Notice of Violation of IEEE Publication PrinciplesNeural networks and genetic algorithm based intelligent robot for face recognition and obstacle avoidance

Notice of Violation of IEEE Publication Principles

"Neural Networks and Genetic Algorithm Based Intelligent Robot for Face Recognition and Obstacle Avoidance"
by S. Arun, G. Harish, K. Salomon, R. Saravanan, K. Kalpana, J.Jaya
in the Proceedings of the International Conference on Current Trends in Engineering and Technology (ICCTET), July 2013, pp. 356-361

After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE\´s Publication Principles.

This paper is a duplication of the original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.

Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:

"Soft-I-Robot Using Neural Networks and Genetic Algorithm"
by Santhosh S.
Indian Journal of Innovations and Development, Vol 1, No 6, June 2012

The Internet-based security Soft-i-Robot is modeled using Soft computing paradigms for problem solving and decision-making in complex and ill-structured situations. Soft-i-Robot monitors the workspace with multimedia devices and sensor using an Internet application program. The model has sensory subsystems such as Intruder detection which, detects intruder, captures image and sends to server, and an Obstacle Avoidance Unit to detect the objects in the path of the mobile robot. These multiple features with hybrid Soft computing techniques depart the developed Soft-i-Robot from the existing developments, proving that the streaming technology-based approach greatly improves the sensibility of robot tele-operation. The relatively powerful online robots available today provoke the simple question, in terms of two compe- ing goals: recognition accuracy and computing time. Improved recognition accuracy and reduced computing time for face recognition of the intruder is obtained using Morphological Shared Weight Neural Network. To obtain a collision-free optimized path, Soft-i-Robot uses derivative free Genetic Algorithm. With rapid expansion of Robotics and Soft computing paradigms, robotic technology touches upon self-understanding of humans, socio-economic, legal and ethical issues leading to improved performance rate and information processing capabilities.