Using the United States National Initiative for Cybersecurity Education framework as a guide, we propose CyberReco - Workforce Readiness Recommender System, an AI/ML model that attempts to address readiness gaps needed prepare users lacking in some cybersecurity knowledge, skills, and activities be workforce-ready. is built using natural language processing. We present hierarchical-based compos...

Graph-based recommender systems (GRSs) analyze the structural information available in graphical representation of data to make better recommendations, especially when direct user-item relation is sparse. Ranking-oriented GRSs mostly use preference (or rank) for measuring node similarities, from which they can infer recommendations using neighborhood-based methods. In this paper, we propose PGR...

Abstract Molecular nature of cancer is the foundation systematic studies genomes, providing exceptional insights and allowing treatments advancement in clinic. We combine techniques image processing for feature enhancement recommender systems proposing a personalized ranking drugs. use database containing drug sensitivity data more than 310.000 IC50, describing response 300 anticancer drugs acr...

 Recommender systems are a branch of retrieval systems and information matching, which through identifying the interests and requires of the user, help the users achieve the desired information or service through a massive selection of choices. In recent years, the recommender systems apply describing information in the terms of the user, such as location, time, and task, in order to produce re...

Recommender systems, also known as recommender engines, have become an important research area and are now being applied in various fields. In addition, the techniques behind the recommender systems have been improved over the time. In general, such systems help users to find their required products or services (e.g. books, music) through analyzing and aggregating other users’ activities and be...

Collaborative filtering (CF) recommender systems are typically unable to generate adequate recommendations in sparse datasets. Empirical evidence suggests that incorporation of a trust network among the users of a recommender system can significantly help to alleviate this problem. For this reason, some studies have been done on combining CF with trust-enhanced recommender system. In this study...