Department of Computer Science
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Item Vision Based Rehabilitation and Information System for Patients(Library Information Services, COMSATS University, Lahore Campus, 2019-06-19) Syyam Noor, Muhammad Hasham; FA15-BCS-127 , FA15-BCS-058; Dr. Allah Bux Sargano; LHR TP 5898The system is an action verification system for rehabilitation of patients with limited mobility. In this regard, a Kinect depth sensor is used to capture the image-sequences of patients for identification of different actions in real time during rehabilitation exercisers at home. The system is trained to make decisions whether the person is performing the rehabilitation exercises correctly or not, each exercise contain some passing parameters, and this is to ensure that all the exercises are done in a complete and perfect manner. A desktop based application is designed for user’s interaction. This application contains a list of exercises available, the user will have an option to choose any of them according to the need. Physiotherapists using this system at their clinics will be hugely benefitted with this, it will act as their personal application based assistant for mobility exercises. Especially, the report generation feature will help them in tracking the patient’s progress. The report can be generated at any given time while doing an exercise that will brief the user about the angles and distances between the joints and what is the ideal one. The main ambition behind making this rehabilitation system is to recover pre-injury stages in all phases of physical fitness. The equipment required by the system are economical ones to help those patients who genuinely need this system which everyone can set up at their own places. This would allow patients to perform the rehabilitation exercises in sitting position at their relaxing place while preventing patients from falling down during training. The proposed system is 100% real-time (gives results on run time), effective to vision-based action identification, and demands low-cost system requirement (hardware and software), which is affordable for almost everyone.Item Voice Assistant for WEGO(Library Information Services, CUI Lahore, 2023) Muhammad Shafay Abid Shafayan; sp 19-BCS-040; Dr. Allah Bux SarganoThis project has been developed as a part of an industrial FYP with WEGO. WEGO is a travel assistant application that allows the user to search for and book flights and hotels. We were assigned the task of automating the process of searching the flight through WEGO with the help of a voice assistant. This project will improve the user experience of searching for a flight through WEGO and make the user less prone to errors. The voice assistant for the project has been trained using Google’s Dialogflow and the application has been developed using Kotlin and Jetpack Compose. This is a standalone Android application and has not been integrated into the existing WEGO application. Moreover, the application provides a complete chatbot experience where the user can communicate with the voice assistant regarding the flight they want to search. Once the user has communicated all of the required information with the voice assistant, they will be shown their search results on a WebView page from WEGO within the application. Since the majority of WEGO’s user base lies in the middle east, we have added Arabic and Urdu language support to the application as well. Further functionalities of our application include user accounts and user history. If the user is registered with the application, their history will be saved and they can visit their old search results from the history. User accounts are maintained using Firebase authentication and history is maintained using Firebase Realtime Database. We have succeeded at solving the problem that we set out to solve with this application. The application is working as it should and showing the relevant results. As far as the accuracy of the search results is concerned it is 100% when it comes to taking input in the English language. However, the accuracy is slightly compromised when it comes to Urdu and Arabic. We have used Google’s on-device translation models for adding Urdu and Arabic languages which compromises the accuracy of the search results shown. However, the search results are still respectable.Item Augmented Reality App for Basic Human Organs(Library Information Services, COMSATS University Islamabad, Lahore Campus., 2019-11-20) Fatima Zulfiqar; CIIT/FA15-BCS-030/LHR; Dr. Allah Bux Sargano; LHR TP 5810The proposed system targets a mobile phone android application using Augmented Reality (AR) as means of user interface. AR is a concept of integrating virtual 3-dimensional objects on 2-dimensioanl user screen creating an illusion of a 3-dimensional, real time user experience of an inherently simulated object in the real environment. The system will serve as a medium for extensive class of students focusing on assisting them to build better semantics for biological concepts through E-learning. The application focuses on four main human organs (heart, brain, stomach and lungs). The user will be able to view the 3D model of the four mentioned organs augmented on their books, saving not only learning time but also to provide a unique perspective to the curious nature of the human mind to understand the inherited complexity of its constituent anatomy. The proposed system achieves this by providing unique interfaces for the user to understand the working functionalities of human anatomy by simulating the 3D models. Converging to the recursively natural approach in machine learning we choose, state of the art, pre-trained convolutional neural network (CNN) as system’s classifier to recognize particular human organs such as Brain, Heart Lungs and Stomach from input camera frames. Once recognized, the particular target model is loaded from the model repository and simulated on user screen for interaction. To ensure maximal student access, the system is deployed as an android application, fine tuned with respect to the execution parameters such as CNN and anatomy model’s storage size kept to the minimum. For modelling and animation of organs, 3D modelling software like Blender, Cinema 4D and Autodesk Maya is used respectively. Unity