Department of Computer Science

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    Data Driven Prediction of Student Performance: Integrating Academic, Social, Personal and Behavioral Factors in Pakistan Higher Education
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2024) Frazeen Babar; FA22-RCS-026; LHR TP 9490; Dr. Farooq Ahmad
    Student performance is an important aspect of any educational institution. Several factors affect a student’s performance, e.g., learning assessments, co-curricular activities, previous achievements, personal and social Interactions, etc. Currently, in Pakistan, there is a lack of any such system that helps to analyze and predict student performance. There are two primary reasons for this, 1) lack of research into existing prediction methods to determine the best suitable approach for evaluating student performance in Pakistani institutions, 2) limited research on the factors that influence specifically Pakistani students' performance. The essential goal of this research is to foster predictive models for GPA evaluation using machine learning (ML) and deep learning (DL) techniques. The main research problems explore the efficiency of various features and algorithms in predicting students' cumulative GPAs (CGPA) and pass/fail outcomes. To close this gap, we first gather a sizable dataset of several characteristics that influence a student's academic success in this study project. The COMSATS University, Lahore Campus students studying computer science and software engineering are the source of the data. Additionally, we assessed the suggested dataset using (ML & DL) models to forecast students' performance in terms of regression (CGPA) and classification (pass/fail). The assessment is conducted utilizing f-score, accuracy, precision, and recall. The examination of RMSE, R2 , and MAE is used to assess the performance of regression techniques. The results of this study might be advantageous to academic institutions, teachers, and students. The study concludes by promoting the use of predictive models to backing academic interferences and institutional planning, with the potential for wider approval in several educational situations
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    Performance Analysis of Machine Learning Algorithms for Security and Privacy Vulnerabilities in IoT
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) Muhammad Sarwar; SP21-RCS-018; LHR TP 8469; Dr. Tariq Umar
    The rapid growth of the Internet of Things (IoT) has brought forth numerous security and privacy challenges. In this MS thesis, we propose a comprehensive research study on the performance analysis of machine learning algorithms for addressing these vulnerabilities in the IoT environment. We employ three datasets, namely the NSL-KDD Dataset, CIC_IoT_2022 Dataset, and a SQL injection dataset, to evaluate the effectiveness of various machine learning algorithms. To ensure the reliability of our analysis, the datasets undergo multiple pre-processing operations, including data cleaning, normalization, and feature engineering. Dominant features are then extracted to develop benchmark datasets that capture the essential characteristics of IoT security and privacy vulnerabilities. Multiple classification algorithms, including Decision Tree, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Logistic Regression, are applied to the benchmark datasets. The performance of these algorithms is evaluated using standard evaluation metrics such as accuracy, precision, recall, F1-score, and area under the curve (AUC). In addition, we explore NLP-based algorithms specifically designed for the SQL injection dataset to detect and mitigate SQL injection attacks. The dataset was extensively analyzed using the python Plotly library where histograms,Using feature extraction, the major vulnerabilities were extracted as specific attributes later used to train the machine learning model separately. In conclusion, the proposed methodology demonstrates enormous potential for practical applications in improving the security and privacy of IoT systems. By leveraging machine learning algorithms and conducting a comprehensive analysis, we can effectively detect and mitigate security vulnerabilities, safeguarding IoT devices and networks. The insights gained from this research contribute to the development of robust security mechanisms and guide the implementation of proactive measures to protect IoT systems from potential attacks. Future work includes exploring advanced machine learning techniques such as deep learning and ensemble methods to further enhance the performance of algorithms in addressing IoT vulnerabilities. Additionally, investigating the impact of different feature engineering techniques and exploring real-time detection and response mechanisms can lead to more effective security measures. Overall, this research paves the way for future studies on securing IoT systems and promotes the adoption of machine learning in IoT security and privacy. The applied algorithms perform well for the datasets, where Decision Tree algorithm surpassed the other algorithms for both NSL-KDD and CIC-IoT2022. For NSL-KDD dataset Decision Tree produced a training and validation accuracy of 99.00% and 98.54% respectively. For CIC-IoT2022 dataset Decision Tree produced a training and validation accuracy of 98.92% and 98.55% respectively. For the SQL Injection Attack dataset Naïve Bayes produced the best accuracy of 99.36% for training process and 98.13% for validation process, respectively.
