Department of Computer Science

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Subject: search.filters.subject.Dr. Rao Muhammad Adeel Nawab

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    A Benchmark Corpus and Methods for the Urdu Qur’anic Question-Answering System
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Abida Ashraf; SP23-RCS-002; Dr. Rao Muhammad Adeel Nawab; LHR TP 9497
    Question Answering System aims to provide answers to a question posed in natural language. A question can be factoid and non-factoid. With the rapid increase in technologies, the QA task become one of the most challenging and important tasks of Natural Language Processing (NLP). In previous studies, the problem of the QA system for the Holy Quran has been explored for Arabic, English, and Indonesian languages. Urdu has more than 170 million speakers worldwide yet considered low low-resource language. However, the problem of the QA system for the Holy Quran has not been explored for the Urdu language due to the unavailability of the dataset. To overcome this research gap, this study developed a benchmark corpus of the Holy Quran in the Urdu language. We developed the corpus by carefully mapping the QRCD corpus which was originally in Arabic language into Urdu. For the mapping of answers, we take the expertise of data annotators who have a deep knowledge of the Holy Quran in both Arabic and Urdu language. We conducted experiments in a state-of-the-art BERT based LLMs. We evaluate the proposed techniques using Exact Match, partial Reciprocal Rank, and F1 score, and expert judgment.
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    Toxicity Detection in Speech
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Mohsin Nazakat , Samiya Ijaz, Ghulam Rasool; FA18 BCS-052, FA18 BCS-143, FA18 BCS-112; Dr. Rao Muhammad Adeel Nawab
    Speech is a fundamental source of communication. We express our ideas, thoughts, and sentiments through speech. What we say (our speech) has a very significant effect on our personalities and the personalities of our listeners. A positive speech spreads positivity in society, gives peace to its speaker and pleasure to the listeners. On the other hand, negative or toxic speech cause great damage to our society. It not only has a negatively effects the minds of the speaker and listener but also spoils their personalities. Ultimately toxic conversation reshapes human personality in such a way that might leads speakers and listeners to get involved in toxic actions. So, it is momentous to put a stop to these toxic conversations in our society. According to the best of our knowledge, there is no existing system that can classify a digital audio conversation (digital speech) as toxic or normal. To fulfil this gap, we have built an administrative android mobile app that will notify the parent whenever an audio speech is classified as toxic. This will allow parent to guide their children to avoid having such toxic conversations. Using the digital audio data of day-to-day conversations, we have trained a Machine Learning model, and using the REST API call, we will send audio to the model and in response, we get a prediction of whether the audio is toxic or normal. In the case of Toxicity, a notification is dispatched to the parent to notify them that a toxic conversation is detected.
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    Developing a Performance Evaluation and Course Recommendation System for Student
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Chaudary Atif Raza Warraich , Asghar Afzal , Muham; SP17-BSE-128 , SP17-BSE-050 , SP17-BSE-059; Dr. Rao Muhammad Adeel Nawab; LHR TP 7120
    Recommenders systems are widely used in a number of field, especially in e-commerce. In past few years, these systems also occupy the major areas of e-learning systems. We will be carrying out detailed analysis on student performance and course recommendation in this final year project. The input to our system is the student marks and our system will fetch all the relative facts and figures that will lead to check the student performance and on this performance, we will recommend a course to student. We will apply various pre-processing techniques to prepare the data for detailed analytics. An efficient and effective Dashboard will be developed to show student performance and recommended courses to the student.
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    Information Retrieval System For Urdu Language
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) HASSAN NAWAZ , SAJID DIN MUHAMMAD; SP17-BCS-125 , SP17-BCS-059; Dr. Rao Muhammad Adeel Nawab; LHR TP 7188
    Internet Searches in the learning environment differs from traditional “Query” or “known objects” research: Students or common person are typically looking for or master a specific topic, and search engines help them to find that information. This report presents IRS (Information Retrieval System), an exploratory search engine for learning resources based on Wikipedia articles: provides set of topics in Urdu language and relationship between Wikipedia articles provides suggestion to Urdu native speakers to deepen research in a specific field. IRS indexes several hundred thousand Wikipedia articles. This report also contains the results of evaluation of IRS show that the exploratory research paradigm using Wikipedia knowledge is a promising method that can be used to create information retrieval systems for learning environments.
