Browsing by Author "Dr. Muhammad Sharjeel"

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Analysis and Evaluation of Risk Prediction using Software Requirements
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2022) Shanzeh Shaukat; SP20-RCS-011; LHR TP 8335; Dr. Muhammad Sharjeel
Software requirements play a pivotal role in the planning of a software project and they are a key input to project size and effort estimation processes. The increasing dependence of all aspects of life on software products has made the success of software projects more critical. It has become a challenge to predict software project risks as early as possible and create contingency plans to overcome or mitigate future risks. Furthermore, the risk is different for different projects. Currently, there is no consensus within the software development community on requirement properties to determine the cause of risk in software projects. There is a need for a systematic analysis that combines risk prediction factors, risk prediction methods, risk datasets, and their analysis in a single framework. This research aims to identify requirement risk attributes and project success factors that are later on validated using SEM (Structural Equation Modeling) to analyze that requirement risk properties can be linked to software risks. Mainly, the purpose of conducting a systematic mapping study (SMS) is to find out the properties of requirements that are the major cause of risk occurring in software projects. Later, we designed a survey based on the requirement properties extracted from SMS to find out risk prediction practices used by software professionals and their views related to these factors in the Pakistani software industry. The proposed SMS and survey in the Pakistani industry based on the risk prediction process using SEM is missing in the published research. That’s why, this research will help project managers to understand, initiate and evaluate the risk prediction process in their software development organizations. Additionally, it will help us to identify the gap between research on risk prediction and actual practices in the software industry
Automic Paraphras Generation for Urdu Language
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Ali Awais; CIIT/CIIT/SP23-RCS-004/LHR; Dr. Muhammad Sharjeel; LHR TP 9994
Automatic paraphrase generation is an important task in Natural Language Processing (NLP) that focuses on producing alternative sentences with the same meaning as the original text. This study aims to develop an automatic paraphrase generation system specifically for the Urdu language. Due to the limited availability of linguistic resources and computational tools for Urdu, generating accurate paraphrases remains a challenging problem. The research explores different NLP and machine learning techniques to generate meaningful paraphrases while preserving the original context and semantics. A dataset of Urdu sentences is used to train and evaluate the proposed model. Various approaches such as rule-based methods, statistical techniques, and deep learning models are analyzed to improve paraphrasing quality. The results demonstrate that the proposed system can effectively generate alternative Urdu sentences while maintaining the original meaning. This study contributes to the advancement of Urdu language processing and can support applications such as text summarization, question answering, machine translation, and plagiarism detection.
Celebrity Profiling on Short Urdu Text using Twitter Follower’s Feed
(Library Information Services, CUI Lahore, 2024) Muhammad Hamza; SP21-RCS-022/; Dr. Muhammad Sharjeel
It is rightly said that current age is a digital age and social media shares a crucial chunk of it. People used to communicate, interact, and build relationships through social media. Celebrities are prolific authors and most of their personal information is public knowledge. There are some digital celebrities who exist only on social media, e.g., Twitter. Twitter is a social networking service which provides general populace as well as celebrities to interact with their fans. The demographics of celebrities could be predicted by the text of their followers as both shares same interest. However, most of the work on celebrity profiling has been performed on English and other similar languages except Urdu. On the contrary, majority of the sub-continent celebrities and their fans tweets in Urdu. To fulfill this gap, in this research work we used Urdu tweets (short text) of 10 followers of a celebrity to build the first celebrity profiling based on followers’ tweets corpus. Furthermore, the corpus was preprocessed, and Machine Learning (Logistic Regression, Support Vector Machines etc.) and Deep Learning (CNN, LSTM etc.) algorithms were used to train models for the prediction task. The trained model will be evaluated using state-of-the-art evaluation measures, i.e., precision, recall, and F1. The accuracy of the demographics of the celebrities are as follow; for the age the cumulative cRank is 0.45 , profession has the accuracy of 0.4, while the gender has cRank 0.65 and finally the cRank of fame is 0.45.
