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Artificial intelligence is an emerging technology that revolutionizes human lives. Despite the fact that this technology is used in higher education, many professors are unaware of it. In this current scenario, there is a huge need to arise, implement information bridge technology, and enhance communication in the classroom. Through this paper, the authors try to predict the future of higher education with the help of artificial intelligence. This research article throws light on the current education system the problems faced by the subject faculties, students, changing government rules, and regulations in the educational sector. Various arguments and challenges on the implementation of artificial intelligence are prevailing in the educational sector. In this concern, we have built a use case model by using a student assessment data of our students and then built a synthesized using generative adversarial network (GAN). The dataset analyzed, visualized, and fed to different machine learning algorithms such as logistic Regression (LR), linear discriminant analysis (LDA), K-nearest neighbors (KNN), classification and regression trees (CART), naive Bayes (NB), support vector machines (SVM), and finally random forest (RF) algorithm and achieved a maximum accuracy of 58%. This article aims to bridge the gap between human lecturers and the machine. We are also concerned about the psychological emotions of the faculty and the students when artificial intelligence takes control., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 K. Hemachandran et al.)

Artificial intelligence (AI) based automated disease prediction has recently taken a significant place in the field of health informatics. However, due to unavailability of real time large scale medical data, the dynamic learning of prediction models remains principally subsided. This paper, therefore proposes a dynamic predictive modelling framework for chronic diseases prediction in real-time. The framework premise suggests creation of a centralized patient-indexed medical database to dynamically train machine learning (ML) models and predict risk levels of chronic diseases in real time. In this study, comprehensive empirical evaluations to train seven state-of-the-art ML models for diabetes risk prediction are performed in context of phase 2 of the suggested framework. The selected optimal model can then be dynamically applied to predict diabetes in phase 3 of the framework. Various metrics such as accuracy, precision, Recall, F1-score and receiver operating characteristic (ROC) curve are employed for evaluating performances of the trained models. Parameter tunings using different type of kernels, different number of neighbors and estimators are rigorously performed in order to create a suggestive literature for healthcare prediction ecosystem. Comparative analysis indicates high prediction accuracies on diabetes test data records for neural network and support vector machine (SVM) models as compared to other applied models. [ABSTRACT FROM AUTHOR]

The paper demonstrates an efficient use of intelligent system for solving the classification problem in the sector of health insurance. A model based on adaptive neuro-fuzzy inference system (ANFIS) is proposed to deal with the fuzziness in the real-life environments. This approach enables the interpretation of majority of health factors of an insurance seeker through a set of fuzzy rules to determine the degree of risk to an individual. A fuzzy neural network has been trained with fuzzy inputs like age, occupation, family size, smoking habits, drinking habits, diabetes history, heart disease and other relevant inputs of individual for risk calculation. The model gets importance in health insurance sector because risk determination is fuzzy in nature, and fuzzy calculations are done more accurately by machines rather than human beings especially for the problems which are repetitive in nature and have large number of vague parameters. The proposed model can help the insurance seeker to identify the degree of risk he is having if he is not taking health insurance. This serves a dual purpose of attracting the insurance seeker to acquire the insurance and facilitate generating business to insurance company. Indicative results are presented and discussed in detail in terms of accuracy and solution interpretability. [ABSTRACT FROM AUTHOR]

This paper is a study of life insurance underwriting for individuals and the various processes involved in providing cover to the insurance seeker. Underwriting is the process of classification, rating and selection of risks among the diverse insurance applications. The paper explores various steps taken in insurance underwriting to rate the insurance application, the various risk factors considered for taking an insurance underwriting decision and proposes a model for insurance underwriting. A robust method for automating the underwriting process through the hybrid methodology is presented, which comprises of Neural Network and Fuzzy Logic. Neural Network in the proposed model can be trained to traditionally perform the insurance underwriting for individuals, while the fuzzy rules are expected to take care of the fuzzy inputs. An insurance application is compared against various pre-defined standards and is classified into one of the risk classes. Based on the risk classes, the premium to be paid by the applicant is determined - higher the risk class, higher is the premium. The application of Artificial Neuro-fuzzy Network in insurance underwriting process can considerably reduce the time and insurance losses. [ABSTRACT FROM AUTHOR]