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 Automated White Matter Hyper Intensities Segmentation in MR Images(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2022) YAQEEN ALI; FA17-RCS-008; LHR TP 8345; Dr.Muhammad Aksam IftikharWhite matter hyperintensities (WMHs) are high-intensity pixels in MRI, especially in FLAIR images. The presence of white matter hyperintensitiesn brain MRI is associated with cerebral small vessel diseases (CSVD), aging-related brain diseases, strokes, and dementia. The extraction of hyperrintensities from FLAIR MRI images of the brain is called WMH segmentation, which is important because it provides details about the volume, location, and shape of WMH lesions. These measurements and the quantity of WMHs are important in the research and diagnosis of a patient. In usual practice, the field expert (radiologist) performs manual segmentation of medical imaging, which is time consuming, costly, and subjective. A few years ago, several different semi-automated and fully automated methods were proposed for different tasks such as WHM detection, stroke lesion segmentation, and WMH segmentation. Some of these methods use supervised learning algorithms with handcrafted features or more recently learned features (representations). On the other hand, some methods also use unsupervised learning for segmentation. These days, deep neural networks are quite popular and robust for learning problems and claim more than human performance in several problems. A convolutional neural network is a deep network structure and is very attractive and effective in computer vision domain problems, especially as a robust method for image classification and segmentation. Recently, many algorithms have been proposed for WMH segmentation, including CNN, but the automatic WMH segmentation task is still challenging due to coexisting with other abnormalities and spatial variability of white matter lesions. This study used an ensemble model of three variants of the U-NET based models. A U-NET network is a network-advanced version of CNN for bio-medical image segmentation tasks. x Method The proposed architecture is an ensemble of three U-net base models. One model is fully convolution neural network and other two are Residual U-Net models. We create ensemble model by giving weights to three trained models. These weights are tuned through experiments. U-NET has two parts. One part is called a contracting path that learns features of an input image and reduces spatial information, and the other part is an expansive path that gradually combines features with spatial information from previous contracting path layers to produce a segmented mask of the input image. The proposed architecture uses reverse convolution or transpose of convolution operators in the second path instead of the convolution and pooling operations that are used in CNN and uses a large number of feature channels in the up-sampling part. Due to this, the model can better carry context information with spatial information to deeper layers of the network as compared to CNN. The residual blocks helps to train deep network without facing vanishing and exploding gradients problems. These characteristics of the proposed model makes it better than CNN and a simple U-NET for the WMHs segmentation problem. Result To evaluate the proposed system, the MICCIA WMHs Segmentation Challenge 2017 (http://wmh.isi.uu.nl/) dataset is used. Our study used some state-of-the-art evaluation measures like Dice Similarity Coefficient (DSC), a modified Hausdorff distance (H95), the absolute percentage volume difference (AVD), recall, and F1 Score. The DSC, AVD, H95, recall, and F1 scores respectively 0.93, 2.21, 5.13, 94%, and 93% against the ground truths for FLAIR and T1 sequences in the WMHs Segmentation Challenge 2017 dataset. Conclusion It is concluded that the proposed system provides a close match to expert delineation across the state-of-the-art dataset used by WMHs Segmentation Challenge 2017 training, and the proposed system is more efficient and effective for automated WHMs segmentation. The proposed system performs better in the standard evaluation measures than the state-of-art techniqueItem Understanding User Mobility Behaviour: A Study of Check-in Patterns in Location-Based Social Networks(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Sajeel Ur Rehman; FA17-RCS-014; LHR TP 7297; Dr. Hamid Turab MirzaIncreasingly there is a tendency amongst social media users to ‘check-in’ i.e., an action allowing individuals to share their location at any given time, to places they physically visit through various social media applications like Facebook, Foursquare, etc. Statistics from these applications serve as a huge reservoir of user data which can be used to understand and predict the mobility behaviour of these users through formulation of a recommender system. The two basic methods for developing such a recommender system are memory-based and model-based system. Owing to pronounced limitations of the memory-based system such as the provision of a huge amount of data being essential for effective performance, this work develops a recommender system using model-based method. User mobility data of New York and Tokyo city (collected over 10 months) is obtained from Foursquare.com. This data is utilized to decipher the mobility behaviour of users: ‘checking-in’ to parks, hotels, recreational centres etc. This data only contains time stamps against the ‘checked-in’ locations. To develop a nuanced recommendation system, further parameters are added qualifying these ‘check-ins’ so that more personalized and precise recommendations are furthered. Firstly, the data is enriched by adding weather as an additional parameter. Secondly, Deep Neural Network” (DNN) is formed by embedding features which are then used to develop the DNN Model. Thirdly, model’s