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

Artificial Intelligence (AI) and chatbot technology have emerged as promising solutions to improve healthcare services. AI chatbots can mimic human-like interactions and assist with tasks such as triaging patients and providing medical advice. This review focuses on the use of AI chatbots in predicting diseases, aiming to explore their effectiveness and potential for early intervention and treatment. The purpose of this systematic literature review is to analyze studies related to AI chatbot technology in disease prediction. A systematic literature review was conducted, analyzing a total of 24 selected journals based on predefined inclusion and exclusion criteria. The review protocol involved examining studies published from various years, with a particular emphasis on articles from 2020. The findings indicate that AI chatbots have the potential to play a significant role in predicting diseases and assisting healthcare professionals in making informed decisions. AI chatbot technology shows tremendous potential in disease prediction. By utilizing machine learning algorithms and techniques, chatbots can enhance the accuracy and quickness of disease diagnosis. Continued research and development efforts are necessary to refine AI chatbots' capabilities and revolutionize healthcare delivery for improved patient outcomes and disease management.

AI Chatbot Technology to Predict Disease: A Systematic Literature Review

A Fuzzy Inference System for Sustainable Outfit Recommendations in the Fashion Industry

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

Diversity in the workplace refers to the differences among employees, including cultural diversity, gender equality, and workspace equality. It's becoming increasingly important for organizations to recognize the benefits of a diverse workforce. Embracing diversity is not only ethically correct but also advantageous for organizational success. Several methods are being used by organizations to handle the diverse work environment which includes policies and rules for employees. Such policies are to be unbiased and for that employees related data need to be analyzed crucially. Data analysis empowers organizations to efficiently manage, explore and visualize data. This is where the role of data visualization tools, such as Tableau, becomes pivotal. Renowned for its usage in data analytics, Tableau serves as a cost-efficient software solution. This review paper showcases data analysis for diverse work environment in organizations, aiding in identifying patterns within the ‘Real-World Fake Data’ dataset.

Gender and Cultural Diversity in Workspace - A Significant Data Analysis

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.

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