• GNAPIKA REDDY
• UDAY KIRAN
• JEEVAN REDDY
• DEVDAS SARASWAT

This research extensively tests a variety of state-of-the-art approaches for estimating approximate nearest neighbours. We examined sixteen distinct algorithms from different domains, tested them with different kinds of queries, and assessed them using different metrics on twenty datasets. To determine how well each strategy worked, we carefully examined the findings. Furthermore, we developed a new approach that appears to do quite well in terms of efficiently identifying the appropriate neighbours in the majority of datasets and configurations. A critical issue in many domains, including databases, machine learning, multimedia, and computer vision, is determining estimated nearest neighbours. Every year, a large number of algorithms are released, but their effectiveness hasn't been thoroughly evaluated.

APPROXIMATE NEAREST NEIGHBOUR SEARCH,HIGH DIMENSIONAL DATA,ANN

"Cloud-Assisted Privacy-Preserving Approximate Nearest Neighbor Search for High-Dimensional Data Applications", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.p624-p629, April-2024, Available :http://www.jetir.org/papers/JETIR2404G82.pdf

"Cloud-Assisted Privacy-Preserving Approximate Nearest Neighbor Search for High-Dimensional Data Applications", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppp624-p629, April-2024, Available at : http://www.jetir.org/papers/JETIR2404G82.pdf