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    Cross-Lingual Abstractive Text Summarization for English-Urdu Language Pair
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Duaa-e-Nadeem; SP20-RCS-001; LHR TP 7600; Dr. Rao Muhammad Adeel Nawab
    Cross-Lingual Abstractive Text Summarization (CLATS) is a process to generate an abstractive summary in Language B (target language) of the given document in Language A (source language). CLATS has the potential to allow cross-lingual information access for millions of people around the world who do not speak or understand languages with a broad online presence and it makes documents originally written in native languages available to a wide audience. The popularity of digital documents has resulted in a rapid increase in the amount of information available online. This massive volume of data, however, cannot be manually analyzed. A summary of a big written document allows individuals to quickly grasp the topic's material without reading the entire document. CLATS plays a vital role in news summarization, literature summarization, timeline/event summarization and summarization of Wikipedia or any information source articles for better understanding. This research aims at exploring the problem of CLATS. Previously, CLATS systems have been developed for different language pair like Chinese-English, German-English, Arabic-English and vice versa but this problem is not explored for English-Urdu language pair, even though Urdu is a widely spoken South Asian language. The twofold aim of this research is to fill the gap: (1) develop a large benchmark corpus CLATS for English-Urdu language pair and (2) develop, apply, evaluate and compare state-of the-art deep learning techniques on proposed corpus for CLATS for English-Urdu language pair. For developing CLATS-EU corpus, News Summary and All the News are used as base corpus. The following steps were taken to create the proposed corpus: (1) automatic translation of base corpus (2) manual inspection and correction of testing data of base corpus (3) corpus standardization After development of CLATS-EU corpus, Deep learning sequence to sequence techniques (RNN, LSTM, GRU, BRNN, BLSTM and BGRU) are developed by using CLATS-EU corpus. Then RNN, LSTM and GRU models are implemented by applying attention mechanism. ROUGE1, ROUGE2 and ROUGEL are used as evaluation x measures to evaluate the performance of the techniques that we have developed. For CLATS, GRU gave the best results on our proposed CLATS-EU corpus
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    Periodic Mining of Users Routine Desktop Behavior for Task Assistance
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2019) SEYYAD ZISHAN ALI; FA16-RCS-028; LHR TP 5622
    Now-a-days the use of technology is rapidly growing in the workplaces and a large number of people are using desktop computers for their personal and professional tasks. As a result of this growth, the large amount of data is been gathered and it’s become difficult to retrieve and manage this huge amount of data in a beneficial way. People usually face difficulty in managing documents and finding relevant files when they want to do specific task in hand. We believe that pervasive monitoring of user’s desktop activities and then mining of user’s periodic behavior can be useful in assisting the future tasks of user. A number of studies have been carried out on monitoring user desktop routine activity. However, these studies have not utilized user’s temporal routine activity patterns for assistance. It was a need to develop a desktop assistant by mining user routine behavior in accordance with the time and date more specifically weekdays and weekends. This work detects and classifies meaningful activities from large data and then models these activities into user’s one day and routine behaviors. This system reads log file and detect some meaningful activities from the large data, after identifying activities the system model these activities into user’s one day activity, then system model routine activities based on one day activity models and evaluates probability measures of user’s routine activities. After getting best results the system finds the best models of routine behavior which may use for the prediction of next possible routine activity of the user based on logged data set. It mines the best models of routine activities which may be useful for the prediction of next possible activity of the user. After a comparative results analysis of standard machine learning and data mining algorithms this research found Decision Tree best for classification of user’s activities with highest mean score of 0.92. Moreover this research has found Decision Tree as best algorithm for prediction of user’s future possible routine activities with 0.84 as highest score. The system has predicted future routine activities of a single user on Tuesday with an accuracy of 92%. This research is basically the implementation of a novel approach for mining user’s periodic desktop routine activity in accordance with the time, date and day.
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    Colored Petri Net Based Modeling And Analysis
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2018) Usman Younas; FA16-RCS-027; Dr. Farooq Ahmad; LHR TP 5621
    Efficient and safe transportation systems are essential part of modern society and developed cities. Logistics and communications play a vital role in the economy of any country. Rail transport systems are considered as backbone for freight and passenger movement across long distances. Developing a secure rail transportation system for a developing country like Pakistan is a challenging task due to weak infrastructure and less public awareness. A secure transport system can reduce chances of collision and result in saving human lives and economy. Formal modeling of a framework or a system can assist us with checking unwavering quality, precision and consistency of the framework or system. It helps us to check the validity and authenticity of the system before the actual product comes in hand. System can be viewed from different aspects through formal specification for better understanding. Modifications in the model at design level is also possible if there are any flaws in our formal model. Railway transportation systems are safety, monetary and environmentally critical systems since its failure may cause serious outcomes, for example, loss of human life, extreme wounds, and huge size of natural harms or extensive financial disasters. Rail transport is a distributed system consist of many concurrent process occurring in parallel fashion. So the safety and heterogeneous nature of this framework requires formal modeling for its development and advancement. The fundamental focus of this study is to build up a moving block railway operation system with dispatching segments to keep away trains from crashes and wrecking. Formal modeling and analysis of the operation system will also be performed to check the consistency and reliability. A core technique for the proposed research work would be the colored Petri nets (CPNs) based on discrete and concurrent system modeling, which will be used for simulation and verification of the operation model of system. CPN is the third class of Petri nets and it is a combination of Petri Nets and Standard Meta Language (SML). After model simulation, a series of analysis checks will make sure the authenticity and consistency of the model.
