Department of Computer Science
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Item A Machine Learning Approach for Mutation Identification in Gene Sequences to Detect Lungs Cancer(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2022) Umer Farooq; SP19-RCS-029; LHR TP 8334; Dr. Allah Bux SarganoCancer is one of the leading causes of death worldwide. The mutation is one of the basic causes of cancer, which is caused by any change in the gene sequences of the human body. The abnormal and uncontrolled growth in the cells of the lungs is a cause of lung cancer. It is the second most common type of cancer worldwide, considered 11.2% of all cancers. According to the cancer statistics of the United States of America (USA), about 236,740 people were diagnosed with lung cancer in the year 2021, and about 130,180 people died from lung cancer in the same year. This study aims to develop a dataset, machine learning, and deep learning-based techniques to address the above-mentioned issues. In this regard, the asia.ensembl.org website is used to extract normal gene sequences from the lung cancer dataset, while the mutated gene sequences for lung cancer are obtained from the intogen.org website. Thus, the dataset of 1588 samples consisting of 2576 mutations is developed by following the standard dataset creation method. Then, different feature extraction methods are applied to extract the discriminative features from the dataset; these features include raw moment, Hahn moment, central moment, position relative incident matrix (PRIM), reverse position relative incident matrix (RPRIM) calculation, feature vector determination, accumulative absolute position incidence vector (AAPIV), reverse accumulative absolute position incidence vector (RAAPIV). These features are then fed to the traditional machine learning (ML) algorithms such as Random Forest (RF), Logistic Regression (LR), Multi-Layer Perceptron (MLP), and an ensemble of these traditional ML algorithms. These extracted features are also fed to deep learning (DL) methods such as LSTM, GRU, Bi-LSTM, and an ensemble of these DL algorithms. An averaging technique is applied to ensemble the classification algorithms. Three validation methods are used for evaluation: self consistency testing, 10-fold cross-validation, and independent set testing. The final classification result of lung cancer is generated as accuracy, specificity, Mathew's Correlation Coefficient (MCC), sensitivity, and Cohen's Kappa. The efficacy of this study can be measured with the accuracy of 94.5% obtained from the ensemble of x machine learning models using 10-FCV and with the accuracy of 97.3% obtained from the ensemble of deep learning models using the independent set test.Item Cross-Lingual Paraphrase Detection for English Urdu Language Pa(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Nida Waheed; SP19-RCS-025; LHR TP 7598; Dr. Rao Muhammad Adeel NawabThe main aim of the Cross-Lingual Paraphrase Detection (CLPD) task is to identify whether a text pair (text 01 in language A and text 02 in language B) are paraphrased of one another or not. With the massive increase in technological advancements in digital media and internet facilities, the availability of digital content across multiple languages has become free as well as easy to access. Besides paraphrasing, there has been a rise in cross-lingual paraphrasing since the development of advanced text processing tools. Moreover, the facility of translating existing texts from the original language into any other desired language using freely available translation tools has worsened the problems and triggered an increase in cross-lingual paraphrasing. Previously, there has been plenty of work done for the cross-lingual paraphrase detection in different languages but the downside is that under-resourced languages are still neglected. Urdu is a widely spoken language, despite the fact, the work done for the Urdu language in the CLPD domain is notably less because of the complexity and challenges of the Urdu language. It still comes in the category of low-resource languages. The research in the text analysis domain is mostly focused on well-resourced languages such as English and other European languages. The CLPD problem has formerly been explored for English-Czech, English German, English-Spanish, English Chinese, etc. language pairs. However, the CLPD problem has not yet been studied and reported for the English-Urdu language pair. Although, Cross-Lingual Paraphrasing is now being used enormously in various domains such as content writing, blog posts, and academics, especially in research works related to different languages. To overcome this research gap, a benchmark corpus and approaches are presented through this study for CLPD. The first objective of this research work is to develop a large benchmark Cross-Lingual Paraphrase Detection Corpus (CLPD-EU) for English-Urdu language pair at the sentence