generalization is evaluated through Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Square Error (MSE). Based on these evaluations, after testing, it is concluded that MAE produced best result which is only 1.25 on the dataset and value for RMSE is 1.45 which is second best. Moreover, MSE results are not satisfactory being 2.11. It is anticipated that this recommender system will help local e-commerce and other non-e-commerce venues of the region to increase the visibility of their products or services sales by attracting customers through this developed modelItem Software Measurements at Small and Medium Enterprises(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2017) Muhammad Noman; FA14-MSCS-018; LHR TP 6871; Dr. Ghulam RasoolSoftware measurement processes are crucial to recognize, assess, identify and measure software products and resources. However, the successful measurement processes implementation is a challenging task in small and medium enterprises (SMEs). The main goal of this thesis is to propose a measurement framework to assess the significant factors that may influence the successful measurement processes implementation in Software SMEs. A multi-method research technique is used to perform this research. A systematic mapping study is conducted to extract the significant factors, challenges, and limitations that may affect the successful implementation of measurement processes in software organizations. A search string is prepared and, applied to different databases (IEEE, ACM, Springer etc) to identify the relevant studies. In total, 24 primary studies are finalized after applying inclusion and exclusion criteria. All the primary studies are explored comprehensively and, identified success factors are categorized in seven major constructs. A measurement framework is proposed for adaptation of measurement processes in software SMEs based on the findings of the mapping study. A total of seven constructs (success factors) are defined in the proposed framework. The proposed framework is evaluated using an online survey. An online survey is conducted from the software professionals in software organizations of Pakistan. In total, 217 responses were collected and a data set is prepared. Structural equation modeling (SEM) technique is used to evaluate the data set. Statistical tools (AMOS and SPSS) are used for factor analysis and hypothesis testing. The statistical results shows that the proposed framework is acceptable due to satisfactory values of model fit indices and use of reliable items in the path diagram. In hypotheses testing, two of the seven constructs (External Forces, Management Commitment) are found to have a significant effect on successful measurement processes in software organizations. The proposed framework can be extended by adding more factors and it can also be verified in software industries of different countries.Item Multi Layered Rule-Based Technique for Explicit Aspect Extraction from Online Reviews(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2020) Dr. M. Aksam Iftikhar; Mubashar Hussain; FA17-RCS-030; LHR TP 6402Aspect is a feature of product, about which the user is expressing his/her opinion. In the field of sentiment analysis, the key task is to extract aspects or opinion targets from blogs, product reviews or discussions, users have expressed their opinions about a product. Extracting just positive or negative opinion is not much useful without knowing the targeted Aspect or Feature. If the aspect or feature is explicitly mentioned in the user‘s review, that aspect is called explicit aspect, otherwise it is termed as implicit aspect. Extracting explicit aspects from the opinion statements has been an active area of research. To achieve this milestone, multiple methodologies have been developed including supervised, rule-based and unsupervised approaches. Many researchers used dependency relation of the sentence also called syntactical dependencies to formulate the rules for aspect extraction. However this approach heavily depends on the authenticity of the employed POS tagger and dependency parser. Their outputs can be misleading as the users may not follow the grammatical rules strictly while writing their opinions. Another popular approach for explicit aspect extraction is sequential rules based approach, wherein the rules are formed by learning from user‘s behavior. However, in general, the sequential rule-based approaches have poor generalization capability. Moreover, existing approaches mostly considers the aspects as noun or noun phrase, so these approaches are unable to extract verb aspects. In this thesis, for overcoming the said problems with the existing approaches, we have proposed a multi-layered rule-based (ML-RB) technique using the syntactic dependency parser based rules along with some selective sequential rules. These rules are applied one after another (i.e. sequential rules after syntactic rules) for extracting noun explicit aspects form customer‘s reviews, thereby forming layers of the application. We also have crafted rules for extraction of verb aspects after rigorous analysis. In third layer, x we have used these verb rules, which are primarily based on the association among verb and opinion words. The proposed multi-layer technique compensates for the weaknesses of individual layers and yields improved results. As the proposed approach has capability to extract both noun and verb aspect, it has better Recall and F1-score on two publically available bench mark customer review datasets, as compared to other famous approachesItem A Study on Diversification of Online Product Reviews(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2020) Abid Ali; FA17-RCS-034; LHR TP 5982; Dr. Hamid Turab MirzaRecent studies have boosted the e¬commerce industry which has resulted in increased significance of online