M.Phil / MS

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A Deep Learning Based Prediction of Stock Market Trend using Social Medi
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2022) Aroma Javed; FA20-RCS-006/; LHR TP 8339; Dr. Hamid Turab Mirza
Machine learning and deep learning are becoming more and more effective techniques for evaluating financial data, encompassing textual, statistical, and digital information. Future stock prediction is a prominent and challenging deep learning topic in the industry. The difficulty in predicting future stock market stems from too many diverse elements that simultaneously influence the amplitude and frequency of stock market rise and falls. In this research work, the main focus is on the problem of stock market trends predictions using social media as a tool. Digital networks are a fast-growing area of information on the Internet. Perhaps one of the most important features is the instant availability of more knowledge and the users' ability to converse swiftly. Different Deep Learning algorithms (like CNN, RNN, GRU, and Bi Directional RNN) were used to forecast stock market trends based on information from social media, as this data might influence investor behavior. Algorithms were used to investigate the impact of social media accounts on stock market prediction performance. The dataset chosen was an expert and public Twitter post from two prominent technology firms, Alphabet Inc. (Google) and Apple Inc, and news data related to these famous firms. The thesis employed deep learning methods, a pre trained language model for economic sentiment analysis, to extract sentiments from tweets. With the help of this research, it will become easy for an investor to invest his money in companies whose stock market values are high on the basis of sentiment classification and will not lead them to any financial crises. SMP aims to anticipate how the stock value of an economic trade will fluctuate in the foreseeable. If shareholders can precisely estimate stock market progression, investors will indeed be able to turn a profit. Finally, the study predicted the trends by modeling the Data on the proposed GRU model, which outperforms the result of other algorithms. The GRU model has shown significant results with an accuracy of 82.41%.
Predicting Tourist Destinations based on Interests and Travel Backgrounds using Text Analytics
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2022) ANAM KHURSHID; FA20-RCS-001; LHR TP 8050; Dr. Hamid Turab Mirza
Tourist destination prediction has arisen as a new topic within text analytics because of the growing adoption of social media. Tourist data is always beneficial to tourism management since it allows them to give personalized services, products, and destinations to future guests. In predicting tourist destinations, machine learning plays a significant role as it is a way to discover hidden patterns among the dataset; researchers have attempted to get valuable understandings by using data from the real world to train models. Many efforts have been made in this regard; however, most of the models still cannot precisely forecast the tourist’s preferences for destinations. In short, the purpose of this study is to narrow down this gap by introducing a system that uses standard machine learning algorithms to forecast destinations chosen by tourists according to their interests and travel histories. This study extracts a dataset from the CouchSurfing.com website to identify the correlation between tourists' interests and travel backgrounds. The data consists of 9575 records of Pakistan users' profile information. To develop a system with standard accuracy, destinations were divided into 8 regions using Google, and interests were divided into 11 categories using the Yahoo category. Using K-means, clustering has been used several times in this study. This research aims to use classification algorithms to cluster the dataset to identify which interests relate to which countries or regions including KNN, Random Forest, AdaBoost, Gradient Boosting, and LDA. After a comparative results analysis of machine learning algorithms, this research found Gradient Boosting has performed best in classification with the highest accuracy of 99.74% and a kappa Score is 0.99. In the end, the system has successfully predicted tourist destinations related to their interests and past travel experiences. The visitors’ interests according to their destination selections have hardly been studied. This research takes a unique strategy from previous studies in that it focuses on this relationship. For text analysis and future travel destination data, the system has also shown good accuracy and surprising results. As a result, it may help travel companies to build marketing plans for tourists with particular interest categories by offering and advertising places.

M.Phil / MS

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