Artificial Intelligence and Machine Learning Methodologies for Research in Management

Data modelling, and, learning for decision sciences is for real-time, and, large datasets’ studies and accuracy levels in such studies increase with the Artificial Intelligence and Machine Learning analysis methodologies.,The probability estimation intervals are narrower and False Negative, and False Positive numbers are lower. Business Intelligence and risk assessment studies employ neural networks.,Literature Review and Research Methods,The kind of work of particular interest is Enzo Grossi and Massimo Buscema’s Artificial Intelligence and Outcome Research, 2006, Drug Development Research using Artificial Neural Networks, and Evolutionary Algorithms for replication of human brain processes for prediction of response to a drug against a disease basis data on patient traits. The classifier predicts whether the drug will work or not. The accuracy of such work in Drug Development Research has been recorded to be 88-90%. It is a combination of Artificial Neural Networks and Evolutionary Algorithms. EA outcomes can be used for prescription on maintaining values at certain levels for classification results on the positive side. This is the Artificial Adaptive Systems methodology.,ANN accuracy is more than the accuracy for logistic regression classifiers. There is over-estimation by a huge margin in case of logistic regression studies. A classifier system usage in an organisational setup encounters endogenous and exogenous problems. A research work corrects these for an accurate model.,An extension of the work will be in services and manufacturing sectors and the use of large datasets for a detailed study using unsupervised learning algorithms.