PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed
AliAmbra – Enhancing Customer Experience through the Application of Machine Learning and Deep Learning Techniques for Survey Data Assessment and Analysis
Mpouziotas, D.; Besharat, J.; Tsoulos, I.G.; Stylios, C. AliAmvra—Enhancing Customer Experience through the Application of Machine Learning Techniques for Survey Data Assessment and Analysis. Information2024, 15, 83.
Mpouziotas, D.; Besharat, J.; Tsoulos, I.G.; Stylios, C. AliAmvra—Enhancing Customer Experience through the Application of Machine Learning Techniques for Survey Data Assessment and Analysis. Information 2024, 15, 83.
Mpouziotas, D.; Besharat, J.; Tsoulos, I.G.; Stylios, C. AliAmvra—Enhancing Customer Experience through the Application of Machine Learning Techniques for Survey Data Assessment and Analysis. Information2024, 15, 83.
Mpouziotas, D.; Besharat, J.; Tsoulos, I.G.; Stylios, C. AliAmvra—Enhancing Customer Experience through the Application of Machine Learning Techniques for Survey Data Assessment and Analysis. Information 2024, 15, 83.
Abstract
AliAmbra is a project developed to explore and promote high-quality catches of the Amvrakikos Gulf GP to Artas’ wider regions. In addition, this project aimed to implement an integrated plan of action, to form a business identity with high-added value and achieve integrated business services adapted to the special characteristics of the area. The action plan for this project was to actively search for new markets, create a collective identity for the products, promote their quality and added value, engage in gastronomes and tasting exhibitions, dissemination and publicity actions, as well as enhance the quality of the products and markets based on the customer needs. The primary focus of this publication is to observe and analyze the data retrieved from various tasting exhibitions of the AliAmbra project, with a target goal of improving customer experience and product quality.
Keywords
Grammatical evolution; Computational Intelligence; Neural networks; Feature Construction; Data Analysis; Recommendation System
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.