level by using a semi-automatic translation approach. For the creation of the CLPD-EU corpus, an existing corpus named Microsoft Research Paraphrase x Corpus (MRPC) is used. The second chief objective of this research work is to make a thorough comparison of classical machine learning (Bilingual Dictionary Based Approaches), Cross-lingual Word Embedding Based Approaches, Transfer Learning (Sentence Transformers Based Approaches) Approaches, and Feature Fusion Approaches on our proposed corpus. The results exhibited that our proposed Feature Fusion Approach 1 has shown notable performance for CLPD. The evaluation of all these approaches is carried out using the weighted average of Precision, Recall, and F1 scores.Item Urdu Short Text Reuse Detection Using Sentence Transformers(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Hamza Hafeez; SP19-RCS-014; LHR TP 7589; Dr. Rao Muhammad Adeel NawabThe process of creating new text(s) using an existing one is called Text Reuse. The goal of text reuse detection is to identify whether a new text is created using a source text or not. A source text can be reused at different rewrite levels to generate either partially (Paraphrased) or fully (Verbatim) reused texts pairs. The Paraphrased Text Reuse shows the relationship between two text pairs conveying the same idea differently. Paraphrased text reuse text is difficult to detect since different edit operations can be used to generate it which varies considerably according to the context where it is being used. In literature, the Paraphrase Text Reuse Detection problem has been explored for English as well as some other European languages, but it has not been investigated for the Urdu language because of the non-availability of the Urdu paraphrase text reuse detection corpus. Therefore, to fulfill this gap this study aims to develop a large-scale gold standard benchmark Urdu Paraphrase Text Reuse Corpus (UPTRC) for the Urdu text reuse detection with special emphasizes on paraphrase text reuse detection for Urdu language. In addition, we have applied the baseline approaches (N-Gram Based Approaches) and proposed other approaches including Word Embedding Based Approaches, Sentence Transformers Based Approaches, and other Feature Fusion Approaches for Urdu Paraphrase Text Reuse Detection (UPTRD). However, Urdu Short Text Reuse Detection (USTRD) has been explored at the sentence level, passage level and document level along with different approaches that have been previously applied on Urdu Short Text Reuse Corpus (USTRC). Nonetheless, the USTRD task has only been explored with lexical and semantic approaches on USTRC but has not been explored with state-of-the-art Sentence Transformers Based Approaches and Word Embedding Based Approaches. The Sentence Transformers Based Approaches are used in a variety of applications and natural language processing tasks, especially for semantic text similarity and duplicate pair detection. But the Sentence Transformers has not been explored for Urdu text reuse detection. So, there is a need to explore the Sentence Transformers Based Approaches on the USTRC corpus for the task of USTRD. In this study, along with the corpus construction, we have applied the N-Gram Based Approaches as baseline and proposed new approaches including Word Embedding Based Approaches, Sentence Transformers Based Approaches, and Feature Fusion Approaches for our proposed UPTRC corpus. We have proposed applied compared and evaluated same approaches for USTRC corpus as well as our proposed corpus to investigate the performance of our proposed approaches. The best results obtained were the weighted average of F1 = 0.855607 by our proposed Feature Fusion Approaches for the task of UPTRD on our proposed UPTRC corpus. In the case of the USTRC corpus, our proposed Feature Fusion Approaches still outperformed the baseline approaches for ternary classification with F1 = 0.715164. The performance of the proposed approaches for both corpora is evaluated and reported using the weighted average of Precision (P), Recall (R), and F1 evaluation measures.Item Automatic Code Generation of Petri Net-Based Business Process Models(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Rana Marwat Hussain; SP19-RCS-021; LHR TP 7292; Dr. Farooq AhmadTo verify software system flows and different components before the implementation of the system formal modeling techniques become very useful. Modeling helps users to understand software systems better by defining the system’s components formally. Many mathematical modeling languages are used for this purpose but the widely used language for formal modeling is Petri Nets. Traditionally all the efforts involved in the modeling phase is nothing more than to support the documentation because the development process manual transfer the modeling into implementation which because information lost is the real system implementation (program Code Generation). To reduce