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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    Anchor Free Motorbike Detection in Surveillance Videos of Dense Traffic
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2020) Mahwish Irshad; FA16-RCS-029; LHR TP 5972; Dr. Zulfiqar Habib
    Object detection and tracking have become the most significant and challenging task within the field of Computer Vision that attempts to detect, recognize, and track objects over any sequence of frames (images) called video. Its purpose is to locate objects motion in a video file or surveillance camera. Object detection is the procedure of locating one or multiple objects by utilizing a single camera, multiple cameras, or a provided video file. Urban area expansions have increased the demand for proper traffic surveillance. Traffic monitoring is important for detecting road accidents, collecting evidence for the investigation, tracking criminals, and traffic violators. To meet these requirements, intelligent systems can be developed to extract and analyze traffic information. This research work is concerned with the detection of motorbikes in videos. Detection of motorbikes object is very helpful for organizations and investigators, which are mostly concerned in dealing with potential violations of human rights on roads. For the detection of motorbike objects a state-of-the-art dataset called Velastin7500 is used. The thesis focused on the detection of motorbikes from videos by using two pre-trained, RetinaNet, and YOLOv3 architectures on the Velastin7500 dataset to explore the importance of object detection from images, captured by a drone camera. This research work has also inspected the domain shift issue of learning of features from images to detect objects and by pertaining this information to real-world imagery in the context of detection of motorbike. The proposed work also explored serious issues in the existing algorithm based on anchor free technique for some practical applications. The results produced for the Velastin7500 dataset by implementing above mentioned two architectures of RetinaNet and YOLOv3 have mean average precision (mAP) value of 18.25% and 76.5%, respectively. This research will also be helpful to understand why the anchor free approach did not work well with partially visible objects.
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    Formal Modeling and Analysis of Security Schemes in Internet of Things
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2020) Muhammad Amar Sohail; FA16-RCS-020; LHR TP 5971; Dr. Farooq Ahmad
    The Internet of Things (IoT) has provided us with the notion of a smartly connected world. Internet of Things is a system in which gadgets, computing machines or mechanical devices, are connected with each other through Unique IDs. Those objects can accumulate and disseminate the packets from one node to other nodes without any human participation. As IoT is related to the network-based distributed systems such as i.e. Cloud Computing, Fog Computing, and Body Area Network etc. These networks when applied to sensitive and confidential infrastructure then the role of its security becomes very crucial. To this end, routing strategy is responsible and literature suggests different techniques for security and privacy in IoT Systems. Formal modeling of a system can help us to check the reliability, accuracy and consistency of the system. Colored Petri Nets is graphic-based language and it has taken premium space in design and specification, validation and verification of systems which are concurrent in its behavior. Security Scheme is the mechanism of securing a network system by setting up routing strategy through topology. Analysing the attacks and mitigating them is a small subset of security schemes. In this study Routing over Low Power and Lossy networks (RPL) is used as a case study and the aim was to present the idea of translation of into formal language and ultimately analyze its security with the means of Formal Specification Methods. RPL is an IPv6 protocol and prominent for the routing in Internet of Things. Eventually, the focus will be shifted towards the evaluation of the existing security standards for RPL through formal validation and verification to check if those standards can achieve the desired security level.
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    Urdu-English Machine Transliteration using Neural Networks
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2019) Usman Mohy ud Din; FA16-RCS-007; LHR TP 5776; Dr. Muhammad Waqas Anwar
    Machine translation has gained much attention in recent years. It is a sub-field of computational linguistic which focus on translating text from one language to other language. Among different translation techniques, neural network currently leading the domain with its capabilities of providing a single large neural network with attention mechanism, sequence-to-sequence and long-short term modelling. Despite significant progress in domain of machine translation, translation of out-of-vocabulary words(OOV) which include technical terms, named-entities, foreign words are still a challenge for current state-of-art translation systems, and this situation becomes even worse while translating between low resource languages or languages having different structures. Due to morphological richness of a language, a word may have different meninges in different context. In such scenarios, translation of word is not only enough in order provide the correct/quality translation. Transliteration is a way to consider the context of word/sentence during translation. For low resource language like Urdu, it is very difficult to have/find parallel corpus for transliteration which is large enough to train the system. In this work, we presented transliteration technique based on Expectation Maximization (EM) which is un-supervised and language independent. Systems learns the pattern and out-of-vocabulary (OOV) words from parallel corpus and there is no need to train it on transliteration corpus explicitly. This approach is tested on three models of statistical machine translation (SMT) which include phrase based, hierarchical phrase-based and factor based models and two models of neural machine translation which include LSTM and transformer model. On SMT models, there is gain of 0.63 to 0.91 in BLEU score while on NMT models, there is gain of 1.28 to 2.05 in BLEU which are better than previous baseline scores. Our approach shows promising results in translation of Urdu text into English which is mostly neglected due to its complexities. We also discussed the results, different challenges faced during this work and effect of right pre-processing techniques
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    Automated Detection of Early Pulmonary Nodule in Computed Tomography Images
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2019) Ahmad Usama Tariq; FA16-RCS-012; LHR TP 5777; Dr. Usama Ijaz Bajwa
    Classification of lung cancer in CT scans majorly have two steps, detect all suspicious lesions also known as pulmonary nodules and calculate the malignancy. Currently, a lot of studies are about nodules detection, but some are about the evaluation of nodule malignancy. Since the presence of nodule does not unquestionably define the presence lung cancer and the morphology of nodule has a complex association with malignant growth, the diagnosis of lung cancer requests cautious examinations on each suspicious nodule and integrateed information every nodule. We propose a 3D CNN CAD system to solve this problem. The system consists of two modules a 3D CNN for nodule detec tion, which outputs all suspicious nodules for a subject and second module train on XGBoost classifier with selective data to acquire the probability of lung malignancy for the subject
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    Periodic Mining of Users Routine Desktop Behavior for Task Assistance
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2019) SEYYAD ZISHAN ALI; FA16-RCS-028; LHR TP 5622
    Now-a-days the use of technology is rapidly growing in the workplaces and a large number of people are using desktop computers for their personal and professional tasks. As a result of this growth, the large amount of data is been gathered and it’s become difficult to retrieve and manage this huge amount of data in a beneficial way. People usually face difficulty in managing documents and finding relevant files when they want to do specific task in hand. We believe that pervasive monitoring of user’s desktop activities and then mining of user’s periodic behavior can be useful in assisting the future tasks of user. A number of studies have been carried out on monitoring user desktop routine activity. However, these studies have not utilized user’s temporal routine activity patterns for assistance. It was a need to develop a desktop assistant by mining user routine behavior in accordance with the time and date more specifically weekdays and weekends. This work detects and classifies meaningful activities from large data and then models these activities into user’s one day and routine behaviors. This system reads log file and detect some meaningful activities from the large data, after identifying activities the system model these activities into user’s one day activity, then system model routine activities based on one day activity models and evaluates probability measures of user’s routine activities. After getting best results the system finds the best models of routine behavior which may use for the prediction of next possible routine activity of the user based on logged data set. It mines the best models of routine activities which may be useful for the prediction of next possible activity of the user. After a comparative results analysis of standard machine learning and data mining algorithms this research found Decision Tree best for classification of user’s activities with highest mean score of 0.92. Moreover this research has found Decision Tree as best algorithm for prediction of user’s future possible routine activities with 0.84 as highest score. The system has predicted future routine activities of a single user on Tuesday with an accuracy of 92%. This research is basically the implementation of a novel approach for mining user’s periodic desktop routine activity in accordance with the time, date and day.
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