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Öğe Malaria parasite detection using deep learning algorithms based on (CNNs) technique(Elsevier Ltd, 2022) Alnussairi, Muqdad Hanoon Dawood; İbrahim, Abdullahi AbduMalaria is a life-threatening disease caused by female anopheles mosquito bites that are prevalent in many regions of the world. We introduce a deep convolutional neural network (CNN) to improve malaria diagnosis accuracy using patches segmented from microscopic images of red blood cell smears. We design the automatic parasite detection in blood from Giemsa-stained smears using three CNN pre-trained models such as VGG19, ResNet50, and MobileNetV2. As the CNNs are poorly performing for small datasets, we introduce the transfer learning technique. Transfer learning involves acquiring visual features from large general datasets and resolving issues using small datasets. We use a transfer learning approach to detect and classify malaria parasites with three CNN pre-trained models. We evaluated proposed CNN models experimentally using the National Institute of Health (NIH) Malaria Dataset. Our proposed model achieves an accuracy of almost 100%.Öğe Quality of Service-aware clustered triad layer architecturefor critical data transmission in multi-body area network environment(John Wiley and Sons Inc., 2021) İbrahim, Abdullahi AbduWireless Body Area Network (WBAN) is an important element of future smart healthcare services in smart cities. For this reason, many research works have been undergone in WBAN. The significant research issue in the WBAN environment is Quality of Service (QoS) provisioning. In literature, some of the works have been focused on QoS aspect of WBAN. However, they have only marginal impact due to ineffective network design and the usage of a single sink node. Also, most of the research works have been tested within a single BAN and present intra-BAN communication. In practical, an effective multi-BAN communication system is emerging due to the need of large-scale healthcare system. This paper addresses these issues and designs a novel QoS-aware Clustered Triad Layer (QC-TriL) architecture for multi-BAN environment. The novel QC-TriL uses dual sink nodes in each BAN and both sinks are deployed in the optimal positions. To determine the optimal position, Type-II Fuzzy Logic (T2FL) is proposed with different criteria. Next, we form clusters based on the positions of dual sink nodes. QC-TriL adopts criticality-aware routing by presenting Delay-aware One-Hop Transmission and Fused Rank Scheme (FuRank) for critical data and normal data routing respectively. The data transmission is organized by a novel 1D-Dragonfly topology-based Priority Dual TDMA (PD-TDMA) scheduling protocol. Finally, multi-BAN communication is enabled with an optimal route selected by QoS-aware Emperor Penguin Colony (QoS-EPC) algorithm. The QC-TriL multi-BAN environment is modeled in OMNeT++ simulator and evaluated in terms of energy consumption, packet delivery ratio, throughput, and delay. The experiments show promising results for the multi-BAN environment.