platform is used for integration of different components and to develop Augmented Reality based mobile application.Item Smart Home Surveillance System(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Muhammad Ibrahim Arif; SP19 BCS 033; Dr. Allah Bux SarganoThe Smart Home Surveillance System is an advanced intelligent security system that makes our residence safe and secure. The system aims to transform the regular CCTVs into intelligent cameras that will have some additional features like Suspicious Activity Detection, Smart Video Recording, Object Detection, and Notification alerts. The system is built not only for residential security but also for commercial areas. In fact, it can be installed in any environment either working or residential to track and minimize the occurrence of criminal activities. It detects the activities like Robbery, Assault, Stealing, Burglary, and Wall Climbing. The system's mobile application uses a trained deep learning model (CNN) that classifies the activities as suspicious or non-suspicious. The model has trained on the UCF crime dataset as well as our custom-made dataset, based on which the binary classification is being done. If the activity is classified as 'Suspicious', the user will get notified immediately on the app through the snapshot of the scene. Yolov5 has been used for object detection. Further, there's a smart recording option that can be enabled through the mobile application. By enabling the smart recording option, the system will start recording only on the detection of objects in the frame. There's an admin portal of the system, which is a web app that would be used by the administrators. The user will be assigned the login credentials through this portal, and it is also responsible for monitoring the whole system. There is also a web portal on the user side that shows the live streaming once the user logged into the portal through the provided credentials. Thus, the whole system facilitates the users by providing a secure and intelligent security system. To evaluate the model, the confusion matrix has been used. The confusion matrix is being created using a test set which is obtained from the UCF crime dataset and some custom recorded videos. The model has an accuracy of 89% with the best confusion matrix obtained so far.Item Magnification Independent Approach to Diagnose the Breast Cancer from Histopathological Images(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2022) SHEHROZ TARIQ; SP20-RCS-017; LHR TP 8354; Dr. Allah Bux SarganoBreast cancer is one of the top cancers that cause death globally. Hematoxylin and Eosin-stained images diagnose biopsy tissue, and experts are usually upset with the closing opinion. Computer-assisted diagnosis techniques enable to cut costs and improve efficiency in this practice. Automation of breast cancer multi class classification from microscopic images has a significant impact on computer-assisted breast cancer identification or prediction. The purpose of breast cancer multiclass classification is to classify different subtypes of breast cancer (Papillary, Adenosis, Mucinous, etc.) after identifying the Benign or Malignant Class. Yet, multi-class classification of breast cancer from microscopic imageries aspects dual major encounters: First, the excessive problems in methods of breast cancer multi-class classification compared to binary class classification (benign vs. malignant), and second, the slight variances in several classes because of the high-quality image inconsistency forms, high cohesiveness of malignant cells, and inconsistency of color distribution. As a result, although the automatic multi-class classification of breast cancer from microscopic images has considerable clinical importance, it has never been investigated. As we are dealing with microscopic imaging, magnification plays a vital role while classifying cancer. Existing literature techniques exclusively emphasize magnification-dependent binary or multi class classification and do not continue the effort for magnification-independent breast cancer diagnosis. Using a proposed model inspired by Vision Transformer (ViT) model with multiple variations, this work offers a robust and novel breast cancer multi-class classification approach combined with a binary classification method from a clinical standpoint. The evaluation methods that are utilized in our work are the accuracy score and confusion matrix. The comprehensive experiments are conducted using the publicly accessible benchmark breast cancer dataset named as BreaKHis. As we have trained three models; first for binary classification and the other two for multiclass classification. We achieved 89.4% accuracy for our first model, 74.57%, and x 57.41% accuracies for the remaining two multi-class models. These outcomes are astonishing as related to existing literature. As existing literature technique claims 88.9%, 63.6%, and 52.7% results against these models. The outcomes showed that the proposed methodology surpassed existing methods in breast cancer diagnosis by a significant margin. This is because of the Convolutional layers, we added before passing to the transformer in our proposed model. This convolution allows the features of the breast legion to be further prominent before applying the multi-head attentionItem A Robust Multi-class Skin Cancer Classification using an Attention-based CNN Model(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2022) Sara Younas; FA20-RCS-015; LHR TP 8352; Dr. Allah Bux SarganoAmong lesions having high mortality rate, skin lesion is at top of the list. The patient‟s life could be rescued through early diagnosis. The manual identification period could be prolonged, as different categories of skin cancer have high similarities between their structure, color, and size which could lead to misclassification. An automatic and robust system is crucial for the timely and accurate categorization of skin cancer. In the last few decades, deep learning emerges as revolutionizing field, especially in the area of medical imaging. Most state-of-the-art work adopted transfer learning and ensemble learning based techniques for this problem, in which models used for TL and EL are designed for different types of problems and mostly trained on the huge amount of data which sometimes