Department of Computer Science

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Author: search.filters.author.Dr. Usama Ijaz BajwaHas files: Yes

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    Sentinel: Smart Surveillance System with Automated Anomaly Detection
    (Library Information Services, CUI Lahore, 2022) Hammad Ali; FA20-BCS-087; Dr. Usama Ijaz Bajwa
    This project addresses the critical need for efficient and real-time anomaly detection in surveillance systems, considering the widespread deployment of surveillance cameras in diverse environments. Traditional server-based approaches pose challenges related to cost, network strain, and responsiveness. Leveraging edge computing and deep learning, our project aims to develop a cost- effective solution. Success criteria involve achieving state-of-the-art accuracy, real-time inference and streaming, and seamless integration with existing networks. Our goal is to explore existing solutions and propose an architecture that optimizes model performance on edge devices while
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    Panoptes
    (Library Information Services, CUI Lahore, 2022) M. Abid Ullah; SP18-BCS-120; Dr. Usama Ijaz Bajwa
    Nowadays we can find security cameras everywhere, from roads to malls, we are under surveillance. A lot of people install security cameras in their houses and the sole reason for installing security cameras is safety. Everyone is worried about the safety of someone, some people are worried about themselves, some about their family, and some are concerned about the safety of their friends. In short, everyone cares about being safe. But how about increasing the security up a notch? How about people installing software with their security cameras that will automatically detect suspicious activities and report authorities? The primary goal of this project is to develop a system that is capable to detect any anomalous and suspicious activities from given video frames captured by surveillance cameras and reporting them to authorized personnel for immediate actions. It is very common in surveillance that anomalies go unnoticed by the guards on the spot. Thousands of cameras are installed on streets and roads that record everything, but that recording is only used later because no one was watching a live video stream at that exact moment. But what if the software is always watching these thousands of live video streams? This means that every anomalous activity or crime that occurs and is recorded by these cameras will be reported instantly. This software will use an advanced Machine learning model to recognize suspicious activities and report them to proper authorities.
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    TalkSee
    (Library Information Services, CUI Lahore, 2021) Zenia Kiran; LHR TP 7504; FA17-BCS-129; Dr. Usama Ijaz Bajwa
    Communication has a crucial role in our daily whether at work, at home, abroad, in education or to enhance our social as well as business network. To have an effective communication, a language plays a vital part that helps to build connection among people from different cultures. there are still some gaps while interacting with people from different cultures and these are due to language barriers. In the increasingly integrated global business community, due to an increasing need to communicate with the people all around the world, the language barrier between different cultures needs to be removed. One solution is to make people learn multiple language, that is worst one, because it is impossible mankind to learn all the native tongue of world. Therefore, by keeping in mind, the problem of language barrier we proposed a solution named TalkSee. TalkSee is a web-based chat application that will allow people to communicate with each other on one platform by translating their text messages, written in their chosen language, to some other language on the receiver’s end in the form of their talking Avatar to provide them with the best possible experience of communication
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    GAME FOR DYSLEXICS
    (Library Information Services, COMSATS University Islamabad, Lahore Campus., 2020-11-20) Noor E Eman; FA16-BCS-286; Dr. Usama Ijaz Bajwa; LHR TP 6120
    The present project will be an empirical endeavour for the development of a reliable and valid diagnostic and rehabilitation tool for the children with reading disorder. It is seen that the activity of diagnosis and rehabilitation is boring for the children, consequently being less effective for them. Therefore, to devise a technique through which children learn more is the focus of this project. Children now a days show keen interest in computer games. Due to the proposed tool, which is a series of games, children will be able to differentiate between different sounds, words and letters. This would be done by different techniques and creating different scenarios and levels. This report presents our serious games created to screen and train different abilities which are usually impaired in children with dyslexia. We primarily test the cognitive abilities which are deeply related to the children’s future reading skills, i.e., visual search ability, rapid identification of visual inputs, visual sequential and auditory memory and the capability to associate visual and auditory stimuli. To this aim, we discuss here a series of serious games designed to train specific skills that have been proven to be effective against dyslexia according to Orton Gallium Study. This game would be a good platform for rehabilitation along with providing the results of performance at the end of game. The game would be developed using unity 3D engine, which provides variety of tools for game creation.
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    Human Action Recognition in Low-Resolution Videos Based on Spatio-Temporal Features
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) Samia Akram; FA21-RCS-015; LHR TP 8678; Dr. Usama Ijaz Bajwa
    In recent years, the field of computer vision and pattern recognition has devoted a lot of research focus to the study of human action. Most of the current action recognition research focuses on high-quality videos with clearly apparent actions. Most actions are of limited quality and take place at a distance, making it difficult to identify them. Therefore, the issue of low video quality is still under-researched and difficult to solve in practical implementation. The goal of this study is to establish a deep learning based framework for recognizing human actions in low-quality video by utilizing spatial-temporal features. This research focuses on the use of annotated dataset TinyVIRAT-v2 of action recognition videos recorded in low quality in order to examine the applicability of deep learning architecture for action recognition from videos of low perceptual quality. First, in the proposed framework datasets will be pre-processed and spatial-temporal features will be retrieved using a CNN-based feature extractor. The extracted features will then be fed into a 3D custom model for classification, which is based on resnet50 as backbone network and c3d with a SoftMax layer for multiclass prediction, allowing for its implementation as a real-world practical application. Upon classifying, every testing video is allocated a predicted category, and the cumulative outcomes are evaluated using performance measure F1-Score which is 0.68.