product reviews. However, this usefulness of product reviews has also attracted the people who try to manipulate overall product perception by generating fake reviews. Another challenge due to boost in e¬commerce is the information overload which is caused by generation of huge reviews data. This study paves a complete pathway by presenting techniques for removal of spam reviews and by proposing a novel algorithm to retrieve a diversified subset of reviews to reduce the burden of information overload. A diversified set of reviews attempts to cover maximum features of the selected product within a limited number of reviews that ultimately leads to reduction in decision time as well as enhances the credibility and reliability for the user. Spam detection techniques were formulated based on deep learning models whereas novel SENTIMENT AND FEATURE ORIENTED DIVERSIFICATION (SeFOD) algorithm was constructed on the features addressed in each review and the sentiments of the review separately. The proposed models showed prominent results and achieved a maximum spam accuracy of 95.78%, 96.38% and 96.18% for LSTM, GRU and CNN models re spectively. The same results were validated on Yelp hotel reviews dataset. Whereas a new measure for calculating the diversity of the reviews set was adopted named as DivScore. The score nearer to 0 means there is no diversity in the set and hence all the retrieved reviews contain similar features. The far this score goes from 0, the more diversity exists in the diversified set. A DivScore of 7.14 was achieved for the selected product from Daraz reviews dataset while 10.88 was the score when a product was diversified from Yelp reviews dataset. This study can be used by e¬commerce industry to maximize their profits as well as is equally relevant for the general users to better choose relevant product for themItem Pipeline for Identification of Terrorist Organization and Classification of Their Members Using Social Media(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2020) Sana kousar; FA17-RCS-023; LHR TP 5978; Dr. Zeeshan GillaniSocial media has a tendency to change perception or opinion of people. The surge in use of social media has made people venerable to exploitation by different banned outfits. The aim of this research is to analyze social media content and to identify potential individual or group of individual and their target. We generated a dataset using Twint API and use natural language processing techniques to preprocess the data and later use network analysis and different machine learning ensemble techniques to identify potential individuals that are targeting and recruiting people in the name of NGO’s.Item 3D Surface Reconstruction using Point Cloud Segmentation with Machine Learning(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2020) Naveed Ahsan Ali Raza; FA17-RCS-018; LHR TP 5977; Dr. Zulfiqar Habib3D point cloud is a kind of geometric data-structure, which is easily acquired using highly advanced 3D sensors. These sensing devices are capable of producing a considerable amount of 3D point clouds by capturing the 3D surface geometries and dimensions from targeted objects and real-world scenes. The captured 3D point clouds are utilized to reconstruct 3D objects, surfaces, shapes, and parts. 3D surface reconstruction is widely used in reverse engineering, 3D medical imaging, 3D printing, and automated medical surgery. Discrete surface reconstruction from 3D point clouds is still a challenging task, because captured data is un-ordered, noisy, redundant, and has topological imperfections. Due to these issues, it is not adequate for 3D surface reconstruction in its current form. In this direction, techniques based on geometric modeling have already been proposed to solve 3D surface reconstruction problems from point cloud data. Due to the irregular format of 3D point clouds, many researchers convert the data into 3D voxel grids or collection of images, which add unnecessary volume and cause issues. On the other hand, most of the deep learning-based techniques have focused on regular input data for surface reconstruction, and little attention has been paid towards 3D point cloud data. Therefore, to address the issues mentioned above, the proposed model is based on unified deep net architecture. The proposed deep net architecture takes raw 3D point clouds as an input. It performs a segmentation technique to tackle un-ordered data and further utilizes this segmentation information for 3D surface reconstruction of objects and their parts. To evaluate the performance of proposed model, experiments have been performed on the benchmark ShapeNet dataset. This dataset is a large-scale repository of 3D point clouds containing 16,880 objects of 16 different categories. The results show that the proposed method achieves superior results as compared to state-of-the art methods with an accuracy of 82% for 3D surface reconstructionItem Mitigation of Denial of Service (DoS) attack in Vehicle to Grid (V2G) Network.