Department of Computer Science

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Author: search.filters.author.Dr. Atif SaeedSubject: search.filters.subject.Computer Science

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    Identification of Citation in Computer Science using Deep Learning and Cyberpsychology During COVID-19
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) Muhammad Sohail Farooq; SP21-RCS-021; LHR TP 8673; Dr. Atif Saeed
    In December 2019, a novel strain of Covid surfaced, unleashing a pervasive and inescapable illness upon the world. This inquiry aims to meticulously explore bibliometric facets, specifically scrutinizing publications in the field of computer science throughout 2020 in the aftermath of the global epidemic outbreak. The data, meticulously sourced from the Google Scholar website, involves the random selection of profiles belonging to computer science scholars, initiating an exhaustive exploration into citation rates. It anticipates unveiling that 2020 witnessed the culmination of a substantial body of scholarly work, surpassing the output of the preceding four years. Furthermore, leveraging this data, the study aspires to prognosticate future citation trends. Deep learning applications have emerged as a key revelation, acknowledged for their capacity to furnish superior data representations, consequently yielding more enlightening outcomes. The subsequent phase embarks on an odyssey to unravel the causative factors behind the surge in citation rates, adopting a psychological vantage point that encompasses elements such as home isolation and dedicated quarantine. The study goes beyond the statistical analyses, delving into the psyche of scholars, thereby presenting a more nuanced understanding of the underlying factors contributing to increased citation rates. This illuminating descriptive-analytical exploration not only establishes a correlation between scholars' isolation experiences and their learning trajectories but also encapsulates this connection in terms of recorded academic citations. The research findings not only offer a comprehensive comprehension of the scholar isolation phenomenon but also serve as a clarion call for heightened awareness among stakeholders. This heightened awareness, rooted in both qualitative and quantitative evidence, not only validates the hypotheses posited but also provides a robust foundation for future research initiatives. In summation, this groundbreaking study unveils, for the first time, the critical interplay between quarantine and addiction factors in the realm of global research, underscoring the paramount significance of these elements in shaping scholarly discourse on a worldwide scale.
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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