of efficiency, reliability, and precision. We have tested the proposed framework by analyzing three-dimensional data within 100 km radial area from 34.708o N, 72.5478o E in Western Himalayas. The results of the proposed framework have been validated against instrumentally recorded regional seismic data of last thirty six years, and 88.87% prediction accuracy has been recorded while using FL technique. Implementation of proposed framework can serve as a significant component for developing early warning earthquakes systems.Item A COGNITIVE FRAMEWORK FOR THE IMPROVEMENT OF LEARNING ANALYTICS EFFICIENCY IN INTRODUCTORY PROGRAMMING COURSES(UMT, Lahore, 2021) Uzma OmerLearning analytics (LA) has become a popular discipline among educationists and researchers as it has a potential to reveal new facets of teaching and learning that could be utilized to improve the efficiencies of related learning environments. Introductory programming courses (IPCs) hold special significance as these courses lay down the foundation for subsequent higher level courses in computer science and associated disciplines. The LA studies in IPCs are mostly anecdotal as less or no attention is given to examine learning at various cognitive levels. This research is designed to find improvements in learning analytics in IPCs by evaluating the cognitive aspects of students’ learning. It aims to explore more granular technique of LA that could lead to enhance the efficiency of LA in IPCs. The objectives of this work are addressed by proposing a framework for cognitive learning analytics in IPCs which serves as a platform that provides structure to the concept data using the technique of concept mapping and examines proliferation of cognitive learning on related concepts using assessment data. The framework is evaluated by predicting performance of learners on a number of IPC concepts through the metrics established from cognitive maps of learners, acquired by deploying the related layers of framework. It was identified that performance predictions through proposed metrics helped in improving efficiency of learning analytics performed in existing work. The research is concluded by presenting prediction accuracies acquired while evaluating the framework which are comparable to the related studies where the proposed technique showed better accuracies as compared to most of the related work. This work contributes by proposing a framework of cognitive learning analytics in introductory programming courses and presenting metrics to measure the cognitive performances which predict the learning performances with improved accuracies.