Protecting Accounting Information Systems Using Machine Learning Based Intrusion Detection

The key goal of this paper is to look at network data and identify whether it is normal traffic data or anomaly traffic data. In this paper, we are using supervised machine learning techniques. Classification models are used to train and validate data. Using these algorithms we are training the system using a training dataset then we use this trained system to detect intrusion from the testing dataset. In our proposed method, we detect whether the network data is normal or an anomaly. The Decision Tree and K-Nearest Neighbor are applied to the proposed model to classify abnormal to normal behaviors of network traffic data. In addition, Logistic Regression Classifier and Support Vector Classification algorithms are used in our model to support proposed concepts. A feature selection method is used to collect valuable information from the dataset to enhance the efficiency of the proposed approach. The experimental findings revealed that the suggested method has a neglected false alarm rate, with the accuracy expected to be between 95% and 100%

Developing a framework of service convenience in health care: An exploratory study for a primary care provider

From retail health clinics and online appointment scheduling to (mobile) kiosks that enable patient check-in and automate the collection of copays and open balances, convenience has become an important topic in the health care sector over the last few years. While service convenience has also gained much interest in academia, one common limitation is that authors have adopted a “goods-centered” perspective focusing primarily on retail settings. Results of this exploratory study reveal that health care service convenience encompasses seven different dimensions: decision, access, scheduling, registration and check-in, transaction, care delivery, and postconsultation convenience. Implications and future research suggestions are discussed