Informatics Publishing Limited

F. Destonius Dhiraviam ¹, J. Saranya ¹, E. Sivasankari ¹, S. Susikala ¹

Affiliations
1 Department of CSE, V.R.S. College of Engineering and Technology, Arasur, Villupuram, IN

Abstract

We propose a cam based assistive content perusing system to help blind persons read content names and item bundling from hand-held questions in their day by day lives. To confine the item from jumbled foundations or other encompassing protests in the cam view, we first propose a proficient and compelling movement based system to characterize a locale of investment (return for capital invested) in the feature by asking the client to shake the article. This system concentrates moving article locale by a mixture-of-Gaussians-based foundation sub-footing strategy. In the removed return for capital invested, content restriction and recognition are directed to secure content data. To consequently confine the content districts from the item return for money invested, we propose a novel content limitation calculation by learning angle gimmicks of stroke introductions and circulations of edge pixels in an Adaboost model. Content characters in the confined content areas are then binarized and perceived by off-the-rack optical character distinguishment delicate product. The perceived content codes are yield to visually impaired clients in discourse. Execution of the proposed content limitation calculation is quantitatively assessed on ICDAR-2003 and ICDAR-2011 Strong Reading Datasets. Exploratory results exhibit that our calculation accomplishes the condition of human expressions. The verification of-idea model is additionally assessed on a dataset gathered utilizing ten visually impaired persons to assess the viability of the framework's equipment. We investigate client between face issues and survey strength of the calculation in removing and perusing content from distinctive articles with complex foundations.

Keywords

Assistive Devices, Visual Insufficiency, Scattering of Edge Pixels, Hand-Held Things, Optical Character Recognition (OCR), Stroke Presentation, Substance Scrutinizing, Content Zone Confinement.

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J. Saranya ¹, G. Padmavathi ¹

Affiliations
1 Dept. of Computer Science, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, IN

Abstract

Wireless Sensor Networks (WSN) consist of a number of resource constrained sensors to collect and monitor data from unattended environments. Hence, security is a crucial task as the nodes are not provided with tamper-resistance hardware. Provision for secured communication in WSN is a challenging task especially due to the environment in which they are deployed. One of the main challenges is detection of intrusions. Intrusion detection system gathers and analyzes information from various areas within a computer or a network to identify possible security breaches. Different intrusion detection methods have been proposed in the literature to identify attacks in the network. Out of these detection methods, machine-learning based methods are observed to be efficient in terms of detection accuracy and alert generations for the system to act immediately. A brief study on different intrusions along with the machine learning based anomaly detection methods are reviewed in this work. The study also classifies the machine learning algorithms into supervised, unsupervised and semi-supervised learning-based anomaly detection. The performances of the algorithms are compared and efficient methods are identified.

Keywords

Anomaly Detection, Intrusions, Intrusion Detection System, Machine-Learning Algorithms.

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