Final Year Projects (FYPs) - Undergraduates
Permanent URI for this collectionhttps://repository.cuilahore.edu.pk/handle/123456789/37
This collection archives the complete set of theses produced by students of the COMSATS University Islamabad, Lahore Campus.
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Item Vehicle Tracking System(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Rao Mubashar Ali , Ali Suhaib Shahid , Ammar Yasee; FA17-BCS-124 , FA17-BCS-082 , FA17-BCS-033; Dr. Zeeshan Gillani; LHR TP 7519The increase in population in metropolitan cities has given rise to severe traffic management problems and security issues. The rise in CCTV (Closed-circuit television) based solution has enabled us to monitor traffic but the sheer number of the cameras has given rise to another challenge of monitoring these systems and analyse this rich information. The advancement in computer vision techniques and GPU (Graphical processing unit) has now enable techniques than can automatically task which was only possible by humans in the past. CNN (Convolutional neural networks) based models can enable us to use real time videos of traffic data to aid in analysing the traffic conditions which in turn helps us in effective decision making regarding our cities to make them intelligent and safe. We will employ futuristic algorithms on the NVIDIA AI city datasets to evaluate the traffic detection and tracking systems.Item Artistify – Artist Identification of an Artwork using CNN(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Usama Anwar Butt; SP17-BCS-128; Dr. Zeeshan Gillani; LHR TP 7169Identifying the artist of the painting is a particularly difficult problem if there are no other metadata concerning the accessible image. Historians and Librarians in the past used to catalogue of notable artworks and used to debate over possible painters that might have originally painted the piece. This is a topic of active research in the computer vision field because of the challenging nature of the problem. We will apply a class of deep neural networks which is a Convolution Neural Network to analysing visual imagery. Using a dataset that contains artwork from the most creative artists of all time like Andy Warhol and Vincent Van Gogh, my network will learn the pattern and characteristics of the art and classify the given artwork to best predict the likely artist. Our goal is to create a model that learns to identify the artist analyzing new pictures.Item Plant Disease Phenotype Identification and Classification via Deep Learning(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Haider Ali Khichi , Muhammad Abdullah Aqib; SP17-BCS-038 , SP17-BCS-062; Dr. Zeeshan Gillani; LHR TP 7163Humans face global food shortages in the upcoming years, and we must maximize the yield of common crops to feed the growing population. Plant diseases are a major threat to small and large farm owners alike. These diseases reduce the potential yield of the crops, and in some severe but frequent cases, up to 100%. The identification of these diseases remains a challenge despite government efforts to educate farmers. There is a need for identification of these diseases early on so that they can be treated quickly. The pervasiveness of smartphones among farmers around the world offers the potential of adopting recent technological developments in computer science to develop a solution that can help in plant disease phenotype identification. This rich ecosystem of diverse communities can be a great advantage to crop heath and consequently the human race. To utilize the potential of these technologies we use a dataset curated by PlantVillage that contains over 50,000 images of 14 different crops that can be utilized to perform disease identification using modern deep learning algorithms. The models can then be employed by smartphones to identify the diseases and recommend possible treatmentsItem Deep learning approach for mango varieties identification using UAV imagery(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) ANISH HASSAN; SP19-BCS-009; Dr. Zeeshan GillaniIn this study, we propose a deep learning approach for the detection, variety identification, and health assessment of mango trees using UAV imagery. We used a DJI P4 Multispectral camera to collect images of mango orchards which were then used to train YOLOv5, YOLOv7, and Detectron2, three state-of-the-art object detection models. Our models were able to accurately detect trees in an image and classify the variety of the mango tree, as well as identify whether the tree is healthy or not. This approach can be useful for monitoring and managing mango orchards, as it allows for the efficient and accurate identification of tree variety and health status. This can be helpful for farmers as it can assist in early identification of diseased or unproductive trees, enabling timely action to be taken, such as providing targeted treatment or replacing the unproductive trees. Additionally, identifying the variety of the tree can help farmers in making better decision regarding harvesting, pruning and other orchard management tasks. The use of DJI P4 Multispectral camera enabled us to acquire both RGB and NDVI data, which helped us to differentiate the healthy trees from the unhealthy ones. The results of our study showed that the YOLOv5 model performed the best, achieving an overall accuracy of more than 80% in tree detection, variety identification and health assessment, demonstrating the potential of UAV imagery, multispectral sensor and deep learning in fruit orchard management. The proposed approach can be easily scaled and applied to other crop fields as well, providing farmers with a reliable and efficient tool for monitoring and management of their orchards.Item CNN based techniques for Detecting Anomalies in the Crop(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Rimsha Urooj , Shifa Allah Baksh , Fatima Batool; FA17-BSE-154 , FA17-BSE-164 , FA17-BSE-017; Dr. Zeeshan Gillani; LHR TP 7027The population across the globe