%0 Journal Article %1 mengs1974einfluss %A Chintamani Bagwe, Kinil Doshi %D 2024 %J International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI %K learning machine %N 2 %P 10 %R 10.5121/ijscai.2024.13202 %T ACOMPREHENSIVE GUIDE TO TESTING AI APPLICATION METRICS %U https://aircconline.com/ijscai/V13N2/13224ijscai01.pdf %V 13

@article{mengs1974einfluss, added-at = {2024-05-07T07:53:51.000+0200}, author = {Chintamani Bagwe, Kinil Doshi}, biburl = {https://www.bibsonomy.org/bibtex/28efd2006ea07e9b5fcfa0c33f417ff0a/leninsha}, description = {This study examines key metrics for assessing the performance of AI applications. With AI rapidly expanding across industries, these metrics ensure systems are reliable, efficient, and effective. The paper analyzes measures like Return on Investment, Customer Satisfaction, Business Process Efficiency, Accuracy and Predictability, and Risk Mitig}, dnbtitleid = {751157570}, doi = {10.5121/ijscai.2024.13202}, interhash = {da5ad95feebaab5bff3562595168f686}, intrahash = {8efd2006ea07e9b5fcfa0c33f417ff0a}, journal = {International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI}, keywords = {learning machine}, month = may, number = 2, pages = 10, school = {Uni Bonn}, timestamp = {2024-05-07T07:53:51.000+0200}, title = {ACOMPREHENSIVE GUIDE TO TESTING AI APPLICATION METRICS}, url = {https://aircconline.com/ijscai/V13N2/13224ijscai01.pdf}, volume = 13, year = 2024 }