http://hdl.handle.net/123456789/51 Bahria University Lahore Campus Sat, 17 Oct 2026 00:55:49 GMT 2026-10-17T00:55:49Z http://localhost:8080/xmlui/bitstream/id/4/Bahria-University-Lahore.jpg http://hdl.handle.net/123456789/51 http://hdl.handle.net/123456789/21741 Human-Computer Interaction Based Usability Study of Mobile Banking Applications 03-135222-047, Sumavia Ali Mobile banking apps are really important to us in our lives especially in countries where more and more people are using mobile phones. Even though we use them all the time people still have a lot of trouble with these apps like it is hard to navigate and figure out how to do things. They do not have enough features to help older people or people with disabilities and the security measures are not clear and that makes people not trust them and not want to use them. We undertook a detailed analysis of the mobile banking applications through the HCI approach that studies interaction between users and computer systems. Multiple approaches have been used for the analysis of the applications, among which include heuristic evaluation that makes use of ten usability heuristics for determining user-friendliness. Other approaches included user testing whereby tasks are completed by the user and questionnaires filled out. Further, the applications underwent testing on various mobile platforms to determine their functionality and suitability. We ensured that there was diversity in our experiment through selection of participants of varied age, gender, culture, and proficiency in the use of technology. While conducting the test, we assessed the ease with which users could accomplish fundamental actions like logging into their accounts, viewing their balance, and making transactions. We also tested the ease with which users could accomplish more complicated activities such as setting up preferences and using the accessibility options available to assist disabled users. In the analysis process, we applied both quantitative and qualitative techniques, thus incorporating numerical information with user reviews. v From our findings, improved mobile banking interfaces were designed using Figma software, and then we proceeded to test these prototypes through user experience. The use of advanced technology such as biometrics (fingerprint and facial recognition), as well as voice commands, was explored as part of the research process. This project will help to guide future designers of mobile banking applications on how to design such apps successfull. Dr Junaid Nasir Qureshi Mon, 01 Jun 2026 00:00:00 GMT http://hdl.handle.net/123456789/21741 2026-06-01T00:00:00Z http://hdl.handle.net/123456789/21743 Deep Learning based Leukemia Detection System using Microscopic Images 03-135222-051, UMAMA ZINOOR Blood cancer creates a disruption to healthy white blood cell production in bone marrow; this type of cancer is known as leukemia. Current methods used to diagnose leukemia rely on manually analyzing blood smears, but have a slow turnaround, require a high level of expertise and suffer from inter-observer variability. These limitations create an urgent need for automated, reliable methods for classifying leukemia sub types (ALL, AML, APML, CLL and CML) using a data set called LeukemiaAttri. This study presents innovative approach based on a hybrid deep learning architecture to assist with the automatic classification of five sub types of leukemia using this previously unexamined data set.. Five CNN models and seven Vision Transformer models were trained across two augmentation phases. MobileNetV2 achieved the best CNN accuracy of 95.58% while Swin Base Patch4 Window7 achieved the best ViT accuracy of 95.02%. The two models were fused using Feature-wise Linear Modulation (FiLM), which allows the ViT branch to intelligently modulate CNN features at three scales through learned parameters, enabling richer feature interaction than simple concatenation. A 97% accuracy on the final test set and an AUC-ROC of 0.9993 were achieved with the fused model. The external validation based on 4,915 independent images demonstrated the model's ability to generalise, having an accuracy of 94.85% and AUC-ROC of 0.9949. Grad-CAM++ heat maps confirmed that the model attends to clinically relevant regions of cells, supporting interpretable and trustworthy predictions.The system was deployed as LeukoDetect AI, a Flask-based web application offering real-time classification, confidence scores, and Grad-CAM++ visualizations to support clinical decision making. Dr Shahid Mehmood Mon, 01 Jun 2026 00:00:00 GMT http://hdl.handle.net/123456789/21743 2026-06-01T00:00:00Z http://hdl.handle.net/123456789/21737 SKYLYTICS AN AI POWERED FLIGHT DELAY PREDICTION SYSTEM 03-134222-003, ABDUL MUTLIB ALI Flight delays cost the aviation industry over $28 billion annually, yet most systems remain reactive, and existing research often fails to translate predictive models into real-world operational tools. Skylytics addresses this by providing a full-stack, proactive flight delay prediction system designed for both operational managers and passengers. It uses a hybrid stacking ensemble combining XGBoost and Bidirectional LSTM models, integrated through a Logistic Regression meta-learner, leveraging their complementary strengths. The system is trained on around 14 million flight records (2024–2025), enriched with weather data from Open-Meteo and live air traffic data from OpenSky Network. Multiple models were evaluated under a leakage-free setup, with XGBoost achieving a ROC-AUC of 0.7935, while the BiLSTM contributed significantly to the ensemble. The system is deployed via a FastAPI backend, a Next.js operations dashboard, and a SwiftUI iOS app for passenger tracking. It also includes. SHAP-based interpretability and an AI assistant for natural-language insights. Overall, Skylytics bridges the gap between academic research and production systems, enabling proactive decision-making and improving reliability for both airlines and passengers. DR. NADEEM SARWAR Mon, 01 Jun 2026 00:00:00 GMT http://hdl.handle.net/123456789/21737 2026-06-01T00:00:00Z http://hdl.handle.net/123456789/21740 Bachelor of Science in Information Technology 03-135222-009, Arooj Ishtiaq The goal of this project is to create a complete digital platform called ‘Skillink' that will access a single ecosystem for connecting students, mentors and recruiters. People typically use several individual platforms to find jobs, find mentors, and acquire new skills in the digital world. Skillink overcomes this by incorporating all these features into a single, easy-to-use mobile app. This report dives deep into the development process of a complete mobile app developed with the Flutter platform and connected to a Supabase database.This system is capable of providing authentication, role based access control, real time database. Administrative controls and manipulation of user management. Additionally, the project includes a data analytics aspect which uses Microsoft Power BI to create customer engagement, app trends visualization. Mentoring and income opportunities Dashboard offers useful information for making decisions and growing platforms. The application is developed following a structured approach: requirement Analysis, design of systems, database modeling, implementation and testing. User management, mentorship system, job application tracking and admin dashboard. Among the main modules are to monitor and control platform activities. The system has been tested by various users and implemented successfully. essential features of login, job applications, mentorship requests and the administrative aspects. The findings suggest that the proposed solution offers an improvement in the use of the system and in its efficiency in relation to the current disorganized platforms. Dr. Junaid Nasir Mon, 01 Jun 2026 00:00:00 GMT http://hdl.handle.net/123456789/21740 2026-06-01T00:00:00Z