the implementation and modeling gap Model-Driven Software Engineering (MDSE) plays a very crucial role as MDSE approaches favor code generation from models which helps to improve productivity, quality of the application and reduce the time spent on implementation. As code generation is the fundamental part of the MDSE and model play a key role that is why tool support proposed in this thesis work tool provides a modeling interface and an approach to generate code from Petri net in multiple high-level programming languages such as Python, C, C++, Java, etc. Through the use of modeling interface, four models have created these models were used to generate code and the tool successfully generated code in multiple high-level programming languages which can be compiled and run-on supportive IDEs. However, the proposed tool had flaws in the modeling such as the CTRL+ key sometimes misses the draw arc operation, and code generated from complex models has logical flaws. The proposed tool can be useful in future research work for providing support to extend the code generation approach, and modeling interface to modeled other domain’s model which based on another formal modeling also to support the other informal modeling languages such as UML class diagram, etc. and help to generate codeItem Super-Resolution of Medical Images using CNN(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Maha Anis; SP19-RCS-023; LHR TP 7288; Dr. Allah Bux SarganoSuper-resolution is a technique that improves the quality of an image. Super-resolution images contain more detail because they have a high density of pixels. Super-resolution images provide complementary detail for clinical diagnosis, astronomy, and biometry. The medical images should be of good resolution for perceiving fine-grained details in the image so that the patients are diagnosed at their early stages of tumours. If the images have low resolution, doctors cannot diagnose the tumours or segment the malignant regions. For super-resolution images, the cost in terms of machines and the health of patients also matter. If patients are at high risk of cancerous cells, continuous radiation exposure will lead to activation of these cancerous cells. So the goal is to enhance the low-quality scans. This thesis emphasises single image super-resolution (SISR). Previously used techniques for the task of super-resolution were interpolation and reconstruction. However, these techniques have limitations in the loss of information in images like edges and boundaries. Recently, deep learning was introduced and aimed to features extraction from the related images for enhancement automatically. For this purpose, Convolutional Neural Networks (CNNs) are employed due to their excellent results in similar domains. These CNN based approaches achieved encouraging results. Transfer learning have been successfully used for image recognition and human action detection. This research work foremostly in-depth examined the learned representation models incorporating transfer learning and fusion of different models. EDSR and WDSR are the winners of NTIRE challenges for the super resolution of natural images. These models, including SRGAN, are evaluated with and without transfer learning. The proposed concepts unleash the integrated potential of these three models in the medical domain with higher PSNR and SSIM as compared to contemporary models. The transfer learning EDSR, WDSR, and SRGAN achieved 40.38, 39.02, and 38.15 PSNR and 0.964, 0.963, and 0.953 SSIM respectively. The fusion of these three models shows PSNR, and SSIM 37.84 and 0.964 respectively. The results report that proposed approaches outperformed state-of-the-art methods by learning super resolution features on NAMIC datasets. The results highlight the importance of transfer learningItem Long and Short Term User Preference Learning for Next POI Recommendation(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Muhammad Umair Khalid; SP19-RCS-005; LHR TP 7299; Dr. Hamid Turab MirzaRecommendation systems in the latest years have play an essential part and it is becoming an emerging trend in daily life and business. The Point-of Interest (POI) - recommendation depends on the check-in histories of individual users at an explicit time. The goal is to develop a comprehensive recommendation system, which has the aptitude to learn the user long and short-term preference for the next POI recommendation and is a considerable attractive research interest. Since users check-in activities are independent and indefinable and can be viewed as long and short term sequence, which equally determine the user’s preference for the next destination. Moreover, the previous methods focuses on the geographical relationship of users among recently visited (POIs) and neglect the user's general interest. To addresses, the issue of (POI)-recommendation, the comprehensive model is given for learning user long and short term