Department of Computer Science

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Author: search.filters.author.Dr. Atif SaeedSubject: search.filters.subject.SP23

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    Securing Email Communications: Advanced Approaches to Detecting Phishing Through Spam
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Amna Asif; SP23-RCS-006; Dr. Atif Saeed; LHR TP 9484
    The growing complexity of email-based communication has made it difficult to correctly identify the spam emails which provide significant hazards such as phishing, malware distribution and data breaches. Conventional spam detection algorithms that use static rule-based techniques like Keyword-Based Filtering, Blacklist Filtering, or rely on individual machine learning algorithms like Naïve Bayes, Logistic Regression, K-Nearest Neighbors, usually fall behind in increasing false positive and decreasing accuracy. This study used an optimal ensemble-based approach for spam email identification using Gradient Boosting Machine and Extreme Gradient Boosting algorithms and aims to enhance spam email detection accuracy through efficient hyper parameter tuning of these machine learning algorithms. Primarily, baseline models were trained on default parameters and then performance of these models was improved through randomized search cross validation method to examine the tuning space of hyper-parameters for efficient hyper-parameter values. By using the Enron dataset, a publicly available extensive collection of actual email data containing 33639 labeled emails, models were assessed using key metrics such as accuracy, F1-score, recall, precision, and ROC-AUC. The experimental results of this study presented that the performance of both algorithms was enhanced by the hyper-parameter tuning when contrasted with the baseline models. The modified XGBoost model outperformed the baseline version and other competitive models with an accuracy of 98.65%. Furthermore, on tuned parameters GBM performed well, demonstrating the effectiveness of ensemble algorithm approach. The results highlight the superiority of ensemble algorithms for challenging classification problems and validate the significance of hyper parameters modification in improving model performance. This research offers useful insights for enhancing cybersecurity measures in email communication systems by developing strong spam detection frameworks.
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    Intelligent Hotel Recommendation System Using Sentiment Classification and Machine Learning
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Muhammad Ahmad Yousaf; CIIT/SP23-RCS-019/LHR; Dr. Atif Saeed; LHR TP 9704
    Travellers are depending more and more on digital platforms to select appropriate lodgings as their dependence on online evaluations grows. However, current hotel recommendation systems sometimes offer general recommendations without being able to comprehend user-specific requirements or evaluate reviews according to specific service elements like location, cleanliness, food quality, and security. Additionally, these systems frequently ignore neutral thoughts, which lowers the recommendations' accuracy and personalization. This study's goal is to create an intelligent hotel recommendation system that combines cutting- edge machine learning and sentiment analysis methods to provide tailored, aspect-based recommendations. To achieve robust classification, the suggested model combines Random Forest with BERT (Bidirectional Encoder Representations from Transformers) for deep contextual sentiment interpretation. To handle a sizable dataset of hotel reviews gathered from Booking.com, the system uses natural language processing techniques including lemmatization, tokenization, stop-word removal, and feature extraction using TF-IDF. By classifying and analyzing reviews based on different hotel features, consumers may do query-based filtering. For instance, they might request hotels with high ratings for cleanliness or food quality. Common issues with current systems, such as cold- start issues, a lack of aspect-level insights, and inadequate user personalization, are addressed by this hybrid paradigm. The method improves the accuracy and applicability of hotel suggestions by precisely reading user preferences and attitudes. The study shows how deep learning and ensemble techniques may be used to create recommendation systems that are more context-aware and user- centric, which enhances decision- making in actual travel situations