Hock Chuan Lim

Parkinson’s disease (PD) is a central sensory system-based progressive illness with no cure. The origin of this illness is unknown. According to various research, it has been found that it is caused due to genetics or environmental factors. It is usually found in older people. However, there is no accurate treatment for this disease. So, the patient must be monitored periodically. It usually starts with deterioration in speech performance. The major problem with this disease is that it’s very costly to treat. The paper aims to report details of numerous aspects of detection of PD published in recent years based on the focus and benefits of the study, the methodology being used, accuracy of the system, and future research suggested for the study. A systematic study was done based on a search of the literature. A total of 50 articles were discovered.

Mental health has become an increasingly important topic to address as a surge in mental health issues can be seen across the globe significantly owing to the COVID-19 pandemic. Several factors such as unawareness, daily life interruptions, and social stigma lead to people hesitating in acquiring appropriate mental health support, thereby arising the need for effective and round-the-clock mental health support solutions. Hence, the purpose of this paper is to address how Artificial Intelligence (AI) in conjunction with the portability of smartphones, can be leveraged to provide a type of first aid for mental health to users in their moment of educational need. This article proposes a new mental health assistance solution in the form of a mobile application called Therapily. The ideation, design, and implementation aspects of this application will be explored along with discussing the technical details of the key modules to aid the education of the health domain. An emotion detection module employing a Deep Neural Network to analyze the user’s facial expressions for recognizing in-the-moment emotion(s) followed by suggesting therapeutic activities based on the results, will be explored. Moreover, the creation of a Natural Language Processing (NLP) - based chatbot and the functionality of online consultations with licensed and experienced therapists will also be covered. Finally, the different ways in which Therapily is likely to be extended or modified in the future will also be identified.

The agent-based modeling paradigm has been actively applied to address social normative issues such as: values, cognition, morality and behaviors. The abstraction of human and human-like social processes and mechanisms result in misalignment of computational model with existing (old) verification and validation techniques and expose significant challenges. We argue that human sources represent a sound approach for verification and validation of agent-based social simulation models. We propose a novel conceptual gaming framework that extracts required information from relevant sources as part of game play.

In many agent-based models theoretical and computational mechanisms are needed for model abstraction and design. However, it can be challenging to arrive at the appropriate mechanisms and models. This research on the interplay of ethical trust and so

The need for guiding model formulation of normative social systems in support of a digital ecosystem is introduced. Normative social systems improve the understanding of computational social processes in simulation and experimentation, and provide support for digital ecosystem developments. However, a successful simulation requires the appropriate implementation of a conceptual model. It is proposed that an heuristic formalism of agents, networks and environments, complements the conventional creative approach to model formulation by guiding the formulation of conceptual models via abstract components and facilitate interface with other components in a digital environment

The understanding of the micro-macro link is an urgent need in the study of social systems. The complex adaptive nature of social systems adds to the challenges of understanding social interactions and system feedback and presents substantial scope and potential for extending the frontiers of computer-based research tools such as simulations and agent-based technologies. In this project, we seek to understand key research questions concerning the interplay of ethical trust at the individual level and the development of collective social moral norms as representative sample of the bigger micro-macro link of social systems. We outline our computational model of ethical trust (CMET) informed by research findings from trust, machine ethics and neural science. Guided by the CMET architecture, we discuss key implementation ideas for the simulations of ethical trust and social moral norms.