K. Martin Sagayam

Mind zoomers have a habit of less velocity thinking along with creative pictures to remember in their brain. Augmented reality enhances such mind zooming disorders as a scientific art of cure. We have implemented augmented reality techniques to overcome such mind zooming disorders in a compact advanced manner. The implementation of mind zooming advancement on augmented reality involves edutainment experiences. The conceptual pretended play for mind zoomers exhibits applications involving, augmenting 3d objects, animating an augmented 3D object interacting with an augmented 3D object through virtual button ensuring the curity for mind zoomers through augmenting ideas. The proposed system for mind zoomers as researches say is evaluated to be augmenting techniques to make the remember visioning objects. It's been a lot of prescribed ideas in the proposed history of technologies says that mind zoomers like autism affected children face the disability to remember visioning objects around their environment. This leading behaviour of mind zoomers will be extracted by the implementation of augmenting and virtual ideas for the visioning object of their sight.

Physically impaired people are often challenged by their physical inability to move around. In no other way are they lesser beings than a fully physically fit human being. Even though they are fit to think independently they are mostly challenged by constrained movement. Our projects targets in making their movement independent and free of external help. The existing wheelchairs need either a physically fit person to help the challenged or requires the physically challenged to have strong hands to control the directions of the wheelchair. Our project aims to solve these issues by motorising the wheelchair and making control with less dampening. We aim to remove the joystick with an electronic hand gesture recognition system that makes directing the wheelchair easier and hassle-free. The hand gesture recognition systems need a clear background and bright surrounding. Camera-based image recognition systems are slow and need perfect surrounding. Here we try to go for an alternate option with hand gesture recognition using electromagnetic field pad.

Fingerprint recognition is an advanced approach for security management which supports by authentication to identify the individuals helps in identifying a person based on his personal attributes or characteristics, To identify a person, the fingerprint is analyzed by its features such as loops, whorl, arch, ridges, minutiae and so on. The process of identification happens from pre-processing, minutiae extraction, and classification. Pre-processing involves the enhancement of fingerprint ridges then noise filtering. Minutiae is extracted and classified with the help of the Euclidean distance classifier. The acquired result is then implemented with the Neural Network classifier for determining the performance. The simulation result shows the speed performance of the fingerprint recognition system.

Wireless sensor networks have gained significant attention industrially and academically due to their wide range of uses in various fields. Because of their vast amount of applications, wireless sensor networks are vulnerable to a variety of security attacks. The protection of wireless sensor networks remains a challenge due to their resource-constrained nature, which is why researchers have begun applying several branches of artificial intelligence to advance the security of these networks. Research is needed on the development of security practices in wireless sensor networks by using smart technologies. Deep Learning Strategies for Security Enhancement in Wireless Sensor Networks provides emerging research exploring the theoretical and practical advancements of security protocols in wireless sensor networks using artificial intelligence-based techniques. Featuring coverage on a broad range of topics such as clustering protocols, intrusion detection, and energy harvesting, this book is ideally designed for researchers, developers, IT professionals, educators, policymakers, practitioners, scientists, theorists, engineers, academicians, and students seeking current research on integrating intelligent techniques into sensor networks for more reliable security practices.