Pusat Pengajian Sains Komputer - Tesis
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- PublicationCloud Resource Management Framework Using Monarch Butterfly Harmony Search And Case Based Reasoning(2017-08)Ahmed Mohamed Ghetas, Mohamed RezkCloud services have evolved rapidly and some have adopted a multi-tier architecture for flexibility and reusability. Various rule- and model-based approaches have designed to manage quality of service for these services. A few of existing resource management approaches aim to increase the cloud provider (cp) service provisioning profits. However, they are based on local search optimization algorithms, which may not obtain the best resource provisioning decision in a large-scale cloud environment. This research proposes a new resource optimization and provisioning (rop) framework to detect, solve the bottlenecks, and satisfy the service-level qos requirements of several multi-tier cloud services and to increase the cp service provisioning profits. The rop framework consists of two main components: global resource optimizer (gro) and resource identifier (ri). This research enhances the butterfly optimization algorithm and plugs the resulting algorithm into the rop as a gro. In addition, a new ri is developed using case-based reasoning and is then plugged into the rop framework. To demonstrate the effectiveness of the proposed rop against rule- and model-based approaches, a prototype running on a cloud platform is developed, and a workload generator and multi-tier service model are adopted.
- PublicationVehicle To Vehicle Physical Sidelink Shared Channel Capacity Estimation And Resource Allocation For 5g Network(2024-01)Abubakar, Saadatu5G V2V users can communicate with the neighboring vehicles using Device to Device (D2D) communication to exchange safety messages; Cooperative Awareness Message (CAM) and Decentralized Environmental Notification Message (DENM). Transmitting these messages over the same 5G V2V channel also called the Physical Sidelink Shared Channel (PSSCH) becomes a challenge to the channel capacity as the channel is heavily utilized and congested and the communication must ensure satisfying 5G V2V QoS requirements.
- PublicationCerebrovascular Segmentation Architecture With Channel Attention And Spatial Kernel Filtering For Tof-Mra Images(2024-01)Goni, Mohammad RaihanThis thesis introduces a deep learning approach to automatically segment cerebrovascular structures in magnetic resonance angiography (MRA) images. This study utilizes an approach that excels in segmenting the entire vessel structure while placing increased emphasis on accurately capturing small vessels (< 5 mm radius). The proposed method was evaluated on the MIDAS dataset, demonstrating its competitive performance with exceptional evaluation results.
- PublicationA Feature Selection Approach Based On Hybridizing Flower Pollination Algorithm With Particle Swarm Optimization For Enhancing The Performance Of Ipv6 Intrusion Detection System(2023-12)Al Ghuraibawi, Adnan Hasan BdairThe proposed approach is evaluated using the “ICMPv6 dataset on different attacks”. The experimental results show that the first proposed approach achieved the best classification accuracy, i.e., 97.96% in terms of the number of features, and it reduced the number of features from 19 to 10 features. In addition, the experimental findings demonstrate that the second proposed strategy achieved the best classification accuracy, i.e., 97.99% in terms of the number of characteristics. It reduced the number of features from 19 to 8 features. Finally, the experimental results showed that the third proposed approach achieved the best classification accuracy, i.e., 97.01% in terms of the number of features. It reduced the number of features from 19 to 4 features.
- PublicationSimilarity Segmentation Approach For Sensor-Based Human Activity Recognition(2024-01)Baraka, Abdulrahman M. A.,The researchers attempted to enhance the segmentation method by proposing various techniques. However, most of them focus on each window’s features, and few consider the temporal relationships between the adjacent windows. Therefore, an analysis of the impact of window size on the performance of basic and transitional activity recognition is performed using a deep learning model.