don‟t perform well for distinctive skin cancer problems due to very challenging datasets. This work proposed novel architecture based on a deep learning technique, which is designed especially for skin cancer classification problem. For learning, this method used dermoscopic images, which are excellent quality images captured through a high-level magnifying device, to obtain clear insights of skin containing cancer cells. The proposed architecture‟s main contribution includes, enhancing performance by embedding inception residual (IR) blocks in which, inception block is warily designed with several parallel layers that are merged together. Moreover, residual connections are established in each block to learn from low and high-level features concomitantly. These connections accelerate the learning process along with coping with the vanishing gradient problem. Deployment of the attention unit in the network boosted performance by suppressing the value of noise-containing features and assigning maximum value to important and relevant features during the learning process. Reducing the number of parameters by keenly optimizing the size and no of filters is another milestone attained through suggested model. The presented architecture achieved promising results by attaining 91.63% accuracy, 91.60% x precision, 91.60% sensitivity, 91.52% specificity, and 91.60% f1-score on the ISIC-19 dataset.Item 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 Person Re-identification for Real-World Scenarios using Deep Learning(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) Muhammad Shahzaib Minhas; FA21-RCS-027; LHR TP 8680; Dr. Allah Bux SarganoWith the increasing need for efficient monitoring and tracking systems in surveillance, Person Re-identification (Re-ID) has emerged as a pivotal solution for monitoring public spaces and automatically tracking suspicious individuals. While significant advancements have been made in the person Re-ID domain, the challenge of continuously evolving new data remains a critical concern for practical model deployment. Most existing works rely on offline learning, which poses difficulties in adapting to dynamic environments, accommodating evolving identities, and ensuring efficient resource utilization in real world scenarios. To address these limitations, there is a pressing need for an active learning system capable of being incrementally updated with new data. In this research, titled "Person Re-identification for Real-World Scenarios using Deep Learning," we propose an innovative framework for iterative model training on progressively evolving data. We selected two benchmark datasets, Market-1501 and DukeMTMC-reID, and divided them into manageable chunks. Our approach involved training a deep learning model incrementally on these chunks. Following each training session, we updated a Representative Memory with the most characteristic images of each identity from each chunk. These images, stored in the Representative Memory, were then utilized in subsequent model updates, where they were combined with new data for further training. To evaluate the efficacy of our model, we conducted a cross-domain evaluation after each iterative training phase. The results were promising with the model achieving accuracy rates of 77.2% and 68.9% in terms of Rank-5 metrics on the Market-1501 and DukeMTMC-reID test sets, respectively. This research not only demonstrates the feasibility of incremental learning in person Re-ID but also paves the way for future advancements in applying deep learning techniques to real-world surveillance and security applications.Item A Machine Learning Approach for Mutation Identification in Gene Sequences to Detect Lungs Cancer(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2022) Umer Farooq; SP19-RCS-029; LHR TP 8334; Dr. Allah Bux SarganoCancer is one of the leading causes of death worldwide. The mutation is one of the basic causes of cancer, which is caused by any change in the gene sequences of the human body. The abnormal and uncontrolled growth in the cells of the lungs is a cause of lung cancer. It is the second most common type of cancer worldwide, considered 11.2% of all cancers. According to the cancer statistics of the United States of America (USA), about 236,740 people were diagnosed with lung cancer in the year 2021, and about 130,180 people died from lung cancer in the same year. This study aims to develop a dataset, machine learning, and deep learning-based techniques to address the above-mentioned issues. In this regard, the asia.ensembl.org website is used to extract normal gene sequences from the lung cancer dataset, while the mutated gene sequences for lung cancer are obtained from the intogen.org website. Thus, the dataset of 1588 samples consisting of 2576 mutations is developed by following the standard dataset creation method. Then, different feature extraction methods are applied to extract the discriminative features from the dataset; these features include raw moment, Hahn moment, central moment, position relative incident matrix (PRIM), reverse position relative incident matrix (RPRIM) calculation, feature vector determination, accumulative absolute position incidence vector (AAPIV), reverse accumulative absolute position incidence vector (RAAPIV). These features are then fed to the traditional machine learning (ML) algorithms such as Random Forest (RF), Logistic Regression (LR), Multi-Layer Perceptron (MLP), and an ensemble of these traditional ML algorithms. These extracted features are also fed to deep learning (DL) methods such as LSTM, GRU, Bi-LSTM, and an ensemble of these DL algorithms. An averaging technique is applied to ensemble the classification algorithms. Three validation methods are used for evaluation: self consistency testing, 10-fold cross-validation, and independent set testing. The final classification result of lung cancer is generated as accuracy, specificity, Mathew's Correlation Coefficient (MCC), sensitivity, and Cohen's Kappa. The efficacy of this study can be measured with the accuracy of 94.5% obtained from the ensemble of x machine learning models using 10-FCV and with the accuracy of 97.3% obtained from the ensemble of deep learning models using the independent set test.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%