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    Toxic Comment Classification of Roman Urdu Text
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2019) Waheed Abbas; FA17-RCS-013; LHR TP 5624; Dr. Rao Muhammad Adeel Nawab
    We are living in an era of technology where most of us have easy access to the inter net. Due to internet bloom, the use of social media and discussion forum, in particular for communication, increase tremendously in recent years. But this advancement also opens doors for trolls who poison these social media and forums by their abusive behav ior toward others. Detection of toxic language online is becoming an important issue in recent years. A toxic comment is defined as rude, unreasonable, or disrespectful com ments which likely to make you leave a conversation. Current methods for handling toxicity online are often heavily dependent on manual moderation, and not scalable enough to handle growing users day by day. In this thesis, we investigated the problem of toxic language detection for Roman Urdu (transliteration of Urdu in English) text because Roman Urdu is widely used to communicate online in Pakistani and Indian community. Various work has been done to detect toxicity in the English language, but due to unavailability of large Roman Urdu corpora, there is no work done so far in Roman Urdu. The aim of this thesis work is two folds: first is to develop large scale corpus for detection of toxic language in Roman Urdu, second is to apply different proven classical machine learning and state-of-the-art deep learning methods on the developed corpus. In the first step, we collected data from the YouTube comment section to develop Roman Urdu corpus. The final corpus consists of 10,863 unique comments where we have 5,887 toxic and 4,976 non-toxic comments. We performed manual annotation of corpus and got it reviewed from another annotator. We applied proven classical ma chine learning algorithms like Logistic Regression, K-Nearest Neighbors, Naive Bayes, Random Forest, Support Vector Machine and Multi-layer Perceptron along with state of-the-art deep learning methods like Long Short-Term Memory, Bidirectional Long Short-Term Memory, and Convolutional Neural Networks. Best results obtained from Random Forest with character 4-gram (Accuracy = 0.964, Precision = 0.980, Recall = 0.953, F1 = 0.966).
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    Software Measurement Programs: Evaluations and Solutions
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2019) Touseef Tahir; SP13-PCS-002; LHR TP 5943; Dr. Ghulam Rasool
    Every software development process either generates or uses measurement data that is essential to understand, evaluate, improve and predict the software processes, products, and resources. Software development organizations implement measurement programs (MPs) as part of their measurement processes. However, implementing successful MPs still remains a challenge. The aim of this thesis is to comprehensively evaluate and then contribute towards planning and implementation issues of MPs. This research is conducted in three phases: 1) problem identification 2) solution design and 3) industrial evaluation of solutions. A mix of qualitative and quantitative research methods is used appropriately for each phase. In the first phase, comprehensive literature reviews of the studies on MPs are conducted to enable a better understanding of the existing measurement planning models, tools, challenges, success factors, and metrics selection methods of implementing MPs in general and specifically for small and medium enterprises (SMEs). In addition, challenges of implementing MPs in large organizations and SMEs are analyzed. In the second phase, multiple solutions are proposed to improve the planning of MPs. The solutions are based on the analysis of research gaps involving the challenges and success factors of implementing MPs. First, a set of key components of MPs is identified for planning a MP. Second, a model of success factors for planning MPs in general is proposed. Third, a model of success factors for planning MPs specifically in SMEs is proposed. Fourth, a model called data-driven measurement for software prediction (D2MSP) is proposed to overcome the challenge of using already collected measurement data for prediction of software attributes e.g., defects. In the third phase, the solutions are evaluated in the software development industry with the help of surveys and empirical data of software projects. A set of key components and a model of success factors are evaluated based on a survey among 200 software professionals working in the Pakistani software development industry. A model of success for planning MPs specifically in SMEs is evaluated based on a survey among 217 software professionals working in SMEs of Pakistan. The D2MSP xi model is evaluated using a dataset of 760 projects and later applied in a software development organization as a case study. Overall, this research contributes to the theory and practice of software measurement by providing an improved understanding of planning software MPs and proposing and validating models of success factors and the D 2MSP model for planning MPs.