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    Multiclass Weeds and Crops Classification and Segmentation using Deep Learning
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) Seemab Ayub; FA21-RCS-009; LHR TP 8675; Dr. Usama Ijaz Bajwa
    The world's population has increased exponentially with the increase in food demand, so it's essential to focus on factors that affect the quality and quantity of food. Some basic natural factors are climate, soil, pests, and weeds. Weeds are unwanted plants in yields that compete with the crops and consume a lot of nutrients that affect the quality and growth of crops. The separation of these toxic weeds is a challenging task because of their color and quantity. Pakistan is an agricultural country, and almost a big part of its economy depends on agriculture. The growth of this department does not match the population growth; the reason behind this is the presence of weeds. Automated technological interventions represent the optimal approach for weed removal. However, notable challenges persist within the technological landscape, including the absence of comprehensive datasets and the need for well-automated systems specifically tailored for crop weed classification and segmentation. Focusing on these issues, most of the researchers are working on different techniques, but the inadequate size of the dataset is still a problem for researchers, so this study performed to generate an efficient large dataset by merging different small datasets on the base to pick only those classes of weed and crops that belong to Pakistan; also an effective system using UNet variations (UNet, Attention UNet, and Attention Residual UNet(ARUNet)) and ResNet where ARUNet has performed very well for segmentation process and especially the Attention mechanism boosted the segmentation process, on the other hand ResNet has performed best for classification process on our dataset (24- classes and 120000 images). The accuracy of 99% achieved for classification process and 95% for segmentation.
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    A Robust Attention-based 3D CNN Model for Fire and Smoke Detection from Videos
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) Rimsha Shoukat; FA21-RCS-014; LHR TP 8677; Dr. Usama Ijaz Bajwa
    The unforeseen occurrence of a fire eruption has significant and adverse impacts on all aspects of human existence, including human life, land, industry, forests, and animal populations. The emergence of smoke before the apparent signs of fire makes smoke detection very advantageous in terms of promptly identifying fires. This early detection capability holds significant potential for preserving numerous lives and mitigating the occurrence of catastrophic events and associated losses. The problem of detecting smoke and fire using video-based methods presents significant challenges due to the considerable variability in their color, brightness, and shape. Most prior researchers conducted individual studies focused on the categorization of fire or smoke. Furthermore, video classification studies focused on a 2D approach, which was inadequate due to its inability to capture temporal information, so it was not able to learn the difference between frames. The convolutional neural network (CNN) has demonstrated exceptional performance in several domains, hence establishing itself as the leading approach. This study presents a robust classifier based on a three-dimensional convolutional neural network (3D-CNN) specifically developed to classify events into three distinct classes: fire, smoke, and neutral. The classifier is meant to effectively process video data. 3D models have the capability to acquire spatial and temporal characteristics from video sequences, rendering them appropriate for undertaking such tasks. The proposed 3D-CNN model had a notable test accuracy of 90% and showcased outstanding performance with an accuracy of 96% when subject to cross data [1] that have seven smoke videos. To enhance its capabilities, the model integrates the CBAM (Convolutional Block Attention Module) attention mechanism. The utilization of this approach allows the model to concentrate on significant regions of interest (ROI) throughout the entirety of the frame, hence enhancing its precision in categorizing complex attributes inside video frames. By integrating the attention mechanism, the model attained a remarkable gain of 6% in test accuracy with an accuracy rate of 96% and an F1-score of 98%.
