Department of Computer Science
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Item Data Augmentation using Machine Translation for Urdu Document Classification(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2022) Khizer But; FA19-RCS-018; LHR TP 8355; Dr Muhammad Waqas AnwarA huge volume of textual data is being created due to the advancement in technology and widespread use of the internet. The exponential growth of textual data in the Urdu language has given rise to its applications in spam filtering, fake news detection, website classification, sentiment analysis, etc. However, Urdu is a low-resource language and lacks the support of automated tools for its applications. This research focuses on using data from a resource-rich language to translate it into the Urdu language to overcome the data scarcity concern. For this purpose, the BBC English news articles dataset was acquired and translated using the Google-trans-new library. Further, an Urdu dataset of fake news was enriched with 1200 articles which were collected from an online resource named Urdu Point. Urdu and machine-translated datasets were merged to come up with a diverse and large augmented dataset and augmented downsized dataset. A supervised classification method was employed with TextCNN, LSTM, simple Bi-LSTM, Bi-LSTM with attention, and SVM on the datasets. Different training experiments were applied to evaluate the effectiveness of the classification with Urdu, machine translated, and augmented datasets. Training and testing on the same dataset gave good results whereas the cross-testing from machine translated to Urdu and Urdu to Machin-translated dataset resulted in a performance decline. Models trained with an augmented dataset achieved promising results when tested on the original Urdu test dataset and results were improved as compared to results achieved with no augmentation. These promising results are suggestive of the applicability of the proposed strategy of creating datasets on large scale using machine translation to automatically classify the documents in the Urdu language. Bi-LSTM resulted in the highest F-1 score of 0.97 with the augmented dataset.Item Segmentation of Brain Tumor using Deep Learning Techniques(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2022) Muhammad Hashir Khan; FA19-RCS-026; Dr. Muhammad Aksam Iftikhar; LHR TP 8357Cancer is one of the fatal types of disease in the World. A brain tumor is a type of cancer that appears in the glial cells of the brain. There exist two types of brain tumors. One is High-grade glioma and the other is low-grade glioma. The survival rate for HGG is very low while according to history most patients diagnose with LGG survive this disease. To detect the tumor in the brain one of the popular techniques is the Magnetic Imaging Resonance (MRI). It has four different modalities and neurologists after observing the different MRI modalities diagnose the tumor location and category. But manually detecting the tumor from MRI scans is a difficult task. There is always a need to segment the tumor region automatically from an MRI scan. To solve this problem, many researchers proposed different solutions. Many researchers use deep learning models to address this issue. One of the earliest networks used for this was Convolutional Neural Network (CNN) and it gives good results for tumor segmentation tasks. In this research, the approach we used is based on the ensemble method. In this approach, we use three different U-Net models to train them from scratch and predict the results on each model separately. After that, we ensemble all three models predicted results applying the majority voting technique and produce a result on testing data. Our proposed ensemble method produces a dice score of 0.86, 0.88, 0.89, and Hausdorff distance 2.0, 2.0., 2.0 for the three categories of a tumor, Enhancing tumor, Whole tumor, and Tumor core respectively on the testing dataset. Our results are better than many comparable state of the art method including the Brats 2019 challenge prominent papers.Item An Improved Technique for Software Requirement Prioritization using Functional Requirements(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2022) Zunaira Jamil; FA19-RCS-016; Dr. Farooq Ahmad; LHR TP 8341Earlier recognition, categorization, and prioritization of elicited requirements in the software development is essential for the project's success because it allows problems to be identified and classified in early stages. The most critical and difficult stage of the Software Development Life Cycle is gathering requirements Following this stage, the requirement priority should be handled and saved for upcoming projects. Using machine learning classification approaches software requirement prioritization can be predicted during the requirement gathering stage. To predict the priority of functional requirements for developing new software, a dataset having the attributes of software requirements is required. In this research, a dataset named software requirement prioritization(SRP) is presented that includes requirements from Software Requirement Specification (SRS) of several open-source projects' and ranking value of different factors that help in priority prediction. Ranking value is obtained from the IT experts through survey on rating scale of 1 to 5 where 1 ranks lowest and 5 ranks highest. Further for our proposed SRP system, content-based n-gram approach is considered at word and character level. After that combine multi-label classifier i.e. One Vs Rest, Label Powerset, with six classical machine learning classifiers i.e. Linear SVC, Logistic Regression etc. Micro F1 score, Accuracy score, and Hamming loss are used for evaluation. However, when compared to the other combination of OVR and Label power set with Gradient Boost Classifier, Random