In the context of medical diagnosis, the identification of human blood groups plays a significant role. To perform the identification of human blood groups, usually invasive identification methods are used. However, due to the limitations of the invasive methods of blood group identification, the use of fingerprint-based identification of human blood groups gained significance recently. In this paper, an accurate comparison of the recently developed deep learning models of fingerprint-based blood group identification techniques is provided. Five CNN-based models, such as ConvNeXt-Tiny, ConvNeXt-Small, EfficientNetV2-S, RepVGG-B3g4, and MobileViT-V2 models for the identification of human blood groups, are implemented. The results obtained in the experiment, considering the accuracy of the models, have proved the EfficientNetV2-S model to have the highest accuracy of 98.84%, followed by the ConvNeXt-Small, ConvNeXt-Tiny, MobileViT-V2, and RepVGG-B3g4 models with accuracies of 97.66%, 97.21%, 97.08%, and 96.98%, respectively.

Because of the mobility of its nodes, MANET plays a significant role in mobile communication. As a result, network infrastructure is frequently changed, resulting in data loss and communication overheads. Despite this, the large packet size causes network congestion or traffic. The difficult task is efficient routing through a dynamic network. For node generation and energy management, the proposed approach in this paper employs GAODM (Geography-based Ad-hoc On Demand disjoint multipath) and E-AODM (Energy Ad-hoc On Demand Vector routing). The proposed GAODM routing protocol reduces congestion using Spider Monkey (SM) Optimization. The E- AODM protocol assesses the energy management solution based on parameters such as delay, energy consumption, routing overhead, and node energy. By choosing the best path through the network, the proposed protocol's effectiveness is increased. The proposed protocol reduces routing overload, delay, and congestion. The simulated results show that increasing the number of packets transmitted in the network using the proposed GAODM and E-AODM routing protocols over the existing protocols on NS 2 reduces node energy and, as a result, overload and delay.