Investigating Cyclic Translation for Urdu Text Reuse Detection using Deep and Transfer Learning Methods
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) Muhammad Arslan; SP20-RCS-025; LHR TP 8466; Dr. Muhammad Sharjeel
This research study addresses the complex problem of generating text reuse corpora using Machine Translation (MT), effectively detecting text reuse in paraphrased text through a cyclic translation approach and investigating the behavior of newly proposed deep learning and transfer learning methods on various Urdu text reuse corpora. Prior research endeavors addressing the challenge of text reuse paraphrase detection through the application of deep learning methodologies have predominantly focused on the English language. However, a comprehensive investigation into the problem of text reuse detection in the Urdu language, specifically utilizing a cyclic translation approach in the processing of data for training and testing purposes, alongside the utilization of deep learning methods, remains relatively limited. To tackle these challenges, a translated dataset is constructed by employing the Python Google Translate API on the Counter Dataset. Two variations of deep neural networks, namely Siamese Bidirectional LSTM (BiLSTM) and Manhattan LSTM (MaLSTM), are employed to achieve optimal accuracy in the multi-classification task. The training process incorporates two well-known optimizers, RMSProp (Root Mean Square Propagation) and Adam (Adaptive Moment Estimation). The experimental results substantiate the effectiveness of the Bidirectional LSTM, which achieves an accuracy of 77.22%, and the MALSTM, which attains an accuracy of 76.20%. These findings provide valuable insights for researchers and practitioners, paving the way for further advancements in the study of text reuse and its implications in diverse linguistic contexts
Urdu Cyclic Text Reuse Detection using Sentence Transformers
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2024) Areej Waseem; FA22-RCS-002; Dr. Muhammad Sharjeel; LHR TP 9334
Detection of cyclic text reuse is an increasingly growing need in the Urdu language with the greater use of digital technologies. The growth of text reuse, which is the process in which the passage from an original document is copied and then reused with slight changes in wording or rewording without proper credit, has become very easy due to the vast availability of digital text and free online machine translation tools. The latest AI-powered machine translation systems make the process of iterative translation of text across multiple languages and back to the origin very easy, thus generating paraphrases which can be reused without due credit. Detection of such text reuse is important for the whole gamut of applications in the journalism domain, near-duplicate document detection, content reuse in blogs, and plagiarism detection. While lots of research has been done on the detection of text reuses in English, there exist only a few manually created corpora in the case of Urdu text reuse detection, which are real and simulated instances of text reuse. And up to now, no huge corpus or research targets the detection of cyclic text reuse in Urdu. This study makes an effort to fill this gap by developing a huge benchmark corpus through an automated approach based on real cases from journalism. The proposed corpus contains 6,000 manually annotated documents as either partially derived, wholly derived, or non-derived. In the paper, more research is conducted on the effectiveness of different sentence-transformer methods such LaBSE,1in detecting cyclic text reuse. Besides, we have fine-tuned the LaBSE, paraphrase-multilingual-mpnet-base-v2, distiluse-base-multilingual-cased-v2, xlm-r-100langs-bert-base-nli-stsb-mean-tokens, xlm-r-distilroberta-base-paraphrase-v1 model to increase its accuracy with the comparison to the baseline of the N-gram approach. The results demonstrate that when the implemented change is applied to Urdu, the cyclic text reuse detection of the given document using the proposed transformer-based approach with the LaBSE yields favorable results with the best F1 score being 0. 61. The results revived the capacity of the model in dealing with other intricate translation tasks as well as increasing its accuracy in expounding the cyclic text reuse. The contributions from this research are two-fold: first, the construction of suitable and large scale corpus corpora for Urdu cyclic text reuse detection, and, second, the analysis of performance of existing and specifically state-of-art sentence transformer techniques such as LaBSE for improving the performance of the methods for this specific task. The techniques and collection employed through this study will provide immense help to the further improvement of cyclical text reuse in Urdu language and can be applied for other similar languages of the world. This thesis, therefore, does not only provide coverage of the various approaches to detecting cyclic text reuses using the sentence-transformer-based methods but also comes with the creation of a stable set of corpus that will be used to analyze the methods. We believe that the outcomes and the materials provided in this research can be the basis for further investigations and realizations testing for text reuse in various languages.