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    Developing a Speech Recognition System for the Urdu Language
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Muhammad Ibtesam Arshad; FA17-BSE-061; Dr. Rao Muhammad Adeel Nawab; LHR TP 7015
    Automatic Speech Recognition Systems (ASR) are used to convert the acoustic signals which are caught through the microphones into the sequence of words. The Automatic Speech Recognition Systems (ASR) empower the machines to react correctly, reliably, and effectively to human speech or voice and offer helpful and important services to the users. The interaction with computers or gadgets is faster, simpler, and easier, through voice or speech instead of typing through the keyboard or console, so the people will prefer Speech Recognition Systems. Speech Recognition Systems will facilitate the users in workplaces, in marketing, in banking, in health care, in language learning, and in the education field also. This project aims to design and the implementation a Speech Recognition System for the Urdu Language. In this first phase, we have collected data for the Urdu Language. To build a general Speech Recognizer, a huge amount of data is needed. In the second phase, we have implemented our System. In the final phase, we have trained and tested our Model. This model is Similar to Deep Speech 2 Models. The evaluation is carried out using Word Error Rate and Character Error Rate.
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    Developing a Gold Standard Benchmark Corpus and Methods for Multi-label Emotion Classification for Urdu Language
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) Azqa Abdul Ghani; SP22-RCS-025; LHR TP 8685; Dr. Rao Muhammad Adeel Nawab
    Emotion Classification (EC) involves detecting the emotional tone conveyed by text, while Multi-Label Emotion Classification (MLEC) aims to assign relevant emotion labels that accurately reflect the author's state of mind. This research focuses on MLEC in Urdu text. In previous studies, only one work has been done for the problem of MLEC, including the development of a dataset based on Urdu Tweets using six emotions [5]. However, the problem of MLEC has not been explored for 12 emotions and diverse genres. To fulfill this research gap, this research study has developed a large benchmark corpus of 16,011 Urdu reviews, manually annotated with 12 emotions and gathered from 9 different genres. Furthermore, this research study has Developed, Applied, Evaluated, and Compared Classical Machine Learning, Deep Learning and Transfer Learning based methods. Whereas in Transfer Learning methods, we have implied 6 distinct sentence transformers (xlm-r-100langs-bert-base-nli-stsb-mean-tokens, xlm-r-100langs-bert-base nli-mean-tokens, gtr-t5-large, all-mpnet-base-v2, distiluse-base-multilingual-cased-v2, and LaBSE), Also the Combined Feature-based approach is applied which averages the features of all the columns of the sentence transformer model and then the Machine Learning models are applied. The proposed transformers-based approach outperforms with an F1 score of 0.4562 using the ‘xlm-r-100langs-bert-base-nli-stsb-mean-tokens’ sentence transformer
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    Cross-genre Multi-label Emotion Classification on Mono-lingual and Code-mixed Texts
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) Ahmad Mahmood; LHR TP 8472; FA21-RCS-008; Dr. Rao Muhammad Adeel Nawab
    This research focuses on This research focuses on Emotion Classification (EC) in textual data and explores the problem of Cross-genre Multi-Label Emotion Classification (CGMLEC). EC involves detecting the emotional attitude conveyed by text, while Multi-Label Emotion Classification aims to assign relevant emotion labels that accurately reflect the author's state of mind. In previous studies most of the work has been done using Same-genre Multi-label Emotion Classification [2], [14]–[16]. Whereas there is only one article that solves the problem of CGMLEC, but the language is the same. However, the problem of CGMLEC has not explored using mono-lingual (English tweets) and code-mixed (SMS messages). Secondly, this research study has Developed, Applied, Evaluated, and Compared Classical Machine Learning, Deep Learning and Transfer Learning based methods. Whereas in Transfer Learning methods we have implied 5 distinct sentence transformers (all-distilroberta-v1, all-MiniLM-L12-v2, all-mpnet-base-v2, facebook-dpr-question_encoder-single-nq-base, and LaBSE), Also the Feature Fussion based approach is applied which combines the features of all the transformers and then the Machine Learning models are applied. The proposed transformers-based approach outperforms with an F1 score of 0.3333 using all-distilroberta-v1 transformer. ) in textual data and explores the problem of Cross-genre Multi-Label Emotion Classification (CGMLEC). EC involves detecting the emotional attitude conveyed by text, while Multi-Label Emotion Classification aims to assign relevant emotion labels that accurately reflect the author's state of mind. In previous studies most of the work has been done using Same-genre Multi-label Emotion Classification [2], [14]–[16]. Whereas there is only one article that solves the problem of CGMLEC, but the language is the same. However, the problem of CGMLEC has not explored using mono-lingual (English tweets) and code-mixed (SMS messages). Secondly, this research study has Developed, Applied, Evaluated, and Compared Classical Machine Learning, Deep Learning and Transfer Learning based methods. Whereas in Transfer Learning methods we have implied 5 distinct sentence transformers (all-distilroberta-v1, all-MiniLM-L12-v2, all-mpnet-base-v2, facebook-dpr-question_encoder-single-nq-base, and LaBSE), Also the Feature Fussion based approach is applied which combines the features of all the transformers and then the Machine Learning models are applied. The proposed transformers-based approach outperforms with an F1 score of 0.3333 using all-distilroberta-v1 transformer.
