Informatics Publishing Limited

A. Bhuvaneshwari , S. Sakthitharan , J. Pavithra

Abstract

Building a robot requires expertise and complex programming techniques. It is all about building systems and combining together motors, flame sensors and wires, among other important components. This paper includes the design and construction of a robot that is able to sense, find the shortest path and detect the obstacles. This robot implements the following concepts: environmental sensing and proportional obstacle detection. This robot processes information from its various sensors and key hardware elements via ARM processors. It uses ultrasonic sensor to detect the obstacle and LCD display to visualize the path selected by the robot. This robot is capable of moving from one coordinate point to another coordinate point, that is, from a fixed source to destination. Then it detects the shortest path from source to destination among many available paths. In Rovers, the robot movements and its positions can be measured. The dijkstra’s algorithm is used to detect the shortest path and also enhance the path efficiency.  The experimental results show the shortest path as a MATLAB graph.

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S. Britto Ramesh Kumar ¹, A. Bhuvaneshwari ¹

Affiliations
1 Department of Computer Science, Bharathidasan University, IN

Abstract

Artificial Intelligence edge detection refers to a set of mathematical techniques used to recognize digital image locations. The picture brightness plays a vital role in detecting dissimilarities and making decisions. Edges are the sharp changes in pictures with respect to the brightness and are commonly categorized into a collection of curved line segments. The main focus of this paper is to find sharp corner edges and the false edges present in the MRI images. The canny edge algorithm is a popular method for detecting these types of edges. The traditional canny edge detection technique has various issues that are discussed in this paper. This study analyses the canny edge algorithm and enhances the smoothing filter, pixel identifier, and feature selection. The proposed Britwari technique, Tabu Search Heuristic Pattern Identifier (TSHPI) enhances the edge detection using SUSAN Filter. Feature Selection is performed to improvise the canny edge method. Deep Learning algorithm is used for classification of pre-trained neural networks to find a greater number of edge pixels. The implementation results show that the Britwari proposed technique (SUSAN Filter Tabu Search Heuristic Pattern Identifier Hill Climbing) reached better accuracy than the traditional Canny Edge Detection algorithms. The results produced better feature set selection using edge detection in MRI images.

Keywords

Britwari Technique, Edge Detection, Deep Learning, Image Processing

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A. Bhuvaneshwari ¹, S. Irulappan ²

Affiliations
1 Assistant Professor, PG Department & Research Centre in Commerce, Rajapalayam Rajus’ College, Rajapalayam, IN
2 Principal, Head and Associate Professor, Department of Commerce, M. K. University College, Madurai, IN

Abstract

The rise of e-commerce and its future must be a major focus in the highly competitive e-commerce business. Online firms must always be thinking of new ways to keep their brand and items in front of their customers' eyes. Staying awake and current with e-commerce trends is one of the finest ways to do this. India is now a large E-Commerce sector, with people of all ages comfortably transacting online – and preferring to shop online rather than visiting physical stores for a wider range of options and offers. Trends in the e-commerce market are influenced by a variety of factors. What kind of shopping habits do customers have? What they buy and how they react to marketing strategies used by companies? Many new trends have emerged in the recent decade.

Keywords

E-Commerce, B2C, Retailing, Internet, Digital Payment, Trends in Online Shopping, Growth of Online Shopping

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