(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2020) Sajjad Ahmad; FA17-RCS-012; LHR TP 5976; Dr. Hasan jamalVehicle to Grid (V2G) network will become an emerging technology very soon because of the efficient use of electrical energy and the reduction of environmental pollution. V2G network plays a vital role in the advancement of charging and discharging the batteries of Electric Vehicles (EVs). An EV charges or discharges the batteries to the Smart Grid (SG) through Central Aggregator (CAG). So that SG and CAG are the important component of V2G network and CAG is an intermediate between SG and EV. CAG is connected with SG through the internet (i.e., Wi-Fi) and also connected with EV through a Dedicated Short-Range Communication (DSRC). Therefore, CAG has a special significance in V2G network. However, CAG’s network security is a major issue as compared to the other wired network because it has an open access wireless media and no fixed cybersecurity mechanism. There are various cybersecurity attacks (i.e. Denial of Service (DoS), Wormhole, Grey hole, Sybil, sinkhole, timing, Man in the Middle (MITM) and eavesdropping, etc.) that reduce the performance of the V2G network. So, it is important to develop a security mechanism that can protect against cybersecurity attacks on CAG. In this research, we investigate the effect of a cybersecurity attack on V2G network regarding CAG. So that, the deploying of a reliable Intrusion Detection System (IDS) can increase the security of V2G network and detects and mitigates the DoS attack before damaging the V2G network. But the existing IDSes cannot work properly in V2G network because of V2G network has special requirements and characteristics. The focus of this thesis to develop IDS for V2G network that can detect the DoS attack. So we establish V2G network cybersecurity attack detection and mitigation mechanism called Central Aggregator Intrusion Detection System (CA-IDS) against DoS attack that is deployed on CAG and can efficiently detect and mitigate the DoS attack launched by a malicious EV. The proposed system’s experiment is performed in NS-3 (V-3.30) that generates normal network traffic and DoS attack traffic and various tests are conducted. These tests generate results (both normal and DoS attack traffic) that are used to measure the performance of V2G network. IX Our proposed system is compared with existing systems by using different parameters such as throughput, jitter, and accuracy. The analysis shows that the proposed CA-IDS has higher throughput, lower jitter and higher accuracy as compared to the existing schemes.Item Recovery of Anti-Patterns from Microservices(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2020) Sabahat Atta; FA17-RCS-028; LHR TP 5980; Dr. Ghulam RasoolMicroservices have brought an evolution in the Service Oriented Architecture by introducing high independence at various levels. These services provide independent development, service to service communication, individual database per service, independent deployment, and maintenance, etc. The service-oriented systems support the quick implementation of customized changes to software applications. These frequent changes might result in introducing some feeble solutions. These solutions commonly known as “Anti-Patterns”, can negatively impact the quality of the microservices. Another reason for anti-patterns occurrence may be due to the migration of software applications developed on monolithic architecture to microservices architecture. This process demands a high knowledge of microservices-based design patterns and best practices to avoid flaws that might instigate while migration from centralized to a distributed environment. In this research, we have selected 18 microservices related anti-patterns and proposed an approach for their detection. We found only a single approach that presented an algorithm to detect microservices specific Anti-Patterns [1]; the study focused on detecting five anti-patterns. However, our proposed approach differs from the state-of-the-art approach, as, firstly, we focused on the detection of a large number of anti-patterns. Secondly, we have implemented reverse engineering on microservices-based systems and then detected anti-patterns from these systems. Our research focuses on the detection of microservices specific anti-patterns from microservices-based software applications. Our approach is developed as an automated approach supplemented with an add-in for Sparx System EA for the automatic detection of anti-patterns from microservices-based systems. We have evaluated our approach using precision and recall metricsItem Development of Large Scale English-Urdu Machine Translation Corpus for Statistical and Neural Machine Translation Systems(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2019) Moodser Hussain; FA17-RCS-005; LHR TP 5623; Dr. Rao Muhammad Adeel NawabThe process of automatically converting the text of one natural language into an other is known as Machine Translation (MT). The two state-of-the-art machine translation techniques are Statistical Machine Translation (SMT) and Neural Ma chine Translation (NMT). In both MT techniques, a large-scale parallel aligned corpus of source & target language text is the most basic requirement to develop quality models. For the Urdu language, there are minimal parallel resources de veloped until now, and these existing resources are not enough to produce some quality translation systems. This research study is majorly focusing on: (1) the development of a large-scale semi-automatically aligned English-Urdu parallel corpus of more than 150k sen tences by collecting the data from different domains. (2) the compilation of large sized monolingual data for English and Urdu language by merging existing cor pora. (3) the development of a character-separated parallel corpus for supervised transliteration (by using Wikipedia titles) (4) the evaluation of impact generated by language model and monolingual corpus on translation quality (by using differ ent data and word-order in language models training). (5) development of Phrase based SMT (PB-SMT) and Recurrent Neural Network-based NMT (RNN-NMT) systems to evaluate and compare their performance on the proposed parallel cor pus. (6) the usage of supervised transliteration and ‘minimum error rate training’ to enhance the quality of SMT. To evaluate the performance of MT systems, ‘Bilingual Evaluation Understudy (BLEU)’ is used as an evaluation measure. The best-achieved scores for English to-Urdu translations are 37.39 and 32.70 by using PBSMT and RNN-NMT, re spectively. While for Urdu-to-English translations, PBSMT and RNN-NMT yield the best score of 28.20 and 30.63 respectively