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    Text Reuse Detection for the Urdu Language at Lexical Level
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) Ayesha Noreen; FA21-RCS-026; LHR TP 8461; 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, sentence/passage level, and document level, along with benchmark corpora and methods. However, the problem of UTRD has not been explored at the Lexical level with respect to corpora and methods. To fulfill this research gap, this research study has developed a large benchmark manually annotated corpus of 22,184 text pairs at two levels of rewrite: 1) Derived = 8,660 and 2) Non-Derived = 13,524. Secondly, this research study has developed, applied, evaluated, and compared baseline approaches (Uni-gram Overlap and Word Embedding-based approaches) with proposed transformer-based approaches on the proposed UTRD-Lex-22 corpus. The proposed transformers-based approach (using a combination of 7 different Sentence Transformers including: distiluse-base-multilingual-cased-v1, LaBSE, paraphrase-multilingual-MiniLM-L12-v2, paraphrase-multilingual-mpnet-base-v2, xlm-r-100langs-bert-base-nli-mean-tokens, xlm-r-bert- base-nli-stsb-mean-tokens, xlm-r-distilroberta-base-paraphrase-v1) outperforms with an F1 score of 0.69 compared to the best results obtained using feature fusion approach (F1 = 0.70).
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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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    Image Caption Generation for Urdu Language – Data and Methods
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Nimra Shafiq; SP20-RCS-010; LHR TP 7602; Dr. Rao Muhammad Adeel Nawab
    Image Captioning (IC) generates a suitable caption for the given query image. The focus of IC is understanding the contextual meaning of visual contents in an image and then describing them in semantically and syntactically correct description in the natural language. The most salient part of an image is described in a sentence to increase the visual understanding of people. It has many applications like navigation guidance for blind persons, image-based optimized search engines, visual context understanding, early babyhood education, image description in an e-commerce site, and image indexing. Previously most of the work done on Image Captioning was in English and other high resource languages like Chinese, Arabic, and Hindi, etc. To the best of our knowledge, the Urdu language has not been explored previously for Urdu Image Captioning (UIC). The primary reason for this is the unavailability of benchmark corpora for UIC. So, the foremost aim of this research is to develop a benchmark Image Captioning corpus for the Urdu language (known as UIC-21 corpus). The proposed corpus is based on the Fliker-8k English corpus. It is developed by translating the English image caption into Urdu image captions using a semi-automatic translation approach. In this approach, automated translation tools are used to automatically translate the image captions in Urdu language and then a manual inspection and correction approach is used to improve the quality of the captions. To demonstrate how UIC systems can be developed and evaluated on our proposed UIC-21 corpus, we applied deep learning techniques (CNN for images and RNN, LSTM, and GRU for Urdu textual data) and state-of-the-art transfer learning techniques (InceptionV3 with ImageNet weights and VGG16 with ImageNet weights for images, and pre-trained word2vec urduvec and W2V models for textual data) for UIC systems, which are further evaluated using BELU-1, BLEU-2, BLEU-3, and BLEU-4. Their experimental results show that the transfer learning InceptionV3-GRU with the pretrained word embedding method achieved the highest BLEU-1 score of 0.60 on our proposed UIC-21 corpus