Mobile recommender system based on smart city graph

Abstract

<span>Mobile recommender systems have changed the way people find items, purposes of intrigue, administrations, or even new companions. The innovation behind mobile recommender systems has developed to give client inclinations and social impacts. This paper introduces a first way to build a mobile recommendation system based on smart city graphs that appear topic features, user profiles, and impacts acquired from social connections. It exploits graph centrality measures to expand customized recommendations from the semantic information represented in the graph. The graph shows and chooses graph algorithms for computing chart centrality that is the center of the mobile recommender system are exhibited. Semantic ideas, for example, semantic transcendence and likeness measures, are adjusted to the graph model. Usage challenges confronted to settle execution issues are additionally examined.</span>

Unveiling Breast Tumor Characteristics: A ResNet152V2 and Mask R-CNN Based Approach for Type and Size Recognition in Mammograms

“…The remarkable advancements in deep learning models have supplanted the process of manual feature creation. Instead, a deep learning-based model automatically learns and identifies essential features that are most relevant to our case, enabling the detection of necessary tumors [11]. An additional advantage of deep learning models over traditional ones is their ability to learn at multiple levels of representation.…”