Recent transmission of large volumes of data through mobile ad hoc networks (MANETs) has resulted in degraded Quality of Service (QoS) due to factors such as packet loss, delay, and packet drop in multipath routing. To address this issue, a traffic-aware Enhanced QoS-Aware Multipath Routing Protocol (EQMRP) has been proposed for real-time IoT applications in MANETs. EQMRP efficiently switches between multiple paths and monitors traffic conditions to maintain an optimal data transmission rate. The proposed method considers different delay sensitivity levels and link expiration time (LTE) to maintain QoS in each path. Through IoT application data analysis, EQMRP maintains QoS in each path more efficiently than conventional methods. The proposed method has been simulated and validated using MATLAB, and the performance analysis shows that EQMRP achieves a higher packet delivery ratio, lower delay, and reduced packet drop compared to conventional methods. In conclusion, the traffic-aware EQMRP protocol offers a significant improvement in QoS for real-time IoT applications in MANETs.
Cervical cancer is the second most common cancer among women globally. Colposcopy plays a vital role in assessing cervical intraepithelial neoplasia (CIN) and screening for cervical cancer. However, existing colposcopy methods mainly rely on physician experience, leading to misdiagnosis and limited medical resources. This study proposes a cervical lesion recognition method based on ShuffleNetV2-CA. A dataset of 6,996 cervical images was created from Hebei University Affiliated Hospital, including normal, cervical cancer, low-grade squamous intraepithelial lesions (LSIL, CIN 1), high-grade squamous intraepithelial lesions (HSIL, CIN 2/CIN 3), and cervical tumor data. Images were preprocessed using data augmentation, and the dataset was divided into training and validation sets at a 9:1 ratio during the training phase. This study introduces a coordinate attention mechanism (CA) to the original ShuffleNetV2 model, enabling the model to focus on larger areas during the image feature extraction process. Experimental results show that compared to other classic networks, the ShuffleNetV2-CA network achieves higher recognition accuracy with smaller model parameters and computation, making it suitable for resource-limited embedded devices such as mobile terminals and offering high clinical applicability.
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