Forest, Nave Baise, linear SVC, Bagging, and Adaboost, Label Powerset with Linear SVC performed well.Item A Study of Crime Analysis and Prediction using Data Mining Techniques(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2022) Syed Huzaifa Ansar; FA19-RCS-030; LHR TP 8338; Dr. Hamid Turab MirzaCrime is considered an offense in the social or moral values of a society, due to the constant increase in crimes the safety and security of people in the world are at high risk, because of the severity of this problem, crime analysis is one of the most highlighted research topics from the past few years. In criminology, data mining plays a major role as data mining is a way to discover hidden patterns among the dataset. Researchers are trying to get useful insights for crime prediction by training different machine learning models from real-world data, although a lot of efforts have been made in this context. But most of the models cannot still predict the crime and crime variables such as crime location, date and time. This process becomes even more complex and time-consuming when trying to predict the crime variables. In short, the main goal of this study is to narrow down this gap by developing a system for crime analysis and prediction by using state-of-the-art data mining techniques. In this study, three different deep learning models including Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN) with LSTM layers hybrid model, and Bidirectional Long Short Term Memory (Bi-LSTM) are used to perform a time-series analysis of Chicago Crime dataset from 2001 to 2022. The District-wise time series analysis is performed on the number of crimes for a Month, Week, Day, and night. Mean Square Error (RMSE) and Mean Absolute Error (MAE) are used as evaluation measures to evaluate the performance of models. After the experimentation, results showed that the BILSTM model obtained the best results for the District wise predictions of crimes on the Chicago dataset. The results indicated that the BILSTM model gives the highest performance results (MSE = 7.16, MAE = 2.01) and outperformed all other models in forecasting crimes by day.Item Survival Prediction of Glioma Patients Based on Radiomic Features(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) Talha Naeem; FA19-RCS-029; LHR TP 8464; Dr. Muhammad Aksam IftikharOur brain is the most important part of our body that controls and commands all the functions. It Takes information from different organs of the body and five main senses and then commands the tissues to perform specific tasks. Brain administrates the motions, creativity, emotions, and memory. The brain is enclosed within the skull, and it consists of Cerebrum, Cerebellum and Brainstem. Cerebrum hemisphere portion is further split into lobes. Each hemisphere includes the Temporal Lobe, Frontal Lobe, Parietal Lobe, and Occipital Lobe. All these hemispheres are once further divided into their areas to accomplish the specific factions. Each lobe cannot accomplish any of the body function without the help of other lobes separately. The relationship between the lobes of the brain and left and right hemisphere is very complex. The left hemisphere deals and commands to the right portion of body and right hemisphere deals and commands to the left portion of body. Gliomas are unique and very extensively recognized categories of the brain tumor. Glioma is a brain tumor category that starts producing in the brain or the spine glial cabals. It is a kind of sturdy cell in our brain. For precise and strong diagnosis of glioma patients both the segmentation into sub-regions and overall survival (OS) prediction tasks are very important. These tasks are used for the better treatment arrangement and risk influence investigation. Form the past couple of year’s investigation in brain tumor has seen remarkable progress. Many researchers present admirable work for better treatment of glioma patients, better treatment arrangement and OS prediction time. However, still to the current era, huge gap exists for precise segmentation and OS prediction task of the glioma patients. Researchers are trying to progress in the complexities and challenges of brain tumor. In this proposed methodology, our set of rules achieve good performance in OS prediction task of glioma patients by using machine learning outline. We perform segmentation of brain sub-regions on magnetic resonance imaging (MRI) scans. Based on segmented sub- regions, we extract the radiomic features such as intensity, shape, and texture. The proposed approach employed on multimodal brain x tumor segmentation (BraTS) with OS prediction task for BraTS-2020 dataset and trained on the random forest (RF) model for the OS task for the prediction of survival days for brain tumor patients. The evaluation of the performance measures is carried out for segmentation process by a pre-processing technique and OS prediction by measuring accuracy of the RF model and we obtained the results of 59% and 51% for training and testing of glioma patients respectively.Item Activity Recognition for Assisted Living based on Multimodal Features using Deep Learning(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Hafsa Yaseen; FA19-RCS-022; LHR TP 7599; Dr. Allah Bux SarganoHuman activity recognition (HAR) is a prominent field in computer vision and signal processing that analyzes the information obtained from numerous sensors, including vision sensors and wearable sensors. The purpose of HAR is to recognize actions from a sequence of observations on the activities of individuals and environment events. It provides a broad variety of applications, including ambient assisted living, robotic technology, intelligent surveillance, human-computer interaction, smart home, transportation, and smart healthcare. As assisted living indicates the technological