http://hdl.handle.net/123456789/13169 2026-07-13T09:04:39Z http://hdl.handle.net/123456789/19845 Recommendation System Using Personalized Chat-Bot Muddasir Aziz Khattak, 01-249231-007 Predicting a person’s personality can significantly enhance personalized recommendations across various domains. This study introduces PersonalityBERT, a deep learning model designed to determine personality traits from user-generated text. We utilized the MBTI (Myers-Briggs Type Indicator) dataset, which categorizes personalities into 16 distinct types, and fine-tuned BERT (Bidirectional Encoder Representations from Transformers) to analyze text input effectively. BERT’s contextaware and transformer-based multi-head self-attention mechanisms enable a deeper understanding of semantic meaning, improving personality classification accuracy.To extract meaningful personality insights, preprocessing techniques such as stopword removal, lemmatization, and text vectorization were applied. The fine-tuned BERT model was trained on the MBTI dataset and optimized using hyperparameter tuning, including learning rate adjustments, gradient accumulation, and weight decay regularization. Our PersonalityBERT model achieved an accuracy of 88%, surpassing baseline models, including traditional NLP classifiers and LSTMs. Additionally, the model produced a precision of 0.86, recall of 0.90, F1-score of 0.88, and an AUC-ROC score of 0.94, demonstrating high reliability in personality classification. Beyond classification, this model was integrated into a personality-based recommendation system. Using Sentence Transformers and Cosine Similarity, PersonalityBERT provides personalized book recommendations based on a user’s personality type and textual input. The chatbot framework was developed using Streamlit, with backend deployment. The model successfully matches users to book reviews with high semantic relevance, ensuring enhanced recommendation accuracy. Currently, PersonalityBERT is optimized for book recommendations, but its applications can be extended to various domains, such as movie recommendations on Netflix, career guidance platforms, and AI-driven personalized learning systems. This research demonstrates that deep learning-based personality prediction models using BERT provide accurate and meaningful recommendations. Future work involves further semantic integration, cross-domain applications, and expanding the model to multilingual personality analysis to improve recommendation diversity and accuracy. Supervised by Dr. Asfand-e-Yar 2025-01-01T00:00:00Z http://hdl.handle.net/123456789/19846 A Multi-Modal Video Recommendation Approach for Cold-Start Problem Fawad Ur Rehman, 01-249231-005 Rapid growth of short-video content on platforms such as TikTok, YouTube, Facebook, and Instagram has intensified the demand for robust recommendation systems (RS) that can effectively handle the cold-start problem. Traditional methods, such as collaborative filtering (CF) and content-based (CB) approaches, struggle in these scenarios due to their reliance on historical interaction data, which are often unavailable or sparse for new users and items. To address these challenges, this thesis proposes a novel multimodal recommendation framework that integrates textual, visual, aural, and metadata features using advanced deep learning models to enhance recommendation accuracy and mitigate cold-start limitations. The proposed framework employs a hybrid architecture featuring modalityspecific encoders that extract rich representations from different content types. A Graph Neural Network (GNN)-based fusion encoder models relationships between items by aggregating features across modalities, while mutual information maximization ensures alignment between latent representations and raw inputs, improving cross-modal consistency. Additionally, a generative decoder reconstructs the original features to preserve semantic fidelity, enabling robust latent space learning even in sparse interaction scenarios. Empirical evaluation of the MicroLens dataset, a large-scale benchmark for short-video recommendations, demonstrates that the proposed framework outperforms state-of-the-art baselines in both standard and cold-start conditions. These findings highlight the potential of multimodal learning to bridge the gap between sparse interaction data and rich content representations, offering practical insights for social media platforms and streaming services aiming to improve user engagement and retention. Supervised by Dr. Fatima Khallique 2025-01-01T00:00:00Z http://hdl.handle.net/123456789/19844 Predicting Efficient Outcomes in Trademark and Copyright Cases using NLP Methods Roheen, 01-249231-017 Natural Language Processing (NLP) in the legal domain has been a vibrant area of research for many years, while the ability to process text has effectively increased with the development of AI and NLP techniques. Due to the increasing number of court proceedings, particularly those related to Intellectual Property Rights (IPR) in Pakistani judiciary, it is difficult and time-consuming for lawyers to navigate and extract valuable insights from legal data. Thus, there is a growing need for an efficient legal assistance system that can provide major improvements in the efficiency of court procedures. A novel semantic search engine is designed in this research to assist lawyers in managing and drafting IPR cases and extract relevant legal data. This search assistance system can predict court judgments and extract relevant data from Trademark and copyright cases as well as Ordinance Data based on the user’s input query. For judgment forecasting of legal scenarios, XGBoost, SVM, Random Forest (RF) were used, with the mean cross-validation score as 75%, 88%, and 91%. The use of pre-trained BERT model in the designed system further enhances the efficiency of data retrieval. In terms of cases and ordinance data extraction, the Mean Average Precisions (MAP) of PAK-LEGAL-BERT and legal-bert-base ranges between 67% to 71%. The models are then fine-tuned on domain-specific data and then used to extract relevant data, thus MAP values increase from 85% to 95% effectively. Supervised by Dr. Asfand-e-Yar 2025-01-01T00:00:00Z http://hdl.handle.net/123456789/20726 Explainable AI for Stock Market Price Prediction: A Hybrid Model for Improved Transparency and Decision-Making Arifa Amir, 01-249232-004 Stock markets are dynamic, stochastic, and non-linear in nature, and the price prediction of stock markets has long remained a complex problem. In recent years, deep learning has gained a lot of hype and depth, with models like Long Short-Term Memory (LSTM) modules providing tremendous results when applied to understanding temporal relations, and Graph Neural Networks (GNNs) have also proved effective in describing relational structures. Although these advances have made it possible to deal with both temporal dynamics and structural dependencies, there is a tendency for the models to commonly fail to address them simultaneously, and, in most cases, they are black boxes with limited interpretability. This gap contributes towards their weaknesses and impairs their application in high stakes even in areas like in financial applications where the process of explaining and understanding is vital. To address this issue, this research paper proposes a hybrid LSTM-GNN model that uses Explainable AI (XAI). The LSTM unit represents the long-term temporal dynamics, and GNN model represents the patterns of relations between the stock characteristics. The LSTM with GNN combination offers a combined structure of learning that deploys both sequential and structural learning. Moreover, the Local Interpretable Model-Agnostic Explanations (LIME) is incorporated to increase transparency by identifying important features in making model predictions therefore providing enhanced trust and explainability. The empirical experiments involving the stock market data of Tata Motors (2000-2021) revealed that the proposed hybrid model of LSTM-GNN was stable and has performed better as compared to those of independent LSTM, GNN, and the traditional statistical models. The hybrid architecture was found to be a superior reliable architecture with an RMSE of 4.12, MAE of 2.90, and an R 2 of 0.89 that translates to an accuracy of almost 80-90 percent in terms of forecasting the market trends. In addition, the model was found to be resistant to sharp market volatility, where standalone models performed poorly. The integration of LIME also showed that the closing price and trading volume were the most significant features, confirming the feasibility of interpretation and logic on the predictions. Collectively, this study presents an episodic Interpretable deep learning model of financial forecasting, delivering superior decision assistance to investors and clearing a path to the present-time usage of transparent AI in the financial ministries. Supervised by Dr. Hafiz Ishfaq Ahmad 2025-01-01T00:00:00Z