RESUMEN
The transportation infrastructure of the future will be based on autonomous vehicles. When it comes to transportation, both emerging and established nations are keen on perfecting systems based on autonomous vehicles. Transportation authorities in the United States report that driver error accounts for over 60% of all accidents each year. Almost everywhere in the world is the same. Since the idea of self-driving cars involves a fusion of hardware and software. Despite the rapid expansion of the software business and the widespread adoption of cutting-edge technologies like AI, ML, Data Science, Big Data, etc. However, the identification of natural disasters and the exchange of data between vehicles present the greatest hurdle to the development of autonomous vehicles. The suggested study primarily focused on data cleansing from the cars, allowing for seamless interaction amongst autonomous vehicles. This study's overarching goal is to look at creating a novel kind of Support Vector Machine kernel specifically for P2P networks. To meet the kernel constraints of Mercer's theorem, a newly proposed W-SVM (Weighted-SVM) kernel was produced by using an appropriately converted weight vector derived through hybrid optimization. Given the advantages of both the Grey Wolf Optimizer (GWO) and the Elephant Herding Optimisation (EHO), combining them for hybridization would be fantastic. Combining the GWO algorithm with the EHO algorithm increases its convergence speed, as well as its exploitation and exploration performances. Therefore, a new hybrid optimization approach is proposed in this study for selecting weights in SVM optimally. When compared to other machine learning methods, the suggested model is shown to be superior in its ability to handle such issues and to produce optimal solutions.
RESUMEN
Handwritten character recognition is one of the classical problems in the field of image classification. Supervised learning techniques using deep learning models are highly effective in their application to handwritten character recognition. However, they require a large dataset of labeled samples to achieve good accuracies. Recent supervised learning techniques for Kannada handwritten character recognition have state of the art accuracy and perform well over a large range of input variations. In this work, a framework is proposed for the Kannada language that incorporates techniques from semi-supervised learning. The framework uses features extracted from a convolutional neural network backbone and uses regularization to improve the trained features and label propagation to classify previously unseen characters. The episodic learning framework is used to validate the framework. Twenty-four classes are used for pre-training, 12 classes are used for testing and 11 classes are used for validation. Fine-tuning is tested using one example per unseen class and five examples per unseen class. Through experimentation the components of the network are implemented in Python using the Pytorch library. It is shown that the accuracy obtained 99.13% make this framework competitive with the currently available supervised learning counterparts, despite the large reduction in the number of labeled samples available for the novel classes.