An Artificial Intelligence-Based Approach to Model User Behavior on the Adoption of E-Payment

The growth of internet usage during the COVID-19 pandemic creates a new business avenue on e-payment for organizations to expand their business horizon. However, challenges on user-related factors arise with this new avenue. This study aims to investigate the association of these factors on the adoption of e-payment services using machine learning inference. An artificial intelligence-based analysis pipeline is established to study the impact of individual items of the dependent factors on the usage of e-payment. In the analysis pipeline, the important items were extracted using a hybrid artificial intelligence method, and the relationships of these items were inferred using the tree algorithm. The results show that items related to expectancy, facilitating conditions, user attitude, and performance expectancy affect usage of e-payment services. Participants below 25 years old require a gamification solution to adopt e-payment, and participants above 40 years old need social support.

IoT Application of Transfer Learning in Hybrid Artificial Intelligence Systems for Acute Lymphoblastic Leukemia Classification

Acute lymphoblastic leukemia is the most common cancer in children, and its diagnosis mainly includes microscopic blood tests of the bone marrow. Therefore, there is a need for a correct classification of white blood cells. The approach developed in this article is based on an optimized and small IoT-friendly neural network architecture. The application of learning transfer in hybrid artificial intelligence systems is offered. The hybrid system consisted of a MobileNet v2 encoder pre-trained on the ImageNet dataset and machine learning algorithms performing the role of the head. These were the XGBoost, Random Forest, and Decision Tree algorithms. In this work, the average accuracy was over 90%, reaching 97.4%. This work proves that using hybrid artificial intelligence systems for tasks with a low computational complexity of the processing units demonstrates a high classification accuracy. The methods used in this study, confirmed by the promising results, can be an effective tool in diagnosing other blood diseases, facilitating the work of a network of medical institutions to carry out the correct treatment schedule.