Department of Computer Science

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Author: search.filters.author.Dr. Tariq UmerSubject: search.filters.subject.SP23

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    A Digital Twin Assisted Disease Detection for Leafy Green Vegetables Using Federated Learning in Smart Greenhouse
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Iqra Pervez; CIIT/SP23-RCS-016/LHR; Dr. Tariq Umer; LHR TP 9703
    Agricultural production is seriously vulnerable to the rapid evolution of plant diseases in greenhouse environment. In modern agriculture, ensuring timely and accurate disease detection in leafy green vegetables is crucial for improving crop yield and sustainability. The effects of environmental temperature, humidity on plant’s health which restricts the efficacy of current disease management techniques in greenhouses. This research presents a Digital Twin-assisted model for disease detection in lettuce plants, leveraging Federated-based Learning to enable privacy-preserving and distributed model training within a smart greenhouse environment. The study integrates five pre-trained models AlexNet, MobileNet, ResNet50, EfficientNetB3, and VGG16 to evaluate their effectiveness in classifying plant diseases. This study utilise a lettuce diseases dataset from Kaggle. Among the tested models, VGG16 achieved the highest accuracy of 98%, demonstrating its superior capability for disease classification in resource-constrained settings. Additionally, a correlation analysis between humidity, temperature and disease occurrence was conducted using Unity 3D and Microsoft Azure, providing deeper insights into the environmental conditions influencing plant health. The proposed model is useful for monitoring crops in real time and shows that combining Digital Twin technology with Federated-based Learning can improve decision-making in smart farming systems.
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    Neural Network-Based Anomaly Detection in Cryptocurrency Transactions
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Rashida Noreen; CIIT/SP23-RCS-025/LHR; Dr. Tariq Umer; LHR TP 9705
    Cryptocurrency, especially the rapid growth in adopting bitcoins has led to a significant increase in fraud and illegal transactions, which faces serious challenges for financial integrity and digital trust. Methods of detecting traditional fraud are often reduced to detecting sophisticated patterns within the blockchain network due to decentralized and dynamic nature of transaction flows. This thesis presents a graph-based deep learning approach to detect discrepancy in cryptocurrency transactions, which uses a large, label dataset of bitcoin transactions collected over time. The functioning begins with the collection and prepressing of the elliptical dataset, which includes more than 230,000 edges representing more than 200,000 transactions and money flows represented as nodes. Dataset consists of three classes: license, illegal and unknown transactions. To extract meaningful insight from this data, a broad time-based and network-based feature engineering was performed. After feature engineering, the dataset was converted into a graph structure suitable for processing by the graph neural network (GNNS). Four state -of -the -art models trained and evaluated: Graph Attention Network (GAT), GATV2, Siamese GNN, Relational Graph Convolution Network (RGCN), and graph transformers. The model was trained and validated using stratified partitions to ensure balanced class representation. In the models, the Siamese GNN achieved the highest performance with an accuracy of 95.67% GAT followed closely with 94.90% accuracy, while graph transformers 94.60% and GATV2 achieved 94.46% competitive but slightly less results. RGCN showed 93.77% the least effective performance, especially struggling with unknown transactions classification. The results strongly suggest that graphs can play an important role in increasing the accuracy and reliability of the system that detects fraud in the blockchain environment, especially in accordance with dynamic and asymmetrical graphs.
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    Design and Development of Trust-Based Secure Access Control in Metaverse for Wearable Devices
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Zonash Faysal; CIIT/SP23-RCS-030/LHR; Dr. Tariq Umer; LHR TP 9707
    The sudden emergence of the metaverse has expanded options for immersive digital environments, especially through head-worn wearables that function as portals to virtual spaces. But then the integration of wearables with the metaverse has some serious security, privacy, and trust implications. In this dissertation, we propose a trust- based secure access control mechanism dedicated to wearable devices inside the metaverse. The proposed approach makes sure the trustworthiness of the network by examining four trust parameters, i.e., security, privacy, integrity, and availability. The trustworthiness measurement of the system is described, and a weighted trust score is computed to measure the reliability of the system, and a comprehensive authentication protocol is designed, combining multi-factor authentication principles, encryption, certain hashing mechanisms and a session key generation process. Using the AVISPA tool, the architecture is validated and discusses the resilience to the well known attacks impersonation, replay and theft. The results give a story to the efficacy of the lightweight framework to establish a secure environment for wearables in the metaverse. This research adds to the growing bank of knowledge in the field of improving the safety, security and reliability of virtual environments for end users.