services that help impaired people and senior citizens to spend independent life. Therefore, HAR, which facilitates proactive gestures and interactions with their surroundings, has become a significant precondition for assisted living applications. For all that, tremendous efforts have been made to reliably capture human action and behavior by manipulating single modality data, but the combined analysis of multimodal data has received less attention. Different modalities usually contain complementary information that must be combined for better learning of action recognition for Ambient Assisted Living. In this research, a novel framework called “Activity Recognition for Assisted Living based on Multimodal Features using Deep learning” is proposed to leverage intra-modality discriminative features as well as inter-modality connection in visual and inertial data using deep neural networks. Two separate unimodal, i.e., visual and inertial models, are proposed to learn action recognition classifiers for these modalities effectively. These models automatically acquire high-quality discriminative action-related images and inertial features. Finally, these heterogeneous models are combined into an end-to-end approach via decision-level fusion. The comprehensive experiments are conducted using the publicly accessible benchmark C-MHAD dataset. The outcomes showed that the proposed methodology surpassed existing methods in action recognition by a significant margin, with an F1-score of 89%Item Attention-based Multimodal Sentiment Analysis and Emotion Recognition using Deep Neural Networks(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Ajwa Aslam; FA19-RCS-009; LHR TP 7284; Dr. Allah Bux SarganoIn recent years, multimodal sentiment analysis and emotion recognition has become a hot research topic among researchers. Instead of manually scrutinizing the sentiments and emotions, there is a need for an automatic process that would easily be applied to numerous tasks, such as product analysis, social media monitoring, election prediction, etc. Though great efforts have been devoted to gauging the people’s sentiments and emotions by manipulating the single modality data like text, or images or audio, but less attention is paid to the joint analysis of multimodal data in social media. Different modalities usually contain complementary information that must be combined for better learning of sentiments and emotions. In this research work, a novel framework: Attention-based Multimodal Sentiment Analysis and Emotion Recognition (AMSAER) using deep neural networks is proposed to exploit intra-modality discriminative features, as well as inter-modality correlation in visual, audio, and text modalities. Three separate unimodal i.e., visual, acoustic and semantic attention models are proposed to effectively learn sentiment and emotion classifiers for these modalities. These models automatically acquire the high-quality discriminative sentiment and emotion-related semantic words, image-regions, and raw audio features. Subsequently, deep hierarchical multimodal based on intermediate fusion is proposed to learn an internal- correlation between these modality features hierarchically. This model first learns the correlation among bimodal features and then among the trimodal features for the joint classification of sentiments and emotions. Finally, all four distinct models are fused into a holistic framework through decision level fusion for multimodal sentiment analysis and emotion recognition. Extensive experiments have been conducted on publically available benchmark IEMOCAP corpus for both sentiment and emotion prediction. The results demonstrated that our proposed architecture outperformed existing techniques with a solid margin in both sentiment and emotion classification, achieving 84% and 92% accuracy, respectivelyItem Smart Reply Suggestion for an Email in Multiple Languages – Data and Metho(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Nazia Jehan; FA19-RCS-002; LHR TP 7295; Dr. Rao Muhammad Adeel NawabSmart Reply Suggestion (SRS) is a novel end-to-end system that refers toward the method of suggesting the three short email responses. These responses are themselves complete short email replies, suggested in diverse form for (monolingual) email i.e. English. In literature, mostly the model is specifically designed for monolingual emails like English. There is a paucity of state-of-the-art techniques that handle both monolingual and multilingual email at a time. Moreover, there is a paucity of benchmark corpus in multiple languages-based emails. This research has tried to overcome the limitations of an earlier study of the SRS system by proposing a novel method for the SRS system which handle both the monolingual and multilingual email at a time. In this research, we treat this problem as a multilabel text classification problem. we developed the " (ML-SRS) Email corpus collected from academia, consisting of three different languages including English, Roman Urdu and a combination of Roman Urdu and English emails (text) and developed the list of smart response prepared to annotate that dataset manually the inter-annotator agreement calculated and then standardized the dataset in .CSV format. For our proposed SRS system, we considered two approaches. In the first approach, we considered content-based n-gram approach at word and character level with the combination of multi-label classifier i.e. One Vs Rest, Label Powerset, with eight classical machine learning classifiers i.e. Linear SVC, Logistic Regression etc. In the second approach, we consider LSTM, CNN, GRU, Bi GRU, Bi-LSTM models. Both approaches are evaluated on Micro Precision, Micro Recall, Micro F1 score and Hamming loss. However, Label Powerset with the combination of Linear SVC performed well as compared to others