Department of Computer Science
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Item RECOVERY OF 60 TONS/DAY OF SULFUR FROM ACID GAS(2023) Asad Jalal Lagari (FA19-CHE-011); Ahsan Haider (FA19-CHE-029); Hammad Anjum (FA19-CHE-039); Wilayat Ali (FA19-CHE-051); M Mubeen Javed (FA19-CHE-069); Dr. Khaliq MajeedThis is the project of Sulfur Recovery Unit (SRU). Many processes are utilized for the desulfurization of acid gas to recover elemental sulfur but here we are more interested in Claus Process because of its efficiency and economy. The plant's efficiency and economy both are favorable conditions for the selection of the process. The purpose is to design a plant which produces 60 tons of elemental sulfur per day. The report includes the introduction of sulfur, production and processes, various processes used to recover sulfur, mass and energy balances over this particular recovery unit, design of all the equipment's, instrumentation, cost estimation, and safety of the plant. In short, this project is unique, crucial and very much important for educating current or future engineers because it focuses at the futuristic improvements to the Claus Process used to recover the elemental sulfur.Item Smart Tenant System(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2024) FURQAN ALI BUTT; FA19-BSE-002; ZAHEER A. GONDALWith the advent of the Internet, a lot of life trends are changing. The internet has a profound impact on many aspects of our lives, from how we communicate and socialize to how we work and learn. The proposed project is an online website that act as a platform which connects room owner and tenants. Being a citizen of Pakistan I have a brief idea of how much our citizens needs an online platform to find rooms as they prefer specially students and people that travel to other cities. This website will act as bridge between both and hence providing the users with better room options and thus saving time and burden.Item A Comparative Analysis of Offensive Language Detection in Roman Urdu(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2022) Muneeb Rehman Khan; FA19-RCS-019; LHR TP 8305; Dr. Muhammad Waqas AnwarThe rise of social media has encouraged experts to conduct several studies based on user generated data. The widespread use of online platforms like Facebook, Twitter and other web forums has created challenges to keep check for inappropriate language. Pakistan has about 44 million online social network users that speak Roman Urdu to interact with each other. The prevalence of user-generated information on social media caused a massive rise in the content of offensive texts. However, this growth also offers avenues for trolls who contaminate these social platforms with their nasty remarks. In South Asia, Roman Urdu is the most widely used communication language on social media. Compared to other resource-rich languages such as English, less work has been done on offensive language classification in Roman Urdu. In this research (1) a lexicon of offensive terms in Roman Urdu is presented, (2) developed a dataset called RU-OLD “Roman Urdu Offensive Language Detection” with 15000 tweets, and (3) conducted a comparative analysis of machine learning and several deep learning architectures. Comparative analysis was performed by well-known machine learning classifiers such as Naive Bayes, RF, SVM, and Logistic Regression as well as cutting-edge deep neural networks such as Long Short-Term Memory, Convolutional Neural Networks, XLNET and BERT. XLNET trained with developed corpus outperformed the rest of the models and resulted in the highest accuracy of 93%. The results are suggestive of the scalability of the proposed approach on large scale to counter offensive language in Roman UrduItem Vehicle Detection for Traffic Monitoring from Urban Video Surveillance Cameras using Deep Learning(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2022) Ather Rafiq; FA19-RCS-032; LHR TP 8344; Dr. Allah Bux SarganoIn recent years, vehicle detection for traffic monitoring from urban video surveillance cameras has become a hot research topic among researchers because of an increase in anomalous or unusual vehicle activities from video sequences captured from the traffic surveillance cameras. Instead of manually analyzing the video for detection of anomalies, there is a need for an automatic process that would easily be easily applied to a large number of videos, because the number of video surveillance cameras is increasing in the public places causing the increase in automated analysis of traffic by capturing videos. Therefore, automatic video surveillance of traffic is considered one of its main applications. The main purpose of the video-based surveillance system is to analyze patterns and behavior, vehicle tracking, detection of anomalies, and abnormal event prediction. In this research work, a novel framework: Vehicle Detection for Traffic Monitoring from Urban Video Surveillance Camera (VDTMUVSC) using deep neural networks is proposed to get better results as compared to other state-of-the-art methods which are being used for automobile detection. In this method, to reduce the time for training, pre-trained weights are used in terms of transfer learning and some initial layers from the backbone of architecture are frozen. In the second part, the hyper-parameter tuning technique is used to achieve higher accuracy. Further, extensive experiments have been conducted on the benchmark dataset UA-DETRAC which is introduced recently, especially for the purpose of vehicle detection and tracking. The results demonstrated that our proposed architecture outperformed existing techniques with a margin of 3% to 5% in object detection for vehicles, achieving 80.3% mean average precisioItem Metropolis Violation Detector Using Deep Neural Network(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Iqra Khalid; FA19-RCS-014; LHR TP 7298; Dr. Zeeshan GillaniAnomaly discovery on road traffic is a significant assignment because of its extraordinary potential in metropolitan traffic the board and street wellbeing. It's anything but an extremely difficult assignment since the strange occasion happens seldom and shows various practices. In this work, we present a model to recognize abnormality in street traffic by gaining from the vehicle movement designs in two particular yet connected modes, i.e., the static mode and the powerful mode, of the vehicles. The static mode investigation of the vehicles is gained from the foundation demonstrating followed by vehicle identification technique to find the unusual vehicles that keep still out and about. The unique mode investigation of the vehicles is gained from identified and followed vehicle directions to find the strange direction which is atypical from the predominant movement designs. The outcomes from the double mode investigations are finally melded by driven a re identification model to acquire the final abnormality. This study was based on three classes of anomalies (car crash, car stall and lane change). It was divided into a two class problem with 63 anomaly folders and 86 no anomaly folders. Dataset was provided by NVIDIA AI city challenge track 4 containing 100 train and 100 test videos. Firstly videos were annotated according to anomaly event time. The anomaly time was converted into seconds and then specific frames were cropped. Then 30 frames per video were selected to fed into ResNet18 for high feature extraction. Then LSTM architecture and 3D-CNN was trained and results were evaluated on accuracy evaluation measure. Satisfying results was obtained from both architectures i-e; 80 % accuracy of 3D-CNN and 83 % accuracy of ResNet with LSTM. As it is an on-growing field many work can be done in future by using advanced models for the improvements