Izzat Alsmadi1 and Mohammad Al-Ramahi2, 1Department of Computing Science, Texas A&M University, San Antonio, USA, 2Department of Accounting and Finance, Texas A&M University, San Antonio, USA
This paper gives complete guidelines for authors submitting papers for the AIRCC Journals. Citizens in large cities utilize public transportation as an alternative to self-driving due to several reasons such as avoiding traffic congestion, parking costs and utilize their time for other things, (e.g. reading a book, or responding to emails). While large cities provide public transportation as services to their citizens, yet they need to consider optimizing their budget and ensure that public transportation is available and reliable. Using our case study, public bus transit system in the city of San Antonio, Texas, in this paper, we used predictive analytics models to evaluate performance of public bus transportation. We used time point stops as the target variable in order to evaluate their impact on the overall performance of the system. We also evaluated methods for the detection of protentional bus-time savings and reported several examples of possible saving.
Predictive analytics, GTFS, Transportation intelligence.
Simone Malacaria1, Michele Grimaldi3, Marco Greco4 and Andrea De Mauro2, 1Department of Enterprise Engineering, University of “Tor Vergata”, Rome, Italy, 2, 3Department of Civil and Mechanical Engineering, University of Cassino and Southern Lazio, Cassino, Italy, 4Department of Enterprise Engineering, University of “Tor Vergata”, Rome, Italy
Generative AI applications offer transformative potential for business operations, yet their adoption introduces substantial challenges. This paper utilizes the CBDAS data maturity model to pinpoint pivotal success factors for seamless generative AI integration in businesses. Through a comprehensive analysis of these factors, we underscore the essentials of generative AI deployment: cohesive architecture, robust data governance, and a data-centric corporate ethos. The study also highlights the hurdles and facilitators influencing its implementation. Key findings suggest that fostering a data-friendly culture, combined with structured governance, optimizes generative AI adoption. The paper culminates in presenting the practical implications of these insights, urging further exploration into the real-world efficacy of the proposed recommendations.
Artificial Intelligence, Generative AI, Analytics, Maturity Model, Big Data.
Stergos Afantenos, Henri Prade and Leonardo Cortez Bernardes, Institut de Recherche en Informatique de Toulouse (IRIT), Université Paul Sabatier,118 route de Narbonne, France
Analogical proportions are statements of the form “ is to as is to”, which expresses that the comparisons of the elements in pair ( , ) and in pair ( , ) yield similar results. Analogical proportions are creative in the sense that given 3 distinct items, one can calculate the representation of a fourth item (distinct from the previous items) that forms an analogical proportion with them, provided certain conditions are met. After providing an introduction to analogical proportions and their properties, the paper reports the results of an experiment made with a database of animal descriptions and their class, where we try to “create” new animals from existing ones, retrieving rare animals such as platypus. Descriptions of animals use either Boolean features, or word embeddings.
Analogical inference, analogical proportion, creativity.