Department of 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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    Automatic Transformation of BPMN Models Onto Petri Nets
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) Samia Nazeer; SP21-RCS-003; LHR TP 8672; Dr. Abid Sohail Bhutta
    Software success depends on the accuracy of the business process modelling notation (BPMN), and the correctness verification procedure is built upon the formalization of BPMN. The primary drawbacks of the BPMN formalization under shifting business needs are, however, the inability to dynamically adapt the formalized BPMN model to modifications in the BPMN model. For the purpose of modelling business processes, the Business Process Model and Notation standard is frequently utilized. Its primary flaw, though, is that it lacks formal semantics, which results in several unwanted qualities like deadlocks and livelocks which produces models with semantic flaws. Petri nets are a formal tool based on sound mathematical ideas and can handle analysis. Additionally, it provides several analytical methods. To officially validate them, we must convert it into a formal language, like Petri nets. The majority of earlier studies focused on manual transformations by mapping rules which creates static petri net model. In order to perform conformity verification, the BPMN model must be transformed into a formal process modelling representation, such as Petri nets. In order to get over these restrictions, this study suggests a method for developing a Petri net model automatically in accordance with BPMN modification rules. Firstly, this study automatically converts the Business Process Model into Petri net Model and produces dynamic output. Secondly, the converted model is compared with the manually transformed model created by expert users according to verified mapping figure’s rules and then analyze the result. Thus, the proposed approach obtains a reliable and well organized Petri net model that works well with other Petri net analysis methods as well as with human analysts
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    Design and Development of a Secure and Light Weight User Authentication Scheme for Smart Home
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2022) Shiza Gul; SP21-RCS-011; LHR TP 8060; Dr. Tariq Umer
    The Internet of Things (IoT) is becoming more advanced day by day, in which smart homes provide different services of home to people. Users like the comfort of the use of home service that smart devices provide. But here is privacy and security in smart homes because smart devices and users perform communication across insecure channels. As smart devices with limited resources do not have the computing and storage capacity to create and execute existing methods that often require complicated computations, Smart Home Area Network (HAN) system experiences major problems in establishing strong authentication methods. Furthermore, the diverse nature of IoT nodes makes it difficult to create end-to-end device authentication in IoT systems. IoT devices commonly use poor authentication protocols. The primary barrier against numerous security attacks on IoT networks is authentication. To strengthen IoT privacy and security against frequent attacks like impersonation and replay attacks, this thesis analyses the research gaps and provides a new security mechanism and authentication technique. By providing a lightweight mutual-authentication and key exchange protocol for IoT based identity and continuously chained hash in HAN, this research improves IoT privacy and security. Using temporary symmetric keys and identities, devices may mutually authenticate and create a connection with the gateway node in an untraceable method. To verify the sender's identity, the Continuous chained-hash approach is presented (through a response challenge). The session keys are often updated after each data transmission to maintain forward secrecy. The proposed method provides authentication while ensuring that IoT device transactions cannot be tracked. Three different techniques are used to assess and simulate the security of the protocols; formal analysis that used the Burrows-Abadi-Needham logic (BAN), informal analysis, and model-checking that used the automated validation of Internet security protocols and applications (AVISPA) tool. The cost and effectiveness of the proposed method are evaluated and compared with other relevant protocols. The outcomes demonstrated that our techniques are more effective.
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    Classification of Non-Functional Requirements by using Machine Learning and Natural Language Processing Techniques
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2022) Nimra Zia; SP21-RCS-008; LHR TP 8059; Dr.Touseef Tahir
    The software development process consists of a series of phases e.g., requirement engineering, design, coding, and testing, and each phase is critical for fulfilling the needs of a software user. The behavior and development of a software system are restricted by NFRs, which define the general characteristics or attributes the final system must possess. An automated technique for identifying NFRs is necessary to find NFRs for the software development lifecycle. The formatting and classification of NFRs subclasses such as performance efficiency, usability, maintainability, portability, security, and reliability are less discussed and there is a lack of a larger publicly available dataset. In addition, the datasets are not semi-formated to overcome inherent ambiguities and incompleteness in NFRs specified in natural language. NFRs are specified in natural language which faces the challenges of overcoming confusion, redundancy, and ambiguity due to a lack of structure in the requirements specification. The NFRs such as performance efficiency, usability, maintainability, portability, security, reliability, etc., will be the focus of our project. This research will use a larger dataset with over more than 2000 NFRs which were created through processing 315 software requirement specifications (SRS) documents of BS (CS) and BS (SE) final year projects (FYP) of CUI, Lahore, and requirements will be semi-formally specified using Rupp's boilerplate to avoid natural language specification problems. The software requirements were passed through several steps including normalization and feature extraction methods like TF-IDF. Several Machine Learning (ML) and Deep Learning (DL) experiments were carried out, including Logistic Regression (LR), Bernoulli Naive Bayes (BNB), Decision Tree (DT), Support Vector Machine (SVM), Multinomial Nave Bayes (MNB), Random Forest (RF), CNN, and Long Short-Term Memory (LSTM), and Bi-LSTM algorithms for the classification of NFRs. SVM performed well in the classification of NFRs with F1-score of 0.82.
