AI and Space Safety: Collision Risk Assessment

Sánchez, Luís Enrique; Vasile, Massimiliano; Minisci, Edmondo

doi:10.1007/978-3-030-22786-9_136-1

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…Despite this, in developed countries, AI remains crucial for various finance-related tasks that involve routine-based activities and do not require complex decision-making. There is a forecast that AI will take over up to 40% of current transactional accounting [9,10,11]. Examples of such tasks include internal performance reporting, purchase-to-pay, and record-to-report, all of which utilize robotic automation.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Artificial Intelligence and Efficacy of Audit Practice in Nigeria

Owonifari

Igbekoyi

Awotomilusi

et al. 2023

AJEBA

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Artificial Intelligence (AI) has become increasingly popular globally as a crucial tool for auditing financial statements, but in Nigeria, the adoption and use of AI tools by auditors is still in its early stages. Attention has been primarily focused on the Big 4 accounting firms, with little attention given to small-scale audit practitioners in Nigeria. This study seeks to examine the impact of AI on audit practice in Nigeria by employing a survey research design. The population of this study comprises 89 accounting firms operating in the Ikeja Local Government area of Lagos State, with a sample size of 62 firms selected using purposive sampling. Data was collected through a well-structured questionnaire, and the reliability of the research instrument was confirmed with a Cronbach Alpha test result of an average of 70%. Descriptive analysis and regression analysis were used to analyze the data, and the results indicated that data mining, machine learning, and image recognition exhibited a significant positive relationship with audit practice in Nigeria. The study concluded that the use of AI will enable auditors to predict future trends and make more informed decisions that focus on improving audit practice. The study recommended constant training of accountants and audit personnel on the use of data mining techniques to improve audit practice, investment in machine learning tools by audit firms in Nigeria, and increased use of image recognition to assist in object classification.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Artificial Intelligence and Efficacy of Audit Practice in Nigeria

Owonifari

Igbekoyi

Awotomilusi

et al. 2023

AJEBA

View full text Add to dashboard Cite

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Synthetic database of space objects encounter events subject to epistemic uncertainty

Sánchez

Vasile

2020

Data in Brief

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The databases included on this article refers to variables and parameters belonging to the Space Traffic Management (STM), Evidence Theory and Machine Learning (ML) fields. They have been used for implementing ML for autonomously predict risk associated to a close encounter between two space (Sanchez and Vasile, On the Use of Machine Learning and Evidence Theory to Improve Collision Risk Management, Acta Astronautica, Special Issue for ICSSA2020, In Press [1] ). The position of the objected is assumed to be affected by epistemic uncertainty, which has been modeled according to Dempster-Shafer Evidence theory (DSt) [2] . Six datasets are presented. Two ( DB1 and DB2 , respectively) include samples of space object close encounters subject to epistemic uncertainty on the relative position. Other two databases ( DB3 and DB4 , respectively) include the values of the Cumulative Plausibility and Belief Curves ( CPC and CBC , respectively) of each sample included in DB1 . The remaining databases ( DB5 and DB6 ), contain the value of the CPC and CBC of each sample included in DB2 . All of them are synthetic databases created using computer simulation to obtain the results presented in [1] . DB1 database is constituted by 9,000 samples and 45 columns and a header, while DB2 is formed by 28,800 samples and 45 columns and a header. These databases come from a set of, respectively, 5 and 14 different families of encounter geometries defined by the range of values that can be assigned to the bounds of the intervals for the uncertain variables, assumed to be affected by epistemic uncertainty, considered to have been provided by two sources of information. The uncertain variables are: the miss distance, [µ x , µ y ], on the impact plane (B plane), the standard deviation of the relative position projected on the B plane, [σ x , σ y ] , and the Hard Body Radius of the combined objects, HBR . The dataset is completed with STM related parameters: miss distance and covariance matrix of the uncertain ellipse projected on the B plane enclosing all samples defined by the uncertainty intervals, the Probability of Collision ( P C ) of this ellipse or the elapsed time to the Time of Closest Approach ( TCA ); with DSt related parameters: Belief and Plausibility of certain values of Pc ; and the class of the event according to the classification detailed in ...

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