is increasing at an alarming rate across the globe. This is giving rise to many challenges and one of the primary challenges is to feed an ever-increasing population with the same resources availed. This will lead to a food security and food crisis if we are not able to adapt our farming methods to modern technologies. With the advancement in computer vision techniques by using advanced CNN-based methods we can now analyse crops to detect anomalies in the crop. These applications could provide a foundation for the development of expertise aid or automatic screening tools. Such tools could contribute to further sustainable agricultural traditions and greater food production safety.Item Terrific Traffic(Library Information Services, COMSATS University Islamabad, Lahore Campus., 2020-11-20) Sarmad Khan; FA16-BCS-099; Dr. Zeeshan Gillani; LHR TP 6158The proposed is a system to detect traffic violations through anomalies detection using Machine Learning techniques from within a live traffic video stream. An anomaly is considered an irregularity or an event which would not occur in normal circumstances. Keeping traffic violations in account the circumstances will be such as Accidents, car crashes and lane violation. The proposed system will be able to differentiate not only between a normal and an abnormal event based on the events in the video stream but will also classify the anomaly accordingly. The purpose of this system is to automate and facilitate the traffic surveillance tasks in daily life. This result is achieved through implementing Image Processing and Deep learning techniques to predict the anomalous section of a video.Item Crop Analytic Using Drone Imaging(Library Information Services, COMSATS University Islamabad, Lahore Campus., 2020) Shehroz Shafiq Khan; FA16-BCS-153; Dr. Zeeshan Gillani; LHR TP 6149The population around the globe is increasing at an alarming rate, which results in the food crisis in the following couple of decades. The use of advanced technologies such as remote sensing with the help of satellite or unmanned aerial vehicle (UAV) could be used to overcome this problem. Drone-based image techniques could be used for crop yield prediction, crop identification, water management, weather, and environmental factors could be observed. With the help of Convolutional neural network (CNN) based models, we can extract some important features from it, and with the help of those features, we can estimate our crop yield.Item Driving Behaviour Analysis on GPS data(Library Information Services, COMSATS University Islamabad, Lahore Campus., 2020-11-20) Rehan Aslam; Sp16-bse-103; Usman Akram; LHR TP 6327; Dr. Zeeshan Gillani; LHR TP 6299The purpose of this project is to provide an ecommerce solution or ecommerce stores to the users who want to sell products online. Ecommerce sites are very expensive. Users have to hire developers or companies for developing an ecommerce site either they are selling two products or two thousand products. Users which have an existing website they can get advantage of traffic of that website. They can add an ecommerce store on sub domain of existing website.This project provides an ecommerce store to all these types of users who want to sell products online.This project will provide ecommerce stores in very cheap rates and easy to use for layman. Users which are less familiar with technology. This project will provide an admin panel to users for their stores where they can manage products as they can add, delete and edit their products. They can add all details of product like name, brand, description, price, SKU number, list of variants with their options, list of features of the product and upload images of the product. They can add categories which will be displayed on the navigation bar of their store. They will upload logo for their store. Users will choose theme from list of themes which they want to use in their store. They can add About-Us page where they will add their store or any type of detail they want to show to their buyers. Contact-Us page also will be added where they will enter their email, phone and map detail. This project is based on multi-tenant architecture to provide stores in which backend will be same for all stores.Link will be created for each store which will be used to register on domain or sub-domain. In this way users will generate traffic on its own domain or sub-domain. Buyers will come their domain name and store will be displayed to them. They will add products in to cart which they want to buy. They would open product page by navigating the categories menu from navigation bar. Finally, they would checkout from cart page after checking their added product in cart and order will be placed after successful payment. All the placed orders will be displayed to the admin on admin panel that are provider on this system.Item Driving Behaviour Analysis on GPS data(Library Information Services, COMSATS University Islamabad, Lahore Campus., 2020-11-20) Fazeen Masood; SP16-BCS-029; Mishual Fatima; SP16-BCS-117; Dr. Zeeshan Gillani; LHR TP 6299Driving behaviour analysis is required to understand the major cause of road crashes and harsh driving today. It is important to understand the different factors of driving when we talk about better safety concerns. Our project will be going to analyse the driving behaviour on GPS data. The project will have information of the car, driver and environment. And will apply different techniques and algorithms of machine learning to analyse the driving behaviours. It will focus on the driving behaviour, risk patterns, travel trajectories, speed, location, time, acceleration, deceleration, dimension of the car etc. We will apply naturalistic driving approach in which driving data will be recorded through GPS after time intervals. We will determine the driving pattern of the driver, his route choices, his acceleration, deceleration, speed limit and score of the trip. And then the application will classify the driver’s behaviour after his daily trip score.