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    Urdu Text Reuse Detection (UTRD) at Phrasal Level using Transformer-Based Approached
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2022) Gull Mehak; SP21-RCS-006; LHR TP 8058; Dr. Rao Muhammad Adeel Nawab
    Text reuse is a process of creating new text(s) from pre-existing text(s). In recent years, Urdu Text Reuse Detection (UTRD) has gained the attention of researchers because the text is readily available in digital format all over the internet and can be copied or paraphrased from another source without proper attribution, which makes it easier to reuse but hard to detect. In previous studies, the problem of UTRD has been explored at the sentence level [1], sentence/passage level [2], and document level [3], along with benchmark corpora and methods. However, the problem of UTRD has not been explored at the Phrasal level with respect to corpora and methods. To fulfill this research gap, this research study has developed a large benchmark manually annotated corpus of 25,001 text pairs at two levels of rewrite: 1) Derived = 15,105 and 2) Non Derived = 9,896. Secondly, this research study has developed, applied, evaluated, and compared baseline approaches (N-gram Overlap and Word Embedding-based approaches) with proposed transformer based approaches on the proposed UTRD-Phr-22 corpus. The proposed transformers-based approach (using a combination of 8 different Sentence Transformers including: paraphrase- multilingual-mpnet-base-v2, distiluse-base-multilingual-cased-v, paraphrase multilingual-MiniLM-L12-v2, LaBSE, xlm-r-distilroberta-base-paraphrase-v1, xlm-r 100langs-bert-base-nli-mean-tokens, xlm-r-bert- base-nli-stsb-mean-tokens, and xlm-r 100langs-bert-base-nli-stsb-mean-tokens) outperforms with an F1 score of 0.63 compared to the best results obtained uisng N-gram Overlap approach (F1 = 0.53)
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    Exploring Property Price Prediction via Machine Learning and Deep Learning Approaches: The case of Pakistan
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2022) Ghulam Fatim; SP21-RCS-014; LHR TP 8062; Dr. Touseef Tahir
    In many real-world applications, predicting a property price is more realistic and appealing. Property price prediction helps individuals make better decisions when buying or selling a property. However, a large number of realistic tests must be conducted to determine the best methodologies and algorithms to find the optimal combination of these strategies for a reliable property price prediction model. In this research, we created a dataset of around 50k properties of Pakistan. This dataset has been used to train machine learning and deep learning models. In addition, we have applied feature selection methods to identify which attributes of a property can help to predict the best results in terms of evaluation metrics e.g., MAPE, RMSE, MAE, R2 , and MSE. Furthermore, after applying feature selection approaches such as the Pearson correlation coefficient, Mutual Information, Chi-Square, and the VIF on our dataset, we selected those characteristics that improved the reliability of our model. Then we applied ML and DL algorithms and evaluated them using various performance metrics. Afterward, the results revealed that the Extra Trees Reg. performed well as compared to other models of ML and DL. Moreover, after observing the outperforming results of ML & DL models, this research work finds that the data from Pakistan's real estate e-markets can be used for predicting property rates
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    Data Driven Vehicular Heterogeneity based Intelligent Collision Avoidance System for IoV
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2022) Iqra Adnan; SP21-RCS-004; LHR TP 8056; Dr. Tariq Umer
    The Internet of Vehicles (IoV) is becoming a trending field of research for researchers. Its implementation in the real world is increasing day by day to facilitate drivers in driving and provide safe road conditions to travelers to be safe from accidents. Many research studies on IoV focused on the issue of collision avoidance between vehicles on the road. Companies like the world’s biggest car company TESLA use IoV and certain machine learning algorithms to make their cars autonomous. Keeping in view the importance of collision avoidance issues between vehicles on the road. Our proposed work is to perform an analysis of real-life vehicular data based on the characteristics of different types of vehicles moving on the roads. Due to the heterogeneousness of the types of vehicles (cars, buses), their parameters are also different. From the vehicular data based on this heterogeneousness, we intended to find out the different parameters’ roles for the collision avoidance system. Using the capabilities of machine learning techniques on the gathered data, a framework for the collision avoidance system is suggested for vehicles on the roads. Performance analysis of the framework for predicting the accident well before the time for the drivers is presented for better road conditions.