preferences. Which considers both recent successive information of users and their general taste simultaneously. The long term module utilizes fusion technique to capture the user's next (POI) preference, by computing the historical visit frequency of users to a particular venue representing their general interest. Whereas the short term preference module utilizes an attention mechanism, to learn the users’ recently visited locations at a specific time and location using spatio temporal based attention model. The given model in this thesis has shown prominent results with an accuracy of 57.7% at N=30 for cold-start-users and 60.7% for Neural network and Random forest respectively. However these results is improved using on non-cold-start users to 79.1% and 77.9% at N=30. Besides the existing methods which neglect the user’s long-term behavior. This work targets to achieve improvement by using fusion model on real-word check-in information and access the model performance in terms of non-cold start and cold-start users which will overcome data sparseness problem in user check-ins, to achieve effective results, and will help in different applications of recommender systemItem University and Medical Colleges Finder System(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) Zeeshan Fiaz (SP19-BSE-034), Hamayoun Masroor (SP19-BSE-101), Ahsan Ali Akram (SP19-BSE-134); LHR TP 8260; Akhzar NazirPlanning before developing something is important as it allows us to track our progress and match it with the project specification. In this report, we specify the requirements and represent it by using graphical and textual visualization techniques. Therefore, the scenario is, after intermediate students have to buy prospectus for each universities in order to get information about the courses, fees structure and other important details. Therefore, it is hectic process for students to find perfect universities and medical college is based on the different factors. We have purposed a universities and medical college’s finder system that helps student to find the perfect universities and medical colleges for their bachelors. System will display universities to the user based on the data students have entered. We have three entities interacting with the system Admin, Students and universities. Two entities have to login into their account to perform any activity. However, student can roam freely. Students first have to select the given filters like fee, location, discipline etc.Item Dystopian Action Adventure(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) Abdul Rauf (sp19-bse--043), Qasim Tariq (sp19-bse-004); LHR TP 8241The dystopian action adventure gives an immersive third person action adventure experience with a story telling gameplay and a fun multiplayer with two different deathmatch modes. The game has custom built assets for weapons and enemy agents. The story mode part of the game has 10 levels, each with a different story to tell. The game has a cloud architecture with a client application and a cloud provider for storage, multiplayer matchmaking and backend unity game server instance with an ability to scale with increasing number of users. The game is developed for PC users and is able to run on a wide variety of modern machinesItem Inner Persona(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) Adil shakeel (SP19-BSE-027). Mahrukh Aqdas (SP19-BSE-039), Areej Malik (SP19-BSE-051); LHR TP 8240; Mohsin MehdiAs we all know, the gaming area is spreading widely, and many people are playing games to get a break from their hectic routines. And over time, these are becoming more advanced, having short stories, advanced functionality, better quality, and graphics. In this project, we will also add these advancements so players can enjoy it to its fullest. Our Game genre is the psychological thriller named Inner persona. Psychological Horror games are pretty different from the other genre of games as these games have a substantial effect on the player’s mind and emotions. These games are judged by the quality of intensity it provides to the player. Most Horror games are based on fictional storylines. But our game deals with real-life mental issues. Our game will represent these real-life issues with a high level of creativity. This will also help the user to understand the importance of mental health. The game is about a doctor who is our player in the game and will diagnose a patient with psychological issues. The patient had faced traumatic and Negative incidents that caused severe mental disorders. Each disorder represents a different episode with different storylines and objectives. The game aims to diagnose and treat those mental disorders. The doctor will teleport himself into the patient’s mind with the help of advanced technology. The doctor will experience thrills and horror in the patient's mind. The Game will end when the patient will completely recover from the disorder.