Authentication is the process of keeping the user's personal information as confidential in digital applications. Moreover, the user authentication process in the digital platform is employed to verify the own users by some authentication methods like biometrics, voice recognition, and so on. Traditionally, a one-time login based credential verification method was utilized for user authentication. Recently, several new approaches were proposed to enhance the user authentication framework but those approaches have been found inconsistent during the authentication execution process. Hence, the main motive of this review article is to analyze the advantage and disadvantages of authentication systems such as voice recognition, keystroke, and mouse dynamics. These authentication models are evaluated in a continuous non-user authentication environment and their results have been presented in way of tabular and graphical representation. Also, the common merits and demerits of the discussed authentication systems are broadly explained discussion section. Henceforth, this study will help the researchers to adopt the best suitable method at each stage to build an authentication framework for non-intrusive active authentication.

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Heart Failure is one of the common diseases that can lead to dangerous situations. There are several data available within the healthcare systems. However, there was an absence of successful analysis methods to find connections and patterns in health care data. Some Machine learning methods can help us remedy this circumstance. This helps in getting a better insight into the concept of a classification problem. In many classification problems, it is difficult to learn good classifiers before removing these unwanted features due to the huge size of the data. In my work, we have used an artificial neural network-based autoencoder for effective feature selection The aim of feature selection is improving prediction performance and providing a better understanding of the process data. Hybrid Classification method with a dynamic integration algorithm for classification that aims at finding optimal features by applying machine learning techniques resulting in improving the performance in the prediction of cardiovascular disease.

In recent years, user authentication based on mouse and keystroke dynamic is the most wanted topic to identify the external user and to secure information. Additionally based on the movement of the mouse and typing speed of keystroke the correct user was identified. But the problems of existing approaches are complicated data, data error, and malicious events. To overcome these threats, a novel cat recurrent neural model (CRNM) is proposed to identify the correct user and improve the accuracy rate. In this work, the CRNM approach is introduced to minimize the error rate, to detect unauthorized users by analyzing the user's mouse and keystroke dynamic. Consequently, the trained datasets verify the inputs and identify the correct user. Thus the proposed CRNM has been implemented in the python framework, to identify the correct user. Moreover, the proposed model is validated with other existing deep learning models in terms of accuracy, false acceptance rate (FAR), F-measure, recall, false negative rate (FNR), precision, and error rate.

Security issues have only been compounded by the advent of distributed networks and global internet availability. Combating these security issues depends on being able to correctly authenticate a valid user. This paper presents variants of our CRNM framework which is an efficient cat swarm optimized deep learning model to accurately authenticate a valid user through signature behavioral patterns and biometric information of the user. The behavioral patterns considered here are keystroke and mouse dynamics. Face recognition has been included as a means to decrease the false rejection rate of the system. The major contributions of this work include comparison of various cat optimization variants for active authentication, performance analysis of our model with different state of the art systems. The fitness functions tested include Rosenbrock, Rastrigin and Griewank while CSO variants studied are ADCSO, AICSO and PCSO. Results of our experiments indicate that the proposed authentication system is faster and more efficient than existing frameworks. We achieve accuracy 98.29%, FAR 0.01 and FRR 1.02.