Editor's NoteThe impact of information technologies is creating a new world yet not fully understood. The extent and speed of economic, life style and social changes already perceived in everyday life is hard to estimate without understanding the technological driving forces behind it. This series presents contributed volumes featuring the latest research and development in the various information engineering technologies that play a key role in this process.The range of topics, focusing primarily on communications and computing engineering include, but are not limited to, wireless networks; mobile communication; design and learning; gaming; interaction; e-health and pervasive healthcare; energy management; smart grids; internet of things; cognitive radio networks; computation; cloud computing; ubiquitous connectivity, and in mode general smart living, smart cities, Internet of Things and more. The series publishes a combination of expanded papers selected from hosted and sponsored European Alliance for Innovation (EAI) conferences that present cutting edge, global research as well as provide new perspectives on traditional related engineering fields. This content, complemented with open calls for contribution of book titles and individual chapters, together maintain Springer's and EAI's high standards of academic excellence. The audience for the books consists of researchers, industry professionals, advanced level students as well as practitioners in related fields of activity include information and communication specialists, security experts, economists, urban planners, doctors, and in general representatives in all those walks of life affected ad contributing to the information revolution.Indexing: This series is indexed in Scopus, Ei Compendex, and zbMATH.
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In this paper we present a Visual feature extraction using improvised SVM and KNN classifiers. The proposed method is an automatic, stable, quick response automatic segmentation, followed by feature extraction and classification to detect spam from the images and the text. The KNN classifier is used to extract features by predicting nearest neighbour while SVM, analyze the data for classification and regression. The hybrid-based Visual feature extraction and classification is elaborated wherein this work discuss the proposed approach which incorporated using improvised SVM and KNN classifier. Moreover, identified patterns via feature extraction method by means of a minimum number of features that are effective in discriminating pattern classes. With all the aforementioned concepts elaborated, the experimental set-up was elaborated with the experimental task, and the results of the character recognition component are further elucidated.
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