M.Phil / MS
Permanent URI for this collectionhttps://repository.cuilahore.edu.pk/handle/123456789/36
This collection archives the complete set of theses produced by students of the COMSATS University Islamabad, Lahore Campus.
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Item Semantic Precision: Forecasting Healthy Practices for Diabetic Patients Using Social Media Perspective and Medical Models(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Hina Idrees; SP23-RCS-015; Dr. Abid Sohail; LHR TP 9483Hyperglycemia mellitus is a chronic health condition that affects millions of people globally, posing significant challenges to both individuals and healthcare systems. The condition arises due to the body's inability to produce enough insulin, as seen in Type 1 diabetes, or its inability to effectively utilize insulin, as in Type 2 diabetes [1]. Managing diabetes requires a multifaceted approach, including regular monitoring of blood sugar levels, adherence to medication regimens, maintaining a balanced diet, engaging in physical activity, and being vigilant for potential symptoms [2]. Poor management can lead to severe complications such as cardiovascular diseases, kidney damage, nerve damage, and vision impairment [3]. This thesis proposes a novel framework to simplify and enhance diabetes care by leveraging real world data, process modeling, and advanced machine learning techniques.Item A Methodological Approach for Classifying and Differentiating Business Processes Using Tasks Label(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Sidra Anwar; SP23-RCS-027; Dr. Abid Sohail; LHR TP 9492It is crucial for two organizations to integrate their business process models when one acquires the other or merges with another organization. This research is motivated by the problem of matching processes with semantically similar activity names and similar process maps that make alignment difficult. Introducing a new approach based on the Domain Specification Mapper along with the current state-of-art transformer models, BERT for semantic augmentation and contextual interpretation of activity labels. The method combines semantic analysis, syntactic features and machine learning algorithms to show that DSM does not only increase the effectiveness of the transformer models such as BERT, but also increases the effectiveness of other machine learning models. Although traditional classifiers and semantic syntactic features failed to provide a correct classification of processes, DSM with BERT can successfully address these problems. Furthermore, the integration of DSM with other classifiers also showed good results and improved the classification rate. This approach facilitates integration of process, increases accuracy and aligns operations which makes it ideal for mergers and acquisitions.Item Towards Better USD/PKR Exchange Rate Forecasting Using Public Sentiment Analysis(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Muhammad Rizwan; CIIT/SP23-RCS-022/LHR; Dr. Abid Sohail; LHR TP 9995Exchange rate forecasting plays a crucial role in financial planning, investment decisions, and economic policy formulation. Traditional forecasting models primarily rely on historical financial data and macroeconomic indicators; however, these models often fail to capture the influence of public perception and market sentiment on currency movements. This study aims to improve the forecasting accuracy of the USD/PKR exchange rate by incorporating public sentiment analysis derived from online sources such as social media platforms, news articles, and financial forums. The research collects historical exchange rate data along with large-scale textual data reflecting public opinions related to economic conditions and currency fluctuations. Natural Language Processing (NLP) techniques are applied to preprocess the textual data and perform sentiment analysis, categorizing the information into positive, negative, and neutral sentiments. These sentiment indicators are then integrated with traditional time-series forecasting models and machine learning techniques to predict future movements of the USD/PKR exchange rate.Item A Methodological Exploration of BPM Community(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2017) Saman R. Abdi; FA15-RCS-007; LHR TP 6831; Dr. Abid SohailThe study is motivated to explore the collaboration between the authors, the patterns which are being used by the authors and the areas on which the authors are working. The end product of the research is a set of generic guidelines for the field of business process management. Business process management conference, which was one of the most famous conference is been used to develop the dataset for the study. Business process management conference started since 2003 so the papers since 2003 till 2016 were collected to create the dataset. The collaboration of authors is been studied by applying certain filters over the raw data of business process management conference. The research pattern of different authors was been studied individually from each paper and later they were all combined by identifying the common or similar aspects of the processes to form a set of frameworks. The study has created a generic set of five frameworks. Each for a specific domain of business process management i.e. business process similarity, business process abstraction, business process model redesign, business process monitoring, and business process mining. The study also provides some key facts about the most collaborative authors and the domains on which the authors or the research community is mainly focusing.Item A Structural Variants Process Models Collection for Process Similarities Evaluations(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2017) Rimsha Anam; FA15-RCS-017; LHR TP 6822; Dr. Abid SohailOrganizations having years of involvement in business process management, regularly keep up business process model repository containing hundreds or even a large number of process models. The process models in their repositories typically evolve from different sources and are created by various stakeholders. A new process models may be by expanding or refining existing models. Therefore, process models have a tendency to collect duplicate process models which, if left unconsolidated, may develop autonomously and cause irregularities. Similarly, it frequently happens that many organizations manage the business process models that have similar objectives but involve different business unit which may differentiate one another. The similarity (Correspondence) of process models can be explored through label, structure and behavior. This study is mainly focusing structural similarity measurement. The variations in the process model though structural correspondences have already been proposed in literature. But, majority of them are conceptual i.e. not providing the implication and verification on process model collections. Moreover, the previous studies on structural changes are not yet defining any proper classification for the structural variations. The first contribution of this study is the provision process model design classification through structural variations. We have tried to explore the availability of BPMN dataset and found some existences, one of which is SAP business process repository but, that was not available publically. So, as a contribution the complete repository was created with 600 different variants of 150 original models. The original models were taken from a verified source of our research group available dataset. Furthermore, the regenerated models were once verified from domain experts and finally included. The rigor evaluations were performed on dataset both manually and automated.