Sparklinglight Transactions on Artificial Intelligence and Quantum Computing (STAIQC) <p>Sparklinglight Transactions on Artificial Intelligence and Quantum Computing is a peer-reviewed, double blinded, biannual journal, with a strong Editorial Board and a tested rapid review system with <strong>ISSN (Online): 2583-0732</strong>. The purpose of STAIQC is to contribute to the development and dissemination of knowledge on various engineering disciplines and management topics as well as to increase communication among scholars, researchers and practitioners.</p> <p>STAIQC is an open access journal. All the manuscripts which are submitted, including symposium papers, will be peer-reviewed by qualified scholars assigned by the editorial board.<br />All the submitted articles should report original, previously unpublished research results, experimental or theoretical, and will be peer-reviewed. The articles submitted to the journal should meet these criteria and must not be under consideration for publication elsewhere. </p> <p>Sparklinglight Transactions on Artificial Intelligence and Quantum Computing (STAIQC) is devoted to fields of Engineering and Management, tutorials and overviews. STAIQC is a peer-reviewed, open access scientific journal, published in electronic form as well as print form. The journal will publish research surveys, tutorials and expository overviews in Artificial Intelligence and Quantum Computing. Articles from supplementary fields are welcome, as long as they are relevant to Artificial Intelligence and Quantum Computing.</p> Sparkling Light Publisher en-US Sparklinglight Transactions on Artificial Intelligence and Quantum Computing (STAIQC) 2583-0732 Classification of Factors Used to Predict Vehicle Break down in Commercial Vehicles <p>Prediction of vehicle breakdowns in commercial vehicles is of utmost importance to ensure the safety, reliability, and cost-effectiveness of road transport. In today's fast-paced and competitive business landscape, the smooth operation of commercial vehicles is crucial for ensuring efficient logistics and supply chain management. Unplanned breakdowns of commercial vehicles can lead to significant financial losses, delays in delivery schedules, and potential damage to the reputation of the transportation companies. Commercial vehicle breakdowns pose significant challenges to both fleet operators and the broader transportation industry. The unexpected failure of a commercial vehicle can result in substantial costs, operational disruptions, and safety hazards. In light of these considerations, the development of effective breakdown prediction models has become an essential endeavor. Predicting when and why a breakdown might occur not only allows for proactive maintenance but also minimizes the economic and safety risks associated with breakdowns.</p> Harshitha K Jagadeesha S N Rajermani Thinakaran T S Copyright (c) 2024 2024-01-18 2024-01-18 4 1 1 16 10.55011/STAIQC.2024.4101