This work presents the incorporating of congestion control on the integrated routing protocol of the opportunistic networks. Pre-emptive congestion control strategies were incorporated into the integrated routing protocol. Results showed that the duplication avoidance improved the integrated routing protocol because it reduced the packet loss and improved the delivery probability. Duplication avoidance reduced the packet loss by 58% and improved the delivery probability by 4% at the end of the simulation time when compared with the delivery probability and packet loss of the integrated routing protocol without congestion control. The use of acknowledgement, buffer size advertisement, data centric method reduced the packet loss by 2.5%, 57% and 57% respectively but did not improve on the delivery probability significantly
General TermsOpportunistic network, congestion control, ONE simulator
KeywordsProphet routing protocol, epidemic routing protocol and integrated routing protocol.
Manual grid-search tuning of machine learning hyperparameters is very time-consuming. Hence, to curb this problem, we propose the use of a genetic algorithm (GA) for the selection of optimal radial-basis-function based support vector machine (RBF-SVM) hyperparameters; regularization parameter C and cost-factor γ. The resulting optimal parameters were used during the training of face recognition models. To train the models, we independently extracted features from the ORL face image dataset using local binary patterns (handcrafted) and deep learning architectures (pretrained variants of VGGNet). The resulting features were passed as input to either linear-SVM or optimized RBF-SVM. The results show that the models from optimized RBFSVM combined with deep learning or hand-crafted features yielded performances that surpass models obtained from Linear-SVM combined with the aforementioned features in most of the data splits. The study demonstrated that it is profitable to optimize the hyperparameters of an SVM to obtain the best classification performance.
Keywords: Face Recognition, Feature Extraction, Local Binary Patterns, Transfer Learning, Genetic Algorithm and Support Vector Machines.
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