Machine Learning (ML) for Artillery War Strategic Readiness Prediction, and Postmortem Analysis for Optimum Performance
Abstract
Computer soft-ware based-tool, a tool to process data that produces accurate results and does not make mistakes like human beings. Based on its efficient storage to secure and protect information stored inside it as well as fast access to information stored. These qualities were needed as aids for artillery war strategic readiness prediction, and postmortem analysis for optimum performance achievement already developed by the authors of this article. Hence, the development of Machine learning (ML) which is a branch of artificial intelligence (AI) that focused on enabling computers and machines to imitate the way that humans learn, to perform tasks autonomously, and to improve their performance and accuracy through experience and exposure to more data. The Problem was defined, the data used in the manual computation were collected, the right algorithm was developed using Python Programming Language, the data were split, the develop model was trained, the models were evaluated and optimized before being deployed. The results were found to be exactly the same as the manually computed results of Akinnuli et al. (2025). This Machine Learning successfully predicts the case studied artillery level of readiness for an operation at this point in time as 74% and the risk involved (that is unreadiness) as 26%. The score of this case study was 233 out of 315 points. The score is Less than 248 but greater than 186 which fell into “Scenario C” of Decision Conditions (248 > SDSp ≥ 186). The quantitative assessment is (Good). Time used for manual computation was one hour twenty minutes (1Hr. 20 Minutes) that is eighty minutes (80 minutes) while computer used fifteen (15) minutes, data input time and processing time added for these computations. This is a very good saving time of sixty-five (65) minute. Comparing the Machine Learning results with the manually computed results Statistic correlation model (Product moment coefficient of correlation (r)) was used to prove the reliability of the developed ML algorithm. The correlation r = 1.0071 (Approximately r = 1.00 ) which shows that it is strong positive perfect correlation. This is scientific base that gave approval for deployment of the Machine Learning algorithm developed for its artillery corps usage.
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