Machine Learning (ML) is a sub-domain of Artificial Intelligence, and it focuses on the statistical methods to analyse data. Analysis of data can help in understanding the hidden patterns. As the internet is growing, one can expect a plethora of data getting generated. Almost every field, be it medicine, education, businesses across the world, stock exchange, agriculture etc., all are contributing to this data generation. Research is going on unprecedentedly on data collected for useful insights. Mental Health is one of the fields where ML is being used for understanding the patients’ behavior, symptoms, effectiveness of the treatments used and helps the medical practitioners in decision making. The presented study aims to showcase the overview of the machine learning technologies used in health care majorly concerned to mental health and depression along with their pitfalls and future directions. The presented analysis laid a foundation for future work in the domain of mental health and depression analysis using machine learning techniques. The study focuses mainly on analyzing how innovation and health could be inter-related. The challenge is to find such techniques that minimize the incorrect outcomes by the machine learning models and help the medical practitioners to take timely decisions.

A Brief Review of Machine Learning Methods used in Mental Health Research

One of the successful biometric technologies has been face recognition. Despite its enormous success, face recognition has drawbacks, much like any biometric technology. Face spoofing attacks, which are still possible with facial recognition technologies, have become a common threat to biometric applications. Many algorithms have recently been applied to publicly available datasets. However, efficiency has been a concern. One model cannot be a generalized solution for all attacks, so testing datasets over different models is needed to know all the possibilities. In this paper, a deep-learning method MobileNetV2 is proposed, which uses transfer learning and is a lighter convolutional network that is tested over the NUAA Dataset, which is publicly available. The proposed method shows better results than state-of-the-art methods over the same dataset.

Improved Facial Biometric Authentication Using MobileNetV2

The Covid-19 pandemic has brought many changes in the healthcare industry lately. As things are going normal with time, many health projects designed and used during emergencies are left unexploited. To make the perpetual use of those technologies, the current need should be taken into consideration along with necessary ideas and frameworks to evolve the existing system into. To demonstrate the same, in this paper, we have presented a package of healthcare services powered by Artificial Intelligence via a chatbot system, where a user can entertain the services either by an interactive Graphical User Interface or a conversational chatbot system. This proposed system showcases how a similar Covid-19 system can be developed into a sophisticated healthcare service. This paper emphasises adding Artificial Intelligence to any conventional software via chatbot services which would broaden the services it provides even further. In order to find out the probable best technology to integrate AI with, about 50 papers have been analysed and out of which 27 relevant papers have been included in the literature review. In future, we intend to add medical support and other intelligence-based services to our system in order to meet user requirements and essential features in the field of healthcare.

Yunish Pokharel

Papers

An Interactive AI-Powered Web Healthcare System