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    Classification of MGMT Promoter Methylation Status in Brain Tumor MR Images using Deep Learning
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) Muhammad Sohaib Iqbal; FA21-RCS-005; LHR TP 8470; Dr. Usama Ijaz Bajwa
    Glioblastoma is the most dangerous brain tumor type (worst prognosis), having the least survival rate of less than a year. O6-methylguanine-DNA methyltransferase (MGMT) promoter status “methylation” adds a favorable prognosis and is a reliable indication of cancer's chemosensitivity. Therefore, determining the MGMT status of a brain tumor patient is essential for treatment planning. Surgical tissue sample removal is required to conduct genetic testing for tumors. The genetic characterization of the tumors may then take a few weeks to establish after that. Non-invasive diagnosis using MR images plays a significant role in the early detection of the deadly disease by using Magnetic Resonance Imaging (MRI) to detect the tumorous regions using deep learning techniques. The main requirement for detecting MGMT status is obtaining the sub-regions of brain tumor from MRIs. Previous studies have worked on Brain tumor segmentation and MGMT classification separately, few of the studies have worked on segmentation and classification together but have used selective modalities for MGMT Prediction. Since each modality has its own significance and MGMT promoter status lies in the tumorous region, this study proposed a pipeline of segmentation and classification models using stacked multimodalities of MRI scans of the benchmark dataset BraTS2021. The pipeline is divided into two phases. The first phase uses a stack of multiple MRI modalities to segment the brain tumor into sub-region using a 3D Residual U-Net Architecture, and the second phase uses the segmentation model's output (stacked multimodalities tumor voxel) to determine the MGMT promoter status using a 3D ResNet 10 Classifier. With the help of pipeline, classification model received precise information about the tumorous regions which helped in efficient prediction of the MGMT status present in the brain tumor. The proposed pipeline has therapeutic value because it can assist radiologists in diagnosing brain tumors more efficiently and precisely by reducing the subjectivity and variability of human interpretation. The segmentation and classification pipeline for brain tumors x can potentially increase the consistency and objectivity of diagnoses. Furthermore, it can also assist radiologists in predicting the MGMT status without using surgical equipment and can help in treatment decision making. The segmentation phase of the proposed pipeline yielded promising results with average dice scores of 0.81, 0.84, and 0.80 for the tumor core (TC), whole tumor (WT), and enhancing tumor (ET) on validation. The classification phase achieved ROC-AUC score of 0.66 on validation.
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    Enhancing Crime Classification in Surveillance Videos for Real Time Monitoring
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Muhammad Salman Ghauri; SP19-RCS-027; LHR TP 7590; Dr. Usama Ijaz Bajwa
    Anomalies can be detected with the help of patterns and events that differ from the normal flow of events. The paradigms of surveillance may relate to abuse, fights, road accidents and snatchings, etc. In real-world surveillance, finding unusual events in these massive video streams is a difficult endeavor, since they often occur inconsistently. However, deep learning-based anomaly detection helps reduce human labor and its decision-making ability can be compared to that of humans, thus ensuring the safety of the public. In the majority of reported studies, anomalies are detected from surveillance videos based on binary classification. The reported approaches did not cover other anomalous events from surveillance videos, including abuse, fights, vehicle accidents, shootings, stealing, vandalism, and robberies. This paper proposes an intelligent anomaly detection framework based on deep features that can operate more efficiently in surveillance networks. In the proposed framework, spatial-temporal features are first extracted from a series of frames by passing them through a CNN model that has been pretrained. Analyzing the frames in a sequence can be beneficial in detecting anomalous events. Once the deep features have been extracted, the data is then passed to the Long Short-Term Memory (LSTM) model. The model can accurately classify ongoing anomalies/normal events in complex surveillance scenes of smart cities. A dataset from the University of Central Florida (UCF) Crime video dataset is used to perform extensive experiments on anomaly detection. We report an increase in data accuracy of 47.83% over state-of-the-art methods for UCF-Crime datasets.
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    Deep Learning-Based Prediction of SARS-CoV 2 (COVID-19) and its Severity Classification using Multimodal Chest Radiography Images
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Mughees Ahmad; SP20-RCS-019; LHR TP 7595; Dr. Usama Ijaz Bajwa
    The new COVID-19 emerged in a town in China named Wuhan in December 2019 and since then this deadly virus transmitted all over the world. This transmittable disease has infected 262 million people worldwide and 5.2 million deaths till November 2021. As we can see the rapidly spreading of this pandemic, different countries are facing limited resources such as medical test kits and ventilators because of the number of positive cases that have been increased out of control. In this devastating situation, it is very compulsory to develop an easily available, low-priced, and automatic deep learning model for COVID-19 prediction through chest radiography images (CRIs) such as X-rays, and CTs. The proposed study is using chest radiography images to detect chest infections such as bacterial, viral, and COVID-19 infection as X-rays and CTs give significant information about this deadly infection. The recent hybrid deep learning (DL) techniques can be used along with chest radiography images (CRIs) for the faster and accurate detection of different chest infections including COVID-19 and its severity levels (i.e., negative for pneumonia, atypical appearance, indeterminate appearance, and typical appearance). Therefore, a novel hybrid model named Lightweight Residual_Bi-GRU uses residual blocks and Bi-directional gated recurrent unit (Bi-GRU) for automatic and correct detection of non-COVID and COVID-19 infections by using preprocessed chest radiography images (CRIs). Lightweight Residual_Bi-GRU is used for the recognition of two-class classification (normal and COVID-19), three-class classification (normal vs COVID-19 vs viral pneumonia), four class classification (normal vs COVID-19 vs viral pneumonia vs bacterial pneumonia), and different COVID-19 severity types' classification. The presented model provides a classification accuracy of 99.5%, 98.4%, 90.2%, and 80.7% for 2 class, 3 class, 4 class, and COVID-19 severity levels classifications. The results prove that radiologists and medical officers can adopt this method for the screening of chest infections where test kits are limited.